From 4c03b228870fe170964b133be006c22bbda5a92f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 16 Sep 2025 10:00:06 -0400
Subject: [PATCH 001/230] turn on debug/api/docs by default

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/installer.py b/installer.py
index 8609ff953..c0d8c7bad 100644
--- a/installer.py
+++ b/installer.py
@@ -1634,7 +1634,7 @@ def add_args(parser):
     group_http.add_argument("--cors-regex", type=str, default=os.environ.get("SD_CORSREGEX", None), help="Allowed CORS origins as regular expression, default: %(default)s")
     group_http.add_argument('--subpath', type=str, default=os.environ.get("SD_SUBPATH", None), help='Customize the URL subpath for usage with reverse proxy')
     group_http.add_argument("--autolaunch", default=os.environ.get("SD_AUTOLAUNCH", False), action='store_true', help="Open the UI URL in the system's default browser upon launch")
-    group_http.add_argument('--docs', default=os.environ.get("SD_DOCS", False), action='store_true', help = "Mount API docs, default: %(default)s")
+    group_http.add_argument('--docs', default=not os.environ.get("SD_NODOCS", False), action='store_true', help = "Mount API docs, default: %(default)s")
     group_http.add_argument("--auth", type=str, default=os.environ.get("SD_AUTH", None), help='Set access authentication like "user:pwd,user:pwd""')
     group_http.add_argument("--auth-file", type=str, default=os.environ.get("SD_AUTHFILE", None), help='Set access authentication using file, default: %(default)s')
     group_http.add_argument("--allowed-paths", nargs='+', default=[], type=str, required=False, help="add additional paths to paths allowed for web access")
@@ -1655,11 +1655,11 @@ def add_args(parser):
 
     group_log = parser.add_argument_group('Logging')
     group_log.add_argument("--log", type=str, default=os.environ.get("SD_LOG", None), help="Set log file, default: %(default)s")
-    group_log.add_argument('--debug', default=os.environ.get("SD_DEBUG",False), action='store_true', help="Run with debug logging, default: %(default)s")
+    group_log.add_argument('--debug', default=not os.environ.get("SD_NODEBUG",False), action='store_true', help="Run with debug logging, default: %(default)s")
     group_log.add_argument("--trace", default=os.environ.get("SD_TRACE", False), action='store_true', help="Run with trace logging, default: %(default)s")
     group_log.add_argument("--profile", default=os.environ.get("SD_PROFILE", False), action='store_true', help="Run profiler, default: %(default)s")
     group_log.add_argument('--docs', default=os.environ.get("SD_DOCS", False), action='store_true', help="Mount API docs, default: %(default)s")
-    group_log.add_argument("--api-log", default=os.environ.get("SD_APILOG", False), action='store_true', help="Log all API requests")
+    group_log.add_argument("--api-log", default=not os.environ.get("SD_NOAPILOG", False), action='store_true', help="Log all API requests")
 
     group_nargs = parser.add_argument_group('Other')
     group_nargs.add_argument('args', type=str, nargs='*', help=argparse.SUPPRESS)

From 39027ccfea6003d65b12943db91e10d881b8d3d2 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 16 Sep 2025 17:12:00 +0300
Subject: [PATCH 002/230] SDNQ fix conv matmul

---
 modules/sdnq/__init__.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index a7829d44d..a82038f68 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -75,6 +75,10 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
                 use_quantized_matmul = group_channel_size >= 32 and output_channel_size >= 32
                 if use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"]:
                     use_quantized_matmul = output_channel_size % 16 == 0 and group_channel_size % 16 == 0
+            if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
+                result_shape = layer.weight.shape
+                layer.weight.data = layer.weight.flatten(1,-1)
+                reduction_axes = -1
         elif layer_class_name in conv_transpose_types:
             if not quant_conv:
                 return layer
@@ -128,7 +132,8 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             num_of_groups = int(num_of_groups)
 
             if num_of_groups > 1:
-                result_shape = layer.weight.shape
+                if result_shape is None:
+                    result_shape = layer.weight.shape
                 new_shape = list(result_shape)
                 if is_conv_type:
                     # output_channel_size, channel_size, X, X
@@ -165,9 +170,6 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
 
         re_quantize_for_matmul = (num_of_groups > 1 or zero_point is not None)
         if use_quantized_matmul and not re_quantize_for_matmul:
-            if is_conv_type:
-                result_shape = layer.weight.shape
-                layer.weight.data = layer.weight.reshape(output_channel_size, -1)
             scale.transpose_(0,1)
             layer.weight.transpose_(0,1)
             if not dtype_dict[weights_dtype]["is_integer"]:

From f8f958244f72e02038c84dbbe65e6dd1ae88fcbb Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 16 Sep 2025 10:17:28 -0400
Subject: [PATCH 003/230] use new pydantic/albumentations/insightface

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 TODO.md                     |  5 -----
 installer.py                | 18 ++++++++++--------
 modules/face/insightface.py |  6 ++----
 scripts/infiniteyou_ext.py  |  6 ++----
 scripts/pulid_ext.py        |  8 +++-----
 5 files changed, 17 insertions(+), 26 deletions(-)

diff --git a/TODO.md b/TODO.md
index dce3ad49e..d20d250ea 100644
--- a/TODO.md
+++ b/TODO.md
@@ -13,11 +13,6 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 - Video Core: API  
 - Video LTX: TeaCache and others, API, Conditioning preprocess Video: LTX API  
 
-### Blocked items
-
-- Upgrade: `albumentations`  
-  - blocked by `insightface`  
-
 ### Under Consideration
 
 - [X-Omni](https://github.com/X-Omni-Team/X-Omni/blob/main/README.md)
diff --git a/installer.py b/installer.py
index c0d8c7bad..ef1d2da95 100644
--- a/installer.py
+++ b/installer.py
@@ -1256,12 +1256,16 @@ def install_gradio():
 
 
 def install_pydantic():
-    if args.new:
-        install('pydantic==2.11.7', ignore=True, quiet=True)
-        reload('pydantic', '2.11.7')
-    else:
-        install('pydantic==1.10.21', ignore=True, quiet=True)
-        reload('pydantic', '1.10.21')
+    install('pydantic==2.11.7', ignore=True, quiet=True)
+    reload('pydantic', '2.11.7')
+
+
+def install_insightface():
+    install('git+https://github.com/deepinsight/insightface@29b6cd65aa0e9ae3b6602de3c52e9d8949c8ee86#subdirectory=python-package', 'insightface') # insightface==0.7.3 with patches
+    # install('albumentations==1.4.3', ignore=True, quiet=True)
+    uninstall('albumentations')
+    install('albumentationsx')
+    install_pydantic()
 
 
 def install_optional():
@@ -1277,8 +1281,6 @@ def install_optional():
     install('nvidia-ml-py', ignore=True, quiet=True)
     install('ultralytics==8.3.40', ignore=True, quiet=True)
     install('Cython', ignore=True, quiet=True)
-    install('git+https://github.com/deepinsight/insightface@554a05561cb71cfebb4e012dfea48807f845a0c2#subdirectory=python-package', 'insightface') # insightface==0.7.3 with patches
-    install('albumentations==1.4.3', ignore=True, quiet=True)
     install('av', ignore=True, quiet=True)
     install('gguf', ignore=True)
     try:
diff --git a/modules/face/insightface.py b/modules/face/insightface.py
index b69648904..99be2d32e 100644
--- a/modules/face/insightface.py
+++ b/modules/face/insightface.py
@@ -10,11 +10,9 @@ instightface_mp = None
 def get_app(mp_name, threshold=0.5, resolution=640):
     global insightface_app, instightface_mp # pylint: disable=global-statement
 
-    from installer import install, installed, install_pydantic
+    from installer import install, installed, install_insightface
     if not installed('insightface', reload=False, quiet=True):
-        install('git+https://github.com/deepinsight/insightface@554a05561cb71cfebb4e012dfea48807f845a0c2#subdirectory=python-package', 'insightface') # insightface==0.7.3 with patches
-        install('albumentations==1.4.3', ignore=False, reinstall=True)
-        install_pydantic()
+        install_insightface()
     if not installed('ip_adapter', reload=False, quiet=True):
         install('git+https://github.com/tencent-ailab/IP-Adapter.git', 'ip_adapter', ignore=False)
 
diff --git a/scripts/infiniteyou_ext.py b/scripts/infiniteyou_ext.py
index e38185aa4..677d56cca 100644
--- a/scripts/infiniteyou_ext.py
+++ b/scripts/infiniteyou_ext.py
@@ -13,11 +13,9 @@ orig_pipeline, orig_prompt_attention = None, None
 
 
 def verify_insightface():
-    from installer import installed, install, install_pydantic
+    from installer import installed, install_insightface
     if not installed('insightface', reload=False, quiet=True):
-        install('git+https://github.com/deepinsight/insightface@554a05561cb71cfebb4e012dfea48807f845a0c2#subdirectory=python-package', 'insightface') # insightface==0.7.3 with patches
-        install('albumentations==1.4.3', ignore=False, reinstall=True)
-        install_pydantic()
+        install_insightface()
 
 
 def load_infiniteyou(model: str):
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index a92480824..df9a099e6 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -28,11 +28,9 @@ class Script(scripts_manager.Script):
         return True
 
     def dependencies(self):
-        from installer import install, installed, install_pydantic
-        if not installed('insightface==0.7.3', reload=False, quiet=True):
-            install('git+https://github.com/deepinsight/insightface@554a05561cb71cfebb4e012dfea48807f845a0c2#subdirectory=python-package', 'insightface') # insightface==0.7.3 with patches
-            install('albumentations==1.4.3', ignore=False, reinstall=True)
-            install_pydantic()
+        from installer import installed, install_insightface
+        if not installed('insightface', reload=False, quiet=True):
+            install_insightface()
 
     def register(self): # register xyz grid elements
         global registered # pylint: disable=global-statement

From 352d3c25460f446cd7a91ef73f605733667506fb Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 16 Sep 2025 13:54:52 -0400
Subject: [PATCH 004/230] fix typo

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/installer.py b/installer.py
index ef1d2da95..f307ba7ba 100644
--- a/installer.py
+++ b/installer.py
@@ -626,7 +626,7 @@ def check_transformers():
     t_start = time.time()
     if args.skip_all or args.skip_git or args.experimental:
         return
-    pkg_transofmers = pkg_resources.working_set.by_key.get('transformers', None)
+    pkg_transformers = pkg_resources.working_set.by_key.get('transformers', None)
     pkg_tokenizers = pkg_resources.working_set.by_key.get('tokenizers', None)
     if args.use_directml:
         target_transformers = '4.52.4'
@@ -634,11 +634,11 @@ def check_transformers():
     else:
         target_transformers = '4.56.1'
         target_tokenizers = '0.22.0'
-    if (pkg_transofmers is None) or ((pkg_transofmers.version != target_transformers) or (pkg_tokenizers is None) or ((pkg_tokenizers.version != target_tokenizers) and (not args.experimental))):
-        if pkg_transofmers is None:
+    if (pkg_transformers is None) or ((pkg_transformers.version != target_transformers) or (pkg_tokenizers is None) or ((pkg_tokenizers.version != target_tokenizers) and (not args.experimental))):
+        if pkg_transformers is None:
             log.info(f'Transformers install: version={target_transformers}')
         else:
-            log.info(f'Transformers update: current={pkg_transofmers.version} target={target_transformers}')
+            log.info(f'Transformers update: current={pkg_transformers.version} target={target_transformers}')
         pip('uninstall --yes transformers', ignore=True, quiet=True, uv=False)
         pip(f'install --upgrade tokenizers=={target_tokenizers}', ignore=False, quiet=True, uv=False)
         pip(f'install --upgrade transformers=={target_transformers}', ignore=False, quiet=True, uv=False)

From 6a021e7743559033f5233e353013d8f1add458a7 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 17 Sep 2025 14:44:44 +0300
Subject: [PATCH 005/230] ipex cleanup tensor_to hijack

---
 modules/intel/ipex/hijacks.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index 9f2c5a542..feb31bec2 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -257,17 +257,15 @@ def torch_tensor(data, *args, dtype=None, device=None, **kwargs):
 torch.Tensor.original_Tensor_to = torch.Tensor.to
 @wraps(torch.Tensor.to)
 def Tensor_to(self, device=None, *args, **kwargs):
+    global device_supports_fp64
     if check_cuda(device):
-        if not device_supports_fp64 and kwargs.get("dtype", None) == torch.float64:
+        device = return_xpu(device)
+    if not device_supports_fp64:
+        if kwargs.get("dtype", None) == torch.float64 and ((device is None and self.device.type == "xpu") or (device is not None and torch.device(device).type == "xpu")):
             kwargs["dtype"] = torch.float32
-        return self.original_Tensor_to(return_xpu(device), *args, **kwargs)
-    else:
-        if not device_supports_fp64:
-            if kwargs.get("dtype", None) == torch.float64 and ((device is None and self.device.type == "xpu") or (device is not None and torch.device(device).type == "xpu")):
-                kwargs["dtype"] = torch.float32
-            elif device == torch.float64 and self.device.type == "xpu":
-                device = torch.float32
-        return self.original_Tensor_to(device, *args, **kwargs)
+        elif device == torch.float64 and self.device.type == "xpu":
+            device = torch.float32
+    return self.original_Tensor_to(device, *args, **kwargs)
 
 original_Tensor_cuda = torch.Tensor.cuda
 @wraps(torch.Tensor.cuda)

From cc6101ecb2a3cb254c62e4384fc8dd2fbb1976b5 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 17 Sep 2025 12:32:27 -0400
Subject: [PATCH 006/230] jumbo update: add wan22-vace, improve offloading, add
 offloading-using-streams, change logging-levels, refactor some default
 packages

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                             | 14 +++++
 README.md                                |  1 +
 TODO.md                                  |  4 +-
 extensions-builtin/sdnext-modernui       |  2 +-
 html/previews.json                       |  9 +--
 html/reference.json                      | 79 +++++++++++++-----------
 installer.py                             |  5 +-
 modules/processing_args.py               | 10 +++
 modules/processing_diffusers.py          |  2 +-
 modules/processing_helpers.py            |  6 +-
 modules/sd_offload.py                    | 38 +++++++++---
 modules/shared.py                        |  1 +
 modules/ui_extra_networks_checkpoints.py |  2 +
 modules/video_models/models_def.py       | 10 ++-
 modules/video_models/video_overrides.py  | 15 +++--
 modules/video_models/video_run.py        | 10 ++-
 pipelines/model_wanai.py                 | 24 ++++---
 17 files changed, 156 insertions(+), 76 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a14c843c2..cc6fe5ecf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,19 @@
 # Change Log for SD.Next
 
+## Update for 2025-09-17
+
+- **Models**
+  - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
+    available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
+- **Offloading**
+  - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
+  - add timers to measure onload/offload times during generate  
+  - experimental offloading using `torch.streams`  
+    enable in settings -> model offloading  
+- **Other**
+  - **logging** enable `debug`, `docs` and `api-docs` by default  
+  - refactor to use new libraries  
+
 ## Update for 2025-09-15
 
 ### Highlights for 2025-09-15
diff --git a/README.md b/README.md
index e37f77479..f57feb916 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,7 @@
 ![Last update](https://img.shields.io/github/last-commit/vladmandic/sdnext?svg=true)
 ![License](https://img.shields.io/github/license/vladmandic/sdnext?svg=true)
 [![Discord](https://img.shields.io/discord/1101998836328697867?logo=Discord&svg=true)](https://discord.gg/VjvR2tabEX)
+[![DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/vladmandic/sdnext)
 [![Sponsors](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 
 [Docs](https://vladmandic.github.io/sdnext-docs/) | [Wiki](https://github.com/vladmandic/sdnext/wiki) | [Discord](https://discord.gg/VjvR2tabEX) | [Changelog](CHANGELOG.md)
diff --git a/TODO.md b/TODO.md
index d20d250ea..26f5fa202 100644
--- a/TODO.md
+++ b/TODO.md
@@ -15,6 +15,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ### Under Consideration
 
+- [Inf-DiT](https://github.com/zai-org/Inf-DiT)
 - [X-Omni](https://github.com/X-Omni-Team/X-Omni/blob/main/README.md)
 - [DiffSynth Studio](https://github.com/modelscope/DiffSynth-Studio)
 - [IPAdapter negative guidance](https://github.com/huggingface/diffusers/discussions/7167)  
@@ -36,13 +37,14 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 ### New models
 
 - [HunyuanImage](https://huggingface.co/tencent/HunyuanImage-2.1)
+- [Phantom HuMo](https://github.com/Phantom-video/Phantom)
 - [Lumina-DiMOO](https://huggingface.co/Alpha-VLLM/Lumina-DiMOO)
-- [Wan2.2 Fun](https://huggingface.co/collections/alibaba-pai/wan22-fun-68958eabec343b948f1225c5) (includes VACE, Control, etc)
 - [Magi](https://github.com/SandAI-org/MAGI-1)(https://github.com/huggingface/diffusers/pull/11713)  
 - [SEVA](https://github.com/huggingface/diffusers/pull/11440)  
 - [Ming](https://github.com/inclusionAI/Ming)  
 - [Liquid](https://github.com/FoundationVision/Liquid)  
 - [Step1X](https://github.com/stepfun-ai/Step1X-Edit)  
+- [LucyEdit](https://github.com/huggingface/diffusers/pull/12340)
 - [SD3 UltraEdit](https://github.com/HaozheZhao/UltraEdit)  
 - [WAN2GP](https://github.com/deepbeepmeep/Wan2GP)  
 - [SelfForcing](https://github.com/guandeh17/Self-Forcing)  
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 430c8140c..049386fd9 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 430c8140c0cdda5e82a80f44ee6961e0d803e516
+Subproject commit 049386fd9d5c2d9b30cc9136be1e1bcda13d81c6
diff --git a/html/previews.json b/html/previews.json
index 6f77db164..4f3354c86 100644
--- a/html/previews.json
+++ b/html/previews.json
@@ -20,10 +20,11 @@
   "HiDream-I1-Full": "models/Reference/HiDream-I1 Full",
   "lodestones--Chroma1-Base": "models/Reference/lodestones--Chroma-Base.jpg",
   "lodestones--Chroma1-HD": "models/Reference/lodestones--Chroma-HD.jpg",
-  "chroma-unlocked-v50": "models/Reference/lodestones Chroma Unlocked HD",
-  "chroma-unlocked-v50-annealed": "models/Reference/lodestones Chroma Unlocked HD",
+  "chroma-unlocked-v50": "models/Reference/lodestones--Chroma-detail.jpg",
+  "chroma-unlocked-v50-annealed": "models/Reference/lodestones--Chroma-annealed.jpg",
   "vladmandic--Qwen-Lightning": "models/Reference/Qwen-Lightning.jpg",
   "vladmandic--Qwen-Lightning-Edit": "models/Reference/Qwen-Lightning.jpg",
-  "Wan-AI--Wan2.2-T2V-A14B-Diffusers": "models/Reference/Wan2.2-T2V-A14B.jpg",
-  "Wan-AI--Wan2.1-T2V-14B-Diffusers": "models/Reference/Wan-AI--Wan2.1.jpg"
+  "Wan-AI--Wan2.2-T2V-A14B-Diffusers": "models/Reference/Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
+  "Wan-AI--Wan2.1-T2V-14B-Diffusers": "models/Reference/Wan-AI--Wan2.1-T2V-14B-Diffusers.jpg",
+  "linoyts--Wan2.2-VACE-Fun-14B-diffusers": "models/Reference/Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg"
 }
diff --git a/html/reference.json b/html/reference.json
index ee0ccf103..b21dd95f7 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -139,6 +139,42 @@
     "extras": "sampler: Default, cfg_scale: 4.5"
   },
 
+  "Qwen-Image": {
+    "path": "Qwen/Qwen-Image",
+    "preview": "Qwen--Qwen-Image.jpg",
+    "desc": " Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing.",
+    "skip": true,
+    "extras": ""
+  },
+  "Qwen-Image-Edit": {
+    "path": "Qwen/Qwen-Image-Edit",
+    "preview": "Qwen--Qwen-Image-Edit.jpg",
+    "desc": "Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.",
+    "skip": true,
+    "extras": ""
+  },
+  "Qwen-Image-Lightning": {
+    "path": "vladmandic/Qwen-Lightning",
+    "preview": "vladmandic--Qwen-Lightning.jpg",
+    "desc": "Qwen-Lightning is step-distilled from Qwen-Image to allow for generation in 8 steps.",
+    "skip": true,
+    "extras": "steps: 8"
+  },
+  "Qwen-Image-Distill": {
+    "path": "SahilCarterr/Qwen-Image-Distill-Full",
+    "preview": "SahilCarterr--Qwen-Image-Distill-Full.jpg",
+    "desc": "Qwen-Image-Distill is a distilled and accelerated version of Qwen-Image by DiffSynth-Studio.",
+    "skip": true,
+    "extras": "steps: 15"
+  },
+  "Qwen-Image-Lightning-Edit": {
+    "path": "vladmandic/Qwen-Lightning-Edit",
+    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "desc": " Qwen-Lightning-Edit is step-distilled from Qwen-Image-Edit to allow for generation in 8 steps.",
+    "skip": true,
+    "extras": "steps: 8"
+  },
+
   "lodestones Chroma1 HD": {
     "path": "lodestones/Chroma1-HD",
     "preview": "lodestones--Chroma-HD.jpg",
@@ -182,42 +218,6 @@
     "extras": ""
   },
 
-  "Qwen-Image": {
-    "path": "Qwen/Qwen-Image",
-    "preview": "Qwen--Qwen-Image.jpg",
-    "desc": " Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing.",
-    "skip": true,
-    "extras": ""
-  },
-  "Qwen-Image-Edit": {
-    "path": "Qwen/Qwen-Image-Edit",
-    "preview": "Qwen--Qwen-Image-Edit.jpg",
-    "desc": "Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.",
-    "skip": true,
-    "extras": ""
-  },
-  "Qwen-Image-Lightning": {
-    "path": "vladmandic/Qwen-Lightning",
-    "preview": "vladmandic--Qwen-Lightning.jpg",
-    "desc": "Qwen-Lightning is step-distilled from Qwen-Image to allow for generation in 8 steps.",
-    "skip": true,
-    "extras": "steps: 8"
-  },
-  "Qwen-Image-Distill": {
-    "path": "SahilCarterr/Qwen-Image-Distill-Full",
-    "preview": "SahilCarterr--Qwen-Image-Distill-Full.jpg",
-    "desc": "Qwen-Image-Distill is a distilled and accelerated version of Qwen-Image by DiffSynth-Studio.",
-    "skip": true,
-    "extras": "steps: 15"
-  },
-  "Qwen-Image-Lightning-Edit": {
-    "path": "vladmandic/Qwen-Lightning-Edit",
-    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
-    "desc": " Qwen-Lightning-Edit is step-distilled from Qwen-Image-Edit to allow for generation in 8 steps.",
-    "skip": true,
-    "extras": "steps: 8"
-  },
-
   "Ostris Flex.2 Preview": {
     "path": "ostris/Flex.2-preview",
     "preview": "ostris--Flex.2-preview.jpg",
@@ -268,6 +268,13 @@
     "skip": true,
     "extras": "sampler: Default"
   },
+  "Wan-AI Wan2.2 14B VACE": {
+    "path": "linoyts/Wan2.2-VACE-Fun-14B-diffusers",
+    "preview": "Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
+    "desc": "Wan2.2, offering more powerful capabilities, better performance, and superior visual quality. With Wan2.2, we have focused on incorporating the following technical innovations: MoE Architecture, Data Scalling, Cinematic Aesthetics, Efficient High-Definition Hybrid",
+    "skip": true,
+    "extras": "sampler: Default"
+  },
 
   "Freepik F-Lite": {
     "path": "Freepik/F-Lite",
diff --git a/installer.py b/installer.py
index f307ba7ba..40c56348b 100644
--- a/installer.py
+++ b/installer.py
@@ -605,7 +605,7 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = '5e181eddfe7e44c1444a2511b0d8e21d177850a0' # diffusers commit hash
+    sha = 'efb7a299af46d739dec6a57a5d2814165fba24b5' # diffusers commit hash
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''
@@ -1636,7 +1636,6 @@ def add_args(parser):
     group_http.add_argument("--cors-regex", type=str, default=os.environ.get("SD_CORSREGEX", None), help="Allowed CORS origins as regular expression, default: %(default)s")
     group_http.add_argument('--subpath', type=str, default=os.environ.get("SD_SUBPATH", None), help='Customize the URL subpath for usage with reverse proxy')
     group_http.add_argument("--autolaunch", default=os.environ.get("SD_AUTOLAUNCH", False), action='store_true', help="Open the UI URL in the system's default browser upon launch")
-    group_http.add_argument('--docs', default=not os.environ.get("SD_NODOCS", False), action='store_true', help = "Mount API docs, default: %(default)s")
     group_http.add_argument("--auth", type=str, default=os.environ.get("SD_AUTH", None), help='Set access authentication like "user:pwd,user:pwd""')
     group_http.add_argument("--auth-file", type=str, default=os.environ.get("SD_AUTHFILE", None), help='Set access authentication using file, default: %(default)s')
     group_http.add_argument("--allowed-paths", nargs='+', default=[], type=str, required=False, help="add additional paths to paths allowed for web access")
@@ -1660,7 +1659,7 @@ def add_args(parser):
     group_log.add_argument('--debug', default=not os.environ.get("SD_NODEBUG",False), action='store_true', help="Run with debug logging, default: %(default)s")
     group_log.add_argument("--trace", default=os.environ.get("SD_TRACE", False), action='store_true', help="Run with trace logging, default: %(default)s")
     group_log.add_argument("--profile", default=os.environ.get("SD_PROFILE", False), action='store_true', help="Run profiler, default: %(default)s")
-    group_log.add_argument('--docs', default=os.environ.get("SD_DOCS", False), action='store_true', help="Mount API docs, default: %(default)s")
+    group_log.add_argument('--docs', default=not os.environ.get("SD_NODOCS", False), action='store_true', help = "Mount API docs, default: %(default)s")
     group_log.add_argument("--api-log", default=not os.environ.get("SD_NOAPILOG", False), action='store_true', help="Log all API requests")
 
     group_nargs = parser.add_argument_group('Other')
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 5d40d8db0..267d779f1 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -128,6 +128,8 @@ def task_specific_kwargs(p, model):
         }
     if model.__class__.__name__ == 'WanImageToVideoPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
         task_args['image'] = p.init_images[0]
+    if model.__class__.__name__ == 'WanVACEPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
+        task_args['reference_images'] = p.init_images
 
     if debug_enabled:
         debug_log(f'Process task specific args: {task_args}')
@@ -307,6 +309,14 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
             args['control_strength'] = p.denoising_strength
             args['width'] = p.width
             args['height'] = p.height
+    if 'WanVACEPipeline' in model.__class__.__name__:
+        if isinstance(args['prompt'], list):
+            args['prompt'] = args['prompt'][0] if len(args['prompt']) > 0 else ''
+        if isinstance(args.get('negative_prompt', None), list):
+            args['negative_prompt'] = args['negative_prompt'][0] if len(args['negative_prompt']) > 0 else ''
+        if isinstance(args['generator'], list) and len(args['generator']) > 0:
+            args['generator'] = args['generator'][0]
+
     # set callbacks
     if 'prior_callback_steps' in possible:  # Wuerstchen / Cascade
         args['prior_callback_steps'] = 1
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 8353be172..d29cfc0e5 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -487,7 +487,7 @@ def validate_pipeline(p: processing.StableDiffusionProcessing):
             if m.repo_cls is not None:
                 models_cls.append(m.repo_cls.__name__)
     is_video_model = shared.sd_model.__class__.__name__ in models_cls
-    override_video_pipelines = ['WanPipeline', 'WanImageToVideoPipeline']
+    override_video_pipelines = ['WanPipeline', 'WanImageToVideoPipeline', 'WanVACEPipeline']
     is_video_pipeline = ('video' in p.__class__.__name__.lower()) or (shared.sd_model.__class__.__name__ in override_video_pipelines)
     if is_video_model and not is_video_pipeline:
         shared.log.error(f'Mismatch: type={shared.sd_model_type} cls={shared.sd_model.__class__.__name__} request={p.__class__.__name__} video model with non-video pipeline')
diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py
index 976ab0c32..15ee19822 100644
--- a/modules/processing_helpers.py
+++ b/modules/processing_helpers.py
@@ -372,7 +372,7 @@ def calculate_base_steps(p, use_denoise_start, use_refiner_start):
     if len(getattr(p, 'timesteps', [])) > 0:
         return None
     cls = shared.sd_model.__class__.__name__
-    if 'Flex' in cls or 'Kontext' in cls or 'Edit' in cls:
+    if 'Flex' in cls or 'Kontext' in cls or 'Edit' in cls or 'Wan' in cls:
         steps = p.steps
     elif not is_txt2img():
         if cls in sd_models.i2i_pipes:
@@ -393,7 +393,7 @@ def calculate_base_steps(p, use_denoise_start, use_refiner_start):
 
 def calculate_hires_steps(p):
     cls = shared.sd_model.__class__.__name__
-    if 'Flex' in cls or 'HiDreamImageEditingPipeline' in cls or 'Kontext' in cls:
+    if 'Flex' in cls or 'Kontext' in cls or 'Edit' in cls or 'Wan' in cls:
         steps = p.steps
     elif p.hr_second_pass_steps > 0:
         steps = (p.hr_second_pass_steps // p.denoising_strength) + 1
@@ -407,7 +407,7 @@ def calculate_hires_steps(p):
 
 def calculate_refiner_steps(p):
     cls = shared.sd_model.__class__.__name__
-    if 'Flex' in cls or 'HiDreamImageEditingPipeline' in cls or 'Kontext' in cls:
+    if 'Flex' in cls or 'Kontext' in cls or 'Edit' in cls or 'Wan' in cls:
         steps = p.steps
     elif "StableDiffusionXL" in shared.sd_refiner.__class__.__name__:
         if p.refiner_start > 0 and p.refiner_start < 1:
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index bf951e2c5..be0379f66 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -18,6 +18,7 @@ offload_post = ['h1']
 offload_hook_instance = None
 balanced_offload_exclude = ['CogView4Pipeline', 'MeissonicPipeline']
 accelerate_dtype_byte_size = None
+move_stream = None
 
 
 def dtype_byte_size(dtype: torch.dtype):
@@ -176,7 +177,7 @@ class OffloadHook(accelerate.hooks.ModelHook):
         self.last_post = None
         self.last_cls = None
         gpu = f'{(shared.gpu_memory * shared.opts.diffusers_offload_min_gpu_memory):.2f}-{(shared.gpu_memory * shared.opts.diffusers_offload_max_gpu_memory):.2f}:{shared.gpu_memory:.2f}'
-        shared.log.info(f'Offload: type=balanced op=init watermark={self.min_watermark}-{self.max_watermark} gpu={gpu} cpu={shared.cpu_memory:.3f} limit={shared.opts.cuda_mem_fraction:.2f} always={self.offload_always} never={self.offload_never} pre={shared.opts.diffusers_offload_pre}')
+        shared.log.info(f'Offload: type=balanced op=init watermark={self.min_watermark}-{self.max_watermark} gpu={gpu} cpu={shared.cpu_memory:.3f} limit={shared.opts.cuda_mem_fraction:.2f} always={self.offload_always} never={self.offload_never} pre={shared.opts.diffusers_offload_pre} streams={shared.opts.diffusers_offload_streams}')
         self.validate()
         super().__init__()
 
@@ -210,8 +211,12 @@ class OffloadHook(accelerate.hooks.ModelHook):
 
     def pre_forward(self, module, *args, **kwargs):
         _id = id(module)
-        if (self.last_pre != _id) and (module.__class__.__name__ != self.last_cls) and self.offload_allowed(module): # offload every other module first time when new module starts pre-forward
+
+        do_offload = (self.last_pre != _id) or (module.__class__.__name__ != self.last_cls)
+
+        if do_offload and self.offload_allowed(module): # offload every other module first time when new module starts pre-forward
             if shared.opts.diffusers_offload_pre:
+                t0 = time.time()
                 debug_move(f'Offload: type=balanced op=pre module={module.__class__.__name__}')
                 for pipe in get_pipe_variants():
                     for module_name in get_module_names(pipe):
@@ -220,15 +225,16 @@ class OffloadHook(accelerate.hooks.ModelHook):
                         if (_id != id(module_instance)) and (module_cls not in self.offload_never) and (not devices.same_device(module_instance.device, devices.cpu)):
                             apply_balanced_offload_to_module(module_instance, op='pre')
                 self.last_cls = module.__class__.__name__
-        self.last_pre = _id
+                process_timer.add('offload', time.time() - t0)
 
         if not devices.same_device(module.device, devices.device): # move-to-device
+            t0 = time.time()
             device_index = torch.device(devices.device).index
             if device_index is None:
                 device_index = 0
             max_memory = { device_index: self.gpu, "cpu": self.cpu }
             device_map = getattr(module, "balanced_offload_device_map", None)
-            if device_map is None or max_memory != getattr(module, "balanced_offload_max_memory", None):
+            if (device_map is None) or (max_memory != getattr(module, "balanced_offload_max_memory", None)):
                 device_map = accelerate.infer_auto_device_map(module, max_memory=max_memory)
             offload_dir = getattr(module, "offload_dir", os.path.join(shared.opts.accelerate_offload_path, module.__class__.__name__))
             if devices.backend == "directml":
@@ -241,13 +247,15 @@ class OffloadHook(accelerate.hooks.ModelHook):
             module._hf_hook.execution_device = torch.device(devices.device) # pylint: disable=protected-access
             module.balanced_offload_device_map = device_map
             module.balanced_offload_max_memory = max_memory
+            process_timer.add('onload', time.time() - t0)
 
         if debug:
-            for pipe in get_pipe_variants():
+            for _i, pipe in enumerate(get_pipe_variants()):
                 for module_name in get_module_names(pipe):
                     module_instance = getattr(pipe, module_name, None)
-                    shared.log.trace(f'Offload: type=balanced op=pre:status module={module_instance.__class__.__name__} device={module_instance.device} dtype={module_instance.dtype}')
+                    shared.log.trace(f'Offload: type=balanced op=pre:status forward={module.__class__.__name__} module={module_name} class={module_instance.__class__.__name__} pipe={_i} device={module_instance.device} dtype={module_instance.dtype}')
 
+        self.last_pre = _id
         return args, kwargs
 
     def post_forward(self, module, output):
@@ -283,6 +291,7 @@ def get_module_names(pipe=None, exclude=[]):
         modules_names = get_signature(pipe).keys()
     modules_names = [m for m in modules_names if m not in exclude and not m.startswith('_')]
     modules_names = [m for m in modules_names if isinstance(getattr(pipe, m, None), torch.nn.Module)]
+    modules_names = list(sorted(set(modules_names)))
     return modules_names
 
 
@@ -308,6 +317,17 @@ def get_module_sizes(pipe=None, exclude=[]):
 
 
 def move_module_to_cpu(module, op='unk', force:bool=False):
+    def do_move(module):
+        if shared.opts.diffusers_offload_streams:
+            global move_stream # pylint: disable=global-statement
+            if move_stream is None:
+                move_stream = torch.cuda.Stream(device=devices.device)
+            with torch.cuda.stream(move_stream):
+                module = module.to(devices.cpu)
+        else:
+            module = module.to(devices.cpu)
+        return module
+
     try:
         module_name = getattr(module, "module_name", module.__class__.__name__)
         module_size = offload_hook_instance.offload_map.get(module_name, offload_hook_instance.model_size())
@@ -318,17 +338,17 @@ def move_module_to_cpu(module, op='unk', force:bool=False):
         op = f'{op}:skip'
         if force:
             op = f'{op}:force'
-            module = module.to(devices.cpu)
+            module = do_move(module)
             used_gpu -= module_size
         elif module_cls in offload_hook_instance.offload_never:
             op = f'{op}:never'
         elif module_cls in offload_hook_instance.offload_always:
             op = f'{op}:always'
-            module = module.to(devices.cpu)
+            module = do_move(module)
             used_gpu -= module_size
         elif perc_gpu > shared.opts.diffusers_offload_min_gpu_memory:
             op = f'{op}:mem'
-            module = module.to(devices.cpu)
+            module = do_move(module)
             used_gpu -= module_size
         if debug:
             quant = getattr(module, "quantization_method", None)
diff --git a/modules/shared.py b/modules/shared.py
index a969c81b0..25db15490 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -170,6 +170,7 @@ options_templates.update(options_section(('offload', "Model Offloading"), {
     "diffusers_offload_nonblocking": OptionInfo(False, "Non-blocking move operations"),
     "offload_balanced_sep": OptionInfo("<h2>Balanced Offload</h2>", "", gr.HTML),
     "diffusers_offload_pre": OptionInfo(True, "Offload during pre-forward"),
+    "diffusers_offload_streams": OptionInfo(False, "Offload using streams"),
     "diffusers_offload_min_gpu_memory": OptionInfo(startup_offload_min_gpu, "Offload low watermark", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01 }),
     "diffusers_offload_max_gpu_memory": OptionInfo(startup_offload_max_gpu, "Offload GPU high watermark", gr.Slider, {"minimum": 0.1, "maximum": 1, "step": 0.01 }),
     "diffusers_offload_max_cpu_memory": OptionInfo(0.90, "Offload CPU high watermark", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False }),
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index 1d320f760..fb77c855b 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -14,6 +14,8 @@ version_map = {
     "SDXL Hyper": "SD XL",
     "StableDiffusion3": "SD 3",
     "StableDiffusionXL": "SD XL",
+    "WanToVideo": "Wan",
+    "WanVACE": "Wan",
 }
 
 class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
diff --git a/modules/video_models/models_def.py b/modules/video_models/models_def.py
index 6d92db375..b9ff5c260 100644
--- a/modules/video_models/models_def.py
+++ b/modules/video_models/models_def.py
@@ -169,6 +169,12 @@ models = {
               te_cls=transformers.T5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel,
               dit_folder=("transformer", "transformer_2")),
+        Model(name='WAN 2.2 14B VACE',
+              url='https://huggingface.co/Wan-AI/Wan2.2-14B-VACE-T2V-Diffusers',
+              repo='linoyts/Wan2.2-VACE-Fun-14B-diffusers',
+              repo_cls=diffusers.WanVACEPipeline,
+              te_cls=transformers.T5EncoderModel,
+              dit_cls=diffusers.WanVACETransformer3DModel),
         Model(name='WAN 2.1 1.3B T2V',
               url='https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
               repo='Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
@@ -204,13 +210,13 @@ models = {
               repo='Wan-AI/Wan2.1-VACE-1.3B-diffusers',
               repo_cls=diffusers.WanVACEPipeline,
               te_cls=transformers.T5EncoderModel,
-              dit_cls=diffusers.WanTransformer3DModel),
+              dit_cls=diffusers.WanVACETransformer3DModel),
         Model(name='WAN 2.1 VACE 14B',
               url='https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-diffusers',
               repo='Wan-AI/Wan2.1-VACE-14B-diffusers',
               repo_cls=diffusers.WanVACEPipeline,
               te_cls=transformers.T5EncoderModel,
-              dit_cls=diffusers.WanTransformer3DModel),
+              dit_cls=diffusers.WanVACETransformer3DModel),
     ],
     'SkyReels V2': [
         Model(name='None'),
diff --git a/modules/video_models/video_overrides.py b/modules/video_models/video_overrides.py
index 5ce02e20a..53c2915ee 100644
--- a/modules/video_models/video_overrides.py
+++ b/modules/video_models/video_overrides.py
@@ -32,19 +32,22 @@ def set_overrides(p: processing.StableDiffusionProcessingVideo, selected: Model)
     if selected.name == 'Latte 1 T2V':
         p.task_args['enable_temporal_attentions'] = True
         p.task_args['video_length'] = 16 * (max(p.frames // 16, 1))
+    # SkyReels
+    if 'SkyReelsV2DiffusionForcing' in cls:
+        p.task_args['overlap_history'] = 17
     # LTX
     if cls == 'LTXImageToVideoPipeline' or cls == 'LTXConditionPipeline':
         p.task_args['generator'] = None
     if cls == 'LTXConditionPipeline':
         p.task_args['strength'] = p.denoising_strength
+    if 'LTX' in cls:
+        p.task_args['width'] = 32 * (p.width // 32)
+        p.task_args['height'] = 32 * (p.height // 32)
     # WAN
     if 'Wan' in cls:
         p.task_args['width'] = 16 * (p.width // 16)
         p.task_args['height'] = 16 * (p.height // 16)
         p.frames = 4 * (max(p.frames // 4, 1)) + 1
-    # LTX
-    if 'LTX' in cls:
-        p.task_args['width'] = 32 * (p.width // 32)
-        p.task_args['height'] = 32 * (p.height // 32)
-    if 'SkyReelsV2DiffusionForcing' in cls:
-        p.task_args['overlap_history'] = 17
+    # WAN VACE
+    if 'WanVACEPipeline' in cls:
+        p.task_args['reference_images'] = 1
diff --git a/modules/video_models/video_run.py b/modules/video_models/video_run.py
index 508980bad..33d4f5d13 100644
--- a/modules/video_models/video_run.py
+++ b/modules/video_models/video_run.py
@@ -53,7 +53,10 @@ def generate(*args, **kwargs):
     p.do_not_save_grid = True
     p.do_not_save_samples = not save_frames
     p.outpath_samples = shared.opts.outdir_samples or shared.opts.outdir_video
-    if 'I2V' in model:
+    if 'T2V' in model:
+        if init_image is not None:
+            shared.log.warning('Video: op=T2V init image not supported')
+    elif 'I2V' in model:
         if init_image is None:
             return video_utils.queue_err('init image not set')
         p.task_args['image'] = images.resize_image(resize_mode=2, im=init_image, width=p.width, height=p.height, upscaler_name=None, output_type='pil')
@@ -66,9 +69,10 @@ def generate(*args, **kwargs):
         p.task_args['image'] = images.resize_image(resize_mode=2, im=init_image, width=p.width, height=p.height, upscaler_name=None, output_type='pil')
         p.task_args['last_image'] = images.resize_image(resize_mode=2, im=last_image, width=p.width, height=p.height, upscaler_name=None, output_type='pil')
         shared.log.debug(f'Video: op=FLF2V init={init_image} last={last_image} resized={p.task_args["image"]}')
-    elif 'T2V' in model:
+    elif 'VACE' in model:
         if init_image is not None:
-            shared.log.warning('Video: op=T2V init image not supported')
+            p.task_args['reference_images'] = [images.resize_image(resize_mode=2, im=init_image, width=p.width, height=p.height, upscaler_name=None, output_type='pil')]
+            shared.log.debug(f'Video: op=VACE reference={init_image} resized={p.task_args["reference_images"]}')
     else:
         shared.log.warning(f'Video: unknown model type "{model}"')
 
diff --git a/pipelines/model_wanai.py b/pipelines/model_wanai.py
index b0a7d8d85..e44d1eb78 100644
--- a/pipelines/model_wanai.py
+++ b/pipelines/model_wanai.py
@@ -8,6 +8,11 @@ def load_transformer(repo_id, diffusers_load_config={}, subfolder='transformer')
     load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model', device_map=True)
     fn = None
 
+    if 'VACE' in repo_id:
+        transformer_cls = diffusers.WanVACETransformer3DModel
+    else:
+        transformer_cls = diffusers.WanTransformer3DModel
+
     if shared.opts.sd_unet is not None and shared.opts.sd_unet != 'Default':
         from modules import sd_unet
         if shared.opts.sd_unet not in list(sd_unet.unet_dict):
@@ -20,7 +25,7 @@ def load_transformer(repo_id, diffusers_load_config={}, subfolder='transformer')
         transformer = None
     elif fn is not None and 'safetensors' in fn.lower():
         shared.log.debug(f'Load model: type=WanAI {subfolder}="{fn}" quant="{model_quant.get_quant(repo_id)}" args={load_args}')
-        transformer = diffusers.WanTransformer3DModel.from_single_file(
+        transformer = transformer_cls.from_single_file(
             fn,
             cache_dir=shared.opts.hfcache_dir,
             **load_args,
@@ -28,7 +33,7 @@ def load_transformer(repo_id, diffusers_load_config={}, subfolder='transformer')
         )
     else:
         shared.log.debug(f'Load model: type=WanAI {subfolder}="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
-        transformer = diffusers.WanTransformer3DModel.from_pretrained(
+        transformer = transformer_cls.from_pretrained(
             repo_id,
             subfolder=subfolder,
             cache_dir=shared.opts.hfcache_dir,
@@ -60,7 +65,7 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
     repo_id = sd_models.path_to_repo(checkpoint_info)
     sd_models.hf_auth_check(checkpoint_info)
 
-    if 'a14b' in repo_id.lower():
+    if 'a14b' in repo_id.lower() or 'fun-14b' in repo_id.lower():
         if shared.opts.model_wan_stage == 'high noise' or shared.opts.model_wan_stage == 'first':
             transformer = load_transformer(repo_id, diffusers_load_config, 'transformer')
             transformer_2 = None
@@ -83,13 +88,18 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
     boundary_ratio = shared.opts.model_wan_boundary if transformer_2 is not None else None
 
     if 'Wan2.2-I2V' in repo_id:
-        cls = diffusers.WanImageToVideoPipeline
+        pipe_cls = diffusers.WanImageToVideoPipeline
         diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanImageToVideoPipeline
+    elif 'Wan2.2-VACE' in repo_id:
+        pipe_cls = diffusers.WanVACEPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanVACEPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanVACEPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["wanai"] = diffusers.WanVACEPipeline
     else:
-        cls = diffusers.WanPipeline
+        pipe_cls = diffusers.WanPipeline
         diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanPipeline
-    shared.log.debug(f'Load model: type=WanAI model="{checkpoint_info.name}" repo="{repo_id}" cls={cls.__name__} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args} stage="{shared.opts.model_wan_stage}" boundary={boundary_ratio}')
-    pipe = cls.from_pretrained(
+    shared.log.debug(f'Load model: type=WanAI model="{checkpoint_info.name}" repo="{repo_id}" cls={pipe_cls.__name__} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args} stage="{shared.opts.model_wan_stage}" boundary={boundary_ratio}')
+    pipe = pipe_cls.from_pretrained(
         repo_id,
         transformer=transformer,
         transformer_2=transformer_2,

From e83df5071719ff223497590b44ae4bb5ca5af2b9 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 17 Sep 2025 19:40:25 +0300
Subject: [PATCH 007/230] IPEX fix missing dtype from randn

---
 modules/intel/ipex/hijacks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index feb31bec2..e617eed3a 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -322,9 +322,9 @@ def torch_randn(*args, device=None, dtype=None, **kwargs):
     if dtype is bytes:
         dtype = None
     if check_cuda(device):
-        return original_torch_randn(*args, device=return_xpu(device), **kwargs)
+        return original_torch_randn(*args, device=return_xpu(device), dtype=dtype, **kwargs)
     else:
-        return original_torch_randn(*args, device=device, **kwargs)
+        return original_torch_randn(*args, device=device, dtype=dtype, **kwargs)
 
 original_torch_ones = torch.ones
 @wraps(torch.ones)

From cc51f79508fa831a15918074e4670b4302eb062d Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 17 Sep 2025 19:53:05 +0300
Subject: [PATCH 008/230] IPEX remove outdated hijacks

---
 modules/intel/ipex/hijacks.py | 168 ++++++++--------------------------
 1 file changed, 37 insertions(+), 131 deletions(-)

diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index e617eed3a..e52327971 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -16,14 +16,6 @@ torch_version[0], torch_version[1] = int(torch_version[0]), int(torch_version[1]
 
 device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp64_dtype") else torch.xpu.get_device_properties(devices.device).has_fp64
 
-if os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '0':
-    if torch_version[0] > 2 or (torch_version[0] == 2 and torch_version[1] >= 7):
-        use_dynamic_attention = False # torch 2.7 has flash atten support
-    else:
-        use_dynamic_attention = True
-else:
-    use_dynamic_attention = bool(os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '1')
-
 # pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
 
 class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
@@ -106,6 +98,30 @@ def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corn
         align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)
 
 
+# SwinIR BF16:
+original_functional_pad = torch.nn.functional.pad
+@wraps(torch.nn.functional.pad)
+def functional_pad(input, pad, mode='constant', value=None):
+    if mode == 'reflect' and input.dtype == torch.bfloat16:
+        return original_functional_pad(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16)
+    else:
+        return original_functional_pad(input, pad, mode=mode, value=value)
+
+# Diffusers FreeU
+original_fft_fftn = torch.fft.fftn
+@wraps(torch.fft.fftn)
+def fft_fftn(input, s=None, dim=None, norm=None, *, out=None):
+    return_dtype = input.dtype
+    return original_fft_fftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype)
+
+# Diffusers FreeU
+original_fft_ifftn = torch.fft.ifftn
+@wraps(torch.fft.ifftn)
+def fft_ifftn(input, s=None, dim=None, norm=None, *, out=None):
+    return_dtype = input.dtype
+    return original_fft_ifftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype)
+
+
 # Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
 original_from_numpy = torch.from_numpy
 @wraps(torch.from_numpy)
@@ -126,120 +142,6 @@ def as_tensor(data, dtype=None, device=None):
         return original_as_tensor(data, dtype=dtype, device=device)
 
 
-if not use_dynamic_attention:
-    original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
-else:
-    # 32 bit attention workarounds for Alchemist:
-    try:
-        from .attention import dynamic_scaled_dot_product_attention as original_scaled_dot_product_attention
-    except ImportError:
-        original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
-
-@wraps(torch.nn.functional.scaled_dot_product_attention)
-def scaled_dot_product_attention(query: torch.FloatTensor, key: torch.FloatTensor, value: torch.FloatTensor, attn_mask: Optional[torch.FloatTensor] = None, dropout_p: float = 0.0, is_causal: bool = False, scale: Optional[float] = None, enable_gqa: bool = False, **kwargs) -> torch.FloatTensor:
-    if query.dtype != key.dtype:
-        key = key.to(dtype=query.dtype)
-    if query.dtype != value.dtype:
-        value = value.to(dtype=query.dtype)
-    if attn_mask is not None and query.dtype != attn_mask.dtype:
-        attn_mask = attn_mask.to(dtype=query.dtype)
-    if enable_gqa:
-        kwargs["enable_gqa"] = enable_gqa
-    result = original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
-    if result.dtype != query.dtype:
-        result = result.to(dtype=query.dtype)
-    return result
-
-# Data Type Errors:
-original_torch_bmm = torch.bmm
-@wraps(torch.bmm)
-def torch_bmm(input, mat2, *, out=None):
-    if input.dtype != mat2.dtype:
-        mat2 = mat2.to(dtype=input.dtype)
-    return original_torch_bmm(input, mat2, out=out)
-
-# Diffusers FreeU
-original_fft_fftn = torch.fft.fftn
-@wraps(torch.fft.fftn)
-def fft_fftn(input, s=None, dim=None, norm=None, *, out=None):
-    return_dtype = input.dtype
-    return original_fft_fftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype)
-
-# Diffusers FreeU
-original_fft_ifftn = torch.fft.ifftn
-@wraps(torch.fft.ifftn)
-def fft_ifftn(input, s=None, dim=None, norm=None, *, out=None):
-    return_dtype = input.dtype
-    return original_fft_ifftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype)
-
-# A1111 FP16
-original_functional_group_norm = torch.nn.functional.group_norm
-@wraps(torch.nn.functional.group_norm)
-def functional_group_norm(input, num_groups, weight=None, bias=None, eps=1e-05):
-    if weight is not None and input.dtype != weight.data.dtype:
-        input = input.to(dtype=weight.data.dtype)
-    if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype:
-        bias.data = bias.data.to(dtype=weight.data.dtype)
-    return original_functional_group_norm(input, num_groups, weight=weight, bias=bias, eps=eps)
-
-# A1111 BF16
-original_functional_layer_norm = torch.nn.functional.layer_norm
-@wraps(torch.nn.functional.layer_norm)
-def functional_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
-    if weight is not None and input.dtype != weight.data.dtype:
-        input = input.to(dtype=weight.data.dtype)
-    if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype:
-        bias.data = bias.data.to(dtype=weight.data.dtype)
-    return original_functional_layer_norm(input, normalized_shape, weight=weight, bias=bias, eps=eps)
-
-# Training
-original_functional_linear = torch.nn.functional.linear
-@wraps(torch.nn.functional.linear)
-def functional_linear(input, weight, bias=None):
-    if input.dtype != weight.data.dtype:
-        input = input.to(dtype=weight.data.dtype)
-    if bias is not None and bias.data.dtype != weight.data.dtype:
-        bias.data = bias.data.to(dtype=weight.data.dtype)
-    return original_functional_linear(input, weight, bias=bias)
-
-original_functional_conv1d = torch.nn.functional.conv1d
-@wraps(torch.nn.functional.conv1d)
-def functional_conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if input.dtype != weight.data.dtype:
-        input = input.to(dtype=weight.data.dtype)
-    if bias is not None and bias.data.dtype != weight.data.dtype:
-        bias.data = bias.data.to(dtype=weight.data.dtype)
-    return original_functional_conv1d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-original_functional_conv2d = torch.nn.functional.conv2d
-@wraps(torch.nn.functional.conv2d)
-def functional_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if input.dtype != weight.data.dtype:
-        input = input.to(dtype=weight.data.dtype)
-    if bias is not None and bias.data.dtype != weight.data.dtype:
-        bias.data = bias.data.to(dtype=weight.data.dtype)
-    return original_functional_conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-# LTX Video
-original_functional_conv3d = torch.nn.functional.conv3d
-@wraps(torch.nn.functional.conv3d)
-def functional_conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if input.dtype != weight.data.dtype:
-        input = input.to(dtype=weight.data.dtype)
-    if bias is not None and bias.data.dtype != weight.data.dtype:
-        bias.data = bias.data.to(dtype=weight.data.dtype)
-    return original_functional_conv3d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-# SwinIR BF16:
-original_functional_pad = torch.nn.functional.pad
-@wraps(torch.nn.functional.pad)
-def functional_pad(input, pad, mode='constant', value=None):
-    if mode == 'reflect' and input.dtype == torch.bfloat16:
-        return original_functional_pad(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16)
-    else:
-        return original_functional_pad(input, pad, mode=mode, value=value)
-
-
 original_torch_tensor = torch.tensor
 @wraps(torch.tensor)
 def torch_tensor(data, *args, dtype=None, device=None, **kwargs):
@@ -438,18 +340,10 @@ def ipex_hijacks():
     torch.amp.autocast_mode.autocast.__init__ = autocast_init
 
     torch.nn.functional.interpolate = interpolate
-    torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
-    torch.nn.functional.group_norm = functional_group_norm
-    torch.nn.functional.layer_norm = functional_layer_norm
-    torch.nn.functional.linear = functional_linear
-    torch.nn.functional.conv1d = functional_conv1d
-    torch.nn.functional.conv2d = functional_conv2d
-    torch.nn.functional.conv3d = functional_conv3d
     torch.nn.functional.pad = functional_pad
-
-    torch.bmm = torch_bmm
     torch.fft.fftn = fft_fftn
     torch.fft.ifftn = fft_ifftn
+
     if not device_supports_fp64:
         torch.from_numpy = from_numpy
         torch.as_tensor = as_tensor
@@ -460,6 +354,18 @@ def ipex_hijacks():
     except Exception:
         pass
 
+    if os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '0':
+        if torch_version[0] > 2 or (torch_version[0] == 2 and torch_version[1] >= 7):
+            use_dynamic_attention = False # torch 2.7 has flash atten support
+        else:
+            use_dynamic_attention = True
+    else:
+        use_dynamic_attention = bool(os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '1')
+
+    if use_dynamic_attention:
+        from .attention import dynamic_scaled_dot_product_attention
+        torch.nn.functional.scaled_dot_product_attention = dynamic_scaled_dot_product_attention
+
     # AMP:
     torch.amp.grad_scaler.GradScaler.__init__ = GradScaler_init
     torch.is_autocast_enabled = torch_is_autocast_enabled

From 2842f5e3aa60ff3a5daad33eccd7f43015617c06 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 17 Sep 2025 19:58:21 +0300
Subject: [PATCH 009/230] cleanup

---
 modules/intel/ipex/hijacks.py | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index e52327971..da24dcbcb 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 import os
 from functools import wraps
 from contextlib import nullcontext
@@ -18,28 +16,27 @@ device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp6
 
 # pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
 
-class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
-    def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument
-        if isinstance(device_ids, list) and len(device_ids) > 1:
-            errors.log.error("IPEX backend doesn't support DataParallel on multiple XPU devices")
-        return module.to(devices.device)
 
 def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
     return nullcontext()
 
+
 @property
 def is_cuda(self):
     return self.device.type == "xpu" or self.device.type == "cuda"
 
+
 def check_device_type(device, device_type: str) -> bool:
     if device is None or type(device) not in {str, int, torch.device}:
         return False
     else:
         return bool(torch.device(device).type == device_type)
 
+
 def check_cuda(device) -> bool:
     return bool(isinstance(device, int) or check_device_type(device, "cuda"))
 
+
 def return_xpu(device): # keep the device instance type, aka return string if the input is string
     return devices.device if device is None else f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device(f"xpu:{device.index}" if device.index is not None else "xpu") if isinstance(device, torch.device) else "xpu"
 
@@ -107,6 +104,7 @@ def functional_pad(input, pad, mode='constant', value=None):
     else:
         return original_functional_pad(input, pad, mode=mode, value=value)
 
+
 # Diffusers FreeU
 original_fft_fftn = torch.fft.fftn
 @wraps(torch.fft.fftn)
@@ -114,6 +112,7 @@ def fft_fftn(input, s=None, dim=None, norm=None, *, out=None):
     return_dtype = input.dtype
     return original_fft_fftn(input.to(dtype=torch.float32), s=s, dim=dim, norm=norm, out=out).to(dtype=return_dtype)
 
+
 # Diffusers FreeU
 original_fft_ifftn = torch.fft.ifftn
 @wraps(torch.fft.ifftn)
@@ -131,6 +130,7 @@ def from_numpy(ndarray):
     else:
         return original_from_numpy(ndarray)
 
+
 original_as_tensor = torch.as_tensor
 @wraps(torch.as_tensor)
 def as_tensor(data, dtype=None, device=None):
@@ -156,6 +156,7 @@ def torch_tensor(data, *args, dtype=None, device=None, **kwargs):
                 dtype = torch.float32
     return original_torch_tensor(data, *args, dtype=dtype, device=device, **kwargs)
 
+
 torch.Tensor.original_Tensor_to = torch.Tensor.to
 @wraps(torch.Tensor.to)
 def Tensor_to(self, device=None, *args, **kwargs):
@@ -169,6 +170,7 @@ def Tensor_to(self, device=None, *args, **kwargs):
             device = torch.float32
     return self.original_Tensor_to(device, *args, **kwargs)
 
+
 original_Tensor_cuda = torch.Tensor.cuda
 @wraps(torch.Tensor.cuda)
 def Tensor_cuda(self, device=None, *args, **kwargs):
@@ -177,6 +179,7 @@ def Tensor_cuda(self, device=None, *args, **kwargs):
     else:
         return original_Tensor_cuda(self, device, *args, **kwargs)
 
+
 original_Tensor_pin_memory = torch.Tensor.pin_memory
 @wraps(torch.Tensor.pin_memory)
 def Tensor_pin_memory(self, device=None, *args, **kwargs):
@@ -185,6 +188,7 @@ def Tensor_pin_memory(self, device=None, *args, **kwargs):
     else:
         return original_Tensor_pin_memory(self, device, *args, **kwargs)
 
+
 original_UntypedStorage_init = torch.UntypedStorage.__init__
 @wraps(torch.UntypedStorage.__init__)
 def UntypedStorage_init(*args, device=None, **kwargs):
@@ -193,6 +197,7 @@ def UntypedStorage_init(*args, device=None, **kwargs):
     else:
         return original_UntypedStorage_init(*args, device=device, **kwargs)
 
+
 if torch_version[0] > 2 or (torch_version[0] == 2 and torch_version[1] >= 4):
     original_UntypedStorage_to = torch.UntypedStorage.to
     @wraps(torch.UntypedStorage.to)
@@ -210,6 +215,7 @@ if torch_version[0] > 2 or (torch_version[0] == 2 and torch_version[1] >= 4):
         else:
             return original_UntypedStorage_cuda(self, device=device, non_blocking=non_blocking, **kwargs)
 
+
 original_torch_empty = torch.empty
 @wraps(torch.empty)
 def torch_empty(*args, device=None, **kwargs):
@@ -218,6 +224,7 @@ def torch_empty(*args, device=None, **kwargs):
     else:
         return original_torch_empty(*args, device=device, **kwargs)
 
+
 original_torch_randn = torch.randn
 @wraps(torch.randn)
 def torch_randn(*args, device=None, dtype=None, **kwargs):
@@ -228,6 +235,7 @@ def torch_randn(*args, device=None, dtype=None, **kwargs):
     else:
         return original_torch_randn(*args, device=device, dtype=dtype, **kwargs)
 
+
 original_torch_ones = torch.ones
 @wraps(torch.ones)
 def torch_ones(*args, device=None, **kwargs):
@@ -236,6 +244,7 @@ def torch_ones(*args, device=None, **kwargs):
     else:
         return original_torch_ones(*args, device=device, **kwargs)
 
+
 original_torch_zeros = torch.zeros
 @wraps(torch.zeros)
 def torch_zeros(*args, device=None, **kwargs):
@@ -244,6 +253,7 @@ def torch_zeros(*args, device=None, **kwargs):
     else:
         return original_torch_zeros(*args, device=device, **kwargs)
 
+
 original_torch_full = torch.full
 @wraps(torch.full)
 def torch_full(*args, device=None, **kwargs):
@@ -252,6 +262,7 @@ def torch_full(*args, device=None, **kwargs):
     else:
         return original_torch_full(*args, device=device, **kwargs)
 
+
 original_torch_linspace = torch.linspace
 @wraps(torch.linspace)
 def torch_linspace(*args, device=None, **kwargs):
@@ -260,6 +271,7 @@ def torch_linspace(*args, device=None, **kwargs):
     else:
         return original_torch_linspace(*args, device=device, **kwargs)
 
+
 original_torch_eye = torch.eye
 @wraps(torch.eye)
 def torch_eye(*args, device=None, **kwargs):
@@ -268,6 +280,7 @@ def torch_eye(*args, device=None, **kwargs):
     else:
         return original_torch_eye(*args, device=device, **kwargs)
 
+
 original_torch_load = torch.load
 @wraps(torch.load)
 def torch_load(f, map_location=None, *args, **kwargs):
@@ -276,6 +289,7 @@ def torch_load(f, map_location=None, *args, **kwargs):
     else:
         return original_torch_load(f, *args, map_location=map_location, **kwargs)
 
+
 @wraps(torch.cuda.synchronize)
 def torch_cuda_synchronize(device=None):
     if check_cuda(device):
@@ -283,6 +297,7 @@ def torch_cuda_synchronize(device=None):
     else:
         return torch.xpu.synchronize(device)
 
+
 @wraps(torch.cuda.device)
 def torch_cuda_device(device):
     if check_cuda(device):
@@ -290,6 +305,7 @@ def torch_cuda_device(device):
     else:
         return torch.xpu.device(device)
 
+
 @wraps(torch.cuda.set_device)
 def torch_cuda_set_device(device):
     if check_cuda(device):
@@ -297,6 +313,7 @@ def torch_cuda_set_device(device):
     else:
         torch.xpu.set_device(device)
 
+
 # torch.Generator has to be a class for isinstance checks
 original_torch_Generator = torch.Generator
 class torch_Generator(original_torch_Generator):
@@ -335,7 +352,6 @@ def ipex_hijacks():
     torch._C.Generator = torch_Generator
 
     torch.backends.cuda.sdp_kernel = return_null_context
-    torch.nn.DataParallel = DummyDataParallel
     torch.UntypedStorage.is_cuda = is_cuda
     torch.amp.autocast_mode.autocast.__init__ = autocast_init
 

From 6f969958aec01ed9a9736f677e25c6c39d89ed9b Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 17 Sep 2025 20:15:00 +0300
Subject: [PATCH 010/230] remove sdp_kernel hijack

---
 modules/intel/ipex/hijacks.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index da24dcbcb..c9be1f789 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -3,7 +3,7 @@ from functools import wraps
 from contextlib import nullcontext
 import torch
 import numpy as np
-from modules import devices, errors
+from modules import devices
 
 
 torch_version = torch.__version__[:4]
@@ -17,10 +17,6 @@ device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp6
 # pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
 
 
-def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
-    return nullcontext()
-
-
 @property
 def is_cuda(self):
     return self.device.type == "xpu" or self.device.type == "cuda"
@@ -351,7 +347,6 @@ def ipex_hijacks():
     torch.Generator = torch_Generator
     torch._C.Generator = torch_Generator
 
-    torch.backends.cuda.sdp_kernel = return_null_context
     torch.UntypedStorage.is_cuda = is_cuda
     torch.amp.autocast_mode.autocast.__init__ = autocast_init
 

From e43956460fa1118a75f55583aef62dd539210a13 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 09:08:08 -0400
Subject: [PATCH 011/230] revert force new pydantic

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/installer.py b/installer.py
index 40c56348b..28590d142 100644
--- a/installer.py
+++ b/installer.py
@@ -1256,8 +1256,12 @@ def install_gradio():
 
 
 def install_pydantic():
-    install('pydantic==2.11.7', ignore=True, quiet=True)
-    reload('pydantic', '2.11.7')
+    if args.new:
+        install('pydantic==2.11.7', ignore=True, quiet=True)
+        reload('pydantic', '2.11.7')
+    else:
+        install('pydantic==1.10.21', ignore=True, quiet=True)
+        reload('pydantic', '1.10.21')
 
 
 def install_insightface():

From daa193be374302435850d3c3e54f3c253d332af4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 09:28:58 -0400
Subject: [PATCH 012/230] delete agent-scheduler

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sd-webui-agent-scheduler | 1 -
 1 file changed, 1 deletion(-)
 delete mode 160000 extensions-builtin/sd-webui-agent-scheduler

diff --git a/extensions-builtin/sd-webui-agent-scheduler b/extensions-builtin/sd-webui-agent-scheduler
deleted file mode 160000
index a33753321..000000000
--- a/extensions-builtin/sd-webui-agent-scheduler
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit a33753321b914c6122df96d1dc0b5117d38af680

From 14804259d37208bac3fd710d5bf40eb77d3e3993 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 09:29:04 -0400
Subject: [PATCH 013/230] delete agent-scheduler

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .gitmodules  | 4 ----
 CHANGELOG.md | 6 +++++-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.gitmodules b/.gitmodules
index 0ead18186..41ed87300 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -14,10 +14,6 @@
   path = extensions-builtin/stable-diffusion-webui-rembg
   url = https://github.com/vladmandic/sd-extension-rembg
   ignore = dirty
-[submodule "extensions-builtin/sd-webui-agent-scheduler"]
-  path = extensions-builtin/sd-webui-agent-scheduler
-  url = https://github.com/ArtVentureX/sd-webui-agent-scheduler
-  ignore = dirty
 [submodule "extensions-builtin/sdnext-modernui"]
   path = extensions-builtin/sdnext-modernui
   url = https://github.com/BinaryQuantumSoul/sdnext-modernui
diff --git a/CHANGELOG.md b/CHANGELOG.md
index cc6fe5ecf..d5613d2b1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,10 +1,14 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-17
+## Update for 2025-09-18
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
+- **Extensions**
+  - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
+    it also does not work with control and video tabs which are the core of sdnext nowadays  
+    so it has been removed from built-in extensions: manual installation is still possible  
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  

From 94cb993e99969e253299433d2ea5c98f0e37ba10 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 10:22:06 -0400
Subject: [PATCH 014/230] framepack add hf auth check

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/framepack/framepack_load.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/framepack/framepack_load.py b/modules/framepack/framepack_load.py
index 0e80e8485..ed9fdde43 100644
--- a/modules/framepack/framepack_load.py
+++ b/modules/framepack/framepack_load.py
@@ -124,6 +124,10 @@ def load_model(variant:str=None, pipeline:str=None, text_encoder:str=None, text_
         sd_models.unload_model_weights()
         t0 = time.time()
 
+        sd_models.hf_auth_check(model["transformer"]["repo"])
+        sd_models.hf_auth_check(model["text_encoder"]["repo"])
+        sd_models.hf_auth_check(model["text_encoder_2"]["repo"])
+
         shared.log.debug(f'FramePack load: module=llm {model["text_encoder"]}')
         load_args, quant_args = model_quant.get_dit_args({}, module='TE', device_map=True)
         text_encoder = LlamaModel.from_pretrained(model["text_encoder"]["repo"], subfolder=model["text_encoder"]["subfolder"], cache_dir=shared.opts.hfcache_dir, **load_args, **quant_args)

From d2b9930d560a93fc7315b4546eeea10254c9636e Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 10:28:02 -0400
Subject: [PATCH 015/230] update system-info extension

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                                | 12 ++++++++----
 extensions-builtin/sd-extension-system-info |  2 +-
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d5613d2b1..b9c657cc7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,18 +5,22 @@
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
-- **Extensions**
-  - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
-    it also does not work with control and video tabs which are the core of sdnext nowadays  
-    so it has been removed from built-in extensions: manual installation is still possible  
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  
   - experimental offloading using `torch.streams`  
     enable in settings -> model offloading  
+- **Extensions**
+  - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
+    it also does not work with control and video tabs which are the core of sdnext nowadays  
+    so it has been removed from built-in extensions: manual installation is still possible  
 - **Other**
   - **logging** enable `debug`, `docs` and `api-docs` by default  
+  - **ipex** simplify internal implementation  
   - refactor to use new libraries  
+- **Fixes**
+  - add explicit hf-login before framepack load  
+  - remove forced sampler from system info benchmark  
 
 ## Update for 2025-09-15
 
diff --git a/extensions-builtin/sd-extension-system-info b/extensions-builtin/sd-extension-system-info
index 615d2f810..19a1b1b72 160000
--- a/extensions-builtin/sd-extension-system-info
+++ b/extensions-builtin/sd-extension-system-info
@@ -1 +1 @@
-Subproject commit 615d2f8103fb61779037bd81523906457fbf7277
+Subproject commit 19a1b1b722b768b821f67831a6d3ba83847bcb61

From 5606519085b40c81cca771ecbdde9af92ca56f8d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 11:40:01 -0400
Subject: [PATCH 016/230] wan init image fix

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_args.py              | 4 ++--
 modules/video_models/video_overrides.py | 3 ++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 267d779f1..98a295ca1 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -126,9 +126,9 @@ def task_specific_kwargs(p, model):
             'target_subject_category': getattr(p, 'prompt', '').split()[-1],
             'output_type': 'pil',
         }
-    if model.__class__.__name__ == 'WanImageToVideoPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
+    if (model.__class__.__name__ == 'WanImageToVideoPipeline') and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
         task_args['image'] = p.init_images[0]
-    if model.__class__.__name__ == 'WanVACEPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
+    if (model.__class__.__name__ == 'WanVACEPipeline') and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
         task_args['reference_images'] = p.init_images
 
     if debug_enabled:
diff --git a/modules/video_models/video_overrides.py b/modules/video_models/video_overrides.py
index 53c2915ee..057ae3f8a 100644
--- a/modules/video_models/video_overrides.py
+++ b/modules/video_models/video_overrides.py
@@ -50,4 +50,5 @@ def set_overrides(p: processing.StableDiffusionProcessingVideo, selected: Model)
         p.frames = 4 * (max(p.frames // 4, 1)) + 1
     # WAN VACE
     if 'WanVACEPipeline' in cls:
-        p.task_args['reference_images'] = 1
+        if (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
+            p.task_args['reference_images'] = p.init_images

From f971550bfbc97b128f75dacc6d9b348888788328 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 12:05:56 -0400
Subject: [PATCH 017/230] server recommend restart on package updates

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 1 +
 installer.py | 3 +++
 launch.py    | 3 +++
 3 files changed, 7 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b9c657cc7..7640a9655 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,7 @@
     it also does not work with control and video tabs which are the core of sdnext nowadays  
     so it has been removed from built-in extensions: manual installation is still possible  
 - **Other**
+  - server will note when restart is recommended due to package updates  
   - **logging** enable `debug`, `docs` and `api-docs` by default  
   - **ipex** simplify internal implementation  
   - refactor to use new libraries  
diff --git a/installer.py b/installer.py
index 28590d142..618e9958d 100644
--- a/installer.py
+++ b/installer.py
@@ -56,6 +56,7 @@ args = Dot({
 })
 git_commit = "unknown"
 diffusers_commit = "unknown"
+restart_required = False
 extensions_commit = { # force specific commit for extensions
     'sd-webui-controlnet': 'ecd33eb',
     'adetailer': 'a89c01d'
@@ -345,6 +346,8 @@ def installed(package, friendly: str = None, reload = False, quiet = False): # p
                             log.warning(f'Install: package="{p[0]}" installed={pkg_version} required={p[1]} allowing experimental')
                         else:
                             log.warning(f'Install: package="{p[0]}" installed={pkg_version} required={p[1]} version mismatch')
+                            global restart_required # pylint: disable=global-statement
+                            restart_required = True
                     ok = ok and (exact or args.experimental)
             else:
                 if not quiet:
diff --git a/launch.py b/launch.py
index 8201b8275..1f55a848d 100755
--- a/launch.py
+++ b/launch.py
@@ -297,6 +297,9 @@ def main():
     installer.log.info(f'Installer time: {init_summary()}')
     get_custom_args()
 
+    if installer.restart_required:
+        installer.log.warning('Restart is recommended due to packages updates...')
+
     uv, instance = start_server(immediate=True, server=None)
     t_server = time.time()
     t_monitor = time.time()

From 710983d0988b3ee19aabd9a0909ab9e85346dcb5 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 13:36:29 -0400
Subject: [PATCH 018/230] add flux.1-dev-srpo

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    | 2 ++
 html/reference.json             | 7 +++++++
 pipelines/flux/flux_nunchaku.py | 4 +++-
 3 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7640a9655..0ab6a5880 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,8 @@
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
+  - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
+    SRPO is trained by with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference  
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  
diff --git a/html/reference.json b/html/reference.json
index b21dd95f7..fc070b320 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -138,6 +138,13 @@
     "skip": true,
     "extras": "sampler: Default, cfg_scale: 4.5"
   },
+  "Tencent FLUX.1 Dev SRPO": {
+    "path": "vladmandic/flux.1-dev-SRPO",
+    "preview": "vladmandic--flux.1-dev-SRPO.jpg",
+    "desc": "FLUX.1 Dev SRPO is Tencent trained with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference",
+    "skip": true,
+    "extras": "sampler: Default, cfg_scale: 4.5"
+  },
 
   "Qwen-Image": {
     "path": "Qwen/Qwen-Image",
diff --git a/pipelines/flux/flux_nunchaku.py b/pipelines/flux/flux_nunchaku.py
index 1aba177aa..49fd77b3d 100644
--- a/pipelines/flux/flux_nunchaku.py
+++ b/pipelines/flux/flux_nunchaku.py
@@ -6,7 +6,9 @@ def load_flux_nunchaku(repo_id):
     nunchaku_precision = nunchaku.utils.get_precision()
     nunchaku_repo = None
     transformer = None
-    if 'flux.1-dev' in repo_id.lower():
+    if 'srpo' in repo_id.lower():
+        pass
+    elif 'flux.1-dev' in repo_id.lower():
         nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-dev/svdq-{nunchaku_precision}_r32-flux.1-dev.safetensors"
     elif 'flux.1-schnell' in repo_id.lower():
         nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-schnell/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"

From c8dd137b22b091a9f7db8ed7c596f6361e9d6fd9 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 18 Sep 2025 13:45:14 -0400
Subject: [PATCH 019/230] add srpo reference model

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .../Reference/vladmandic--flux.1-dev-SRPO.jpg   | Bin 0 -> 41057 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 models/Reference/vladmandic--flux.1-dev-SRPO.jpg

diff --git a/models/Reference/vladmandic--flux.1-dev-SRPO.jpg b/models/Reference/vladmandic--flux.1-dev-SRPO.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..436efbba25c7150baaedfea85f75d86a32045dbe
GIT binary patch
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literal 0
HcmV?d00001


From 47cba6b0a728f3f7358c5d8eb25543485ecb47a2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 19 Sep 2025 09:24:49 -0400
Subject: [PATCH 020/230] fix xyz with random seed

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md           |  7 ++++---
 scripts/xyz_grid.py    | 10 +++++-----
 scripts/xyz_grid_on.py |  5 +++++
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0ab6a5880..622d64498 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-18
+## Update for 2025-09-19
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -22,8 +22,9 @@
   - **ipex** simplify internal implementation  
   - refactor to use new libraries  
 - **Fixes**
-  - add explicit hf-login before framepack load  
-  - remove forced sampler from system info benchmark  
+  - framepack: add explicit hf-login before framepack load  
+  - benchmark: remove forced sampler from system info benchmark  
+  - xyz-grid: fix xyz grid with random seeds  
 
 ## Update for 2025-09-15
 
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index 2ec2452da..3874deb5e 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -54,9 +54,9 @@ class Script(scripts_manager.Script):
 
         with gr.Row():
             with gr.Column():
-                csv_mode = gr.Checkbox(label='Text inputs', value=False, elem_id=self.elem_id("csv_mode"), container=False)
-                draw_legend = gr.Checkbox(label='Legend', value=True, elem_id=self.elem_id("draw_legend"), container=False)
-                no_fixed_seeds = gr.Checkbox(label='Random seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"), container=False)
+                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"), container=False)
+                csv_mode = gr.Checkbox(label='Use text inputs', value=False, elem_id=self.elem_id("csv_mode"), container=False)
+                no_fixed_seeds = gr.Checkbox(label='Use random seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"), container=False)
                 include_time = gr.Checkbox(label='Add time info', value=False, elem_id=self.elem_id("include_time"), container=False)
                 include_text = gr.Checkbox(label='Add text info', value=False, elem_id=self.elem_id("include_text"), container=False)
             with gr.Column():
@@ -168,8 +168,6 @@ class Script(scripts_manager.Script):
             create_video, video_type, video_duration, video_loop, video_pad, video_interpolate,
            ): # pylint: disable=W0221
         jobid = shared.state.begin('XYZ Grid')
-        if not no_fixed_seeds:
-            processing.fix_seed(p)
         if not shared.opts.return_grid:
             p.batch_size = 1
 
@@ -251,6 +249,8 @@ class Script(scripts_manager.Script):
             xs = fix_axis_seeds(x_opt, xs)
             ys = fix_axis_seeds(y_opt, ys)
             zs = fix_axis_seeds(z_opt, zs)
+        else:
+            processing.fix_seed(p)
 
         total_jobs = len(xs) * len(ys) * len(zs)
         if x_opt.label == 'Steps':
diff --git a/scripts/xyz_grid_on.py b/scripts/xyz_grid_on.py
index f5a910f93..f79be6636 100644
--- a/scripts/xyz_grid_on.py
+++ b/scripts/xyz_grid_on.py
@@ -329,6 +329,11 @@ class Script(scripts_manager.Script):
             pc = copy(p)
             pc.override_settings_restore_afterwards = False
             pc.styles = pc.styles[:]
+            if no_fixed_seeds:
+                pc.seed = -1
+                processing.fix_seed(pc)
+                pc.all_seeds = None
+                pc.all_subseeds = None
             x_opt.apply(pc, x, xs)
             y_opt.apply(pc, y, ys)
             z_opt.apply(pc, z, zs)

From cd79f92dff3d65464d3348b9975bf7202a1fba55 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 19 Sep 2025 11:21:54 -0400
Subject: [PATCH 021/230] add opts models_not_to_offload

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                  |  2 ++
 TODO.md                       |  1 +
 modules/sd_offload.py         |  2 ++
 modules/shared.py             |  3 ++-
 modules/ui.py                 |  4 ++-
 modules/ui_caption.py         |  1 +
 modules/ui_control_helpers.py |  2 +-
 modules/ui_docs.py            |  1 +
 modules/ui_extensions.py      |  1 +
 modules/ui_gallery.py         |  1 +
 modules/ui_img2img.py         |  2 +-
 modules/ui_loadsave.py        | 48 +++++++++++++++++++++++++++--------
 modules/ui_models.py          |  1 +
 modules/ui_postprocessing.py  |  1 +
 modules/ui_settings.py        |  1 +
 modules/ui_txt2img.py         |  2 +-
 modules/ui_video.py           |  2 +-
 17 files changed, 59 insertions(+), 16 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 622d64498..569e5c194 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,6 +12,8 @@
   - add timers to measure onload/offload times during generate  
   - experimental offloading using `torch.streams`  
     enable in settings -> model offloading  
+  - new feature to specify which models types not to offload  
+    in *settings -> model offloading -> model types not to offload*  
 - **Extensions**
   - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
     it also does not work with control and video tabs which are the core of sdnext nowadays  
diff --git a/TODO.md b/TODO.md
index 26f5fa202..e796a3740 100644
--- a/TODO.md
+++ b/TODO.md
@@ -39,6 +39,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 - [HunyuanImage](https://huggingface.co/tencent/HunyuanImage-2.1)
 - [Phantom HuMo](https://github.com/Phantom-video/Phantom)
 - [Lumina-DiMOO](https://huggingface.co/Alpha-VLLM/Lumina-DiMOO)
+- [Wan2.2-Animate-14B](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B)
 - [Magi](https://github.com/SandAI-org/MAGI-1)(https://github.com/huggingface/diffusers/pull/11713)  
 - [SEVA](https://github.com/huggingface/diffusers/pull/11440)  
 - [Ming](https://github.com/inclusionAI/Ming)  
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index be0379f66..cf7a92a36 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -207,6 +207,8 @@ class OffloadHook(accelerate.hooks.ModelHook):
             return False
         if hasattr(module, 'nets') and any(hasattr(n, "offload_never") for n in module.nets):
             return False
+        if shared.sd_model_type.lower() in [m.lower().strip() for m in re.split(r'[ ,]+', shared.opts.models_not_to_offload)]:
+            return False
         return True
 
     def pre_forward(self, module, *args, **kwargs):
diff --git a/modules/shared.py b/modules/shared.py
index 25db15490..9577535c0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -174,6 +174,7 @@ options_templates.update(options_section(('offload', "Model Offloading"), {
     "diffusers_offload_min_gpu_memory": OptionInfo(startup_offload_min_gpu, "Offload low watermark", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01 }),
     "diffusers_offload_max_gpu_memory": OptionInfo(startup_offload_max_gpu, "Offload GPU high watermark", gr.Slider, {"minimum": 0.1, "maximum": 1, "step": 0.01 }),
     "diffusers_offload_max_cpu_memory": OptionInfo(0.90, "Offload CPU high watermark", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False }),
+    "models_not_to_offload": OptionInfo("", "Model types not to offload"),
     "diffusers_offload_always": OptionInfo(startup_offload_always, "Modules to always offload"),
     "diffusers_offload_never": OptionInfo(startup_offload_never, "Modules to never offload"),
     "offload_group_sep": OptionInfo("<h2>Group Offload</h2>", "", gr.HTML),
@@ -184,7 +185,7 @@ options_templates.update(options_section(('offload', "Model Offloading"), {
 }))
 
 options_templates.update(options_section(("quantization", "Model Quantization"), {
-    "models_not_to_quant": OptionInfo("", "List of model types not to quantize"),
+    "models_not_to_quant": OptionInfo("", "Model types not to quantize"),
 
     "sdnq_quantize_sep": OptionInfo("<h2>SDNQ: SD.Next Quantization</h2>", "", gr.HTML),
     "sdnq_quantize_weights": OptionInfo([], "Quantization enabled", gr.CheckboxGroup, {"choices": ["Model", "TE", "LLM", "Control", "VAE"]}),
diff --git a/modules/ui.py b/modules/ui.py
index d87f02c70..f9daddb35 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -28,6 +28,7 @@ plaintext_to_html = ui_common.plaintext_to_html # compatibility item
 infotext_to_html = ui_common.infotext_to_html # compatibility item
 create_sampler_and_steps_selection = ui_sections.create_sampler_and_steps_selection # compatibility item
 ui_system_tabs = None # required for system-info
+interfaces = []
 
 
 if not shared.cmd_opts.share and not shared.cmd_opts.listen:
@@ -132,7 +133,8 @@ def create_ui(startup_timer = None):
         ui_extensions.create_ui()
         timer.startup.record("ui-extensions")
 
-    interfaces = []
+    global interfaces # pylint: disable=global-statement
+    interfaces.clear()
     interfaces += [(txt2img_interface, "Text", "txt2img")]
     interfaces += [(img2img_interface, "Image", "img2img")]
     if control_interface is not None:
diff --git a/modules/ui_caption.py b/modules/ui_caption.py
index 85d3ca0bc..290ec74f8 100644
--- a/modules/ui_caption.py
+++ b/modules/ui_caption.py
@@ -28,6 +28,7 @@ def update_clip_params(*args):
 
 
 def create_ui():
+    shared.log.debug('UI initialize: tab=caption')
     with gr.Row(equal_height=False, variant='compact', elem_classes="caption", elem_id="caption_tab"):
         with gr.Column(variant='compact', elem_id='interrogate_input'):
             with gr.Row():
diff --git a/modules/ui_control_helpers.py b/modules/ui_control_helpers.py
index a04512863..c5757c56f 100644
--- a/modules/ui_control_helpers.py
+++ b/modules/ui_control_helpers.py
@@ -24,7 +24,7 @@ def initialize():
     from modules.control.units import xs # vislearn ControlNet-XS
     from modules.control.units import lite # vislearn ControlNet-XS
     from modules.control.units import t2iadapter # TencentARC T2I-Adapter
-    shared.log.debug(f'UI initialize: control models="{shared.opts.control_dir}"')
+    shared.log.debug(f'UI initialize: tab=control models="{shared.opts.control_dir}"')
     controlnet.cache_dir = os.path.join(shared.opts.control_dir, 'controlnet')
     xs.cache_dir = os.path.join(shared.opts.control_dir, 'xs')
     lite.cache_dir = os.path.join(shared.opts.control_dir, 'lite')
diff --git a/modules/ui_docs.py b/modules/ui_docs.py
index d8b4e3dce..8b1381ce8 100644
--- a/modules/ui_docs.py
+++ b/modules/ui_docs.py
@@ -275,6 +275,7 @@ def create_ui_docs():
 
 
 def create_ui():
+    log.debug('UI initialize: tab=info')
     with gr.Tabs(elem_id="tabs_info"):
         with gr.TabItem("Docs", id="docs", elem_id="system_tab_docs"):
             create_ui_docs()
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 28d792393..43158211a 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -430,6 +430,7 @@ def create_html(search_text, sort_column):
 
 
 def create_ui():
+    shared.log.debug('UI initialize: tab=extensions')
     extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "user", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all", visible=False)
     extensions_disabled_list = gr.Textbox(elem_id="extensions_disabled_list", visible=False, container=False)
     extensions_update_list = gr.Textbox(elem_id="extensions_update_list", visible=False, container=False)
diff --git a/modules/ui_gallery.py b/modules/ui_gallery.py
index e6cb3f297..283b720a8 100644
--- a/modules/ui_gallery.py
+++ b/modules/ui_gallery.py
@@ -54,6 +54,7 @@ def read_media(fn):
 
 
 def create_ui():
+    shared.log.debug('UI initialize: tab=gallery')
     with gr.Blocks() as tab:
         with gr.Row(elem_id='tab-gallery-sort-buttons'):
             sort_buttons = []
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index c1597321d..d08999c8c 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -33,7 +33,7 @@ def process_interrogate(mode, ii_input_files, ii_input_dir, ii_output_dir, *ii_s
 
 
 def create_ui():
-    shared.log.debug('UI initialize: img2img')
+    shared.log.debug('UI initialize: tab=img2img')
     import modules.img2img # pylint: disable=redefined-outer-name
     modules.scripts_manager.scripts_current = modules.scripts_manager.scripts_img2img
     modules.scripts_manager.scripts_img2img.initialize_scripts(is_img2img=True, is_control=False)
diff --git a/modules/ui_loadsave.py b/modules/ui_loadsave.py
index a8e3dce9b..3b6484e06 100644
--- a/modules/ui_loadsave.py
+++ b/modules/ui_loadsave.py
@@ -35,7 +35,6 @@ class UiLoadsave:
             saved_value = self.ui_settings.get(key, None)
             self.ui_defaults[key] = getattr(obj, field)
             if saved_value is None:
-                # self.ui_settings[key] = getattr(obj, field)
                 pass
             elif condition and not condition(saved_value):
                 pass
@@ -126,15 +125,37 @@ class UiLoadsave:
         writefile(current_ui_settings, self.filename)
 
     def dump_defaults(self):
-        """saves default values to a file unless the file is present and there was an error loading default values at start"""
         if os.path.exists(self.filename):
             return
         self.write_to_file(self.ui_settings)
 
+    def iter_all(self, values):
+        updates = []
+        for i, name in enumerate(self.component_mapping):
+            component = self.component_mapping[name]
+            choices = getattr(component, 'choices', None)
+            if type(choices) is list and len(choices) > 0: # fix gradio radio button choices being tuples
+                if type(choices[0]) is tuple:
+                    choices = [c[0] for c in choices]
+            new_value = values[i]
+            if isinstance(new_value, int) and choices:
+                if new_value >= len(choices):
+                    updates.append(None)
+                new_value = choices[new_value]
+            old_value = self.ui_settings.get(name, None)
+            default_value = self.ui_defaults.get(name, '')
+            if old_value == new_value:
+                updates.append(None)
+            elif old_value is None and (new_value == '' or new_value == []):
+                updates.append(None)
+            elif (new_value == default_value) and (old_value is None):
+                updates.append(None)
+            else:
+                updates.append((name, old_value, new_value, default_value))
+        return updates
+
     def iter_changes(self, values):
         for i, name in enumerate(self.component_mapping):
-            # if '__init__' in name:
-            #    continue
             component = self.component_mapping[name]
             choices = getattr(component, 'choices', None)
             if type(choices) is list and len(choices) > 0: # fix gradio radio button choices being tuples
@@ -201,16 +222,23 @@ class UiLoadsave:
 
     def ui_apply(self, *values):
         num_changed = 0
+        num_unchanged = 0
         current_ui_settings = self.read_from_file()
-        for name, old_value, new_value, default_value in self.iter_changes(values):
-            component = self.component_mapping[name]
-            errors.log.debug(f'Settings: name={name} component={component} old={old_value} default={default_value} new={new_value}')
-            num_changed += 1
-            current_ui_settings[name] = new_value
+        for x in self.iter_all(values):
+            if x is None:
+                num_unchanged += 1
+            else:
+                name, old_value, new_value, default_value = x
+                component = self.component_mapping[name]
+                errors.log.debug(f'Settings: name={name} component={component} old={old_value} default={default_value} new={new_value}')
+                num_changed += 1
+                current_ui_settings[name] = new_value
+                # what = name.split('/')[-1]
+                # setattr(component, what, new_value)
         if num_changed == 0:
             return "No changes"
         self.write_to_file(current_ui_settings)
-        errors.log.info(f'UI defaults saved: {self.filename}')
+        errors.log.info(f'UI defaults saved: {self.filename} changes={num_changed} unchanged={num_unchanged}')
         return f"Wrote {num_changed} changes"
 
     def ui_submenu_apply(self, items):
diff --git a/modules/ui_models.py b/modules/ui_models.py
index bfb876372..34563e891 100644
--- a/modules/ui_models.py
+++ b/modules/ui_models.py
@@ -12,6 +12,7 @@ extra_ui = []
 
 
 def create_ui():
+    log.debug('UI initialize: tab=models')
     dummy_component = gr.Label(visible=False)
     with gr.Row(elem_id="models_tab"):
         with gr.Column(elem_id='models_output_container', scale=1):
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index b575fa67a..5ed90446a 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -16,6 +16,7 @@ def submit_process(tab_index, extras_image, image_batch, extras_batch_input_dir,
 
 
 def create_ui():
+    shared.log.debug('UI initialize: tab=process')
     tab_index = gr.State(value=0) # pylint: disable=abstract-class-instantiated
     with gr.Row(equal_height=False, variant='compact', elem_classes="extras", elem_id="extras_tab"):
         with gr.Column(variant='compact'):
diff --git a/modules/ui_settings.py b/modules/ui_settings.py
index 9b24464cc..4cee32959 100644
--- a/modules/ui_settings.py
+++ b/modules/ui_settings.py
@@ -172,6 +172,7 @@ def run_settings_single(value, key, progress=False):
 
 
 def create_ui():
+    shared.log.debug('UI initialize: tab=settings')
     global text_settings # pylint: disable=global-statement
     text_settings = gr.Textbox(elem_id="settings_json", elem_classes=["settings_json"], value=lambda: shared.opts.dumpjson(), visible=False)
     with gr.Row(elem_id="system_row"):
diff --git a/modules/ui_txt2img.py b/modules/ui_txt2img.py
index fe9a19c50..a7efadba1 100644
--- a/modules/ui_txt2img.py
+++ b/modules/ui_txt2img.py
@@ -5,7 +5,7 @@ from modules.ui_components import ToolButton # pylint: disable=unused-import
 
 
 def create_ui():
-    shared.log.debug('UI initialize: txt2img')
+    shared.log.debug('UI initialize: tab=txt2img')
     import modules.txt2img # pylint: disable=redefined-outer-name
     modules.scripts_manager.scripts_current = modules.scripts_manager.scripts_txt2img
     modules.scripts_manager.scripts_txt2img.initialize_scripts(is_img2img=False, is_control=False)
diff --git a/modules/ui_video.py b/modules/ui_video.py
index bb9cace94..02c787c6e 100644
--- a/modules/ui_video.py
+++ b/modules/ui_video.py
@@ -7,7 +7,7 @@ debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None e
 
 
 def create_ui():
-    shared.log.debug('UI initialize: video')
+    shared.log.debug('UI initialize: tab=video')
     with gr.Blocks(analytics_enabled=False) as _video_interface:
         prompt, styles, negative, generate_btn, _reprocess, paste, networks_button, _token_counter, _token_button, _token_counter_negative, _token_button_negative = ui_sections.create_toprow(
             is_img2img=False,

From e6715ba8d363dc278b02081b00b8fdfea3e1c433 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Fri, 19 Sep 2025 19:29:36 +0300
Subject: [PATCH 022/230] Cleanup SDNQ compile

---
 extensions-builtin/sd-webui-agent-scheduler   |  1 +
 modules/sdnq/common.py                        |  6 ++++
 modules/sdnq/dequantizer.py                   | 28 ++++++-------------
 modules/sdnq/layers/conv/conv_fp8.py          |  5 ++--
 .../sdnq/layers/conv/conv_fp8_tensorwise.py   |  5 ++--
 modules/sdnq/layers/conv/conv_int8.py         |  5 ++--
 modules/sdnq/layers/linear/linear_fp8.py      |  5 ++--
 .../layers/linear/linear_fp8_tensorwise.py    |  5 ++--
 modules/sdnq/layers/linear/linear_int8.py     |  5 ++--
 9 files changed, 28 insertions(+), 37 deletions(-)
 create mode 160000 extensions-builtin/sd-webui-agent-scheduler

diff --git a/extensions-builtin/sd-webui-agent-scheduler b/extensions-builtin/sd-webui-agent-scheduler
new file mode 160000
index 000000000..a33753321
--- /dev/null
+++ b/extensions-builtin/sd-webui-agent-scheduler
@@ -0,0 +1 @@
+Subproject commit a33753321b914c6122df96d1dc0b5117d38af680
diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index fab48c4b9..8ea1d76ca 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -2,6 +2,8 @@
 
 import os
 import torch
+from functools import partial
+
 from modules import shared
 
 torch_version = float(torch.__version__[:3])
@@ -42,3 +44,7 @@ allowed_types = linear_types + conv_types + conv_transpose_types
 if use_torch_compile:
     torch._dynamo.config.cache_size_limit = max(8192, torch._dynamo.config.cache_size_limit)
     torch._dynamo.config.accumulated_recompile_limit = max(8192, torch._dynamo.config.accumulated_recompile_limit)
+    compile_func = partial(torch.compile, fullgraph=True, dynamic=False)
+else:
+    def compile_func(fn, **kwargs):
+        return fn
diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 9ac902c57..be68ab96d 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from .common import dtype_dict, use_torch_compile
+from .common import dtype_dict, compile_func
 from .packed_int import pack_int_symetric, unpack_int_symetric, pack_int_asymetric, unpack_int_asymetric
 
 
@@ -226,21 +226,11 @@ dequantizer_dict = {
 }
 
 
-if use_torch_compile:
-    dequantize_asymmetric_compiled = torch.compile(dequantize_asymmetric, fullgraph=True, dynamic=False)
-    dequantize_symmetric_compiled = torch.compile(dequantize_symmetric, fullgraph=True, dynamic=False)
-    dequantize_packed_int_asymmetric_compiled = torch.compile(dequantize_packed_int_asymmetric, fullgraph=True, dynamic=False)
-    dequantize_packed_int_symmetric_compiled = torch.compile(dequantize_packed_int_symmetric, fullgraph=True, dynamic=False)
-    re_quantize_matmul_asymmetric_compiled = torch.compile(re_quantize_matmul_asymmetric, fullgraph=True, dynamic=False)
-    re_quantize_matmul_symmetric_compiled = torch.compile(re_quantize_matmul_symmetric, fullgraph=True, dynamic=False)
-    re_quantize_matmul_packed_int_asymmetric_compiled = torch.compile(re_quantize_matmul_packed_int_asymmetric, fullgraph=True, dynamic=False)
-    re_quantize_matmul_packed_int_symmetric_compiled = torch.compile(re_quantize_matmul_packed_int_symmetric, fullgraph=True, dynamic=False)
-else:
-    dequantize_asymmetric_compiled = dequantize_asymmetric
-    dequantize_symmetric_compiled = dequantize_symmetric
-    dequantize_packed_int_asymmetric_compiled = dequantize_packed_int_asymmetric
-    dequantize_packed_int_symmetric_compiled = dequantize_packed_int_symmetric
-    re_quantize_matmul_asymmetric_compiled = re_quantize_matmul_asymmetric
-    re_quantize_matmul_symmetric_compiled = re_quantize_matmul_symmetric
-    re_quantize_matmul_packed_int_asymmetric_compiled = re_quantize_matmul_packed_int_asymmetric
-    re_quantize_matmul_packed_int_symmetric_compiled = re_quantize_matmul_packed_int_symmetric
+dequantize_asymmetric_compiled = compile_func(dequantize_asymmetric)
+dequantize_symmetric_compiled = compile_func(dequantize_symmetric)
+dequantize_packed_int_asymmetric_compiled = compile_func(dequantize_packed_int_asymmetric)
+dequantize_packed_int_symmetric_compiled = compile_func(dequantize_packed_int_symmetric)
+re_quantize_matmul_asymmetric_compiled = compile_func(re_quantize_matmul_asymmetric)
+re_quantize_matmul_symmetric_compiled = compile_func(re_quantize_matmul_symmetric)
+re_quantize_matmul_packed_int_asymmetric_compiled = compile_func(re_quantize_matmul_packed_int_asymmetric)
+re_quantize_matmul_packed_int_symmetric_compiled = compile_func(re_quantize_matmul_packed_int_symmetric)
diff --git a/modules/sdnq/layers/conv/conv_fp8.py b/modules/sdnq/layers/conv/conv_fp8.py
index cfce8994a..2d1407a47 100644
--- a/modules/sdnq/layers/conv/conv_fp8.py
+++ b/modules/sdnq/layers/conv/conv_fp8.py
@@ -4,7 +4,7 @@ from typing import List
 
 import torch
 
-from ...common import use_torch_compile # noqa: TID252
+from ...common import compile_func # noqa: TID252
 from ..linear.linear_fp8 import quantize_fp8_matmul_input # noqa: TID252
 from .forward import get_conv_args, process_conv_input
 
@@ -68,5 +68,4 @@ def quantized_conv_forward_fp8_matmul(self, input) -> torch.FloatTensor:
     )
 
 
-if use_torch_compile:
-    conv_fp8_matmul = torch.compile(conv_fp8_matmul, fullgraph=True, dynamic=False)
+conv_fp8_matmul = compile_func(conv_fp8_matmul)
diff --git a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
index a21699855..bc968e519 100644
--- a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
+++ b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
@@ -4,7 +4,7 @@ from typing import List
 
 import torch
 
-from ...common import use_torch_compile # noqa: TID252
+from ...common import compile_func # noqa: TID252
 from ...dequantizer import dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 from ..linear.linear_fp8_tensorwise import quantize_fp8_matmul_input_tensorwise # noqa: TID252
 from .forward import get_conv_args, process_conv_input
@@ -63,5 +63,4 @@ def quantized_conv_forward_fp8_matmul_tensorwise(self, input) -> torch.FloatTens
     )
 
 
-if use_torch_compile:
-    conv_fp8_matmul_tensorwise = torch.compile(conv_fp8_matmul_tensorwise, fullgraph=True, dynamic=False)
+conv_fp8_matmul_tensorwise = compile_func(conv_fp8_matmul_tensorwise)
diff --git a/modules/sdnq/layers/conv/conv_int8.py b/modules/sdnq/layers/conv/conv_int8.py
index cd18b1db6..6c59f6d60 100644
--- a/modules/sdnq/layers/conv/conv_int8.py
+++ b/modules/sdnq/layers/conv/conv_int8.py
@@ -4,7 +4,7 @@ from typing import List
 
 import torch
 
-from ...common import use_torch_compile # noqa: TID252
+from ...common import compile_func # noqa: TID252
 from ...packed_int import unpack_int_symetric # noqa: TID252
 from ...dequantizer import dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 from ..linear.linear_int8 import quantize_int8_matmul_input # noqa: TID252
@@ -75,5 +75,4 @@ def quantized_conv_forward_int8_matmul(self, input) -> torch.FloatTensor:
     )
 
 
-if use_torch_compile:
-    conv_int8_matmul = torch.compile(conv_int8_matmul, fullgraph=True, dynamic=False)
+conv_int8_matmul = compile_func(conv_int8_matmul)
diff --git a/modules/sdnq/layers/linear/linear_fp8.py b/modules/sdnq/layers/linear/linear_fp8.py
index 5707ad225..63eee40b4 100644
--- a/modules/sdnq/layers/linear/linear_fp8.py
+++ b/modules/sdnq/layers/linear/linear_fp8.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from ...common import use_torch_compile # noqa: TID252
+from ...common import compile_func # noqa: TID252
 from ...dequantizer import quantize_fp8 # noqa: TID252
 
 
@@ -34,5 +34,4 @@ def quantized_linear_forward_fp8_matmul(self, input: torch.FloatTensor) -> torch
     return fp8_matmul(input, self.weight, self.bias, self.sdnq_dequantizer.scale)
 
 
-if use_torch_compile:
-    fp8_matmul = torch.compile(fp8_matmul, fullgraph=True, dynamic=False)
+fp8_matmul = compile_func(fp8_matmul)
diff --git a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
index aa80251ae..0343087c5 100644
--- a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
+++ b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from ...common import use_torch_compile # noqa: TID252
+from ...common import compile_func # noqa: TID252
 from ...dequantizer import quantize_fp8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 
 
@@ -39,5 +39,4 @@ def quantized_linear_forward_fp8_matmul_tensorwise(self, input: torch.FloatTenso
     return fp8_matmul_tensorwise(input, self.weight, self.bias, self.sdnq_dequantizer.scale)
 
 
-if use_torch_compile:
-    fp8_matmul_tensorwise = torch.compile(fp8_matmul_tensorwise, fullgraph=True, dynamic=False)
+fp8_matmul_tensorwise = compile_func(fp8_matmul_tensorwise)
diff --git a/modules/sdnq/layers/linear/linear_int8.py b/modules/sdnq/layers/linear/linear_int8.py
index 3a1b82c1f..bcc4d775c 100644
--- a/modules/sdnq/layers/linear/linear_int8.py
+++ b/modules/sdnq/layers/linear/linear_int8.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from ...common import use_torch_compile # noqa: TID252
+from ...common import compile_func # noqa: TID252
 from ...packed_int import unpack_int_symetric # noqa: TID252
 from ...dequantizer import quantize_int8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 
@@ -50,5 +50,4 @@ def quantized_linear_forward_int8_matmul(self, input: torch.FloatTensor) -> torc
     return int8_matmul(input, weight, self.bias, scale, quantized_weight_shape, self.sdnq_dequantizer.weights_dtype)
 
 
-if use_torch_compile:
-    int8_matmul = torch.compile(int8_matmul, fullgraph=True, dynamic=False)
+int8_matmul = compile_func(int8_matmul)

From a9fb09852c9f4432b85e17eaf75f2ea2f229dc8a Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Fri, 19 Sep 2025 19:38:52 +0300
Subject: [PATCH 023/230] re-remove agent scheduler

---
 extensions-builtin/sd-webui-agent-scheduler | 1 -
 1 file changed, 1 deletion(-)
 delete mode 160000 extensions-builtin/sd-webui-agent-scheduler

diff --git a/extensions-builtin/sd-webui-agent-scheduler b/extensions-builtin/sd-webui-agent-scheduler
deleted file mode 160000
index a33753321..000000000
--- a/extensions-builtin/sd-webui-agent-scheduler
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit a33753321b914c6122df96d1dc0b5117d38af680

From 9667fffd3a1e440cfbdce9cde607272043325fa4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 20 Sep 2025 19:39:41 -0400
Subject: [PATCH 024/230] fix download for sd15/sdxl reference models

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                        | 3 ++-
 modules/civitai/download_civitai.py | 9 +++++----
 modules/lora/lora_overrides.py      | 4 +++-
 3 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 569e5c194..df9219de2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-19
+## Update for 2025-09-20
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -27,6 +27,7 @@
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
   - xyz-grid: fix xyz grid with random seeds  
+  - fix download for sd15/sdxl reference models  
 
 ## Update for 2025-09-15
 
diff --git a/modules/civitai/download_civitai.py b/modules/civitai/download_civitai.py
index 508bd0a8f..2c6c47412 100644
--- a/modules/civitai/download_civitai.py
+++ b/modules/civitai/download_civitai.py
@@ -103,10 +103,11 @@ def download_civit_model_thread(model_name: str, model_url: str, model_path: str
     if os.path.isfile(temp_file):
         starting_pos = os.path.getsize(temp_file)
         headers['Range'] = f'bytes={starting_pos}-'
-    if token is None or len(token) == 0:
-        token = shared.opts.civitai_token
-    if token is not None and len(token) > 0:
-        headers['Authorization'] = f'Bearer {token}'
+    if ('civit' in model_url.lower()):
+        if token is None or len(token) == 0:
+            token = shared.opts.civitai_token
+        if (token is not None) and (len(token) > 0):
+            headers['Authorization'] = f'Bearer {token}'
 
     r = shared.req(model_url, headers=headers, stream=True)
     total_size = int(r.headers.get('content-length', 0))
diff --git a/modules/lora/lora_overrides.py b/modules/lora/lora_overrides.py
index 61f2dad46..5889e1dec 100644
--- a/modules/lora/lora_overrides.py
+++ b/modules/lora/lora_overrides.py
@@ -61,7 +61,9 @@ def get_method(shorthash=''):
         use_diffusers = use_diffusers or any(x.startswith(shorthash) for x in maybe_diffusers)
     if shared.opts.lora_force_diffusers and len(shorthash) > 4:
         use_diffusers = use_diffusers or any(x.startswith(shorthash) for x in force_diffusers)
-    use_nunchaku = hasattr(shared.sd_model, 'transformer') and 'Nunchaku' in shared.sd_model.transformer.__class__.__name__
+    nunchaku_dit = hasattr(shared.sd_model, 'transformer') and 'Nunchaku' in shared.sd_model.transformer.__class__.__name__
+    nunchaku_unet = hasattr(shared.sd_model, 'unet') and 'Nunchaku' in shared.sd_model.unet.__class__.__name__
+    use_nunchaku = nunchaku_dit or nunchaku_unet
     if use_nunchaku:
         return 'nunchaku'
     elif use_diffusers:

From 5b43c66a92fe20ff6a9b72a95ee244ded8efccb2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 20 Sep 2025 21:01:21 -0400
Subject: [PATCH 025/230] nunchaku sdxl and sdxl-turbo support

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                         |   5 ++
 html/reference.json                  |  16 +++-
 modules/civitai/download_civitai.py  |   2 +-
 modules/sd_detect.py                 |   2 +
 modules/sd_models.py                 | 123 +++++++++++++++------------
 modules/sd_offload.py                |   2 +-
 modules/sd_unet.py                   |  39 ++++++++-
 modules/sdnq/common.py               |   4 +-
 pipelines/flux/flux_legacy_loader.py |   4 +-
 pipelines/flux/flux_nunchaku.py      |   7 +-
 pipelines/model_qwen.py              |   2 -
 pipelines/model_sana.py              |   3 +-
 pipelines/qwen/qwen_nunchaku.py      |   7 +-
 13 files changed, 142 insertions(+), 74 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index df9219de2..07adf4e96 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,11 @@
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
     SRPO is trained by with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference  
+  - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
+    impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
+    note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
+    *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
+    *note*: sdxl support for nunchaku is not in released version of `nunchaku==1.0.0`, so you need to build [nunchaku](https://nunchaku.tech/docs/nunchaku/installation/installation.html) from source
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  
diff --git a/html/reference.json b/html/reference.json
index fc070b320..79a0980fe 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -55,11 +55,21 @@
     "desc": "This stable-diffusion-2 model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on 768x768 images",
     "extras": "width: 768, height: 768, sampler: DEIS, steps: 20, cfg_scale: 6.0"
   },
-  "StabilityAI StableDiffusion XL 1.0 Base": {
-    "path": "sd_xl_base_1.0.safetensors@https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors?download=true",
+  "StabilityAI StableDiffusion XL": {
+    "path": "stabilityai/stable-diffusion-xl-base-1.0",
     "preview": "sd_xl_base_1.0.jpg",
     "desc": "Stable Diffusion XL (SDXL) is the latest AI image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition compared to previous SD models, including SD 2.1. It can make realistic faces, legible text within the images, and better image composition, all while using shorter and simpler prompts at a greatly increased base resolution of 1024x1024. Just like its predecessors, SDXL has the ability to generate image variations using image-to-image prompting, inpainting (reimagining of the selected parts of an image), and outpainting (creating new parts that lie outside the image borders).",
-    "extras": "sampler: DEIS, steps: 20, cfg_scale: 6.0"
+    "skip": true,
+    "variant": "fp16",
+    "extras": ""
+  },
+  "StabilityAI StableDiffusion XL Turbo": {
+    "path": "stabilityai/sdxl-turbo",
+    "preview": "sd_xl_base_1.0.jpg",
+    "desc": "SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.",
+    "skip": true,
+    "variant": "fp16",
+    "extras": ""
   },
   "StabilityAI Stable Cascade": {
     "path": "huggingface/stabilityai/stable-cascade",
diff --git a/modules/civitai/download_civitai.py b/modules/civitai/download_civitai.py
index 2c6c47412..d12a00830 100644
--- a/modules/civitai/download_civitai.py
+++ b/modules/civitai/download_civitai.py
@@ -103,7 +103,7 @@ def download_civit_model_thread(model_name: str, model_url: str, model_path: str
     if os.path.isfile(temp_file):
         starting_pos = os.path.getsize(temp_file)
         headers['Range'] = f'bytes={starting_pos}-'
-    if ('civit' in model_url.lower()):
+    if 'civit' in model_url.lower(): # downloader can be used for other urls too
         if token is None or len(token) == 0:
             token = shared.opts.civitai_token
         if (token is not None) and (len(token) > 0):
diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index b945a6457..838c68770 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -115,6 +115,8 @@ def guess_by_name(fn, current_guess):
         return 'Kandinsky 3.0'
     elif 'hunyuanimage' in fn.lower():
         return 'HunyuanImage'
+    elif 'sdxl-turbo' in fn.lower() or 'stable-diffusion-xl' in fn.lower():
+        return 'Stable Diffusion XL'
     return current_guess
 
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index a251495c3..4423c780d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -411,65 +411,70 @@ def load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_c
     files = shared.walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt'])
     if 'variant' not in diffusers_load_config and any('diffusion_pytorch_model.fp16' in f for f in files): # deal with diffusers lack of variant fallback when loading
         diffusers_load_config['variant'] = 'fp16'
-    if (model_type is not None) and (pipeline is not None) and ('ONNX' in model_type): # forced pipeline
-        try:
-            sd_model = pipeline.from_pretrained(checkpoint_info.path)
-        except Exception as e:
-            shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}')
-            if debug_load:
-                errors.display(e, 'Load')
-            return None
-    else:
-        err1, err2, err3 = None, None, None
-        if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
-            if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
-                shared.log.debug(f'Load {op}: type=pickle')
-                diffusers_load_config['use_safetensors'] = False
+
+    err0, err1, err2, err3 = None, None, None, None
+    if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
+        if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
+            shared.log.debug(f'Load {op}: type=pickle')
+            diffusers_load_config['use_safetensors'] = False
+    if debug_load:
+        shared.log.debug(f'Load {op}: args={diffusers_load_config}')
+
+    try: #0 - using detected model type and pipeline
+        if (model_type is not None) and (pipeline is not None):
+            sd_model = pipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+            sd_model.model_type = sd_model.__class__.__name__
+    except Exception as e:
+        err0 = e
         if debug_load:
-            shared.log.debug(f'Load {op}: args={diffusers_load_config}')
-        try: # 1 - autopipeline, best choice but not all pipelines are available
-            try:
+            errors.display(e, 'Load Detected')
+
+    try: # 1 - autopipeline, best choice but not all pipelines are available
+        try:
+            if err0 is not None:
                 sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
                 sd_model.model_type = sd_model.__class__.__name__
-            except ValueError as e:
-                if 'no variant default' in str(e):
-                    shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
-                    diffusers_load_config.pop('variant', None)
-                    sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
-                    sd_model.model_type = sd_model.__class__.__name__
-                elif 'safetensors found in directory' in str(err1):
-                    shared.log.warning(f'Load {op}: type=pickle')
-                    diffusers_load_config['use_safetensors'] = False
-                    sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
-                    sd_model.model_type = sd_model.__class__.__name__
-                else:
-                    raise ValueError from e # reraise
-        except Exception as e:
-            err1 = e
-            if debug_load:
-                errors.display(e, 'Load AutoPipeline')
-            # shared.log.error(f'AutoPipeline: {e}')
-        try: # 2 - diffusion pipeline, works for most non-linked pipelines
-            if err1 is not None:
-                sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+        except ValueError as e:
+            if 'no variant default' in str(e):
+                shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
+                diffusers_load_config.pop('variant', None)
+                sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
                 sd_model.model_type = sd_model.__class__.__name__
-        except Exception as e:
-            err2 = e
-            if debug_load:
-                errors.display(e, "Load DiffusionPipeline")
-            # shared.log.error(f'DiffusionPipeline: {e}')
-        try: # 3 - try basic pipeline just in case
-            if err2 is not None:
-                sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+            elif 'safetensors found in directory' in str(err1):
+                shared.log.warning(f'Load {op}: type=pickle')
+                diffusers_load_config['use_safetensors'] = False
+                sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
                 sd_model.model_type = sd_model.__class__.__name__
-        except Exception as e:
-            err3 = e  # ignore last error
-            shared.log.error(f"StableDiffusionPipeline: {e}")
-            if debug_load:
-                errors.display(e, "Load StableDiffusionPipeline")
-        if err3 is not None:
-            shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
-            return None
+            else:
+                raise ValueError from e # reraise
+    except Exception as e:
+        err1 = e
+        if debug_load:
+            errors.display(e, 'Load AutoPipeline')
+
+    try: # 2 - diffusion pipeline, works for most non-linked pipelines
+        if err1 is not None:
+            sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+            sd_model.model_type = sd_model.__class__.__name__
+    except Exception as e:
+        err2 = e
+        if debug_load:
+            errors.display(e, "Load DiffusionPipeline")
+
+    try: # 3 - try basic pipeline just in case
+        if err2 is not None:
+            sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+            sd_model.model_type = sd_model.__class__.__name__
+    except Exception as e:
+        err3 = e  # ignore last error
+        shared.log.error(f"StableDiffusionPipeline: {e}")
+        if debug_load:
+            errors.display(e, "Load StableDiffusionPipeline")
+
+    if err3 is not None:
+        shared.log.error(f'Load {op}: {checkpoint_info.path} detected={err0} auto={err1} diffusion={err2} base={err3}')
+        return None
+
     return sd_model
 
 
@@ -667,7 +672,7 @@ def load_diffuser(checkpoint_info=None, op='model', revision=None): # pylint: di
             sd_model.scheduler.name = 'DDIM'
 
         if hasattr(sd_model, "unet") and model_type not in ['Stable Cascade']: # others calls load_diffuser again
-            sd_unet.load_unet(sd_model)
+            sd_unet.load_unet(sd_model, checkpoint_info.path)
 
         add_noise_pred_to_diffusers_callback(sd_model)
 
@@ -1029,7 +1034,13 @@ def set_diffusers_attention(pipe, quiet:bool=False):
             return
         # other models uses their own attention processor
         if pipe.__class__.__name__.startswith("StableDiffusion") and hasattr(pipe, "unet"):
-            pipe.unet.set_attn_processor(attention)
+            try:
+                pipe.unet.set_attn_processor(attention)
+            except Exception as e:
+                if 'Nunchaku' in pipe.unet.__class__.__name__:
+                    pass
+                else:
+                    shared.log.error(f"Attention: {name if name is not None else attention.__class__.__name__} pipe={pipe.__class__.__name__} {e}")
         elif not quiet:
             shared.log.warning(f"Attention: {name if name is not None else attention.__class__.__name__} is not compatible with {pipe.__class__.__name__}")
 
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index cf7a92a36..19e59de3d 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -293,7 +293,7 @@ def get_module_names(pipe=None, exclude=[]):
         modules_names = get_signature(pipe).keys()
     modules_names = [m for m in modules_names if m not in exclude and not m.startswith('_')]
     modules_names = [m for m in modules_names if isinstance(getattr(pipe, m, None), torch.nn.Module)]
-    modules_names = list(sorted(set(modules_names)))
+    modules_names = sorted(set(modules_names))
     return modules_names
 
 
diff --git a/modules/sd_unet.py b/modules/sd_unet.py
index 2bd1741f4..6d3d6c936 100644
--- a/modules/sd_unet.py
+++ b/modules/sd_unet.py
@@ -1,5 +1,5 @@
 import os
-from modules import shared, devices, files_cache, sd_models
+from modules import shared, devices, files_cache, sd_models, model_quant
 
 
 unet_dict = {}
@@ -8,22 +8,55 @@ failed_unet = []
 debug = os.environ.get('SD_LOAD_DEBUG', None) is not None
 
 
-dit_models = ['Flux', 'StableDiffusion3', 'HiDream', 'Lumina2', 'Chroma', 'Wan']
+dit_models = ['Flux', 'StableDiffusion3', 'HiDream', 'Lumina2', 'Chroma', 'Wan', 'Qwen']
 
 
-def load_unet(model):
+def load_unet_sdxl_nunchaku(repo_id):
+    try:
+        from nunchaku.models.unets.unet_sdxl import NunchakuSDXLUNet2DConditionModel
+    except Exception:
+        shared.log.error(f'Load module: quant=Nunchaku module=unet repo="{repo_id}" low nunchaku version')
+        return None
+    if 'turbo' in repo_id.lower():
+        nunchaku_repo = 'nunchaku-tech/nunchaku-sdxl-turbo/svdq-int4_r32-sdxl-turbo.safetensors'
+    else:
+        nunchaku_repo = 'nunchaku-tech/nunchaku-sdxl/svdq-int4_r32-sdxl.safetensors'
+
+    shared.log.debug(f'Load module: quant=Nunchaku module=unet repo="{nunchaku_repo}" offload={shared.opts.nunchaku_offload}')
+    unet = NunchakuSDXLUNet2DConditionModel.from_pretrained(
+        nunchaku_repo,
+        offload=shared.opts.nunchaku_offload,
+        torch_dtype=devices.dtype,
+        cache_dir=shared.opts.hfcache_dir,
+    )
+    unet.quantization_method = 'SVDQuant'
+    return unet
+
+
+def load_unet(model, repo_id:str=None):
     global loaded_unet # pylint: disable=global-statement
+
+    if ("StableDiffusionXLPipeline" in model.__class__.__name__) and (('stable-diffusion-xl-base' in repo_id) or ('sdxl-turbo' in repo_id)):
+        if model_quant.check_nunchaku('Model'):
+            unet = load_unet_sdxl_nunchaku(repo_id)
+            if unet is not None:
+                model.unet = unet
+                return
+
     if shared.opts.sd_unet == 'Default' or shared.opts.sd_unet == 'None':
         return
+
     if shared.opts.sd_unet not in list(unet_dict):
         shared.log.error(f'Load module: type=UNet not found: {shared.opts.sd_unet}')
         return
+
     config_file = os.path.splitext(unet_dict[shared.opts.sd_unet])[0] + '.json'
     if os.path.exists(config_file):
         config = shared.readfile(config_file)
     else:
         config = None
         config_file = 'default'
+
     try:
         if shared.opts.sd_unet == loaded_unet or shared.opts.sd_unet in failed_unet:
             pass
diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 8ea1d76ca..e6ed21d4d 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -1,8 +1,8 @@
 # pylint: disable=redefined-builtin,no-member,protected-access
 
 import os
-import torch
 from functools import partial
+import torch
 
 from modules import shared
 
@@ -46,5 +46,5 @@ if use_torch_compile:
     torch._dynamo.config.accumulated_recompile_limit = max(8192, torch._dynamo.config.accumulated_recompile_limit)
     compile_func = partial(torch.compile, fullgraph=True, dynamic=False)
 else:
-    def compile_func(fn, **kwargs):
+    def compile_func(fn, **kwargs): # pylint: disable=unused-argument
         return fn
diff --git a/pipelines/flux/flux_legacy_loader.py b/pipelines/flux/flux_legacy_loader.py
index 6b6f9d294..cb3dad3c1 100644
--- a/pipelines/flux/flux_legacy_loader.py
+++ b/pipelines/flux/flux_legacy_loader.py
@@ -130,7 +130,7 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant): # pylint: disable=unus
                 shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
             if nunchaku_repo is not None:
                 shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} offload={shared.opts.nunchaku_offload} attention={shared.opts.nunchaku_attention}')
-                kwargs['transformer'] = nunchaku.NunchakuFluxTransformer2dModel.from_pretrained(nunchaku_repo, offload=shared.opts.nunchaku_offload, torch_dtype=devices.dtype)
+                kwargs['transformer'] = nunchaku.NunchakuFluxTransformer2dModel.from_pretrained(nunchaku_repo, offload=shared.opts.nunchaku_offload, torch_dtype=devices.dtype, cache_dir=cache_dir)
                 kwargs['transformer'].quantization_method = 'SVDQuant'
                 if shared.opts.nunchaku_attention:
                     kwargs['transformer'].set_attention_impl("nunchaku-fp16")
@@ -142,7 +142,7 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant): # pylint: disable=unus
             nunchaku_precision = nunchaku.utils.get_precision()
             nunchaku_repo = 'mit-han-lab/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors'
             shared.log.debug(f'Load module: quant=Nunchaku module=t5 repo="{nunchaku_repo}" precision={nunchaku_precision}')
-            kwargs['text_encoder_2'] = nunchaku.NunchakuT5EncoderModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
+            kwargs['text_encoder_2'] = nunchaku.NunchakuT5EncoderModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype, cache_dir=cache_dir)
             kwargs['text_encoder_2'].quantization_method = 'SVDQuant'
         if 'text_encoder_2' not in kwargs and model_quant.check_quant('TE'):
             load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
diff --git a/pipelines/flux/flux_nunchaku.py b/pipelines/flux/flux_nunchaku.py
index 49fd77b3d..9a737d103 100644
--- a/pipelines/flux/flux_nunchaku.py
+++ b/pipelines/flux/flux_nunchaku.py
@@ -26,7 +26,12 @@ def load_flux_nunchaku(repo_id):
         shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
     if nunchaku_repo is not None:
         shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} offload={shared.opts.nunchaku_offload} attention={shared.opts.nunchaku_attention}')
-        transformer = nunchaku.NunchakuFluxTransformer2dModel.from_pretrained(nunchaku_repo, offload=shared.opts.nunchaku_offload, torch_dtype=devices.dtype)
+        transformer = nunchaku.NunchakuFluxTransformer2dModel.from_pretrained(
+            nunchaku_repo,
+            offload=shared.opts.nunchaku_offload,
+            torch_dtype=devices.dtype,
+            cache_dir=shared.opts.hfcache_dir,
+        )
         transformer.quantization_method = 'SVDQuant'
         if shared.opts.nunchaku_attention:
             transformer.set_attention_impl("nunchaku-fp16")
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index bb65df53e..a89c272a1 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -26,8 +26,6 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
     if model_quant.check_nunchaku('Model'):
         from pipelines.qwen.qwen_nunchaku import load_qwen_nunchaku
         transformer = load_qwen_nunchaku(repo_id)
-        # if transformer is not None:
-        #     cls_name = nunchaku.pipeline.pipeline_qwenimage.NunchakuQwenImagePipeline # we dont need this
 
     if 'Qwen-Image-Distill-Full' in repo_id:
         repo_transformer = repo_id
diff --git a/pipelines/model_sana.py b/pipelines/model_sana.py
index d4ec574f6..9584263a3 100644
--- a/pipelines/model_sana.py
+++ b/pipelines/model_sana.py
@@ -10,9 +10,8 @@ def load_quants(kwargs, repo_id, cache_dir):
         import nunchaku
         nunchaku_precision = nunchaku.utils.get_precision()
         nunchaku_repo = "nunchaku-tech/nunchaku-sana/svdq-int4_r32-sana1.6b.safetensors"
-        # https://huggingface.co/nunchaku-tech/nunchaku-sana/blob/main/svdq-int4_r32-sana1.6b.safetensors
         shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} attention={shared.opts.nunchaku_attention}')
-        kwargs['transformer'] = nunchaku.NunchakuSanaTransformer2DModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
+        kwargs['transformer'] = nunchaku.NunchakuSanaTransformer2DModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype, cache_dir=cache_dir)
     elif model_quant.check_quant('Model'):
         load_args, quant_args = model_quant.get_dit_args(kwargs_copy, module='Model')
         kwargs['transformer'] = diffusers.SanaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, **load_args, **quant_args)
diff --git a/pipelines/qwen/qwen_nunchaku.py b/pipelines/qwen/qwen_nunchaku.py
index 084b24a1e..2fa723565 100644
--- a/pipelines/qwen/qwen_nunchaku.py
+++ b/pipelines/qwen/qwen_nunchaku.py
@@ -23,6 +23,11 @@ def load_qwen_nunchaku(repo_id):
         shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
     if nunchaku_repo is not None:
         shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} offload={shared.opts.nunchaku_offload} attention={shared.opts.nunchaku_attention}')
-        transformer = NunchakuQwenImageTransformer2DModel.from_pretrained(nunchaku_repo, offload=shared.opts.nunchaku_offload, torch_dtype=devices.dtype) # pylint: disable=no-member
+        transformer = NunchakuQwenImageTransformer2DModel.from_pretrained(
+            nunchaku_repo,
+            offload=shared.opts.nunchaku_offload,
+            torch_dtype=devices.dtype,
+            cache_dir=shared.opts.hfcache_dir,
+        ) # pylint: disable=no-member
         transformer.quantization_method = 'SVDQuant'
     return transformer

From 2d14a6412d06f3d9ce5e08ebfa6e76f089dcc7ec Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 20 Sep 2025 21:01:47 -0400
Subject: [PATCH 026/230] update changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 07adf4e96..b3bea1252 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,7 +6,7 @@
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
-    SRPO is trained by with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference  
+    SRPO is trained by Tencent with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference  
   - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
     impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  

From aaf26673f85663f72715d16ed4c1dacaa1808858 Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Sun, 21 Sep 2025 14:08:27 +0300
Subject: [PATCH 027/230] Cover images: embed prompts to BRIA, Kandinsky.
 Rename juggernautXL. Generate Wan 2.2 VACE.

---
 html/previews.json                            |   2 +-
 html/reference.json                           |   6 +++---
 models/Reference/briaai--BRIA-3.2.jpg         | Bin 31678 -> 48630 bytes
 ... => juggernautXL_juggXIByRundiffusion.jpg} | Bin
 ...g => juggernautXL_juggXILightningByRD.jpg} | Bin
 .../kandinsky-community--kandinsky-2-1.jpg    | Bin 83911 -> 134110 bytes
 ...insky-community--kandinsky-2-2-decoder.jpg | Bin 64475 -> 40047 bytes
 .../kandinsky-community--kandinsky-3.jpg      | Bin 53882 -> 68205 bytes
 ...linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg | Bin 0 -> 69556 bytes
 9 files changed, 4 insertions(+), 4 deletions(-)
 rename models/Reference/{juggernautXL_v9Rundiffusionphoto2.jpg => juggernautXL_juggXIByRundiffusion.jpg} (100%)
 rename models/Reference/{juggernautXL_v9Rdphoto2Lightning.jpg => juggernautXL_juggXILightningByRD.jpg} (100%)
 create mode 100644 models/Reference/linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg

diff --git a/html/previews.json b/html/previews.json
index 4f3354c86..e27e2513f 100644
--- a/html/previews.json
+++ b/html/previews.json
@@ -26,5 +26,5 @@
   "vladmandic--Qwen-Lightning-Edit": "models/Reference/Qwen-Lightning.jpg",
   "Wan-AI--Wan2.2-T2V-A14B-Diffusers": "models/Reference/Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
   "Wan-AI--Wan2.1-T2V-14B-Diffusers": "models/Reference/Wan-AI--Wan2.1-T2V-14B-Diffusers.jpg",
-  "linoyts--Wan2.2-VACE-Fun-14B-diffusers": "models/Reference/Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg"
+  "linoyts--Wan2.2-VACE-Fun-14B-diffusers": "models/Reference/linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg"
 }
diff --git a/html/reference.json b/html/reference.json
index 79a0980fe..e2615f0e4 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -14,13 +14,13 @@
 
   "Juggernaut XL XI": {
     "path": "juggernautXL_juggXIByRundiffusion.safetensors@https://civitai.com/api/download/models/782002",
-    "preview": "juggernautXL_v9Rundiffusionphoto2.jpg",
+    "preview": "juggernautXL_juggXIByRundiffusion.jpg",
     "desc": "Showcase finetuned model based on Stable diffusion XL",
     "extras": "sampler: DEIS, steps: 20, cfg_scale: 6.0"
   },
   "Juggernaut XL XI Lightning": {
     "path": "juggernautXL_juggXILightningByRD.safetensors@https://civitai.com/api/download/models/920957",
-    "preview": "juggernautXL_v9Rdphoto2Lightning.jpg",
+    "preview": "juggernautXL_juggXILightningByRD.jpg",
     "desc": "Showcase finetuned model based on Stable diffusion XL",
     "extras": "sampler: DPM SDE, steps: 6, cfg_scale: 2.0"
   },
@@ -287,7 +287,7 @@
   },
   "Wan-AI Wan2.2 14B VACE": {
     "path": "linoyts/Wan2.2-VACE-Fun-14B-diffusers",
-    "preview": "Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
+    "preview": "linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg",
     "desc": "Wan2.2, offering more powerful capabilities, better performance, and superior visual quality. With Wan2.2, we have focused on incorporating the following technical innovations: MoE Architecture, Data Scalling, Cinematic Aesthetics, Efficient High-Definition Hybrid",
     "skip": true,
     "extras": "sampler: Default"
diff --git a/models/Reference/briaai--BRIA-3.2.jpg b/models/Reference/briaai--BRIA-3.2.jpg
index dc7067ec55b3e74d6099abbf5e78d3791a87fb95..91476ba26fb7c2af87484288ab182c9301064d4b 100644
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From 2ac299a5a40310cfe8da2f4a535dc108b5017590 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 21 Sep 2025 12:33:54 -0400
Subject: [PATCH 028/230] nunchaku flux.1-pulid

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                |  4 +++-
 installer.py                |  9 ++++++---
 scripts/pulid/__init__.py   |  1 +
 scripts/pulid/pulid_flux.py | 35 +++++++++++++++++++++++++++++++++++
 scripts/pulid_ext.py        | 22 ++++++++++++++++++----
 5 files changed, 63 insertions(+), 8 deletions(-)
 create mode 100644 scripts/pulid/pulid_flux.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b3bea1252..1f890337f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-20
+## Update for 2025-09-21
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -12,6 +12,8 @@
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
     *note*: sdxl support for nunchaku is not in released version of `nunchaku==1.0.0`, so you need to build [nunchaku](https://nunchaku.tech/docs/nunchaku/installation/installation.html) from source
+  - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
+    automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  
diff --git a/installer.py b/installer.py
index 618e9958d..b31ef0e7e 100644
--- a/installer.py
+++ b/installer.py
@@ -1269,9 +1269,12 @@ def install_pydantic():
 
 def install_insightface():
     install('git+https://github.com/deepinsight/insightface@29b6cd65aa0e9ae3b6602de3c52e9d8949c8ee86#subdirectory=python-package', 'insightface') # insightface==0.7.3 with patches
-    # install('albumentations==1.4.3', ignore=True, quiet=True)
-    uninstall('albumentations')
-    install('albumentationsx')
+    if args.new:
+        uninstall('albumentations')
+        install('albumentationsx')
+    else:
+       uninstall('albumentationsx')
+       install('albumentations==1.4.3', ignore=True, quiet=True)
     install_pydantic()
 
 
diff --git a/scripts/pulid/__init__.py b/scripts/pulid/__init__.py
index 958ee9c1e..864b758d1 100644
--- a/scripts/pulid/__init__.py
+++ b/scripts/pulid/__init__.py
@@ -8,6 +8,7 @@ from modules.errors import log
 sys.path.append(os.path.dirname(__file__))
 try:
     from pulid_sdxl import StableDiffusionXLPuLIDPipeline, StableDiffusionXLPuLIDPipelineImage, StableDiffusionXLPuLIDPipelineInpaint
+    from pulid_flux import apply_flux, unapply_flux
     from pulid_utils import resize_numpy_image_long as resize
     import attention_processor as attention
     import pulid_sampling as sampling
diff --git a/scripts/pulid/pulid_flux.py b/scripts/pulid/pulid_flux.py
new file mode 100644
index 000000000..a419ca9c0
--- /dev/null
+++ b/scripts/pulid/pulid_flux.py
@@ -0,0 +1,35 @@
+from types import MethodType
+import accelerate
+from diffusers import FluxPipeline
+from modules import shared, sd_models
+
+
+def apply_flux(pipe: FluxPipeline):
+    if not hasattr(pipe, 'transformer') or not 'Nunchaku' in pipe.transformer.__class__.__name__:
+        shared.log.error('PuLID: flux support requires nunchaku')
+        return pipe
+
+    from nunchaku.pipeline.pipeline_flux_pulid import PuLIDFluxPipeline
+    if not isinstance(pipe, PuLIDFluxPipeline):
+        from nunchaku.models.pulid.pulid_forward import pulid_forward
+        sd_models.clear_caches(full=True)
+        accelerate.hooks.remove_hook_from_module(pipe.transformer, recurse=True)
+        pipe = sd_models.switch_pipe(PuLIDFluxPipeline, pipe)
+        pipe.transformer.orig_forward = pipe.transformer.forward
+        pipe.transformer.forward = MethodType(pulid_forward, pipe.transformer)
+        pipe = sd_models.apply_balanced_offload(pipe)
+        pipe.pulid_model = sd_models.apply_balanced_offload(pipe.pulid_model)
+        shared.log.info(f'PuLID: flux applied cls={pipe.__class__.__name__} pipe={pipe.pulid_model.__class__.__name__}')
+    return pipe
+
+
+def unapply_flux(pipe: FluxPipeline):
+    from nunchaku.pipeline.pipeline_flux_pulid import PuLIDFluxPipeline
+    if isinstance(pipe, PuLIDFluxPipeline) and hasattr(pipe.transformer, 'orig_forward'):
+        sd_models.clear_caches(full=True)
+        accelerate.hooks.remove_hook_from_module(pipe.transformer, recurse=True)
+        pipe.transformer.forward = MethodType(pipe.transformer.orig_forward, pipe.transformer)
+        del pipe.transformer.orig_forward
+        pipe = sd_models.switch_pipe(FluxPipeline, pipe)
+        pipe = sd_models.apply_balanced_offload(pipe)
+    return pipe
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index df9a099e6..ede691902 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -200,14 +200,12 @@ class Script(scripts_manager.Script):
                 errors.display(e, 'PuLID')
                 return None
         elif shared.sd_model_type == 'f1':
-            # TODO nunchaku: pulid-f1
-            shared.log.error('PuLID: f1 not supported')
-            return None
+            shared.sd_model = self.pulid.apply_flux(shared.sd_model)
 
         if shared.sd_model_type == 'sdxl':
             processed = self.run_sdxl(p, images, strength, zero, sampler, ortho, restore, offload, version)
         elif shared.sd_model_type == 'f1':
-            processed = None
+            processed = self.run_flux(p, images, strength)
         else:
             shared.log.error(f'PuLID: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
             processed = None
@@ -227,6 +225,12 @@ class Script(scripts_manager.Script):
                 shared.sd_model = shared.sd_model.pipe
                 devices.torch_gc(force=True, reason='pulid')
             shared.log.debug(f'PuLID complete: class={shared.sd_model.__class__.__name__} preprocess={self.preprocess:.2f} pipe={"restore" if restore else "cache"}')
+        if shared.sd_model_type == "f1":
+            restore = getattr(p, 'pulid_restore', restore)
+            if restore:
+                shared.sd_model = self.pulid.unapply_flux(shared.sd_model)
+                devices.torch_gc(force=True, reason='pulid')
+            shared.log.debug(f'PuLID complete: class={shared.sd_model.__class__.__name__} pipe={"restore" if restore else "cache"}')
         return processed
 
     def run_sdxl(self, p: processing.StableDiffusionProcessing, images: list, strength: float, zero: int, sampler: str, ortho: str, restore: bool, offload: bool, version: str):
@@ -285,3 +289,13 @@ class Script(scripts_manager.Script):
         # interim = [Image.fromarray(img) for img in shared.sd_model.debug_img_list]
         # shared.log.debug(f'PuLID: time={t1-t0:.2f}')
         return processed
+
+    def run_flux(self, p: processing.StableDiffusionProcessing, images: list, strength: float):
+        image = Image.fromarray(images[0]) # takes single pil image
+        p.task_args['id_image'] = image
+        p.task_args['id_weight'] = strength
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} image={image}')
+        p.extra_generation_params["PuLID"] = f'Strength={strength}'
+
+        processed: processing.Processed = processing.process_images(p) # runs processing using main loop
+        return processed

From 82504e96d042361beaa56331d5b9552bc4da46e2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 21 Sep 2025 13:26:18 -0400
Subject: [PATCH 029/230] nunchaku flux.1-ipadapter

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ipadapter.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/modules/ipadapter.py b/modules/ipadapter.py
index 676045d1f..88a01e2cd 100644
--- a/modules/ipadapter.py
+++ b/modules/ipadapter.py
@@ -354,6 +354,18 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
                     adapter_scales[i] = 0.00
             pipe.set_ip_adapter_scale(adapter_scales if len(adapter_scales) > 1 else adapter_scales[0])
             ip_str =  [f'{os.path.splitext(adapter)[0]}:{scale}:{start}:{end}:{crop}' for adapter, scale, start, end, crop in zip(adapter_names, adapter_scales, adapter_starts, adapter_ends, adapter_crops)]
+        if hasattr(pipe, 'transformer') and 'Nunchaku' in pipe.transformer.__class__.__name__:
+            if isinstance(repos, str):
+                sd_models.clear_caches(full=True)
+                import accelerate
+                accelerate.hooks.remove_hook_from_module(pipe.transformer, recurse=True)
+                pipe.transformer = pipe.transformer.to(devices.device)
+                from nunchaku.models.ip_adapter.diffusers_adapters import apply_IPA_on_pipe
+                apply_IPA_on_pipe(pipe, ip_adapter_scale=adapter_scales[0], repo_id=repos)
+                pipe = sd_models.apply_balanced_offload(pipe)
+                shared.log.debug(f'IP adapter load: engine=nunchaku scale={adapter_scales[0]} repo="{repos}"')
+            else:
+                shared.log.error('IP adapter: Nunchaku only supports single adapter')
         p.task_args['ip_adapter_image'] = crop_images(adapter_images, adapter_crops)
         if len(adapter_masks) > 0:
             p.cross_attention_kwargs = { 'ip_adapter_masks': adapter_masks }

From 27846fe97d3a83e679c01779e9bea8b048039f8d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 21 Sep 2025 19:43:55 -0400
Subject: [PATCH 030/230] styles and wildcards use main seed

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md      |  1 +
 modules/styles.py | 11 ++++++++++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1f890337f..7dea21326 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -30,6 +30,7 @@
   - **logging** enable `debug`, `docs` and `api-docs` by default  
   - **ipex** simplify internal implementation  
   - refactor to use new libraries  
+  - styles and wildcards now use same seed as main generate for reproducible results  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/modules/styles.py b/modules/styles.py
index ec3ca71e9..4ea4509a6 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -123,7 +123,7 @@ def apply_wildcards_to_prompt(prompt, all_wildcards, seed=-1, silent=False):
     if replaced and not silent:
         shared.log.debug(f'Apply wildcards: {replaced} path="{shared.opts.wildcards_dir}" type=style time={t1-t0:.2f}')
     if (len(replaced_file) > 0 or len(not_found) > 0) and not silent:
-        shared.log.debug(f'Apply wildcards: found={replaced_file} missing={not_found} path="{shared.opts.wildcards_dir}" type=file time={t2-t2:.2f} ')
+        shared.log.debug(f'Apply wildcards: found={replaced_file} missing={not_found} path="{shared.opts.wildcards_dir}" type=file seed={seed} time={t2-t2:.2f}')
     if old_state is not None:
         random.setstate(old_state)
     return prompt
@@ -168,6 +168,7 @@ def apply_styles_to_extra(p, style: Style):
     params = []
     settings = []
     skipped = []
+
     for k, v in extra.items():
         k = k.lower().replace(' ', '_')
         if k in name_map: # rename some fields
@@ -322,16 +323,24 @@ class StyleDatabase:
         jobid = shared.state.begin('Styles')
         parsed_positive = []
         parsed_negative = []
+        random_state = random.getstate()
+
         for i in range(len(prompts)):
+            if seeds[i]> 0:
+                random.seed(seeds[i])
             prompt = prompts[i]
             prompt = apply_styles_to_prompt(prompt, [self.find_style(x).prompt for x in styles])
             prompt = apply_wildcards_to_prompt(prompt, [self.find_style(x).wildcards for x in styles], seeds[i])
             parsed_positive.append(prompt)
         for i in range(len(negatives)):
+            if seeds[i]> 0:
+                random.seed(seeds[i])
             prompt = negatives[i]
             prompt = apply_styles_to_prompt(prompt, [self.find_style(x).negative_prompt for x in styles])
             prompt = apply_wildcards_to_prompt(prompt, [self.find_style(x).wildcards for x in styles], seeds[i])
             parsed_negative.append(prompt)
+
+        random.setstate(random_state)
         shared.state.end(jobid)
         return parsed_positive, parsed_negative
 

From d41bf3d6733ea0901a27df6a34de1d460956f97c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 21 Sep 2025 20:06:45 -0400
Subject: [PATCH 031/230] add api to trigger civitai metadata update

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                        |  2 ++
 modules/civitai/api_civitai.py      |  9 +++++++++
 modules/civitai/metadata_civitai.py | 14 +++++++-------
 3 files changed, 18 insertions(+), 7 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7dea21326..2f490ed68 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -31,6 +31,8 @@
   - **ipex** simplify internal implementation  
   - refactor to use new libraries  
   - styles and wildcards now use same seed as main generate for reproducible results  
+  - **api** new endpoint POST `/sdapi/v1/civitai` to trigger civitai models metadata update  
+    accepts optional `page` parameter to search specific networks page  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/modules/civitai/api_civitai.py b/modules/civitai/api_civitai.py
index 92add902f..4041f3f36 100644
--- a/modules/civitai/api_civitai.py
+++ b/modules/civitai/api_civitai.py
@@ -54,6 +54,15 @@ def get_civitai(
     return JSONResponse(content=[], status_code=200)
 
 
+def post_civitai(page:str=None):
+    from modules.civitai import metadata_civitai
+    result = []
+    for r in metadata_civitai.civit_search_metadata(title=page, raw=True):
+        result = r # get the last yielded result
+    return result
+
+
 def register_api():
     from modules.shared import api
     api.add_api_route("/sdapi/v1/civitai", get_civitai, methods=["GET"], response_model=list)
+    api.add_api_route("/sdapi/v1/civitai", post_civitai, methods=["POST"], response_model=list)
diff --git a/modules/civitai/metadata_civitai.py b/modules/civitai/metadata_civitai.py
index 6d4315860..fab22e78f 100644
--- a/modules/civitai/metadata_civitai.py
+++ b/modules/civitai/metadata_civitai.py
@@ -26,7 +26,7 @@ class CivitModel:
         self.status = 'Not found'
 
 
-def civit_update_metadata():
+def civit_update_metadata(raw:bool=False):
     def create_update_metadata_table(rows: list[CivitModel]):
         html = """
             <table class="simple-table">
@@ -102,8 +102,8 @@ def civit_update_metadata():
                             model.status = 'Update available'
                         break
         results.append(model)
-        yield create_update_metadata_table(results)
-    return create_update_metadata_table(results)
+        yield results if raw else create_update_metadata_table(results)
+    yield results if raw else create_update_metadata_table(results)
 
 
 def civit_search_model(name, tag, model_type):
@@ -224,7 +224,7 @@ def atomic_civit_search_metadata(item, results):
             results.append(result)
 
 
-def civit_search_metadata(title: str = None):
+def civit_search_metadata(title: str = None, raw: bool = False):
     def create_search_metadata_table(rows):
         html = """
             <table class="simple-table">
@@ -258,7 +258,7 @@ def civit_search_metadata(title: str = None):
     re_skip = [r.strip() for r in opts.extra_networks_scan_skip.split(',') if len(r.strip()) > 0]
     for page in get_pages():
         if type(title) == str:
-            if page.title != title:
+            if page.title.lower() != title.lower():
                 continue
         if page.name == 'style' or page.name == 'wildcards':
             continue
@@ -278,8 +278,8 @@ def civit_search_metadata(title: str = None):
             future_items[executor.submit(atomic_civit_search_metadata, fn, results)] = fn
         for future in concurrent.futures.as_completed(future_items):
             future.result()
-            yield create_search_metadata_table(results)
+            yield results if raw else create_search_metadata_table(results)
 
     t1 = time.time()
     log.debug(f'CivitAI search metadata: scanned={scanned} skipped={skipped} time={t1-t0:.2f}')
-    yield create_search_metadata_table(results)
+    yield results if raw else create_search_metadata_table(results)

From 2731b91a55df11c30997e65367bd36e3808b944c Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Mon, 22 Sep 2025 09:42:18 +0300
Subject: [PATCH 032/230] move 14B to the left for Vace, SRPO CFG6 image

---
 ...linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg | Bin 69556 -> 81552 bytes
 .../Reference/vladmandic--flux.1-dev-SRPO.jpg | Bin 41057 -> 54365 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/models/Reference/linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg b/models/Reference/linoyts--Wan2.2-VACE-Fun-14B-diffusers.jpg
index 0bf9f26b981cda1e3a62558b3719f580562a5424..dbde7389be192318559763f022ddd9e4a96449d9 100644
GIT binary patch
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From 41bb446697fe2be2c3ba9424f13b0ec808986fbd Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 22 Sep 2025 13:18:29 -0400
Subject: [PATCH 033/230] support configurable multi-stage models in video tab

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                            |   4 +-
 modules/video_models/models_def.py      |   5 +-
 modules/video_models/video_load.py      |  41 ++++----
 modules/video_models/video_overrides.py |   9 ++
 pipelines/model_wanai.py                |   2 +
 pipelines/wan/wan_image.py              | 119 ++++++++++++++++++++++++
 webui.py                                |   6 +-
 7 files changed, 156 insertions(+), 30 deletions(-)
 create mode 100644 pipelines/wan/wan_image.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2f490ed68..22b3ac43b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-21
+## Update for 2025-09-22
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -33,6 +33,8 @@
   - styles and wildcards now use same seed as main generate for reproducible results  
   - **api** new endpoint POST `/sdapi/v1/civitai` to trigger civitai models metadata update  
     accepts optional `page` parameter to search specific networks page  
+  - **reference models** additional example images, thanks @liutyi  
+  - **video** support for configurable multi-stage models such as WAN-2.2-14B  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/modules/video_models/models_def.py b/modules/video_models/models_def.py
index b9ff5c260..e2605ae50 100644
--- a/modules/video_models/models_def.py
+++ b/modules/video_models/models_def.py
@@ -170,11 +170,12 @@ models = {
               dit_cls=diffusers.WanTransformer3DModel,
               dit_folder=("transformer", "transformer_2")),
         Model(name='WAN 2.2 14B VACE',
-              url='https://huggingface.co/Wan-AI/Wan2.2-14B-VACE-T2V-Diffusers',
+              url='https://huggingface.co/linoyts/Wan2.2-VACE-Fun-14B-diffusers',
               repo='linoyts/Wan2.2-VACE-Fun-14B-diffusers',
               repo_cls=diffusers.WanVACEPipeline,
               te_cls=transformers.T5EncoderModel,
-              dit_cls=diffusers.WanVACETransformer3DModel),
+              dit_cls=diffusers.WanVACETransformer3DModel,
+              dit_folder=("transformer", "transformer_2")),
         Model(name='WAN 2.1 1.3B T2V',
               url='https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
               repo='Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
diff --git a/modules/video_models/video_load.py b/modules/video_models/video_load.py
index 72c411166..874df0231 100644
--- a/modules/video_models/video_load.py
+++ b/modules/video_models/video_load.py
@@ -40,31 +40,24 @@ def load_model(selected: models_def.Model):
 
     # transformer
     try:
+        if selected.dit_folder is None:
+            selected.dit_folder = ['transformer']
         if isinstance(selected.dit_folder, list) or isinstance(selected.dit_folder, tuple):
-            # wan a14b has transformer and transformer_2
-            for dit_folder in selected.dit_folder:
-                # get a new quant arg on every loop to prevent the quant config classes getting entangled
-                load_args, quant_args = model_quant.get_dit_args({}, module='Model', device_map=True)
-                shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" folder="{dit_folder}" cls={selected.dit_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
-                kwargs[dit_folder] = selected.dit_cls.from_pretrained(
-                    pretrained_model_name_or_path=selected.dit or selected.repo,
-                    subfolder=dit_folder,
-                    revision=selected.dit_revision or selected.repo_revision,
-                    cache_dir=shared.opts.hfcache_dir,
-                    **load_args,
-                    **quant_args
-                )
-        else:
-            load_args, quant_args = model_quant.get_dit_args({}, module='Model', device_map=True)
-            shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" folder="{selected.dit_folder}" cls={selected.dit_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
-            kwargs["transformer"] = selected.dit_cls.from_pretrained(
-                pretrained_model_name_or_path=selected.dit or selected.repo,
-                subfolder=selected.dit_folder,
-                revision=selected.dit_revision or selected.repo_revision,
-                cache_dir=shared.opts.hfcache_dir,
-                **load_args,
-                **quant_args
-            )
+            for dit_folder in selected.dit_folder: # wan a14b has transformer and transformer_2
+                if dit_folder is not None and dit_folder not in kwargs:
+                    # get a new quant arg on every loop to prevent the quant config classes getting entangled
+                    load_args, quant_args = model_quant.get_dit_args({}, module='Model', device_map=True)
+                    shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" module="{dit_folder}" folder="{dit_folder}" cls={selected.dit_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
+                    kwargs[dit_folder] = selected.dit_cls.from_pretrained(
+                        pretrained_model_name_or_path=selected.dit or selected.repo,
+                        subfolder=dit_folder,
+                        revision=selected.dit_revision or selected.repo_revision,
+                        cache_dir=shared.opts.hfcache_dir,
+                        **load_args,
+                        **quant_args
+                    )
+                else:
+                    shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" module="{dit_folder}" folder="{dit_folder}" cls={selected.dit_cls.__name__} skip')
     except Exception as e:
         shared.log.error(f'video load: module=transformer cls={selected.dit_cls.__name__} {e}')
         errors.display(e, 'video')
diff --git a/modules/video_models/video_overrides.py b/modules/video_models/video_overrides.py
index 057ae3f8a..856da62e6 100644
--- a/modules/video_models/video_overrides.py
+++ b/modules/video_models/video_overrides.py
@@ -19,6 +19,15 @@ def load_override(selected: Model):
     # WAN
     if 'WAN 2.1 14B' in selected.name:
         kwargs['vae'] = diffusers.AutoencoderKLWan.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir)
+    if 'A14B' in selected.name or '14B VACE' in selected.name:
+        if shared.opts.model_wan_stage == 'combined':
+            kwargs['boundary_ratio'] = shared.opts.model_wan_boundary
+        elif shared.opts.model_wan_stage == 'high noise':
+            kwargs['transformer_2'] = None
+            kwargs['boundary_ratio'] = 0.0
+        elif shared.opts.model_wan_stage == 'low noise':
+            kwargs['boundary_ratio'] = 1.0
+            kwargs['transformer'] = None
     debug(f'Video overrides: model="{selected.name}" kwargs={list(kwargs)}')
     return kwargs
 
diff --git a/pipelines/model_wanai.py b/pipelines/model_wanai.py
index e44d1eb78..718ec63c6 100644
--- a/pipelines/model_wanai.py
+++ b/pipelines/model_wanai.py
@@ -96,8 +96,10 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
         diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanVACEPipeline
         diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["wanai"] = diffusers.WanVACEPipeline
     else:
+        from pipelines.wan.wan_image import WanImagePipeline
         pipe_cls = diffusers.WanPipeline
         diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["wanai"] = WanImagePipeline
     shared.log.debug(f'Load model: type=WanAI model="{checkpoint_info.name}" repo="{repo_id}" cls={pipe_cls.__name__} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args} stage="{shared.opts.model_wan_stage}" boundary={boundary_ratio}')
     pipe = pipe_cls.from_pretrained(
         repo_id,
diff --git a/pipelines/wan/wan_image.py b/pipelines/wan/wan_image.py
new file mode 100644
index 000000000..bd9923e5b
--- /dev/null
+++ b/pipelines/wan/wan_image.py
@@ -0,0 +1,119 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch
+import diffusers
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput
+
+from modules import devices
+
+
+class WanImagePipeline(diffusers.WanPipeline):
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "np",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        strength: float = 0.3, # new
+        image: PipelineImageInput = None, # new
+    ):
+        # get img2img timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=devices.device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
+        # monkey patch original pipeline
+        self.scheduler.timesteps = timesteps
+        # self.scheduler._step_index = 0
+        self.scheduler.orig_set_timesteps = self.scheduler.set_timesteps
+        self.scheduler.set_timesteps = lambda *args, **kwargs: None
+
+        # prepare latents
+        latents = self.img2img_prepare_latents(
+            image=image,
+            timesteps=timesteps,
+            dtype=devices.dtype,
+            device=devices.device,
+            generator=generator,
+        )
+
+        # call original pipeline
+        result = super().__call__( # pylint: disable=no-member
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_frames=num_frames,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            guidance_scale_2=guidance_scale_2,
+            num_videos_per_prompt=num_videos_per_prompt,
+            generator=generator,
+            latents=latents,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            output_type=output_type,
+            return_dict=return_dict,
+            attention_kwargs=attention_kwargs,
+            callback_on_step_end=callback_on_step_end,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        # un-monkey patch original pipeline
+        self.scheduler.set_timesteps = self.scheduler.orig_set_timesteps
+        return result
+
+    def get_timesteps(self, num_inference_steps, strength):
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+        if hasattr(self.scheduler, "set_begin_index"):
+            # self.scheduler.set_begin_index(t_start * self.scheduler.order)
+            self.scheduler.set_begin_index(0)
+        return timesteps, num_inference_steps - t_start
+
+    def img2img_prepare_latents(
+        self,
+        image: torch.Tensor = None,
+        timesteps: torch.Tensor = None,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    ) -> torch.Tensor:
+        from diffusers.utils.torch_utils import randn_tensor
+        from diffusers.video_processor import VideoProcessor
+
+        if isinstance(image, list):
+            image = image[0] # ignore batch for now
+
+        video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+        image_tensor = video_processor.preprocess(image, None, None) # convert PIL to [B, C, H, W] # channels may need rearrange
+        image_tensor = image_tensor.squeeze(0).to(device=device, dtype=dtype)
+        image_tensor = image_tensor[None, :, None, :, :] # expand before encode to [B, C, N, H, W]
+        encoder_output = self.vae.encode(image_tensor)
+        # init_latents = encoder_output.latent_dist.mode() # argmax or sample?
+        init_latents = encoder_output.latent_dist.sample(generator)
+
+        latents_mean = torch.tensor(self.vae.config.latents_mean, device=device, dtype=torch.float32).view(1, self.vae.config.z_dim, 1, 1, 1)
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std, device=device, dtype=torch.float32).view(1, self.vae.config.z_dim, 1, 1, 1)
+        init_latents = ((init_latents.float() - latents_mean) * latents_std).to(dtype) # normalized to standard distribution range
+
+        init_noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
+        init_timestep = timesteps[:1]
+        noised_latents = self.scheduler.add_noise(init_latents, init_noise, init_timestep)
+
+        return noised_latents
diff --git a/webui.py b/webui.py
index ec33c908e..8dc29d5bc 100644
--- a/webui.py
+++ b/webui.py
@@ -152,9 +152,9 @@ def initialize():
 
 def load_model():
     modeldata.model_data.locked = False
-    if not shared.opts.sd_checkpoint_autoload and shared.cmd_opts.ckpt is None:
-        log.info('Model: autoload=False')
-    else:
+    autoload = shared.opts.sd_checkpoint_autoload or shared.cmd_opts.ckpt is not None
+    log.info(f'Model: autoload={autoload} selected="{shared.opts.sd_model_checkpoint}"')
+    if autoload:
         jobid = shared.state.begin('Load model')
         thread_model = Thread(target=lambda: shared.sd_model)
         thread_model.start()

From 4130c6cc8b21207a3ef4556230722f8226588e97 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 22 Sep 2025 15:55:26 -0400
Subject: [PATCH 034/230] experimental cache-dit

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    |  2 ++
 modules/cachedit.py             | 58 +++++++++++++++++++++++++++++++++
 modules/processing.py           |  2 +-
 modules/processing_diffusers.py |  6 ++--
 modules/shared.py               |  4 +++
 5 files changed, 69 insertions(+), 3 deletions(-)
 create mode 100644 modules/cachedit.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 22b3ac43b..61c089574 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -35,6 +35,8 @@
     accepts optional `page` parameter to search specific networks page  
   - **reference models** additional example images, thanks @liutyi  
   - **video** support for configurable multi-stage models such as WAN-2.2-14B  
+  - [cache-dit](https://github.com/vipshop/cache-dit)  
+    *experimental support*, enable in *settings -> pipeline modifers -> cache-dit*  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/modules/cachedit.py b/modules/cachedit.py
new file mode 100644
index 000000000..1b698ff3b
--- /dev/null
+++ b/modules/cachedit.py
@@ -0,0 +1,58 @@
+from installer import install
+from modules import shared
+
+
+def apply_cache_dit(pipe):
+    if not shared.opts.cache_dit_enabled:
+        return
+    install('cache_dit')
+    try:
+        import cache_dit
+    except Exception as e:
+        shared.log.error(f'Cache-DIT: {e}')
+        return
+    _, supported = cache_dit.supported_pipelines()
+    supported = [s.replace('*', '') for s in supported]
+    if not any(pipe.__class__.__name__.startswith(s) for s in supported):
+        shared.log.error(f'Cache-DiT: pipeline={pipe.__class__.__name__} unsupported')
+        return
+
+    if getattr(pipe, 'has_cache_dit', False):
+        unapply_cache_dir(pipe)
+
+    cache_config = cache_dit.BasicCacheConfig()
+    if shared.opts.cache_dit_calibrator == "TaylorSeer":
+        calibrator_config = cache_dit.TaylorSeerCalibratorConfig(taylorseer_order=1)
+    elif shared.opts.cache_dit_calibrator == "FoCa":
+        calibrator_config = cache_dit.FoCaCalibratorConfig()
+    else:
+        calibrator_config = None
+    """
+        Fn_compute_blocks=shared.opts.cache_dit_Fn_compute_blocks, # 8
+        Bn_compute_blocks=shared.opts.cache_dit_Bn_compute_blocks, # 0
+        residual_diff_threshold=shared.opts.cache_dit_residual_diff_threshold, # 0.08
+        max_warmup_steps=shared.opts.cache_dit_max_warmup_steps, # 8
+        max_cached_steps=shared.opts.cache_dit_max_cached_steps, # -1
+        max_continuous_cached_steps=shared.opts.cache_dit_max_continuous_cached_steps, # -1
+        enable_separate_cfg=shared.opts.cache_dit_enable_separate_cfg, # False
+        cfg_compute_first=shared.opts.cache_dit_cfg_compute_first, # False
+    )
+    """
+    shared.log.info(f'Apply Cache-DiT: config="{cache_config.strify()}" calibrator="{calibrator_config.strify() if calibrator_config else "None"}"')
+    cache_dit.enable_cache(
+        pipe,
+        cache_config=cache_config,
+        calibrator_config=calibrator_config,
+    )
+    shared.sd_model.has_cache_dit = True
+
+
+def unapply_cache_dir(pipe):
+    if not shared.opts.cache_dit_enabled or not getattr(pipe, 'has_cache_dit', False):
+        return
+    try:
+        import cache_dit
+        cache_dit.disable_cache(pipe)
+        pipe.has_cache_dit = False
+    except Exception:
+        return
diff --git a/modules/processing.py b/modules/processing.py
index 31378fa4a..e01f88bdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -130,7 +130,7 @@ def get_processed(*args, **kwargs):
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
     timer.process.reset()
-    debug(f'Process images: {vars(p)}')
+    debug(f'Process images: class={p.__class__.__name__} {vars(p)}')
     if not hasattr(p.sd_model, 'sd_checkpoint_info'):
         shared.log.error('Processing: incomplete model')
         return None
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index d29cfc0e5..1d88ab2a0 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -67,7 +67,7 @@ def restore_state(p: processing.StableDiffusionProcessing):
 
 
 def process_pre(p: processing.StableDiffusionProcessing):
-    from modules import ipadapter, sd_hijack_freeu, para_attention, teacache, hidiffusion, ras, pag, cfgzero, transformer_cache, token_merge, linfusion
+    from modules import ipadapter, sd_hijack_freeu, para_attention, teacache, hidiffusion, ras, pag, cfgzero, transformer_cache, token_merge, linfusion, cachedit
     shared.log.info('Processing modifiers: apply')
 
     try:
@@ -86,6 +86,7 @@ def process_pre(p: processing.StableDiffusionProcessing):
         transformer_cache.set_cache()
         para_attention.apply_first_block_cache()
         teacache.apply_teacache(p)
+        cachedit.apply_cache_dit(shared.sd_model)
     except Exception as e:
         shared.log.error(f'Processing apply: {e}')
         errors.display(e, 'apply')
@@ -99,7 +100,7 @@ def process_pre(p: processing.StableDiffusionProcessing):
 
 
 def process_post(p: processing.StableDiffusionProcessing):
-    from modules import ipadapter, hidiffusion, ras, pag, cfgzero, token_merge, linfusion
+    from modules import ipadapter, hidiffusion, ras, pag, cfgzero, token_merge, linfusion, cachedit
     shared.log.info('Processing modifiers: unapply')
 
     try:
@@ -111,6 +112,7 @@ def process_post(p: processing.StableDiffusionProcessing):
         pag.unapply()
         cfgzero.unapply()
         linfusion.unapply(shared.sd_model)
+        cachedit.unapply_cache_dir(shared.sd_model)
     except Exception as e:
         shared.log.error(f'Processing unapply: {e}')
         errors.display(e, 'unapply')
diff --git a/modules/shared.py b/modules/shared.py
index 9577535c0..0cc33fbc3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -356,6 +356,10 @@ options_templates.update(options_section(('advanced', "Pipeline Modifiers"), {
     "pab_spacial_skip_start": OptionInfo(100, "PAB spacial skip start", gr.Slider, {"minimum": 0, "maximum": 1000, "step": 1}),
     "pab_spacial_skip_end": OptionInfo(800, "PAB spacial skip end", gr.Slider, {"minimum": 0, "maximum": 1000, "step": 1}),
 
+    "cache_dit_sep": OptionInfo("<h2>Cache-DiT</h2>", "", gr.HTML),
+    "cache_dit_enabled": OptionInfo(False, "Cache-DiT enabled"),
+    "cache_dit_calibrator": OptionInfo("None", "Cache-DiT calibrator", gr.Radio, {"choices": ["None", "TaylorSeer", "FoCa"]}),
+
     "faster_cache__sep": OptionInfo("<h2>Faster Cache</h2>", "", gr.HTML),
     "faster_cache_enabled": OptionInfo(False, "FasterCache cache enabled"),
     "fc_spacial_skip_range": OptionInfo(2, "FasterCache spacial skip range", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),

From 41e297ba2cc293c2d3e7875c4abcbbf65fe643ae Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 23 Sep 2025 16:17:09 +0300
Subject: [PATCH 035/230] SDNQ fix new transformers

---
 modules/sdnq/__init__.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index a82038f68..d8394cde7 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -422,6 +422,12 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return config
 
+    def update_ep_plan(self, config):
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return config
+
     def update_unexpected_keys(self, model, unexpected_keys: List[str], prefix: str) -> List[str]: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function

From 93659dff4d5083dd084fdab92549ee62462f0165 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 23 Sep 2025 09:58:02 -0400
Subject: [PATCH 036/230] add qwen-image-edit-2509 and update requirements

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .gitignore                 |  8 --------
 CHANGELOG.md               |  4 +++-
 html/reference.json        |  7 +++++++
 installer.py               | 10 +++++-----
 modules/cachedit.py        |  8 ++++++--
 modules/processing_args.py | 10 +++++-----
 pipelines/model_qwen.py    | 15 +++++++++++++--
 requirements.txt           |  2 +-
 wiki                       |  2 +-
 9 files changed, 41 insertions(+), 25 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9a3415c86..9bc6d5a24 100644
--- a/.gitignore
+++ b/.gitignore
@@ -46,14 +46,6 @@ tunableop_results*.csv
 !package.json
 !requirements.txt
 
-# pyinstaller
-*.spec
-build/
-dist/
-
-# dynamically generated
-/repositories/deepseek-vl2/
-
 # all dynamic stuff
 /extensions/**/*
 /outputs/**/*
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 61c089574..769688808 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,10 +1,12 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-22
+## Update for 2025-09-23
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
+  - [Qwen Image Edit 2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509)  
+    improved version of Qwen Image Edit with improved image consistency  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
     SRPO is trained by Tencent with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference  
   - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
diff --git a/html/reference.json b/html/reference.json
index e2615f0e4..530570913 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -170,6 +170,13 @@
     "skip": true,
     "extras": ""
   },
+  "Qwen-Image-Edit-2509": {
+    "path": "Qwen/Qwen-Image-Edit-2509",
+    "preview": "Qwen--Qwen-Image-Edit.jpg",
+    "desc": "Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.",
+    "skip": true,
+    "extras": ""
+  },
   "Qwen-Image-Lightning": {
     "path": "vladmandic/Qwen-Lightning",
     "preview": "vladmandic--Qwen-Lightning.jpg",
diff --git a/installer.py b/installer.py
index b31ef0e7e..97a502d40 100644
--- a/installer.py
+++ b/installer.py
@@ -608,7 +608,7 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = 'efb7a299af46d739dec6a57a5d2814165fba24b5' # diffusers commit hash
+    sha = '1448b035859dd57bbb565239dcdd79a025a85422' # diffusers commit hash
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''
@@ -635,8 +635,8 @@ def check_transformers():
         target_transformers = '4.52.4'
         target_tokenizers = '0.21.4'
     else:
-        target_transformers = '4.56.1'
-        target_tokenizers = '0.22.0'
+        target_transformers = '4.56.2'
+        target_tokenizers = '0.22.1'
     if (pkg_transformers is None) or ((pkg_transformers.version != target_transformers) or (pkg_tokenizers is None) or ((pkg_tokenizers.version != target_tokenizers) and (not args.experimental))):
         if pkg_transformers is None:
             log.info(f'Transformers install: version={target_transformers}')
@@ -1273,8 +1273,8 @@ def install_insightface():
         uninstall('albumentations')
         install('albumentationsx')
     else:
-       uninstall('albumentationsx')
-       install('albumentations==1.4.3', ignore=True, quiet=True)
+        uninstall('albumentationsx')
+        install('albumentations==1.4.3', ignore=True, quiet=True)
     install_pydantic()
 
 
diff --git a/modules/cachedit.py b/modules/cachedit.py
index 1b698ff3b..62575ca1b 100644
--- a/modules/cachedit.py
+++ b/modules/cachedit.py
@@ -5,7 +5,7 @@ from modules import shared
 def apply_cache_dit(pipe):
     if not shared.opts.cache_dit_enabled:
         return
-    install('cache_dit')
+    install('git+https://github.com/vipshop/cache-dit', 'cache_dit')
     try:
         import cache_dit
     except Exception as e:
@@ -20,7 +20,9 @@ def apply_cache_dit(pipe):
     if getattr(pipe, 'has_cache_dit', False):
         unapply_cache_dir(pipe)
 
-    cache_config = cache_dit.BasicCacheConfig()
+    config_args = {}
+
+    cache_config = cache_dit.BasicCacheConfig(**config_args)
     if shared.opts.cache_dit_calibrator == "TaylorSeer":
         calibrator_config = cache_dit.TaylorSeerCalibratorConfig(taylorseer_order=1)
     elif shared.opts.cache_dit_calibrator == "FoCa":
@@ -52,6 +54,8 @@ def unapply_cache_dir(pipe):
         return
     try:
         import cache_dit
+        stats = cache_dit.summary(pipe)
+        shared.log.critical(f'Unapply Cache-DiT: {stats}')
         cache_dit.disable_cache(pipe)
         pipe.has_cache_dit = False
     except Exception:
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 98a295ca1..971adad44 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -103,9 +103,9 @@ def task_specific_kwargs(p, model):
         }
 
     # model specific args
-    if model_cls == 'QwenImageEditPipeline' and len(getattr(p, 'init_images', [])) == 0:
+    if 'QwenImageEdit' in model_cls and len(getattr(p, 'init_images', [])) == 0:
         task_args['image'] = [Image.new('RGB', (p.width, p.height), (0, 0, 0))] # monkey-patch so qwen-image-edit pipeline does not error-out on t2i
-    if model_cls == 'LatentConsistencyModelPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
+    if 'LatentConsistencyModelPipeline' in model_cls and hasattr(p, 'init_images') and len(p.init_images) > 0:
         p.ops.append('lcm')
         init_latents = [processing_vae.vae_encode(image, model=shared.sd_model, vae_type=p.vae_type).squeeze(dim=0) for image in p.init_images]
         init_latent = torch.stack(init_latents, dim=0).to(shared.device)
@@ -116,7 +116,7 @@ def task_specific_kwargs(p, model):
             'width': p.width if hasattr(p, 'width') else None,
             'height': p.height if hasattr(p, 'height') else None,
         }
-    if model_cls == 'BlipDiffusionPipeline':
+    if 'BlipDiffusionPipeline' in model_cls:
         if len(getattr(p, 'init_images', [])) == 0:
             shared.log.error('BLiP diffusion requires init image')
             return task_args
@@ -126,9 +126,9 @@ def task_specific_kwargs(p, model):
             'target_subject_category': getattr(p, 'prompt', '').split()[-1],
             'output_type': 'pil',
         }
-    if (model.__class__.__name__ == 'WanImageToVideoPipeline') and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
+    if ('WanImageToVideoPipeline' in model_cls) and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
         task_args['image'] = p.init_images[0]
-    if (model.__class__.__name__ == 'WanVACEPipeline') and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
+    if ('WanVACEPipeline' in model_cls) and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
         task_args['reference_images'] = p.init_images
 
     if debug_enabled:
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index a89c272a1..cf18e2143 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -12,7 +12,12 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model')
     shared.log.debug(f'Load model: type=Qwen model="{checkpoint_info.name}" repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
-    if 'Edit' in repo_id:
+    if '2509' in repo_id :
+        cls_name = diffusers.QwenImageEditPlusPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageEditPlusPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageEditPlusPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageEditPlusPipeline
+    elif 'Edit' in repo_id:
         cls_name = diffusers.QwenImageEditPipeline
         diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageEditPipeline
         diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageEditPipeline
@@ -36,7 +41,13 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
         transformer_subfolder = "transformer"
 
     if transformer is None:
-        transformer = generic.load_transformer(repo_transformer, subfolder=transformer_subfolder, cls_name=diffusers.QwenImageTransformer2DModel, load_config=diffusers_load_config, modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "transformer_blocks.0.img_mod.1.weight"]})
+        transformer = generic.load_transformer(
+            repo_transformer,
+            subfolder=transformer_subfolder,
+            cls_name=diffusers.QwenImageTransformer2DModel,
+            load_config=diffusers_load_config,
+            modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "transformer_blocks.0.img_mod.1.weight"]},
+        )
 
     repo_te = 'Qwen/Qwen-Image'
     text_encoder = generic.load_text_encoder(repo_te, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
diff --git a/requirements.txt b/requirements.txt
index 25fb3c837..cec622cb1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -44,7 +44,7 @@ tqdm==4.67.1
 accelerate==1.10.1
 opencv-contrib-python-headless==4.11.0.86
 einops==0.8.1
-huggingface_hub==0.34.4
+huggingface_hub==0.35.0
 numexpr==2.11.0
 numpy==2.1.2
 pandas==2.3.1
diff --git a/wiki b/wiki
index 76be4e54b..0172d5d26 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 76be4e54b2c40bac1502cf97caa4a04bec0a033f
+Subproject commit 0172d5d268497e52300423837be3c53c3fc2ec83

From a502cc2ae1020128f50664bf76cfc026b3f4c8a1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 23 Sep 2025 10:13:30 -0400
Subject: [PATCH 037/230] cache-dit support

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md        |  7 +++++--
 modules/cachedit.py | 26 ++++++++++++--------------
 modules/shared.py   |  4 ++++
 3 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 769688808..21b69aa3f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,6 +14,11 @@
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
     *note*: sdxl support for nunchaku is not in released version of `nunchaku==1.0.0`, so you need to build [nunchaku](https://nunchaku.tech/docs/nunchaku/installation/installation.html) from source
+- **Features**
+  - [Cache-DiT](https://github.com/vipshop/cache-dit)  
+    cache-dit is a unified, flexible and training-free cache acceleration framework  
+    compatible with many dit-based models such as FLUX.1, Qwen, HunyuanImage, Wan2.2, Chroma, etc.  
+    enable in *settings -> pipeline modifers -> cache-dit*  
   - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
     automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
 - **Offloading**
@@ -37,8 +42,6 @@
     accepts optional `page` parameter to search specific networks page  
   - **reference models** additional example images, thanks @liutyi  
   - **video** support for configurable multi-stage models such as WAN-2.2-14B  
-  - [cache-dit](https://github.com/vipshop/cache-dit)  
-    *experimental support*, enable in *settings -> pipeline modifers -> cache-dit*  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/modules/cachedit.py b/modules/cachedit.py
index 62575ca1b..f31dcc225 100644
--- a/modules/cachedit.py
+++ b/modules/cachedit.py
@@ -1,3 +1,4 @@
+import os
 from installer import install
 from modules import shared
 
@@ -6,6 +7,7 @@ def apply_cache_dit(pipe):
     if not shared.opts.cache_dit_enabled:
         return
     install('git+https://github.com/vipshop/cache-dit', 'cache_dit')
+    os.environ.setdefault("CACHE_DIT_LOG_LEVEL", "error")
     try:
         import cache_dit
     except Exception as e:
@@ -21,7 +23,14 @@ def apply_cache_dit(pipe):
         unapply_cache_dir(pipe)
 
     config_args = {}
-
+    if shared.opts.cache_dit_fcompute >= 0:
+        config_args['Fn_compute_blocks'] = int(shared.opts.cache_dit_fcompute)
+    if shared.opts.cache_dit_bcompute >= 0:
+        config_args['Bn_compute_blocks'] = int(shared.opts.cache_dit_bcompute)
+    if shared.opts.cache_dit_threshold >= 0:
+        config_args['residual_diff_threshold'] = float(shared.opts.cache_dit_threshold)
+    if shared.opts.cache_dit_warmup >= 0:
+        config_args['max_warmup_steps'] = int(shared.opts.cache_dit_warmup)
     cache_config = cache_dit.BasicCacheConfig(**config_args)
     if shared.opts.cache_dit_calibrator == "TaylorSeer":
         calibrator_config = cache_dit.TaylorSeerCalibratorConfig(taylorseer_order=1)
@@ -29,17 +38,6 @@ def apply_cache_dit(pipe):
         calibrator_config = cache_dit.FoCaCalibratorConfig()
     else:
         calibrator_config = None
-    """
-        Fn_compute_blocks=shared.opts.cache_dit_Fn_compute_blocks, # 8
-        Bn_compute_blocks=shared.opts.cache_dit_Bn_compute_blocks, # 0
-        residual_diff_threshold=shared.opts.cache_dit_residual_diff_threshold, # 0.08
-        max_warmup_steps=shared.opts.cache_dit_max_warmup_steps, # 8
-        max_cached_steps=shared.opts.cache_dit_max_cached_steps, # -1
-        max_continuous_cached_steps=shared.opts.cache_dit_max_continuous_cached_steps, # -1
-        enable_separate_cfg=shared.opts.cache_dit_enable_separate_cfg, # False
-        cfg_compute_first=shared.opts.cache_dit_cfg_compute_first, # False
-    )
-    """
     shared.log.info(f'Apply Cache-DiT: config="{cache_config.strify()}" calibrator="{calibrator_config.strify() if calibrator_config else "None"}"')
     cache_dit.enable_cache(
         pipe,
@@ -54,8 +52,8 @@ def unapply_cache_dir(pipe):
         return
     try:
         import cache_dit
-        stats = cache_dit.summary(pipe)
-        shared.log.critical(f'Unapply Cache-DiT: {stats}')
+        # stats = cache_dit.summary(pipe)
+        # shared.log.critical(f'Unapply Cache-DiT: {stats}')
         cache_dit.disable_cache(pipe)
         pipe.has_cache_dit = False
     except Exception:
diff --git a/modules/shared.py b/modules/shared.py
index 0cc33fbc3..a16d80571 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -359,6 +359,10 @@ options_templates.update(options_section(('advanced', "Pipeline Modifiers"), {
     "cache_dit_sep": OptionInfo("<h2>Cache-DiT</h2>", "", gr.HTML),
     "cache_dit_enabled": OptionInfo(False, "Cache-DiT enabled"),
     "cache_dit_calibrator": OptionInfo("None", "Cache-DiT calibrator", gr.Radio, {"choices": ["None", "TaylorSeer", "FoCa"]}),
+    "cache_dit_fcompute": OptionInfo(-1, "Cache-DiT F-compute blocks", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "cache_dit_bcompute": OptionInfo(-1, "Cache-DiT B-compute blocks", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "cache_dit_threshold": OptionInfo(-1, "Cache-DiT residual diff threshold", gr.Slider, {"minimum": -1.0, "maximum": 1.0, "step": 0.01}),
+    "cache_dit_warmup": OptionInfo(-1, "Cache-DiT warmup steps", gr.Slider, {"minimum": -1, "maximum": 50, "step": 1}),
 
     "faster_cache__sep": OptionInfo("<h2>Faster Cache</h2>", "", gr.HTML),
     "faster_cache_enabled": OptionInfo(False, "FasterCache cache enabled"),

From 81037a6ff956ea35b4dcef76511112549d67a165 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 23 Sep 2025 10:19:19 -0400
Subject: [PATCH 038/230] update changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 21b69aa3f..a4de5cd96 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,9 +6,9 @@
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
   - [Qwen Image Edit 2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509)  
-    improved version of Qwen Image Edit with improved image consistency  
+    updated version of Qwen Image Edit with improved image consistency  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
-    SRPO is trained by Tencent with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference  
+    SRPO is trained by Tencent with specific technique: directly aligning the full diffusion trajectory with fine-grained human preference  
   - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
     impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
@@ -21,6 +21,10 @@
     enable in *settings -> pipeline modifers -> cache-dit*  
   - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
     automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
+- **Extensions**
+  - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
+    it also does not work with control and video tabs which are the core of sdnext nowadays  
+    so it has been removed from built-in extensions: manual installation is still possible  
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  
@@ -28,10 +32,6 @@
     enable in settings -> model offloading  
   - new feature to specify which models types not to offload  
     in *settings -> model offloading -> model types not to offload*  
-- **Extensions**
-  - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
-    it also does not work with control and video tabs which are the core of sdnext nowadays  
-    so it has been removed from built-in extensions: manual installation is still possible  
 - **Other**
   - server will note when restart is recommended due to package updates  
   - **logging** enable `debug`, `docs` and `api-docs` by default  

From d71d540a62823f086d83e358dc5233b482919d08 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 23 Sep 2025 15:25:13 -0400
Subject: [PATCH 039/230] allow lora specifier to select model component. see
 changelog/wiki for docs on how to.

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                        |  9 ++-
 modules/cachedit.py                 | 16 +++--
 modules/lora/extra_networks_lora.py | 37 ++++++++----
 modules/lora/lora_diffusers.py      | 92 +++++++++++++++++++++++++++++
 modules/lora/lora_load.py           | 61 ++++---------------
 pipelines/model_wanai.py            |  9 ++-
 wiki                                |  2 +-
 7 files changed, 155 insertions(+), 71 deletions(-)
 create mode 100644 modules/lora/lora_diffusers.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a4de5cd96..6104c9043 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -130,6 +130,13 @@ And check out new **history** tab in the right panel, it now shows visualization
     *experimental*: requires new pydantic package which *may* break other things, to enable start sdnext with `--new` flag  
     *note*: this is model quantization only, no support for tensorRT inference yet  
 - **Other**
+  - **LoRA** allow specifying module to apply lora on  
+    *example*: `<lora:mylora:1.0:module=unet>` would apply lora *only* on unet regardless of lora content  
+    this is particularly useful when you have multiple loras and you want to apply them on different parts of the model  
+    *example*: `<lora:firstlora:1.0:high>` and `<lora:secondlora:1.0:low>`  
+    *note*: `low` is shorthand for `module=transformer_2` and `high` is shortcut for `module=transformer`  
+  - **Detailer** allow manually setting processing resolution  
+    *note*: this does not impact the actual image resolution, only the resolution at which detailer internally operates  
   - refactor reuse-seed and add functionality to all tabs  
   - refactor modernui js codebase  
   - move zluda flash attenion to *Triton Flash attention* option  
@@ -144,8 +151,6 @@ And check out new **history** tab in the right panel, it now shows visualization
   - add deprecation warning for `python==3.9`  
   - allow setting denoise strength to 0 in control/img2img  
     this allows to run workflows which only refine or detail existing image without changing it   
-  - **Detailer** allow manually setting processing resolution  
-    *note*: this does not impact the actual image resolution, only the resolution at which detailer internally operates  
 - **Fixes**
   - normalize path hanlding when deleting images  
   - unified compile upscalers  
diff --git a/modules/cachedit.py b/modules/cachedit.py
index f31dcc225..057b23804 100644
--- a/modules/cachedit.py
+++ b/modules/cachedit.py
@@ -39,12 +39,16 @@ def apply_cache_dit(pipe):
     else:
         calibrator_config = None
     shared.log.info(f'Apply Cache-DiT: config="{cache_config.strify()}" calibrator="{calibrator_config.strify() if calibrator_config else "None"}"')
-    cache_dit.enable_cache(
-        pipe,
-        cache_config=cache_config,
-        calibrator_config=calibrator_config,
-    )
-    shared.sd_model.has_cache_dit = True
+    try:
+        cache_dit.enable_cache(
+            pipe,
+            cache_config=cache_config,
+            calibrator_config=calibrator_config,
+        )
+        shared.sd_model.has_cache_dit = True
+    except Exception as e:
+        shared.log.error(f'Cache-DiT: {e}')
+        return
 
 
 def unapply_cache_dir(pipe):
diff --git a/modules/lora/extra_networks_lora.py b/modules/lora/extra_networks_lora.py
index d18444ea0..6370ef7df 100644
--- a/modules/lora/extra_networks_lora.py
+++ b/modules/lora/extra_networks_lora.py
@@ -2,7 +2,7 @@ from typing import List
 import os
 import re
 import numpy as np
-from modules.lora import networks, lora_overrides, lora_load
+from modules.lora import networks, lora_overrides, lora_load, lora_diffusers
 from modules.lora import lora_common as l
 from modules import extra_networks, shared, sd_models
 
@@ -88,16 +88,21 @@ def parse(p, params_list, step=0):
     te_multipliers = []
     unet_multipliers = []
     dyn_dims = []
+    lora_modules = []
     for params in params_list:
-        assert params.items
         names.append(params.positional[0])
-        te_multiplier = params.named.get("te", params.positional[1] if len(params.positional) > 1 else shared.opts.extra_networks_default_multiplier)
+
+        default_multiplier = params.positional[1] if len(params.positional) > 1 else shared.opts.extra_networks_default_multiplier
+        if isinstance(default_multiplier, str) and "@" not in default_multiplier:
+            default_multiplier = shared.opts.extra_networks_default_multiplier
+
+        te_multiplier = params.named.get("te", default_multiplier)
         if isinstance(te_multiplier, str) and "@" in te_multiplier:
             te_multiplier = get_stepwise(te_multiplier, step, p.steps)
         else:
             te_multiplier = float(te_multiplier)
-        unet_multiplier = [params.positional[2] if len(params.positional) > 2 else te_multiplier] * 3
-        unet_multiplier = [params.named.get("unet", unet_multiplier[0])] * 3
+
+        unet_multiplier = 3 * [params.named.get("unet", te_multiplier)] # fill all 3 with same value
         unet_multiplier[0] = params.named.get("in", unet_multiplier[0])
         unet_multiplier[1] = params.named.get("mid", unet_multiplier[1])
         unet_multiplier[2] = params.named.get("out", unet_multiplier[2])
@@ -106,12 +111,22 @@ def parse(p, params_list, step=0):
                 unet_multiplier[i] = get_stepwise(unet_multiplier[i], step, p.steps)
             else:
                 unet_multiplier[i] = float(unet_multiplier[i])
-        dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
-        dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
+
+        dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else None
         te_multipliers.append(te_multiplier)
         unet_multipliers.append(unet_multiplier)
         dyn_dims.append(dyn_dim)
-    return names, te_multipliers, unet_multipliers, dyn_dims
+
+        lora_module = []
+        if 'high' in params.positional or 'HIGH 14B' in params.positional[0]:
+            lora_module.append('transformer')
+        if 'low' in params.positional or 'LOW 14B' in params.positional[0]:
+            lora_module.append('transformer_2')
+        if params.named.get('module', None) is not None:
+            lora_module.append(params.named['module'].lower())
+        lora_modules.append(lora_module)
+
+    return names, te_multipliers, unet_multipliers, dyn_dims, lora_modules
 
 
 def unload_diffusers():
@@ -168,7 +183,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
         if len(params_list) > 0 and not self.active: # activate patches once
             self.active = True
             self.model = shared.opts.sd_model_checkpoint
-        names, te_multipliers, unet_multipliers, dyn_dims = parse(p, params_list, step)
+        names, te_multipliers, unet_multipliers, dyn_dims, lora_modules = parse(p, params_list, step)
         requested = self.signature(names, te_multipliers, unet_multipliers)
 
         load_method = lora_overrides.get_method()
@@ -181,7 +196,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
             has_changed = False # diffusers handles its own loading
             if len(exclude) == 0:
                 jobid = shared.state.begin('LoRA')
-                lora_load.network_load(names, te_multipliers, unet_multipliers, dyn_dims) # load only on first call
+                lora_load.network_load(names, te_multipliers, unet_multipliers, dyn_dims, lora_modules) # load only on first call
                 sd_models.set_diffuser_offload(shared.sd_model, op="model")
                 shared.state.end(jobid)
         elif load_method == 'nunchaku':
@@ -208,7 +223,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
                 shared.log.info(f'Network load: type=LoRA apply={[n.name for n in l.loaded_networks]} method={load_method} mode={"fuse" if shared.opts.lora_fuse_diffusers else "backup"} te={te_multipliers} unet={unet_multipliers} time={l.timer.summary}')
 
     def deactivate(self, p):
-        if len(lora_load.diffuser_loaded) > 0:
+        if len(lora_diffusers.diffuser_loaded) > 0:
             if not (shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled is True):
                 unload_diffusers()
         if self.active and l.debug:
diff --git a/modules/lora/lora_diffusers.py b/modules/lora/lora_diffusers.py
new file mode 100644
index 000000000..29402febe
--- /dev/null
+++ b/modules/lora/lora_diffusers.py
@@ -0,0 +1,92 @@
+from typing import Union
+import os
+import time
+import diffusers
+from modules import shared, errors
+from modules.lora import network
+from modules.lora import lora_common as l
+
+
+diffuser_loaded = []
+diffuser_scales = []
+
+
+def load_per_module(sd_model: diffusers.DiffusionPipeline, filename: str, adapter_name: str, lora_modules: list[str]):
+    shared.log.debug(f'LoRA load: modules={lora_modules}')
+    try:
+        state_dict = sd_model.lora_state_dict(filename)
+        if isinstance(state_dict, tuple) and len(state_dict) == 2:
+            state_dict, network_alphas = state_dict
+        else:
+            network_alphas = {}
+    except Exception as e:
+        shared.log.error(f'LoRA load: {e}')
+        if l.debug:
+            errors.display(e, "LoRA")
+        return None
+    for lora_module in lora_modules:
+        if lora_module == 'transformer':
+            if hasattr(sd_model, 'transformer') and sd_model.transformer is not None:
+                sd_model.load_lora_into_transformer(state_dict, transformer=sd_model.transformer, adapter_name=adapter_name)
+            else:
+                shared.log.warning(f'LoRA load: requested={lora_module} missing')
+        elif lora_module == 'transformer_2':
+            if hasattr(sd_model, 'transformer_2') and sd_model.transformer_2 is not None:
+                sd_model.load_lora_into_transformer(state_dict, transformer=sd_model.transformer_2, adapter_name=adapter_name)
+            else:
+                shared.log.warning(f'LoRA load: requested={lora_module} missing')
+        elif lora_module == 'unet':
+            if hasattr(sd_model, 'unet') and sd_model.unet is not None:
+                sd_model.load_lora_into_unet(state_dict, network_alphas, unet=sd_model.unet, adapter_name=adapter_name)
+            else:
+                shared.log.warning(f'LoRA load: requested={lora_module} missing')
+        elif lora_module == 'text_encoder' or lora_module == 'te':
+            if hasattr(sd_model, 'text_encoder') and sd_model.text_encoder is not None:
+                sd_model.load_lora_into_text_encoder(state_dict, network_alphas, text_encoder=sd_model.text_encoder, adapter_name=adapter_name)
+            else:
+                shared.log.warning(f'LoRA load: requested={lora_module} missing')
+        else:
+            shared.log.warning(f'LoRA load: requested={lora_module} unknown')
+    return adapter_name
+
+
+def load_diffusers(name: str, network_on_disk: network.NetworkOnDisk, lora_scale:float=shared.opts.extra_networks_default_multiplier, lora_module=None) -> Union[network.Network, None]:
+    t0 = time.time()
+    name = name.replace(".", "_")
+    sd_model: diffusers.DiffusionPipeline = getattr(shared.sd_model, "pipe", shared.sd_model)
+    shared.log.debug(f'Network load: type=LoRA name="{name}" file="{network_on_disk.filename}" detected={network_on_disk.sd_version} method=diffusers scale={lora_scale} fuse={shared.opts.lora_fuse_diffusers}')
+    if not hasattr(sd_model, 'load_lora_weights'):
+        shared.log.error(f'Network load: type=LoRA class={sd_model.__class__} does not implement load lora')
+        return None
+    try:
+        if lora_module is not None and isinstance(lora_module, list) and len(lora_module) > 0:
+            name = load_per_module(sd_model, network_on_disk.filename, adapter_name=name, lora_modules=lora_module)
+        else:
+            sd_model.load_lora_weights(network_on_disk.filename, adapter_name=name)
+    except Exception as e:
+        if 'already in use' in str(e):
+            pass
+        else:
+            if 'following keys have not been correctly renamed' in str(e):
+                shared.log.error(f'Network load: type=LoRA name="{name}" diffusers unsupported format')
+            elif 'object has no attribute' in str(e):
+                shared.log.error(f'Network load: type=LoRA name="{name}" diffusers empty module')
+            else:
+                shared.log.error(f'Network load: type=LoRA name="{name}" {e}')
+            if l.debug:
+                errors.display(e, "LoRA")
+            return None
+    if name is None:
+        return None
+    if name not in diffuser_loaded:
+        list_adapters = sd_model.get_list_adapters()
+        list_adapters = [adapter for adapters in list_adapters.values() for adapter in adapters]
+        if name not in list_adapters:
+            shared.log.error(f'Network load: type=LoRA name="{name}" adapters={list_adapters} not loaded')
+        else:
+            diffuser_loaded.append(name)
+            diffuser_scales.append(lora_scale)
+    net = network.Network(name, network_on_disk)
+    net.mtime = os.path.getmtime(network_on_disk.filename)
+    l.timer.activate += time.time() - t0
+    return net
diff --git a/modules/lora/lora_load.py b/modules/lora/lora_load.py
index 0b74c6c6d..f629c2772 100644
--- a/modules/lora/lora_load.py
+++ b/modules/lora/lora_load.py
@@ -3,12 +3,10 @@ import os
 import time
 import concurrent
 from modules import shared, errors, sd_models, sd_models_compile, files_cache
-from modules.lora import network, lora_overrides, lora_convert
+from modules.lora import network, lora_overrides, lora_convert, lora_diffusers
 from modules.lora import lora_common as l
 
 
-diffuser_loaded = []
-diffuser_scales = []
 lora_cache = {}
 available_networks = {}
 available_network_aliases = {}
@@ -17,41 +15,6 @@ available_network_hash_lookup = {}
 dump_lora_keys = os.environ.get('SD_LORA_DUMP', None) is not None
 
 
-def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> Union[network.Network, None]:
-    t0 = time.time()
-    name = name.replace(".", "_")
-    sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
-    shared.log.debug(f'Network load: type=LoRA name="{name}" file="{network_on_disk.filename}" detected={network_on_disk.sd_version} method=diffusers scale={lora_scale} fuse={shared.opts.lora_fuse_diffusers}')
-    if not hasattr(sd_model, 'load_lora_weights'):
-        shared.log.error(f'Network load: type=LoRA class={sd_model.__class__} does not implement load lora')
-        return None
-    try:
-        sd_model.load_lora_weights(network_on_disk.filename, adapter_name=name)
-    except Exception as e:
-        if 'already in use' in str(e):
-            pass
-        else:
-            if 'The following keys have not been correctly renamed' in str(e):
-                shared.log.error(f'Network load: type=LoRA name="{name}" diffusers unsupported format')
-            else:
-                shared.log.error(f'Network load: type=LoRA name="{name}" {e}')
-            if l.debug:
-                errors.display(e, "LoRA")
-            return None
-    if name not in diffuser_loaded:
-        list_adapters = sd_model.get_list_adapters()
-        list_adapters = [adapter for adapters in list_adapters.values() for adapter in adapters]
-        if name not in list_adapters:
-            shared.log.error(f'Network load: type=LoRA name="{name}" adapters={list_adapters} not loaded')
-        else:
-            diffuser_loaded.append(name)
-            diffuser_scales.append(lora_scale)
-    net = network.Network(name, network_on_disk)
-    net.mtime = os.path.getmtime(network_on_disk.filename)
-    l.timer.activate += time.time() - t0
-    return net
-
-
 def lora_dump(lora, dct):
     import tempfile
     sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
@@ -73,7 +36,7 @@ def lora_dump(lora, dct):
             f.write(line + "\n")
 
 
-def load_safetensors(name, network_on_disk) -> Union[network.Network, None]:
+def load_safetensors(name, network_on_disk: network.NetworkOnDisk) -> Union[network.Network, None]:
     if not shared.sd_loaded:
         return None
 
@@ -261,15 +224,15 @@ def gather_networks(names):
     return networks_on_disk
 
 
-def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
+def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None, lora_modules=None):
     networks_on_disk = gather_networks(names)
     failed_to_load_networks = []
     recompile_model, skip_lora_load = maybe_recompile_model(names, te_multipliers)
     sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
 
     l.loaded_networks.clear()
-    diffuser_loaded.clear()
-    diffuser_scales.clear()
+    lora_diffusers.diffuser_loaded.clear()
+    lora_diffusers.diffuser_scales.clear()
     t0 = time.time()
 
     for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
@@ -279,11 +242,13 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
             if l.debug:
                 shared.log.debug(f'Network load: type=LoRA name="{name}" file="{network_on_disk.filename}" hash="{shorthash}"')
             try:
+                lora_scale = te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier
+                lora_module = lora_modules[i] if lora_modules and len(lora_modules) > i else None
                 if recompile_model:
-                    shared.compiled_model_state.lora_model.append(f"{name}:{te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier}")
+                    shared.compiled_model_state.lora_model.append(f"{name}:{lora_scale}")
                 lora_method = lora_overrides.get_method(shorthash)
                 if lora_method == 'diffusers':
-                    net = load_diffusers(name, network_on_disk, lora_scale=te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier)
+                    net = lora_diffusers.load_diffusers(name, network_on_disk, lora_scale, lora_module)
                 elif lora_method == 'nunchaku':
                     pass # handled directly from extra_networks_lora.load_nunchaku
                 else:
@@ -311,21 +276,21 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
         name = next(iter(lora_cache))
         lora_cache.pop(name, None)
 
-    if not skip_lora_load and len(diffuser_loaded) > 0:
-        shared.log.debug(f'Network load: type=LoRA loaded={diffuser_loaded} available={sd_model.get_list_adapters()} active={sd_model.get_active_adapters()} scales={diffuser_scales}')
+    if not skip_lora_load and len(lora_diffusers.diffuser_loaded) > 0:
+        shared.log.debug(f'Network load: type=LoRA loaded={lora_diffusers.diffuser_loaded} available={sd_model.get_list_adapters()} active={sd_model.get_active_adapters()} scales={lora_diffusers.diffuser_scales}')
         try:
             t1 = time.time()
             if l.debug:
                 shared.log.trace(f'Network load: type=LoRA list={sd_model.get_list_adapters()}')
                 shared.log.trace(f'Network load: type=LoRA active={sd_model.get_active_adapters()}')
-            sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
+            sd_model.set_adapters(adapter_names=lora_diffusers.diffuser_loaded, adapter_weights=lora_diffusers.diffuser_scales)
         except Exception as e:
             shared.log.error(f'Network load: type=LoRA action=set {e}')
             if l.debug:
                 errors.display(e, 'LoRA')
         try:
             if shared.opts.lora_fuse_diffusers and not lora_overrides.disable_fuse():
-                sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # diffusers with fuse uses fixed scale since later apply does the scaling
+                sd_model.fuse_lora(adapter_names=lora_diffusers.diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # diffusers with fuse uses fixed scale since later apply does the scaling
                 sd_model.unload_lora_weights()
             l.timer.activate += time.time() - t1
         except Exception as e:
diff --git a/pipelines/model_wanai.py b/pipelines/model_wanai.py
index 718ec63c6..6a9f844d5 100644
--- a/pipelines/model_wanai.py
+++ b/pipelines/model_wanai.py
@@ -65,16 +65,20 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
     repo_id = sd_models.path_to_repo(checkpoint_info)
     sd_models.hf_auth_check(checkpoint_info)
 
+    boundary_ratio = None
     if 'a14b' in repo_id.lower() or 'fun-14b' in repo_id.lower():
         if shared.opts.model_wan_stage == 'high noise' or shared.opts.model_wan_stage == 'first':
             transformer = load_transformer(repo_id, diffusers_load_config, 'transformer')
             transformer_2 = None
+            boundary_ratio = 0.0
         elif shared.opts.model_wan_stage == 'low noise' or shared.opts.model_wan_stage == 'second':
-            transformer = load_transformer(repo_id, diffusers_load_config, 'transformer_2')
-            transformer_2 = None
+            transformer = None
+            transformer_2 = load_transformer(repo_id, diffusers_load_config, 'transformer_2')
+            boundary_ratio = 1.0
         elif shared.opts.model_wan_stage == 'combined' or shared.opts.model_wan_stage == 'both':
             transformer = load_transformer(repo_id, diffusers_load_config, 'transformer')
             transformer_2 = load_transformer(repo_id, diffusers_load_config, 'transformer_2')
+            boundary_ratio = shared.opts.model_wan_boundary
         else:
             shared.log.error(f'Load model: type=WanAI stage="{shared.opts.model_wan_stage}" unsupported')
             return None
@@ -85,7 +89,6 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
     text_encoder = load_text_encoder(repo_id, diffusers_load_config)
 
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model')
-    boundary_ratio = shared.opts.model_wan_boundary if transformer_2 is not None else None
 
     if 'Wan2.2-I2V' in repo_id:
         pipe_cls = diffusers.WanImageToVideoPipeline
diff --git a/wiki b/wiki
index 0172d5d26..d86043969 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 0172d5d268497e52300423837be3c53c3fc2ec83
+Subproject commit d86043969fb2ab19c68229a7227e0af8aa4f6c0d

From 696a41fd428cc6ff2da688077e102a5dcda08140 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 23 Sep 2025 15:33:52 -0400
Subject: [PATCH 040/230] fix prompts-from-file state check

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 scripts/prompts_from_file.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 460b39ecf..ae2e8038f 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -147,4 +147,6 @@ class Script(scripts_manager.Script):
             all_negative += proc.all_negative_prompts
             images += proc.images
             infotexts += proc.infotexts
+            if state.interrupted:
+                break
         return get_processed(p, images, p.seed, "", all_prompts=all_prompts, all_seeds=all_seeds, all_negative_prompts=all_negative, infotexts=infotexts)

From 0bbc2e4f5e0de40441dc996f7c2e5b68a1277f22 Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Wed, 24 Sep 2025 10:30:57 +0300
Subject: [PATCH 041/230] improve AuraFlow 0.3 cover image

---
 models/Reference/fal--AuraFlow-v0.3.jpg | Bin 89486 -> 104702 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/models/Reference/fal--AuraFlow-v0.3.jpg b/models/Reference/fal--AuraFlow-v0.3.jpg
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P9xvhJ7$ZfTz%T#Vj@+-r


From a9a39cb64bce7c6dd80076a0b8a3f01b5bf19e92 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 09:31:36 -0400
Subject: [PATCH 042/230] ltx model selector

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md          |  3 ++-
 modules/ltx/ltx_ui.py | 17 +++++++++++++++++
 wiki                  |  2 +-
 3 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6104c9043..6a9831dfb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-23
+## Update for 2025-09-24
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -42,6 +42,7 @@
     accepts optional `page` parameter to search specific networks page  
   - **reference models** additional example images, thanks @liutyi  
   - **video** support for configurable multi-stage models such as WAN-2.2-14B  
+  - **video** new LTX model selection  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/modules/ltx/ltx_ui.py b/modules/ltx/ltx_ui.py
index 27b033af5..e0626bbe4 100644
--- a/modules/ltx/ltx_ui.py
+++ b/modules/ltx/ltx_ui.py
@@ -3,17 +3,34 @@ import gradio as gr
 from modules import shared, ui_sections, ui_symbols, ui_common
 from modules.ui_components import ToolButton
 from modules.video_models.video_utils import get_codecs
+from modules.video_models.models_def import models
 from modules.ltx import ltx_process
 
 
 debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
+def load_model(model_name):
+    if model_name is None or model_name == 'None':
+        shared.log.info('LTX model unload')
+        from modules import sd_models
+        sd_models.unload_model_weights()
+        return
+    else:
+        model_instance = [m for m in models['LTX Video'] if m.name == model_name][0]
+        from modules.video_models import video_load
+        video_load.load_model(model_instance)
+
+
 def create_ui(prompt, negative, styles, overrides):
     with gr.Row():
         with gr.Column(variant='compact', elem_id="ltx_settings", elem_classes=['settings-column'], scale=1):
             with gr.Row():
                 generate = gr.Button('Generate', elem_id="ltx_generate_btn", variant='primary', visible=False)
+            with gr.Row():
+                ltx_models = [m.name for m in models['LTX Video']]
+                model = gr.Dropdown(label='LTX model', choices=ltx_models, value=ltx_models[0])
+                model.change(fn=load_model, inputs=[model], outputs=[], show_progress=True)
             with gr.Accordion(open=True, label="LTX size", elem_id='ltx_generate_accordion'):
                 with gr.Row():
                     width, height = ui_sections.create_resolution_inputs('ltx', default_width=832, default_height=480)
diff --git a/wiki b/wiki
index d86043969..3df7dbfc9 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit d86043969fb2ab19c68229a7227e0af8aa4f6c0d
+Subproject commit 3df7dbfc9c3e2f39c825f8d76541e13d3c3f097d

From 35c319578d46b595c57c7eaef3f2d8e35e6f96d7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 09:38:35 -0400
Subject: [PATCH 043/230] auto uninstall pynvml

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    | 1 +
 installer.py                    | 1 +
 modules/processing_diffusers.py | 2 +-
 3 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6a9831dfb..7bc2a5401 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -43,6 +43,7 @@
   - **reference models** additional example images, thanks @liutyi  
   - **video** support for configurable multi-stage models such as WAN-2.2-14B  
   - **video** new LTX model selection  
+  - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
 - **Fixes**
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
diff --git a/installer.py b/installer.py
index 97a502d40..f5e3bad73 100644
--- a/installer.py
+++ b/installer.py
@@ -877,6 +877,7 @@ def install_torch_addons():
         install('pillow-jxl-plugin==1.3.4', 'pillow-jxl-plugin')
     if not args.experimental:
         uninstall('wandb', quiet=True)
+        uninstall('pynvml', quiet=True)
     ts('addons', t_start)
 
 
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 1d88ab2a0..4de4e0a47 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -80,13 +80,13 @@ def process_pre(p: processing.StableDiffusionProcessing):
         pag.apply(p)
         cfgzero.apply(p)
         linfusion.apply(shared.sd_model)
+        cachedit.apply_cache_dit(shared.sd_model)
 
         # apply-only
         sd_hijack_freeu.apply_freeu(p)
         transformer_cache.set_cache()
         para_attention.apply_first_block_cache()
         teacache.apply_teacache(p)
-        cachedit.apply_cache_dit(shared.sd_model)
     except Exception as e:
         shared.log.error(f'Processing apply: {e}')
         errors.display(e, 'apply')

From 91dab5703aceb135ef0e239ad19d9fa62a672c8b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 11:40:20 -0400
Subject: [PATCH 044/230] modular pipeline prototype

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/modular.py              | 55 +++++++++++++++++++++++++++++++++
 modules/processing_args.py      | 45 ++++++++++++++++++++++++---
 modules/processing_diffusers.py |  7 +++++
 modules/processing_helpers.py   | 22 +++++++++++--
 modules/sd_models.py            |  6 ++++
 5 files changed, 127 insertions(+), 8 deletions(-)
 create mode 100644 modules/modular.py

diff --git a/modules/modular.py b/modules/modular.py
new file mode 100644
index 000000000..bdcd1c470
--- /dev/null
+++ b/modules/modular.py
@@ -0,0 +1,55 @@
+import time
+import diffusers
+from modules import shared
+
+
+modular_map= {
+    'StableDiffusionXLPipeline': 'StableDiffusionXLAutoBlocks',
+    'StableDiffusionXLImg2ImgPipeline': 'StableDiffusionXLAutoBlocks',
+    'FluxPipeline': 'FluxAutoBlocks',
+    'WanPipeline': 'WanAutoBlocks',
+    'QwenImagePipeline': 'QwenImageAutoBlocks',
+    'QwenImageEditPipeline': 'QwenImageEditAutoBlocks',
+}
+
+
+def is_compatible(diffusion_pipeline: diffusers.DiffusionPipeline) -> bool:
+    return diffusion_pipeline.__class__.__name__ in modular_map
+
+
+def convert_to_modular(diffusion_pipeline: diffusers.DiffusionPipeline) -> diffusers.ModularPipeline:
+    modular_pipe = None
+    try:
+        t0 = time.time()
+        modular_cls = modular_map.get(diffusion_pipeline.__class__.__name__, None)
+        if modular_cls is None:
+            raise ValueError(f'unknown: cls={diffusion_pipeline.__class__.__name__}')
+        modular_cls = getattr(diffusers, modular_cls, None)
+        if modular_cls is None:
+            raise ValueError(f'invalid: cls={diffusion_pipeline.__class__.__name__}')
+        modular_blocks = modular_cls()
+        modular_pipe = modular_blocks.init_pipeline()
+        components_dct = {k: v for k, v in diffusion_pipeline.components.items() if v is not None}
+        modular_pipe.update_components(**components_dct, **diffusion_pipeline.parameters)
+        modular_pipe.original_pipe = diffusion_pipeline
+        t1 = time.time()
+        shared.log.debug(f'Modular: from={diffusion_pipeline.__class__.__name__} to={modular_pipe.__class__.__name__} time={t1 - t0:.2f}')
+
+        """
+        for expected_input_param in modular_pipe.blocks.inputs:
+            name = expected_input_param.name
+            default = expected_input_param.default
+            kwargs_type = expected_input_param.kwargs_type
+            shared.log.trace(f'Modular input: name={name} type={kwargs_type} default={default}')
+        """
+
+    except Exception as e:
+        shared.log.error(f'Modular: {e}')
+        raise e
+    return modular_pipe
+
+
+def restore_standard(modular_pipe):
+    if hasattr(modular_pipe, 'original_pipe'):
+        shared.log.debug(f'Modular: from={modular_pipe.__class__.__name__} to={modular_pipe.original_pipe.__class__.__name__}')
+        return modular_pipe.original_pipe
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 971adad44..7770f210d 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -18,6 +18,26 @@ debug_log = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
 disable_pbar = os.environ.get('SD_DISABLE_PBAR', None) is not None
 
 
+def task_modular_kwargs(p, model):
+    model_cls = model.__class__.__name__ # pylint: disable=unused-variable
+    task_args = {}
+    p.ops.append('modular')
+
+    processing_helpers.resize_init_images(p)
+    task_args['width'] = p.width
+    task_args['height'] = p.height
+    if len(getattr(p, 'init_images', [])) > 0:
+        task_args['image'] = p.init_images
+        task_args['strength'] = p.denoising_strength
+    mask_image = p.task_args.get('image_mask', None) or getattr(p, 'image_mask', None) or getattr(p, 'mask', None)
+    if mask_image is not None:
+        task_args['mask_image'] = mask_image
+
+    if debug_enabled:
+        debug_log(f'Process task specific args: {task_args}')
+    return task_args
+
+
 def task_specific_kwargs(p, model):
     model_cls = model.__class__.__name__
     vae_scale_factor = sd_vae.get_vae_scale_factor(model)
@@ -136,6 +156,16 @@ def task_specific_kwargs(p, model):
     return task_args
 
 
+def get_params(model):
+    if hasattr(model, 'blocks') and hasattr(model.blocks, 'inputs'): # modular pipeline
+        possible = [input_param.name for input_param in model.blocks.inputs]
+        return possible
+    else:
+        signature = inspect.signature(type(model).__call__, follow_wrapped=True)
+        possible = list(signature.parameters)
+        return possible
+
+
 def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:typing.Optional[list]=None, negative_prompts_2:typing.Optional[list]=None, prompt_attention:typing.Optional[str]=None, desc:typing.Optional[str]='', **kwargs):
     t0 = time.time()
     shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
@@ -151,8 +181,8 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
             model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba', disable=disable_pbar)
         else:
             model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba')
-    signature = inspect.signature(type(model).__call__, follow_wrapped=True)
-    possible = list(signature.parameters)
+
+    possible = get_params(model)
 
     if debug_enabled:
         debug_log(f'Process pipeline possible: {possible}')
@@ -357,7 +387,11 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
             args[arg] = kwargs[arg]
 
     # handle task specific args
-    task_kwargs = task_specific_kwargs(p, model)
+    if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.MODULAR:
+        task_kwargs = task_modular_kwargs(p, model)
+    else:
+        task_kwargs = task_specific_kwargs(p, model)
+
     pipe_args = getattr(p, 'task_args', {})
     model_args = getattr(model, 'task_args', {})
     task_kwargs.update(pipe_args or {})
@@ -407,8 +441,9 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
 
     # handle implicit controlnet
     if 'control_image' in possible and 'control_image' not in args and 'image' in args:
-        debug_log('Process: set control image')
-        args['control_image'] = args['image']
+        if sd_models.get_diffusers_task(model) != sd_models.DiffusersTaskType.MODULAR:
+            debug_log('Process: set control image')
+            args['control_image'] = args['image']
 
     sd_hijack_hypertile.hypertile_set(p, hr=len(getattr(p, 'init_images', [])) > 0)
 
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 4de4e0a47..75f74a11b 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -96,6 +96,13 @@ def process_pre(p: processing.StableDiffusionProcessing):
     #     sd_models.move_model(shared.sd_model.unet, devices.device)
     # if hasattr(shared.sd_model, 'transformer'):
     #     sd_models.move_model(shared.sd_model.transformer, devices.device)
+
+    from modules import modular
+    if modular.is_compatible(shared.sd_model):
+        modular_pipe = modular.convert_to_modular(shared.sd_model)
+        if modular_pipe is not None:
+            shared.sd_model = modular_pipe
+
     timer.process.record('pre')
 
 
diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py
index 15ee19822..7906261b9 100644
--- a/modules/processing_helpers.py
+++ b/modules/processing_helpers.py
@@ -9,6 +9,7 @@ import cv2
 from PIL import Image
 from blendmodes.blend import blendLayers, BlendType
 from modules import shared, devices, images, sd_models, sd_samplers, sd_vae, sd_hijack_hypertile, processing_vae, timer
+from modules.api import helpers
 
 
 debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -16,6 +17,10 @@ debug_steps = shared.log.trace if os.environ.get('SD_STEPS_DEBUG', None) is not
 debug_steps('Trace: STEPS')
 
 
+def is_modular():
+    return sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.MODULAR
+
+
 def is_txt2img():
     return sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE
 
@@ -278,7 +283,10 @@ def validate_sample(tensor):
 def resize_init_images(p):
     if getattr(p, 'image', None) is not None and getattr(p, 'init_images', None) is None:
         p.init_images = [p.image]
+
     if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
+        if isinstance(p.init_images[0], str):
+            p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images]
         vae_scale_factor = sd_vae.get_vae_scale_factor()
         tgt_width, tgt_height = vae_scale_factor * math.ceil(p.init_images[0].width / vae_scale_factor), vae_scale_factor * math.ceil(p.init_images[0].height / vae_scale_factor)
         if p.init_images[0].size != (tgt_width, tgt_height):
@@ -287,11 +295,17 @@ def resize_init_images(p):
             p.height = tgt_height
             p.width = tgt_width
             sd_hijack_hypertile.hypertile_set(p)
-        if getattr(p, 'mask', None) is not None and p.mask.size != (tgt_width, tgt_height):
+        if getattr(p, 'mask', None) is not None and p.mask is not None and p.mask.size != (tgt_width, tgt_height):
+            if isinstance(p.mask[0], str):
+                p.mask = [helpers.decode_base64_to_image(i, quiet=True) for i in p.mask]
             p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
-        if getattr(p, 'init_mask', None) is not None and p.init_mask.size != (tgt_width, tgt_height):
+        if getattr(p, 'init_mask', None) is not None and p.init_mask is not None and p.init_mask.size != (tgt_width, tgt_height):
+            if isinstance(p.init_mask[0], str):
+                p.init_mask = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_mask]
             p.init_mask = images.resize_image(1, p.init_mask, tgt_width, tgt_height, upscaler_name=None)
-        if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
+        if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
+            if isinstance(p.mask_for_overlay, str):
+                p.mask_for_overlay = helpers.decode_base64_to_image(p.mask_for_overlay, quiet=True)
             p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
         return tgt_width, tgt_height
     return p.width, p.height
@@ -374,6 +388,8 @@ def calculate_base_steps(p, use_denoise_start, use_refiner_start):
     cls = shared.sd_model.__class__.__name__
     if 'Flex' in cls or 'Kontext' in cls or 'Edit' in cls or 'Wan' in cls:
         steps = p.steps
+    elif is_modular():
+        steps = p.steps
     elif not is_txt2img():
         if cls in sd_models.i2i_pipes:
             steps = p.steps
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4423c780d..9b9ec7d68 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -743,6 +743,7 @@ class DiffusersTaskType(Enum):
     IMAGE_2_IMAGE = 2
     INPAINTING = 3
     INSTRUCT = 4
+    MODULAR = 5
 
 
 def get_diffusers_task(pipe: diffusers.DiffusionPipeline) -> DiffusersTaskType:
@@ -753,6 +754,8 @@ def get_diffusers_task(pipe: diffusers.DiffusionPipeline) -> DiffusersTaskType:
         return DiffusersTaskType.IMAGE_2_IMAGE
     elif 'Instruct' in cls:
         return DiffusersTaskType.INSTRUCT
+    elif 'Modular' in cls:
+        return DiffusersTaskType.MODULAR
     elif pipe.__class__ in diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING.values():
         return DiffusersTaskType.IMAGE_2_IMAGE
     elif pipe.__class__ in diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING.values():
@@ -950,6 +953,9 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     if get_diffusers_task(pipe) == new_pipe_type:
         return pipe
 
+    if get_diffusers_task(pipe) == DiffusersTaskType.MODULAR:
+        return pipe
+
     # skip specific pipelines
     cls = pipe.__class__.__name__
     if cls in pipe_switch_task_exclude:

From 6f409deedfdbe5c9903a1707480e08462c60e3e0 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 12:33:57 -0400
Subject: [PATCH 045/230] ui separate guidance and detail sections

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui |  2 +-
 modules/control/run.py             |  4 ++--
 modules/processing_class.py        | 16 ++++++++--------
 modules/processing_diffusers.py    |  4 ++--
 modules/ui_control.py              |  3 ++-
 modules/ui_img2img.py              |  5 +++--
 modules/ui_sections.py             | 26 ++++++++++++--------------
 modules/ui_txt2img.py              |  4 ++--
 8 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 049386fd9..a445b6b09 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 049386fd9d5c2d9b30cc9136be1e1bcda13d81c6
+Subproject commit a445b6b09f3974f95d410f19575200109e1a8b83
diff --git a/modules/control/run.py b/modules/control/run.py
index 6d1b885b7..586958a49 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -396,9 +396,9 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
     # hires/refine defined outside of main init
     vae_scale_factor = sd_vae.get_vae_scale_factor()
     if p.enable_hr and (p.hr_resize_x == 0 or p.hr_resize_y == 0):
-        p.hr_upscale_to_x, p.hr_upscale_to_y = vae_scale_factor * int(p.width_before * p.hr_scale / vae_scale_factor), vae_scale_factor * int(p.height_before * p.hr_scale / vae_scale_factor)
+        p.hr_upscale_to_x, p.hr_upscale_to_y = int(vae_scale_factor * int(p.width_before * p.hr_scale / vae_scale_factor)), int(vae_scale_factor * int(p.height_before * p.hr_scale / vae_scale_factor))
     elif p.enable_hr and (p.hr_upscale_to_x == 0 or p.hr_upscale_to_y == 0):
-        p.hr_upscale_to_x, p.hr_upscale_to_y = 8 * int(p.hr_resize_x / vae_scale_factor), vae_scale_factor * int(p.hr_resize_y / vae_scale_factor)
+        p.hr_upscale_to_x, p.hr_upscale_to_y = 8 * int(p.hr_resize_x / vae_scale_factor), int(vae_scale_factor * int(p.hr_resize_y / vae_scale_factor))
 
     global p_extra_args # pylint: disable=global-statement
     for k, v in p_extra_args.items():
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 7ea9c2eab..d0d634cfa 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -445,14 +445,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.hr_upscale_to_y = int(self.height * scale)
         else:
             if self.hr_resize_y == 0:
-                self.hr_upscale_to_x = self.hr_resize_x
-                self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+                self.hr_upscale_to_x = int(self.hr_resize_x)
+                self.hr_upscale_to_y = int(self.hr_resize_x * self.height // self.width)
             elif self.hr_resize_x == 0:
-                self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
-                self.hr_upscale_to_y = self.hr_resize_y
+                self.hr_upscale_to_x = int(self.hr_resize_y * self.width // self.height)
+                self.hr_upscale_to_y = int(self.hr_resize_y)
             elif self.hr_resize_x > 0 and self.hr_resize_y > 0:
-                self.hr_upscale_to_x = self.hr_resize_x
-                self.hr_upscale_to_y = self.hr_resize_y
+                self.hr_upscale_to_x = int(self.hr_resize_x)
+                self.hr_upscale_to_y = int(self.hr_resize_y)
         shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.hr_sampler_name}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
 
 
@@ -572,9 +572,9 @@ class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
         self.hr_force = force
         if use_scale:
             vae_scale_factor = sd_vae.get_vae_scale_factor()
-            self.hr_upscale_to_x, self.hr_upscale_to_y = vae_scale_factor * int(self.width * scale / vae_scale_factor), vae_scale_factor * int(self.height * scale / vae_scale_factor)
+            self.hr_upscale_to_x, self.hr_upscale_to_y = int(vae_scale_factor * int(self.width * scale / vae_scale_factor)), int(vae_scale_factor * int(self.height * scale / vae_scale_factor))
         else:
-            self.hr_upscale_to_x, self.hr_upscale_to_y = self.hr_resize_x, self.hr_resize_y
+            self.hr_upscale_to_x, self.hr_upscale_to_y = int(self.hr_resize_x), int(self.hr_resize_y)
 
 
 def switch_class(p: StableDiffusionProcessing, new_class: type, dct: dict = None):
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 75f74a11b..6453516c0 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -238,8 +238,8 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 p.hr_upscaler = p.resize_name
                 p.hr_resize_mode = p.resize_mode
                 p.hr_resize_context = p.resize_context
-            p.hr_upscale_to_x = p.width * p.hr_scale if p.hr_resize_x == 0 else p.hr_resize_x
-            p.hr_upscale_to_y = p.height * p.hr_scale if p.hr_resize_y == 0 else p.hr_resize_y
+            p.hr_upscale_to_x = int(p.width * p.hr_scale) if p.hr_resize_x == 0 else p.hr_resize_x
+            p.hr_upscale_to_y = int(p.height * p.hr_scale) if p.hr_resize_y == 0 else p.hr_resize_y
 
         # hires runs on original pipeline
         if hasattr(shared.sd_model, 'restore_pipeline') and (shared.sd_model.restore_pipeline is not None) and (not shared.opts.control_hires):
diff --git a/modules/ui_control.py b/modules/ui_control.py
index b75842aee..0fe361aa2 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -163,7 +163,8 @@ def create_ui(_blocks: gr.Blocks=None):
 
                 mask_controls = masking.create_segment_ui()
 
-                vae_type, tiling, hidiffusion, cfg_scale, clip_skip, image_cfg_scale, guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_advanced_inputs('control')
+                cfg_scale, image_cfg_scale, guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_guidance_inputs('control')
+                vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('control')
                 hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('control')
 
                 with gr.Accordion(open=False, label="Video", elem_id="control_video", elem_classes=["small-accordion"]):
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index d08999c8c..7e7db44cb 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -132,9 +132,10 @@ def create_ui():
                             denoising_strength = gr.Slider(minimum=0.00, maximum=0.99, step=0.01, label='Denoising strength', value=0.30, elem_id="img2img_denoising_strength")
                             refiner_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise start', value=0.0, elem_id="img2img_refiner_start")
 
-                    vae_type, tiling, hidiffusion, cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_advanced_inputs('img2img')
+                    cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_guidance_inputs('img2img')
+                    vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('img2img')
                     hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('img2img')
-                    enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, hr_refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('txt2img')
+                    enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, hr_refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('img2img')
                     detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution = shared.yolo.ui('img2img')
 
                     # with gr.Group(elem_id="inpaint_controls", visible=False) as inpaint_controls:
diff --git a/modules/ui_sections.py b/modules/ui_sections.py
index 07ce95b3c..829c502bd 100644
--- a/modules/ui_sections.py
+++ b/modules/ui_sections.py
@@ -146,26 +146,24 @@ def create_video_inputs(tab:str, show_always:bool=False):
     return video_type, video_duration, video_loop, video_pad, video_interpolate
 
 
-def create_cfg_inputs(tab):
-    with gr.Row(elem_id=f"{tab}_cfg_row", elem_classes=['flexbox']):
-        cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
-        cfg_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance end', value=1.0, elem_id=f"{tab}_cfg_end")
-    return cfg_scale, cfg_end
-
-
-def create_advanced_inputs(tab, base=True):
-    with gr.Accordion(open=False, label="Advanced", elem_id=f"{tab}_advanced", elem_classes=["small-accordion"]):
+def create_guidance_inputs(tab):
+    with gr.Accordion(open=False, label="Guidance", elem_id=f"{tab}_guidance", elem_classes=["small-accordion"]):
         with gr.Group():
-            if base:
-                cfg_scale, cfg_end = create_cfg_inputs(tab)
-            else:
-                cfg_scale, cfg_end = None, None
+            with gr.Row(elem_id=f"{tab}_cfg_row", elem_classes=['flexbox']):
+                cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
+                cfg_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance end', value=1.0, elem_id=f"{tab}_cfg_end")
             with gr.Row():
                 image_cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Refine guidance', value=6.0, elem_id=f"{tab}_image_cfg_scale")
                 diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Rescale guidance', value=0.0, elem_id=f"{tab}_image_cfg_rescale")
             with gr.Row():
                 diffusers_pag_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='Attention guidance', value=0.0, elem_id=f"{tab}_pag_scale")
                 diffusers_pag_adaptive = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Adaptive scaling', value=0.5, elem_id=f"{tab}_pag_adaptive")
+    return cfg_scale, image_cfg_scale, diffusers_guidance_rescale, diffusers_pag_scale, diffusers_pag_adaptive, cfg_end
+
+
+def create_advanced_inputs(tab):
+    with gr.Accordion(open=False, label="Advanced", elem_id=f"{tab}_advanced", elem_classes=["small-accordion"]):
+        with gr.Group():
             with gr.Row():
                 clip_skip = gr.Slider(label='CLiP skip', value=1, minimum=0, maximum=12, step=0.1, elem_id=f"{tab}_clip_skip", interactive=shared.opts.clip_skip_enabled)
             with gr.Row(elem_id=f"{tab}_vae_options"):
@@ -173,7 +171,7 @@ def create_advanced_inputs(tab, base=True):
             with gr.Row(elem_id=f"{tab}_advanced_options"):
                 tiling = gr.Checkbox(label='Texture tiling', value=False, elem_id=f"{tab}_tiling")
                 hidiffusion = gr.Checkbox(label='HiDiffusion', value=False, elem_id=f"{tab}_hidiffusion")
-    return vae_type, tiling, hidiffusion, cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, diffusers_pag_scale, diffusers_pag_adaptive, cfg_end
+    return vae_type, tiling, hidiffusion, clip_skip
 
 
 def create_correction_inputs(tab):
diff --git a/modules/ui_txt2img.py b/modules/ui_txt2img.py
index a7efadba1..0fae0c606 100644
--- a/modules/ui_txt2img.py
+++ b/modules/ui_txt2img.py
@@ -27,14 +27,14 @@ def create_ui():
                     width, height = ui_sections.create_resolution_inputs('txt2img')
 
                 batch_count, batch_size = ui_sections.create_batch_inputs('txt2img', accordion=False)
-                cfg_scale, cfg_end = ui_sections.create_cfg_inputs('txt2img')
                 steps, sampler_index = ui_sections.create_sampler_and_steps_selection(None, "txt2img")
 
                 with gr.Group(elem_classes="settings-accordion"):
                     with gr.Accordion(open=False, label="Samplers", elem_classes=["small-accordion"], elem_id="txt2img_sampler_group"):
                         ui_sections.create_sampler_options('txt2img')
                     seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = ui_sections.create_seed_inputs('txt2img')
-                    vae_type, tiling, hidiffusion, _cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, _cfg_end = ui_sections.create_advanced_inputs('txt2img', base=False)
+                    cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_guidance_inputs('txt2img')
+                    vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('txt2img')
                     hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('txt2img')
                     enable_hr, hr_sampler_index, denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('txt2img')
                     detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution  = shared.yolo.ui('txt2img')

From c96af0d428b7759bc3a8ea865c86fe4648ed2e89 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 12:55:11 -0400
Subject: [PATCH 046/230] fix lora filter by model type

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                             | 3 +++
 javascript/extraNetworks.js              | 5 +++--
 modules/ui_extra_networks_checkpoints.py | 1 +
 modules/ui_extra_networks_lora.py        | 2 +-
 4 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7bc2a5401..7138182b0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -32,6 +32,9 @@
     enable in settings -> model offloading  
   - new feature to specify which models types not to offload  
     in *settings -> model offloading -> model types not to offload*  
+- **UI**
+  - separate guidance and detail sections  
+  - networks ability to filter lora by base model version  
 - **Other**
   - server will note when restart is recommended due to package updates  
   - **logging** enable `debug`, `docs` and `api-docs` by default  
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 3508ac572..95b5aca10 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -293,10 +293,11 @@ function extraNetworksSearchButton(event) {
 }
 
 function extraNetworksFilterVersion(event) {
-  // log('extraNetworksFilterVersion', event);
+  log('extraNetworksFilterVersion', event);
   const version = event.target.textContent.trim();
   const activeTab = getENActiveTab();
-  const cardContainer = gradioApp().querySelector(`#${activeTab}_model_cards`);
+  const activePage = getENActivePage().toLowerCase();
+  const cardContainer = gradioApp().querySelector(`#${activeTab}_${activePage}_cards`);
   if (!cardContainer) return;
   if (cardContainer.dataset.activeVersion === version) {
     cardContainer.dataset.activeVersion = '';
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index fb77c855b..427de5f34 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -7,6 +7,7 @@ from modules import shared, ui_extra_networks, sd_models, modelstats, paths
 
 version_map = {
     "QwenEdit": "Qwen",
+    "QwenEditPlus": "Qwen",
     "Flux.1 D": "Flux",
     "Flux.1 S": "Flux",
     "FluxKontext": "Flux",
diff --git a/modules/ui_extra_networks_lora.py b/modules/ui_extra_networks_lora.py
index d390fe914..6b9705674 100644
--- a/modules/ui_extra_networks_lora.py
+++ b/modules/ui_extra_networks_lora.py
@@ -85,7 +85,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
                 "metadata": json.dumps(l.metadata, indent=4) if l.metadata else None,
                 "mtime": mtime,
                 "size": size,
-                "version": version.get("baseModel", l.sd_version) if info else l.sd_version,
+                "version": version.get("baseModel", l.sd_version),
                 "info": info,
                 "description": self.find_description(l.filename, info),
                 "tags": self.get_tags(l, info, version),

From 0d2579302ecfdc259c4f7c43130564747efe482e Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 13:03:18 -0400
Subject: [PATCH 047/230] cleanup modular init

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/modular.py | 12 ++++++++----
 modules/shared.py  |  2 ++
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/modules/modular.py b/modules/modular.py
index bdcd1c470..b2b6a8124 100644
--- a/modules/modular.py
+++ b/modules/modular.py
@@ -14,7 +14,12 @@ modular_map= {
 
 
 def is_compatible(diffusion_pipeline: diffusers.DiffusionPipeline) -> bool:
-    return diffusion_pipeline.__class__.__name__ in modular_map
+    if not shared.opts.model_modular_enable:
+        return False
+    compatible = diffusion_pipeline.__class__.__name__ in modular_map
+    if not compatible:
+        shared.log.debug(f'Modular: source={diffusion_pipeline.__class__.__name__} incompatible pipeline')
+    return compatible
 
 
 def convert_to_modular(diffusion_pipeline: diffusers.DiffusionPipeline) -> diffusers.ModularPipeline:
@@ -33,8 +38,7 @@ def convert_to_modular(diffusion_pipeline: diffusers.DiffusionPipeline) -> diffu
         modular_pipe.update_components(**components_dct, **diffusion_pipeline.parameters)
         modular_pipe.original_pipe = diffusion_pipeline
         t1 = time.time()
-        shared.log.debug(f'Modular: from={diffusion_pipeline.__class__.__name__} to={modular_pipe.__class__.__name__} time={t1 - t0:.2f}')
-
+        shared.log.debug(f'Modular: source={diffusion_pipeline.__class__.__name__} target={modular_pipe.__class__.__name__} time={t1 - t0:.2f}')
         """
         for expected_input_param in modular_pipe.blocks.inputs:
             name = expected_input_param.name
@@ -51,5 +55,5 @@ def convert_to_modular(diffusion_pipeline: diffusers.DiffusionPipeline) -> diffu
 
 def restore_standard(modular_pipe):
     if hasattr(modular_pipe, 'original_pipe'):
-        shared.log.debug(f'Modular: from={modular_pipe.__class__.__name__} to={modular_pipe.original_pipe.__class__.__name__}')
+        shared.log.debug(f'Modular: source={modular_pipe.__class__.__name__} target={modular_pipe.original_pipe.__class__.__name__}')
         return modular_pipe.original_pipe
diff --git a/modules/shared.py b/modules/shared.py
index a16d80571..c8a645128 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -155,6 +155,8 @@ options_templates.update(options_section(('sd', "Model Loading"), {
 }))
 
 options_templates.update(options_section(('model_options', "Model Options"), {
+    "model_modular_sep": OptionInfo("<h2>Modular Pipelines</h2>", "", gr.HTML),
+    "model_modular_enable": OptionInfo(False, "Enable modular pipelines (experimental)"),
     "model_sd3_sep": OptionInfo("<h2>Stable Diffusion 3.x</h2>", "", gr.HTML),
     "model_sd3_disable_te5": OptionInfo(False, "Disable T5 text encoder"),
     "model_h1_sep": OptionInfo("<h2>HiDream</h2>", "", gr.HTML),

From a776c7c61534bb8f240a5dd13d135d2eef284ab3 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 24 Sep 2025 21:32:26 +0300
Subject: [PATCH 048/230] SDNQ fix new transformers

---
 modules/sdnq/__init__.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index d8394cde7..71db2127f 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -440,6 +440,12 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return missing_keys
 
+    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict:
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return state_dict
+
     def update_expected_keys(self, model, expected_keys: List[str], loaded_keys: List[str]) -> List[str]: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function

From faa18321267daaa883f3e9d9f94e941d9fe5ff4b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 18:27:02 -0400
Subject: [PATCH 049/230] nunchaku-qwen-image-edit-2509

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    | 2 +-
 pipelines/qwen/qwen_nunchaku.py | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7138182b0..05680394d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,7 +5,7 @@
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
-  - [Qwen Image Edit 2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509)  
+  - [Qwen Image Edit 2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) and [Nunchaku Qwen Image Edit 2509](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit-2509)  
     updated version of Qwen Image Edit with improved image consistency  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
     SRPO is trained by Tencent with specific technique: directly aligning the full diffusion trajectory with fine-grained human preference  
diff --git a/pipelines/qwen/qwen_nunchaku.py b/pipelines/qwen/qwen_nunchaku.py
index 2fa723565..7c689c7a3 100644
--- a/pipelines/qwen/qwen_nunchaku.py
+++ b/pipelines/qwen/qwen_nunchaku.py
@@ -15,6 +15,8 @@ def load_qwen_nunchaku(repo_id):
         nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image.safetensors" # r32 vs r128
     elif repo_id.lower().endswith('qwen-lightning'):
         nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image-lightningv1.1-8steps.safetensors" # 8-step variant
+    elif repo_id.lower().endswith('qwen-image-edit-2509'):
+        nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image-edit-2509/svdq-{nunchaku_precision}_r128-qwen-image-edit-2509.safetensors" # 8-step variant
     elif repo_id.lower().endswith('qwen-image-edit'):
         nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image-edit/svdq-{nunchaku_precision}_r128-qwen-image-edit.safetensors" # 8-step variant
     elif repo_id.lower().endswith('qwen-lightning-edit'):

From 7a3001170b8e4fe0239141d05ccb96a76df68f0d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 24 Sep 2025 20:16:07 -0400
Subject: [PATCH 050/230] add daam script

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                  |   1 +
 .ruff.toml                 |   1 +
 CHANGELOG.md               |   8 +-
 modules/modular.py         |   1 +
 modules/processing_args.py |   4 +-
 scripts/daam/__init__.py   |   5 +
 scripts/daam/evaluate.py   | 122 +++++++++++++
 scripts/daam/experiment.py | 344 +++++++++++++++++++++++++++++++++++++
 scripts/daam/heatmap.py    | 185 ++++++++++++++++++++
 scripts/daam/hook.py       | 127 ++++++++++++++
 scripts/daam/trace.py      | 323 ++++++++++++++++++++++++++++++++++
 scripts/daam/utils.py      | 109 ++++++++++++
 scripts/daam_ext.py        |  57 ++++++
 13 files changed, 1283 insertions(+), 4 deletions(-)
 create mode 100644 scripts/daam/__init__.py
 create mode 100644 scripts/daam/evaluate.py
 create mode 100644 scripts/daam/experiment.py
 create mode 100644 scripts/daam/heatmap.py
 create mode 100644 scripts/daam/hook.py
 create mode 100644 scripts/daam/trace.py
 create mode 100644 scripts/daam/utils.py
 create mode 100644 scripts/daam_ext.py

diff --git a/.pylintrc b/.pylintrc
index 5bd9493dd..7b6cdb7cb 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -44,6 +44,7 @@ ignore-paths=/usr/lib/.*$,
              pipelines/segmoe,
              scripts/consistory,
              scripts/ctrlx,
+             scripts/daam,
              scripts/demofusion,
              scripts/freescale,
              scripts/infiniteyou,
diff --git a/.ruff.toml b/.ruff.toml
index 74b734878..297515402 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -24,6 +24,7 @@ exclude = [
     "pipelines/segmoe",
 
     "scripts/lbm",
+    "scripts/daam",
     "scripts/xadapter",
     "scripts/pulid",
     "scripts/instantir",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 05680394d..bcfa7def8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -21,10 +21,14 @@
     enable in *settings -> pipeline modifers -> cache-dit*  
   - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
     automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
-- **Extensions**
-  - **agent-scheduler** was a high-value built-in extension, but it has not been maintained for 1.5 years  
+- **Extensions**  
+  - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
+    was a high-value built-in extension, but it has not been maintained for 1.5 years  
     it also does not work with control and video tabs which are the core of sdnext nowadays  
     so it has been removed from built-in extensions: manual installation is still possible  
+  - [DAAM: Diffusion Attentive Attribution Maps](https://github.com/castorini/daam)  
+    create heatmap visualizations of which parts of the prompt influenced which parts of the image  
+    available in scripts for sdxl text-to-image workflows  
 - **Offloading**
   - improve offloading for pipelines with multiple stages such as *wan-2.2-14b*  
   - add timers to measure onload/offload times during generate  
diff --git a/modules/modular.py b/modules/modular.py
index b2b6a8124..595b860f7 100644
--- a/modules/modular.py
+++ b/modules/modular.py
@@ -57,3 +57,4 @@ def restore_standard(modular_pipe):
     if hasattr(modular_pipe, 'original_pipe'):
         shared.log.debug(f'Modular: source={modular_pipe.__class__.__name__} target={modular_pipe.original_pipe.__class__.__name__}')
         return modular_pipe.original_pipe
+    return modular_pipe
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 7770f210d..c1f0b9cdb 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -18,8 +18,8 @@ debug_log = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
 disable_pbar = os.environ.get('SD_DISABLE_PBAR', None) is not None
 
 
-def task_modular_kwargs(p, model):
-    model_cls = model.__class__.__name__ # pylint: disable=unused-variable
+def task_modular_kwargs(p, model): # pylint: disable=unused-argument
+    # model_cls = model.__class__.__name__
     task_args = {}
     p.ops.append('modular')
 
diff --git a/scripts/daam/__init__.py b/scripts/daam/__init__.py
new file mode 100644
index 000000000..a407cfd4d
--- /dev/null
+++ b/scripts/daam/__init__.py
@@ -0,0 +1,5 @@
+from .experiment import *
+from .heatmap import *
+from .hook import *
+from .utils import *
+from .trace import *
diff --git a/scripts/daam/evaluate.py b/scripts/daam/evaluate.py
new file mode 100644
index 000000000..ec9e1567b
--- /dev/null
+++ b/scripts/daam/evaluate.py
@@ -0,0 +1,122 @@
+from collections import defaultdict
+from typing import List, Union
+
+from scipy.optimize import linear_sum_assignment
+import PIL.Image as Image
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+__all__ = ['compute_iou', 'MeanEvaluator', 'load_mask', 'compute_ioa']
+
+
+def compute_iou(a: torch.Tensor, b: torch.Tensor) -> float:
+    if a.shape[0] != b.shape[0]:
+        a = F.interpolate(a.unsqueeze(0).unsqueeze(0).float(), size=b.shape, mode='bicubic').squeeze()
+        a[a < 1] = 0
+        a[a >= 1] = 1
+
+    intersection = (a * b).sum()
+    union = a.sum() + b.sum() - intersection
+
+    return (intersection / (union + 1e-8)).item()
+
+
+def compute_ioa(a: torch.Tensor, b: torch.Tensor) -> float:
+    if a.shape[0] != b.shape[0]:
+        a = F.interpolate(a.unsqueeze(0).unsqueeze(0).float(), size=b.shape, mode='bicubic').squeeze()
+        a[a < 1] = 0
+        a[a >= 1] = 1
+
+    intersection = (a * b).sum()
+    area = a.sum()
+
+    return (intersection / (area + 1e-8)).item()
+
+
+def load_mask(path: str) -> torch.Tensor:
+    mask = np.array(Image.open(path))
+    mask = torch.from_numpy(mask).float()[:, :, 3]  # use alpha channel
+    mask = (mask > 0).float()
+
+    return mask
+
+
+class UnsupervisedEvaluator:
+    def __init__(self, name: str = 'UnsupervisedEvaluator'):
+        self.name = name
+        self.ious = defaultdict(list)
+        self.num_samples = 0
+
+    def log_iou(self, preds: Union[torch.Tensor, List[torch.Tensor]], truth: torch.Tensor, gt_idx: int = 0, pred_idx: int = 0):
+        if not isinstance(preds, list):
+            preds = [preds]
+
+        iou = max(compute_iou(pred, truth) for pred in preds)
+        self.ious[gt_idx].append((pred_idx, iou))
+
+    @property
+    def mean_iou(self) -> float:
+        n = max(max(self.ious), max([y[0] for x in self.ious.values() for y in x])) + 1
+        iou_matrix = np.zeros((n, n))
+        count_matrix = np.zeros((n, n))
+
+        for gt_idx, ious in self.ious.items():
+            for pred_idx, iou in ious:
+                iou_matrix[gt_idx, pred_idx] += iou
+                count_matrix[gt_idx, pred_idx] += 1
+
+        row_ind, col_ind = linear_sum_assignment(iou_matrix, maximize=True)
+        return iou_matrix[row_ind, col_ind].sum() / count_matrix[row_ind, col_ind].sum()
+
+    def increment(self):
+        self.num_samples += 1
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def __str__(self):
+        return f'{self.name}<{self.mean_iou:.4f} (mIoU) {len(self)} samples>'
+
+
+class MeanEvaluator:
+    def __init__(self, name: str = 'MeanEvaluator'):
+        self.ious: List[float] = []
+        self.intensities: List[float] = []
+        self.name = name
+
+    def log_iou(self, preds: Union[torch.Tensor, List[torch.Tensor]], truth: torch.Tensor):
+        if not isinstance(preds, list):
+            preds = [preds]
+
+        self.ious.append(max(compute_iou(pred, truth) for pred in preds))
+        return self
+
+    def log_intensity(self, pred: torch.Tensor):
+        self.intensities.append(pred.mean().item())
+        return self
+
+    @property
+    def mean_iou(self) -> float:
+        return np.mean(self.ious)
+
+    @property
+    def mean_intensity(self) -> float:
+        return np.mean(self.intensities)
+
+    @property
+    def ci95_miou(self) -> float:
+        return 1.96 * np.std(self.ious) / np.sqrt(len(self.ious))
+
+    def __len__(self) -> int:
+        return max(len(self.ious), len(self.intensities))
+
+    def __str__(self):
+        return f'{self.name}<{self.mean_iou:.4f} (±{self.ci95_miou:.3f} mIoU) {self.mean_intensity:.4f} (mInt) {len(self)} samples>'
+
+
+if __name__ == '__main__':
+    mask = load_mask('truth/output/452/sink.gt.png')
+
+    print(MeanEvaluator().log_iou(mask, mask))
diff --git a/scripts/daam/experiment.py b/scripts/daam/experiment.py
new file mode 100644
index 000000000..4465a3054
--- /dev/null
+++ b/scripts/daam/experiment.py
@@ -0,0 +1,344 @@
+from pathlib import Path
+from typing import List, Optional, Dict, Any, Union
+from dataclasses import dataclass
+import json
+
+from transformers import PreTrainedTokenizer, AutoTokenizer
+import PIL.Image
+import numpy as np
+import torch
+
+from .utils import auto_autocast
+from .evaluate import load_mask
+
+
+__all__ = ['GenerationExperiment', 'COCO80_LABELS', 'COCOSTUFF27_LABELS', 'COCO80_INDICES', 'build_word_list_coco80']
+
+
+COCO80_LABELS: List[str] = [
+    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+    'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+    'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+    'hair drier', 'toothbrush'
+]
+
+COCO80_INDICES: Dict[str, int] = {x: i for i, x in enumerate(COCO80_LABELS)}
+
+UNUSED_LABELS: List[str] = [f'__unused_{i}__' for i in range(1, 200)]
+
+COCOSTUFF27_LABELS: List[str] = [
+    'electronic', 'appliance', 'food', 'furniture', 'indoor', 'kitchen', 'accessory', 'animal', 'outdoor', 'person',
+    'sports', 'vehicle', 'ceiling', 'floor', 'food', 'furniture', 'rawmaterial', 'textile', 'wall', 'window',
+    'building', 'ground', 'plant', 'sky', 'solid', 'structural', 'water'
+]
+
+COCO80_ONTOLOGY = {
+    'two-wheeled vehicle': ['bicycle', 'motorcycle'],
+    'vehicle': ['two-wheeled vehicle', 'four-wheeled vehicle'],
+    'four-wheeled vehicle': ['bus', 'truck', 'car'],
+    'four-legged animals': ['livestock', 'pets', 'wild animals'],
+    'livestock': ['cow', 'horse', 'sheep'],
+    'pets': ['cat', 'dog'],
+    'wild animals': ['elephant', 'bear', 'zebra', 'giraffe'],
+    'bags': ['backpack', 'handbag', 'suitcase'],
+    'sports boards': ['snowboard', 'surfboard', 'skateboard'],
+    'utensils': ['fork', 'knife', 'spoon'],
+    'receptacles': ['bowl', 'cup'],
+    'fruits': ['banana', 'apple', 'orange'],
+    'foods': ['fruits', 'meals', 'desserts'],
+    'meals': ['sandwich', 'hot dog', 'pizza'],
+    'desserts': ['cake', 'donut'],
+    'furniture': ['chair', 'couch', 'bench'],
+    'electronics': ['monitors', 'appliances'],
+    'monitors': ['tv', 'cell phone', 'laptop'],
+    'appliances': ['oven', 'toaster', 'refrigerator']
+}
+
+COCO80_TO_27 = {
+    'bicycle': 'vehicle', 'car': 'vehicle', 'motorcycle': 'vehicle', 'airplane': 'vehicle', 'bus': 'vehicle',
+    'train': 'vehicle', 'truck': 'vehicle', 'boat': 'vehicle', 'traffic light': 'accessory', 'fire hydrant': 'accessory',
+    'stop sign': 'accessory', 'parking meter': 'accessory', 'bench': 'furniture', 'bird': 'animal', 'cat': 'animal',
+    'dog': 'animal', 'horse': 'animal', 'sheep': 'animal', 'cow': 'animal', 'elephant': 'animal', 'bear': 'animal',
+    'zebra': 'animal', 'giraffe': 'animal', 'backpack': 'accessory', 'umbrella': 'accessory', 'handbag': 'accessory',
+    'tie': 'accessory', 'suitcase': 'accessory', 'frisbee': 'sports', 'skis': 'sports', 'snowboard': 'sports',
+    'sports ball': 'sports', 'kite': 'sports', 'baseball bat': 'sports', 'baseball glove': 'sports',
+    'skateboard': 'sports', 'surfboard': 'sports', 'tennis racket': 'sports', 'bottle': 'food', 'wine glass': 'food',
+    'cup': 'food', 'fork': 'food', 'knife': 'food', 'spoon': 'food', 'bowl': 'food', 'banana': 'food', 'apple': 'food',
+    'sandwich': 'food', 'orange': 'food', 'broccoli': 'food', 'carrot': 'food', 'hot dog': 'food', 'pizza': 'food',
+    'donut': 'food', 'cake': 'food', 'chair': 'furniture', 'couch': 'furniture', 'potted plant': 'plant',
+    'bed': 'furniture', 'dining table': 'furniture', 'toilet': 'furniture', 'tv': 'electronic', 'laptop': 'electronic',
+    'mouse': 'electronic', 'remote': 'electronic', 'keyboard': 'electronic', 'cell phone': 'electronic',
+    'microwave': 'appliance', 'oven': 'appliance', 'toaster': 'appliance', 'sink': 'appliance',
+    'refrigerator': 'appliance', 'book': 'indoor', 'clock': 'indoor', 'vase': 'indoor', 'scissors': 'indoor',
+    'teddy bear': 'indoor', 'hair drier': 'indoor', 'toothbrush': 'indoor'
+}
+
+
+def build_word_list_coco80() -> Dict[str, List[str]]:
+    words_map = COCO80_ONTOLOGY.copy()
+    words_map = {k: v for k, v in words_map.items() if not any(item in COCO80_ONTOLOGY for item in v)}
+
+    return words_map
+
+
+def _add_mask(masks: Dict[str, torch.Tensor], word: str, mask: torch.Tensor, simplify80: bool = False) -> Dict[str, torch.Tensor]:
+    if simplify80:
+        word = COCO80_TO_27.get(word, word)
+
+    if word in masks:
+        masks[word] = masks[word.lower()] + mask
+        masks[word].clamp_(0, 1)
+    else:
+        masks[word] = mask
+
+    return masks
+
+
+@dataclass
+class GenerationExperiment:
+    """Class to hold experiment parameters. Pickleable."""
+    image: PIL.Image.Image
+    global_heat_map: torch.Tensor
+    prompt: str
+
+    seed: int = None
+    id: str = '.'
+    path: Optional[Path] = None
+
+    truth_masks: Optional[Dict[str, torch.Tensor]] = None
+    prediction_masks: Optional[Dict[str, torch.Tensor]] = None
+    annotations: Optional[Dict[str, Any]] = None
+    subtype: Optional[str] = '.'
+    tokenizer: AutoTokenizer = None
+
+    def __post_init__(self):
+        if isinstance(self.path, str):
+            self.path = Path(self.path)
+
+        self.path = None if self.path is None else self.path / self.id
+
+    def nsfw(self) -> bool:
+        return np.sum(np.array(self.image)) == 0
+
+    def heat_map(self, tokenizer: AutoTokenizer = None):
+        if tokenizer is None:
+            tokenizer = self.tokenizer
+
+        from daam import GlobalHeatMap
+        return GlobalHeatMap(tokenizer, self.prompt, self.global_heat_map)
+
+    def clear_checkpoint(self):
+        path = self if isinstance(self, Path) else self.path
+
+        (path / 'generation.pt').unlink(missing_ok=True)
+
+    def save(self, path: str = None, heat_maps: bool = True, tokenizer: AutoTokenizer = None):
+        if path is None:
+            path = self.path
+        else:
+            path = Path(path) / self.id
+
+        if tokenizer is None:
+            tokenizer = self.tokenizer
+
+        (path / self.subtype).mkdir(parents=True, exist_ok=True)
+        torch.save(self, path / self.subtype / 'generation.pt')
+        self.image.save(path / self.subtype / 'output.png')
+
+        with (path / 'prompt.txt').open('w') as f:
+            f.write(self.prompt)
+
+        with (path / 'seed.txt').open('w') as f:
+            f.write(str(self.seed))
+
+        if self.truth_masks is not None:
+            for name, mask in self.truth_masks.items():
+                im = PIL.Image.fromarray((mask * 255).unsqueeze(-1).expand(-1, -1, 4).byte().numpy())
+                im.save(path / f'{name.lower()}.gt.png')
+
+        if heat_maps and tokenizer is not None:
+            self.save_all_heat_maps(tokenizer)
+
+        self.save_annotations()
+
+    def save_annotations(self, path: Path = None):
+        if path is None:
+            path = self.path
+
+        if self.annotations is not None:
+            with (path / 'annotations.json').open('w') as f:
+                json.dump(self.annotations, f)
+
+    def _load_truth_masks(self, simplify80: bool = False) -> Dict[str, torch.Tensor]:
+        masks = {}
+
+        for mask_path in self.path.glob('*.gt.png'):
+            word = mask_path.name.split('.gt.png')[0].lower()
+            mask = load_mask(str(mask_path))
+            _add_mask(masks, word, mask, simplify80)
+
+        return masks
+
+    def _load_pred_masks(self, pred_prefix, composite=False, simplify80=False, vocab=None):
+        # type: (str, bool, bool, List[str] | None) -> Dict[str, torch.Tensor]
+        masks = {}
+
+        if vocab is None:
+            vocab = UNUSED_LABELS
+
+        if composite:
+            try:
+                im = PIL.Image.open(self.path / self.subtype / f'composite.{pred_prefix}.pred.png')
+                im = np.array(im)
+
+                for mask_idx in np.unique(im):
+                    mask = torch.from_numpy((im == mask_idx).astype(np.float32))
+                    _add_mask(masks, vocab[mask_idx], mask, simplify80)
+            except FileNotFoundError:
+                pass
+        else:
+            for mask_path in (self.path / self.subtype).glob(f'*.{pred_prefix}.pred.png'):
+                mask = load_mask(str(mask_path))
+                word = mask_path.name.split(f'.{pred_prefix}.pred')[0].lower()
+                _add_mask(masks, word, mask, simplify80)
+
+        return masks
+
+    def clear_prediction_masks(self, name: str):
+        path = self if isinstance(self, Path) else self.path
+        path = path / self.subtype
+
+        for mask_path in path.glob(f'*.{name}.pred.png'):
+            mask_path.unlink()
+
+    def save_prediction_mask(self, mask: torch.Tensor, word: str, name: str):
+        path = self if isinstance(self, Path) else self.path
+        im = PIL.Image.fromarray((mask * 255).unsqueeze(-1).expand(-1, -1, 4).cpu().byte().numpy())
+        im.save(path / self.subtype / f'{word.lower()}.{name}.pred.png')
+
+    def save_heat_map(
+            self,
+            word: str,
+            tokenizer: PreTrainedTokenizer = None,
+            crop: int = None,
+            output_prefix: str = '',
+            absolute: bool = False
+    ) -> Path:
+        from .trace import GlobalHeatMap  # because of cyclical import
+
+        if tokenizer is None:
+            tokenizer = self.tokenizer
+
+        with auto_autocast(dtype=torch.float32):
+            path = self.path / self.subtype / f'{output_prefix}{word.lower()}.heat_map.png'
+            heat_map = GlobalHeatMap(tokenizer, self.prompt, self.global_heat_map)
+            heat_map.compute_word_heat_map(word).expand_as(self.image, color_normalize=not absolute, out_file=path, plot=True)
+
+        return path
+
+    def save_all_heat_maps(self, tokenizer: PreTrainedTokenizer = None, crop: int = None) -> Dict[str, Path]:
+        path_map = {}
+
+        if tokenizer is None:
+            tokenizer = self.tokenizer
+
+        for word in self.prompt.split(' '):
+            try:
+                path = self.save_heat_map(word, tokenizer, crop=crop)
+                path_map[word] = path
+            except:
+                pass
+
+        return path_map
+
+    @staticmethod
+    def contains_truth_mask(path: Union[str, Path], prompt_id: str = None) -> bool:
+        if prompt_id is None:
+            return any(Path(path).glob('*.gt.png'))
+        else:
+            return any((Path(path) / prompt_id).glob('*.gt.png'))
+
+    @staticmethod
+    def read_seed(path: Union[str, Path], prompt_id: str = None) -> int:
+        if prompt_id is None:
+            return int(Path(path).joinpath('seed.txt').read_text())
+        else:
+            return int(Path(path).joinpath(prompt_id).joinpath('seed.txt').read_text())
+
+    @staticmethod
+    def has_annotations(path: Union[str, Path]) -> bool:
+        return Path(path).joinpath('annotations.json').exists()
+
+    @staticmethod
+    def has_experiment(path: Union[str, Path], prompt_id: str) -> bool:
+        return (Path(path) / prompt_id / 'generation.pt').exists()
+
+    @staticmethod
+    def read_prompt(path: Union[str, Path], prompt_id: str = None) -> str:
+        if prompt_id is None:
+            prompt_id = '.'
+
+        with (Path(path) / prompt_id / 'prompt.txt').open('r') as f:
+            return f.read().strip()
+
+    def _try_load_annotations(self):
+        if not (self.path / 'annotations.json').exists():
+            return None
+
+        return json.load((self.path / 'annotations.json').open())
+
+    def annotate(self, key: str, value: Any) -> 'GenerationExperiment':
+        if self.annotations is None:
+            self.annotations = {}
+
+        self.annotations[key] = value
+
+        return self
+
+    @classmethod
+    def load(
+            cls,
+            path,
+            pred_prefix='daam',
+            composite=False,
+            simplify80=False,
+            vocab=None,
+            subtype='.',
+            all_subtypes=False
+    ):
+        # type: (str, str, bool, bool, List[str] | None, str, bool) -> GenerationExperiment | List[GenerationExperiment]
+        if all_subtypes:
+            experiments = []
+
+            for directory in Path(path).iterdir():
+                if not directory.is_dir():
+                    continue
+
+                try:
+                    experiments.append(cls.load(
+                        path,
+                        pred_prefix=pred_prefix,
+                        composite=composite,
+                        simplify80=simplify80,
+                        vocab=vocab,
+                        subtype=directory.name
+                    ))
+                except:
+                    pass
+
+            return experiments
+
+        path = Path(path)
+        exp = torch.load(path / subtype / 'generation.pt')
+        exp.subtype = subtype
+        exp.path = path
+        exp.truth_masks = exp._load_truth_masks(simplify80=simplify80)
+        exp.prediction_masks = exp._load_pred_masks(pred_prefix, composite=composite, simplify80=simplify80, vocab=vocab)
+        exp.annotations = exp._try_load_annotations()
+
+        return exp
diff --git a/scripts/daam/heatmap.py b/scripts/daam/heatmap.py
new file mode 100644
index 000000000..0f7a311f0
--- /dev/null
+++ b/scripts/daam/heatmap.py
@@ -0,0 +1,185 @@
+import io
+from collections import defaultdict
+from dataclasses import dataclass
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Tuple, Set, Iterable
+
+from matplotlib import pyplot as plt
+import numpy as np
+import PIL.Image
+import spacy.tokens
+import torch
+import torch.nn.functional as F
+
+from .evaluate import compute_ioa
+from .utils import compute_token_merge_indices, cached_nlp, auto_autocast
+
+__all__ = ['GlobalHeatMap', 'RawHeatMapCollection', 'WordHeatMap', 'ParsedHeatMap', 'SyntacticHeatMapPair']
+
+
+def plot_overlay_heat_map(im, heat_map, word=None, out_file=None, crop=None, color_normalize=True, ax=None, cmap='jet'):
+    # type: (PIL.Image.Image | np.ndarray, torch.Tensor, str, Path, int, bool, plt.Axes) -> None
+    if ax is None:
+        plt.rcParams['font.size'] = 16
+        plt.rcParams['figure.facecolor'] = 'black'
+        plt.rcParams['text.color'] = 'white'
+        plt.rcParams['axes.labelcolor'] = 'white'
+        plt.rcParams['xtick.color'] = 'black'
+        plt.rcParams['ytick.color'] = 'black'
+        plt.clf()
+        plt_ = plt
+    else:
+        plt_ = ax
+
+    with auto_autocast(dtype=torch.float32):
+        im = np.array(im)
+
+        if crop is not None:
+            heat_map = heat_map.squeeze()[crop:-crop, crop:-crop]
+            im = im[crop:-crop, crop:-crop]
+
+        if color_normalize:
+            plt_.imshow(heat_map.squeeze().cpu().numpy(), cmap=cmap)
+        else:
+            heat_map = heat_map.clamp_(min=0, max=1)
+            plt_.imshow(heat_map.squeeze().cpu().numpy(), cmap=cmap, vmin=0.0, vmax=1.0)
+
+        im = torch.from_numpy(im).float() / 255
+        im = torch.cat((im, (1 - heat_map.unsqueeze(-1))), dim=-1)
+        plt_.imshow(im)
+
+        if word is not None:
+            if ax is None:
+                plt.title(word)
+            else:
+                ax.set_title(word)
+
+        if out_file is not None:
+            plt.savefig(out_file)
+
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight')
+        buf.seek(0)
+        image = PIL.Image.open(buf)
+        return image
+
+
+class WordHeatMap:
+    def __init__(self, heatmap: torch.Tensor, word: str = None, word_idx: int = None):
+        self.word = word
+        self.word_idx = word_idx
+        self.heatmap = heatmap
+
+    @property
+    def value(self):
+        return self.heatmap
+
+    def plot_overlay(self, image, out_file=None, color_normalize=True, ax=None, cmap='jet', **expand_kwargs):
+        # type: (PIL.Image.Image | np.ndarray, Path, bool, plt.Axes, Dict[str, Any]) -> None
+        return plot_overlay_heat_map(
+            image,
+            self.expand_as(image, **expand_kwargs),
+            word=self.word,
+            out_file=out_file,
+            color_normalize=color_normalize,
+            ax=ax,
+            cmap=cmap,
+        )
+
+    def expand_as(self, image, absolute=False, threshold=None, plot=False, **plot_kwargs):
+        # type: (PIL.Image.Image, bool, float, bool, Dict[str, Any]) -> torch.Tensor
+        im = self.heatmap.unsqueeze(0).unsqueeze(0)
+        im = F.interpolate(im.float().detach(), size=(image.size[0], image.size[1]), mode='bicubic')
+
+        if not absolute:
+            im = (im - im.min()) / (im.max() - im.min() + 1e-8)
+
+        if threshold:
+            im = (im > threshold).float()
+
+        im = im.cpu().detach().squeeze()
+
+        if plot:
+            self.plot_overlay(image, **plot_kwargs)
+
+        return im
+
+    def compute_ioa(self, other: 'WordHeatMap'):
+        return compute_ioa(self.heatmap, other.heatmap)
+
+
+@dataclass
+class SyntacticHeatMapPair:
+    head_heat_map: WordHeatMap
+    dep_heat_map: WordHeatMap
+    head_text: str
+    dep_text: str
+    relation: str
+
+
+@dataclass
+class ParsedHeatMap:
+    word_heat_map: WordHeatMap
+    token: spacy.tokens.Token
+
+
+class GlobalHeatMap:
+    def __init__(self, tokenizer: Any, prompt: str, heat_maps: torch.Tensor):
+        self.tokenizer = tokenizer
+        self.heat_maps = heat_maps
+        self.prompt = prompt
+        self.compute_word_heat_map = lru_cache(maxsize=50)(self.compute_word_heat_map)
+
+    def compute_word_heat_map(self, word: str, word_idx: int = None, offset_idx: int = 0) -> WordHeatMap:
+        merge_idxs, word_idx = compute_token_merge_indices(self.tokenizer, self.prompt, word, word_idx, offset_idx)
+        return WordHeatMap(self.heat_maps[merge_idxs].mean(0), word, word_idx)
+
+    def parsed_heat_maps(self) -> Iterable[ParsedHeatMap]:
+        for token in cached_nlp(self.prompt):
+            try:
+                heat_map = self.compute_word_heat_map(token.text)
+                yield ParsedHeatMap(heat_map, token)
+            except ValueError:
+                pass
+
+    def dependency_relations(self) -> Iterable[SyntacticHeatMapPair]:
+        for token in cached_nlp(self.prompt):
+            if token.dep_ != 'ROOT':
+                try:
+                    dep_heat_map = self.compute_word_heat_map(token.text)
+                    head_heat_map = self.compute_word_heat_map(token.head.text)
+
+                    yield SyntacticHeatMapPair(head_heat_map, dep_heat_map, token.head.text, token.text, token.dep_)
+                except ValueError:
+                    pass
+
+
+RawHeatMapKey = Tuple[int, int, int]  # factor, layer, head
+
+
+class RawHeatMapCollection:
+    def __init__(self):
+        self.ids_to_heatmaps: Dict[RawHeatMapKey, torch.Tensor] = defaultdict(lambda: 0.0)
+        self.ids_to_num_maps: Dict[RawHeatMapKey, int] = defaultdict(lambda: 0)
+
+    def update(self, factor: int, layer_idx: int, head_idx: int, heatmap: torch.Tensor):
+        with auto_autocast(dtype=torch.float32):
+            key = (factor, layer_idx, head_idx)
+            self.ids_to_heatmaps[key] = self.ids_to_heatmaps[key] + heatmap
+
+    def factors(self) -> Set[int]:
+        return set(key[0] for key in self.ids_to_heatmaps.keys())
+
+    def layers(self) -> Set[int]:
+        return set(key[1] for key in self.ids_to_heatmaps.keys())
+
+    def heads(self) -> Set[int]:
+        return set(key[2] for key in self.ids_to_heatmaps.keys())
+
+    def __iter__(self):
+        return iter(self.ids_to_heatmaps.items())
+
+    def clear(self):
+        self.ids_to_heatmaps.clear()
+        self.ids_to_num_maps.clear()
diff --git a/scripts/daam/hook.py b/scripts/daam/hook.py
new file mode 100644
index 000000000..f82762c4e
--- /dev/null
+++ b/scripts/daam/hook.py
@@ -0,0 +1,127 @@
+from typing import List, Generic, TypeVar, Callable, Union, Any
+import functools
+import itertools
+
+from diffusers import UNet2DConditionModel
+from diffusers.models.attention_processor import Attention
+import torch.nn as nn
+
+
+__all__ = ['ObjectHooker', 'ModuleLocator', 'AggregateHooker', 'UNetCrossAttentionLocator']
+
+
+ModuleType = TypeVar('ModuleType')
+ModuleListType = TypeVar('ModuleListType', bound=List)
+
+
+class ModuleLocator(Generic[ModuleType]):
+    def locate(self, model: nn.Module) -> List[ModuleType]:
+        raise NotImplementedError
+
+
+class ObjectHooker(Generic[ModuleType]):
+    def __init__(self, module: ModuleType):
+        self.module: ModuleType = module
+        self.hooked = False
+        self.old_state = dict()
+
+    def __enter__(self):
+        self.hook()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.unhook()
+
+    def hook(self):
+        if self.hooked:
+            raise RuntimeError('Already hooked module')
+
+        self.old_state = dict()
+        self.hooked = True
+        self._hook_impl()
+
+        return self
+
+    def unhook(self):
+        if not self.hooked:
+            raise RuntimeError('Module is not hooked')
+
+        for k, v in self.old_state.items():
+            if k.startswith('old_fn_'):
+                setattr(self.module, k[7:], v)
+
+        self.hooked = False
+        self._unhook_impl()
+
+        return self
+
+    def monkey_patch(self, fn_name, fn, strict: bool = True):
+        try:
+            self.old_state[f'old_fn_{fn_name}'] = getattr(self.module, fn_name)
+            setattr(self.module, fn_name, functools.partial(fn, self.module))
+        except AttributeError:
+            if strict:
+                raise
+
+    def monkey_super(self, fn_name, *args, **kwargs):
+        return self.old_state[f'old_fn_{fn_name}'](*args, **kwargs)
+
+    def _hook_impl(self):
+        raise NotImplementedError
+
+    def _unhook_impl(self):
+        pass
+
+
+class AggregateHooker(ObjectHooker[ModuleListType]):
+    def _hook_impl(self):
+        for h in self.module:
+            h.hook()
+
+    def _unhook_impl(self):
+        for h in self.module:
+            h.unhook()
+
+    def register_hook(self, hook: ObjectHooker):
+        self.module.append(hook)
+
+
+class UNetCrossAttentionLocator(ModuleLocator[Attention]):
+    def __init__(self, restrict: bool = None, locate_middle_block: bool = False):
+        self.restrict = restrict
+        self.layer_names = []
+        self.locate_middle_block = locate_middle_block
+
+    def locate(self, model: UNet2DConditionModel) -> List[Attention]:
+        """
+        Locate all cross-attention modules in a UNet2DConditionModel.
+
+        Args:
+            model (`UNet2DConditionModel`): The model to locate the cross-attention modules in.
+
+        Returns:
+            `List[Attention]`: The list of cross-attention modules.
+        """
+        self.layer_names.clear()
+        blocks_list = []
+        up_names = ['up'] * len(model.up_blocks)
+        down_names = ['down'] * len(model.down_blocks)
+
+        for unet_block, name in itertools.chain(
+                zip(model.up_blocks, up_names),
+                zip(model.down_blocks, down_names),
+                zip([model.mid_block], ['mid']) if self.locate_middle_block else [],
+        ):
+            if 'CrossAttn' in unet_block.__class__.__name__:
+                blocks = []
+
+                for spatial_transformer in unet_block.attentions:
+                    for transformer_block in spatial_transformer.transformer_blocks:
+                        blocks.append(transformer_block.attn2)
+
+                blocks = [b for idx, b in enumerate(blocks) if self.restrict is None or idx in self.restrict]
+                names = [f'{name}-attn-{i}' for i in range(len(blocks)) if self.restrict is None or i in self.restrict]
+                blocks_list.extend(blocks)
+                self.layer_names.extend(names)
+
+        return blocks_list
diff --git a/scripts/daam/trace.py b/scripts/daam/trace.py
new file mode 100644
index 000000000..1b6a6cd3f
--- /dev/null
+++ b/scripts/daam/trace.py
@@ -0,0 +1,323 @@
+from pathlib import Path
+from typing import List, Type, Any, Dict, Tuple, Union
+import math
+
+from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import Attention
+import numpy as np
+import PIL.Image as Image
+import torch
+import torch.nn.functional as F
+
+from .utils import cache_dir, auto_autocast
+from .experiment import GenerationExperiment
+from .heatmap import RawHeatMapCollection, GlobalHeatMap
+from .hook import ObjectHooker, AggregateHooker, UNetCrossAttentionLocator
+
+
+__all__ = ['trace', 'DiffusionHeatMapHooker', 'GlobalHeatMap']
+
+
+class DiffusionHeatMapHooker(AggregateHooker):
+    def __init__(
+            self,
+            pipeline: Union[StableDiffusionPipeline, StableDiffusionXLPipeline],
+            low_memory: bool = False,
+            load_heads: bool = False,
+            save_heads: bool = False,
+            data_dir: str = None
+    ):
+        self.all_heat_maps = RawHeatMapCollection()
+        h = (pipeline.unet.config.sample_size * pipeline.vae_scale_factor)
+        self.latent_hw = 4096 if h == 512 or h == 1024 else 9216  # 64x64 or 96x96 depending on if it's 2.0-v or 2.0
+        locate_middle = load_heads or save_heads
+        self.locator = UNetCrossAttentionLocator(restrict={0} if low_memory else None, locate_middle_block=locate_middle)
+        self.last_prompt: str = ''
+        self.last_image: Image = None
+        self.time_idx = 0
+        self._gen_idx = 0
+
+        modules = [
+            UNetCrossAttentionHooker(
+                x,
+                self,
+                layer_idx=idx,
+                latent_hw=self.latent_hw,
+                load_heads=load_heads,
+                save_heads=save_heads,
+                data_dir=data_dir
+            ) for idx, x in enumerate(self.locator.locate(pipeline.unet))
+        ]
+
+        modules.append(PipelineHooker(pipeline, self))
+
+        if type(pipeline) == StableDiffusionXLPipeline:
+            modules.append(ImageProcessorHooker(pipeline.image_processor, self))
+
+        super().__init__(modules)
+        self.pipe = pipeline
+
+    def time_callback(self, *args, **kwargs):
+        self.time_idx += 1
+
+    @property
+    def layer_names(self):
+        return self.locator.layer_names
+
+    def to_experiment(self, path, seed=None, id='.', subtype='.', **compute_kwargs):
+        # type: (Union[Path, str], int, str, str, Dict[str, Any]) -> GenerationExperiment
+        """Exports the last generation call to a serializable generation experiment."""
+
+        return GenerationExperiment(
+            self.last_image,
+            self.compute_global_heat_map(**compute_kwargs).heat_maps,
+            self.last_prompt,
+            seed=seed,
+            id=id,
+            subtype=subtype,
+            path=path,
+            tokenizer=self.pipe.tokenizer,
+        )
+
+    def compute_global_heat_map(self, prompt=None, factors=None, head_idx=None, layer_idx=None, normalize=False):
+        # type: (str, List[float], int, int, bool) -> GlobalHeatMap
+        """
+        Compute the global heat map for the given prompt, aggregating across time (inference steps) and space (different
+        spatial transformer block heat maps).
+
+        Args:
+            prompt: The prompt to compute the heat map for. If none, uses the last prompt that was used for generation.
+            factors: Restrict the application to heat maps with spatial factors in this set. If `None`, use all sizes.
+            head_idx: Restrict the application to heat maps with this head index. If `None`, use all heads.
+            layer_idx: Restrict the application to heat maps with this layer index. If `None`, use all layers.
+
+        Returns:
+            A heat map object for computing word-level heat maps.
+        """
+        heat_maps = self.all_heat_maps
+
+        if prompt is None:
+            prompt = self.last_prompt
+
+        if factors is None:
+            factors = {0, 1, 2, 4, 8, 16, 32, 64}
+        else:
+            factors = set(factors)
+
+        all_merges = []
+        x = int(np.sqrt(self.latent_hw))
+
+        with auto_autocast(dtype=torch.float32):
+            for (factor, layer, head), heat_map in heat_maps:
+                if factor in factors and (head_idx is None or head_idx == head) and (layer_idx is None or layer_idx == layer):
+                    heat_map = heat_map.unsqueeze(1)
+                    # The clamping fixes undershoot.
+                    all_merges.append(F.interpolate(heat_map, size=(x, x), mode='bicubic').clamp_(min=0))
+
+            try:
+                maps = torch.stack(all_merges, dim=0)
+            except RuntimeError:
+                if head_idx is not None or layer_idx is not None:
+                    raise RuntimeError('No heat maps found for the given parameters.')
+                else:
+                    raise RuntimeError('No heat maps found. Did you forget to call `with trace(...)` during generation?')
+
+            maps = maps.mean(0)[:, 0]
+            maps = maps[:len(self.pipe.tokenizer.tokenize(prompt)) + 2]  # 1 for SOS and 1 for padding
+
+            if normalize:
+                maps = maps / (maps[1:-1].sum(0, keepdim=True) + 1e-6)  # drop out [SOS] and [PAD] for proper probabilities
+
+        return GlobalHeatMap(self.pipe.tokenizer, prompt, maps)
+
+
+class ImageProcessorHooker(ObjectHooker[VaeImageProcessor]):
+    def __init__(self, processor: VaeImageProcessor, parent_trace: 'trace'):
+        super().__init__(processor)
+        self.parent_trace = parent_trace
+
+    def _hooked_postprocess(hk_self, _: VaeImageProcessor, *args, **kwargs):
+        images = hk_self.monkey_super('postprocess', *args, **kwargs)
+        hk_self.parent_trace.last_image = images[0]
+
+        return images
+
+    def _hook_impl(self):
+        self.monkey_patch('postprocess', self._hooked_postprocess)
+
+
+class PipelineHooker(ObjectHooker[StableDiffusionPipeline]):
+    def __init__(self, pipeline: StableDiffusionPipeline, parent_trace: 'trace'):
+        super().__init__(pipeline)
+        self.heat_maps = parent_trace.all_heat_maps
+        self.parent_trace = parent_trace
+
+    def _hooked_run_safety_checker(hk_self, self: StableDiffusionPipeline, image, *args, **kwargs):
+        image, has_nsfw = hk_self.monkey_super('run_safety_checker', image, *args, **kwargs)
+
+        if self.image_processor:
+            if torch.is_tensor(image):
+                images = self.image_processor.postprocess(image, output_type='pil')
+            else:
+                images = self.image_processor.numpy_to_pil(image)
+        else:
+            images = self.numpy_to_pil(image)
+
+        hk_self.parent_trace.last_image = images[len(images)-1]
+
+        return image, has_nsfw
+
+    def _hooked_check_inputs(hk_self, _: StableDiffusionPipeline, prompt: Union[str, List[str]], *args, **kwargs):
+        if not isinstance(prompt, str) and len(prompt) > 1:
+            raise ValueError('Only single prompt generation is supported for heat map computation.')
+        elif not isinstance(prompt, str):
+            last_prompt = prompt[0]
+        else:
+            last_prompt = prompt
+
+        hk_self.heat_maps.clear()
+        hk_self.parent_trace.last_prompt = last_prompt
+
+        return hk_self.monkey_super('check_inputs', prompt, *args, **kwargs)
+
+    def _hook_impl(self):
+        self.monkey_patch('run_safety_checker', self._hooked_run_safety_checker, strict=False)  # not present in SDXL
+        self.monkey_patch('check_inputs', self._hooked_check_inputs)
+
+
+class UNetCrossAttentionHooker(ObjectHooker[Attention]):
+    def __init__(
+            self,
+            module: Attention,
+            parent_trace: 'trace',
+            context_size: int = 77,
+            layer_idx: int = 0,
+            latent_hw: int = 9216,
+            load_heads: bool = False,
+            save_heads: bool = False,
+            data_dir: Union[str, Path] = None,
+    ):
+        super().__init__(module)
+        self.heat_maps = parent_trace.all_heat_maps
+        self.context_size = context_size
+        self.layer_idx = layer_idx
+        self.latent_hw = latent_hw
+
+        self.load_heads = load_heads
+        self.save_heads = save_heads
+        self.trace = parent_trace
+
+        if data_dir is not None:
+            data_dir = Path(data_dir)
+        else:
+            data_dir = cache_dir() / 'heads'
+
+        self.data_dir = data_dir
+        self.data_dir.mkdir(parents=True, exist_ok=True)
+
+    @torch.no_grad()
+    def _unravel_attn(self, x):
+        # type: (torch.Tensor) -> torch.Tensor
+        # x shape: (heads, height * width, tokens)
+        """
+        Unravels the attention, returning it as a collection of heat maps.
+
+        Args:
+            x (`torch.Tensor`): cross attention slice/map between the words and the tokens.
+            value (`torch.Tensor`): the value tensor.
+
+        Returns:
+            `List[Tuple[int, torch.Tensor]]`: the list of heat maps across heads.
+        """
+        h = w = int(math.sqrt(x.size(1)))
+        maps = []
+        x = x.permute(2, 0, 1)
+
+        with auto_autocast(dtype=torch.float32):
+            for map_ in x:
+                map_ = map_.view(map_.size(0), h, w)
+                # For Instruct Pix2Pix, divide the map into three parts: text condition, image condition and unconditional,
+                # and only keep the text condition part, which is first of the three parts(as per diffusers implementation).
+                if map_.size(0) == 24:
+                    map_ = map_[:((map_.size(0) // 3)+1)]  # Filter out unconditional and image condition
+                else:
+                    map_ = map_[map_.size(0) // 2:] #  # Filter out unconditional
+                maps.append(map_)
+
+        maps = torch.stack(maps, 0)  # shape: (tokens, heads, height, width)
+        return maps.permute(1, 0, 2, 3).contiguous()  # shape: (heads, tokens, height, width)
+
+    def _save_attn(self, attn_slice: torch.Tensor):
+        torch.save(attn_slice, self.data_dir / f'{self.trace._gen_idx}.pt')
+
+    def _load_attn(self) -> torch.Tensor:
+        return torch.load(self.data_dir / f'{self.trace._gen_idx}.pt')
+
+    def __call__(
+            self,
+            attn: Attention,
+            hidden_states,
+            encoder_hidden_states=None,
+            attention_mask=None,
+    ):
+        """Capture attentions and aggregate them."""
+        batch_size, sequence_length, _ = hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross is not None:
+            encoder_hidden_states = attn.norm_cross(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+
+        # DAAM save heads
+        if self.save_heads:
+            self._save_attn(attention_probs)
+        elif self.load_heads:
+            attention_probs = self._load_attn()
+
+        # compute shape factor
+        factor = int(math.sqrt(self.latent_hw // attention_probs.shape[1]))
+        self.trace._gen_idx += 1
+
+        # skip if too large
+        if attention_probs.shape[-1] == self.context_size and factor != 8:
+            # shape: (batch_size, 64 // factor, 64 // factor, 77)
+            maps = self._unravel_attn(attention_probs)
+
+            for head_idx, heatmap in enumerate(maps):
+                self.heat_maps.update(factor, self.layer_idx, head_idx, heatmap)
+
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        return hidden_states
+
+    def _hook_impl(self):
+        self.original_processor = self.module.processor
+        self.module.set_processor(self)
+
+    def _unhook_impl(self):
+        self.module.set_processor(self.original_processor)
+
+    @property
+    def num_heat_maps(self):
+        return len(next(iter(self.heat_maps.values())))
+
+
+trace: Type[DiffusionHeatMapHooker] = DiffusionHeatMapHooker
diff --git a/scripts/daam/utils.py b/scripts/daam/utils.py
new file mode 100644
index 000000000..8cfde13f2
--- /dev/null
+++ b/scripts/daam/utils.py
@@ -0,0 +1,109 @@
+from functools import lru_cache
+from pathlib import Path
+import os
+import sys
+import random
+from typing import TypeVar
+
+import PIL.Image
+import matplotlib.pyplot as plt
+import numpy as np
+import spacy
+import torch
+import torch.nn.functional as F
+
+
+__all__ = ['set_seed', 'compute_token_merge_indices', 'plot_mask_heat_map', 'cached_nlp', 'cache_dir', 'auto_device', 'auto_autocast']
+
+
+T = TypeVar('T')
+
+
+def auto_device(obj: T = torch.device('cpu')) -> T:
+    if isinstance(obj, torch.device):
+        return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    if torch.cuda.is_available():
+        return obj.to('cuda')
+
+    return obj
+
+
+def auto_autocast(*args, **kwargs):
+    if not torch.cuda.is_available():
+        kwargs['enabled'] = False
+
+    return torch.cuda.amp.autocast(*args, **kwargs)
+
+
+def plot_mask_heat_map(im: PIL.Image.Image, heat_map: torch.Tensor, threshold: float = 0.4):
+    im = torch.from_numpy(np.array(im)).float() / 255
+    mask = (heat_map.squeeze() > threshold).float()
+    im = im * mask.unsqueeze(-1)
+    plt.imshow(im)
+
+
+def set_seed(seed: int) -> torch.Generator:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+    gen = torch.Generator(device=auto_device())
+    gen.manual_seed(seed)
+
+    return gen
+
+
+def cache_dir() -> Path:
+    # *nix
+    if os.name == 'posix' and sys.platform != 'darwin':
+        xdg = os.environ.get('XDG_CACHE_HOME', os.path.expanduser('~/.cache'))
+        return Path(xdg, 'daam')
+    elif sys.platform == 'darwin':
+        # Mac OS
+        return Path(os.path.expanduser('~'), 'Library/Caches/daam')
+    else:
+        # Windows
+        local = os.environ.get('LOCALAPPDATA', None) \
+                or os.path.expanduser('~\\AppData\\Local')
+        return Path(local, 'daam')
+
+
+def compute_token_merge_indices(tokenizer, prompt: str, word: str, word_idx: int = None, offset_idx: int = 0):
+    merge_idxs = []
+    tokens = tokenizer.tokenize(prompt.lower())
+    tokens = [x.replace('</w>', '') for x in tokens]  # New tokenizer uses wordpiece markers.
+
+    if word_idx is None:
+        word = word.lower()
+        search_tokens = [x.replace('</w>', '') for x in tokenizer.tokenize(word)]  # New tokenizer uses wordpiece markers.
+        start_indices = [x + offset_idx for x in range(len(tokens)) if tokens[x:x + len(search_tokens)] == search_tokens]
+
+        for indice in start_indices:
+            merge_idxs += [i + indice for i in range(0, len(search_tokens))]
+
+        if not merge_idxs:
+            raise ValueError(f'Search word {word} not found in prompt!')
+    else:
+        merge_idxs.append(word_idx)
+
+    return [x + 1 for x in merge_idxs], word_idx  # Offset by 1.
+
+
+nlp = None
+
+
+@lru_cache(maxsize=100000)
+def cached_nlp(prompt: str, type='en_core_web_md'):
+    global nlp
+
+    if nlp is None:
+        try:
+            nlp = spacy.load(type)
+        except OSError:
+            import os
+            os.system(f'python -m spacy download {type}')
+            nlp = spacy.load(type)
+
+    return nlp(prompt)
diff --git a/scripts/daam_ext.py b/scripts/daam_ext.py
new file mode 100644
index 000000000..5ae16a855
--- /dev/null
+++ b/scripts/daam_ext.py
@@ -0,0 +1,57 @@
+# https://github.com/genforce/ctrl-x
+
+import gradio as gr
+from installer import install
+from modules import shared, scripts_manager, processing
+
+
+COLORMAP = ['autumn', 'bone', 'jet', 'winter', 'rainbow', 'ocean', 'summer', 'spring', 'cool', 'hsv', 'pink', 'hot', 'parula', 'magma', 'inferno', 'plasma', 'viridis', 'cividis', 'twilight', 'shifted', 'turbo', 'deepgreen']
+
+
+class Script(scripts_manager.Script):
+    def title(self):
+        return 'DAAM: Diffusion Attentive Attribution Maps'
+
+    def show(self, is_img2img):
+        return not is_img2img
+
+    def ui(self, _is_img2img):
+        with gr.Row():
+            gr.HTML('<a href="https://github.com/castorini/daam">&nbsp DAAM: Diffusion Attentive Attribution Maps</a><br>')
+        with gr.Row():
+            append_images = gr.Checkbox(label='Append heatmaps to results', value=True, elem_id='daam_append_images')
+            colormap = gr.Dropdown(label='Colormap', choices=COLORMAP, value='jet', type='value', elem_id='daam_colormap')
+        return append_images, colormap
+
+    def run(self, p: processing.StableDiffusionProcessing, append_images, colormap): # pylint: disable=arguments-differ
+        c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
+        if shared.sd_model_type != 'sdxl':
+            shared.log.warning(f'DAAM: pipeline={c} required=StableDiffusionXLPipeline')
+            return None
+
+        install('thinc==8.3.4')
+        install('spacy==3.8.4')
+
+        from scripts import daam # pylint: disable=no-name-in-module
+        orig_prompt_attention = shared.opts.prompt_attention
+        shared.opts.data['prompt_attention'] = 'fixed'
+
+        # process
+        with daam.trace(shared.sd_model) as tc:
+            processed: processing.Processed = processing.process_images(p)
+            global_heat_map = tc.compute_global_heat_map()
+            shared.log.info(f'DAAM: prompt="{global_heat_map.prompt}" heatmaps={global_heat_map.heat_maps.shape}')
+
+            # word_heat_map = global_heat_map.compute_word_heat_map('woman')
+            parsed_heat_maps = global_heat_map.parsed_heat_maps()
+            if append_images:
+                image = processed.images[0]
+                for parsed_heat_map in parsed_heat_maps:
+                    if len(parsed_heat_map.token.text) > 1:
+                        shared.log.debug(f'DAAM: token="{parsed_heat_map.token.text}"')
+                        overlay = parsed_heat_map.word_heat_map.plot_overlay(image=image, color_normalize=True, cmap=colormap)
+                        processed.images.append(overlay)
+
+        # restore and return
+        shared.opts.data['prompt_attention'] = orig_prompt_attention
+        return processed

From c010111c236f71f6b799571a6d9bb10e057e684e Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Thu, 25 Sep 2025 14:06:57 +0900
Subject: [PATCH 051/230] install AMD's preview pytorch on Windows if user
 wants

---
 installer.py | 51 +++++++++++++++++++++++++++------------------------
 1 file changed, 27 insertions(+), 24 deletions(-)

diff --git a/installer.py b/installer.py
index f5e3bad73..f05a5bb32 100644
--- a/installer.py
+++ b/installer.py
@@ -717,36 +717,39 @@ def install_rocm_zluda():
         msg += f', using agent {device.name}'
     log.info(msg)
 
-    if sys.platform == "win32": # TODO install: enable ROCm for windows when available
+    if sys.platform == "win32":
         #check_python(supported_minors=[10, 11, 12, 13], reason='ZLUDA backend requires a Python version between 3.10 and 3.13')
 
-        if args.device_id is not None:
-            if os.environ.get('HIP_VISIBLE_DEVICES', None) is not None:
-                log.warning('Setting HIP_VISIBLE_DEVICES and --device-id at the same time may be mistake.')
-            os.environ['HIP_VISIBLE_DEVICES'] = args.device_id
-            del args.device_id
+        if args.use_rocm and args.experimental and (sys.version_info.major, sys.version_info.minor) == (3, 12): # TODO install: switch to pytorch source when it becomes available
+            torch_command = os.environ.get('TORCH_COMMAND', '--no-cache-dir https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torch-2.8.0a0%2Bgitfc14c65-cp312-cp312-win_amd64.whl https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torchvision-0.24.0a0%2Bc85f008-cp312-cp312-win_amd64.whl')
+        else:
+            if args.device_id is not None:
+                if os.environ.get('HIP_VISIBLE_DEVICES', None) is not None:
+                    log.warning('Setting HIP_VISIBLE_DEVICES and --device-id at the same time may be mistake.')
+                os.environ['HIP_VISIBLE_DEVICES'] = args.device_id
+                del args.device_id
 
-        error = None
-        from modules import zluda_installer
-        try:
-            if args.reinstall or zluda_installer.is_reinstall_needed():
-                zluda_installer.uninstall()
-            zluda_installer.install()
-            zluda_installer.set_default_agent(device)
-        except Exception as e:
-            error = e
-            log.warning(f'Failed to install ZLUDA: {e}')
-
-        if error is None:
+            error = None
+            from modules import zluda_installer
             try:
-                zluda_installer.load()
-                torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+cu118 torchvision==0.22.1+cu118 --index-url https://download.pytorch.org/whl/cu118')
+                if args.reinstall or zluda_installer.is_reinstall_needed():
+                    zluda_installer.uninstall()
+                zluda_installer.install()
+                zluda_installer.set_default_agent(device)
             except Exception as e:
                 error = e
-                log.warning(f'Failed to load ZLUDA: {e}')
-        if error is not None:
-            log.info('Using CPU-only torch')
-            torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
+                log.warning(f'Failed to install ZLUDA: {e}')
+
+            if error is None:
+                try:
+                    zluda_installer.load()
+                    torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+cu118 torchvision==0.22.1+cu118 --index-url https://download.pytorch.org/whl/cu118')
+                except Exception as e:
+                    error = e
+                    log.warning(f'Failed to load ZLUDA: {e}')
+            if error is not None:
+                log.info('Using CPU-only torch')
+                torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
     else:
         #check_python(supported_minors=[10, 11, 12, 13], reason='ROCm backend requires a Python version between 3.10 and 3.13')
 

From 27c3476da79707131ca0791c406ff86993f2f706 Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Thu, 25 Sep 2025 14:32:25 +0300
Subject: [PATCH 052/230] new SDXL Turbo cover, Qwen Edit 2509, some renaming

---
 html/previews.json                              |   2 +-
 html/reference.json                             |  14 +++++++-------
 models/Reference/Qwen--Qwen-Image-Edit-2509.jpg | Bin 0 -> 87125 bytes
 models/Reference/Qwen-Image-Distill.jpg         | Bin 76711 -> 0 bytes
 ...undai--playground-v2.5-1024px-aesthetic.jpg} | Bin
 models/Reference/stabilityai--sdxl-turbo.jpg    | Bin 0 -> 36684 bytes
 ...abilityai--stable-diffusion-xl-base-1.0.jpg} | Bin
 7 files changed, 8 insertions(+), 8 deletions(-)
 create mode 100644 models/Reference/Qwen--Qwen-Image-Edit-2509.jpg
 delete mode 100644 models/Reference/Qwen-Image-Distill.jpg
 rename models/Reference/{playgroundai--playground-v2_5-1024px-aesthetic.jpg => playgroundai--playground-v2.5-1024px-aesthetic.jpg} (100%)
 create mode 100644 models/Reference/stabilityai--sdxl-turbo.jpg
 rename models/Reference/{sd_xl_base_1.0.jpg => stabilityai--stable-diffusion-xl-base-1.0.jpg} (100%)

diff --git a/html/previews.json b/html/previews.json
index e27e2513f..07af08547 100644
--- a/html/previews.json
+++ b/html/previews.json
@@ -1,6 +1,6 @@
 {
   "sd-v21-512-ema": "models/Reference/stabilityai--stable-diffusion-2-1-base.jpg",
-  "stabilityai--stable-diffusion-xl-base-1.0": "models/Reference/sd_xl_base_1.0.jpg",
+  "stabilityai--stable-diffusion-xl-base-1.0": "models/Reference/stabilityai--stable-diffusion-xl-base-1.0.jpg",
   "stabilityai--stable-diffusion-3-medium-diffusers": "models/Reference/stabilityai--stable-diffusion-3.jpg",
   "stabilityai--stable-diffusion-3.5-medium": "models/Reference/stabilityai--stable-diffusion-3_5-medium.jpg",
   "stabilityai--stable-diffusion-3.5-large": "models/Reference/stabilityai--stable-diffusion-3_5-large.jpg",
diff --git a/html/reference.json b/html/reference.json
index 530570913..c8886af6b 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -57,19 +57,19 @@
   },
   "StabilityAI StableDiffusion XL": {
     "path": "stabilityai/stable-diffusion-xl-base-1.0",
-    "preview": "sd_xl_base_1.0.jpg",
-    "desc": "Stable Diffusion XL (SDXL) is the latest AI image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition compared to previous SD models, including SD 2.1. It can make realistic faces, legible text within the images, and better image composition, all while using shorter and simpler prompts at a greatly increased base resolution of 1024x1024. Just like its predecessors, SDXL has the ability to generate image variations using image-to-image prompting, inpainting (reimagining of the selected parts of an image), and outpainting (creating new parts that lie outside the image borders).",
+    "preview": "stabilityai--stable-diffusion-xl-base-1.0.jpg",
+    "desc": "Stable Diffusion XL (SDXL) is AI image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition compared to previous SD models, including SD 2.1. It can make realistic faces and better image composition, all while using shorter and simpler prompts at a greatly increased base resolution of 1024x1024. Just like its predecessors, SDXL has the ability to generate image variations using image-to-image prompting, inpainting (reimagining of the selected parts of an image), and outpainting (creating new parts that lie outside the image borders).",
     "skip": true,
     "variant": "fp16",
     "extras": ""
   },
   "StabilityAI StableDiffusion XL Turbo": {
     "path": "stabilityai/sdxl-turbo",
-    "preview": "sd_xl_base_1.0.jpg",
-    "desc": "SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.",
+    "preview": "stabilityai--sdxl-turbo.jpg",
+    "desc": "SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a 1-4 steps.",
     "skip": true,
     "variant": "fp16",
-    "extras": ""
+    "extras": "steps: 4, cfg_scale: 0.0"
   },
   "StabilityAI Stable Cascade": {
     "path": "huggingface/stabilityai/stable-cascade",
@@ -172,7 +172,7 @@
   },
   "Qwen-Image-Edit-2509": {
     "path": "Qwen/Qwen-Image-Edit-2509",
-    "preview": "Qwen--Qwen-Image-Edit.jpg",
+    "preview": "Qwen--Qwen-Image-Edit-2509.jpg",
     "desc": "Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.",
     "skip": true,
     "extras": ""
@@ -609,7 +609,7 @@
   "Playground v2.5": {
     "path": "playgroundai/playground-v2.5-1024px-aesthetic",
     "desc": "Playground v2.5 is a diffusion-based text-to-image generative model, and a successor to Playground v2. Playground v2.5 is the state-of-the-art open-source model in aesthetic quality.",
-    "preview": "playgroundai--playground-v2_5-1024px-aesthetic.jpg",
+    "preview": "playgroundai--playground-v2.5-1024px-aesthetic.jpg",
     "variant": "fp16",
     "extras": "sampler: DPM++ 2M EDM"
   },
diff --git a/models/Reference/Qwen--Qwen-Image-Edit-2509.jpg b/models/Reference/Qwen--Qwen-Image-Edit-2509.jpg
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HcmV?d00001

diff --git a/models/Reference/sd_xl_base_1.0.jpg b/models/Reference/stabilityai--stable-diffusion-xl-base-1.0.jpg
similarity index 100%
rename from models/Reference/sd_xl_base_1.0.jpg
rename to models/Reference/stabilityai--stable-diffusion-xl-base-1.0.jpg

From 9cee2cd1ed1b1360fed2416e6da99b18561427aa Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 25 Sep 2025 13:44:56 -0400
Subject: [PATCH 053/230] fix metadata on batched images

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                   |   3 +-
 modules/processing_info.py     |   8 +-
 modules/ui_common.py           |  20 +-
 modules/ui_control.py          | 318 +-------------------------------
 modules/ui_control_elements.py | 321 +++++++++++++++++++++++++++++++++
 5 files changed, 343 insertions(+), 327 deletions(-)
 create mode 100644 modules/ui_control_elements.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bcfa7def8..45ac06a67 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-24
+## Update for 2025-09-25
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -52,6 +52,7 @@
   - **video** new LTX model selection  
   - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
 - **Fixes**
+  - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
   - benchmark: remove forced sampler from system info benchmark  
   - xyz-grid: fix xyz grid with random seeds  
diff --git a/modules/processing_info.py b/modules/processing_info.py
index 694fd52b3..e1d4d6379 100644
--- a/modules/processing_info.py
+++ b/modules/processing_info.py
@@ -31,13 +31,13 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
     if all_subseeds is None:
         all_subseeds = p.all_subseeds or [p.subseed]
     while len(all_prompts) <= index:
-        all_prompts.append(all_prompts[-1])
+        all_prompts.insert(0, p.prompt)
     while len(all_seeds) <= index:
-        all_seeds.append(all_seeds[-1])
+        all_seeds.insert(0, int(p.seed))
     while len(all_subseeds) <= index:
-        all_subseeds.append(all_subseeds[-1])
+        all_subseeds.insert(0, int(p.subseed))
     while len(all_negative_prompts) <= index:
-        all_negative_prompts.append(all_negative_prompts[-1])
+        all_negative_prompts.insert(0, p.negative_prompt)
     comment = ', '.join(comments) if comments is not None and type(comments) is list else None
     ops = list(set(p.ops))
     args = {
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 1c5499cf7..fa0d1322f 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -5,7 +5,7 @@ import shutil
 import platform
 import subprocess
 import gradio as gr
-from modules import call_queue, shared, errors, ui_sections, ui_symbols, ui_components, generation_parameters_copypaste, images, scripts_manager, script_callbacks, infotext
+from modules import call_queue, shared, errors, ui_sections, ui_symbols, ui_components, generation_parameters_copypaste, images, scripts_manager, script_callbacks, infotext, processing
 
 
 folder_symbol = ui_symbols.folder
@@ -19,6 +19,8 @@ def gr_show(visible=True):
 
 def update_generation_info(generation_info, html_info, img_index):
     try:
+        if len(generation_info) == 0 and processing.processed is not None:
+            generation_info = processing.processed.js() or {}
         generation_json = json.loads(generation_info)
         if len(generation_json["infotexts"]) == 0:
             return html_info, 'no infotexts found'
@@ -29,8 +31,8 @@ def update_generation_info(generation_info, html_info, img_index):
         info = generation_json["infotexts"][img_index]
         html_info_formatted = infotext_to_html(info)
         return html_info, html_info_formatted
-    except Exception:
-        pass
+    except Exception as e:
+        shared.log.warning(f'Update info: info="{generation_info}" {e}')
     return html_info, html_info
 
 
@@ -238,7 +240,7 @@ def open_folder(result_gallery, gallery_index = 0):
             subprocess.Popen(["xdg-open", path]) # pylint: disable=consider-using-with
 
 
-def create_output_panel(tabname, preview=True, prompt=None, height=None, transfer=True, scale=1):
+def create_output_panel(tabname, preview=True, prompt=None, height=None, transfer=True, scale=1, result_info=None):
     with gr.Column(variant='panel', elem_id=f"{tabname}_results", scale=scale):
         with gr.Group(elem_id=f"{tabname}_gallery_container"):
             if tabname == "txt2img":
@@ -286,10 +288,11 @@ def create_output_panel(tabname, preview=True, prompt=None, height=None, transfe
                 generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
                 generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
 
+                result_field = result_info or html_info_formatted
                 generation_info_button.click(fn=update_generation_info, show_progress=False,
                     _js="(x, y, z) => [x, y, selected_gallery_index()]", # triggered on gallery change from js
                     inputs=[generation_info, html_info, html_info],
-                    outputs=[html_info, html_info_formatted],
+                    outputs=[html_info, result_field],
                 )
                 save.click(fn=call_queue.wrap_gradio_call(save_files), show_progress=False,
                     _js="(x, y, z, i) => [x, y, z, selected_gallery_index()]",
@@ -354,11 +357,12 @@ def create_override_inputs(tab): # pylint: disable=unused-argument
 def reuse_seed(seed_component: gr.Number, reuse_button: gr.Button, subseed:bool=False):
     def reuse_click(selected_gallery_index):
         selected_gallery_index = int(selected_gallery_index)
-        from modules import processing
         if processing.processed is None:
             seed = -1
-        elif selected_gallery_index >= len(processing.processed.all_seeds):
-            selected_gallery_index -= len(processing.processed.images) - len(processing.processed.all_seeds) # if we have more images than seeds it is likely the grid image
+        elif len(processing.processed.images) > len(processing.processed.all_seeds): # if we have more images than seeds it is likely the grid image
+            selected_gallery_index -= (len(processing.processed.images) - len(processing.processed.all_seeds))
+            seed = processing.processed.all_seeds[selected_gallery_index] if not subseed else processing.processed.all_subseeds[selected_gallery_index]
+        elif selected_gallery_index <= len(processing.processed.all_seeds):
             seed = processing.processed.all_seeds[selected_gallery_index] if not subseed else processing.processed.all_subseeds[selected_gallery_index]
         elif len(processing.processed.all_seeds) > 0:
             seed = processing.processed.all_seeds[0] if not subseed else processing.processed.all_subseeds[0]
diff --git a/modules/ui_control.py b/modules/ui_control.py
index 0fe361aa2..ecfe1fa01 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -1,15 +1,8 @@
 import os
 import time
 import gradio as gr
-import matplotlib.pyplot as plt
 from modules.control import unit
-from modules.control import processors # patrickvonplaten controlnet_aux
-from modules.control.units import controlnet # lllyasviel ControlNet
-from modules.control.units import xs # vislearn ControlNet-XS
-from modules.control.units import lite # vislearn ControlNet-XS
-from modules.control.units import t2iadapter # TencentARC T2I-Adapter
-from modules.control.units import reference # reference pipeline
-from modules import errors, shared, progress, ui_components, ui_symbols, ui_common, ui_sections, generation_parameters_copypaste, call_queue, scripts_manager, masking, images, processing_vae, timer # pylint: disable=ungrouped-imports
+from modules import errors, shared, progress, ui_common, ui_sections, generation_parameters_copypaste, call_queue, scripts_manager, masking, images, processing_vae, timer # pylint: disable=ungrouped-imports
 from modules import ui_control_helpers as helpers
 
 
@@ -218,7 +211,7 @@ def create_ui(_blocks: gr.Blocks=None):
                     gr.HTML('<span id="control-output-button">Output</p>')
                     with gr.Tabs(elem_classes=['control-tabs'], elem_id='control-tab-output') as output_tabs:
                         with gr.Tab('Gallery', id='out-gallery'):
-                            output_gallery, _output_gen_info, _output_html_info, _output_html_info_formatted, output_html_log = ui_common.create_output_panel("control", preview=False, prompt=prompt, height=gr_height)
+                            output_gallery, output_gen_info, _output_html_info, _output_html_info_formatted, output_html_log = ui_common.create_output_panel("control", preview=False, prompt=prompt, height=gr_height, result_info=result_txt)
                         with gr.Tab('Image', id='out-image'):
                             output_image = gr.Image(label="Output", show_label=False, type="pil", interactive=False, tool="editor", height=gr_height, elem_id='control_output_image', elem_classes=['control-image'])
                         with gr.Tab('Video', id='out-video'):
@@ -229,312 +222,9 @@ def create_ui(_blocks: gr.Blocks=None):
                         with gr.Tab('Preview', id='preview-image') as _tab_preview:
                             preview_process = gr.Image(label="Preview", show_label=False, type="pil", interactive=False, height=gr_height, visible=True, elem_id='control_preview', elem_classes=['control-image'])
 
-            with gr.Accordion('Control elements', open=False, elem_id="control_elements"):
-                with gr.Tabs(elem_id='control-tabs') as _tabs_control_type:
 
-                    with gr.Tab('ControlNet') as _tab_controlnet:
-                        gr.HTML('<a href="https://github.com/lllyasviel/ControlNet">ControlNet</a>')
-                        with gr.Row():
-                            extra_controls = [
-                                gr.Checkbox(label="Guess mode", value=False, scale=3),
-                            ]
-                            num_controlnet_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
-                        controlnet_ui_units = [] # list of hidable accordions
-                        for i in range(max_units):
-                            enabled = True if i==0 else False
-                            with gr.Accordion(f'ControlNet unit {i+1}', visible= i < num_controlnet_units.value, elem_classes='control-unit') as unit_ui:
-                                with gr.Row():
-                                    enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
-                                    process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
-                                    model_id = gr.Dropdown(label="ControlNet", choices=controlnet.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
-                                    ui_common.create_refresh_button(model_id, controlnet.list_models, lambda: {"choices": controlnet.list_models(refresh=True)}, f'controlnet_models_{i}_refresh')
-                                    control_mode = gr.Dropdown(label="CN Mode", choices=['default'], value='default', visible=False, elem_id=f'control_unit-{i}-mode')
-                                    model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=2.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
-                                    control_start = gr.Slider(label="CN Start", minimum=0.0, maximum=1.0, step=0.05, value=0, elem_id=f'control_unit-{i}-start')
-                                    control_end = gr.Slider(label="CN End", minimum=0.0, maximum=1.0, step=0.05, value=1.0, elem_id=f'control_unit-{i}-end')
-                                    control_tile = gr.Dropdown(label="CN Tiles", choices=[x.strip() for x in shared.opts.control_tiles.split(',') if 'x' in x], value='1x1', visible=False, elem_id=f'control_unit-{i}-tile')
-                                    reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'controlnet_unit-{i}-reset')
-                                    image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'controlnet_unit-{i}-upload')
-                                    image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'controlnet_unit-{i}-reuse')
-                                    btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'controlnet_unit-{i}-preview')
-                                    image_preview = gr.Image(label="Input", type="pil", height=128, width=128, visible=False, interactive=True, show_label=False, show_download_button=False, container=False, elem_id=f'controlnet_unit-{i}-override')
-                            controlnet_ui_units.append(unit_ui)
-                            units.append(unit.Unit(
-                                unit_type = 'controlnet',
-                                index = i,
-                                enabled = enabled,
-                                result_txt = result_txt,
-                                enabled_cb = enabled_cb,
-                                reset_btn = reset_btn,
-                                process_id = process_id,
-                                model_id = model_id,
-                                model_strength = model_strength,
-                                preview_process = preview_process,
-                                preview_btn = btn_preview,
-                                image_upload = image_upload,
-                                image_reuse = image_reuse,
-                                image_preview = image_preview,
-                                control_start = control_start,
-                                control_end = control_end,
-                                control_mode = control_mode,
-                                control_tile = control_tile,
-                                extra_controls = extra_controls,
-                                )
-                            )
-                            if i == 0:
-                                units[-1].enabled = True # enable first unit in group
-                        num_controlnet_units.change(fn=helpers.display_units, inputs=[num_controlnet_units], outputs=controlnet_ui_units)
-
-                    with gr.Tab('T2I Adapter') as _tab_t2iadapter:
-                        gr.HTML('<a href="https://github.com/TencentARC/T2I-Adapter">T2I-Adapter</a>')
-                        with gr.Row():
-                            extra_controls = [
-                                gr.Slider(label="Control factor", minimum=0.0, maximum=1.0, step=0.05, value=1.0, scale=3),
-                            ]
-                            num_adapter_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
-                        adapter_ui_units = [] # list of hidable accordions
-                        for i in range(max_units):
-                            enabled = True if i==0 else False
-                            with gr.Accordion(f'T2I-Adapter unit {i+1}', visible= i < num_adapter_units.value, elem_classes='control-unit') as unit_ui:
-                                with gr.Row():
-                                    enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
-                                    process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
-                                    model_id = gr.Dropdown(label="Adapter", choices=t2iadapter.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
-                                    ui_common.create_refresh_button(model_id, t2iadapter.list_models, lambda: {"choices": t2iadapter.list_models(refresh=True)}, f'adapter_models_{i}_refresh')
-                                    model_strength = gr.Slider(label="T2I Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
-                                    reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'adapter_unit-{i}-reset')
-                                    image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'adapter_unit-{i}-upload')
-                                    image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'adapter_unit-{i}-reuse')
-                                    btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'adapter_unit-{i}-preview')
-                                    image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'adapter_unit-{i}-override')
-                            adapter_ui_units.append(unit_ui)
-                            units.append(unit.Unit(
-                                unit_type = 't2i adapter',
-                                index = i,
-                                enabled = enabled,
-                                result_txt = result_txt,
-                                enabled_cb = enabled_cb,
-                                reset_btn = reset_btn,
-                                process_id = process_id,
-                                model_id = model_id,
-                                model_strength = model_strength,
-                                preview_process = preview_process,
-                                preview_btn = btn_preview,
-                                image_upload = image_upload,
-                                image_reuse = image_reuse,
-                                image_preview = image_preview,
-                                extra_controls = extra_controls,
-                                )
-                            )
-                            if i == 0:
-                                units[-1].enabled = True # enable first unit in group
-                        num_adapter_units.change(fn=helpers.display_units, inputs=[num_adapter_units], outputs=adapter_ui_units)
-
-                    with gr.Tab('XS') as _tab_controlnetxs:
-                        gr.HTML('<a href="https://vislearn.github.io/ControlNet-XS/">ControlNet XS</a>')
-                        with gr.Row():
-                            extra_controls = [
-                                gr.Slider(label="Time embedding mix", minimum=0.0, maximum=1.0, step=0.05, value=0.0, scale=3)
-                            ]
-                            num_controlnet_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
-                        controlnetxs_ui_units = [] # list of hidable accordions
-                        for i in range(max_units):
-                            enabled = True if i==0 else False
-                            with gr.Accordion(f'ControlNet-XS unit {i+1}', visible= i < num_controlnet_units.value, elem_classes='control-unit') as unit_ui:
-                                with gr.Row():
-                                    enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
-                                    process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
-                                    model_id = gr.Dropdown(label="ControlNet-XS", choices=xs.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
-                                    ui_common.create_refresh_button(model_id, xs.list_models, lambda: {"choices": xs.list_models(refresh=True)}, f'xs_models_{i}_refresh')
-                                    model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
-                                    control_start = gr.Slider(label="Start", minimum=0.0, maximum=1.0, step=0.05, value=0, elem_id=f'control_unit-{i}-start')
-                                    control_end = gr.Slider(label="End", minimum=0.0, maximum=1.0, step=0.05, value=1.0, elem_id=f'control_unit-{i}-end')
-                                    reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'controlnetxs_unit-{i}-reset')
-                                    image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'controlnetxs_unit-{i}-upload')
-                                    image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'controlnetxs_unit-{i}-reuse')
-                                    btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'controlnetxs_unit-{i}-preview')
-                                    image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'controlnetxs_unit-{i}-override')
-                            controlnetxs_ui_units.append(unit_ui)
-                            units.append(unit.Unit(
-                                unit_type = 'xs',
-                                index = i,
-                                enabled = enabled,
-                                result_txt = result_txt,
-                                enabled_cb = enabled_cb,
-                                reset_btn = reset_btn,
-                                process_id = process_id,
-                                model_id = model_id,
-                                model_strength = model_strength,
-                                preview_process = preview_process,
-                                preview_btn = btn_preview,
-                                image_upload = image_upload,
-                                image_reuse = image_reuse,
-                                image_preview = image_preview,
-                                control_start = control_start,
-                                control_end = control_end,
-                                extra_controls = extra_controls,
-                                )
-                            )
-                            if i == 0:
-                                units[-1].enabled = True # enable first unit in group
-                        num_controlnet_units.change(fn=helpers.display_units, inputs=[num_controlnet_units], outputs=controlnetxs_ui_units)
-
-                    with gr.Tab('Lite') as _tab_lite:
-                        gr.HTML('<a href="https://huggingface.co/kohya-ss/controlnet-lllite">Control LLLite</a>')
-                        with gr.Row():
-                            extra_controls = [
-                            ]
-                            num_lite_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
-                        lite_ui_units = [] # list of hidable accordions
-                        for i in range(max_units):
-                            enabled = True if i==0 else False
-                            with gr.Accordion(f'Control-LLLite unit {i+1}', visible= i < num_lite_units.value, elem_classes='control-unit') as unit_ui:
-                                with gr.Row():
-                                    enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
-                                    process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
-                                    model_id = gr.Dropdown(label="Model", choices=lite.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
-                                    ui_common.create_refresh_button(model_id, lite.list_models, lambda: {"choices": lite.list_models(refresh=True)}, f'lite_models_{i}_refresh')
-                                    model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
-                                    reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'lite_unit-{i}-reset')
-                                    image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'lite_unit-{i}-upload')
-                                    image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'lite_unit-{i}-reuse')
-                                    image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'lite_unit-{i}-override')
-                                    btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'lite_unit-{i}-preview')
-                            lite_ui_units.append(unit_ui)
-                            units.append(unit.Unit(
-                                unit_type = 'lite',
-                                index = i,
-                                enabled = enabled,
-                                result_txt = result_txt,
-                                enabled_cb = enabled_cb,
-                                reset_btn = reset_btn,
-                                process_id = process_id,
-                                model_id = model_id,
-                                model_strength = model_strength,
-                                preview_process = preview_process,
-                                preview_btn = btn_preview,
-                                image_upload = image_upload,
-                                image_reuse = image_reuse,
-                                image_preview = image_preview,
-                                extra_controls = extra_controls,
-                                )
-                            )
-                            if i == 0:
-                                units[-1].enabled = True # enable first unit in group
-                        num_lite_units.change(fn=helpers.display_units, inputs=[num_lite_units], outputs=lite_ui_units)
-
-                    with gr.Tab('Reference') as _tab_reference:
-                        gr.HTML('<a href="https://github.com/Mikubill/sd-webui-controlnet/discussions/1236">ControlNet reference-only control</a>')
-                        with gr.Row():
-                            extra_controls = [
-                                gr.Radio(label="Reference context", choices=['Attention', 'Adain', 'Attention Adain'], value='Attention', interactive=True),
-                                gr.Slider(label="Style fidelity", minimum=0.0, maximum=1.0, step=0.05, value=0.5, interactive=True), # prompt vs control importance
-                                gr.Slider(label="Reference query weight", minimum=0.0, maximum=1.0, step=0.05, value=1.0, interactive=True),
-                                gr.Slider(label="Reference adain weight", minimum=0.0, maximum=2.0, step=0.05, value=1.0, interactive=True),
-                            ]
-                        for i in range(1): # can only have one reference unit
-                            enabled = True if i==0 else False
-                            with gr.Accordion(f'Reference unit {i+1}', visible=True, elem_classes='control-unit') as unit_ui:
-                                with gr.Row():
-                                    enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
-                                    model_id = gr.Dropdown(label="Reference", choices=reference.list_models(), value='Reference', visible=False, elem_id=f'control_unit-{i}-model_name')
-                                    model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, visible=False, elem_id=f'control_unit-{i}-strength')
-                                    reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'reference_unit-{i}-reset')
-                                    image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'reference_unit-{i}-upload')
-                                    image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'reference_unit-{i}-reuse')
-                                    image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'reference_unit-{i}-override')
-                                    btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'reference_unit-{i}-preview')
-                            units.append(unit.Unit(
-                                unit_type = 'reference',
-                                index = i,
-                                enabled = enabled,
-                                result_txt = result_txt,
-                                enabled_cb = enabled_cb,
-                                reset_btn = reset_btn,
-                                process_id = process_id,
-                                model_id = model_id,
-                                model_strength = model_strength,
-                                preview_process = preview_process,
-                                preview_btn = btn_preview,
-                                image_upload = image_upload,
-                                image_reuse = image_reuse,
-                                image_preview = image_preview,
-                                extra_controls = extra_controls,
-                                )
-                            )
-                            if i == 0:
-                                units[-1].enabled = True # enable first unit in group
-
-                with gr.Accordion('Control settings', open=False, elem_classes=['control-settings']) as _tab_settings:
-                    with gr.Group(elem_classes=['processor-group']):
-                        settings = []
-                        with gr.Accordion('Global', open=True, elem_classes=['processor-settings']):
-                            control_hires = gr.Checkbox(label="Use control during hires", value=shared.opts.control_hires, elem_id='control_hires')
-                            def set_control_hires(value):
-                                shared.opts.control_active = value
-                            control_hires.change(fn=set_control_hires, inputs=[control_hires], outputs=[])
-                            control_max_units = gr.Slider(label="Maximum units", minimum=1, maximum=10, step=1, value=shared.opts.control_max_units, elem_id='control_max_units')
-                            def set_control_max_units(value):
-                                shared.opts.control_max_units = value
-                            control_max_units.change(fn=set_control_max_units, inputs=[control_max_units], outputs=[])
-                            control_tiles = gr.Textbox(label="Tiling options", value=shared.opts.control_tiles, elem_id='control_tiles')
-                            def set_control_tiles(value):
-                                shared.opts.control_tiles = value
-                            control_tiles.change(fn=set_control_tiles, inputs=[control_tiles], outputs=[])
-                            control_move_processor = gr.Checkbox(label="Move processor to CPU after use", value=shared.opts.control_move_processor, elem_id='control_move_processor')
-                            def set_control_move_processor(value):
-                                shared.opts.control_move_processor = value
-                            control_move_processor.change(fn=set_control_move_processor, inputs=[control_move_processor], outputs=[])
-                            control_unload_processor = gr.Checkbox(label="Unload processor after use", value=shared.opts.control_unload_processor, elem_id='control_unload_processor')
-                            def set_control_unload_processor(value):
-                                shared.opts.control_unload_processor = value
-                            control_unload_processor.change(fn=set_control_unload_processor, inputs=[control_unload_processor], outputs=[])
-
-                        with gr.Accordion('HED', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Scribble", value=False))
-                        with gr.Accordion('Midas depth', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Slider(label="Background threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.1))
-                            settings.append(gr.Checkbox(label="Depth and normal", value=False))
-                        with gr.Accordion('MLSD', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Slider(label="Score threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.1))
-                            settings.append(gr.Slider(label="Distance threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.1))
-                        with gr.Accordion('OpenBody', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Body", value=True))
-                            settings.append(gr.Checkbox(label="Hands", value=False))
-                            settings.append(gr.Checkbox(label="Face", value=False))
-                        with gr.Accordion('PidiNet', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Scribble", value=False))
-                            settings.append(gr.Checkbox(label="Apply filter", value=False))
-                        with gr.Accordion('LineArt', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Coarse", value=False))
-                        with gr.Accordion('Leres Depth', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Boost", value=False))
-                            settings.append(gr.Slider(label="Near threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.0))
-                            settings.append(gr.Slider(label="Depth threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.0))
-                        with gr.Accordion('MediaPipe Face', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Slider(label="Max faces", minimum=1, maximum=10, step=1, value=1))
-                            settings.append(gr.Slider(label="Face confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.5))
-                        with gr.Accordion('Canny', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Slider(label="Low threshold", minimum=0, maximum=1000, step=1, value=100))
-                            settings.append(gr.Slider(label="High threshold", minimum=0, maximum=1000, step=1, value=200))
-                        with gr.Accordion('DWPose', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Radio(label="Pose Model", choices=['Tiny', 'Medium', 'Large'], value='Tiny'))
-                            settings.append(gr.Slider(label="Pose confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.3))
-                        with gr.Accordion('SegmentAnything', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Radio(label="Segment Model", choices=['Base', 'Large'], value='Base'))
-                        with gr.Accordion('Edge', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Parameter free", value=True))
-                            settings.append(gr.Radio(label="Edge mode", choices=['edge', 'gradient'], value='edge'))
-                        with gr.Accordion('Zoe Depth', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Checkbox(label="Gamma corrected", value=False))
-                        with gr.Accordion('Marigold Depth', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Dropdown(label="Color map", choices=['None'] + plt.colormaps(), value='None'))
-                            settings.append(gr.Slider(label="Denoising steps", minimum=1, maximum=99, step=1, value=10))
-                            settings.append(gr.Slider(label="Ensemble size", minimum=1, maximum=99, step=1, value=10))
-                        with gr.Accordion('Depth Anything', open=True, elem_classes=['processor-settings']):
-                            settings.append(gr.Dropdown(label="Depth map", choices=['none'] + masking.COLORMAP, value='inferno'))
-                        for setting in settings:
-                            setting.change(fn=processors.update_settings, inputs=settings, outputs=[])
+            from modules.ui_control_elements import create_ui_elements
+            create_ui_elements(units, result_txt, preview_process)
 
             with gr.Row(elem_id="control_script_container"):
                 input_script_args = scripts_manager.scripts_current.setup_ui(parent='control', accordion=True)
diff --git a/modules/ui_control_elements.py b/modules/ui_control_elements.py
new file mode 100644
index 000000000..21f7b4ca5
--- /dev/null
+++ b/modules/ui_control_elements.py
@@ -0,0 +1,321 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+from modules.control import unit
+from modules.control import processors # patrickvonplaten controlnet_aux
+from modules.control.units import controlnet # lllyasviel ControlNet
+from modules.control.units import xs # vislearn ControlNet-XS
+from modules.control.units import lite # vislearn ControlNet-XS
+from modules.control.units import t2iadapter # TencentARC T2I-Adapter
+from modules.control.units import reference # reference pipeline
+from modules import shared, ui_components, ui_symbols, ui_common, masking # pylint: disable=ungrouped-imports
+from modules import ui_control_helpers as helpers
+
+
+def create_ui_elements(units, result_txt, preview_process):
+    max_units = shared.opts.control_max_units
+    with gr.Accordion('Control elements', open=False, elem_id="control_elements"):
+        with gr.Tabs(elem_id='control-tabs') as _tabs_control_type:
+
+            with gr.Tab('ControlNet') as _tab_controlnet:
+                gr.HTML('<a href="https://github.com/lllyasviel/ControlNet">ControlNet</a>')
+                with gr.Row():
+                    extra_controls = [
+                        gr.Checkbox(label="Guess mode", value=False, scale=3),
+                    ]
+                    num_controlnet_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
+                controlnet_ui_units = [] # list of hidable accordions
+                for i in range(max_units):
+                    enabled = True if i==0 else False
+                    with gr.Accordion(f'ControlNet unit {i+1}', visible= i < num_controlnet_units.value, elem_classes='control-unit') as unit_ui:
+                        with gr.Row():
+                            enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
+                            process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
+                            model_id = gr.Dropdown(label="ControlNet", choices=controlnet.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
+                            ui_common.create_refresh_button(model_id, controlnet.list_models, lambda: {"choices": controlnet.list_models(refresh=True)}, f'controlnet_models_{i}_refresh')
+                            control_mode = gr.Dropdown(label="CN Mode", choices=['default'], value='default', visible=False, elem_id=f'control_unit-{i}-mode')
+                            model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=2.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
+                            control_start = gr.Slider(label="CN Start", minimum=0.0, maximum=1.0, step=0.05, value=0, elem_id=f'control_unit-{i}-start')
+                            control_end = gr.Slider(label="CN End", minimum=0.0, maximum=1.0, step=0.05, value=1.0, elem_id=f'control_unit-{i}-end')
+                            control_tile = gr.Dropdown(label="CN Tiles", choices=[x.strip() for x in shared.opts.control_tiles.split(',') if 'x' in x], value='1x1', visible=False, elem_id=f'control_unit-{i}-tile')
+                            reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'controlnet_unit-{i}-reset')
+                            image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'controlnet_unit-{i}-upload')
+                            image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'controlnet_unit-{i}-reuse')
+                            btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'controlnet_unit-{i}-preview')
+                            image_preview = gr.Image(label="Input", type="pil", height=128, width=128, visible=False, interactive=True, show_label=False, show_download_button=False, container=False, elem_id=f'controlnet_unit-{i}-override')
+                    controlnet_ui_units.append(unit_ui)
+                    units.append(unit.Unit(
+                        unit_type = 'controlnet',
+                        index = i,
+                        enabled = enabled,
+                        result_txt = result_txt,
+                        enabled_cb = enabled_cb,
+                        reset_btn = reset_btn,
+                        process_id = process_id,
+                        model_id = model_id,
+                        model_strength = model_strength,
+                        preview_process = preview_process,
+                        preview_btn = btn_preview,
+                        image_upload = image_upload,
+                        image_reuse = image_reuse,
+                        image_preview = image_preview,
+                        control_start = control_start,
+                        control_end = control_end,
+                        control_mode = control_mode,
+                        control_tile = control_tile,
+                        extra_controls = extra_controls,
+                        )
+                    )
+                    if i == 0:
+                        units[-1].enabled = True # enable first unit in group
+                num_controlnet_units.change(fn=helpers.display_units, inputs=[num_controlnet_units], outputs=controlnet_ui_units)
+
+            with gr.Tab('T2I Adapter') as _tab_t2iadapter:
+                gr.HTML('<a href="https://github.com/TencentARC/T2I-Adapter">T2I-Adapter</a>')
+                with gr.Row():
+                    extra_controls = [
+                        gr.Slider(label="Control factor", minimum=0.0, maximum=1.0, step=0.05, value=1.0, scale=3),
+                    ]
+                    num_adapter_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
+                adapter_ui_units = [] # list of hidable accordions
+                for i in range(max_units):
+                    enabled = True if i==0 else False
+                    with gr.Accordion(f'T2I-Adapter unit {i+1}', visible= i < num_adapter_units.value, elem_classes='control-unit') as unit_ui:
+                        with gr.Row():
+                            enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
+                            process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
+                            model_id = gr.Dropdown(label="Adapter", choices=t2iadapter.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
+                            ui_common.create_refresh_button(model_id, t2iadapter.list_models, lambda: {"choices": t2iadapter.list_models(refresh=True)}, f'adapter_models_{i}_refresh')
+                            model_strength = gr.Slider(label="T2I Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
+                            reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'adapter_unit-{i}-reset')
+                            image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'adapter_unit-{i}-upload')
+                            image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'adapter_unit-{i}-reuse')
+                            btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'adapter_unit-{i}-preview')
+                            image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'adapter_unit-{i}-override')
+                    adapter_ui_units.append(unit_ui)
+                    units.append(unit.Unit(
+                        unit_type = 't2i adapter',
+                        index = i,
+                        enabled = enabled,
+                        result_txt = result_txt,
+                        enabled_cb = enabled_cb,
+                        reset_btn = reset_btn,
+                        process_id = process_id,
+                        model_id = model_id,
+                        model_strength = model_strength,
+                        preview_process = preview_process,
+                        preview_btn = btn_preview,
+                        image_upload = image_upload,
+                        image_reuse = image_reuse,
+                        image_preview = image_preview,
+                        extra_controls = extra_controls,
+                        )
+                    )
+                    if i == 0:
+                        units[-1].enabled = True # enable first unit in group
+                num_adapter_units.change(fn=helpers.display_units, inputs=[num_adapter_units], outputs=adapter_ui_units)
+
+            with gr.Tab('XS') as _tab_controlnetxs:
+                gr.HTML('<a href="https://vislearn.github.io/ControlNet-XS/">ControlNet XS</a>')
+                with gr.Row():
+                    extra_controls = [
+                        gr.Slider(label="Time embedding mix", minimum=0.0, maximum=1.0, step=0.05, value=0.0, scale=3)
+                    ]
+                    num_controlnet_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
+                controlnetxs_ui_units = [] # list of hidable accordions
+                for i in range(max_units):
+                    enabled = True if i==0 else False
+                    with gr.Accordion(f'ControlNet-XS unit {i+1}', visible= i < num_controlnet_units.value, elem_classes='control-unit') as unit_ui:
+                        with gr.Row():
+                            enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
+                            process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
+                            model_id = gr.Dropdown(label="ControlNet-XS", choices=xs.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
+                            ui_common.create_refresh_button(model_id, xs.list_models, lambda: {"choices": xs.list_models(refresh=True)}, f'xs_models_{i}_refresh')
+                            model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
+                            control_start = gr.Slider(label="Start", minimum=0.0, maximum=1.0, step=0.05, value=0, elem_id=f'control_unit-{i}-start')
+                            control_end = gr.Slider(label="End", minimum=0.0, maximum=1.0, step=0.05, value=1.0, elem_id=f'control_unit-{i}-end')
+                            reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'controlnetxs_unit-{i}-reset')
+                            image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'controlnetxs_unit-{i}-upload')
+                            image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'controlnetxs_unit-{i}-reuse')
+                            btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'controlnetxs_unit-{i}-preview')
+                            image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'controlnetxs_unit-{i}-override')
+                    controlnetxs_ui_units.append(unit_ui)
+                    units.append(unit.Unit(
+                        unit_type = 'xs',
+                        index = i,
+                        enabled = enabled,
+                        result_txt = result_txt,
+                        enabled_cb = enabled_cb,
+                        reset_btn = reset_btn,
+                        process_id = process_id,
+                        model_id = model_id,
+                        model_strength = model_strength,
+                        preview_process = preview_process,
+                        preview_btn = btn_preview,
+                        image_upload = image_upload,
+                        image_reuse = image_reuse,
+                        image_preview = image_preview,
+                        control_start = control_start,
+                        control_end = control_end,
+                        extra_controls = extra_controls,
+                        )
+                    )
+                    if i == 0:
+                        units[-1].enabled = True # enable first unit in group
+                num_controlnet_units.change(fn=helpers.display_units, inputs=[num_controlnet_units], outputs=controlnetxs_ui_units)
+
+            with gr.Tab('Lite') as _tab_lite:
+                gr.HTML('<a href="https://huggingface.co/kohya-ss/controlnet-lllite">Control LLLite</a>')
+                with gr.Row():
+                    extra_controls = [
+                    ]
+                    num_lite_units = gr.Slider(label="Units", minimum=1, maximum=max_units, step=1, value=1, scale=1)
+                lite_ui_units = [] # list of hidable accordions
+                for i in range(max_units):
+                    enabled = True if i==0 else False
+                    with gr.Accordion(f'Control-LLLite unit {i+1}', visible= i < num_lite_units.value, elem_classes='control-unit') as unit_ui:
+                        with gr.Row():
+                            enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
+                            process_id = gr.Dropdown(label="Processor", choices=processors.list_models(), value='None', elem_id=f'control_unit-{i}-process_name')
+                            model_id = gr.Dropdown(label="Model", choices=lite.list_models(), value='None', elem_id=f'control_unit-{i}-model_name')
+                            ui_common.create_refresh_button(model_id, lite.list_models, lambda: {"choices": lite.list_models(refresh=True)}, f'lite_models_{i}_refresh')
+                            model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, elem_id=f'control_unit-{i}-strength')
+                            reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'lite_unit-{i}-reset')
+                            image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'lite_unit-{i}-upload')
+                            image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'lite_unit-{i}-reuse')
+                            image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'lite_unit-{i}-override')
+                            btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'lite_unit-{i}-preview')
+                    lite_ui_units.append(unit_ui)
+                    units.append(unit.Unit(
+                        unit_type = 'lite',
+                        index = i,
+                        enabled = enabled,
+                        result_txt = result_txt,
+                        enabled_cb = enabled_cb,
+                        reset_btn = reset_btn,
+                        process_id = process_id,
+                        model_id = model_id,
+                        model_strength = model_strength,
+                        preview_process = preview_process,
+                        preview_btn = btn_preview,
+                        image_upload = image_upload,
+                        image_reuse = image_reuse,
+                        image_preview = image_preview,
+                        extra_controls = extra_controls,
+                        )
+                    )
+                    if i == 0:
+                        units[-1].enabled = True # enable first unit in group
+                num_lite_units.change(fn=helpers.display_units, inputs=[num_lite_units], outputs=lite_ui_units)
+
+            with gr.Tab('Reference') as _tab_reference:
+                gr.HTML('<a href="https://github.com/Mikubill/sd-webui-controlnet/discussions/1236">ControlNet reference-only control</a>')
+                with gr.Row():
+                    extra_controls = [
+                        gr.Radio(label="Reference context", choices=['Attention', 'Adain', 'Attention Adain'], value='Attention', interactive=True),
+                        gr.Slider(label="Style fidelity", minimum=0.0, maximum=1.0, step=0.05, value=0.5, interactive=True), # prompt vs control importance
+                        gr.Slider(label="Reference query weight", minimum=0.0, maximum=1.0, step=0.05, value=1.0, interactive=True),
+                        gr.Slider(label="Reference adain weight", minimum=0.0, maximum=2.0, step=0.05, value=1.0, interactive=True),
+                    ]
+                for i in range(1): # can only have one reference unit
+                    enabled = True if i==0 else False
+                    with gr.Accordion(f'Reference unit {i+1}', visible=True, elem_classes='control-unit') as unit_ui:
+                        with gr.Row():
+                            enabled_cb = gr.Checkbox(enabled, label='Active', container=False, show_label=True, elem_id=f'control_unit-{i}-enabled')
+                            model_id = gr.Dropdown(label="Reference", choices=reference.list_models(), value='Reference', visible=False, elem_id=f'control_unit-{i}-model_name')
+                            model_strength = gr.Slider(label="CN Strength", minimum=0.01, maximum=1.0, step=0.01, value=1.0, visible=False, elem_id=f'control_unit-{i}-strength')
+                            reset_btn = ui_components.ToolButton(value=ui_symbols.reset, elem_id=f'reference_unit-{i}-reset')
+                            image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'], elem_id=f'reference_unit-{i}-upload')
+                            image_reuse= ui_components.ToolButton(value=ui_symbols.reuse, elem_id=f'reference_unit-{i}-reuse')
+                            image_preview = gr.Image(label="Input", show_label=False, type="pil", interactive=False, height=128, width=128, visible=False, elem_id=f'reference_unit-{i}-override')
+                            btn_preview= ui_components.ToolButton(value=ui_symbols.preview, elem_id=f'reference_unit-{i}-preview')
+                    units.append(unit.Unit(
+                        unit_type = 'reference',
+                        index = i,
+                        enabled = enabled,
+                        result_txt = result_txt,
+                        enabled_cb = enabled_cb,
+                        reset_btn = reset_btn,
+                        process_id = process_id,
+                        model_id = model_id,
+                        model_strength = model_strength,
+                        preview_process = preview_process,
+                        preview_btn = btn_preview,
+                        image_upload = image_upload,
+                        image_reuse = image_reuse,
+                        image_preview = image_preview,
+                        extra_controls = extra_controls,
+                        )
+                    )
+                    if i == 0:
+                        units[-1].enabled = True # enable first unit in group
+
+        with gr.Accordion('Control settings', open=False, elem_classes=['control-settings']) as _tab_settings:
+            with gr.Group(elem_classes=['processor-group']):
+                settings = []
+                with gr.Accordion('Global', open=True, elem_classes=['processor-settings']):
+                    control_hires = gr.Checkbox(label="Use control during hires", value=shared.opts.control_hires, elem_id='control_hires')
+                    def set_control_hires(value):
+                        shared.opts.control_active = value
+                    control_hires.change(fn=set_control_hires, inputs=[control_hires], outputs=[])
+                    control_max_units = gr.Slider(label="Maximum units", minimum=1, maximum=10, step=1, value=shared.opts.control_max_units, elem_id='control_max_units')
+                    def set_control_max_units(value):
+                        shared.opts.control_max_units = value
+                    control_max_units.change(fn=set_control_max_units, inputs=[control_max_units], outputs=[])
+                    control_tiles = gr.Textbox(label="Tiling options", value=shared.opts.control_tiles, elem_id='control_tiles')
+                    def set_control_tiles(value):
+                        shared.opts.control_tiles = value
+                    control_tiles.change(fn=set_control_tiles, inputs=[control_tiles], outputs=[])
+                    control_move_processor = gr.Checkbox(label="Move processor to CPU after use", value=shared.opts.control_move_processor, elem_id='control_move_processor')
+                    def set_control_move_processor(value):
+                        shared.opts.control_move_processor = value
+                    control_move_processor.change(fn=set_control_move_processor, inputs=[control_move_processor], outputs=[])
+                    control_unload_processor = gr.Checkbox(label="Unload processor after use", value=shared.opts.control_unload_processor, elem_id='control_unload_processor')
+                    def set_control_unload_processor(value):
+                        shared.opts.control_unload_processor = value
+                    control_unload_processor.change(fn=set_control_unload_processor, inputs=[control_unload_processor], outputs=[])
+
+                with gr.Accordion('HED', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Scribble", value=False))
+                with gr.Accordion('Midas depth', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Slider(label="Background threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.1))
+                    settings.append(gr.Checkbox(label="Depth and normal", value=False))
+                with gr.Accordion('MLSD', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Slider(label="Score threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.1))
+                    settings.append(gr.Slider(label="Distance threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.1))
+                with gr.Accordion('OpenBody', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Body", value=True))
+                    settings.append(gr.Checkbox(label="Hands", value=False))
+                    settings.append(gr.Checkbox(label="Face", value=False))
+                with gr.Accordion('PidiNet', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Scribble", value=False))
+                    settings.append(gr.Checkbox(label="Apply filter", value=False))
+                with gr.Accordion('LineArt', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Coarse", value=False))
+                with gr.Accordion('Leres Depth', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Boost", value=False))
+                    settings.append(gr.Slider(label="Near threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.0))
+                    settings.append(gr.Slider(label="Depth threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.0))
+                with gr.Accordion('MediaPipe Face', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Slider(label="Max faces", minimum=1, maximum=10, step=1, value=1))
+                    settings.append(gr.Slider(label="Face confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.5))
+                with gr.Accordion('Canny', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Slider(label="Low threshold", minimum=0, maximum=1000, step=1, value=100))
+                    settings.append(gr.Slider(label="High threshold", minimum=0, maximum=1000, step=1, value=200))
+                with gr.Accordion('DWPose', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Radio(label="Pose Model", choices=['Tiny', 'Medium', 'Large'], value='Tiny'))
+                    settings.append(gr.Slider(label="Pose confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.3))
+                with gr.Accordion('SegmentAnything', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Radio(label="Segment Model", choices=['Base', 'Large'], value='Base'))
+                with gr.Accordion('Edge', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Parameter free", value=True))
+                    settings.append(gr.Radio(label="Edge mode", choices=['edge', 'gradient'], value='edge'))
+                with gr.Accordion('Zoe Depth', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Checkbox(label="Gamma corrected", value=False))
+                with gr.Accordion('Marigold Depth', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Dropdown(label="Color map", choices=['None'] + plt.colormaps(), value='None'))
+                    settings.append(gr.Slider(label="Denoising steps", minimum=1, maximum=99, step=1, value=10))
+                    settings.append(gr.Slider(label="Ensemble size", minimum=1, maximum=99, step=1, value=10))
+                with gr.Accordion('Depth Anything', open=True, elem_classes=['processor-settings']):
+                    settings.append(gr.Dropdown(label="Depth map", choices=['none'] + masking.COLORMAP, value='inferno'))
+                for setting in settings:
+                    setting.change(fn=processors.update_settings, inputs=settings, outputs=[])

From ab82c172d5f1d6d1c190133c4981978726da912a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 25 Sep 2025 14:02:21 -0400
Subject: [PATCH 054/230] handle nunchaku lora with unsupported models

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/lora/lora_nunchaku.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/lora/lora_nunchaku.py b/modules/lora/lora_nunchaku.py
index 63a71b46f..d01238449 100644
--- a/modules/lora/lora_nunchaku.py
+++ b/modules/lora/lora_nunchaku.py
@@ -8,6 +8,9 @@ previously_loaded = [] # we maintain private state here
 
 def load_nunchaku(names, strengths):
     global previously_loaded # pylint: disable=global-statement
+    if not hasattr(shared.sd_model, 'transformer') or not hasattr(shared.sd_model.transformer, 'update_lora_params'):
+        shared.log.error(f'Network load: type=LoRA method=nunchaku model={shared.sd_model.__class__.__name__} unsupported')
+        return False
     strengths = [s[0] if isinstance(s, list) else s for s in strengths]
     networks = lora_load.gather_networks(names)
     networks = [(network, strength) for network, strength in zip(networks, strengths) if network is not None and strength > 0]
@@ -27,7 +30,7 @@ def load_nunchaku(names, strengths):
         lora_common.timer.load = t1 - t0
         shared.log.debug(f"Network load: type=LoRA method=nunchaku loras={names} strength={strengths} time={t1-t0:.3f}")
     except Exception as e:
-        shared.log.errors(f'Network load: type=LoRA method=nunchaku {e}')
+        shared.log.error(f'Network load: type=LoRA method=nunchaku {e}')
         if lora_common.debug:
             errors.display(e, 'LoRA')
     return is_changed

From 01e8211d4b2d181ff09e31afd704623d72643f79 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 25 Sep 2025 14:17:40 -0400
Subject: [PATCH 055/230] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ui_common.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui_common.py b/modules/ui_common.py
index fa0d1322f..f328351f5 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -22,7 +22,7 @@ def update_generation_info(generation_info, html_info, img_index):
         if len(generation_info) == 0 and processing.processed is not None:
             generation_info = processing.processed.js() or {}
         generation_json = json.loads(generation_info)
-        if len(generation_json["infotexts"]) == 0:
+        if len(generation_json.get("infotexts", [])) == 0:
             return html_info, 'no infotexts found'
         if img_index == -1:
             img_index = 0
@@ -32,7 +32,7 @@ def update_generation_info(generation_info, html_info, img_index):
         html_info_formatted = infotext_to_html(info)
         return html_info, html_info_formatted
     except Exception as e:
-        shared.log.warning(f'Update info: info="{generation_info}" {e}')
+        shared.log.trace(f'Update info: info="{generation_info}" {e}')
     return html_info, html_info
 
 

From ae25cb88805d0046733b698ca5681e6bff6e23b1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 25 Sep 2025 14:33:21 -0400
Subject: [PATCH 056/230] linting

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/localize.js          | 7 ++++---
 modules/sdnq/__init__.py | 2 +-
 modules/ui_control.py    | 2 +-
 scripts/daam/hook.py     | 6 +++---
 scripts/daam/trace.py    | 2 +-
 scripts/daam/utils.py    | 2 --
 6 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/cli/localize.js b/cli/localize.js
index da870d455..12d289867 100755
--- a/cli/localize.js
+++ b/cli/localize.js
@@ -7,9 +7,10 @@ const { GoogleGenerativeAI } = require('@google/generative-ai');
 
 const api_key = process.env.GOOGLE_AI_API_KEY;
 const model = 'gemini-2.5-flash';
-const prompt = `
-// eslint-disable-next-line max-len
-Translate attached JSON from English to {language} using following rules: fields id, label and reload should be preserved from original, field localized should be a translated version of field label and field hint should be translated in-place. if field is less than 3 characters, do not translate it and keep it as is. Every JSON entry should have id, label, localized, reload and hint fields. Output should be pure JSON without any additional text. To better match translation, context of the text is related to Stable Diffusion and topic of Generative AI.`;
+const prompt = `Translate attached JSON from English to {language} using following rules: fields id, label and reload should be preserved from original, field localized should be a translated version of field label and field hint should be translated in-place.
+if field is less than 3 characters, do not translate it and keep it as is.
+Every JSON entry should have id, label, localized, reload and hint fields.
+Output should be pure JSON without any additional text. To better match translation, context of the text is related to Stable Diffusion and topic of Generative AI.`;
 const languages = {
   hr: 'Croatian',
   de: 'German',
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 71db2127f..6ba49cbb1 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -440,7 +440,7 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return missing_keys
 
-    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict:
+    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
diff --git a/modules/ui_control.py b/modules/ui_control.py
index ecfe1fa01..a1c77848d 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -211,7 +211,7 @@ def create_ui(_blocks: gr.Blocks=None):
                     gr.HTML('<span id="control-output-button">Output</p>')
                     with gr.Tabs(elem_classes=['control-tabs'], elem_id='control-tab-output') as output_tabs:
                         with gr.Tab('Gallery', id='out-gallery'):
-                            output_gallery, output_gen_info, _output_html_info, _output_html_info_formatted, output_html_log = ui_common.create_output_panel("control", preview=False, prompt=prompt, height=gr_height, result_info=result_txt)
+                            output_gallery, _output_gen_info, _output_html_info, _output_html_info_formatted, output_html_log = ui_common.create_output_panel("control", preview=False, prompt=prompt, height=gr_height, result_info=result_txt)
                         with gr.Tab('Image', id='out-image'):
                             output_image = gr.Image(label="Output", show_label=False, type="pil", interactive=False, tool="editor", height=gr_height, elem_id='control_output_image', elem_classes=['control-image'])
                         with gr.Tab('Video', id='out-video'):
diff --git a/scripts/daam/hook.py b/scripts/daam/hook.py
index f82762c4e..7c2866b63 100644
--- a/scripts/daam/hook.py
+++ b/scripts/daam/hook.py
@@ -1,4 +1,4 @@
-from typing import List, Generic, TypeVar, Callable, Union, Any
+from typing import List, Generic, TypeVar
 import functools
 import itertools
 
@@ -23,7 +23,7 @@ class ObjectHooker(Generic[ModuleType]):
     def __init__(self, module: ModuleType):
         self.module: ModuleType = module
         self.hooked = False
-        self.old_state = dict()
+        self.old_state = {}
 
     def __enter__(self):
         self.hook()
@@ -36,7 +36,7 @@ class ObjectHooker(Generic[ModuleType]):
         if self.hooked:
             raise RuntimeError('Already hooked module')
 
-        self.old_state = dict()
+        self.old_state = {}
         self.hooked = True
         self._hook_impl()
 
diff --git a/scripts/daam/trace.py b/scripts/daam/trace.py
index 1b6a6cd3f..625e3d402 100644
--- a/scripts/daam/trace.py
+++ b/scripts/daam/trace.py
@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import List, Type, Any, Dict, Tuple, Union
+from typing import List, Type, Any, Dict, Union
 import math
 
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
diff --git a/scripts/daam/utils.py b/scripts/daam/utils.py
index 8cfde13f2..15e2c1b5e 100644
--- a/scripts/daam/utils.py
+++ b/scripts/daam/utils.py
@@ -10,7 +10,6 @@ import matplotlib.pyplot as plt
 import numpy as np
 import spacy
 import torch
-import torch.nn.functional as F
 
 
 __all__ = ['set_seed', 'compute_token_merge_indices', 'plot_mask_heat_map', 'cached_nlp', 'cache_dir', 'auto_device', 'auto_autocast']
@@ -102,7 +101,6 @@ def cached_nlp(prompt: str, type='en_core_web_md'):
         try:
             nlp = spacy.load(type)
         except OSError:
-            import os
             os.system(f'python -m spacy download {type}')
             nlp = spacy.load(type)
 

From c2548353513de9ac1c3af1cddf6f5aa322d71e50 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 26 Sep 2025 10:14:02 -0400
Subject: [PATCH 057/230] fix lora strength

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 configs/Dockerfile.cuda             |  4 ++--
 modules/loader.py                   | 21 +++++++++++++--------
 modules/lora/extra_networks_lora.py | 12 ++++++++++--
 modules/ui_common.py                |  2 ++
 4 files changed, 27 insertions(+), 12 deletions(-)

diff --git a/configs/Dockerfile.cuda b/configs/Dockerfile.cuda
index 4e5a496e1..b53a8872f 100644
--- a/configs/Dockerfile.cuda
+++ b/configs/Dockerfile.cuda
@@ -41,11 +41,11 @@ ENV SD_DOCKER=true
 # tcmalloc is not required but it is highly recommended
 ENV LD_PRELOAD=libtcmalloc.so.4
 # sdnext will run all necessary pip install ops and then exit
-RUN ["python", "/app/launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test", "--optional"]
+RUN ["python", "/app/launch.py", "--uv", "--use-cuda", "--log", "sdnext.log", "--test", "--optional"]
 # preinstall additional packages to avoid installation during runtime
 
 # actually run sdnext
-CMD ["python", "launch.py", "--debug", "--skip-all", "--listen", "--quick", "--api-log", "--log", "sdnext.log"]
+CMD ["python", "launch.py", "--listen", "--quick", "--log", "sdnext.log"]
 
 # expose port
 EXPOSE 7860
diff --git a/modules/loader.py b/modules/loader.py
index efd58ff11..7ee969962 100644
--- a/modules/loader.py
+++ b/modules/loader.py
@@ -123,15 +123,20 @@ timer.startup.record("pydantic")
 import tqdm as tqdm_lib # pylint: disable=C0411
 from tqdm.rich import tqdm # pylint: disable=W0611,C0411
 
-import diffusers.utils.import_utils # pylint: disable=W0611,C0411
-diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git
-diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access
+try:
+    import diffusers.utils.import_utils # pylint: disable=W0611,C0411
+    diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git
+    diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access
 
-import diffusers # pylint: disable=W0611,C0411
-import diffusers.loaders.single_file # pylint: disable=W0611,C0411
-diffusers.loaders.single_file.logging.tqdm = partial(tqdm, unit='C')
-logging.getLogger("diffusers.loaders.single_file").setLevel(logging.ERROR)
-timer.startup.record("diffusers")
+    import diffusers # pylint: disable=W0611,C0411
+    import diffusers.loaders.single_file # pylint: disable=W0611,C0411
+    diffusers.loaders.single_file.logging.tqdm = partial(tqdm, unit='C')
+    logging.getLogger("diffusers.loaders.single_file").setLevel(logging.ERROR)
+    timer.startup.record("diffusers")
+except Exception as e:
+    errors.log.error(f'Loader: diffusers=={diffusers.__version__ if "diffusers" in sys.modules else None} {e}')
+    errors.log.error('Please restart re-run the installer')
+    sys.exit(1)
 
 import huggingface_hub # pylint: disable=W0611,C0411
 timer.startup.record("hfhub")
diff --git a/modules/lora/extra_networks_lora.py b/modules/lora/extra_networks_lora.py
index 6370ef7df..ee657d2d1 100644
--- a/modules/lora/extra_networks_lora.py
+++ b/modules/lora/extra_networks_lora.py
@@ -83,6 +83,13 @@ def infotext(p):
             p.extra_generation_params["LoRA hashes"] = ", ".join(network_hashes)
 
 
+def to_float(value):
+    try:
+        return float(value)
+    except (ValueError, TypeError):
+        return value
+
+
 def parse(p, params_list, step=0):
     names = []
     te_multipliers = []
@@ -93,6 +100,7 @@ def parse(p, params_list, step=0):
         names.append(params.positional[0])
 
         default_multiplier = params.positional[1] if len(params.positional) > 1 else shared.opts.extra_networks_default_multiplier
+        default_multiplier = to_float(default_multiplier)
         if isinstance(default_multiplier, str) and "@" not in default_multiplier:
             default_multiplier = shared.opts.extra_networks_default_multiplier
 
@@ -100,7 +108,7 @@ def parse(p, params_list, step=0):
         if isinstance(te_multiplier, str) and "@" in te_multiplier:
             te_multiplier = get_stepwise(te_multiplier, step, p.steps)
         else:
-            te_multiplier = float(te_multiplier)
+            te_multiplier = to_float(te_multiplier)
 
         unet_multiplier = 3 * [params.named.get("unet", te_multiplier)] # fill all 3 with same value
         unet_multiplier[0] = params.named.get("in", unet_multiplier[0])
@@ -110,7 +118,7 @@ def parse(p, params_list, step=0):
             if isinstance(unet_multiplier[i], str) and "@" in unet_multiplier[i]:
                 unet_multiplier[i] = get_stepwise(unet_multiplier[i], step, p.steps)
             else:
-                unet_multiplier[i] = float(unet_multiplier[i])
+                unet_multiplier[i] = to_float(unet_multiplier[i])
 
         dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else None
         te_multipliers.append(te_multiplier)
diff --git a/modules/ui_common.py b/modules/ui_common.py
index f328351f5..7fa684190 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -21,6 +21,8 @@ def update_generation_info(generation_info, html_info, img_index):
     try:
         if len(generation_info) == 0 and processing.processed is not None:
             generation_info = processing.processed.js() or {}
+        if len(generation_info) == 0:
+            return html_info, html_info
         generation_json = json.loads(generation_info)
         if len(generation_json.get("infotexts", [])) == 0:
             return html_info, 'no infotexts found'

From bd048e8efc9b5180c2b339ce65e82664f9f8ebf4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 26 Sep 2025 12:28:52 -0400
Subject: [PATCH 058/230] framepack: patch solver for unsupported gpus

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                            |  5 ++--
 modules/framepack/framepack_worker.py   |  4 +--
 modules/framepack/pipeline/uni_pc_fm.py | 36 ++++++++++++++++++++++---
 wiki                                    |  2 +-
 4 files changed, 39 insertions(+), 8 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 45ac06a67..05964ddde 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -54,10 +54,11 @@
 - **Fixes**
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
+  - framepack: patch solver for unsupported gpus  
   - benchmark: remove forced sampler from system info benchmark  
   - xyz-grid: fix xyz grid with random seeds  
-  - fix download for sd15/sdxl reference models  
-
+  - reference: fix download for sd15/sdxl reference models  
+  
 ## Update for 2025-09-15
 
 ### Highlights for 2025-09-15
diff --git a/modules/framepack/framepack_worker.py b/modules/framepack/framepack_worker.py
index 1b87334ec..eedbad13b 100644
--- a/modules/framepack/framepack_worker.py
+++ b/modules/framepack/framepack_worker.py
@@ -54,7 +54,7 @@ def worker(
 
     from modules.framepack.pipeline import hunyuan
     from modules.framepack.pipeline import utils
-    from modules.framepack.pipeline.k_diffusion_hunyuan import sample_hunyuan
+    from modules.framepack.pipeline import k_diffusion_hunyuan
 
     is_f1 = variant == 'forward-only'
     total_generated_frames = 0
@@ -244,7 +244,7 @@ def worker(
                 transformer.initialize_teacache(enable_teacache=use_teacache, num_steps=steps, rel_l1_thresh=shared.opts.teacache_thresh)
 
                 t_sample = time.time()
-                generated_latents = sample_hunyuan(
+                generated_latents = k_diffusion_hunyuan.sample_hunyuan(
                     transformer=transformer,
                     sampler='unipc',
                     width=width,
diff --git a/modules/framepack/pipeline/uni_pc_fm.py b/modules/framepack/pipeline/uni_pc_fm.py
index 6ce7c15ce..2066cd8e3 100644
--- a/modules/framepack/pipeline/uni_pc_fm.py
+++ b/modules/framepack/pipeline/uni_pc_fm.py
@@ -5,7 +5,7 @@
 
 
 import torch
-
+import numpy as np
 from tqdm.auto import trange
 
 
@@ -13,6 +13,36 @@ def expand_dims(v, dims):
     return v[(...,) + (None,) * (dims - 1)]
 
 
+torch_linalg_solve = None
+
+
+def test_solver():
+    from modules import devices, shared
+    try:
+        a = torch.randn(50, 50).to(device=devices.device, dtype=torch.float32)
+        b = torch.randn(50, 2).to(device=devices.device, dtype=torch.float32)
+        _x = torch.linalg.solve(a, b)
+        return True
+    except Exception as e:
+        shared.log.debug(f'FramePack: solver=cpu {e}')
+        return False
+
+
+def linalg_solve(A, B, device):
+    global torch_linalg_solve # pylint: disable=global-statement
+    if torch_linalg_solve is None:
+        torch_linalg_solve = test_solver()
+    if torch_linalg_solve:
+        X = torch.linalg.solve(A, B)
+        return X
+    else:
+        A_np = A.float().cpu().numpy()
+        B_np = B.float().cpu().numpy()
+        X_np = np.linalg.solve(A_np, B_np)
+        X = torch.from_numpy(X_np).to(device=device, dtype=A.dtype)
+        return X
+
+
 class FlowMatchUniPC:
     def __init__(self, model, extra_args, variant='bh1'):
         self.model = model
@@ -78,7 +108,7 @@ class FlowMatchUniPC:
             if order == 2:
                 rhos_p = torch.tensor([0.5], device=b.device)
             else:
-                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+                rhos_p = linalg_solve(R[:-1, :-1], b[:-1], x.device)
         else:
             D1s = None
             rhos_p = None
@@ -86,7 +116,7 @@ class FlowMatchUniPC:
         if order == 1:
             rhos_c = torch.tensor([0.5], device=b.device)
         else:
-            rhos_c = torch.linalg.solve(R, b)
+            rhos_c = linalg_solve(R, b, x.device)
 
         x_t_ = expand_dims(t / t_prev_0, dims) * x - expand_dims(h_phi_1, dims) * model_prev_0
 
diff --git a/wiki b/wiki
index 3df7dbfc9..bd9905932 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 3df7dbfc9c3e2f39c825f8d76541e13d3c3f097d
+Subproject commit bd990593287f36d4c76828a2db0eaa019ca0630f

From 7bc6edf4832cd6c536e13393695b56ed444244a0 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 26 Sep 2025 13:17:52 -0400
Subject: [PATCH 059/230] ui add connection monitor

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .eslintrc.json                     |  3 ++-
 extensions-builtin/sdnext-modernui |  2 +-
 html/locale_en.json                |  1 -
 javascript/monitor.js              | 33 ++++++++++++++++++++++++++++++
 javascript/ui.js                   |  1 +
 5 files changed, 37 insertions(+), 3 deletions(-)
 create mode 100644 javascript/monitor.js

diff --git a/.eslintrc.json b/.eslintrc.json
index 32283e620..81c196021 100644
--- a/.eslintrc.json
+++ b/.eslintrc.json
@@ -116,7 +116,8 @@
     "idbDel": "readonly",
     "idbAdd": "readonly",
     "initChangelog": "readonly",
-    "sendNotification": "readonly"
+    "sendNotification": "readonly",
+    "monitorConnection": "readonly"
   },
   "ignorePatterns": [
     "node_modules",
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index a445b6b09..b189fac67 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit a445b6b09f3974f95d410f19575200109e1a8b83
+Subproject commit b189fac67d24bf21c9b65cf45add5b8c29f7456f
diff --git a/html/locale_en.json b/html/locale_en.json
index 8113a811b..056637d2b 100644
--- a/html/locale_en.json
+++ b/html/locale_en.json
@@ -32,7 +32,6 @@
   {"id":"","label":"","localized":"","reload":"","hint":"Sort by time, descending"}
 ],
 "main": [
-  {"id":"","label":"SD.Next","localized":"","reload":"","hint":"SD.Next<br>All-in-one WebUI for AI generative image and video creation"},
   {"id":"","label":"Prompt","localized":"","reload":"","hint":"Describe image you want to generate"},
   {"id":"","label":"Start","localized":"","reload":"","hint":"Start"},
   {"id":"","label":"End","localized":"","reload":"","hint":"End"},
diff --git a/javascript/monitor.js b/javascript/monitor.js
new file mode 100644
index 000000000..1c194af08
--- /dev/null
+++ b/javascript/monitor.js
@@ -0,0 +1,33 @@
+async function updateIndicator(online, data, msg) {
+  const el = document.getElementById('logo_nav');
+  if (!el || !data) return;
+  const status = online ? '<b style="color:lime">online</b>' : '<b style="color:darkred">offline</b>';
+  const template = `
+    Version: <b>${data.updated}</b><br>
+    Commit: <b>${data.hash}</b><br>
+    Branch: <b>${data.branch}</b><br>
+    Status: ${status}<br>
+  `;
+  if (online) {
+    el.dataset.hint = template;
+    el.style.backgroundColor = 'var(--sd-main-accent-color)';
+    log('monitorConnection: online', data);
+  } else {
+    el.dataset.hint = template;
+    el.style.backgroundColor = 'var(--color-error)';
+    log('monitorConnection: offline', msg);
+  }
+}
+
+async function monitorConnection() {
+  try {
+    const res = await fetch(`${window.api}/version`);
+    const data = await res.json();
+    const url = res.url.split('/sdapi')[0].replace('http', 'ws'); // update global url as ws need fqdn
+    const ws = new WebSocket(`${url}/queue/join`);
+    ws.onopen = () => updateIndicator(true, data, '');
+    ws.onclose = () => updateIndicator(false, data, '');
+    ws.onerror = (e) => updateIndicator(false, data, e.message);
+    ws.onmessage = (evt) => log('monitorConnection: message', evt.data);
+  } catch { /**/ }
+}
diff --git a/javascript/ui.js b/javascript/ui.js
index a61297a54..8c5cd6630 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -601,4 +601,5 @@ async function reconnectUI() {
   const sd_model_observer = new MutationObserver(sd_model_callback);
   sd_model_observer.observe(sd_model, { attributes: true, childList: true, subtree: true });
   log('reconnectUI');
+  monitorConnection();
 }

From 07a66b48154c653545a2605a0a8dc839e960074c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 26 Sep 2025 13:25:17 -0400
Subject: [PATCH 060/230] ui add server connection status indicators

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md             | 2 ++
 javascript/logMonitor.js | 1 +
 2 files changed, 3 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 05964ddde..12627f6bd 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -37,6 +37,8 @@
   - new feature to specify which models types not to offload  
     in *settings -> model offloading -> model types not to offload*  
 - **UI**
+  - **connection monitor**  
+    main logo in top-left corner now indicates server connection status and hovering over it shows connection details  
   - separate guidance and detail sections  
   - networks ability to filter lora by base model version  
 - **Other**
diff --git a/javascript/logMonitor.js b/javascript/logMonitor.js
index 08f104b55..21c03e88d 100644
--- a/javascript/logMonitor.js
+++ b/javascript/logMonitor.js
@@ -78,6 +78,7 @@ async function logMonitor() {
       for (const line of lines) addLogLine(line);
       if (!logConnected) {
         logConnected = true;
+        monitorConnection();
         xhrPost(`${window.api}/log`, { debug: 'connected' });
       }
     } else {

From d63bc05b6ccc2b7ca2d22a6846599f4c121317a4 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 11:12:13 +0900
Subject: [PATCH 061/230] update rocm.py to detect rocm-sdk packages

---
 installer.py               |   8 ++-
 modules/rocm.py            | 112 ++++++++++++++++++++++++++++---------
 modules/zluda_installer.py |  11 ++--
 3 files changed, 98 insertions(+), 33 deletions(-)

diff --git a/installer.py b/installer.py
index f05a5bb32..bc0612393 100644
--- a/installer.py
+++ b/installer.py
@@ -677,6 +677,10 @@ def install_rocm_zluda():
     if args.skip_all or args.skip_requirements:
         return torch_command
     from modules import rocm
+    if rocm.err is not None:
+        log.warning(f'ROCm: error checking ROCm toolkit: {rocm.err}')
+        log.info('Using CPU-only torch')
+        return os.environ.get('TORCH_COMMAND', 'torch torchvision')
     if not rocm.is_installed:
         log.warning('ROCm: could not find ROCm toolkit installed')
         log.info('Using CPU-only torch')
@@ -720,8 +724,8 @@ def install_rocm_zluda():
     if sys.platform == "win32":
         #check_python(supported_minors=[10, 11, 12, 13], reason='ZLUDA backend requires a Python version between 3.10 and 3.13')
 
-        if args.use_rocm and args.experimental and (sys.version_info.major, sys.version_info.minor) == (3, 12): # TODO install: switch to pytorch source when it becomes available
-            torch_command = os.environ.get('TORCH_COMMAND', '--no-cache-dir https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torch-2.8.0a0%2Bgitfc14c65-cp312-cp312-win_amd64.whl https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torchvision-0.24.0a0%2Bc85f008-cp312-cp312-win_amd64.whl')
+        if args.use_rocm: # TODO install: switch to pytorch source when it becomes available
+            torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{rocm.get_distribution(device)}')
         else:
             if args.device_id is not None:
                 if os.environ.get('HIP_VISIBLE_DEVICES', None) is not None:
diff --git a/modules/rocm.py b/modules/rocm.py
index 554c3af55..bf06ba3b9 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -6,6 +6,7 @@ import subprocess
 import importlib.metadata
 from typing import Union, List
 from enum import Enum
+from installer import installed
 
 
 def resolve_link(path_: str) -> str:
@@ -40,6 +41,33 @@ def conceal():
     os.environ["PATH"] = ";".join(paths_no_rocm)
 
 
+class Environment:
+    hip: ctypes.CDLL
+
+    def __init__(self, path: str):
+        self.hip = ctypes.CDLL(path)
+
+
+# rocm is installed system-wide
+class ROCmEnvironment(Environment):
+    path: str
+
+    def __init__(self, path: str):
+        for v in (6, 7):
+            lib = os.path.join(path, "bin", f"amdhip64_{v}.dll")
+            if os.path.exists(lib):
+                super().__init__(lib)
+                break
+        self.path = path
+
+
+# rocm-sdk package is installed
+class PythonPackageEnvironment(Environment):
+    def __init__(self):
+        import _rocm_sdk_core
+        super().__init__(os.path.join(_rocm_sdk_core.__path__[0], "bin", "amdhip64_7.dll"))
+
+
 class MicroArchitecture(Enum):
     GCN = "gcn"
     RDNA = "rdna"
@@ -81,7 +109,7 @@ class Agent:
         self.blaslt_supported = os.path.exists(os.path.join(blaslt_tensile_libpath, f"Kernels.so-000-{name}.hsaco" if sys.platform == "win32" else f"extop_{name}.co"))
 
     def get_gfx_version(self) -> Union[str, None]:
-        if self.gfx_version >= 0x1101 and self.gfx_version < 0x1200:
+        if self.gfx_version >= 0x1100 and self.gfx_version < 0x1200:
             return "11.0.0"
         elif self.gfx_version != 0x1030 and self.gfx_version >= 0x1000 and self.gfx_version < 0x1100:
             # gfx1010 users had to override gfx version to 10.3.0 in Linux
@@ -101,15 +129,23 @@ def get_version_torch() -> Union[str, None]:
     return version_.split("+rocm")[1]
 
 
-if sys.platform == "win32":
-    def find() -> Union[str, None]:
-        hip_path = shutil.which("hipconfig")
-        if hip_path is not None:
-            return dirname(resolve_link(hip_path), 2)
+def find() -> Union[Environment, None]:
+    hip_path = shutil.which("hipconfig")
+    if hip_path is not None:
+        py_path = os.path.dirname(sys.executable)
+        if hip_path.startswith(py_path):
+            try:
+                import _rocm_sdk_core # pylint: disable=unused-import
+                return PythonPackageEnvironment()
+            except ImportError:
+                pass
+        else:
+            return ROCmEnvironment(dirname(resolve_link(hip_path), 2))
 
+    if sys.platform == "win32":
         hip_path = os.environ.get("HIP_PATH", None)
         if hip_path is not None:
-            return hip_path
+            return ROCmEnvironment(hip_path)
 
         program_files = os.environ.get('ProgramFiles', r'C:\Program Files')
         hip_path = rf'{program_files}\AMD\ROCm'
@@ -146,29 +182,47 @@ if sys.platform == "win32":
         if latest is None:
             return None
 
-        return os.path.join(hip_path, str(latest))
+        return ROCmEnvironment(os.path.join(hip_path, str(latest)))
+    else:
+        if not os.path.exists("/opt/rocm"):
+            return None
+        return ROCmEnvironment(resolve_link("/opt/rocm"))
 
-    def get_version() -> str: # cannot just run hipconfig as it requires Perl installed on Windows.
-        return os.path.basename(path) or os.path.basename(os.path.dirname(path))
 
+def get_version() -> str:
+    version = ctypes.c_int()
+    environment.hip.hipRuntimeGetVersion(ctypes.byref(version))
+    major = version.value // 10000000
+    minor = (version.value // 100000) % 100
+    patch = version.value % 100000
+    return f"{major}.{minor}.{patch}"
+
+
+if sys.platform == "win32":
     def get_agents() -> List[Agent]:
-        return [Agent(x.split(' ')[-1].strip()) for x in spawn("hipinfo", cwd=os.path.join(path, 'bin')).split("\n") if x.startswith('gcnArchName:')]
+        if isinstance(environment, ROCmEnvironment):
+            out = spawn("amdgpu-arch", cwd=os.path.join(environment.path, 'bin'))
+        else:
+            out = spawn("amdgpu-arch")
+        out = out.strip()
+        return [Agent(x.split(' ')[-1].strip()) for x in out.split("\n")]
+
+    def get_distribution(agent: Agent) -> str:
+        if agent.gfx_version >= 0x1100 and agent.gfx_version < 0x1110:
+            return "gfx110X-dgpu"
+        if agent.gfx_version == 0x1151:
+            return "gfx1151"
+        if agent.gfx_version >= 0x1200 and agent.gfx_version < 0x1210:
+            return "gfx120X-all"
+        if agent.gfx_version >= 0x940 and agent.gfx_version < 0x950:
+            return "gfx94X-dcgpu"
+        if agent.gfx_version == 0x950:
+            return "gfx950-dcgpu"
+        raise Exception(f"Unsupported GPU architecture: {agent.name}")
 
     is_wsl: bool = False
     version_torch = None
 else:
-    def find() -> Union[str, None]:
-        rocm_path = shutil.which("hipconfig")
-        if rocm_path is not None:
-            return dirname(resolve_link(rocm_path), 2)
-        if not os.path.exists("/opt/rocm"):
-            return None
-        return resolve_link("/opt/rocm")
-
-    def get_version() -> str:
-        arr = spawn("hipconfig --version", cwd=os.path.join(path, 'bin')).split(".")
-        return f'{arr[0]}.{arr[1]}' if len(arr) >= 2 else None
-
     def get_agents() -> List[Agent]:
         try:
             agents = spawn("rocm_agent_enumerator").split("\n")
@@ -208,11 +262,17 @@ else:
 
     is_wsl: bool = os.environ.get('WSL_DISTRO_NAME', 'unknown' if spawn('wslpath -w /') else None) is not None
     version_torch = get_version_torch()
-path = find()
+environment = None
+err = None
+try:
+    environment = find()
+except Exception as e:
+    err = e
 blaslt_tensile_libpath = ""
 is_installed = False
 version = None
-if path is not None:
-    blaslt_tensile_libpath = os.environ.get("HIPBLASLT_TENSILE_LIBPATH", os.path.join(path, "bin" if sys.platform == "win32" else "lib", "hipblaslt", "library"))
+if environment is not None:
+    if isinstance(environment, ROCmEnvironment):
+        blaslt_tensile_libpath = os.environ.get("HIPBLASLT_TENSILE_LIBPATH", os.path.join(environment.path, "bin" if sys.platform == "win32" else "lib", "hipblaslt", "library"))
     is_installed = True
     version = get_version()
diff --git a/modules/zluda_installer.py b/modules/zluda_installer.py
index da0ee290f..2922f196f 100644
--- a/modules/zluda_installer.py
+++ b/modules/zluda_installer.py
@@ -119,12 +119,13 @@ def link_or_copy(src: os.PathLike, dst: os.PathLike):
 
 
 def load():
+    assert isinstance(rocm.environment, rocm.ROCmEnvironment)
     global core, ml, hipBLASLt_enabled, MIOpen_enabled # pylint: disable=global-statement
     core = Core(ctypes.windll.LoadLibrary(os.path.join(path, 'nvcuda.dll')))
     ml = ZLUDALibrary(ctypes.windll.LoadLibrary(os.path.join(path, 'nvml.dll')))
     is_nightly = core.get_nightly_flag() == 1
-    hipBLASLt_enabled = is_nightly and os.path.exists(rocm.blaslt_tensile_libpath) and os.path.exists(os.path.join(rocm.path, "bin", "hipblaslt.dll")) and default_agent is not None
-    MIOpen_enabled = is_nightly and os.path.exists(os.path.join(rocm.path, "bin", "MIOpen.dll"))
+    hipBLASLt_enabled = is_nightly and os.path.exists(rocm.blaslt_tensile_libpath) and os.path.exists(os.path.join(rocm.environment.path, "bin", "hipblaslt.dll")) and default_agent is not None
+    MIOpen_enabled = is_nightly and os.path.exists(os.path.join(rocm.environment.path, "bin", "MIOpen.dll"))
 
     if hipBLASLt_enabled:
         if not default_agent.blaslt_supported:
@@ -147,19 +148,19 @@ def load():
     os.environ["ZLUDA_NVRTC_LIB"] = os.path.join([v for v in site.getsitepackages() if v.endswith("site-packages")][0], "torch", "lib", "nvrtc64_112_0.dll")
 
     for v in HIPSDK_TARGETS:
-        ctypes.windll.LoadLibrary(os.path.join(rocm.path, 'bin', v))
+        ctypes.windll.LoadLibrary(os.path.join(rocm.environment.path, 'bin', v))
     for v in DLL_MAPPING.values():
         ctypes.windll.LoadLibrary(os.path.join(path, v))
 
     if hipBLASLt_enabled:
         os.environ.setdefault("DISABLE_ADDMM_CUDA_LT", "0")
-        ctypes.windll.LoadLibrary(os.path.join(rocm.path, 'bin', 'hipblaslt.dll'))
+        ctypes.windll.LoadLibrary(os.path.join(rocm.environment.path, 'bin', 'hipblaslt.dll'))
         ctypes.windll.LoadLibrary(os.path.join(path, 'cublasLt64_11.dll'))
     else:
         os.environ["DISABLE_ADDMM_CUDA_LT"] = "1"
 
     if MIOpen_enabled:
-        ctypes.windll.LoadLibrary(os.path.join(rocm.path, 'bin', 'MIOpen.dll'))
+        ctypes.windll.LoadLibrary(os.path.join(rocm.environment.path, 'bin', 'MIOpen.dll'))
         ctypes.windll.LoadLibrary(os.path.join(path, 'cudnn64_9.dll'))
 
     def conceal():

From 8d2aedd924f85c684cc4a159f5008a665f8542d9 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 11:17:15 +0900
Subject: [PATCH 062/230] hotfix

---
 modules/rocm.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index bf06ba3b9..754540f36 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -53,11 +53,14 @@ class ROCmEnvironment(Environment):
     path: str
 
     def __init__(self, path: str):
-        for v in (6, 7):
-            lib = os.path.join(path, "bin", f"amdhip64_{v}.dll")
-            if os.path.exists(lib):
-                super().__init__(lib)
-                break
+        if sys.platform == "win32":
+            for v in (6, 7):
+                lib = os.path.join(path, "bin", f"amdhip64_{v}.dll")
+                if os.path.exists(lib):
+                    super().__init__(lib)
+                    break
+        else:
+            super().__init__(os.path.join(path, "lib", "libamdhip64.so"))
         self.path = path
 
 
@@ -194,8 +197,8 @@ def get_version() -> str:
     environment.hip.hipRuntimeGetVersion(ctypes.byref(version))
     major = version.value // 10000000
     minor = (version.value // 100000) % 100
-    patch = version.value % 100000
-    return f"{major}.{minor}.{patch}"
+    #patch = version.value % 100000
+    return f"{major}.{minor}"
 
 
 if sys.platform == "win32":

From 4c590993e423fb924e048e69163a6e8901d49c70 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 11:24:38 +0900
Subject: [PATCH 063/230] install preview pytorch if rocm-sdk package not found

---
 installer.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/installer.py b/installer.py
index bc0612393..98ffef561 100644
--- a/installer.py
+++ b/installer.py
@@ -725,7 +725,11 @@ def install_rocm_zluda():
         #check_python(supported_minors=[10, 11, 12, 13], reason='ZLUDA backend requires a Python version between 3.10 and 3.13')
 
         if args.use_rocm: # TODO install: switch to pytorch source when it becomes available
-            torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{rocm.get_distribution(device)}')
+            if isinstance(rocm.environment, rocm.PythonPackageEnvironment): # TheRock
+                torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{rocm.get_distribution(device)}')
+            else:
+                check_python(supported_minors=[12], reason='AMD Windows preview requires a Python version 3.12')
+                torch_command = os.environ.get('TORCH_COMMAND', '--no-cache-dir https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torch-2.8.0a0%2Bgitfc14c65-cp312-cp312-win_amd64.whl https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torchvision-0.24.0a0%2Bc85f008-cp312-cp312-win_amd64.whl')
         else:
             if args.device_id is not None:
                 if os.environ.get('HIP_VISIBLE_DEVICES', None) is not None:

From e3e41298d902162fa77d6bb2b40d72bf02481f80 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 11:29:55 +0900
Subject: [PATCH 064/230] remove unused import

---
 modules/rocm.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 754540f36..3af8eb577 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -6,7 +6,6 @@ import subprocess
 import importlib.metadata
 from typing import Union, List
 from enum import Enum
-from installer import installed
 
 
 def resolve_link(path_: str) -> str:

From f00486c8f39731c494b53b7f2c13a77c0d4fcbb2 Mon Sep 17 00:00:00 2001
From: Luca Beltrame <lbeltrame@users.noreply.github.com>
Date: Sat, 27 Sep 2025 09:23:32 +0200
Subject: [PATCH 065/230] Fix HIP library detection

The code unconditionally checked `lib`, but on modern Linux distributions, 64-bit binaries are under `lib64` instead.

Note that this might be slightly different on Debian-based distributions, but as I don't have one I can't test it.
---
 modules/rocm.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 3af8eb577..0222218c8 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -59,7 +59,13 @@ class ROCmEnvironment(Environment):
                     super().__init__(lib)
                     break
         else:
-            super().__init__(os.path.join(path, "lib", "libamdhip64.so"))
+            # Check 64bit path first, then fall back if it doesn't exist
+            # FIXME: This may be brittle on Debian-based distributions
+            joined_path = os.path.join(path, "lib64", "libamdhip64.so")
+            if not os.path.exists(joined_path):
+                joined_path = os.path.join(path, "lib", "libamdhip64.so")
+
+            super().__init__(joined_path)
         self.path = path
 
 

From d52d84eda59351c4a7ae0c9bfd09a54d9966f819 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 16:49:29 +0900
Subject: [PATCH 066/230] clean up

---
 installer.py    |  7 ++-----
 modules/rocm.py | 21 +++++++++++++--------
 2 files changed, 15 insertions(+), 13 deletions(-)

diff --git a/installer.py b/installer.py
index 98ffef561..1bbb03719 100644
--- a/installer.py
+++ b/installer.py
@@ -1015,11 +1015,8 @@ def check_torch():
     if rocm.is_installed:
         if sys.platform == "win32": # CPU, DirectML, ZLUDA
             rocm.conceal()
-        elif rocm.is_wsl: # WSL ROCm
-            try:
-                rocm.load_hsa_runtime()
-            except OSError:
-                log.error("ROCm: failed to preload HSA runtime")
+        else: # ROCm
+            rocm.load_libraries()
     if args.version:
         return
     if not args.skip_all:
diff --git a/modules/rocm.py b/modules/rocm.py
index 0222218c8..365502413 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -73,7 +73,11 @@ class ROCmEnvironment(Environment):
 class PythonPackageEnvironment(Environment):
     def __init__(self):
         import _rocm_sdk_core
-        super().__init__(os.path.join(_rocm_sdk_core.__path__[0], "bin", "amdhip64_7.dll"))
+        if sys.platform == "win32":
+            path = os.path.join(_rocm_sdk_core.__path__[0], "bin", "amdhip64_7.dll")
+        else:
+            raise NotImplementedError
+        super().__init__(path)
 
 
 class MicroArchitecture(Enum):
@@ -216,13 +220,13 @@ if sys.platform == "win32":
         return [Agent(x.split(' ')[-1].strip()) for x in out.split("\n")]
 
     def get_distribution(agent: Agent) -> str:
-        if agent.gfx_version >= 0x1100 and agent.gfx_version < 0x1110:
+        if (agent.gfx_version & 0xFFF0) == 0x1100:
             return "gfx110X-dgpu"
         if agent.gfx_version == 0x1151:
             return "gfx1151"
-        if agent.gfx_version >= 0x1200 and agent.gfx_version < 0x1210:
+        if (agent.gfx_version & 0xFFF0) == 0x1200:
             return "gfx120X-all"
-        if agent.gfx_version >= 0x940 and agent.gfx_version < 0x950:
+        if (agent.gfx_version & 0xFFF0) == 0x940:
             return "gfx94X-dcgpu"
         if agent.gfx_version == 0x950:
             return "gfx950-dcgpu"
@@ -240,16 +244,17 @@ else:
             agents = [x.strip().split(" ")[-1] for x in agents if x.startswith('  Name:') and "CPU" not in x]
         return [Agent(x) for x in agents]
 
-    def load_hsa_runtime() -> None:
+    def load_libraries() -> None:
         try:
-            # Preload stdc++ library. This will ignore Anaconda stdc++ library.
+            # Preload stdc++ library. This will bypass Anaconda stdc++ library.
             load_library_global("/lib/x86_64-linux-gnu/libstdc++.so.6")
             # Use tcmalloc if possible.
             load_library_global("/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4")
+            if is_wsl:
+                # Preload rocr4wsl.
+                load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
         except OSError:
             pass
-        # Preload HSA Runtime library.
-        load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
 
     def set_blaslt_enabled(enabled: bool) -> None:
         if enabled:

From 579b1f3175a2fe9ea7be049f3e1a19f2db274975 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 17:01:14 +0900
Subject: [PATCH 067/230] do not load global amdhip64, let pytorch load it

---
 modules/rocm.py | 45 ++++++++++++++++++++-------------------------
 1 file changed, 20 insertions(+), 25 deletions(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 365502413..26b8f9f3a 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -41,10 +41,7 @@ def conceal():
 
 
 class Environment:
-    hip: ctypes.CDLL
-
-    def __init__(self, path: str):
-        self.hip = ctypes.CDLL(path)
+    pass
 
 
 # rocm is installed system-wide
@@ -52,32 +49,21 @@ class ROCmEnvironment(Environment):
     path: str
 
     def __init__(self, path: str):
-        if sys.platform == "win32":
-            for v in (6, 7):
-                lib = os.path.join(path, "bin", f"amdhip64_{v}.dll")
-                if os.path.exists(lib):
-                    super().__init__(lib)
-                    break
-        else:
-            # Check 64bit path first, then fall back if it doesn't exist
-            # FIXME: This may be brittle on Debian-based distributions
-            joined_path = os.path.join(path, "lib64", "libamdhip64.so")
-            if not os.path.exists(joined_path):
-                joined_path = os.path.join(path, "lib", "libamdhip64.so")
-
-            super().__init__(joined_path)
         self.path = path
 
 
 # rocm-sdk package is installed
 class PythonPackageEnvironment(Environment):
+    hip: ctypes.CDLL
+
     def __init__(self):
         import _rocm_sdk_core
         if sys.platform == "win32":
             path = os.path.join(_rocm_sdk_core.__path__[0], "bin", "amdhip64_7.dll")
         else:
             raise NotImplementedError
-        super().__init__(path)
+        # This library will be loaded/used by PyTorch. So it won't make conflicts.
+        self.hip = ctypes.CDLL(path)
 
 
 class MicroArchitecture(Enum):
@@ -202,12 +188,21 @@ def find() -> Union[Environment, None]:
 
 
 def get_version() -> str:
-    version = ctypes.c_int()
-    environment.hip.hipRuntimeGetVersion(ctypes.byref(version))
-    major = version.value // 10000000
-    minor = (version.value // 100000) % 100
-    #patch = version.value % 100000
-    return f"{major}.{minor}"
+    if isinstance(environment, ROCmEnvironment):
+        if sys.platform == "win32":
+            # ROCm is system-wide installed. Assume the version is the folder name. (e.g. C:\Program Files\AMD\ROCm\6.4)
+            # hipconfig requires Perl
+            return os.path.basename(environment.path) or os.path.basename(os.path.dirname(environment.path))
+        else:
+            arr = spawn("hipconfig --version", cwd=os.path.join(environment.path, 'bin')).split(".")
+            return f'{arr[0]}.{arr[1]}' if len(arr) >= 2 else None
+    else:
+        version = ctypes.c_int()
+        environment.hip.hipRuntimeGetVersion(ctypes.byref(version))
+        major = version.value // 10000000
+        minor = (version.value // 100000) % 100
+        #patch = version.value % 100000
+        return f"{major}.{minor}"
 
 
 if sys.platform == "win32":

From 71fde8a897ec6b9e4663c2ca89759f581f2b3296 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 27 Sep 2025 11:32:46 +0300
Subject: [PATCH 068/230] ROCm and Zluda don't fallback to CPU and clenup
 strings

---
 installer.py                    | 47 ++++++++++-----------------------
 modules/intel/ipex/__init__.py  |  2 +-
 modules/intel/ipex/attention.py |  4 +--
 modules/intel/ipex/hijacks.py   | 12 ++++-----
 modules/sdnq/__init__.py        |  4 +--
 modules/sdnq/common.py          |  2 +-
 6 files changed, 26 insertions(+), 45 deletions(-)

diff --git a/installer.py b/installer.py
index 1bbb03719..22172970b 100644
--- a/installer.py
+++ b/installer.py
@@ -677,18 +677,9 @@ def install_rocm_zluda():
     if args.skip_all or args.skip_requirements:
         return torch_command
     from modules import rocm
-    if rocm.err is not None:
-        log.warning(f'ROCm: error checking ROCm toolkit: {rocm.err}')
-        log.info('Using CPU-only torch')
-        return os.environ.get('TORCH_COMMAND', 'torch torchvision')
-    if not rocm.is_installed:
-        log.warning('ROCm: could not find ROCm toolkit installed')
-        log.info('Using CPU-only torch')
-        return os.environ.get('TORCH_COMMAND', 'torch torchvision')
 
     log.info('ROCm: AMD toolkit detected')
-    # if not is_windows:
-    #    os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow-rocm')
+    #os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow')
 
     device = None
     try:
@@ -701,8 +692,6 @@ def install_rocm_zluda():
                 index = 0
                 for idx, gpu in enumerate(amd_gpus):
                     index = idx
-                    # if gpu.name.startswith('gfx11') and os.environ.get('TENSORFLOW_PACKAGE') == 'tensorflow-rocm': # do not use tensorflow-rocm for navi 3x
-                    #    os.environ['TENSORFLOW_PACKAGE'] = 'tensorflow==2.13.0'
                     if not gpu.is_apu:
                         # although apu was found, there can be a dedicated card. do not break loop.
                         # if no dedicated card was found, apu will be used.
@@ -722,22 +711,23 @@ def install_rocm_zluda():
     log.info(msg)
 
     if sys.platform == "win32":
-        #check_python(supported_minors=[10, 11, 12, 13], reason='ZLUDA backend requires a Python version between 3.10 and 3.13')
-
         if args.use_rocm: # TODO install: switch to pytorch source when it becomes available
-            if isinstance(rocm.environment, rocm.PythonPackageEnvironment): # TheRock
+            if device is not None and isinstance(rocm.environment, rocm.PythonPackageEnvironment): # TheRock
+                check_python(supported_minors=[11, 12, 13], reason='ROCm backend requires a Python version between 3.11 and 3.13')
                 torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{rocm.get_distribution(device)}')
             else:
-                check_python(supported_minors=[12], reason='AMD Windows preview requires a Python version 3.12')
+                check_python(supported_minors=[12], reason='ROCm Windows preview requires Python version 3.12')
                 torch_command = os.environ.get('TORCH_COMMAND', '--no-cache-dir https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torch-2.8.0a0%2Bgitfc14c65-cp312-cp312-win_amd64.whl https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torchvision-0.24.0a0%2Bc85f008-cp312-cp312-win_amd64.whl')
         else:
+            #check_python(supported_minors=[10, 11, 12, 13], reason='ZLUDA backend requires a Python version between 3.10 and 3.13')
+            torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+cu118 torchvision==0.22.1+cu118 --index-url https://download.pytorch.org/whl/cu118')
+
             if args.device_id is not None:
                 if os.environ.get('HIP_VISIBLE_DEVICES', None) is not None:
                     log.warning('Setting HIP_VISIBLE_DEVICES and --device-id at the same time may be mistake.')
                 os.environ['HIP_VISIBLE_DEVICES'] = args.device_id
                 del args.device_id
 
-            error = None
             from modules import zluda_installer
             try:
                 if args.reinstall or zluda_installer.is_reinstall_needed():
@@ -745,19 +735,12 @@ def install_rocm_zluda():
                 zluda_installer.install()
                 zluda_installer.set_default_agent(device)
             except Exception as e:
-                error = e
                 log.warning(f'Failed to install ZLUDA: {e}')
 
-            if error is None:
-                try:
-                    zluda_installer.load()
-                    torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+cu118 torchvision==0.22.1+cu118 --index-url https://download.pytorch.org/whl/cu118')
-                except Exception as e:
-                    error = e
-                    log.warning(f'Failed to load ZLUDA: {e}')
-            if error is not None:
-                log.info('Using CPU-only torch')
-                torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
+            try:
+                zluda_installer.load()
+            except Exception as e:
+                log.warning(f'Failed to load ZLUDA: {e}')
     else:
         #check_python(supported_minors=[10, 11, 12, 13], reason='ROCm backend requires a Python version between 3.10 and 3.13')
 
@@ -793,7 +776,7 @@ def install_rocm_zluda():
         log.info(f'ROCm: HSA_OVERRIDE_GFX_VERSION auto config skipped: device={device.name if device is not None else None} version={os.environ.get("HSA_OVERRIDE_GFX_VERSION", None)}')
     else:
         gfx_ver = device.get_gfx_version()
-        if gfx_ver is not None:
+        if gfx_ver is not None and device.name.removeprefix("gfx") != gfx_ver.replace(".", ""):
             os.environ.setdefault('HSA_OVERRIDE_GFX_VERSION', gfx_ver)
             log.info(f'ROCm: HSA_OVERRIDE_GFX_VERSION config overridden: device={device.name} version={os.environ.get("HSA_OVERRIDE_GFX_VERSION", None)}')
 
@@ -936,8 +919,8 @@ def check_torch():
     if torch_command != '':
         pass
     else:
-        is_cuda_available = allow_cuda and (shutil.which('nvidia-smi') is not None or args.use_xformers or os.path.exists(os.path.join(os.environ.get('SystemRoot') or r'C:\Windows', 'System32', 'nvidia-smi.exe')))
-        is_rocm_available = allow_rocm and rocm.is_installed
+        is_cuda_available = allow_cuda and (args.use_cuda or shutil.which('nvidia-smi') is not None or args.use_xformers or os.path.exists(os.path.join(os.environ.get('SystemRoot') or r'C:\Windows', 'System32', 'nvidia-smi.exe')))
+        is_rocm_available = allow_rocm and (args.use_rocm or args.use_zluda or rocm.is_installed)
         is_ipex_available = allow_ipex and (args.use_ipex or shutil.which('sycl-ls') is not None or shutil.which('sycl-ls.exe') is not None or os.environ.get('ONEAPI_ROOT') is not None or os.path.exists('/opt/intel/oneapi') or os.path.exists("C:/Program Files (x86)/Intel/oneAPI") or os.path.exists("C:/oneAPI"))
 
         if is_cuda_available and args.use_cuda: # prioritize cuda
@@ -965,8 +948,6 @@ def check_torch():
                     install(torch_command, 'torch torchvision')
                 install('onnxruntime-directml', 'onnxruntime-directml', ignore=True)
             else:
-                if args.use_zluda:
-                    log.warning("ZLUDA failed to initialize: no HIP SDK found")
                 log.warning('Torch: CPU-only version installed')
                 torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
     if 'torch' in torch_command and not args.version:
diff --git a/modules/intel/ipex/__init__.py b/modules/intel/ipex/__init__.py
index 93f94d280..3d49beca7 100644
--- a/modules/intel/ipex/__init__.py
+++ b/modules/intel/ipex/__init__.py
@@ -165,7 +165,7 @@ def ipex_init(): # pylint: disable=too-many-statements
                         pass
 
             # Memory:
-            if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read():
+            if "linux" in sys.platform and "WSL2" in os.popen("uname -a").read():
                 torch.xpu.empty_cache = lambda: None
             torch.cuda.empty_cache = torch.xpu.empty_cache
 
diff --git a/modules/intel/ipex/attention.py b/modules/intel/ipex/attention.py
index c3e8ef8ed..aacccca75 100644
--- a/modules/intel/ipex/attention.py
+++ b/modules/intel/ipex/attention.py
@@ -8,8 +8,8 @@ from functools import cache, wraps
 
 # ARC GPUs can't allocate more than 4GB to a single block so we slice the attention layers
 
-dynamic_attention_slice_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 1))
-dynamic_attention_trigger_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 0.5))
+dynamic_attention_slice_rate = float(os.environ.get("IPEX_SDPA_SLICE_TRIGGER_RATE", "1"))
+dynamic_attention_trigger_rate = float(os.environ.get("IPEX_ATTENTION_SLICE_RATE", "0.5"))
 
 # Find something divisible with the input_tokens
 @cache
diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index c9be1f789..8e8961476 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -80,8 +80,8 @@ def torch_get_autocast_dtype(device_type=None):
 # IPEX 2.5 and above has partial support but doesn't really work most of the time.
 original_interpolate = torch.nn.functional.interpolate
 @wraps(torch.nn.functional.interpolate)
-def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
-    if mode in {'bicubic', 'bilinear'}:
+def interpolate(tensor, size=None, scale_factor=None, mode="nearest", align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
+    if mode in {"bicubic", "bilinear"}:
         return_device = tensor.device
         return_dtype = tensor.dtype
         return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode,
@@ -94,8 +94,8 @@ def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corn
 # SwinIR BF16:
 original_functional_pad = torch.nn.functional.pad
 @wraps(torch.nn.functional.pad)
-def functional_pad(input, pad, mode='constant', value=None):
-    if mode == 'reflect' and input.dtype == torch.bfloat16:
+def functional_pad(input, pad, mode="constant", value=None):
+    if mode == "reflect" and input.dtype == torch.bfloat16:
         return original_functional_pad(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16)
     else:
         return original_functional_pad(input, pad, mode=mode, value=value)
@@ -365,13 +365,13 @@ def ipex_hijacks():
     except Exception:
         pass
 
-    if os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '0':
+    if os.environ.get("IPEX_FORCE_ATTENTION_SLICE", "0") == "0":
         if torch_version[0] > 2 or (torch_version[0] == 2 and torch_version[1] >= 7):
             use_dynamic_attention = False # torch 2.7 has flash atten support
         else:
             use_dynamic_attention = True
     else:
-        use_dynamic_attention = bool(os.environ.get('IPEX_FORCE_ATTENTION_SLICE', '0') == '1')
+        use_dynamic_attention = bool(os.environ.get("IPEX_FORCE_ATTENTION_SLICE", "0") == "1")
 
     if use_dynamic_attention:
         from .attention import dynamic_scaled_dot_product_attention
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 6ba49cbb1..6b8e7c409 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -404,7 +404,7 @@ class SDNQQuantizer(DiffusersQuantizer):
     def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument
         if shared.opts.diffusers_offload_mode != "none":
             model = model.to(devices.cpu)
-        devices.torch_gc(force=True, reason='sdnq')
+        devices.torch_gc(force=True, reason="sdnq")
         return model
 
     def get_accelerator_warm_up_factor(self):
@@ -440,7 +440,7 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return missing_keys
 
-    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict: # pylint: disable=unused-argument
+    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict:
         """
         needed for transformers compatibilty, no-op function
         """
diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index e6ed21d4d..ecd06a653 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -34,7 +34,7 @@ if hasattr(torch, "float8_e5m2fnuz"):
     dtype_dict["float8_e5m2fnuz"] = {"min": -57344, "max": 57344, "num_bits": 8, "target_dtype": "fp8", "torch_dtype": torch.float8_e5m2fnuz, "storage_dtype": torch.float8_e5m2fnuz, "is_unsigned": False, "is_integer": False}
 
 use_torch_compile = shared.opts.sdnq_dequantize_compile # this setting requires a full restart of the webui to apply
-use_tensorwise_fp8_matmul = os.environ.get('SDNQ_USE_TENSORWISE_FP8_MATMUL', "1").lower() not in {"0", "false", "no"} # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
+use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MATMUL", "1").lower() not in {"0", "false", "no"} # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
 
 linear_types = ("Linear",)
 conv_types = ("Conv1d", "Conv2d", "Conv3d")

From 503a1787944070f2d34f0ec62b04120c8c6d0670 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 27 Sep 2025 11:39:24 +0300
Subject: [PATCH 069/230] Don't load rocm hsa on non wsl envs and remove unused
 lib hijacks

---
 modules/rocm.py          | 80 +++++++++++++++-------------------------
 modules/sdnq/__init__.py |  2 +-
 2 files changed, 30 insertions(+), 52 deletions(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 26b8f9f3a..c5fe24d37 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -116,17 +116,6 @@ class Agent:
         return None
 
 
-def get_version_torch() -> Union[str, None]:
-    version_ = None
-    try:
-        version_ = importlib.metadata.version("torch")
-    except importlib.metadata.PackageNotFoundError:
-        return None
-    if "+rocm" not in version_: # unofficial build, non-rocm torch.
-        return None
-    return version_.split("+rocm")[1]
-
-
 def find() -> Union[Environment, None]:
     hip_path = shutil.which("hipconfig")
     if hip_path is not None:
@@ -205,6 +194,29 @@ def get_version() -> str:
         return f"{major}.{minor}"
 
 
+def get_flash_attention_command(agent: Agent):
+    default = "git+https://github.com/ROCm/flash-attention"
+    if agent.gfx_version >= 0x1100 and agent.gfx_version < 0x1200 and os.environ.get("FLASH_ATTENTION_USE_TRITON_ROCM", "false").lower() != "true":
+        # use the navi_rotary_fix fork because the original doesn't support rotary_emb for transformers
+        # original: "git+https://github.com/ROCm/flash-attention@howiejay/navi_support"
+        default = "git+https://github.com/Disty0/flash-attention@navi_rotary_fix"
+    return "--no-build-isolation " + os.environ.get("FLASH_ATTENTION_PACKAGE", default)
+
+
+def get_distribution(agent: Agent) -> str:
+    if (agent.gfx_version & 0xFFF0) == 0x1100:
+        return "gfx110X-dgpu"
+    if agent.gfx_version == 0x1151:
+        return "gfx1151"
+    if (agent.gfx_version & 0xFFF0) == 0x1200:
+        return "gfx120X-all"
+    if (agent.gfx_version & 0xFFF0) == 0x940:
+        return "gfx94X-dcgpu"
+    if agent.gfx_version == 0x950:
+        return "gfx950-dcgpu"
+    raise Exception(f"Unsupported GPU architecture: {agent.name}")
+
+
 if sys.platform == "win32":
     def get_agents() -> List[Agent]:
         if isinstance(environment, ROCmEnvironment):
@@ -214,21 +226,7 @@ if sys.platform == "win32":
         out = out.strip()
         return [Agent(x.split(' ')[-1].strip()) for x in out.split("\n")]
 
-    def get_distribution(agent: Agent) -> str:
-        if (agent.gfx_version & 0xFFF0) == 0x1100:
-            return "gfx110X-dgpu"
-        if agent.gfx_version == 0x1151:
-            return "gfx1151"
-        if (agent.gfx_version & 0xFFF0) == 0x1200:
-            return "gfx120X-all"
-        if (agent.gfx_version & 0xFFF0) == 0x940:
-            return "gfx94X-dcgpu"
-        if agent.gfx_version == 0x950:
-            return "gfx950-dcgpu"
-        raise Exception(f"Unsupported GPU architecture: {agent.name}")
-
     is_wsl: bool = False
-    version_torch = None
 else:
     def get_agents() -> List[Agent]:
         try:
@@ -240,36 +238,16 @@ else:
         return [Agent(x) for x in agents]
 
     def load_libraries() -> None:
-        try:
-            # Preload stdc++ library. This will bypass Anaconda stdc++ library.
-            load_library_global("/lib/x86_64-linux-gnu/libstdc++.so.6")
-            # Use tcmalloc if possible.
-            load_library_global("/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4")
-            if is_wsl:
+        if is_wsl:
+            try:
+                # Preload stdc++ library. This will bypass Anaconda stdc++ library.
+                load_library_global("/lib/x86_64-linux-gnu/libstdc++.so.6")
                 # Preload rocr4wsl.
                 load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
-        except OSError:
-            pass
-
-    def set_blaslt_enabled(enabled: bool) -> None:
-        if enabled:
-            os.environ["HIPBLASLT_TENSILE_LIBPATH"] = blaslt_tensile_libpath
-        else:
-            os.environ["TORCH_BLAS_PREFER_HIPBLASLT"] = "0"
-
-    def get_blaslt_enabled() -> bool:
-        return version == version_torch and bool(int(os.environ.get("TORCH_BLAS_PREFER_HIPBLASLT", "1")))
-
-    def get_flash_attention_command(agent: Agent):
-        default = "git+https://github.com/ROCm/flash-attention"
-        if agent.gfx_version >= 0x1100 and agent.gfx_version < 0x1200 and os.environ.get("FLASH_ATTENTION_USE_TRITON_ROCM", "false").lower() != "true":
-            # use the navi_rotary_fix fork because the original doesn't support rotary_emb for transformers
-            # original: "git+https://github.com/ROCm/flash-attention@howiejay/navi_support"
-            default = "git+https://github.com/Disty0/flash-attention@navi_rotary_fix"
-        return "--no-build-isolation " + os.environ.get("FLASH_ATTENTION_PACKAGE", default)
+            except OSError:
+                pass
 
     is_wsl: bool = os.environ.get('WSL_DISTRO_NAME', 'unknown' if spawn('wslpath -w /') else None) is not None
-    version_torch = get_version_torch()
 environment = None
 err = None
 try:
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 6b8e7c409..82689efca 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -440,7 +440,7 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return missing_keys
 
-    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict:
+    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """

From 059392afc7f3136d74fe75e9867900147cce58d9 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sat, 27 Sep 2025 17:51:59 +0900
Subject: [PATCH 070/230] clean up

---
 installer.py    |  4 ++--
 modules/rocm.py | 19 ++++++++++---------
 2 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/installer.py b/installer.py
index 22172970b..b5ddca5b7 100644
--- a/installer.py
+++ b/installer.py
@@ -996,8 +996,8 @@ def check_torch():
     if rocm.is_installed:
         if sys.platform == "win32": # CPU, DirectML, ZLUDA
             rocm.conceal()
-        else: # ROCm
-            rocm.load_libraries()
+        elif rocm.is_wsl: # ROCm WSL
+            rocm.preload_hsa_runtime()
     if args.version:
         return
     if not args.skip_all:
diff --git a/modules/rocm.py b/modules/rocm.py
index c5fe24d37..5188409d4 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -3,7 +3,6 @@ import sys
 import ctypes
 import shutil
 import subprocess
-import importlib.metadata
 from typing import Union, List
 from enum import Enum
 
@@ -178,6 +177,7 @@ def find() -> Union[Environment, None]:
 
 def get_version() -> str:
     if isinstance(environment, ROCmEnvironment):
+        # We don't load the hip library that will not be used by PyTorch.
         if sys.platform == "win32":
             # ROCm is system-wide installed. Assume the version is the folder name. (e.g. C:\Program Files\AMD\ROCm\6.4)
             # hipconfig requires Perl
@@ -186,6 +186,7 @@ def get_version() -> str:
             arr = spawn("hipconfig --version", cwd=os.path.join(environment.path, 'bin')).split(".")
             return f'{arr[0]}.{arr[1]}' if len(arr) >= 2 else None
     else:
+        # If rocm-sdk package is installed, the hip library may be used by PyTorch.
         version = ctypes.c_int()
         environment.hip.hipRuntimeGetVersion(ctypes.byref(version))
         major = version.value // 10000000
@@ -194,7 +195,7 @@ def get_version() -> str:
         return f"{major}.{minor}"
 
 
-def get_flash_attention_command(agent: Agent):
+def get_flash_attention_command(agent: Agent) -> str:
     default = "git+https://github.com/ROCm/flash-attention"
     if agent.gfx_version >= 0x1100 and agent.gfx_version < 0x1200 and os.environ.get("FLASH_ATTENTION_USE_TRITON_ROCM", "false").lower() != "true":
         # use the navi_rotary_fix fork because the original doesn't support rotary_emb for transformers
@@ -237,15 +238,15 @@ else:
             agents = [x.strip().split(" ")[-1] for x in agents if x.startswith('  Name:') and "CPU" not in x]
         return [Agent(x) for x in agents]
 
-    def load_libraries() -> None:
-        if is_wsl:
-            try:
+    def preload_hsa_runtime():
+        try:
+            if shutil.which("conda") is not None:
                 # Preload stdc++ library. This will bypass Anaconda stdc++ library.
                 load_library_global("/lib/x86_64-linux-gnu/libstdc++.so.6")
-                # Preload rocr4wsl.
-                load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
-            except OSError:
-                pass
+            # Preload rocr4wsl. The user don't have to replace the library file.
+            load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
+        except OSError:
+            pass
 
     is_wsl: bool = os.environ.get('WSL_DISTRO_NAME', 'unknown' if spawn('wslpath -w /') else None) is not None
 environment = None

From 384c2037a2b98ddd923ad0693a7680b587e2306c Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Sat, 27 Sep 2025 14:38:29 +0300
Subject: [PATCH 071/230] new segmind (5) model covers

---
 models/Reference/segmind--SSD-1B.jpg          | Bin 50008 -> 85815 bytes
 models/Reference/segmind--SegMoE-4x2-v0.jpg   | Bin 35297 -> 60715 bytes
 .../Reference/segmind--SegMoE-SD-4x2-v0.jpg   | Bin 21996 -> 52031 bytes
 models/Reference/segmind--Segmind-Vega.jpg    | Bin 75379 -> 77679 bytes
 models/Reference/segmind--tiny-sd.jpg         | Bin 90089 -> 82205 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/models/Reference/segmind--SSD-1B.jpg b/models/Reference/segmind--SSD-1B.jpg
index 51907db27369a369215e1cca5054e0355c76f0fe..11a96eedb2971787b726028b8b08597d2de25798 100644
GIT binary patch
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From 35b1c6b07d41936919ceffcd7f99e56758a7e675 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 05:39:23 +0900
Subject: [PATCH 072/230] windows install rocm if not installed

---
 installer.py               | 56 ++++++++++++++++++++--------------
 modules/rocm.py            | 62 +++++++++++++++++++++++---------------
 modules/windows_hip_ffi.py | 44 +++++++++++++++++++++++++++
 3 files changed, 115 insertions(+), 47 deletions(-)
 create mode 100644 modules/windows_hip_ffi.py

diff --git a/installer.py b/installer.py
index b5ddca5b7..b5e9ba1ad 100644
--- a/installer.py
+++ b/installer.py
@@ -678,32 +678,42 @@ def install_rocm_zluda():
         return torch_command
     from modules import rocm
 
-    log.info('ROCm: AMD toolkit detected')
+    amd_gpus = []
+    try:
+        if sys.platform == "win32" and not rocm.is_installed:
+            amd_gpus = rocm.driver_get_agents()
+        else:
+            amd_gpus = rocm.get_agents()
+            log.info('ROCm: AMD toolkit detected')
+    except Exception as e:
+        log.warning(f'ROCm agent enumerator failed: {e}')
+
     #os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow')
 
     device = None
-    try:
-        amd_gpus = rocm.get_agents()
-        if len(amd_gpus) == 0:
-            log.warning('ROCm: no agent was found')
+    if len(amd_gpus) == 0:
+        log.warning('ROCm: no agent was found')
+    else:
+        log.info(f'ROCm: agents={[gpu.name for gpu in amd_gpus]}')
+        if args.device_id is None:
+            index = 0
+            for idx, gpu in enumerate(amd_gpus):
+                index = idx
+                if not gpu.is_apu:
+                    # although apu was found, there can be a dedicated card. do not break loop.
+                    # if no dedicated card was found, apu will be used.
+                    break
+            os.environ.setdefault('HIP_VISIBLE_DEVICES', str(index))
+            device = amd_gpus[index]
         else:
-            log.info(f'ROCm: agents={[gpu.name for gpu in amd_gpus]}')
-            if args.device_id is None:
-                index = 0
-                for idx, gpu in enumerate(amd_gpus):
-                    index = idx
-                    if not gpu.is_apu:
-                        # although apu was found, there can be a dedicated card. do not break loop.
-                        # if no dedicated card was found, apu will be used.
-                        break
-                os.environ.setdefault('HIP_VISIBLE_DEVICES', str(index))
-                device = amd_gpus[index]
-            else:
-                device_id = int(args.device_id)
-                if device_id < len(amd_gpus):
-                    device = amd_gpus[device_id]
-    except Exception as e:
-        log.warning(f'ROCm agent enumerator failed: {e}')
+            device_id = int(args.device_id)
+            if device_id < len(amd_gpus):
+                device = amd_gpus[device_id]
+
+    if sys.platform == "win32" and args.use_rocm and not rocm.is_installed:
+        check_python(supported_minors=[11, 12, 13], reason='ROCm backend requires a Python version between 3.11 and 3.13')
+        install(f"rocm rocm-sdk-core --index-url https://rocm.nightlies.amd.com/v2-staging/{device.therock}")
+        rocm.refresh()
 
     msg = f'ROCm: version={rocm.version}'
     if device is not None:
@@ -714,7 +724,7 @@ def install_rocm_zluda():
         if args.use_rocm: # TODO install: switch to pytorch source when it becomes available
             if device is not None and isinstance(rocm.environment, rocm.PythonPackageEnvironment): # TheRock
                 check_python(supported_minors=[11, 12, 13], reason='ROCm backend requires a Python version between 3.11 and 3.13')
-                torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{rocm.get_distribution(device)}')
+                torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{device.therock}')
             else:
                 check_python(supported_minors=[12], reason='ROCm Windows preview requires Python version 3.12')
                 torch_command = os.environ.get('TORCH_COMMAND', '--no-cache-dir https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torch-2.8.0a0%2Bgitfc14c65-cp312-cp312-win_amd64.whl https://repo.radeon.com/rocm/windows/rocm-rel-6.4.4/torchvision-0.24.0a0%2Bc85f008-cp312-cp312-win_amd64.whl')
diff --git a/modules/rocm.py b/modules/rocm.py
index 5188409d4..8dfcd9ec8 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -105,6 +105,20 @@ class Agent:
         self.is_apu = (self.gfx_version & 0xFFF0 == 0x1150) or self.gfx_version in (0x801, 0x902, 0x90c, 0x1013, 0x1033, 0x1035, 0x1036, 0x1103,)
         self.blaslt_supported = os.path.exists(os.path.join(blaslt_tensile_libpath, f"Kernels.so-000-{name}.hsaco" if sys.platform == "win32" else f"extop_{name}.co"))
 
+    @property
+    def therock(self) -> str:
+        if (self.gfx_version & 0xFFF0) == 0x1100:
+            return "gfx110X-dgpu"
+        if self.gfx_version == 0x1151:
+            return "gfx1151"
+        if (self.gfx_version & 0xFFF0) == 0x1200:
+            return "gfx120X-all"
+        if (self.gfx_version & 0xFFF0) == 0x940:
+            return "gfx94X-dcgpu"
+        if self.gfx_version == 0x950:
+            return "gfx950-dcgpu"
+        raise Exception(f"Unsupported GPU architecture: {self.name}")
+
     def get_gfx_version(self) -> Union[str, None]:
         if self.gfx_version >= 0x1100 and self.gfx_version < 0x1200:
             return "11.0.0"
@@ -204,29 +218,29 @@ def get_flash_attention_command(agent: Agent) -> str:
     return "--no-build-isolation " + os.environ.get("FLASH_ATTENTION_PACKAGE", default)
 
 
-def get_distribution(agent: Agent) -> str:
-    if (agent.gfx_version & 0xFFF0) == 0x1100:
-        return "gfx110X-dgpu"
-    if agent.gfx_version == 0x1151:
-        return "gfx1151"
-    if (agent.gfx_version & 0xFFF0) == 0x1200:
-        return "gfx120X-all"
-    if (agent.gfx_version & 0xFFF0) == 0x940:
-        return "gfx94X-dcgpu"
-    if agent.gfx_version == 0x950:
-        return "gfx950-dcgpu"
-    raise Exception(f"Unsupported GPU architecture: {agent.name}")
-
-
 if sys.platform == "win32":
     def get_agents() -> List[Agent]:
         if isinstance(environment, ROCmEnvironment):
             out = spawn("amdgpu-arch", cwd=os.path.join(environment.path, 'bin'))
         else:
+            # Assume that amdgpu-arch is in PATH (venv/Scripts/amdgpu-arch.exe)
             out = spawn("amdgpu-arch")
         out = out.strip()
         return [Agent(x.split(' ')[-1].strip()) for x in out.split("\n")]
 
+    def driver_get_agents() -> List[Agent]:
+        # unsafe and experimental feature
+        from modules import windows_hip_ffi
+        hip = windows_hip_ffi.HIP()
+        count = hip.get_device_count()
+        agents = [None] * count
+        for i in range(count):
+            prop = hip.get_device_properties(i)
+            name = prop.gcnArchName.decode('utf-8').strip('\x00')
+            agents[i] = Agent(name)
+        del hip
+        return agents
+
     is_wsl: bool = False
 else:
     def get_agents() -> List[Agent]:
@@ -250,16 +264,16 @@ else:
 
     is_wsl: bool = os.environ.get('WSL_DISTRO_NAME', 'unknown' if spawn('wslpath -w /') else None) is not None
 environment = None
-err = None
-try:
-    environment = find()
-except Exception as e:
-    err = e
 blaslt_tensile_libpath = ""
 is_installed = False
 version = None
-if environment is not None:
-    if isinstance(environment, ROCmEnvironment):
-        blaslt_tensile_libpath = os.environ.get("HIPBLASLT_TENSILE_LIBPATH", os.path.join(environment.path, "bin" if sys.platform == "win32" else "lib", "hipblaslt", "library"))
-    is_installed = True
-    version = get_version()
+
+def refresh():
+    global environment, blaslt_tensile_libpath, is_installed, version
+    environment = find()
+    if environment is not None:
+        if isinstance(environment, ROCmEnvironment):
+            blaslt_tensile_libpath = os.environ.get("HIPBLASLT_TENSILE_LIBPATH", os.path.join(environment.path, "bin" if sys.platform == "win32" else "lib", "hipblaslt", "library"))
+        is_installed = True
+        version = get_version()
+refresh()
diff --git a/modules/windows_hip_ffi.py b/modules/windows_hip_ffi.py
new file mode 100644
index 000000000..cc9e4a1e4
--- /dev/null
+++ b/modules/windows_hip_ffi.py
@@ -0,0 +1,44 @@
+import sys
+
+if sys.platform == "win32":
+    import ctypes
+    import ctypes.wintypes
+
+    class hipDeviceProp(ctypes.Structure):
+        _fields_ = [
+            ('__front__', ctypes.c_byte * 396),
+            ('gcnArchName', ctypes.c_char * 256),
+            ('__rear__', ctypes.c_byte * 820)
+        ]
+
+    class HIP:
+        def __init__(self):
+            ctypes.windll.kernel32.LoadLibraryA.restype = ctypes.wintypes.HMODULE
+            ctypes.windll.kernel32.LoadLibraryA.argtypes = [ctypes.c_char_p]
+            # amdhip64.dll is a part of AMDGPU drivers
+            self.handle = ctypes.windll.kernel32.LoadLibraryA(b"amdhip64.dll")
+            ctypes.windll.kernel32.GetLastError.restype = ctypes.wintypes.DWORD
+            ctypes.windll.kernel32.GetLastError.argtypes = []
+            assert ctypes.windll.kernel32.GetLastError() == 0
+            ctypes.windll.kernel32.GetProcAddress.restype = ctypes.c_void_p
+            ctypes.windll.kernel32.GetProcAddress.argtypes = [ctypes.wintypes.HMODULE, ctypes.c_char_p]
+            self.hipGetDeviceCount = ctypes.CFUNCTYPE(ctypes.c_int, ctypes.POINTER(ctypes.c_int))(
+                ctypes.windll.kernel32.GetProcAddress(self.handle, b"hipGetDeviceCount"))
+            self.hipGetDeviceProperties = ctypes.CFUNCTYPE(ctypes.c_int, ctypes.POINTER(hipDeviceProp), ctypes.c_int)(
+                ctypes.windll.kernel32.GetProcAddress(self.handle, b"hipGetDeviceProperties"))
+
+        def __del__(self):
+            #ctypes.windll.kernel32.FreeLibrary.argtypes = [ctypes.wintypes.HMODULE]
+            #ctypes.windll.kernel32.FreeLibrary(self.handle)
+            # Hopefully it does not make conflicts with amdhip64_7.dll
+            pass
+
+        def get_device_count(self):
+            count = ctypes.c_int()
+            assert self.hipGetDeviceCount(ctypes.byref(count)) == 0
+            return count.value
+
+        def get_device_properties(self, device_id):
+            prop = hipDeviceProp()
+            assert self.hipGetDeviceProperties(ctypes.byref(prop), device_id) == 0
+            return prop

From 587db51e9e0a03d556daab27ff5eb8b096028497 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 28 Sep 2025 00:22:00 +0300
Subject: [PATCH 073/230] Cleanup env vars and set
 TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL to 1

---
 installer.py                   | 58 +++++++++++-----------------------
 modules/intel/ipex/__init__.py |  2 +-
 modules/shared.py              |  3 +-
 3 files changed, 20 insertions(+), 43 deletions(-)

diff --git a/installer.py b/installer.py
index b5e9ba1ad..ea7f5b496 100644
--- a/installer.py
+++ b/installer.py
@@ -754,9 +754,6 @@ def install_rocm_zluda():
     else:
         #check_python(supported_minors=[10, 11, 12, 13], reason='ROCm backend requires a Python version between 3.10 and 3.13')
 
-        if os.environ.get("TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL", None) is None:
-            os.environ.setdefault('TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL', '1')
-
         if args.use_nightly:
             if rocm.version is None or float(rocm.version) >= 6.4: # assume the latest if version check fails
                 torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.4')
@@ -800,27 +797,6 @@ def install_ipex():
     args.use_ipex = True # pylint: disable=attribute-defined-outside-init
     log.info('IPEX: Intel OneAPI toolkit detected')
 
-    if os.environ.get("NEOReadDebugKeys", None) is None:
-        os.environ.setdefault('NEOReadDebugKeys', '1')
-
-    if os.environ.get("ClDeviceGlobalMemSizeAvailablePercent", None) is None:
-        os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
-
-    if os.environ.get("SYCL_CACHE_PERSISTENT", None) is None:
-        os.environ.setdefault('SYCL_CACHE_PERSISTENT', '1') # Jit cache
-
-    if os.environ.get("PYTORCH_ENABLE_XPU_FALLBACK", None) is None:
-        os.environ.setdefault('PYTORCH_ENABLE_XPU_FALLBACK', '1') # CPU fallback for unsupported ops
-
-    if os.environ.get("UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS", None) is None:
-        os.environ.setdefault('UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS', '1') # Work around the 4G alloc limit on Alchemist
-
-    # FP64 emulation causes random UR Errors
-    #if os.environ.get("OverrideDefaultFP64Settings", None) is None:
-    #    os.environ.setdefault('OverrideDefaultFP64Settings', '1')
-    #if os.environ.get("IGC_EnableDPEmulation", None) is None:
-    #    os.environ.setdefault('IGC_EnableDPEmulation', '1') # FP64 Emulation
-
     if args.use_nightly:
         torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/xpu')
     else:
@@ -834,20 +810,17 @@ def install_ipex():
 def install_openvino():
     t_start = time.time()
     log.info('OpenVINO: selected')
-    #check_python(supported_minors=[10, 11, 12, 13], reason='OpenVINO backend requires a Python version between 3.10 and 3.13')
+    os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
 
+    #check_python(supported_minors=[10, 11, 12, 13], reason='OpenVINO backend requires a Python version between 3.10 and 3.13')
     if sys.platform == 'darwin':
         torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.8.0 torchvision==0.23.0')
     else:
         torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.8.0+cpu torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cpu')
 
-    install(os.environ.get('OPENVINO_COMMAND', 'openvino==2025.3.0'), 'openvino')
-    install(os.environ.get('NNCF_COMMAND', 'nncf==2.18.0'), 'nncf')
-    os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
-    if os.environ.get("NEOReadDebugKeys", None) is None:
-        os.environ.setdefault('NEOReadDebugKeys', '1')
-    if os.environ.get("ClDeviceGlobalMemSizeAvailablePercent", None) is None:
-        os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
+    if not (args.skip_all or args.skip_requirements):
+        install(os.environ.get('OPENVINO_COMMAND', 'openvino==2025.3.0'), 'openvino')
+        install(os.environ.get('NNCF_COMMAND', 'nncf==2.18.0'), 'nncf')
     ts('openvino', t_start)
     return torch_command
 
@@ -969,12 +942,12 @@ def check_torch():
         try:
             import torch
             log.info(f'Torch {torch.__version__}')
-            if args.use_ipex and allow_ipex:
-                try:
-                    import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-                    log.info(f'Torch backend: Intel IPEX {ipex.__version__}')
-                except Exception:
-                    log.warning('IPEX: not found')
+            try:
+                import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+                log.info(f'Torch backend: Intel IPEX {ipex.__version__}')
+            except Exception:
+                pass
+            if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
                 if shutil.which('icpx') is not None:
                     log.info(f'{os.popen("icpx --version").read().rstrip()}')
                 for device in range(torch.xpu.device_count()):
@@ -1370,8 +1343,13 @@ def set_environment():
     os.environ.setdefault('PYTORCH_CUDA_ALLOC_CONF', allocator)
     os.environ.setdefault('PYTORCH_HIP_ALLOC_CONF', allocator)
     log.debug(f'Torch allocator: "{allocator}"')
-    if sys.platform == 'darwin':
-        os.environ.setdefault('PYTORCH_ENABLE_MPS_FALLBACK', '1')
+    os.environ.setdefault('TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL', '1')
+    os.environ.setdefault('NEOReadDebugKeys', '1')
+    os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
+    os.environ.setdefault('SYCL_CACHE_PERSISTENT', '1')
+    os.environ.setdefault('UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS', '1')
+    os.environ.setdefault('PYTORCH_ENABLE_XPU_FALLBACK', '1')
+    os.environ.setdefault('PYTORCH_ENABLE_MPS_FALLBACK', '1')
 
 
 def check_extensions():
diff --git a/modules/intel/ipex/__init__.py b/modules/intel/ipex/__init__.py
index 3d49beca7..0a4e19389 100644
--- a/modules/intel/ipex/__init__.py
+++ b/modules/intel/ipex/__init__.py
@@ -152,7 +152,7 @@ def ipex_init(): # pylint: disable=too-many-statements
                 # torch._int_mm directly uses onednn quantized matmul
                 # onednn qlinear is a wrapper around onednn quantized matmul
                 if hasattr(torch.ops, "onednn") and hasattr(torch.ops.onednn, "qlinear_pointwise"):
-                    def onednn_mm(x: torch.Tensor, y: torch.Tensor):
+                    def onednn_mm(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
                         # supports int8, fp32, fp16, and bf16 matmul with accumulation using a different dtype
                         # int8 matmul with onednn is slower than 16 bit with dim_size < 4096
                         return torch.ops.onednn.qlinear_pointwise.default(x, 1.0, 0, y, torch.ones(1, device=y.device), torch.zeros(1, device=y.device), None, 1.0, 0, torch.float32, "none", [], "none")
diff --git a/modules/shared.py b/modules/shared.py
index c8a645128..78ff5ac0c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -70,9 +70,8 @@ state = shared_state.State()
 
 # early select backend
 backend = Backend.DIFFUSERS
-if not hasattr(cmd_opts, "use_openvino"):
-    cmd_opts.use_openvino = False
 if cmd_opts.use_openvino: # override for openvino
+    os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
     from modules.intel.openvino import get_device_list as get_openvino_device_list # pylint: disable=ungrouped-imports
 elif cmd_opts.use_ipex or devices.has_xpu():
     from modules.intel.ipex import ipex_init

From a1214bfe30b39534ed07e9ca4c3b3826226b1eff Mon Sep 17 00:00:00 2001
From: Raphael Stoeckli <github@rabanti.ch>
Date: Sun, 28 Sep 2025 02:32:08 +0200
Subject: [PATCH 074/230] Added option to randomize loopback iterations

---
 scripts/loopback.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/scripts/loopback.py b/scripts/loopback.py
index a22b153a2..571f67665 100644
--- a/scripts/loopback.py
+++ b/scripts/loopback.py
@@ -1,4 +1,5 @@
 import math
+import random
 
 import gradio as gr
 from modules import images, processing, scripts_manager
@@ -21,10 +22,12 @@ class Script(scripts_manager.Script):
             final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
         with gr.Row():
             denoising_curve = gr.Dropdown(label="Strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
+        with gr.Row():
+            randomize_seed = gr.Checkbox(label="Randomize seed after each loop iteration", value=False)
 
-        return [loops, final_denoising_strength, denoising_curve]
+        return [loops, final_denoising_strength, denoising_curve, randomize_seed]
 
-    def run(self, p, loops, final_denoising_strength, denoising_curve): # pylint: disable=arguments-differ
+    def run(self, p, loops, final_denoising_strength, denoising_curve, randomize_seed): # pylint: disable=arguments-differ
         processing.fix_seed(p)
         initial_batch_count = p.n_iter
         p.extra_generation_params['Loopback'] = final_denoising_strength
@@ -76,6 +79,10 @@ class Script(scripts_manager.Script):
                 if initial_seed is None:
                     initial_seed = processed.seed
                     initial_info = processed.info
+                if randomize_seed:
+                    p.seed = random.randrange(4294967294)
+                    p.all_seeds = [p.seed]
+                    log.info(f'Setting random seed {p.seed} for loopback iteration {i}')
                 p.seed = processed.seed + 1 # why?
                 p.denoising_strength = calculate_denoising_strength(i + 1)
                 last_image = processed.images[0]

From 0de373288472d7085e85d24a15e2c2a85d986175 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 11:31:43 +0900
Subject: [PATCH 075/230] prioritize python pacakage check over PATH

---
 modules/rocm.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 8dfcd9ec8..3ffff069a 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -130,17 +130,16 @@ class Agent:
 
 
 def find() -> Union[Environment, None]:
+    try: # TheRock
+        import _rocm_sdk_core # pylint: disable=unused-import
+        return PythonPackageEnvironment()
+    except ImportError:
+        pass
+
+    # system-wide installation
     hip_path = shutil.which("hipconfig")
     if hip_path is not None:
-        py_path = os.path.dirname(sys.executable)
-        if hip_path.startswith(py_path):
-            try:
-                import _rocm_sdk_core # pylint: disable=unused-import
-                return PythonPackageEnvironment()
-            except ImportError:
-                pass
-        else:
-            return ROCmEnvironment(dirname(resolve_link(hip_path), 2))
+        return ROCmEnvironment(dirname(resolve_link(hip_path), 2))
 
     if sys.platform == "win32":
         hip_path = os.environ.get("HIP_PATH", None)

From 95ee0ed29c6d02e581611d72b24d798b6c5c9acf Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 17:15:12 +0900
Subject: [PATCH 076/230] load amdhip64_6.dll instead of amdhip64.dll

---
 modules/windows_hip_ffi.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/modules/windows_hip_ffi.py b/modules/windows_hip_ffi.py
index cc9e4a1e4..c612aa43d 100644
--- a/modules/windows_hip_ffi.py
+++ b/modules/windows_hip_ffi.py
@@ -1,6 +1,7 @@
 import sys
 
 if sys.platform == "win32":
+    import os
     import ctypes
     import ctypes.wintypes
 
@@ -15,8 +16,11 @@ if sys.platform == "win32":
         def __init__(self):
             ctypes.windll.kernel32.LoadLibraryA.restype = ctypes.wintypes.HMODULE
             ctypes.windll.kernel32.LoadLibraryA.argtypes = [ctypes.c_char_p]
-            # amdhip64.dll is a part of AMDGPU drivers
-            self.handle = ctypes.windll.kernel32.LoadLibraryA(b"amdhip64.dll")
+            path = os.environ.get("windir", "C:\\Windows") + "\\System32\\amdhip64_6.dll"
+            if not os.path.isfile(path):
+                path = os.environ.get("windir", "C:\\Windows") + "\\System32\\amdhip64_7.dll"
+            assert os.path.isfile(path)
+            self.handle = ctypes.windll.kernel32.LoadLibraryA(path.encode('utf-8'))
             ctypes.windll.kernel32.GetLastError.restype = ctypes.wintypes.DWORD
             ctypes.windll.kernel32.GetLastError.argtypes = []
             assert ctypes.windll.kernel32.GetLastError() == 0
@@ -28,10 +32,9 @@ if sys.platform == "win32":
                 ctypes.windll.kernel32.GetProcAddress(self.handle, b"hipGetDeviceProperties"))
 
         def __del__(self):
-            #ctypes.windll.kernel32.FreeLibrary.argtypes = [ctypes.wintypes.HMODULE]
-            #ctypes.windll.kernel32.FreeLibrary(self.handle)
-            # Hopefully it does not make conflicts with amdhip64_7.dll
-            pass
+            # Hopefully this will prevent conflicts with amdhip64_7.dll from ROCm Python packages or HIP SDK
+            ctypes.windll.kernel32.FreeLibrary.argtypes = [ctypes.wintypes.HMODULE]
+            ctypes.windll.kernel32.FreeLibrary(self.handle)
 
         def get_device_count(self):
             count = ctypes.c_int()

From e8a507fe2a19ce984f961bd182089fadc57354fd Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 17:34:47 +0900
Subject: [PATCH 077/230] skip rocm.conceal if rocm torch is installed

---
 installer.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/installer.py b/installer.py
index ea7f5b496..48f0588c9 100644
--- a/installer.py
+++ b/installer.py
@@ -977,8 +977,10 @@ def check_torch():
             if not args.ignore:
                 sys.exit(1)
     if rocm.is_installed:
-        if sys.platform == "win32": # CPU, DirectML, ZLUDA
-            rocm.conceal()
+        if sys.platform == "win32":
+            import torch
+            if torch.version.hip is None: # CPU, DirectML, ZLUDA
+                rocm.conceal()
         elif rocm.is_wsl: # ROCm WSL
             rocm.preload_hsa_runtime()
     if args.version:

From 6e8abf8dc38a6ec2362bd572bd08f4aca641dbc0 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 18:03:02 +0900
Subject: [PATCH 078/230] hijack cholesky decomposition for therock pytorch

---
 installer.py               |  7 +----
 modules/rocm.py            | 53 +++++++++++++++++++++++++++++---------
 modules/zluda_installer.py |  4 +--
 3 files changed, 44 insertions(+), 20 deletions(-)

diff --git a/installer.py b/installer.py
index 48f0588c9..1932a20af 100644
--- a/installer.py
+++ b/installer.py
@@ -977,12 +977,7 @@ def check_torch():
             if not args.ignore:
                 sys.exit(1)
     if rocm.is_installed:
-        if sys.platform == "win32":
-            import torch
-            if torch.version.hip is None: # CPU, DirectML, ZLUDA
-                rocm.conceal()
-        elif rocm.is_wsl: # ROCm WSL
-            rocm.preload_hsa_runtime()
+        rocm.postinstall()
     if args.version:
         return
     if not args.skip_all:
diff --git a/modules/rocm.py b/modules/rocm.py
index 3ffff069a..c7aa6771b 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -5,6 +5,7 @@ import shutil
 import subprocess
 from typing import Union, List
 from enum import Enum
+from functools import wraps
 
 
 def resolve_link(path_: str) -> str:
@@ -28,17 +29,6 @@ def load_library_global(path_: str):
     ctypes.CDLL(path_, mode=ctypes.RTLD_GLOBAL)
 
 
-def conceal():
-    os.environ.pop("ROCM_HOME", None)
-    os.environ.pop("ROCM_PATH", None)
-    paths = os.environ["PATH"].split(";")
-    paths_no_rocm = []
-    for path_ in paths:
-        if "rocm" not in path_.lower():
-            paths_no_rocm.append(path_)
-    os.environ["PATH"] = ";".join(paths_no_rocm)
-
-
 class Environment:
     pass
 
@@ -240,6 +230,43 @@ if sys.platform == "win32":
         del hip
         return agents
 
+    def postinstall():
+        import torch
+        if torch.version.hip is None:
+            os.environ.pop("ROCM_HOME", None)
+            os.environ.pop("ROCM_PATH", None)
+            paths = os.environ["PATH"].split(";")
+            paths_no_rocm = []
+            for path_ in paths:
+                if "rocm" not in path_.lower():
+                    paths_no_rocm.append(path_)
+            os.environ["PATH"] = ";".join(paths_no_rocm)
+            return
+
+        cholesky_ex_gpu = torch.linalg.cholesky_ex
+        @wraps(cholesky_ex_gpu)
+        def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.Tensor:
+            if A.device.type != 'cpu':
+                return cholesky_ex_gpu(A, upper=upper, check_errors=check_errors, out=out)
+
+            assert not upper
+            assert not check_errors
+            assert out is None
+
+            n = A.size(0)
+            L = torch.zeros_like(A)
+
+            for i in range(n):
+                for j in range(i + 1):
+                    s = torch.dot(L[i, :j], L[j, :j].conj())
+                    if i == j:
+                        val = A[i, i] - s
+                        L[i, j] = torch.sqrt(val.real)
+                    else:
+                        L[i, j] = (A[i, j] - s) / L[j, j]
+            return L, torch.tensor(0, dtype=torch.int32, device=A.device)
+        torch.linalg.cholesky_ex = cholesky_ex
+
     is_wsl: bool = False
 else:
     def get_agents() -> List[Agent]:
@@ -251,7 +278,9 @@ else:
             agents = [x.strip().split(" ")[-1] for x in agents if x.startswith('  Name:') and "CPU" not in x]
         return [Agent(x) for x in agents]
 
-    def preload_hsa_runtime():
+    def postinstall():
+        if not is_wsl:
+            return
         try:
             if shutil.which("conda") is not None:
                 # Preload stdc++ library. This will bypass Anaconda stdc++ library.
diff --git a/modules/zluda_installer.py b/modules/zluda_installer.py
index 2922f196f..c6fb55c23 100644
--- a/modules/zluda_installer.py
+++ b/modules/zluda_installer.py
@@ -163,7 +163,7 @@ def load():
         ctypes.windll.LoadLibrary(os.path.join(rocm.environment.path, 'bin', 'MIOpen.dll'))
         ctypes.windll.LoadLibrary(os.path.join(path, 'cudnn64_9.dll'))
 
-    def conceal():
+    def postinstall():
         import torch
         torch.version.hip = rocm.version
         platform = sys.platform
@@ -176,4 +176,4 @@ def load():
         def _join_rocm_home(*paths) -> str:
             return os.path.join(cpp_extension.ROCM_HOME, *paths)
         cpp_extension._join_rocm_home = _join_rocm_home # pylint: disable=protected-access
-    rocm.conceal = conceal
+    rocm.postinstall = postinstall

From 9c19e8a9b8c9196be5cf361f6841ecbcf6fbedf9 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 18:34:10 +0900
Subject: [PATCH 079/230] return correct item

---
 modules/rocm.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index c7aa6771b..9728313e0 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -261,10 +261,12 @@ if sys.platform == "win32":
                     s = torch.dot(L[i, :j], L[j, :j].conj())
                     if i == j:
                         val = A[i, i] - s
+                        if val.real <= 0 or (A.dtype.is_complex and val.imag != 0):
+                            return torch.return_types.linalg_cholesky_ex((L, torch.tensor(i + 1, dtype=torch.int32, device='cpu')), {})
                         L[i, j] = torch.sqrt(val.real)
                     else:
                         L[i, j] = (A[i, j] - s) / L[j, j]
-            return L, torch.tensor(0, dtype=torch.int32, device=A.device)
+            return torch.return_types.linalg_cholesky_ex((L, torch.tensor(0, dtype=torch.int32, device='cpu')), {})
         torch.linalg.cholesky_ex = cholesky_ex
 
     is_wsl: bool = False

From 06b14b070b136d8ae36335aedd57b42c8cfa426e Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Sun, 28 Sep 2025 18:43:37 +0900
Subject: [PATCH 080/230] hijack torch.linalg.cholesky()

---
 modules/rocm.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 9728313e0..0d489b72a 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -245,7 +245,7 @@ if sys.platform == "win32":
 
         cholesky_ex_gpu = torch.linalg.cholesky_ex
         @wraps(cholesky_ex_gpu)
-        def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.Tensor:
+        def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.return_types.linalg_cholesky_ex:
             if A.device.type != 'cpu':
                 return cholesky_ex_gpu(A, upper=upper, check_errors=check_errors, out=out)
 
@@ -269,6 +269,15 @@ if sys.platform == "win32":
             return torch.return_types.linalg_cholesky_ex((L, torch.tensor(0, dtype=torch.int32, device='cpu')), {})
         torch.linalg.cholesky_ex = cholesky_ex
 
+        cholesky_gpu = torch.linalg.cholesky
+        @wraps(cholesky_gpu)
+        def cholesky(A: torch.Tensor, upper=False, out=None) -> torch.Tensor:
+            if A.device.type != 'cpu':
+                return cholesky_gpu(A, upper=upper, out=out)
+            L, _ = torch.linalg.cholesky_ex(A, upper=upper, out=out)
+            return L
+        torch.linalg.cholesky = cholesky
+
     is_wsl: bool = False
 else:
     def get_agents() -> List[Agent]:

From a47959b114b68ea8eb1f22e49183af9f70478c53 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 28 Sep 2025 13:33:15 +0300
Subject: [PATCH 081/230] move ROCm Windows hijacks outside of torch install

---
 modules/devices.py |  2 ++
 modules/rocm.py    | 83 +++++++++++++++++++++++-----------------------
 modules/shared.py  |  5 +++
 3 files changed, 49 insertions(+), 41 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index abe979c17..804fdaa9b 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -48,6 +48,8 @@ def has_mps() -> bool:
 def has_xpu() -> bool:
     return bool(hasattr(torch, 'xpu') and torch.xpu.is_available())
 
+def has_rocm() -> bool:
+    return bool(torch.version.hip is not None and torch.cuda.is_available())
 
 def has_zluda() -> bool:
     if not cuda_ok:
diff --git a/modules/rocm.py b/modules/rocm.py
index 0d489b72a..643e02692 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -243,40 +243,39 @@ if sys.platform == "win32":
             os.environ["PATH"] = ";".join(paths_no_rocm)
             return
 
-        cholesky_ex_gpu = torch.linalg.cholesky_ex
-        @wraps(cholesky_ex_gpu)
-        def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.return_types.linalg_cholesky_ex:
-            if A.device.type != 'cpu':
-                return cholesky_ex_gpu(A, upper=upper, check_errors=check_errors, out=out)
+    def rocm_init():
+        try:
+            import torch
+            import numpy as np
 
-            assert not upper
-            assert not check_errors
-            assert out is None
+            cholesky_ex_gpu = torch.linalg.cholesky_ex
+            @wraps(cholesky_ex_gpu)
+            def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.return_types.linalg_cholesky_ex:
+                if A.device.type != 'cpu':
+                    return cholesky_ex_gpu(A, upper=upper, check_errors=check_errors, out=out)
 
-            n = A.size(0)
-            L = torch.zeros_like(A)
+                assert not check_errors
+                L = torch.from_numpy(np.linalg.cholesky(A.numpy(), upper=upper))
+                info = torch.tensor(0, dtype=torch.int32, device='cpu')
+                if out is not None:
+                    out[0].copy_(L)
+                    out[1].copy_(info)
+                return torch.return_types.linalg_cholesky_ex((L, info), {})
+            torch.linalg.cholesky_ex = cholesky_ex
 
-            for i in range(n):
-                for j in range(i + 1):
-                    s = torch.dot(L[i, :j], L[j, :j].conj())
-                    if i == j:
-                        val = A[i, i] - s
-                        if val.real <= 0 or (A.dtype.is_complex and val.imag != 0):
-                            return torch.return_types.linalg_cholesky_ex((L, torch.tensor(i + 1, dtype=torch.int32, device='cpu')), {})
-                        L[i, j] = torch.sqrt(val.real)
-                    else:
-                        L[i, j] = (A[i, j] - s) / L[j, j]
-            return torch.return_types.linalg_cholesky_ex((L, torch.tensor(0, dtype=torch.int32, device='cpu')), {})
-        torch.linalg.cholesky_ex = cholesky_ex
-
-        cholesky_gpu = torch.linalg.cholesky
-        @wraps(cholesky_gpu)
-        def cholesky(A: torch.Tensor, upper=False, out=None) -> torch.Tensor:
-            if A.device.type != 'cpu':
-                return cholesky_gpu(A, upper=upper, out=out)
-            L, _ = torch.linalg.cholesky_ex(A, upper=upper, out=out)
-            return L
-        torch.linalg.cholesky = cholesky
+            cholesky_gpu = torch.linalg.cholesky
+            @wraps(cholesky_gpu)
+            def cholesky(A: torch.Tensor, upper=False, out=None) -> torch.Tensor:
+                if A.device.type != 'cpu':
+                    return cholesky_gpu(A, upper=upper, out=out)
+                L = torch.from_numpy(np.linalg.cholesky(A.numpy(), upper=upper))
+                if out is not None:
+                    out.copy_(L)
+                return L
+            torch.linalg.cholesky = cholesky
+        except Exception as e:
+            return False, e
+        return True, None
 
     is_wsl: bool = False
 else:
@@ -290,16 +289,18 @@ else:
         return [Agent(x) for x in agents]
 
     def postinstall():
-        if not is_wsl:
-            return
-        try:
-            if shutil.which("conda") is not None:
-                # Preload stdc++ library. This will bypass Anaconda stdc++ library.
-                load_library_global("/lib/x86_64-linux-gnu/libstdc++.so.6")
-            # Preload rocr4wsl. The user don't have to replace the library file.
-            load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
-        except OSError:
-            pass
+        if is_wsl:
+            try:
+                if shutil.which("conda") is not None:
+                    # Preload stdc++ library. This will bypass Anaconda stdc++ library.
+                    load_library_global("/lib/x86_64-linux-gnu/libstdc++.so.6")
+                # Preload rocr4wsl. The user don't have to replace the library file.
+                load_library_global("/opt/rocm/lib/libhsa-runtime64.so")
+            except OSError:
+                pass
+
+    def rocm_init():
+        return True, None
 
     is_wsl: bool = os.environ.get('WSL_DISTRO_NAME', 'unknown' if spawn('wslpath -w /') else None) is not None
 environment = None
diff --git a/modules/shared.py b/modules/shared.py
index 78ff5ac0c..6f54bff2c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -83,6 +83,11 @@ elif cmd_opts.use_directml:
     ok, e = directml_init()
     if not ok:
         log.error(f'DirectML initialization failed: {e}')
+elif cmd_opts.use_rocm or devices.has_rocm():
+    from modules.rocm import rocm_init
+    ok, e = rocm_init()
+    if not ok:
+        log.error(f'ROCm initialization failed: {e}')
 devices.backend = devices.get_backend(cmd_opts)
 devices.device = devices.get_optimal_device()
 mem_stat = memory_stats()

From 4a70e82b0c8e021cf48d62445bc4b5e503fa2f7a Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 28 Sep 2025 14:05:20 +0300
Subject: [PATCH 082/230] ROCm always use numpy on cholesky

---
 modules/devices.py |  2 ++
 modules/rocm.py    | 13 +++++--------
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 804fdaa9b..a98b53d02 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -48,9 +48,11 @@ def has_mps() -> bool:
 def has_xpu() -> bool:
     return bool(hasattr(torch, 'xpu') and torch.xpu.is_available())
 
+
 def has_rocm() -> bool:
     return bool(torch.version.hip is not None and torch.cuda.is_available())
 
+
 def has_zluda() -> bool:
     if not cuda_ok:
         return False
diff --git a/modules/rocm.py b/modules/rocm.py
index 643e02692..30e90f000 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -251,12 +251,10 @@ if sys.platform == "win32":
             cholesky_ex_gpu = torch.linalg.cholesky_ex
             @wraps(cholesky_ex_gpu)
             def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.return_types.linalg_cholesky_ex:
-                if A.device.type != 'cpu':
-                    return cholesky_ex_gpu(A, upper=upper, check_errors=check_errors, out=out)
-
                 assert not check_errors
-                L = torch.from_numpy(np.linalg.cholesky(A.numpy(), upper=upper))
-                info = torch.tensor(0, dtype=torch.int32, device='cpu')
+                return_device = A.device
+                L = torch.from_numpy(np.linalg.cholesky(A.to("cpu").numpy(), upper=upper)).to(return_device)
+                info = torch.tensor(0, dtype=torch.int32, device=return_device)
                 if out is not None:
                     out[0].copy_(L)
                     out[1].copy_(info)
@@ -266,9 +264,8 @@ if sys.platform == "win32":
             cholesky_gpu = torch.linalg.cholesky
             @wraps(cholesky_gpu)
             def cholesky(A: torch.Tensor, upper=False, out=None) -> torch.Tensor:
-                if A.device.type != 'cpu':
-                    return cholesky_gpu(A, upper=upper, out=out)
-                L = torch.from_numpy(np.linalg.cholesky(A.numpy(), upper=upper))
+                return_device = A.device
+                L = torch.from_numpy(np.linalg.cholesky(A.to("cpu").numpy(), upper=upper)).to(return_device)
                 if out is not None:
                     out.copy_(L)
                 return L

From 32755358675dd16631c887b49b02cadc53a56431 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 09:09:19 -0400
Subject: [PATCH 083/230] update loopback and note changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md        | 3 ++-
 scripts/loopback.py | 2 +-
 wiki                | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 12627f6bd..28d098a4e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-25
+## Update for 2025-09-28
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -53,6 +53,7 @@
   - **video** support for configurable multi-stage models such as WAN-2.2-14B  
   - **video** new LTX model selection  
   - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
+  - update **loopback** script with radon seed option, thanks @rabanti  
 - **Fixes**
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
diff --git a/scripts/loopback.py b/scripts/loopback.py
index 571f67665..e24683ce6 100644
--- a/scripts/loopback.py
+++ b/scripts/loopback.py
@@ -82,11 +82,11 @@ class Script(scripts_manager.Script):
                 if randomize_seed:
                     p.seed = random.randrange(4294967294)
                     p.all_seeds = [p.seed]
-                    log.info(f'Setting random seed {p.seed} for loopback iteration {i}')
                 p.seed = processed.seed + 1 # why?
                 p.denoising_strength = calculate_denoising_strength(i + 1)
                 last_image = processed.images[0]
                 p.init_images = [last_image]
+                log.info(f'Loopback: iteration={i} seed={p.seed} curve={denoising_curve} strength={p.denoising_strength}:{final_denoising_strength}')
                 if initial_batch_count == 1:
                     history.append(last_image)
                     all_images.append(last_image)
diff --git a/wiki b/wiki
index bd9905932..77e5745f7 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit bd990593287f36d4c76828a2db0eaa019ca0630f
+Subproject commit 77e5745f7a7b8ac2f9d029c6ebb83c954be56006

From 02f03d56a71b2a579af51293dcd7f550fe7dab0a Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 28 Sep 2025 16:23:29 +0300
Subject: [PATCH 084/230] set offload_buffers to true on balanced offload

---
 modules/sd_offload.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index 19e59de3d..4126b81e8 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -245,7 +245,7 @@ class OffloadHook(accelerate.hooks.ModelHook):
                     if isinstance(device_map[v], int):
                         device_map[v] = f"{devices.device.type}:{device_map[v]}" # int implies CUDA or XPU device, but it will break DirectML backend so we add type
             if device_map is not None:
-                module = accelerate.dispatch_model(module, device_map=device_map, offload_dir=offload_dir)
+                module = accelerate.dispatch_model(module, device_map=device_map, offload_dir=offload_dir, offload_buffers=True)
             module._hf_hook.execution_device = torch.device(devices.device) # pylint: disable=protected-access
             module.balanced_offload_device_map = device_map
             module.balanced_offload_max_memory = max_memory

From 9e6fdaa5cca2690abbc29bdbe6662c0d15e5179e Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 09:24:04 -0400
Subject: [PATCH 085/230] vae slicing and tiling enabled on low and med vram,
 off otherwise

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_vae.py | 4 ++--
 modules/sd_hijack_vae.py  | 2 +-
 modules/shared.py         | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index aca549a39..933af425f 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -173,7 +173,7 @@ def full_vae_decode(latents, model):
         log_debug(f'VAE memory: {shared.mem_mon.read()}')
     vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
     vae_scale_factor = sd_vae.get_vae_scale_factor(model)
-    shared.log.debug(f'Decode: vae="{vae_name}" scale={vae_scale_factor} upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device}:{latents.dtype} time={t1-t0:.3f}')
+    shared.log.debug(f'Decode: vae="{vae_name}" scale={vae_scale_factor} upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
     return decoded
 
 
@@ -221,7 +221,7 @@ def taesd_vae_decode(latents):
     else:
         decoded = sd_vae_taesd.decode(latents)
     t1 = time.time()
-    shared.log.debug(f'Decode: vae="taesd" latents={latents.shape}:{latents.dtype}:{latents.device} time={t1-t0:.3f}')
+    shared.log.debug(f'Decode: vae="taesd" latents={latents.shape}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
     return decoded
 
 
diff --git a/modules/sd_hijack_vae.py b/modules/sd_hijack_vae.py
index edd8427e6..2563ccfed 100644
--- a/modules/sd_hijack_vae.py
+++ b/modules/sd_hijack_vae.py
@@ -18,7 +18,7 @@ def hijack_vae_decode(*args, **kwargs):
             latents = args[0].to(device=devices.device, dtype=shared.sd_model.vae.dtype) # upcast to vae dtype
             res = shared.sd_model.vae.orig_decode(latents, *args[1:], **kwargs)
             t1 = time.time()
-            shared.log.debug(f'Decode: vae={shared.sd_model.vae.__class__.__name__} slicing={getattr(shared.sd_model.vae, "use_slicing", None)} tiling={getattr(shared.sd_model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device}:{latents.dtype} time={t1-t0:.3f}')
+            shared.log.debug(f'Decode: vae={shared.sd_model.vae.__class__.__name__} slicing={getattr(shared.sd_model.vae, "use_slicing", None)} tiling={getattr(shared.sd_model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
         else:
             res = shared.sd_model.vae.orig_decode(*args, **kwargs)
     except Exception as e:
diff --git a/modules/shared.py b/modules/shared.py
index 6f54bff2c..c7d507a9e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -256,7 +256,7 @@ options_templates.update(options_section(('vae_encoder', "Variational Auto Encod
     "sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
     "diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
     "no_half_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Full precision (--no-half-vae)"),
-    "diffusers_vae_slicing": OptionInfo(True, "VAE slicing", gr.Checkbox),
+    "diffusers_vae_slicing": OptionInfo(cmd_opts.lowvram or cmd_opts.medvram, "VAE slicing", gr.Checkbox),
     "diffusers_vae_tiling": OptionInfo(cmd_opts.lowvram or cmd_opts.medvram, "VAE tiling", gr.Checkbox),
     "diffusers_vae_tile_size": OptionInfo(0, "VAE tile size", gr.Slider, {"minimum": 0, "maximum": 4096, "step": 8 }),
     "diffusers_vae_tile_overlap": OptionInfo(0.25, "VAE tile overlap", gr.Slider, {"minimum": 0, "maximum": 0.95, "step": 0.05 }),

From 749878289f4904be4188906c6c3f77c4884f69e9 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 09:25:38 -0400
Subject: [PATCH 086/230] document vae changes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md      | 1 +
 modules/shared.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 28d098a4e..b10970e6d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -54,6 +54,7 @@
   - **video** new LTX model selection  
   - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
   - update **loopback** script with radon seed option, thanks @rabanti  
+  - **vae** slicing enable for lowvram/medvram, tiling for lowvram, both disabled otherwise    
 - **Fixes**
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
diff --git a/modules/shared.py b/modules/shared.py
index c7d507a9e..a9600ce71 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -257,7 +257,7 @@ options_templates.update(options_section(('vae_encoder', "Variational Auto Encod
     "diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
     "no_half_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Full precision (--no-half-vae)"),
     "diffusers_vae_slicing": OptionInfo(cmd_opts.lowvram or cmd_opts.medvram, "VAE slicing", gr.Checkbox),
-    "diffusers_vae_tiling": OptionInfo(cmd_opts.lowvram or cmd_opts.medvram, "VAE tiling", gr.Checkbox),
+    "diffusers_vae_tiling": OptionInfo(cmd_opts.lowvram, "VAE tiling", gr.Checkbox),
     "diffusers_vae_tile_size": OptionInfo(0, "VAE tile size", gr.Slider, {"minimum": 0, "maximum": 4096, "step": 8 }),
     "diffusers_vae_tile_overlap": OptionInfo(0.25, "VAE tile overlap", gr.Slider, {"minimum": 0, "maximum": 0.95, "step": 0.05 }),
     "remote_vae_type": OptionInfo('raw', "Remote VAE image type", gr.Dropdown, {"choices": ['raw', 'jpg', 'png']}),

From 6f2145fa570ed34d291a40cd7aef6e610f2bf2c7 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 28 Sep 2025 16:47:31 +0300
Subject: [PATCH 087/230] revert offload_buffers

---
 modules/sd_offload.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index 4126b81e8..19e59de3d 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -245,7 +245,7 @@ class OffloadHook(accelerate.hooks.ModelHook):
                     if isinstance(device_map[v], int):
                         device_map[v] = f"{devices.device.type}:{device_map[v]}" # int implies CUDA or XPU device, but it will break DirectML backend so we add type
             if device_map is not None:
-                module = accelerate.dispatch_model(module, device_map=device_map, offload_dir=offload_dir, offload_buffers=True)
+                module = accelerate.dispatch_model(module, device_map=device_map, offload_dir=offload_dir)
             module._hf_hook.execution_device = torch.device(devices.device) # pylint: disable=protected-access
             module.balanced_offload_device_map = device_map
             module.balanced_offload_max_memory = max_memory

From 9d0d17a54b1b5a67c50a3805671ae99a9a64281f Mon Sep 17 00:00:00 2001
From: Aleri Kaisattera <73682764+alerikaisattera@users.noreply.github.com>
Date: Sun, 28 Sep 2025 21:44:54 +0600
Subject: [PATCH 088/230] Add model size and release date to reference model
 list

---
 html/reference.json | 280 +++++++++++++++++++++++++++++++++-----------
 1 file changed, 210 insertions(+), 70 deletions(-)

diff --git a/html/reference.json b/html/reference.json
index c8886af6b..4a45cb86c 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -37,7 +37,9 @@
     "path": "v1-5-pruned-fp16-emaonly.safetensors@https://huggingface.co/Aptronym/SDNext/resolve/main/Reference/v1-5-pruned-fp16-emaonly.safetensors?download=true",
     "preview": "v1-5-pruned-fp16-emaonly.jpg",
     "desc": "Stable Diffusion 1.5 is the base model all other 1.5 checkpoint were trained from. It's a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512.",
-    "extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0"
+    "extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0",
+    "size": 2.28,
+    "date": "2022 October"
   },
   "StabilityAI StableDiffusion 2.1": {
     "path": "huggingface/stabilityai/stable-diffusion-2-1-base",
@@ -45,7 +47,9 @@
     "skip": true,
     "variant": "fp16",
     "desc": "This stable-diffusion-2-1-base model fine-tunes stable-diffusion-2-base (512-base-ema.ckpt) with 220k extra steps taken",
-    "extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0"
+    "extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0",
+    "size": 2.58,
+    "date": "2022 December"
   },
   "StabilityAI StableDiffusion 2.1 V": {
     "path": "huggingface/stabilityai/stable-diffusion-2-1",
@@ -61,7 +65,9 @@
     "desc": "Stable Diffusion XL (SDXL) is AI image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition compared to previous SD models, including SD 2.1. It can make realistic faces and better image composition, all while using shorter and simpler prompts at a greatly increased base resolution of 1024x1024. Just like its predecessors, SDXL has the ability to generate image variations using image-to-image prompting, inpainting (reimagining of the selected parts of an image), and outpainting (creating new parts that lie outside the image borders).",
     "skip": true,
     "variant": "fp16",
-    "extras": ""
+    "extras": "",
+    "size": 6.94,
+    "date": "2023 July"
   },
   "StabilityAI StableDiffusion XL Turbo": {
     "path": "stabilityai/sdxl-turbo",
@@ -77,7 +83,9 @@
     "variant": "bf16",
     "desc": "Stable Cascade is a diffusion model built upon the Würstchen architecture and its main difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a 1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable Diffusion 1.5",
     "preview": "stabilityai--stable-cascade.jpg",
-    "extras": "sampler: Default, cfg_scale: 4.0, image_cfg_scale: 1.0"
+    "extras": "sampler: Default, cfg_scale: 4.0, image_cfg_scale: 1.0",
+    "size": 11.82,
+    "date": "2024 February"
   },
   "StabilityAI Stable Cascade Lite": {
     "path": "huggingface/stabilityai/stable-cascade-lite",
@@ -85,7 +93,9 @@
     "variant": "bf16",
     "desc": "Stable Cascade is a diffusion model built upon the Würstchen architecture and its main difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a 1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable Diffusion 1.5",
     "preview": "stabilityai--stable-cascade-lite.jpg",
-    "extras": "sampler: Default, cfg_scale: 4.0, image_cfg_scale: 1.0"
+    "extras": "sampler: Default, cfg_scale: 4.0, image_cfg_scale: 1.0",
+    "size": 4.97,
+    "date": "2024 February"
   },
   "StabilityAI Stable Diffusion 3 Medium": {
     "path": "stabilityai/stable-diffusion-3-medium-diffusers",
@@ -93,7 +103,9 @@
     "variant": "fp16",
     "desc": "Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency",
     "preview": "stabilityai--stable-diffusion-3.jpg",
-    "extras": "sampler: Default, cfg_scale: 7.0"
+    "extras": "sampler: Default, cfg_scale: 7.0",
+    "size": 15.14,
+    "date": "2024 June"
   },
   "StabilityAI Stable Diffusion 3.5 Medium": {
     "path": "stabilityai/stable-diffusion-3.5-medium",
@@ -101,7 +113,9 @@
     "variant": "fp16",
     "desc": "Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer with improvements (MMDiT-X) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.",
     "preview": "stabilityai--stable-diffusion-3_5-medium.jpg",
-    "extras": "sampler: Default, cfg_scale: 7.0"
+    "extras": "sampler: Default, cfg_scale: 7.0",
+    "size": 15.89,
+    "date": "2024 October"
   },
   "StabilityAI Stable Diffusion 3.5 Large": {
     "path": "stabilityai/stable-diffusion-3.5-large",
@@ -109,7 +123,9 @@
     "variant": "fp16",
     "desc": "Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.",
     "preview": "stabilityai--stable-diffusion-3_5-large.jpg",
-    "extras": "sampler: Default, cfg_scale: 7.0"
+    "extras": "sampler: Default, cfg_scale: 7.0",
+    "size": 26.98,
+    "date": "2024 October"
   },
   "StabilityAI Stable Diffusion 3.5 Turbo": {
     "path": "stabilityai/stable-diffusion-3.5-large-turbo",
@@ -125,28 +141,36 @@
     "preview": "black-forest-labs--FLUX.1-dev.jpg",
     "desc": "FLUX.1 models are based on a hybrid architecture of multimodal and parallel diffusion transformer blocks, scaled to 12B parameters and builing on flow matching",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 32.93,
+    "date": "2024 August"
   },
   "Black Forest Labs FLUX.1 Schnell": {
     "path": "black-forest-labs/FLUX.1-schnell",
     "preview": "black-forest-labs--FLUX.1-schnell.jpg",
     "desc": "FLUX.1 models are based on a hybrid architecture of multimodal and parallel diffusion transformer blocks, scaled to 12B parameters and builing on flow matching. Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 32.93,
+    "date": "2024 August"
   },
   "Black Forest Labs FLUX.1 Kontext Dev": {
     "path": "black-forest-labs/FLUX.1-Kontext-dev",
     "preview": "black-forest-labs--FLUX.1-Kontext-dev.jpg",
     "desc": "FLUX.1 Kontext [dev] is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions.",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 32.93,
+    "date": "2025 June"
   },
   "Black Forest Labs FLUX.1 Krea Dev": {
     "path": "black-forest-labs/FLUX.1-Krea-dev",
     "preview": "black-forest-labs--FLUX.1-Krea-dev.jpg",
     "desc": "FLUX.1 Krea [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 4.5"
+    "extras": "sampler: Default, cfg_scale: 4.5",
+    "size": 32.93,
+    "date": "2025 July"
   },
   "Tencent FLUX.1 Dev SRPO": {
     "path": "vladmandic/flux.1-dev-SRPO",
@@ -161,42 +185,54 @@
     "preview": "Qwen--Qwen-Image.jpg",
     "desc": " Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 56.1,
+    "date": "2025 August"
   },
   "Qwen-Image-Edit": {
     "path": "Qwen/Qwen-Image-Edit",
     "preview": "Qwen--Qwen-Image-Edit.jpg",
     "desc": "Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 56.1,
+    "date": "2025 August"
   },
   "Qwen-Image-Edit-2509": {
     "path": "Qwen/Qwen-Image-Edit-2509",
     "preview": "Qwen--Qwen-Image-Edit-2509.jpg",
     "desc": "Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 56.1,
+    "date": "2025 September"
   },
   "Qwen-Image-Lightning": {
     "path": "vladmandic/Qwen-Lightning",
     "preview": "vladmandic--Qwen-Lightning.jpg",
     "desc": "Qwen-Lightning is step-distilled from Qwen-Image to allow for generation in 8 steps.",
     "skip": true,
-    "extras": "steps: 8"
+    "extras": "steps: 8",
+    "size": 56.1,
+    "date": "2025 August"
   },
   "Qwen-Image-Distill": {
     "path": "SahilCarterr/Qwen-Image-Distill-Full",
     "preview": "SahilCarterr--Qwen-Image-Distill-Full.jpg",
     "desc": "Qwen-Image-Distill is a distilled and accelerated version of Qwen-Image by DiffSynth-Studio.",
     "skip": true,
-    "extras": "steps: 15"
+    "extras": "steps: 15",
+    "size": 56.1,
+    "date": "2025 August"
   },
   "Qwen-Image-Lightning-Edit": {
     "path": "vladmandic/Qwen-Lightning-Edit",
     "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
     "desc": " Qwen-Lightning-Edit is step-distilled from Qwen-Image-Edit to allow for generation in 8 steps.",
     "skip": true,
-    "extras": "steps: 8"
+    "extras": "steps: 8",
+    "size": 56.1,
+    "date": "2025 August"
   },
 
   "lodestones Chroma1 HD": {
@@ -204,42 +240,54 @@
     "preview": "lodestones--Chroma-HD.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. This is the high-res fine-tune of the Chroma1-Base at a 1024x1024 resolution.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 26.84,
+    "date": "2025 July"
   },
   "lodestones Chroma1 Base": {
     "path": "lodestones/Chroma1-Base",
     "preview": "lodestones--Chroma-Base.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. This is the core 512x512 model. It's a solid, all-around foundation for pretty much any creative project.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 26.84,
+    "date": "2025 July"
   },
   "lodestones Chroma1 Flash": {
     "path": "lodestones/Chroma1-Flash",
     "preview": "lodestones--Chroma-flash.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. A fine-tuned version of the Chroma1-Base made to find the best way to make these flow matching models faster.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 26.84,
+    "date": "2025 July"
   },
   "lodestones Chroma1 v50 Preview Annealed": {
     "path": "vladmandic/chroma-unlocked-v50-annealed",
     "preview": "lodestones--Chroma-annealed.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. Re-tweaked variant with extra noise added.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 26.84,
+    "date": "2025 July"
   },
   "lodestones Chroma1 v48 Preview": {
     "path": "vladmandic/chroma-unlocked-v48",
     "preview": "lodestones--Chroma.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. Last raw version of Chroma before final finetuning.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 26.84,
+    "date": "2025 July"
   },
   "lodestones Chroma1 v48 Preview Calibrated": {
     "path": "vladmandic/chroma-unlocked-v48-detail-calibrated",
     "preview": "lodestones--Chroma-detail.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. Last raw version of Chroma before final finetuning but with some detail calibration.",
     "skip": true,
-    "extras": ""
+    "extras": "",
+    "size": 26.84,
+    "date": "2025 July"
   },
 
   "Ostris Flex.2 Preview": {
@@ -247,14 +295,18 @@
     "preview": "ostris--Flex.2-preview.jpg",
     "desc": "Open Source 8B parameter Text to Image Diffusion Model with universal control and inpainting support built in. Early access preview release. The next version of Flex.1-alpha",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 25.65,
+    "date": "2025 April"
   },
   "Ostris Flex.1 Alpha": {
     "path": "ostris/Flex.1-alpha",
     "preview": "ostris--Flex.1-alpha.jpg",
     "desc": "Flex.1 alpha is a pre-trained base 8 billion parameter rectified flow transformer capable of generating images from text descriptions. It has a similar architecture to FLUX.1-dev, but with fewer double transformer blocks (8 vs 19)",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 25.65,
+    "date": "2025 January"
   },
 
   "Wan-AI Wan2.1 1.3B": {
@@ -262,14 +314,18 @@
     "preview": "Wan-AI--Wan2.1-T2V-1.3B-Diffusers.jpg",
     "desc": "Wan is an advanced and powerful visual generation model developed by Tongyi Lab of Alibaba Group. It can generate videos based on text, images, and other control signals. The Wan2.1 series models are now fully open-source.",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 27.72,
+    "date": "2025 February"
   },
   "Wan-AI Wan2.1 14B": {
     "path": "Wan-AI/Wan2.1-T2V-14B-Diffusers",
     "preview": "Wan-AI--Wan2.1-T2V-14B-Diffusers.jpg",
     "desc": "Wan is an advanced and powerful visual generation model developed by Tongyi Lab of Alibaba Group. It can generate videos based on text, images, and other control signals. The Wan2.1 series models are now fully open-source.",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 78.52,
+    "date": "2025 February"
   },
   "Wan-AI Wan2.2 5B": {
     "path": "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
@@ -305,21 +361,27 @@
     "preview": "Freepik--F-Lite.jpg",
     "desc": "F Lite is a 10B parameter diffusion model created by Freepik and Fal, trained exclusively on copyright-safe and SFW content. The model was trained on Freepik's internal dataset comprising approximately 80 million copyright-safe images, making it the first publicly available model of this scale trained exclusively on legally compliant and SFW content.",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 19.81,
+    "date": "2025 May"
   },
   "Freepik F-Lite Texture": {
     "path": "Freepik/F-Lite-Texture",
     "preview": "Freepik--F-Lite-Texture.jpg",
     "desc": "F Lite is a 10B parameter diffusion model created by Freepik and Fal, trained exclusively on copyright-safe and SFW content. The model was trained on Freepik's internal dataset comprising approximately 80 million copyright-safe images, making it the first publicly available model of this scale trained exclusively on legally compliant and SFW content.",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 19.81,
+    "date": "2025 May"
   },
   "Freepik F-Lite 7B": {
     "path": "Freepik/F-Lite-7B",
     "preview": "Freepik--F-Lite-7B.jpg",
     "desc": "F Lite is a 10B parameter diffusion model created by Freepik and Fal, trained exclusively on copyright-safe and SFW content. The model was trained on Freepik's internal dataset comprising approximately 80 million copyright-safe images, making it the first publicly available model of this scale trained exclusively on legally compliant and SFW content.",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 3.5"
+    "extras": "sampler: Default, cfg_scale: 3.5",
+    "size": 13.89,
+    "date": "2025 May"
   },
 
   "SDXS DreamShaper 512": {
@@ -340,13 +402,17 @@
     "path": "Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers",
     "desc": "Sana is an efficient model with scaling of training-time and inference time techniques. SANA-1.5 delivers: efficient model growth from 1.6B Sana-1.0 model to 4.8B, achieving similar or better performance than training from scratch and saving 60% training cost; efficient model depth pruning, slimming any model size as you want; powerful VLM selection based inference scaling, smaller model+inference scaling > larger model.",
     "preview": "Efficient-Large-Model--SANA1.5_1.6B_1024px_diffusers.jpg",
-    "skip": true
+    "skip": true,
+    "size": 9.49,
+    "date": "2025 March"
   },
   "NVLabs Sana 1.5 4.8B 1k": {
     "path": "Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers",
     "desc": "Sana is an efficient model with scaling of training-time and inference time techniques. SANA-1.5 delivers: efficient model growth from 1.6B Sana-1.0 model to 4.8B, achieving similar or better performance than training from scratch and saving 60% training cost; efficient model depth pruning, slimming any model size as you want; powerful VLM selection based inference scaling, smaller model+inference scaling > larger model.",
     "preview": "Efficient-Large-Model--SANA1.5_4.8B_1024px_diffusers.jpg",
-    "skip": true
+    "skip": true,
+    "size": 15.58,
+    "date": "2025 March"
   },
   "NVLabs Sana 1.5 1.6B 1k Sprint": {
     "path": "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers",
@@ -358,63 +424,83 @@
     "path": "Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers",
     "desc": "Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.",
     "preview": "Efficient-Large-Model--Sana_1600M_4Kpx_BF16_diffusers.jpg",
-    "skip": true
+    "skip": true,
+    "size": 12.63,
+    "date": "2024 November"
   },
   "NVLabs Sana 1.0 1.6B 2k": {
     "path": "Efficient-Large-Model/Sana_1600M_2Kpx_BF16_diffusers",
     "desc": "Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.",
     "preview": "Efficient-Large-Model--Sana_1600M_2Kpx_BF16_diffusers.jpg",
-    "skip": true
+    "skip": true,
+    "size": 12.63,
+    "date": "2024 November"
   },
   "NVLabs Sana 1.0 1.6B 1k": {
     "path": "Efficient-Large-Model/Sana_1600M_1024px_diffusers",
     "desc": "Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.",
     "preview": "Efficient-Large-Model--Sana_1600M_1024px_diffusers.jpg",
-    "skip": true
+    "skip": true,
+    "size": 12.63,
+    "date": "2024 November"
   },
   "NVLabs Sana 1.0 0.6B 0.5k": {
     "path": "Efficient-Large-Model/Sana_600M_512px_diffusers",
     "desc": "Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.",
     "preview": "Efficient-Large-Model--Sana_600M_512px_diffusers.jpg",
-    "skip": true
+    "skip": true,
+    "size": 7.51,
+    "date": "2024 November"
   },
   "nVidia Cosmos-Predict2 T2I 2B": {
     "path": "nvidia/Cosmos-Predict2-2B-Text2Image",
     "desc": "Cosmos-Predict2: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware images, videos and world states for physical AI development.",
     "preview": "nvidia--Cosmos-Predict2-2B-Text2Image.jpg",
-    "skip": true
+    "skip": true,
+    "size": 13.32,
+    "date": "2025 June"
   },
   "nVidia Cosmos-Predict2 T2I 14B": {
     "path": "nvidia/Cosmos-Predict2-14B-Text2Image",
     "desc": "Cosmos-Predict2: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware images, videos and world states for physical AI development.",
     "preview": "nvidia--Cosmos-Predict2-14B-Text2Image.jpg",
-    "skip": true
+    "skip": true,
+    "size": 37.36,
+    "date": "2025 June"
   },
 
   "VectorSpaceLab OmniGen v1": {
     "path": "Shitao/OmniGen-v1-diffusers",
     "desc": "OmniGen is a unified image generation model that can generate a wide range of images from multi-modal prompts. It is designed to be simple, flexible and easy to use.",
     "preview": "Shitao--OmniGen-v1.jpg",
-    "skip": true
+    "skip": true,
+    "size": 15.47,
+    "date": "2024 October"
   },
   "VectorSpaceLab OmniGen v2": {
     "path": "OmniGen2/OmniGen2",
     "desc": "OmniGen2 is a powerful and efficient unified multimodal model. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer.",
     "preview": "OmniGen2--OmniGen2.jpg",
-    "skip": true
+    "skip": true,
+    "size": 30.5,
+    "date": "2025 June"
   },
 
   "AuraFlow 0.3": {
     "path": "fal/AuraFlow-v0.3",
     "desc": "AuraFlow v0.3 is the fully open-sourced flow-based text-to-image generation model. The model was trained with more compute compared to the previous version, AuraFlow-v0.2. Compared to AuraFlow-v0.2, the model is fine-tuned on more aesthetic datasets and now supports various aspect ratio, (now width and height up to 1536 pixels).",
     "preview": "fal--AuraFlow-v0.3.jpg",
-    "skip": true
+    "skip": true,
+    "size": 31.9,
+    "date": "2024 August"
   },
   "AuraFlow 0.2": {
     "path": "fal/AuraFlow-v0.2",
     "desc": "AuraFlow v0.2 is the fully open-sourced largest flow-based text-to-image generation model. The model was trained with more compute compared to the previous version, AuraFlow-v0.1",
     "preview": "fal--AuraFlow-v0.2.jpg",
-    "skip": true
+    "skip": true,
+    "size": 31.9,
+    "date": "2024 July"
   },
 
   "Segmind Vega": {
@@ -423,7 +509,9 @@
     "desc": "The Segmind-Vega Model is a distilled version of the Stable Diffusion XL (SDXL), offering a remarkable 70% reduction in size and an impressive 100% speedup while retaining high-quality text-to-image generation capabilities. Trained on diverse datasets, including Grit and Midjourney scrape data, it excels at creating a wide range of visual content based on textual prompts. Employing a knowledge distillation strategy, Segmind-Vega leverages the teachings of several expert models, including SDXL, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce compelling visual outputs.",
     "variant": "fp16",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 9.0"
+    "extras": "sampler: Default, cfg_scale: 9.0",
+    "size": 6.43,
+    "date": "2023 November"
   },
   "Segmind SSD-1B": {
     "path": "huggingface/segmind/SSD-1B",
@@ -431,13 +519,17 @@
     "desc": "The Segmind Stable Diffusion Model (SSD-1B) offers a compact, efficient, and distilled version of the SDXL model. At 50% smaller and 60% faster than Stable Diffusion XL (SDXL), it provides quick and seamless performance without sacrificing image quality.",
     "variant": "fp16",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 9.0"
+    "extras": "sampler: Default, cfg_scale: 9.0",
+    "size": 8.72,
+    "date": "2023 October"
   },
   "Segmind Tiny": {
     "path": "segmind/tiny-sd",
     "preview": "segmind--tiny-sd.jpg",
     "desc": "Segmind's Tiny-SD offers a compact, efficient, and distilled version of Realistic Vision 4.0 and is up to 80% faster than SD1.5",
-    "extras": "width: 512, height: 512, sampler: Default, cfg_scale: 9.0"
+    "extras": "width: 512, height: 512, sampler: Default, cfg_scale: 9.0",
+    "size": 1.03,
+    "date": "2023 July"
   },
   "Segmind SegMoE SD 4x2": {
     "path": "segmind/SegMoE-SD-4x2-v0",
@@ -461,7 +553,9 @@
     "path": "PixArt-alpha/PixArt-XL-2-1024-MS",
     "desc": "PixArt-α is a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), and the training speed markedly surpasses existing large-scale T2I models. Extensive experiments demonstrate that PIXART-α excels in image quality, artistry, and semantic control. It can directly generate 1024px images from text prompts within a single sampling process.",
     "preview": "PixArt-alpha--PixArt-XL-2-1024-MS.jpg",
-    "extras": "sampler: Default, cfg_scale: 2.0"
+    "extras": "sampler: Default, cfg_scale: 2.0",
+    "size": 21.3,
+    "date": "2023 November"
   },
   "Pixart-Σ Small": {
     "path": "huggingface/PixArt-alpha/PixArt-Sigma-XL-2-512-MS",
@@ -482,14 +576,18 @@
     "desc": "PixArt-Σ, a Diffusion Transformer model (DiT) capable of directly generating images at 4K resolution. PixArt-Σ represents a significant advancement over its predecessor, PixArt-α, offering images of markedly higher fidelity and improved alignment with text prompts.",
     "preview": "PixArt-alpha--PixArt-Sigma-XL-2-2K-MS.jpg",
     "skip": true,
-    "extras": "sampler: Default, cfg_scale: 2.0"
+    "extras": "sampler: Default, cfg_scale: 2.0",
+    "size": 21.3,
+    "date": "2024 April"
   },
 
   "Tencent HunyuanDiT 1.2": {
     "path": "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers",
     "desc": "Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.",
     "preview": "Tencent-Hunyuan--HunyuanDiT-v1.2-Diffusers.jpg",
-    "extras": "sampler: Default, cfg_scale: 2.0"
+    "extras": "sampler: Default, cfg_scale: 2.0",
+    "size": 14.09,
+    "date": "2024 May"
   },
   "Tencent HunyuanDiT 1.2 Distilled": {
     "path": "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers-Distilled",
@@ -515,14 +613,18 @@
     "desc": "The Lumina-Next-SFT is a Next-DiT model containing 2B parameters and utilizes Gemma-2B as the text encoder, enhanced through high-quality supervised fine-tuning (SFT).",
     "preview": "Alpha-VLLM--Lumina-Next-SFT-diffusers.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 8.67,
+    "date": "2024 June"
   },
   "AlphaVLLM Lumina 2": {
     "path": "Alpha-VLLM/Lumina-Image-2.0",
     "desc": "A Unified and Efficient Image Generative Model. Lumina-Image-2.0 is a 2 billion parameter flow-based diffusion transformer capable of generating images from text descriptions.",
     "preview": "Alpha-VLLM--Lumina-Image-2.0.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 20.75,
+    "date": "2025 January"
   },
 
   "HiDream-I1 Fast": {
@@ -530,21 +632,27 @@
     "desc": "HiDream-I1 is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.",
     "preview": "HiDream-ai--HiDream-I1-Fast.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 58.4,
+    "date": "2025 April"
   },
   "HiDream-I1 Dev": {
     "path": "HiDream-ai/HiDream-I1-Dev",
     "desc": "HiDream-I1 is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.",
     "preview": "HiDream-ai--HiDream-I1-Dev.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 58.4,
+    "date": "2025 April"
   },
   "HiDream-I1 Full": {
     "path": "HiDream-ai/HiDream-I1-Full",
     "desc": "HiDream-I1 is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.",
     "preview": "HiDream-ai--HiDream-I1-Full.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 58.4,
+    "date": "2025 April"
   },
   "HiDream-E1 Full": {
     "path": "HiDream-ai/HiDream-E1-Full",
@@ -559,34 +667,44 @@
     "desc": "Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team. Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and proprietary models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs",
     "preview": "Kwai-Kolors--Kolors-diffusers.jpg",
     "skip": true,
-    "extras": "width: 1024, height: 1024"
+    "extras": "width: 1024, height: 1024",
+    "size": 17.40,
+    "date": "2024 July"
   },
 
   "Kandinsky 2.1": {
     "path": "kandinsky-community/kandinsky-2-1",
     "desc": "Kandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. Kandinsky 2.1 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.",
     "preview": "kandinsky-community--kandinsky-2-1.jpg",
-    "extras": "width: 768, height: 768, sampler: Default"
+    "extras": "width: 768, height: 768, sampler: Default",
+    "size": 5.15,
+    "date": "2023 April"
   },
   "Kandinsky 2.2": {
     "path": "kandinsky-community/kandinsky-2-2-decoder",
     "desc": "Kandinsky 2.2 is a text-conditional diffusion model (+0.1!) based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. Kandinsky 2.2 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.",
     "preview": "kandinsky-community--kandinsky-2-2-decoder.jpg",
-    "extras": "width: 768, height: 768, sampler: Default"
+    "extras": "width: 768, height: 768, sampler: Default",
+    "size": 5.15,
+    "date": "2023 July"
   },
   "Kandinsky 3": {
     "path": "kandinsky-community/kandinsky-3",
     "desc": "Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, Kandinsky 3.0 incorporates more data and specifically related to Russian culture, which allows to generate pictures related to Russin culture. Furthermore, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.",
     "preview": "kandinsky-community--kandinsky-3.jpg",
     "variant": "fp16",
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 27.72,
+    "date": "2023 November"
   },
 
   "Playground v1": {
     "path": "playgroundai/playground-v1",
     "desc": "Playground v1 is a latent diffusion model that improves the overall HDR quality to get more stunning images.",
     "preview": "playgroundai--playground-v1.jpg",
-    "extras": "width: 512, height: 512, sampler: Default"
+    "extras": "width: 512, height: 512, sampler: Default",
+    "size": 4.95,
+    "date": "2023 December"
   },
   "Playground v2 Small": {
     "path": "playgroundai/playground-v2-256px-base",
@@ -611,20 +729,26 @@
     "desc": "Playground v2.5 is a diffusion-based text-to-image generative model, and a successor to Playground v2. Playground v2.5 is the state-of-the-art open-source model in aesthetic quality.",
     "preview": "playgroundai--playground-v2.5-1024px-aesthetic.jpg",
     "variant": "fp16",
-    "extras": "sampler: DPM++ 2M EDM"
+    "extras": "sampler: DPM++ 2M EDM",
+    "size": 13.35,
+    "date": "2023 December"
   },
 
   "CogView 4": {
     "path": "zai-org/CogView4-6B",
     "desc": "An innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution.",
     "preview": "THUDM--CogView4-6B.jpg",
-    "skip": true
+    "skip": true,
+    "size": 30.39,
+    "date": "2025 March"
   },
   "CogView 3 Plus": {
     "path": "zai-org/CogView3-Plus-3B",
     "desc": "An innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution.",
     "preview": "THUDM--CogView3-Plus-3B.jpg",
-    "skip": true
+    "skip": true,
+    "size": 24.96,
+    "date": "2024 October"
   },
 
   "ShuttleAI Shuttle 3.0 Diffusion": {
@@ -650,14 +774,18 @@
     "path": "briaai/BRIA-3.2",
     "desc": "Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.",
     "preview": "briaai--BRIA-3.2.jpg",
-    "skip": true
+    "skip": true,
+    "size": 18.66,
+    "date": "2025 June"
   },
 
   "Meissonic": {
     "path": "MeissonFlow/Meissonic",
     "desc": "Meissonic is a non-autoregressive mask image modeling text-to-image synthesis model that can generate high-resolution images. It is designed to run on consumer graphics cards.",
     "preview": "MeissonFlow--Meissonic.jpg",
-    "skip": true
+    "skip": true,
+    "size": 3.64,
+    "date": "2024 October"
   },
 
   "aMUSEd 256": {
@@ -678,7 +806,9 @@
     "path": "warp-ai/wuerstchen",
     "desc": "Würstchen is a diffusion model whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images, is way more expensive than training at 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the paper). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference.",
     "preview": "warp-ai--wuerstchen.jpg",
-    "extras": "sampler: Default, cfg_scale: 4.0, image_cfg_scale: 0.0"
+    "extras": "sampler: Default, cfg_scale: 4.0, image_cfg_scale: 0.0",
+    "size": 12.16,
+    "date": "2023 August"
   },
 
   "KOALA 700M": {
@@ -687,7 +817,9 @@
     "skip": true,
     "desc": "Fast text-to-image model, called KOALA, by compressing SDXL's U-Net and distilling knowledge from SDXL into our model. KOALA-700M can generate a 1024x1024 image in less than 1.5 seconds on an NVIDIA 4090 GPU, which is more than 2x faster than SDXL.",
     "preview": "etri-vilab--koala-700m-llava-cap.jpg",
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 6.58,
+    "date": "2024 January"
   },
 
   "HDM-XUT 340M Anime": {
@@ -702,13 +834,17 @@
     "path": "thu-ml/unidiffuser-v1",
     "desc": "UniDiffuser is a unified diffusion framework to fit all distributions relevant to a set of multi-modal data in one transformer. UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead.\nSpecifically, UniDiffuser employs a variation of transformer, called U-ViT, which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from Stable Diffusion, a pretrained image ViT-B/32 CLIP encoder, a pretrained text ViT-L CLIP encoder, and a GPT-2 text decoder finetuned by ourselves.",
     "preview": "thu-ml--unidiffuser-v1.jpg",
-    "extras": "width: 512, height: 512, sampler: Default"
+    "extras": "width: 512, height: 512, sampler: Default",
+    "size": 5.37,
+    "date": "2023 May"
   },
 
   "SalesForce BLIP-Diffusion": {
     "path": "salesforce/blipdiffusion",
     "desc": "BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation.",
-    "preview": "salesforce--blipdiffusion.jpg"
+    "preview": "salesforce--blipdiffusion.jpg",
+    "size": 7.23,
+    "date": "2023 July"
   },
 
   "InstaFlow 0.9B": {
@@ -721,13 +857,17 @@
     "path": "DeepFloyd/IF-I-M-v1.0",
     "desc": "DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of 6.66 on the COCO dataset. It is modular and composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024.",
     "preview": "DeepFloyd--IF-I-M-v1.0.jpg",
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 12.79,
+    "date": "2023 April"
   },
   "DeepFloyd IF Large": {
     "path": "DeepFloyd/IF-I-L-v1.0",
     "desc": "DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of 6.66 on the COCO dataset. It is modular and composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024.",
     "preview": "DeepFloyd--IF-I-L-v1.0.jpg",
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "size": 15.48,
+    "date": "2023 April"
   }
 
 }

From 1b45c145e9429f1e059335c120037cf04f3f902a Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 28 Sep 2025 20:40:24 +0300
Subject: [PATCH 089/230] SDNQ re-enable dynamic compile

---
 modules/sdnq/common.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index ecd06a653..783a7ab2f 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -44,7 +44,7 @@ allowed_types = linear_types + conv_types + conv_transpose_types
 if use_torch_compile:
     torch._dynamo.config.cache_size_limit = max(8192, torch._dynamo.config.cache_size_limit)
     torch._dynamo.config.accumulated_recompile_limit = max(8192, torch._dynamo.config.accumulated_recompile_limit)
-    compile_func = partial(torch.compile, fullgraph=True, dynamic=False)
+    compile_func = partial(torch.compile, fullgraph=True)
 else:
     def compile_func(fn, **kwargs): # pylint: disable=unused-argument
         return fn

From eb025eaf3139d0a296496816f65603db8f391e2f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 18:01:31 -0400
Subject: [PATCH 090/230] remove split-attention and add attention slicing
 option

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    |   4 +-
 javascript/guidance.js          |  20 +++++
 modules/control/run.py          |  10 ++-
 modules/img2img.py              |  11 ++-
 modules/modular_guiders.py      |  87 ++++++++++++++++++++++
 modules/processing_class.py     |  13 +++-
 modules/processing_diffusers.py |   2 +
 modules/sd_models.py            |  12 +--
 modules/shared.py               |   2 +
 modules/shared_items.py         |   1 -
 modules/txt2img.py              |   6 ++
 modules/ui_control.py           |  17 +++--
 modules/ui_guidance.py          | 125 ++++++++++++++++++++++++++++++++
 modules/ui_img2img.py           |  27 ++++---
 modules/ui_sections.py          |  15 ----
 modules/ui_txt2img.py           |  21 ++++--
 scripts/xyz/xyz_grid_classes.py |   2 +
 scripts/xyz/xyz_grid_shared.py  |   7 ++
 scripts/xyz_grid_on.py          |   1 +
 19 files changed, 331 insertions(+), 52 deletions(-)
 create mode 100644 javascript/guidance.js
 create mode 100644 modules/modular_guiders.py
 create mode 100644 modules/ui_guidance.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b10970e6d..474ab69f6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -54,7 +54,9 @@
   - **video** new LTX model selection  
   - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
   - update **loopback** script with radon seed option, thanks @rabanti  
-  - **vae** slicing enable for lowvram/medvram, tiling for lowvram, both disabled otherwise    
+  - **vae** slicing enable for lowvram/medvram, tiling for lowvram, both disabled otherwise  
+  - **attention** remove split-attention and add explicitly attention slicing enable/disable option  
+    can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
 - **Fixes**
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
diff --git a/javascript/guidance.js b/javascript/guidance.js
new file mode 100644
index 000000000..11c2430d3
--- /dev/null
+++ b/javascript/guidance.js
@@ -0,0 +1,20 @@
+const guiders = {
+  None: '',
+  'LSC: LayerSkipConfig': 'https://github.com/huggingface/diffusers/blob/041501aea92919c9c7f36e189fc9cf7d865ebb96/src/diffusers/hooks/layer_skip.py#L41',
+  'CFG: ClassifierFreeGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.ClassifierFreeGuidance',
+  'Auto: AutoGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.AutoGuidance',
+  'Zero: ClassifierFreeZeroStar': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.ClassifierFreeZeroStarGuidance',
+  'PAG: PerturbedAttentionGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.PerturbedAttentionGuidance',
+  'APG: AdaptiveProjectedGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.AdaptiveProjectedGuidance',
+  'SLG: SkipLayerGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.SkipLayerGuidance',
+  'SEG: SmoothedEnergyGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.SmoothedEnergyGuidance',
+  'TCFG: TangentialClassifierFreeGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.TangentialClassifierFreeGuidance',
+  'FDG: FrequencyDecoupledGuidance': 'https://huggingface.co/docs/diffusers/v0.35.1/en/api/modular_diffusers/guiders#diffusers.FrequencyDecoupledGuidance',
+};
+
+function getGuidanceDocs(guider) {
+  if (guider.label) guider = guider.label;
+  const url = guiders[guider];
+  log('getGuidanceDocs', guider, url);
+  if (url) window.open(url, '_blank');
+}
diff --git a/modules/control/run.py b/modules/control/run.py
index 586958a49..3f6b0f7be 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -255,6 +255,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
                 prompt: str = '', negative_prompt: str = '', styles: List[str] = [],
                 steps: int = 20, sampler_index: int = None,
                 seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1,
+                guidance_name: str = 'Default', guidance_scale: float = 6.0, guidance_rescale: float = 0.0, guidance_start: float = 0.0, guidance_stop: float = 1.0,
                 cfg_scale: float = 6.0, clip_skip: float = 1.0, image_cfg_scale: float = 6.0, diffusers_guidance_rescale: float = 0.7, pag_scale: float = 0.0, pag_adaptive: float = 0.5, cfg_end: float = 1.0,
                 vae_type: str = 'Full', tiling: bool = False, hidiffusion: bool = False,
                 detailer_enabled: bool = True, detailer_prompt: str = '', detailer_negative: str = '', detailer_steps: int = 10, detailer_strength: float = 0.3, detailer_resolution: int = 1024,
@@ -306,7 +307,13 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
         seed_resize_from_h = seed_resize_from_h,
         seed_resize_from_w = seed_resize_from_w,
         denoising_strength = denoising_strength,
-        # advanced
+        # modular guidance
+        guidance_name = guidance_name,
+        guidance_scale = guidance_scale,
+        guidance_rescale = guidance_rescale,
+        guidance_start = guidance_start,
+        guidance_stop = guidance_stop,
+        # legacy guidance
         cfg_scale = cfg_scale,
         cfg_end = cfg_end,
         clip_skip = clip_skip,
@@ -314,6 +321,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
         diffusers_guidance_rescale = diffusers_guidance_rescale,
         pag_scale = pag_scale,
         pag_adaptive = pag_adaptive,
+        # advanced
         vae_type = vae_type,
         tiling = tiling,
         hidiffusion = hidiffusion,
diff --git a/modules/img2img.py b/modules/img2img.py
index 19da4b92f..4809d9fa0 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -161,10 +161,8 @@ def img2img(id_task: str, state: str, mode: int,
             vae_type, tiling, hidiffusion,
             detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution,
             n_iter, batch_size,
-            cfg_scale, image_cfg_scale,
-            diffusers_guidance_rescale,
-            pag_scale, pag_adaptive,
-            cfg_end,
+            guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop,
+            cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end,
             refiner_start,
             clip_skip,
             denoising_strength,
@@ -255,6 +253,11 @@ def img2img(id_task: str, state: str, mode: int,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
+        guidance_name=guidance_name,
+        guidance_scale=guidance_scale,
+        guidance_rescale=guidance_rescale,
+        guidance_start=guidance_start,
+        guidance_stop=guidance_stop,
         cfg_scale=cfg_scale,
         cfg_end=cfg_end,
         clip_skip=clip_skip,
diff --git a/modules/modular_guiders.py b/modules/modular_guiders.py
new file mode 100644
index 000000000..ee9c3655c
--- /dev/null
+++ b/modules/modular_guiders.py
@@ -0,0 +1,87 @@
+import diffusers
+from modules import shared, errors, processing
+
+
+# ['Default', 'CFG', 'Zero', 'PAG', 'APG', 'SLG', 'SEG', 'TCFG', 'FDG']
+guiders = {
+    # 'None': { 'cls': None, 'args': {}, },
+    'Default': { 'cls': None, 'args': {}, },
+    'CFG: ClassifierFreeGuidance': { 'cls': diffusers.ClassifierFreeGuidance, 'args': {} },
+    'Auto: AutoGuidance': { 'cls': diffusers.AutoGuidance, 'args': { 'dropout': 1.0, 'auto_guidance_layers': [7, 8, 9], 'auto_guidance_config': None } },
+    'Zero: ClassifierFreeZeroStar': { 'cls': diffusers.ClassifierFreeZeroStarGuidance, 'args': { 'zero_init_steps': 1 } },
+    'PAG: PerturbedAttentionGuidance': { 'cls': diffusers.PerturbedAttentionGuidance, 'args': { 'perturbed_guidance_scale': 2.8, 'perturbed_guidance_start': 0.01, 'perturbed_guidance_stop': 0.2, 'perturbed_guidance_layers': [7, 8, 9], 'perturbed_guidance_config': None } },
+    'APG: AdaptiveProjectedGuidance': { 'cls': diffusers.AdaptiveProjectedGuidance, 'args': { 'adaptive_projected_guidance_momentum': -1, 'adaptive_projected_guidance_rescale': 15.0 } },
+    'SLG: SkipLayerGuidance': { 'cls': diffusers.SkipLayerGuidance, 'args': { 'skip_layer_guidance_scale': 2.8, 'skip_layer_guidance_start': 0.01, 'skip_layer_guidance_stop': 0.2, 'skip_layer_guidance_layers': [7, 8, 9], 'skip_layer_config': None } },
+    'SEG: SmoothedEnergyGuidance': { 'cls': diffusers.SmoothedEnergyGuidance, 'args': { 'seg_guidance_scale': 3.0, 'seg_blur_sigma': 9999999.0, 'seg_blur_threshold_inf': 9999.0, 'seg_guidance_start': 0.0, 'seg_guidance_stop': 1.0, 'seg_guidance_layers': [7, 8, 9], 'seg_guidance_config': None } },
+    'TCFG: TangentialClassifierFreeGuidance': { 'cls': diffusers.TangentialClassifierFreeGuidance, 'args': {} },
+    'FDG: FrequencyDecoupledGuidance': { 'cls': diffusers.FrequencyDecoupledGuidance, 'args': { 'guidance_scales': [10.0, 5.0], 'parallel_weights': 1.0, 'guidance_rescale_space': "data" } },
+}
+base_args = {
+    'guidance_scale': 6.0,
+    'guidance_rescale': 0.0,
+    'start': 0.0,
+    'stop': 1.0,
+}
+
+
+def set_guider(p: processing.StableDiffusionProcessing):
+    guidance_name = p.guidance_name or 'Default'
+    if guidance_name not in guiders:
+        return None
+
+    if guidance_name == 'Default':
+        if hasattr(shared.sd_model, 'default_guider'):
+            guider_info = shared.sd_model.default_guider
+            shared.sd_model.update_components(guider=guider_info)
+        else:
+            guider_info = shared.sd_model.get_component_spec("guider")
+            shared.sd_model.default_guider = guider_info
+        guider_cls = guider_info.type_hint
+        if guider_info is not None and guider_cls is not None and guider_info.config is not None:
+            guider_args = {k: v for k, v in guider_info.config.items() if not k.startswith('_') and v is not None}
+        else:
+            guider_args = {}
+        shared.log.info(f'Guider: name={guidance_name} cls={guider_cls.__name__} args={guider_args}')
+        return
+    if guidance_name == 'None':
+        shared.sd_model.update_components(guider=None) # breaks the pipeline
+        shared.log.info(f'Guider: name={guidance_name}')
+        return
+
+    guider_info = guiders[guidance_name]
+    guider_cls = guider_info['cls']
+    guider_args = {}
+    for k, v in base_args.items():
+        if v is not None and v >= 0.0:
+            guider_args[k] = v
+    shared.log.warning('Guiders: partially implemented') # TODO: guiders
+    for k, v in guider_info['args'].items():
+        try:
+            if k is None:
+                pass
+            elif k.endswith('_layers') and isinstance(v, str):
+                guider_args[k] = [int(x.strip()) for x in v.split(',') if x.strip().isdigit()]
+            elif k.endswith('_config'):
+                # if lsc_enabled
+                # guider_args[k] = diffusers.LayerSkipConfig(...)
+                pass
+            elif isinstance(v, list) and len(v) > 0:
+                guider_args[k] = v
+            elif isinstance(v, int) and (v >= 0):
+                guider_args[k] = int(v)
+            elif isinstance(v, float) and (v >= 0.0):
+                guider_args[k] = float(v)
+            elif isinstance(v, str) and (len(v) > 0):
+                guider_args[k] = v
+        except Exception as e:
+            shared.log.error(f'Guiders: arg={k} value={v} error={e}')
+            errors.display(e, 'Guiders')
+    # guider_args.update(guider_info['args'])
+    if guider_cls is not None:
+        try:
+            guider_instance = guider_cls(**guider_args)
+            shared.log.info(f'Guider: name={guidance_name} cls={guider_cls.__name__} args={guider_args}')
+            shared.sd_model.update_components(guider=guider_instance)
+        except Exception as e:
+            shared.log.error(f'Guider: name={guidance_name} cls={guider_cls.__name__} args={guider_args} {e}')
+            return
diff --git a/modules/processing_class.py b/modules/processing_class.py
index d0d634cfa..ddad4686e 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -36,7 +36,13 @@ class StableDiffusionProcessing:
                  sampler_name: str = None,
                  hr_sampler_name: str = None,
                  eta: float = None,
-                 # guidance
+                 # modular guidance
+                 guidance_name: str = 'Default',
+                 guidance_scale: float = 6.0,
+                 guidance_rescale: float = 0.0,
+                 guidance_start: float = 0.0,
+                 guidance_stop: float = 1.0,
+                 # legacy guidance
                  cfg_scale: float = 6.0,
                  cfg_end: float = 1,
                  diffusers_guidance_rescale: float = 0.0,
@@ -247,6 +253,11 @@ class StableDiffusionProcessing:
         self.do_not_save_grid = do_not_save_grid
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.eta = eta
+        self.guidance_name = guidance_name
+        self.guidance_scale = guidance_scale
+        self.guidance_rescale = guidance_rescale
+        self.guidance_start = guidance_start
+        self.guidance_stop = guidance_stop
         self.cfg_scale = cfg_scale
         self.cfg_end = cfg_end
         self.diffusers_guidance_rescale = diffusers_guidance_rescale
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 6453516c0..277b5e1c5 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -102,6 +102,8 @@ def process_pre(p: processing.StableDiffusionProcessing):
         modular_pipe = modular.convert_to_modular(shared.sd_model)
         if modular_pipe is not None:
             shared.sd_model = modular_pipe
+            from modules import modular_guiders
+            modular_guiders.set_guider(p)
 
     timer.process.record('pre')
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9b9ec7d68..e759ac56f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1070,17 +1070,19 @@ def set_diffusers_attention(pipe, quiet:bool=False):
             pipe.enable_xformers_memory_efficient_attention()
         else:
             shared.log.warning(f"Attention: xFormers is not compatible with {pipe.__class__.__name__}")
-    elif shared.opts.cross_attention_optimization == "Split attention":
-        if hasattr(pipe, "enable_attention_slicing"):
-            pipe.enable_attention_slicing()
-        else:
-            shared.log.warning(f"Attention: Split attention is not compatible with {pipe.__class__.__name__}")
     elif shared.opts.cross_attention_optimization == "Batch matrix-matrix":
         set_attn(pipe, p.AttnProcessor(), name="Batch matrix-matrix")
     elif shared.opts.cross_attention_optimization == "Dynamic Attention BMM":
         from modules.sd_hijack_dynamic_atten import DynamicAttnProcessorBMM
         set_attn(pipe, DynamicAttnProcessorBMM(), name="Dynamic Attention BMM")
 
+    if shared.opts.attention_slicing != "Default" and hasattr(pipe, "enable_attention_slicing") and hasattr(pipe, "disable_attention_slicing"):
+        if shared.opts.attention_slicing:
+            pipe.enable_attention_slicing()
+        else:
+            pipe.disable_attention_slicing()
+        shared.log.debug(f"Attention: slicing={shared.opts.attention_slicing}")
+
     pipe.current_attn_name = shared.opts.cross_attention_optimization
 
 
diff --git a/modules/shared.py b/modules/shared.py
index a9600ce71..8dcf8f26d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -290,6 +290,8 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
 
     "cross_attention_sep": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
     "cross_attention_optimization": OptionInfo(startup_cross_attention, "Attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention()}),
+    "attention_": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
+    "attention_slicing": OptionInfo('Default', "Attention slicing", gr.CheckboxGroup, {"choices": ['Default', 'Enabled', 'Disabled']}),
     "sdp_options": OptionInfo(startup_sdp_options, "SDP options", gr.CheckboxGroup, {"choices": startup_sdp_choices}),
     "xformers_options": OptionInfo(['Flash attention'], "xFormers options", gr.CheckboxGroup, {"choices": ['Flash attention'] }),
     "dynamic_attention_slice_rate": OptionInfo(0.5, "Dynamic Attention slicing rate in GB", gr.Slider, {"minimum": 0.01, "maximum": max(gpu_memory,4), "step": 0.01}),
diff --git a/modules/shared_items.py b/modules/shared_items.py
index 8eac5e070..f4d98e955 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -120,7 +120,6 @@ def list_crossattention():
         "Scaled-Dot-Product",
         "xFormers",
         "Batch matrix-matrix",
-        "Split attention",
         "Dynamic Attention BMM"
     ]
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 3397d4fe9..08d4f1bf2 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -14,6 +14,7 @@ def txt2img(id_task, state,
             vae_type, tiling, hidiffusion,
             detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution,
             n_iter, batch_size,
+            guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop,
             cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end,
             clip_skip,
             seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
@@ -55,6 +56,11 @@ def txt2img(id_task, state,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
+        guidance_name=guidance_name,
+        guidance_scale=guidance_scale,
+        guidance_rescale=guidance_rescale,
+        guidance_start=guidance_start,
+        guidance_stop=guidance_stop,
         cfg_scale=cfg_scale,
         image_cfg_scale=image_cfg_scale,
         diffusers_guidance_rescale=diffusers_guidance_rescale,
diff --git a/modules/ui_control.py b/modules/ui_control.py
index a1c77848d..3f43701af 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -2,7 +2,8 @@ import os
 import time
 import gradio as gr
 from modules.control import unit
-from modules import errors, shared, progress, ui_common, ui_sections, generation_parameters_copypaste, call_queue, scripts_manager, masking, images, processing_vae, timer # pylint: disable=ungrouped-imports
+from modules import errors, shared, progress, generation_parameters_copypaste, call_queue, scripts_manager, masking, images, processing_vae, timer # pylint: disable=ungrouped-imports
+from modules import ui_common, ui_sections, ui_guidance
 from modules import ui_control_helpers as helpers
 
 
@@ -156,7 +157,7 @@ def create_ui(_blocks: gr.Blocks=None):
 
                 mask_controls = masking.create_segment_ui()
 
-                cfg_scale, image_cfg_scale, guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_guidance_inputs('control')
+                guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_guidance.create_guidance_inputs('control')
                 vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('control')
                 hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('control')
 
@@ -274,7 +275,8 @@ def create_ui(_blocks: gr.Blocks=None):
                 prompt, negative, styles,
                 steps, sampler_index,
                 seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
-                cfg_scale, clip_skip, image_cfg_scale, guidance_rescale, pag_scale, pag_adaptive, cfg_end, vae_type, tiling, hidiffusion,
+                guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop,
+                cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end, vae_type, tiling, hidiffusion,
                 detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution,
                 hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio,
                 resize_mode_before, resize_name_before, resize_context_before, width_before, height_before, scale_by_before, selected_scale_tab_before,
@@ -354,14 +356,19 @@ def create_ui(_blocks: gr.Blocks=None):
                 (mask_controls[4], "Mask erode"),
                 (mask_controls[5], "Mask dilate"),
                 (mask_controls[6], "Mask auto"),
+                # guidance
+                (guidance_name, "Guidance"),
+                (guidance_scale, "Guidance scale"),
+                (guidance_rescale, "Guidance rescale"),
+                (guidance_start, "Guidance start"),
+                (guidance_stop, "Guidance stop"),
                 # advanced
-                (cfg_scale, "Guidance scale"),
                 (cfg_scale, "CFG scale"),
                 (cfg_end, "CFG end"),
                 (clip_skip, "Clip skip"),
                 (image_cfg_scale, "Image CFG scale"),
                 (image_cfg_scale, "Hires CFG scale"),
-                (guidance_rescale, "CFG rescale"),
+                (diffusers_guidance_rescale, "CFG rescale"),
                 (vae_type, "VAE type"),
                 (tiling, "Tiling"),
                 (hidiffusion, "HiDiffusion"),
diff --git a/modules/ui_guidance.py b/modules/ui_guidance.py
new file mode 100644
index 000000000..3f3b3f8f3
--- /dev/null
+++ b/modules/ui_guidance.py
@@ -0,0 +1,125 @@
+import gradio as gr
+from modules import shared, modular_guiders
+from modules import ui_symbols, ui_components
+
+
+def create_guidance_inputs(tab):
+    with gr.Accordion(open=False, label='Guidance', elem_id=f"{tab}_guidance", elem_classes=["small-accordion"]):
+        with gr.Group():
+
+            with gr.Row(elem_id=f"{tab}_guider_row", elem_classes=['flexbox'], visible=shared.opts.model_modular_enable):
+                guidance_name = gr.Dropdown(choices=list(modular_guiders.guiders.keys()), value='Default', label='Guider', elem_id=f"{tab}_guider")
+                guidance_btn = ui_components.ToolButton(value=ui_symbols.book, elem_id=f"{tab}_guider_docs")
+                guidance_btn.click(fn=None, _js='getGuidanceDocs', inputs=[guidance_name], outputs=[])
+            with gr.Row(visible=shared.opts.model_modular_enable):
+                guidance_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_guidance_scale")
+                guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance rescale', value=0.0, elem_id=f"{tab}_guidance_rescale")
+            with gr.Row(visible=shared.opts.model_modular_enable):
+                guidance_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance start', value=0.0, elem_id=f"{tab}_guidance_start")
+                guidance_stop = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance stop', value=1.0, elem_id=f"{tab}_guidance_stop")
+            guidance_args = [guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop]
+
+            lsc_group = gr.Accordion(open=False, label='Layer skip guidance', elem_classes=["small-accordion"], visible=shared.opts.model_modular_enable)
+            with lsc_group:
+                with gr.Row():
+                    guidance_lsc_enabled = gr.Checkbox(label='Enable LayerSkipConfig', value=False)
+                    guidance_lsc_label = gr.Label(value='LSC: LayerSkipConfig', elem_id=f"{tab}_lsc_label", visible=False)
+                    guidance_lsc_btn = ui_components.ToolButton(value=ui_symbols.book, elem_id=f"{tab}_lsc_docs", elem_classes=["guidance-docs"])
+                    guidance_lsc_btn.click(fn=None, _js='getGuidanceDocs', inputs=[guidance_lsc_label], outputs=[])
+                with gr.Row():
+                    guidance_lsc_indices = gr.Textbox(label='LSC layer indices', value='1, 2, 3', placeholder='Comma-separated layer indices to skip')
+                with gr.Row():
+                    guidance_lsc_fqn = gr.Textbox(label='LSC fully qualified name', value='transformer_blocks', placeholder='Fully qualified name of the layer stack')
+                with gr.Row():
+                    guidance_lsc_skip_attention = gr.Checkbox(label='LSC skip attention blocks', value=True)
+                    guidance_lsc_skip_ff = gr.Checkbox(label='LSC skip feed-forward blocks', value=True)
+                    guidance_lsc_skip_attention_scores = gr.Checkbox(label='LSC skip attention scores', value=False)
+                with gr.Row():
+                    guidance_lsc_dropout = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='LSC dropout rate', value=1.0)
+                lsc_args = [guidance_lsc_enabled, guidance_lsc_indices, guidance_lsc_fqn, guidance_lsc_skip_attention, guidance_lsc_skip_ff, guidance_lsc_skip_attention_scores, guidance_lsc_dropout]
+
+            auto_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with auto_group:
+                guidance_auto_dropout = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='AutoGuidance dropout', value=0.1)
+                guidance_auto_layers = gr.Textbox(label='AutoGuidance layers', value='7, 8, 9', placeholder='Comma-separated layer indices, e.g. 7,8,9')
+                guidance_auto_config = gr.Dropdown(choices=[None, 'config1', 'config2'], value=None, label='AutoGuidance config')
+                guidance_auto_args = [guidance_auto_dropout, guidance_auto_layers, guidance_auto_config]
+
+            zero_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with zero_group:
+                guidance_zero_init_steps = gr.Slider(minimum=0, maximum=10, step=1, label='ZeroStar init steps', value=1)
+                guidance_zero_args = [guidance_zero_init_steps]
+
+            pag_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with pag_group:
+                guidance_pag_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='PAG scale', value=2.8)
+                guidance_pag_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='PAG start', value=0.01)
+                guidance_pag_stop = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='PAG stop', value=0.2)
+                guidance_pag_layers = gr.Textbox(label='PAG layers', value='7, 8, 9', placeholder='Comma-separated layer indices, e.g. 7,8,9')
+                guidance_pag_config = gr.Dropdown(choices=[None, 'config1', 'config2'], value=None, label='PAG config')
+                guidance_pag_args = [guidance_pag_scale, guidance_pag_start, guidance_pag_stop, guidance_pag_layers, guidance_pag_config]
+
+            apg_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with apg_group:
+                guidance_apg_momentum = gr.Slider(minimum=-1.0, maximum=1.0, step=0.05, label='APG momentum', value=-1.0)
+                guidance_apg_rescale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='APG rescale', value=15.0)
+                guidance_apg_args = [guidance_apg_momentum, guidance_apg_rescale]
+
+            slg_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with slg_group:
+                guidance_slg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='SLG scale', value=2.8)
+                guidance_slg_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='SLG start', value=0.01)
+                guidance_slg_stop = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='SLG stop', value=0.2)
+                guidance_slg_layers = gr.Textbox(label='SLG layers', value='7, 8, 9', placeholder='Comma-separated layer indices, e.g. 7,8,9')
+                guidance_slg_config = gr.Dropdown(choices=[None, 'config1', 'config2'], value=None, label='SLG config')
+                guidance_slg_args = [guidance_slg_scale, guidance_slg_start, guidance_slg_stop, guidance_slg_layers, guidance_slg_config]
+
+            seg_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with seg_group:
+                guidance_seg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='SEG scale', value=3.0)
+                guidance_seg_blur_sigma = gr.Number(label='SEG blur sigma', value=9999999.0)
+                guidance_seg_blur_threshold_inf = gr.Number(label='SEG blur threshold inf', value=9999.0)
+                guidance_seg_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='SEG start', value=0.0)
+                guidance_seg_stop = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='SEG stop', value=1.0)
+                guidance_seg_layers = gr.Textbox(label='SEG layers', value='7, 8, 9', placeholder='Comma-separated layer indices, e.g. 7,8,9')
+                guidance_seg_config = gr.Dropdown(choices=[None, 'config1', 'config2'], value=None, label='SEG config')
+                guidance_seg_args = [guidance_seg_scale, guidance_seg_blur_sigma, guidance_seg_blur_threshold_inf, guidance_seg_start, guidance_seg_stop, guidance_seg_layers, guidance_seg_config]
+
+            tcfg_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with tcfg_group:
+                pass
+
+            fdg_group = gr.Accordion(open=True, label='Advanced guidance params', elem_classes=["small-accordion"], visible=False)
+            with fdg_group:
+                guidance_fdg_scales = gr.Textbox(label='FDG scales', value='10.0, 5.0', placeholder='Comma-separated scales, e.g. 10.0,5.0')
+                guidance_fdg_weights = gr.Textbox(label='FDG weights', value='1.0', placeholder='Single float or comma-separated weights, e.g. 1.0 or 1.0,0.5')
+                guidance_fdg_rescale_space = gr.Dropdown(choices=['data', 'freq'], value='data', label='FDG rescale space')
+                guidance_fdg_args = [guidance_fdg_scales, guidance_fdg_weights, guidance_fdg_rescale_space]
+
+            def adv_visibility(guidance_name):
+                return [
+                    gr.update(visible=guidance_name.startswith('Auto')),
+                    gr.update(visible=guidance_name.startswith('Zero')),
+                    gr.update(visible=guidance_name.startswith('PAG')),
+                    gr.update(visible=guidance_name.startswith('APG')),
+                    gr.update(visible=guidance_name.startswith('SLG')),
+                    gr.update(visible=guidance_name.startswith('SEG')),
+                    gr.update(visible=guidance_name.startswith('TCFG')),
+                    gr.update(visible=guidance_name.startswith('FDG')),
+                ]
+            guidance_name.change(fn=adv_visibility, inputs=[guidance_name], outputs=[auto_group, zero_group, pag_group, apg_group, slg_group, seg_group, tcfg_group, fdg_group])
+
+            gr.HTML(value='<br><h2>Fallback guidance</h2>', visible=shared.opts.model_modular_enable, elem_id=f"{tab}_guidance_note")
+            with gr.Row(elem_id=f"{tab}_cfg_row", elem_classes=['flexbox']):
+                cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
+                cfg_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance end', value=1.0, elem_id=f"{tab}_cfg_end")
+            with gr.Row():
+                image_cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Refine guidance', value=6.0, elem_id=f"{tab}_image_cfg_scale")
+                diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Rescale guidance', value=0.0, elem_id=f"{tab}_image_cfg_rescale")
+            with gr.Row():
+                diffusers_pag_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='Attention guidance', value=0.0, elem_id=f"{tab}_pag_scale")
+                diffusers_pag_adaptive = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Adaptive scaling', value=0.5, elem_id=f"{tab}_pag_adaptive")
+
+    _modular_args = guidance_args + lsc_args + guidance_auto_args + guidance_zero_args + guidance_pag_args + guidance_apg_args + guidance_slg_args + guidance_seg_args + guidance_fdg_args
+    standard_args = [cfg_scale, image_cfg_scale, diffusers_guidance_rescale, diffusers_pag_scale, diffusers_pag_adaptive, cfg_end]
+    return guidance_args + standard_args
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index 7e7db44cb..cf1324484 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -1,11 +1,11 @@
-import os
-from PIL import Image
 import gradio as gr
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
-from modules import timer, shared, ui_common, ui_sections, generation_parameters_copypaste, processing_vae
+from modules import timer, shared, call_queue, generation_parameters_copypaste, processing_vae
+from modules import ui_common, ui_sections, ui_guidance
 
 
 def process_interrogate(mode, ii_input_files, ii_input_dir, ii_output_dir, *ii_singles):
+    import os
+    from PIL import Image
     from modules.interrogate.interrogate import interrogate
     mode = int(mode)
     if mode in {0, 1, 3, 4}:
@@ -132,7 +132,7 @@ def create_ui():
                             denoising_strength = gr.Slider(minimum=0.00, maximum=0.99, step=0.01, label='Denoising strength', value=0.30, elem_id="img2img_denoising_strength")
                             refiner_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Denoise start', value=0.0, elem_id="img2img_refiner_start")
 
-                    cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_guidance_inputs('img2img')
+                    guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_guidance.create_guidance_inputs('img2img')
                     vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('img2img')
                     hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('img2img')
                     enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, hr_refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('img2img')
@@ -177,8 +177,8 @@ def create_ui():
                 vae_type, tiling, hidiffusion,
                 detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution,
                 batch_count, batch_size,
-                cfg_scale, image_cfg_scale,
-                diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end,
+                guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop,
+                cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end,
                 refiner_start,
                 clip_skip,
                 denoising_strength,
@@ -194,7 +194,7 @@ def create_ui():
                 override_settings,
             ]
             img2img_dict = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', ''], name='Image'),
+                fn=call_queue.wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', ''], name='Image'),
                 _js="submit_img2img",
                 inputs= img2img_args + img2img_script_inputs,
                 outputs=[
@@ -229,8 +229,8 @@ def create_ui():
             )
             interrogate_btn.click(fn=lambda *args: process_interrogate(*args), **interrogate_args)
 
-            img2img_token_button.click(fn=wrap_queued_call(ui_common.update_token_counter), inputs=[img2img_prompt], outputs=[img2img_token_counter], show_progress = False)
-            img2img_negative_token_button.click(fn=wrap_queued_call(ui_common.update_token_counter), inputs=[img2img_negative_prompt], outputs=[img2img_negative_token_counter], show_progress = False)
+            img2img_token_button.click(fn=call_queue.wrap_queued_call(ui_common.update_token_counter), inputs=[img2img_prompt], outputs=[img2img_token_counter], show_progress = False)
+            img2img_negative_token_button.click(fn=call_queue.wrap_queued_call(ui_common.update_token_counter), inputs=[img2img_negative_prompt], outputs=[img2img_negative_token_counter], show_progress = False)
 
             ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
             img2img_paste_fields = [
@@ -254,8 +254,13 @@ def create_ui():
                 (seed, "Seed"),
                 (subseed, "Variation seed"),
                 (subseed_strength, "Variation strength"),
+                # guidance
+                (guidance_name, "Guidance"),
+                (guidance_scale, "Guidance scale"),
+                (guidance_rescale, "Guidance rescale"),
+                (guidance_start, "Guidance start"),
+                (guidance_stop, "Guidance stop"),
                 # advanced
-                (cfg_scale, "Guidance scale"),
                 (cfg_scale, "CFG scale"),
                 (cfg_end, "CFG end"),
                 (image_cfg_scale, "Image CFG scale"),
diff --git a/modules/ui_sections.py b/modules/ui_sections.py
index 829c502bd..683681cd5 100644
--- a/modules/ui_sections.py
+++ b/modules/ui_sections.py
@@ -146,21 +146,6 @@ def create_video_inputs(tab:str, show_always:bool=False):
     return video_type, video_duration, video_loop, video_pad, video_interpolate
 
 
-def create_guidance_inputs(tab):
-    with gr.Accordion(open=False, label="Guidance", elem_id=f"{tab}_guidance", elem_classes=["small-accordion"]):
-        with gr.Group():
-            with gr.Row(elem_id=f"{tab}_cfg_row", elem_classes=['flexbox']):
-                cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
-                cfg_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance end', value=1.0, elem_id=f"{tab}_cfg_end")
-            with gr.Row():
-                image_cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Refine guidance', value=6.0, elem_id=f"{tab}_image_cfg_scale")
-                diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Rescale guidance', value=0.0, elem_id=f"{tab}_image_cfg_rescale")
-            with gr.Row():
-                diffusers_pag_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='Attention guidance', value=0.0, elem_id=f"{tab}_pag_scale")
-                diffusers_pag_adaptive = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Adaptive scaling', value=0.5, elem_id=f"{tab}_pag_adaptive")
-    return cfg_scale, image_cfg_scale, diffusers_guidance_rescale, diffusers_pag_scale, diffusers_pag_adaptive, cfg_end
-
-
 def create_advanced_inputs(tab):
     with gr.Accordion(open=False, label="Advanced", elem_id=f"{tab}_advanced", elem_classes=["small-accordion"]):
         with gr.Group():
diff --git a/modules/ui_txt2img.py b/modules/ui_txt2img.py
index 0fae0c606..6eccbdd82 100644
--- a/modules/ui_txt2img.py
+++ b/modules/ui_txt2img.py
@@ -1,7 +1,6 @@
 import gradio as gr
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
-from modules import timer, shared, ui_common, ui_sections, generation_parameters_copypaste, processing_vae, images
-from modules.ui_components import ToolButton # pylint: disable=unused-import
+from modules import timer, shared, call_queue, generation_parameters_copypaste, processing_vae, images
+from modules import ui_common, ui_sections, ui_guidance
 
 
 def create_ui():
@@ -33,7 +32,7 @@ def create_ui():
                     with gr.Accordion(open=False, label="Samplers", elem_classes=["small-accordion"], elem_id="txt2img_sampler_group"):
                         ui_sections.create_sampler_options('txt2img')
                     seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = ui_sections.create_seed_inputs('txt2img')
-                    cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_sections.create_guidance_inputs('txt2img')
+                    guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_guidance.create_guidance_inputs('txt2img')
                     vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('txt2img')
                     hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('txt2img')
                     enable_hr, hr_sampler_index, denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('txt2img')
@@ -57,6 +56,7 @@ def create_ui():
                 vae_type, tiling, hidiffusion,
                 detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution,
                 batch_count, batch_size,
+                guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop,
                 cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end,
                 clip_skip,
                 seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
@@ -68,7 +68,7 @@ def create_ui():
                 override_settings,
             ]
             txt2img_dict = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', ''], name='Text'),
+                fn=call_queue.wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', ''], name='Text'),
                 _js="submit_txt2img",
                 inputs=txt2img_args + txt2img_script_inputs,
                 outputs=[
@@ -106,8 +106,13 @@ def create_ui():
                 (seed, "Seed"),
                 (subseed, "Variation seed"),
                 (subseed_strength, "Variation strength"),
+                # guidance
+                (guidance_name, "Guidance"),
+                (guidance_scale, "Guidance scale"),
+                (guidance_rescale, "Guidance rescale"),
+                (guidance_start, "Guidance start"),
+                (guidance_stop, "Guidance stop"),
                 # advanced
-                (cfg_scale, "Guidance scale"),
                 (cfg_scale, "CFG scale"),
                 (cfg_end, "CFG end"),
                 (clip_skip, "Clip skip"),
@@ -156,7 +161,7 @@ def create_ui():
             txt2img_bindings = generation_parameters_copypaste.ParamBinding(paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None)
             generation_parameters_copypaste.register_paste_params_button(txt2img_bindings)
 
-            txt2img_token_button.click(fn=wrap_queued_call(ui_common.update_token_counter), inputs=[txt2img_prompt], outputs=[txt2img_token_counter], show_progress = False)
-            txt2img_negative_token_button.click(fn=wrap_queued_call(ui_common.update_token_counter), inputs=[txt2img_negative_prompt], outputs=[txt2img_negative_token_counter], show_progress = False)
+            txt2img_token_button.click(fn=call_queue.wrap_queued_call(ui_common.update_token_counter), inputs=[txt2img_prompt], outputs=[txt2img_token_counter], show_progress = False)
+            txt2img_negative_token_button.click(fn=call_queue.wrap_queued_call(ui_common.update_token_counter), inputs=[txt2img_negative_prompt], outputs=[txt2img_negative_token_counter], show_progress = False)
 
             ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
diff --git a/scripts/xyz/xyz_grid_classes.py b/scripts/xyz/xyz_grid_classes.py
index 306b591f3..0836adbbf 100644
--- a/scripts/xyz/xyz_grid_classes.py
+++ b/scripts/xyz/xyz_grid_classes.py
@@ -20,6 +20,7 @@ from scripts.xyz.xyz_grid_shared import ( # pylint: disable=no-name-in-module, u
     apply_lora,
     apply_lora_strength,
     apply_te,
+    apply_guidance,
     apply_styles,
     apply_upscaler,
     apply_context,
@@ -223,6 +224,7 @@ axis_options = [
     AxisOption("[Sampler] Shift", float, apply_setting("schedulers_shift")),
     AxisOption("[Sampler] eta delta", float, apply_setting("eta_noise_seed_delta")),
     AxisOption("[Sampler] eta multiplier", float, apply_setting("scheduler_eta")),
+    AxisOption("[Guidance] Guidance name", str, apply_guidance, choices=lambda: ['Default', 'CFG', 'Auto', 'Zero', 'PAG', 'APG', 'SLG', 'SEG', 'TCFG', 'FDG']),
     AxisOption("[Refine] Upscaler", str, apply_field("hr_upscaler"), cost=0.3, choices=lambda: [x.name for x in shared.sd_upscalers]),
     AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
     AxisOption("[Refine] Denoising strength", float, apply_field("denoising_strength")),
diff --git a/scripts/xyz/xyz_grid_shared.py b/scripts/xyz/xyz_grid_shared.py
index 0c95d14de..76d4df769 100644
--- a/scripts/xyz/xyz_grid_shared.py
+++ b/scripts/xyz/xyz_grid_shared.py
@@ -254,6 +254,13 @@ def apply_te(p, x, xs):
     shared.log.debug(f'XYZ grid apply text-encoder: "{x}"')
 
 
+def apply_guidance(p, x, xs):
+    from modules.modular_guiders import guiders
+    guiders = list(guiders.keys())
+    p.guidance_name = [g for g in guiders if g.lower().startswith(x.lower())][0]
+    shared.log.debug(f'XYZ grid apply guidance: "{p.guidance_name}"')
+
+
 def apply_styles(p: processing.StableDiffusionProcessingTxt2Img, x: str, _):
     p.styles.extend(x.split(','))
     shared.log.debug(f'XYZ grid apply style: "{x}"')
diff --git a/scripts/xyz_grid_on.py b/scripts/xyz_grid_on.py
index f79be6636..595536067 100644
--- a/scripts/xyz_grid_on.py
+++ b/scripts/xyz_grid_on.py
@@ -324,6 +324,7 @@ class Script(scripts_manager.Script):
 
         def cell(x, y, z, ix, iy, iz):
             if shared.state.interrupted:
+                shared.log.warning('XYZ grid: Interrupted')
                 return processing.Processed(p, [], p.seed, ""), 0
             p.xyz = True
             pc = copy(p)

From c661497b87df1c0712029e82bed69f49d1dae9e3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 18:09:09 -0400
Subject: [PATCH 091/230] linting

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md               |  8 ++++++++
 modules/modular_guiders.py |  2 +-
 modules/rocm.py            | 12 ++++++------
 3 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 474ab69f6..829902abe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -56,7 +56,15 @@
   - update **loopback** script with radon seed option, thanks @rabanti  
   - **vae** slicing enable for lowvram/medvram, tiling for lowvram, both disabled otherwise  
   - **attention** remove split-attention and add explicitly attention slicing enable/disable option  
+    enable in *settings -> compute settings*  
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
+- **Experimental**
+  - `new` command line flag enables new `pydantic` and `albumentations` packages  
+  - **modular pipelines**: enable in *settings -> model options*  
+    only compatible with some pipelines, invalidates preview generation  
+  - **modular guiders**: automatically used for compatible pipelines when *modular pipelines* is enabled  
+    allows for using many different guidance methods:  
+    *CFG, CFGZero, PAG, APG, SLG, SEG, TCFG, FDG*  
 - **Fixes**
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
diff --git a/modules/modular_guiders.py b/modules/modular_guiders.py
index ee9c3655c..556ad52b4 100644
--- a/modules/modular_guiders.py
+++ b/modules/modular_guiders.py
@@ -27,7 +27,7 @@ base_args = {
 def set_guider(p: processing.StableDiffusionProcessing):
     guidance_name = p.guidance_name or 'Default'
     if guidance_name not in guiders:
-        return None
+        return
 
     if guidance_name == 'Default':
         if hasattr(shared.sd_model, 'default_guider'):
diff --git a/modules/rocm.py b/modules/rocm.py
index 30e90f000..000eb039f 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -107,7 +107,7 @@ class Agent:
             return "gfx94X-dcgpu"
         if self.gfx_version == 0x950:
             return "gfx950-dcgpu"
-        raise Exception(f"Unsupported GPU architecture: {self.name}")
+        raise RuntimeError(f"Unsupported GPU architecture: {self.name}")
 
     def get_gfx_version(self) -> Union[str, None]:
         if self.gfx_version >= 0x1100 and self.gfx_version < 0x1200:
@@ -190,10 +190,10 @@ def get_version() -> str:
             return f'{arr[0]}.{arr[1]}' if len(arr) >= 2 else None
     else:
         # If rocm-sdk package is installed, the hip library may be used by PyTorch.
-        version = ctypes.c_int()
-        environment.hip.hipRuntimeGetVersion(ctypes.byref(version))
-        major = version.value // 10000000
-        minor = (version.value // 100000) % 100
+        ver = ctypes.c_int()
+        environment.hip.hipRuntimeGetVersion(ctypes.byref(ver))
+        major = ver.value // 10000000
+        minor = (ver.value // 100000) % 100
         #patch = version.value % 100000
         return f"{major}.{minor}"
 
@@ -306,7 +306,7 @@ is_installed = False
 version = None
 
 def refresh():
-    global environment, blaslt_tensile_libpath, is_installed, version
+    global environment, blaslt_tensile_libpath, is_installed, version # pylint: disable=global-statement
     environment = find()
     if environment is not None:
         if isinstance(environment, ROCmEnvironment):

From 8cc9c23f98b73c74c1b6a49af1a6afa8af9e7914 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 18:44:42 -0400
Subject: [PATCH 092/230] add directml and rocm note to changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md        | 8 ++++++++
 html/reference.json | 7 +++++++
 installer.py        | 3 ++-
 modules/modular.py  | 4 ++++
 modules/shared.py   | 2 +-
 5 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 829902abe..3186996a9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,8 @@
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
   - [Qwen Image Edit 2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) and [Nunchaku Qwen Image Edit 2509](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit-2509)  
     updated version of Qwen Image Edit with improved image consistency  
+  - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
+    updated version of E1 image editing model  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
     SRPO is trained by Tencent with specific technique: directly aligning the full diffusion trajectory with fine-grained human preference  
   - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
@@ -21,6 +23,12 @@
     enable in *settings -> pipeline modifers -> cache-dit*  
   - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
     automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
+- **Compute**
+  - **ROCm** for Windows  
+    support for both official torch preview release of `torch-rocm` for windows and **TheRock** unoffical `torch-rocm` builds for windows  
+    note that rocm for windows is still in preview and has limited gpu support, please check rocm docs for details  
+  - **DirectML** warn as end-of-life  
+    `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
 - **Extensions**  
   - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
     was a high-value built-in extension, but it has not been maintained for 1.5 years  
diff --git a/html/reference.json b/html/reference.json
index c8886af6b..4b5f01c99 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -553,6 +553,13 @@
     "skip": true,
     "extras": "sampler: Default"
   },
+  "HiDream-E1.1": {
+    "path": "HiDream-ai/HiDream-E1-1",
+    "desc": "HiDream-E1 is an image editing model built on HiDream-I1.",
+    "preview": "HiDream-ai--HiDream-E1-Full.jpg",
+    "skip": true,
+    "extras": "sampler: Default"
+  },
 
   "Kwai Kolors": {
     "path": "Kwai-Kolors/Kolors-diffusers",
diff --git a/installer.py b/installer.py
index 1932a20af..7d111d44c 100644
--- a/installer.py
+++ b/installer.py
@@ -967,7 +967,8 @@ def check_torch():
                     if args.use_directml and allow_directml:
                         import torch_directml # pylint: disable=import-error
                         dml_ver = pkg_resources.get_distribution("torch-directml")
-                        log.info(f'Torch backend: DirectML ({dml_ver})')
+                        log.warning(f'Torch backend: DirectML ({dml_ver})')
+                        log.warning('DirectML: end-of-life')
                         for i in range(0, torch_directml.device_count()):
                             log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
                 except Exception:
diff --git a/modules/modular.py b/modules/modular.py
index 595b860f7..5f3bfefc2 100644
--- a/modules/modular.py
+++ b/modules/modular.py
@@ -6,8 +6,12 @@ from modules import shared
 modular_map= {
     'StableDiffusionXLPipeline': 'StableDiffusionXLAutoBlocks',
     'StableDiffusionXLImg2ImgPipeline': 'StableDiffusionXLAutoBlocks',
+    'StableDiffusionXLInpaintPipeline': 'StableDiffusionXLAutoBlocks',
     'FluxPipeline': 'FluxAutoBlocks',
+    'FluxImg2ImgPipeline': 'FluxAutoBlocks',
+    'FluxInpaintPipeline': 'FluxAutoBlocks',
     'WanPipeline': 'WanAutoBlocks',
+    'WanImageToVideoPipeline': 'WanAutoBlocks',
     'QwenImagePipeline': 'QwenImageAutoBlocks',
     'QwenImageEditPipeline': 'QwenImageEditAutoBlocks',
 }
diff --git a/modules/shared.py b/modules/shared.py
index 8dcf8f26d..d35dc6fbb 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -291,7 +291,7 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
     "cross_attention_sep": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
     "cross_attention_optimization": OptionInfo(startup_cross_attention, "Attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention()}),
     "attention_": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
-    "attention_slicing": OptionInfo('Default', "Attention slicing", gr.CheckboxGroup, {"choices": ['Default', 'Enabled', 'Disabled']}),
+    "attention_slicing": OptionInfo('Default', "Attention slicing", gr.Radio, {"choices": ['Default', 'Enabled', 'Disabled']}),
     "sdp_options": OptionInfo(startup_sdp_options, "SDP options", gr.CheckboxGroup, {"choices": startup_sdp_choices}),
     "xformers_options": OptionInfo(['Flash attention'], "xFormers options", gr.CheckboxGroup, {"choices": ['Flash attention'] }),
     "dynamic_attention_slice_rate": OptionInfo(0.5, "Dynamic Attention slicing rate in GB", gr.Slider, {"minimum": 0.01, "maximum": max(gpu_memory,4), "step": 0.01}),

From 70a2c209b144ce795dd050d425a4f85ad85ee373 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 20:22:25 -0400
Subject: [PATCH 093/230] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                  | 2 ++
 html/reference.json           | 3 ++-
 modules/lora/lora_nunchaku.py | 6 +++---
 modules/model_quant.py        | 5 -----
 pipelines/model_qwen.py       | 2 +-
 5 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3186996a9..a036f9cbb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -29,6 +29,8 @@
     note that rocm for windows is still in preview and has limited gpu support, please check rocm docs for details  
   - **DirectML** warn as end-of-life  
     `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
+  - command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible  
+    previously sdnext was performing a fallback to `torch-cpu` which is not desired  
 - **Extensions**  
   - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
     was a high-value built-in extension, but it has not been maintained for 1.5 years  
diff --git a/html/reference.json b/html/reference.json
index ee89edb12..cc7a34eab 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -666,7 +666,8 @@
     "desc": "HiDream-E1 is an image editing model built on HiDream-I1.",
     "preview": "HiDream-ai--HiDream-E1-Full.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "experimental": true
   },
 
   "Kwai Kolors": {
diff --git a/modules/lora/lora_nunchaku.py b/modules/lora/lora_nunchaku.py
index d01238449..de4773158 100644
--- a/modules/lora/lora_nunchaku.py
+++ b/modules/lora/lora_nunchaku.py
@@ -8,9 +8,6 @@ previously_loaded = [] # we maintain private state here
 
 def load_nunchaku(names, strengths):
     global previously_loaded # pylint: disable=global-statement
-    if not hasattr(shared.sd_model, 'transformer') or not hasattr(shared.sd_model.transformer, 'update_lora_params'):
-        shared.log.error(f'Network load: type=LoRA method=nunchaku model={shared.sd_model.__class__.__name__} unsupported')
-        return False
     strengths = [s[0] if isinstance(s, list) else s for s in strengths]
     networks = lora_load.gather_networks(names)
     networks = [(network, strength) for network, strength in zip(networks, strengths) if network is not None and strength > 0]
@@ -18,6 +15,9 @@ def load_nunchaku(names, strengths):
     is_changed = loras != previously_loaded
     if not is_changed:
         return False
+    if not hasattr(shared.sd_model, 'transformer') or not hasattr(shared.sd_model.transformer, 'update_lora_params'):
+        shared.log.error(f'Network load: type=LoRA method=nunchaku model={shared.sd_model.__class__.__name__} unsupported')
+        return False
 
     previously_loaded = loras
     try:
diff --git a/modules/model_quant.py b/modules/model_quant.py
index 33495b327..19b7ae7a7 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -501,11 +501,6 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
     if model.__class__.__name__ == "ChromaTransformer2DModel":
         modules_to_not_convert.append("distilled_guidance_layer")
-    if model.__class__.__name__ == "QwenImageTransformer2DModel":
-        if "minimum_6bit" not in modules_dtype_dict.keys():
-            modules_dtype_dict["minimum_6bit"] = ["img_mod", "pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out"]
-        else:
-            modules_dtype_dict["minimum_6bit"].extend(["img_mod", "pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out"])
 
     sdnq_modules_to_not_convert = [m.strip() for m in re.split(';|,| ', shared.opts.sdnq_modules_to_not_convert) if len(m.strip()) > 1]
     if len(sdnq_modules_to_not_convert) > 0:
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index cf18e2143..8927c0fbb 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -46,7 +46,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
             subfolder=transformer_subfolder,
             cls_name=diffusers.QwenImageTransformer2DModel,
             load_config=diffusers_load_config,
-            modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "transformer_blocks.0.img_mod.1.weight"]},
+            modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "img_mod", "transformer_blocks.0.img_mod.1.weight"]},
         )
 
     repo_te = 'Qwen/Qwen-Image'

From 2a7e545c096d2d6776e37b24fc637a9364163c99 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 20:50:14 -0400
Subject: [PATCH 094/230] update qwen layers to min-8bit

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 pipelines/model_qwen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index 8927c0fbb..f28313603 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -46,7 +46,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
             subfolder=transformer_subfolder,
             cls_name=diffusers.QwenImageTransformer2DModel,
             load_config=diffusers_load_config,
-            modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "img_mod", "transformer_blocks.0.img_mod.1.weight"]},
+            modules_dtype_dict={"minimum_8bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "img_mod", "transformer_blocks.0.img_mod.1.weight"]},
         )
 
     repo_te = 'Qwen/Qwen-Image'

From c03a6928e4c128543f55e3c3b74da10dd8ece47e Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 21:19:36 -0400
Subject: [PATCH 095/230] reference models add size and release date info

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                             |  1 +
 modules/ui_extra_networks.py             |  5 +++++
 modules/ui_extra_networks_checkpoints.py | 13 +++++++++++--
 3 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a036f9cbb..8a47fb014 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -60,6 +60,7 @@
   - **api** new endpoint POST `/sdapi/v1/civitai` to trigger civitai models metadata update  
     accepts optional `page` parameter to search specific networks page  
   - **reference models** additional example images, thanks @liutyi  
+  - **reference models** add model size and release date, thanks @alerikaisattera  
   - **video** support for configurable multi-stage models such as WAN-2.2-14B  
   - **video** new LTX model selection  
   - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 645dab049..7777c73f2 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -828,6 +828,11 @@ def create_ui(container, button_parent, tabname, skip_indexing = False):
 
         if is_valid:
             stat_size, stat_mtime = modelstats.stat(item.filename)
+            if hasattr(item, 'size') and item.size > 0:
+                stat_size = item.size
+            if hasattr(item, 'mtime') and item.mtime is not None:
+                stat_mtime = item.mtime
+            print('HERE', item.name, stat_size, stat_mtime)
             desc = item.description
             fullinfo = shared.readfile(os.path.splitext(item.filename)[0] + '.json', silent=True)
             if 'modelVersions' in fullinfo: # sanitize massive objects
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index 427de5f34..ae4c98422 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -2,6 +2,7 @@ import os
 import html
 import json
 import concurrent
+from datetime import datetime
 from modules import shared, ui_extra_networks, sd_models, modelstats, paths
 
 
@@ -45,8 +46,16 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                     continue
             preview = v.get('preview', v['path'])
             preview_file = self.find_preview_file(os.path.join(paths.reference_path, preview))
-            _size, mtime = modelstats.stat(preview_file)
             name = os.path.normpath(os.path.join(paths.reference_path, k)).replace('\\', '/')
+            size = int(float(v.get('size', 0)) * 1024 * 1024 * 1024)
+            mtime = v.get('date', None)
+            if mtime is None:
+                _size, mtime = modelstats.stat(preview_file)
+            else:
+                try:
+                    mtime = datetime.strptime(mtime, '%Y %B') # 2025 January
+                except Exception:
+                    _size, mtime = modelstats.stat(preview_file)
             yield {
                 "type": 'Model',
                 "name": name,
@@ -57,7 +66,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                 "onclick": '"' + html.escape(f"selectReference({json.dumps(url)})") + '"',
                 "hash": None,
                 "mtime": mtime,
-                "size": 0,
+                "size": size,
                 "info": {},
                 "metadata": {},
                 "description": v.get('desc', ''),

From 5d1bfc2bfcb63d8e34c96b7d2a5d942e7e242aaf Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 28 Sep 2025 21:36:16 -0400
Subject: [PATCH 096/230] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ui_extra_networks.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 7777c73f2..c60c04c86 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -832,7 +832,6 @@ def create_ui(container, button_parent, tabname, skip_indexing = False):
                 stat_size = item.size
             if hasattr(item, 'mtime') and item.mtime is not None:
                 stat_mtime = item.mtime
-            print('HERE', item.name, stat_size, stat_mtime)
             desc = item.description
             fullinfo = shared.readfile(os.path.splitext(item.filename)[0] + '.json', silent=True)
             if 'modelVersions' in fullinfo: # sanitize massive objects

From 93bdca0670c15abcf5e0b227ecf7f67f983fd0ea Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 29 Sep 2025 10:56:03 -0400
Subject: [PATCH 097/230] fix image str decode and update requirements

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       |  3 ++-
 extensions-builtin/sdnext-modernui |  2 +-
 installer.py                       |  2 +-
 modules/processing_helpers.py      | 38 +++++++++++++++++++++++-------
 requirements.txt                   |  6 +----
 5 files changed, 35 insertions(+), 16 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8a47fb014..f73e1bf33 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-28
+## Update for 2025-09-29
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -83,6 +83,7 @@
   - benchmark: remove forced sampler from system info benchmark  
   - xyz-grid: fix xyz grid with random seeds  
   - reference: fix download for sd15/sdxl reference models  
+  - fix checks in init/mask image decode  
   
 ## Update for 2025-09-15
 
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index b189fac67..620fc402a 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit b189fac67d24bf21c9b65cf45add5b8c29f7456f
+Subproject commit 620fc402ad2de2d7446ae6b2586235ee5d75bb58
diff --git a/installer.py b/installer.py
index 7d111d44c..b32a26664 100644
--- a/installer.py
+++ b/installer.py
@@ -608,7 +608,7 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = '1448b035859dd57bbb565239dcdd79a025a85422' # diffusers commit hash
+    sha = '64a5187d96f9376c7cf5123db810f2d2da79d7d0' # diffusers commit hash
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''
diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py
index 7906261b9..b58f0594a 100644
--- a/modules/processing_helpers.py
+++ b/modules/processing_helpers.py
@@ -280,13 +280,38 @@ def validate_sample(tensor):
     return cast
 
 
+def decode_images(image):
+    if isinstance(image, list):
+        decoded = []
+        for i, img in enumerate(image):
+            if isinstance(img, str):
+                try:
+                    decoded.append(helpers.decode_base64_to_image(img, quiet=True))
+                except Exception as e:
+                    shared.log.error(f'Decode image[{i}]: {e}')
+            elif isinstance(img, Image.Image):
+                decoded.append(img)
+            else:
+                shared.log.error(f'Decode image[{i}]: {type(img)} unknown type')
+        return decoded
+    elif isinstance(image, str):
+        try:
+            return helpers.decode_base64_to_image(image, quiet=True)
+        except Exception as e:
+            shared.log.error(f'Decode image: {e}')
+    elif isinstance(image, Image.Image):
+        return image
+    else:
+        shared.log.error(f'Decode image: {type(image)} unknown type')
+    return None
+
+
 def resize_init_images(p):
     if getattr(p, 'image', None) is not None and getattr(p, 'init_images', None) is None:
         p.init_images = [p.image]
 
     if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
-        if isinstance(p.init_images[0], str):
-            p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images]
+        p.init_images = decode_images(p.init_images)
         vae_scale_factor = sd_vae.get_vae_scale_factor()
         tgt_width, tgt_height = vae_scale_factor * math.ceil(p.init_images[0].width / vae_scale_factor), vae_scale_factor * math.ceil(p.init_images[0].height / vae_scale_factor)
         if p.init_images[0].size != (tgt_width, tgt_height):
@@ -296,16 +321,13 @@ def resize_init_images(p):
             p.width = tgt_width
             sd_hijack_hypertile.hypertile_set(p)
         if getattr(p, 'mask', None) is not None and p.mask is not None and p.mask.size != (tgt_width, tgt_height):
-            if isinstance(p.mask[0], str):
-                p.mask = [helpers.decode_base64_to_image(i, quiet=True) for i in p.mask]
+            p.mask = decode_images(p.mask)
             p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
         if getattr(p, 'init_mask', None) is not None and p.init_mask is not None and p.init_mask.size != (tgt_width, tgt_height):
-            if isinstance(p.init_mask[0], str):
-                p.init_mask = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_mask]
+            p.init_mask = decode_images(p.init_mask)
             p.init_mask = images.resize_image(1, p.init_mask, tgt_width, tgt_height, upscaler_name=None)
         if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
-            if isinstance(p.mask_for_overlay, str):
-                p.mask_for_overlay = helpers.decode_base64_to_image(p.mask_for_overlay, quiet=True)
+            p.mask_for_overlay = decode_images(p.mask_for_overlay)
             p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
         return tgt_width, tgt_height
     return p.width, p.height
diff --git a/requirements.txt b/requirements.txt
index cec622cb1..6ca4153e9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -44,7 +44,7 @@ tqdm==4.67.1
 accelerate==1.10.1
 opencv-contrib-python-headless==4.11.0.86
 einops==0.8.1
-huggingface_hub==0.35.0
+huggingface_hub==0.35.3
 numexpr==2.11.0
 numpy==2.1.2
 pandas==2.3.1
@@ -69,7 +69,3 @@ seam-carving
 ruff
 pylint
 pre-commit
-
-# block
-torch!=2.5.0
-torchvision!=0.20.0

From 206c79520723c13d54ff3b9642cdf69f6bc6b17b Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 29 Sep 2025 18:36:16 +0300
Subject: [PATCH 098/230] add  Intel Graphics Software to autodetect

---
 installer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/installer.py b/installer.py
index b32a26664..8e2898a81 100644
--- a/installer.py
+++ b/installer.py
@@ -904,7 +904,7 @@ def check_torch():
     else:
         is_cuda_available = allow_cuda and (args.use_cuda or shutil.which('nvidia-smi') is not None or args.use_xformers or os.path.exists(os.path.join(os.environ.get('SystemRoot') or r'C:\Windows', 'System32', 'nvidia-smi.exe')))
         is_rocm_available = allow_rocm and (args.use_rocm or args.use_zluda or rocm.is_installed)
-        is_ipex_available = allow_ipex and (args.use_ipex or shutil.which('sycl-ls') is not None or shutil.which('sycl-ls.exe') is not None or os.environ.get('ONEAPI_ROOT') is not None or os.path.exists('/opt/intel/oneapi') or os.path.exists("C:/Program Files (x86)/Intel/oneAPI") or os.path.exists("C:/oneAPI"))
+        is_ipex_available = allow_ipex and (args.use_ipex or shutil.which('sycl-ls') is not None or shutil.which('sycl-ls.exe') is not None or os.environ.get('ONEAPI_ROOT') is not None or os.path.exists('/opt/intel/oneapi') or os.path.exists("C:/Program Files (x86)/Intel/oneAPI") or os.path.exists("C:/oneAPI") or os.path.exists("C:/Program Files/Intel/Intel Graphics Software"))
 
         if is_cuda_available and args.use_cuda: # prioritize cuda
             torch_command = install_cuda()

From 00fa1dc59e40234ea73ca6b5f2b32d42e7033405 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 29 Sep 2025 11:45:58 -0400
Subject: [PATCH 099/230] add online/offline since

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui | 2 +-
 javascript/monitor.js              | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 620fc402a..d034f611d 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 620fc402ad2de2d7446ae6b2586235ee5d75bb58
+Subproject commit d034f611df9931d87059a8256a2c77cc82f8e11c
diff --git a/javascript/monitor.js b/javascript/monitor.js
index 1c194af08..276e4bc0e 100644
--- a/javascript/monitor.js
+++ b/javascript/monitor.js
@@ -2,11 +2,13 @@ async function updateIndicator(online, data, msg) {
   const el = document.getElementById('logo_nav');
   if (!el || !data) return;
   const status = online ? '<b style="color:lime">online</b>' : '<b style="color:darkred">offline</b>';
+  const date = new Date();
   const template = `
     Version: <b>${data.updated}</b><br>
     Commit: <b>${data.hash}</b><br>
     Branch: <b>${data.branch}</b><br>
     Status: ${status}<br>
+    Since: ${date.toLocaleString()}<br>
   `;
   if (online) {
     el.dataset.hint = template;

From 3084c3214fc6c47d3d816af5f0177b1ed83fc7f4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 29 Sep 2025 16:33:03 -0400
Subject: [PATCH 100/230] disable transformers TOKENIZERS_PARALLELISM

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/installer.py b/installer.py
index 8e2898a81..cb3153b3e 100644
--- a/installer.py
+++ b/installer.py
@@ -1348,6 +1348,7 @@ def set_environment():
     os.environ.setdefault('UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS', '1')
     os.environ.setdefault('PYTORCH_ENABLE_XPU_FALLBACK', '1')
     os.environ.setdefault('PYTORCH_ENABLE_MPS_FALLBACK', '1')
+    os.environ.setdefault('TOKENIZERS_PARALLELISM', '0')
 
 
 def check_extensions():

From 6b67a9d0c45f76034aeb4736022df885537cef8e Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 30 Sep 2025 01:58:13 +0300
Subject: [PATCH 101/230] SDNQ add check_mats to matmul

---
 modules/rocm.py                                   |  8 ++++----
 modules/sdnq/__init__.py                          | 14 ++++++++------
 modules/sdnq/layers/conv/conv_fp8.py              |  2 ++
 modules/sdnq/layers/conv/conv_fp8_tensorwise.py   |  2 ++
 modules/sdnq/layers/conv/conv_int8.py             |  2 ++
 modules/sdnq/layers/linear/forward.py             | 15 +++++++++++++++
 modules/sdnq/layers/linear/linear_fp8.py          |  2 ++
 .../sdnq/layers/linear/linear_fp8_tensorwise.py   |  2 ++
 modules/sdnq/layers/linear/linear_int8.py         |  2 ++
 9 files changed, 39 insertions(+), 10 deletions(-)

diff --git a/modules/rocm.py b/modules/rocm.py
index 000eb039f..1b03bfd32 100644
--- a/modules/rocm.py
+++ b/modules/rocm.py
@@ -248,8 +248,8 @@ if sys.platform == "win32":
             import torch
             import numpy as np
 
-            cholesky_ex_gpu = torch.linalg.cholesky_ex
-            @wraps(cholesky_ex_gpu)
+            original_cholesky_ex = torch.linalg.cholesky_ex
+            @wraps(original_cholesky_ex)
             def cholesky_ex(A: torch.Tensor, upper=False, check_errors=False, out=None) -> torch.return_types.linalg_cholesky_ex:
                 assert not check_errors
                 return_device = A.device
@@ -261,8 +261,8 @@ if sys.platform == "win32":
                 return torch.return_types.linalg_cholesky_ex((L, info), {})
             torch.linalg.cholesky_ex = cholesky_ex
 
-            cholesky_gpu = torch.linalg.cholesky
-            @wraps(cholesky_gpu)
+            original_cholesky = torch.linalg.cholesky
+            @wraps(original_cholesky)
             def cholesky(A: torch.Tensor, upper=False, out=None) -> torch.Tensor:
                 return_device = A.device
                 L = torch.from_numpy(np.linalg.cholesky(A.to("cpu").numpy(), upper=upper)).to(return_device)
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 82689efca..4f35009ec 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -172,12 +172,14 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         if use_quantized_matmul and not re_quantize_for_matmul:
             scale.transpose_(0,1)
             layer.weight.transpose_(0,1)
-            if not dtype_dict[weights_dtype]["is_integer"]:
-                weight_stride = layer.weight.stride()
-                if not (weight_stride[0] == 1 and weight_stride[1] > 1):
-                    layer.weight.data = layer.weight.t().contiguous().t()
-                if not use_tensorwise_fp8_matmul:
-                    scale = scale.to(torch.float32)
+            weight_stride = layer.weight.stride()
+            if not (weight_stride[0] == 1 and weight_stride[1] > 1):
+                if devices.backend != "ipex":
+                    layer.weight.data = layer.weight.t_().contiguous().t_()
+            elif devices.backend == "ipex":
+                layer.weight.data = layer.weight.t_().contiguous().t_()
+            if not use_tensorwise_fp8_matmul and not dtype_dict[weights_dtype]["is_integer"]:
+                scale = scale.to(torch.float32)
 
         layer.sdnq_dequantizer = dequantizer_dict[weights_dtype](
             scale=scale,
diff --git a/modules/sdnq/layers/conv/conv_fp8.py b/modules/sdnq/layers/conv/conv_fp8.py
index 2d1407a47..7a83bb3a8 100644
--- a/modules/sdnq/layers/conv/conv_fp8.py
+++ b/modules/sdnq/layers/conv/conv_fp8.py
@@ -6,6 +6,7 @@ import torch
 
 from ...common import compile_func # noqa: TID252
 from ..linear.linear_fp8 import quantize_fp8_matmul_input # noqa: TID252
+from ..linear.forward import check_mats # noqa: TID252
 from .forward import get_conv_args, process_conv_input
 
 
@@ -23,6 +24,7 @@ def conv_fp8_matmul(
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
     input, input_scale = quantize_fp8_matmul_input(input)
+    input, weight = check_mats(input, weight)
 
     if groups == 1:
         if bias is not None and bias.dtype != torch.bfloat16:
diff --git a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
index bc968e519..e3b005622 100644
--- a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
+++ b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
@@ -7,6 +7,7 @@ import torch
 from ...common import compile_func # noqa: TID252
 from ...dequantizer import dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 from ..linear.linear_fp8_tensorwise import quantize_fp8_matmul_input_tensorwise # noqa: TID252
+from ..linear.forward import check_mats # noqa: TID252
 from .forward import get_conv_args, process_conv_input
 
 
@@ -24,6 +25,7 @@ def conv_fp8_matmul_tensorwise(
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
     input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
+    input, weight = check_mats(input, weight)
     dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
 
     if groups == 1:
diff --git a/modules/sdnq/layers/conv/conv_int8.py b/modules/sdnq/layers/conv/conv_int8.py
index 6c59f6d60..260acd170 100644
--- a/modules/sdnq/layers/conv/conv_int8.py
+++ b/modules/sdnq/layers/conv/conv_int8.py
@@ -8,6 +8,7 @@ from ...common import compile_func # noqa: TID252
 from ...packed_int import unpack_int_symetric # noqa: TID252
 from ...dequantizer import dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 from ..linear.linear_int8 import quantize_int8_matmul_input # noqa: TID252
+from ..linear.forward import check_mats # noqa: TID252
 from .forward import get_conv_args, process_conv_input
 
 
@@ -29,6 +30,7 @@ def conv_int8_matmul(
     input, scale = quantize_int8_matmul_input(input, scale)
     if quantized_weight_shape is not None:
         weight = unpack_int_symetric(weight, quantized_weight_shape, weights_dtype, dtype=torch.int8)
+    input, weight = check_mats(input, weight)
 
     if groups == 1:
         result = torch._int_mm(input, weight)
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index 20224204c..eff9b0428 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -1,7 +1,22 @@
 # pylint: disable=relative-beyond-top-level,redefined-builtin,protected-access
 
+from typing import Tuple
+
 import torch
 
 
+def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    input_stride = input.stride()
+    if not (input_stride[0] > input_stride[1] and input_stride[1] == 1):
+        input = input.contiguous()
+    weight_stride = weight.stride()
+    if not (weight_stride[0] == 1 and weight_stride[1] > 1):
+        if weight.device.type != "xpu":
+            weight = weight.t().contiguous().t()
+    elif weight.device.type == "xpu":
+        weight = weight.t().contiguous().t()
+    return input, weight
+
+
 def quantized_linear_forward(self, input: torch.FloatTensor) -> torch.FloatTensor:
     return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight), self.bias)
diff --git a/modules/sdnq/layers/linear/linear_fp8.py b/modules/sdnq/layers/linear/linear_fp8.py
index 63eee40b4..d1845b1e7 100644
--- a/modules/sdnq/layers/linear/linear_fp8.py
+++ b/modules/sdnq/layers/linear/linear_fp8.py
@@ -6,6 +6,7 @@ import torch
 
 from ...common import compile_func # noqa: TID252
 from ...dequantizer import quantize_fp8 # noqa: TID252
+from .forward import check_mats
 
 
 def quantize_fp8_matmul_input(input: torch.FloatTensor) -> Tuple[torch.Tensor, torch.FloatTensor]:
@@ -23,6 +24,7 @@ def fp8_matmul(
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
     input, input_scale = quantize_fp8_matmul_input(input)
+    input, weight = check_mats(input, weight)
     if bias is not None and bias.dtype != torch.bfloat16:
         bias = bias.to(dtype=torch.bfloat16)
     return torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16).view(output_shape).to(return_dtype)
diff --git a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
index 0343087c5..a35eb0721 100644
--- a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
+++ b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
@@ -6,6 +6,7 @@ import torch
 
 from ...common import compile_func # noqa: TID252
 from ...dequantizer import quantize_fp8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
+from .forward import check_mats
 
 
 def quantize_fp8_matmul_input_tensorwise(input: torch.FloatTensor, scale: torch.FloatTensor) -> Tuple[torch.Tensor, torch.FloatTensor]:
@@ -27,6 +28,7 @@ def fp8_matmul_tensorwise(
     output_shape = (*input.shape[:-1], weight.shape[-1])
     dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
     input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
+    input, weight = check_mats(input, weight)
     if bias is not None:
         return dequantize_symmetric_with_bias(torch._scaled_mm(input, weight, scale_a=dummy_input_scale, scale_b=dummy_input_scale, bias=None, out_dtype=scale.dtype), scale, bias, return_dtype, output_shape)
     else:
diff --git a/modules/sdnq/layers/linear/linear_int8.py b/modules/sdnq/layers/linear/linear_int8.py
index bcc4d775c..356179b97 100644
--- a/modules/sdnq/layers/linear/linear_int8.py
+++ b/modules/sdnq/layers/linear/linear_int8.py
@@ -7,6 +7,7 @@ import torch
 from ...common import compile_func # noqa: TID252
 from ...packed_int import unpack_int_symetric # noqa: TID252
 from ...dequantizer import quantize_int8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
+from .forward import check_mats
 
 
 def quantize_int8_matmul_input(input: torch.FloatTensor, scale: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
@@ -31,6 +32,7 @@ def int8_matmul(
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
     input, scale = quantize_int8_matmul_input(input, scale)
+    input, weight = check_mats(input, weight)
     if bias is not None:
         return dequantize_symmetric_with_bias(torch._int_mm(input, weight), scale, bias, return_dtype, output_shape)
     else:

From 2621aecaccd92fa93b4b65a6be2a8e982e37235f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 29 Sep 2025 19:25:47 -0400
Subject: [PATCH 102/230] interrrupt will now show last known preview image

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md          |  2 ++
 modules/processing.py | 11 +++++++++--
 modules/shared.py     |  2 +-
 3 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f73e1bf33..cee8bcb0d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -53,6 +53,8 @@
   - networks ability to filter lora by base model version  
 - **Other**
   - server will note when restart is recommended due to package updates  
+  - **interrrupt** will now show last known preview image  
+    *keep incomplete* setting is now *save interrupted*  
   - **logging** enable `debug`, `docs` and `api-docs` by default  
   - **ipex** simplify internal implementation  
   - refactor to use new libraries  
diff --git a/modules/processing.py b/modules/processing.py
index e01f88bdc..d42929b23 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -427,13 +427,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     for script_image, script_infotext in zip(results.images, results.infotexts):
                         output_images.append(script_image)
                         infotexts.append(script_infotext)
+
             if samples is None:
                 from modules.processing_diffusers import process_diffusers
                 samples = process_diffusers(p)
             timer.process.record('process')
 
-            if not shared.opts.keep_incomplete and shared.state.interrupted:
-                samples = []
+            if shared.state.interrupted:
+                shared.log.debug(f'Process: batch={n+1}/{p.n_iter} interrupted')
+                p.do_not_save_samples = not shared.opts.keep_incomplete
+                if shared.state.current_image is not None and isinstance(shared.state.current_image, Image.Image):
+                    samples = [shared.state.current_image]
+                    infotexts = [create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, index=0)]
+                else:
+                    samples = []
 
             if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
                 p.scripts.postprocess_batch(p, samples, batch_number=n)
diff --git a/modules/shared.py b/modules/shared.py
index d35dc6fbb..fab234f94 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -471,7 +471,7 @@ options_templates.update(options_section(('system-paths', "System Paths"), {
 
 options_templates.update(options_section(('saving-images', "Image Options"), {
     "samples_save": OptionInfo(True, "Save all generated images"),
-    "keep_incomplete": OptionInfo(True, "Keep incomplete images"),
+    "keep_incomplete": OptionInfo(True, "Save interrupted images"),
     "samples_format": OptionInfo('jpg', 'File format', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2", "jxl"]}),
     "jpeg_quality": OptionInfo(90, "Image quality", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "img_max_size_mp": OptionInfo(1000, "Maximum image size (MP)", gr.Slider, {"minimum": 100, "maximum": 2000, "step": 1}),

From 0c1d34721c94841a5006c225a5a1d408af86b6be Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 30 Sep 2025 02:37:58 +0300
Subject: [PATCH 103/230] SDNQ use contiguous for intel

---
 modules/sdnq/__init__.py              | 2 +-
 modules/sdnq/layers/linear/forward.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 4f35009ec..1a3686ccf 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -177,7 +177,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
                 if devices.backend != "ipex":
                     layer.weight.data = layer.weight.t_().contiguous().t_()
             elif devices.backend == "ipex":
-                layer.weight.data = layer.weight.t_().contiguous().t_()
+                layer.weight.data = layer.weight.contiguous()
             if not use_tensorwise_fp8_matmul and not dtype_dict[weights_dtype]["is_integer"]:
                 scale = scale.to(torch.float32)
 
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index eff9b0428..d404966c8 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -14,7 +14,7 @@ def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor,
         if weight.device.type != "xpu":
             weight = weight.t().contiguous().t()
     elif weight.device.type == "xpu":
-        weight = weight.t().contiguous().t()
+        weight = weight.contiguous()
     return input, weight
 
 

From 24c44af42fc265c7dea250ffdd4cbae10f6dfe25 Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Tue, 30 Sep 2025 15:27:49 +0300
Subject: [PATCH 104/230] embed prompts to Tempest model, similar prompt for
 base and hyper

---
 html/reference.json                         |   4 ++--
 models/Reference/tempest-by-vlad-base.jpg   | Bin 34916 -> 0 bytes
 models/Reference/tempest-by-vlad-hyper.jpg  | Bin 26074 -> 0 bytes
 models/Reference/tempestByVlad_baseV01.jpg  | Bin 0 -> 76310 bytes
 models/Reference/tempestByVlad_hyperV01.jpg | Bin 0 -> 84660 bytes
 5 files changed, 2 insertions(+), 2 deletions(-)
 delete mode 100644 models/Reference/tempest-by-vlad-base.jpg
 delete mode 100644 models/Reference/tempest-by-vlad-hyper.jpg
 create mode 100644 models/Reference/tempestByVlad_baseV01.jpg
 create mode 100644 models/Reference/tempestByVlad_hyperV01.jpg

diff --git a/html/reference.json b/html/reference.json
index cc7a34eab..8c5901381 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -1,13 +1,13 @@
 {
   "Tempest-by-Vlad XL": {
     "path": "tempestByVlad_baseV01.safetensors@https://civitai.com/api/download/models/1301775",
-    "preview": "tempest-by-vlad-base.jpg",
+    "preview": "tempestByVlad_baseV01.jpg",
     "desc": "Flexible SDXL model with custom encoder and finetuned for larger landscape resolutions with high details and high contrast.",
     "extras": ""
   },
   "Tempest-by-Vlad XL Hyper": {
     "path": "tempestByVlad_hyperV01.safetensors@https://civitai.com/api/download/models/1343512",
-    "preview": "tempest-by-vlad-hyper.jpg",
+    "preview": "tempestByVlad_hyperV01.jpg",
     "desc": "Custom distilled variant with goal to get as-normal-as-possible model that works with low steps and guidance-free",
     "extras": ""
   },
diff --git a/models/Reference/tempest-by-vlad-base.jpg b/models/Reference/tempest-by-vlad-base.jpg
deleted file mode 100644
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GIT binary patch
literal 0
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literal 0
HcmV?d00001


From 201ca283005806fdf7e6ec836a11dd4e4c430f26 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 30 Sep 2025 14:27:43 -0400
Subject: [PATCH 105/230] improve ram monitor

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/memstats.py | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/modules/memstats.py b/modules/memstats.py
index fdc94df37..1bb83998b 100644
--- a/modules/memstats.py
+++ b/modules/memstats.py
@@ -54,11 +54,24 @@ def ram_stats():
             ram_total = 100 * res.rss / process.memory_percent()
             ram_total = min(ram_total, get_docker_limit(), get_runpod_limit())
             ram['total'] = gb(ram_total)
-        ram['used'] = gb(res.rss)
-        ram['free'] = round(ram['total'] - ram['used'])
+        ram['rss'] = gb(res.rss)
     except Exception as e:
         ram['total'] = 0
+        ram['rss'] = 0
+        ram['error'] = str(e)
+        if not fail_once:
+            shared.log.error(f'RAM stats: {e}')
+            errors.display(e, 'RAM stats')
+            fail_once = True
+    try:
+        vmem = psutil.virtual_memory()
+        ram['used'] = gb(vmem.used)
+        ram['free'] = gb(vmem.free)
+        ram['avail'] = gb(vmem.available)
+    except Exception as e:
         ram['used'] = 0
+        ram['free'] = 0
+        ram['avail'] = 0
         ram['error'] = str(e)
         if not fail_once:
             shared.log.error(f'RAM stats: {e}')

From 8c2b76a8daef339a85abe465268a1136e1421d67 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 30 Sep 2025 14:36:55 -0400
Subject: [PATCH 106/230] add buffers and cache info

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md        |  4 +++-
 modules/memstats.py | 10 +++++++---
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cee8bcb0d..d48bce68d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-29
+## Update for 2025-09-30
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -56,6 +56,8 @@
   - **interrrupt** will now show last known preview image  
     *keep incomplete* setting is now *save interrupted*  
   - **logging** enable `debug`, `docs` and `api-docs` by default  
+  - **logging** add detailed ram/vram utilization info to log  
+    logging frequency can be specified using `--monitor x` command line param, where x is number of seconds  
   - **ipex** simplify internal implementation  
   - refactor to use new libraries  
   - styles and wildcards now use same seed as main generate for reproducible results  
diff --git a/modules/memstats.py b/modules/memstats.py
index 1bb83998b..68a7d27ea 100644
--- a/modules/memstats.py
+++ b/modules/memstats.py
@@ -65,13 +65,17 @@ def ram_stats():
             fail_once = True
     try:
         vmem = psutil.virtual_memory()
-        ram['used'] = gb(vmem.used)
-        ram['free'] = gb(vmem.free)
-        ram['avail'] = gb(vmem.available)
+        ram['used'] = gb(vmem.used) if hasattr(vmem, 'used') else 0
+        ram['free'] = gb(vmem.free) if hasattr(vmem, 'free') else 0
+        ram['avail'] = gb(vmem.available) if hasattr(vmem, 'available') else 0
+        ram['buffers'] = gb(vmem.buffers) if hasattr(vmem, 'buffers') else 0
+        ram['cached'] = gb(vmem.cached) if hasattr(vmem, 'cached') else 0
     except Exception as e:
         ram['used'] = 0
         ram['free'] = 0
         ram['avail'] = 0
+        ram['buffers'] = 0
+        ram['cached'] = 0
         ram['error'] = str(e)
         if not fail_once:
             shared.log.error(f'RAM stats: {e}')

From 03382bdd4cc4644c0ac657fafda9b2bbf9ef0038 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 1 Oct 2025 01:35:51 +0300
Subject: [PATCH 107/230] SDNQ simplify check_mats

---
 modules/sdnq/__init__.py              | 3 +--
 modules/sdnq/layers/linear/forward.py | 7 ++-----
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 1a3686ccf..450b43d43 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -172,8 +172,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         if use_quantized_matmul and not re_quantize_for_matmul:
             scale.transpose_(0,1)
             layer.weight.transpose_(0,1)
-            weight_stride = layer.weight.stride()
-            if not (weight_stride[0] == 1 and weight_stride[1] > 1):
+            if layer.weight.is_contiguous():
                 if devices.backend != "ipex":
                     layer.weight.data = layer.weight.t_().contiguous().t_()
             elif devices.backend == "ipex":
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index d404966c8..18009c27b 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -6,11 +6,8 @@ import torch
 
 
 def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    input_stride = input.stride()
-    if not (input_stride[0] > input_stride[1] and input_stride[1] == 1):
-        input = input.contiguous()
-    weight_stride = weight.stride()
-    if not (weight_stride[0] == 1 and weight_stride[1] > 1):
+    input = input.contiguous()
+    if weight.is_contiguous():
         if weight.device.type != "xpu":
             weight = weight.t().contiguous().t()
     elif weight.device.type == "xpu":

From 1fcf12d8747adf1e2482d5ade015346db02b6dab Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 1 Oct 2025 10:24:20 -0400
Subject: [PATCH 108/230] lock diffusers versions for zluda/rocm/directml

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/installer.py b/installer.py
index cb3153b3e..a2ec15fd2 100644
--- a/installer.py
+++ b/installer.py
@@ -609,6 +609,8 @@ def check_diffusers():
         install('diffusers')
         return
     sha = '64a5187d96f9376c7cf5123db810f2d2da79d7d0' # diffusers commit hash
+    if args.use_rocm or args.use_zluda or args.use_directml:
+        sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''

From 5d0a3e5e8a2c208169acf6467add2946bf5e73ff Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 1 Oct 2025 10:58:52 -0400
Subject: [PATCH 109/230] fix microsoft-florence

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       |  2 ++
 extensions-builtin/sdnext-modernui |  2 +-
 modules/interrogate/vqa.py         | 17 ++++++++++++++---
 scripts/prompt_enhance.py          |  2 +-
 4 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d48bce68d..db8625033 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -81,6 +81,8 @@
     allows for using many different guidance methods:  
     *CFG, CFGZero, PAG, APG, SLG, SEG, TCFG, FDG*  
 - **Fixes**
+  - **Microsoft Florence 2** both base and large variants  
+    *note* this will trigger download of the new variant of the model, feel free to delete older variant in `huggingface` folder  
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
   - framepack: patch solver for unsupported gpus  
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index d034f611d..e56b4f695 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit d034f611df9931d87059a8256a2c77cc82f8e11c
+Subproject commit e56b4f69549e6654c953346082ff3947256c9636
diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index 578c96093..5518ff803 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -27,8 +27,10 @@ vlm_models = {
     "Apple FastVLM 0.5B": "apple/FastVLM-0.5B",
     "Apple FastVLM 1.5B": "apple/FastVLM-1.5B",
     "Apple FastVLM 7B": "apple/FastVLM-7B",
-    "Microsoft Florence 2 Base": "microsoft/Florence-2-base-ft", # 0.5GB
-    "Microsoft Florence 2 Large": "microsoft/Florence-2-large-ft", # 1.5GB
+    # "Microsoft Florence 2 Base": "microsoft/Florence-2-base-ft", # 0.5GB
+    # "Microsoft Florence 2 Large": "microsoft/Florence-2-large-ft", # 1.5GB
+    "Microsoft Florence 2 Base": "florence-community/Florence-2-base-ft", # 0.5GB
+    "Microsoft Florence 2 Large": "florence-community/Florence-2-large-ft", # 1.5GB
     "MiaoshouAI PromptGen 1.5 Base": "MiaoshouAI/Florence-2-base-PromptGen-v1.5@c06a5f02cc6071a5d65ee5d294cf3732d3097540", # 1.1GB
     "MiaoshouAI PromptGen 1.5 Large": "MiaoshouAI/Florence-2-large-PromptGen-v1.5@28a42440e39c9c32b83f7ae74ec2b3d1540404f0", # 3.3GB
     "MiaoshouAI PromptGen 2.0 Base": "MiaoshouAI/Florence-2-base-PromptGen-v2.0", # 1.1GB
@@ -354,7 +356,6 @@ def smol(question: str, image: Image.Image, repo: str = None, system_prompt: str
             repo,
             cache_dir=shared.opts.hfcache_dir,
             torch_dtype=devices.dtype,
-            _attn_implementation="eager",
             **quant_args,
             )
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
@@ -536,6 +537,7 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
         shared.log.debug(f'Interrogate load: vlm="{repo}" path="{shared.opts.hfcache_dir}"')
         transformers.dynamic_module_utils.get_imports = get_imports
         model = None
+        """
         model = transformers.AutoModelForCausalLM.from_pretrained(
             repo,
             trust_remote_code=True,
@@ -544,6 +546,15 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
             cache_dir=shared.opts.hfcache_dir,
             **quant_args,
         )
+        """
+        model = transformers.Florence2ForConditionalGeneration.from_pretrained(
+            repo,
+            dtype=torch.bfloat16,
+            revision=revision,
+            torch_dtype=devices.dtype,
+            cache_dir=shared.opts.hfcache_dir,
+            **quant_args,
+        )
         processor = transformers.AutoProcessor.from_pretrained(repo, trust_remote_code=True, revision=revision, cache_dir=shared.opts.hfcache_dir)
         transformers.dynamic_module_utils.get_imports = _get_imports
         loaded = repo
diff --git a/scripts/prompt_enhance.py b/scripts/prompt_enhance.py
index aea0e5730..221d815dc 100644
--- a/scripts/prompt_enhance.py
+++ b/scripts/prompt_enhance.py
@@ -159,7 +159,7 @@ class Script(scripts_manager.Script):
                 trust_remote_code=True,
                 torch_dtype=devices.dtype,
                 cache_dir=shared.opts.hfcache_dir,
-                _attn_implementation="eager",
+                # _attn_implementation="eager",
                 **gguf_args,
                 **quant_args,
             )

From 22074f47272b812ba6efed48aed8a2eafdcce755 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 1 Oct 2025 12:02:55 -0400
Subject: [PATCH 110/230] cleanup vqa

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/interrogate/vqa.py | 25 ++++++++++++++++++-------
 wiki                       |  2 +-
 2 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index 5518ff803..e1a9a095a 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -13,7 +13,7 @@ from modules import shared, devices, errors, model_quant, sd_models, sd_models_c
 processor = None
 model = None
 loaded: str = None
-quant_args = {}
+quant_args = None
 vlm_default = "Alibaba Qwen 2.5 VL 4B"
 vlm_models = {
     "Google Gemma 3 4B": "google/gemma-3-4b-it",
@@ -90,14 +90,19 @@ def b64(image):
 
 def clean(response, question):
     strip = ['---', '\r', '\t', '**', '"', '“', '”', 'Assistant:', 'Caption:', '<|im_end|>', '<pad>']
-    if isinstance(response, dict):
-        if 'task' in response:
-            response = response['task']
+    if isinstance(response, str):
+        response = response.strip()
+    elif isinstance(response, dict):
         if 'answer' in response:
             response = response['answer']
-        response = json.dumps(response)
-    if isinstance(response, list):
+        elif 'task' in response:
+            response = response['task']
+        else:
+            response = json.dumps(response)
+    elif isinstance(response, list):
         response = response[0]
+    else:
+        response = str(response)
     question = question.replace('<', '').replace('>', '').replace('_', ' ')
     if question in response:
         response = response.split(question, 1)[1]
@@ -525,11 +530,13 @@ def moondream(question: str, image: Image.Image, repo: str = None):
 def florence(question: str, image: Image.Image, repo: str = None, revision: str = None):
     global processor, model, loaded # pylint: disable=global-statement
     _get_imports = transformers.dynamic_module_utils.get_imports
+
     def get_imports(f):
         R = _get_imports(f)
         if "flash_attn" in R:
             R.remove("flash_attn") # flash_attn is optional
         return R
+
     revision = None
     if '@' in repo:
         repo, revision = repo.split('@')
@@ -618,7 +625,8 @@ def interrogate(question:str='', system_prompt:str=None, prompt:str=None, image:
     global quant_args # pylint: disable=global-statement
     jobid = shared.state.begin('Interrogate LLM')
     t0 = time.time()
-    quant_args = model_quant.create_config(module='LLM')
+    if quant_args is None:
+        quant_args = model_quant.create_config(module='LLM')
     model_name = model_name or shared.opts.interrogate_vlm_model
     if isinstance(image, list):
         image = image[0] if len(image) > 0 else None
@@ -634,9 +642,12 @@ def interrogate(question:str='', system_prompt:str=None, prompt:str=None, image:
         question = prompt
     if len(question) < 2:
         question = "Describe the image."
+
+    """
     if shared.sd_loaded:
         from modules.sd_models import apply_balanced_offload # prevent circular import
         apply_balanced_offload(shared.sd_model)
+    """
 
     from modules import modelloader
     modelloader.hf_login()
diff --git a/wiki b/wiki
index 77e5745f7..f274489e4 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 77e5745f7a7b8ac2f9d029c6ebb83c954be56006
+Subproject commit f274489e42b97b57ea91b9e94087b8b39820a4ee

From 81bb2b99ef58cea8fbec3161a914c0b82f36e159 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 1 Oct 2025 21:43:02 +0300
Subject: [PATCH 111/230] update florence promptgen repo ids

---
 modules/interrogate/vqa.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index e1a9a095a..baebb62c2 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -31,10 +31,14 @@ vlm_models = {
     # "Microsoft Florence 2 Large": "microsoft/Florence-2-large-ft", # 1.5GB
     "Microsoft Florence 2 Base": "florence-community/Florence-2-base-ft", # 0.5GB
     "Microsoft Florence 2 Large": "florence-community/Florence-2-large-ft", # 1.5GB
-    "MiaoshouAI PromptGen 1.5 Base": "MiaoshouAI/Florence-2-base-PromptGen-v1.5@c06a5f02cc6071a5d65ee5d294cf3732d3097540", # 1.1GB
-    "MiaoshouAI PromptGen 1.5 Large": "MiaoshouAI/Florence-2-large-PromptGen-v1.5@28a42440e39c9c32b83f7ae74ec2b3d1540404f0", # 3.3GB
-    "MiaoshouAI PromptGen 2.0 Base": "MiaoshouAI/Florence-2-base-PromptGen-v2.0", # 1.1GB
-    "MiaoshouAI PromptGen 2.0 Large": "MiaoshouAI/Florence-2-large-PromptGen-v2.0", # 3.3GB
+    #"MiaoshouAI PromptGen 1.5 Base": "MiaoshouAI/Florence-2-base-PromptGen-v1.5@c06a5f02cc6071a5d65ee5d294cf3732d3097540", # 1.1GB
+    #"MiaoshouAI PromptGen 1.5 Large": "MiaoshouAI/Florence-2-large-PromptGen-v1.5@28a42440e39c9c32b83f7ae74ec2b3d1540404f0", # 3.3GB
+    #"MiaoshouAI PromptGen 2.0 Base": "MiaoshouAI/Florence-2-base-PromptGen-v2.0", # 1.1GB
+    #"MiaoshouAI PromptGen 2.0 Large": "MiaoshouAI/Florence-2-large-PromptGen-v2.0", # 3.3GB
+    "MiaoshouAI PromptGen 1.5 Base": "Disty0/Florence-2-base-PromptGen-v1.5", # 0.5GB
+    "MiaoshouAI PromptGen 1.5 Large": "Disty0/Florence-2-large-PromptGen-v1.5", # 1.5GB
+    "MiaoshouAI PromptGen 2.0 Base": "Disty0/Florence-2-base-PromptGen-v2.0", # 0.5GB
+    "MiaoshouAI PromptGen 2.0 Large": "Disty0/Florence-2-large-PromptGen-v2.0", # 1.5GB
     "CogFlorence 2.0 Large": "thwri/CogFlorence-2-Large-Freeze", # 1.6GB
     "CogFlorence 2.2 Large": "thwri/CogFlorence-2.2-Large", # 1.6GB
     "Moondream 2": "vikhyatk/moondream2", # 3.7GB

From ec7b22e38362b2383536ae7ae1606031aa575754 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 1 Oct 2025 12:24:55 -0400
Subject: [PATCH 112/230] warn on startup if cuda/rocm are available and
 torch-cpu is installed

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md |  1 +
 installer.py | 85 +++++++++++++++++++++++++++-------------------------
 2 files changed, 46 insertions(+), 40 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index db8625033..a6214457e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -31,6 +31,7 @@
     `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
   - command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible  
     previously sdnext was performing a fallback to `torch-cpu` which is not desired  
+  - **installer**: warn if cuda or rocm are available and `torch-cpu` is installed  
 - **Extensions**  
   - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
     was a high-value built-in extension, but it has not been maintained for 1.5 years  
diff --git a/installer.py b/installer.py
index a2ec15fd2..cde81c9c2 100644
--- a/installer.py
+++ b/installer.py
@@ -935,54 +935,59 @@ def check_torch():
             else:
                 log.warning('Torch: CPU-only version installed')
                 torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
-    if 'torch' in torch_command and not args.version:
+    if args.version:
+        return
+
+    if 'torch' in torch_command:
         if not installed('torch'):
             log.info(f'Torch: download and install in progress... cmd="{torch_command}"')
             install('--upgrade pip', 'pip', reinstall=True) # pytorch rocm is too large for older pip
         install(torch_command, 'torch torchvision', quiet=True)
-    else:
+
+    try:
+        import torch
         try:
-            import torch
-            log.info(f'Torch {torch.__version__}')
-            try:
-                import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-                log.info(f'Torch backend: Intel IPEX {ipex.__version__}')
-            except Exception:
-                pass
-            if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
-                if shutil.which('icpx') is not None:
-                    log.info(f'{os.popen("icpx --version").read().rstrip()}')
-                for device in range(torch.xpu.device_count()):
-                    log.info(f'Torch detected GPU: {torch.xpu.get_device_name(device)} VRAM {round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} Compute Units {torch.xpu.get_device_properties(device).max_compute_units}')
-            elif torch.cuda.is_available() and (allow_cuda or allow_rocm):
-                # log.debug(f'Torch allocator: {torch.cuda.get_allocator_backend()}')
-                if torch.version.cuda and allow_cuda:
-                    log.info(f'Torch backend: nVidia CUDA {torch.version.cuda} cuDNN {torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}')
-                elif torch.version.hip and allow_rocm:
-                    log.info(f'Torch backend: AMD ROCm HIP {torch.version.hip}')
-                else:
-                    log.warning('Unknown Torch backend')
-                for device in [torch.cuda.device(i) for i in range(torch.cuda.device_count())]:
-                    log.info(f'Torch detected GPU: {torch.cuda.get_device_name(device)} VRAM {round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} Arch {torch.cuda.get_device_capability(device)} Cores {torch.cuda.get_device_properties(device).multi_processor_count}')
+            import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+            log.info(f'Torch backend: type=IPEX version={ipex.__version__}')
+        except Exception:
+            pass
+        if 'cpu' in torch.__version__:
+            if is_cuda_available:
+                log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is available - consider reinstalling')
+            elif is_rocm_available:
+                log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is available - consider reinstalling')
+        if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
+            if shutil.which('icpx') is not None:
+                log.info(f'{os.popen("icpx --version").read().rstrip()}')
+            for device in range(torch.xpu.device_count()):
+                log.info(f'Torch detected: gpu="{torch.xpu.get_device_name(device)}" vram={round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} units={torch.xpu.get_device_properties(device).max_compute_units}')
+        elif torch.cuda.is_available() and (allow_cuda or allow_rocm):
+            if torch.version.cuda and allow_cuda:
+                log.info(f'Torch backend: version="{torch.__version__}" type=CUDA CUDA={torch.version.cuda} cuDNN={torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}')
+            elif torch.version.hip and allow_rocm:
+                log.info(f'Torch backend: version="{torch.__version__}" type=ROCm HIP={torch.version.hip}')
             else:
-                try:
-                    if args.use_directml and allow_directml:
-                        import torch_directml # pylint: disable=import-error
-                        dml_ver = pkg_resources.get_distribution("torch-directml")
-                        log.warning(f'Torch backend: DirectML ({dml_ver})')
-                        log.warning('DirectML: end-of-life')
-                        for i in range(0, torch_directml.device_count()):
-                            log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
-                except Exception:
-                    log.warning("Torch reports CUDA not available")
-        except Exception as e:
-            log.error(f'Torch cannot load: {e}')
-            if not args.ignore:
-                sys.exit(1)
+                log.warning('Unknown Torch backend')
+            for device in [torch.cuda.device(i) for i in range(torch.cuda.device_count())]:
+                log.info(f'Torch detected: gpu="{torch.cuda.get_device_name(device)}" vram={round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} arch={torch.cuda.get_device_capability(device)} cores={torch.cuda.get_device_properties(device).multi_processor_count}')
+        else:
+            try:
+                if args.use_directml and allow_directml:
+                    import torch_directml # pylint: disable=import-error
+                    dml_ver = pkg_resources.get_distribution("torch-directml")
+                    log.warning(f'Torch backend: DirectML ({dml_ver})')
+                    log.warning('DirectML: end-of-life')
+                    for i in range(0, torch_directml.device_count()):
+                        log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
+            except Exception:
+                log.warning("Torch reports CUDA not available")
+    except Exception as e:
+        log.error(f'Torch cannot load: {e}')
+        if not args.ignore:
+            sys.exit(1)
+
     if rocm.is_installed:
         rocm.postinstall()
-    if args.version:
-        return
     if not args.skip_all:
         install_torch_addons()
     check_cudnn()

From 7bae6c2c83ecd921f69a7f94b34cc708ad52073b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 1 Oct 2025 14:45:04 -0400
Subject: [PATCH 113/230] add florence/promptgen info to changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a6214457e..7a68ac79b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-30
+## Update for 2025-10-01
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -84,6 +84,7 @@
 - **Fixes**
   - **Microsoft Florence 2** both base and large variants  
     *note* this will trigger download of the new variant of the model, feel free to delete older variant in `huggingface` folder  
+  - **MiaoshouAI PromptGen** 1.5/2.0 in both base and large variants  
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
   - framepack: patch solver for unsupported gpus  

From 450464aa923522190688488f6f7d8914f47c35f2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 1 Oct 2025 15:24:37 -0400
Subject: [PATCH 114/230] update todo

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 TODO.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/TODO.md b/TODO.md
index e796a3740..1a5475c7d 100644
--- a/TODO.md
+++ b/TODO.md
@@ -36,6 +36,8 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ### New models
 
+- [Kandinsky 5](https://github.com/ai-forever/Kandinsky-5)
+- [Bytedance Lynx](https://github.com/bytedance/lynx)
 - [HunyuanImage](https://huggingface.co/tencent/HunyuanImage-2.1)
 - [Phantom HuMo](https://github.com/Phantom-video/Phantom)
 - [Lumina-DiMOO](https://huggingface.co/Alpha-VLLM/Lumina-DiMOO)

From 78820a14dc4ec2b29e657774f52c233be212d96f Mon Sep 17 00:00:00 2001
From: CalamitousFelicitousness <iowasovereign@gmail.com>
Date: Wed, 1 Oct 2025 20:52:04 +0100
Subject: [PATCH 115/230] Allow VLM temp setting temperature to 0

Allow VLM temp setting temperature to 0
---
 modules/ui_caption.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui_caption.py b/modules/ui_caption.py
index 85d3ca0bc..86e2eade3 100644
--- a/modules/ui_caption.py
+++ b/modules/ui_caption.py
@@ -47,7 +47,7 @@ def create_ui():
                         with gr.Row():
                             vlm_max_tokens = gr.Slider(label='VLM max tokens', value=shared.opts.interrogate_vlm_max_length, minimum=16, maximum=4096, step=1, elem_id='vlm_max_tokens')
                             vlm_num_beams = gr.Slider(label='VLM num beams', value=shared.opts.interrogate_vlm_num_beams, minimum=1, maximum=16, step=1, elem_id='vlm_num_beams')
-                            vlm_temperature = gr.Slider(label='VLM Temperature', value=shared.opts.interrogate_vlm_temperature, minimum=0.1, maximum=1.0, step=0.01, elem_id='vlm_temperature')
+                            vlm_temperature = gr.Slider(label='VLM Temperature', value=shared.opts.interrogate_vlm_temperature, minimum=0.0, maximum=1.0, step=0.01, elem_id='vlm_temperature')
                         with gr.Row():
                             vlm_top_k = gr.Slider(label='Top-K', value=shared.opts.interrogate_vlm_top_k, minimum=0, maximum=99, step=1, elem_id='vlm_top_k')
                             vlm_top_p = gr.Slider(label='Top-P', value=shared.opts.interrogate_vlm_top_p, minimum=0.0, maximum=1.0, step=0.01, elem_id='vlm_top_p')

From f245506bf263f05a73718198ecba07233c7ab902 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 2 Oct 2025 08:17:32 -0400
Subject: [PATCH 116/230] cleanup hf login

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/modelloader.py | 1 +
 modules/sd_models.py   | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 0eaccf1bd..bc2e0b607 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -22,6 +22,7 @@ pbar = None
 def hf_login(token=None):
     global loggedin # pylint: disable=global-statement
     token = token or shared.opts.huggingface_token
+    token = token.replace("\n", "").replace("\r", "").strip() if token is not None else None
     install('hf_xet', quiet=True)
     if token is None or len(token) <= 4:
         log.debug('HF login: no token provided')
diff --git a/modules/sd_models.py b/modules/sd_models.py
index e759ac56f..bb89f6df2 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1220,9 +1220,9 @@ def hf_auth_check(checkpoint_info, force:bool=False):
                 return True
         except Exception:
             pass
+    repo_id = path_to_repo(checkpoint_info)
     try:
         login = modelloader.hf_login()
-        repo_id = path_to_repo(checkpoint_info)
         return hf.auth_check(repo_id)
     except Exception as e:
         shared.log.error(f'Load model: repo="{repo_id}" login={login} {e}')

From a0b375ae3dfa72f3b424ea194d7d4eb34244a72d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 2 Oct 2025 12:19:54 -0400
Subject: [PATCH 117/230] update diffusers to unblock rocm

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 5 +++--
 installer.py | 6 +++---
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7a68ac79b..b85d076d0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-01
+## Update for 2025-10-02
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -92,7 +92,8 @@
   - xyz-grid: fix xyz grid with random seeds  
   - reference: fix download for sd15/sdxl reference models  
   - fix checks in init/mask image decode  
-  
+  - fix hf token with extra chars  
+
 ## Update for 2025-09-15
 
 ### Highlights for 2025-09-15
diff --git a/installer.py b/installer.py
index cde81c9c2..6df4d7129 100644
--- a/installer.py
+++ b/installer.py
@@ -608,9 +608,9 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = '64a5187d96f9376c7cf5123db810f2d2da79d7d0' # diffusers commit hash
-    if args.use_rocm or args.use_zluda or args.use_directml:
-        sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
+    sha = 'b4297967a04cca6ac4493202c02d81c30d0f9ee8' # diffusers commit hash
+    # if args.use_rocm or args.use_zluda or args.use_directml:
+    #     sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''

From 34c2a624aaf6bd443d3e937fed0c4403a620c752 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 2 Oct 2025 19:40:07 +0300
Subject: [PATCH 118/230] SDNQ autodetect fp8 tw fallback and disable dynamic
 compile

---
 modules/sdnq/common.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 783a7ab2f..b70c7b06b 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -4,7 +4,7 @@ import os
 from functools import partial
 import torch
 
-from modules import shared
+from modules import shared, devices
 
 torch_version = float(torch.__version__[:3])
 
@@ -34,7 +34,12 @@ if hasattr(torch, "float8_e5m2fnuz"):
     dtype_dict["float8_e5m2fnuz"] = {"min": -57344, "max": 57344, "num_bits": 8, "target_dtype": "fp8", "torch_dtype": torch.float8_e5m2fnuz, "storage_dtype": torch.float8_e5m2fnuz, "is_unsigned": False, "is_integer": False}
 
 use_torch_compile = shared.opts.sdnq_dequantize_compile # this setting requires a full restart of the webui to apply
-use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MATMUL", "1").lower() not in {"0", "false", "no"} # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
+
+if devices.backend == "cuda" and os.environ.get("SDNQ_USE_TENSORWISE_FP8_MATMUL", None) is None:
+     # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
+    use_tensorwise_fp8_matmul = torch.cuda.get_device_capability(devices.device) < (9,0)
+else:
+    use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MATMUL", "1").lower() not in {"0", "false", "no"}
 
 linear_types = ("Linear",)
 conv_types = ("Conv1d", "Conv2d", "Conv3d")
@@ -44,7 +49,7 @@ allowed_types = linear_types + conv_types + conv_transpose_types
 if use_torch_compile:
     torch._dynamo.config.cache_size_limit = max(8192, torch._dynamo.config.cache_size_limit)
     torch._dynamo.config.accumulated_recompile_limit = max(8192, torch._dynamo.config.accumulated_recompile_limit)
-    compile_func = partial(torch.compile, fullgraph=True)
+    compile_func = partial(torch.compile, fullgraph=True, dynamic=False)
 else:
     def compile_func(fn, **kwargs): # pylint: disable=unused-argument
         return fn

From 7325f9dbaeb7d47275430f1510325c2b6d60502c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 2 Oct 2025 16:28:31 -0400
Subject: [PATCH 119/230] dont import guiders until needed

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ui_guidance.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/ui_guidance.py b/modules/ui_guidance.py
index 3f3b3f8f3..ae0506771 100644
--- a/modules/ui_guidance.py
+++ b/modules/ui_guidance.py
@@ -1,14 +1,17 @@
 import gradio as gr
-from modules import shared, modular_guiders
+from modules import shared
 from modules import ui_symbols, ui_components
 
 
+guiders = ['Default', 'CFG', 'Zero', 'PAG', 'APG', 'SLG', 'SEG', 'TCFG', 'FDG']
+
+
 def create_guidance_inputs(tab):
     with gr.Accordion(open=False, label='Guidance', elem_id=f"{tab}_guidance", elem_classes=["small-accordion"]):
         with gr.Group():
 
             with gr.Row(elem_id=f"{tab}_guider_row", elem_classes=['flexbox'], visible=shared.opts.model_modular_enable):
-                guidance_name = gr.Dropdown(choices=list(modular_guiders.guiders.keys()), value='Default', label='Guider', elem_id=f"{tab}_guider")
+                guidance_name = gr.Dropdown(choices=guiders, value='Default', label='Guider', elem_id=f"{tab}_guider")
                 guidance_btn = ui_components.ToolButton(value=ui_symbols.book, elem_id=f"{tab}_guider_docs")
                 guidance_btn.click(fn=None, _js='getGuidanceDocs', inputs=[guidance_name], outputs=[])
             with gr.Row(visible=shared.opts.model_modular_enable):

From 75773eb3ccfa930425de1dc4042ea1342d433029 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 2 Oct 2025 17:38:07 -0400
Subject: [PATCH 120/230] lock diffusers version for rocm/zluda

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/installer.py b/installer.py
index 6df4d7129..a4fea4002 100644
--- a/installer.py
+++ b/installer.py
@@ -609,8 +609,8 @@ def check_diffusers():
         install('diffusers')
         return
     sha = 'b4297967a04cca6ac4493202c02d81c30d0f9ee8' # diffusers commit hash
-    # if args.use_rocm or args.use_zluda or args.use_directml:
-    #     sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
+    if args.use_rocm or args.use_zluda or args.use_directml:
+        sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''

From c5cab962233b36db28331519743532f45244b686 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Fri, 3 Oct 2025 02:58:14 +0300
Subject: [PATCH 121/230] SDNQ simplify check_mats

---
 modules/sdnq/__init__.py              | 7 +++----
 modules/sdnq/layers/linear/forward.py | 7 +++----
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 450b43d43..a84f48f2e 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -172,11 +172,10 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         if use_quantized_matmul and not re_quantize_for_matmul:
             scale.transpose_(0,1)
             layer.weight.transpose_(0,1)
-            if layer.weight.is_contiguous():
-                if devices.backend != "ipex":
-                    layer.weight.data = layer.weight.t_().contiguous().t_()
-            elif devices.backend == "ipex":
+            if devices.backend == "ipex":
                 layer.weight.data = layer.weight.contiguous()
+            elif layer.weight.is_contiguous():
+                layer.weight.data = layer.weight.t_().contiguous().t_()
             if not use_tensorwise_fp8_matmul and not dtype_dict[weights_dtype]["is_integer"]:
                 scale = scale.to(torch.float32)
 
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index 18009c27b..6977d6566 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -7,11 +7,10 @@ import torch
 
 def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     input = input.contiguous()
-    if weight.is_contiguous():
-        if weight.device.type != "xpu":
-            weight = weight.t().contiguous().t()
-    elif weight.device.type == "xpu":
+    if weight.device.type == "xpu":
         weight = weight.contiguous()
+    elif weight.is_contiguous():
+        weight = weight.t().contiguous().t()
     return input, weight
 
 

From 54acf1760b2abbc160f1735f66a9c9ebea76f28d Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Fri, 3 Oct 2025 18:13:55 +0300
Subject: [PATCH 122/230] Make SDNQ scales compatible with balanced offload

---
 modules/lora/lora_apply.py                    | 30 +++++++++----
 modules/sdnq/__init__.py                      | 11 +++--
 modules/sdnq/dequantizer.py                   | 44 +++++++------------
 modules/sdnq/layers/conv/conv_fp8.py          |  5 +--
 .../sdnq/layers/conv/conv_fp8_tensorwise.py   |  5 +--
 modules/sdnq/layers/conv/conv_int8.py         |  6 +--
 modules/sdnq/layers/conv/forward.py           |  8 ++--
 modules/sdnq/layers/linear/forward.py         |  2 +-
 modules/sdnq/layers/linear/linear_fp8.py      |  4 +-
 .../layers/linear/linear_fp8_tensorwise.py    |  4 +-
 modules/sdnq/layers/linear/linear_int8.py     |  6 +--
 11 files changed, 64 insertions(+), 61 deletions(-)

diff --git a/modules/lora/lora_apply.py b/modules/lora/lora_apply.py
index 0343d7b14..47b408446 100644
--- a/modules/lora/lora_apply.py
+++ b/modules/lora/lora_apply.py
@@ -47,6 +47,11 @@ def network_backup_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.n
                     self.network_weights_backup = weight.clone().to(devices.cpu)
                     if hasattr(self, "sdnq_dequantizer"):
                         self.sdnq_dequantizer_backup = self.sdnq_dequantizer.to(devices.cpu)
+                        self.sdnq_scale_backup = self.scale.clone().to(devices.cpu)
+                        if self.zero_point is not None:
+                            self.sdnq_zero_point_backup = self.zero_point.clone().to(devices.cpu)
+                        else:
+                            self.sdnq_zero_point_backup = None
 
         if bias_backup is None:
             if getattr(self, 'bias', None) is not None:
@@ -80,9 +85,9 @@ def network_calc_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.
         try:
             t0 = time.time()
             if hasattr(self, "sdnq_dequantizer_backup"):
-                weight = self.sdnq_dequantizer_backup.to(devices.device)(self.weight.to(devices.device), skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul)
+                weight = self.sdnq_dequantizer_backup.to(devices.device)(self.weight.to(devices.device), self.sdnq_scale_backup.to(devices.device), self.sdnq_zero_point_backup.to(devices.device) if self.sdnq_zero_point_backup is not None else None, skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul)
             elif hasattr(self, "sdnq_dequantizer"):
-                weight = self.sdnq_dequantizer.to(devices.device)(self.weight.to(devices.device), skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul)
+                weight = self.sdnq_dequantizer.to(devices.device)(self.weight.to(devices.device), self.scale.to(devices.device), self.zero_point.to(devices.device) if self.zero_point is not None else None, skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul)
             else:
                 weight = self.weight.to(devices.device) # must perform calc on gpu due to performance
             updown, ex_bias = module.calc_updown(weight)
@@ -142,16 +147,18 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
         try:
             from modules.sdnq import sdnq_quantize_layer
             if hasattr(self, "sdnq_dequantizer_backup"):
-                sdnq_dequantizer = self.sdnq_dequantizer_backup.to(devices.device)
-            else:
-                sdnq_dequantizer = self.sdnq_dequantizer.to(devices.device)
-            dequant_weight = sdnq_dequantizer(model_weights.to(devices.device), skip_quantized_matmul=sdnq_dequantizer.use_quantized_matmul)
+                weights_dtype = self.sdnq_dequantizer_backup.weights_dtype
+                dequant_weight = self.sdnq_dequantizer_backup.to(devices.device)(model_weights.to(devices.device), self.sdnq_scale_backup.to(devices.device), self.sdnq_zero_point_backup.to(devices.device) if self.sdnq_zero_point_backup is not None else None, skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul)
+            elif hasattr(self, "sdnq_dequantizer"):
+                weights_dtype = self.sdnq_dequantizer.weights_dtype
+                dequant_weight = self.sdnq_dequantizer.to(devices.device)(model_weights.to(devices.device), self.scale.to(devices.device), self.zero_point.to(devices.device) if self.zero_point is not None else None, skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul)
+
             new_weight = dequant_weight.to(devices.device, dtype=torch.float32) + lora_weights.to(devices.device, dtype=torch.float32)
             self.weight = torch.nn.Parameter(new_weight, requires_grad=False)
-            self.sdnq_dequantizer = None
+            del self.sdnq_dequantizer, self.scale, self.zero_point
             self = sdnq_quantize_layer(
                 self,
-                sdnq_dequantizer.weights_dtype,
+                weights_dtype=weights_dtype,
                 torch_dtype=devices.dtype,
                 group_size=shared.opts.sdnq_quantize_weights_group_size,
                 quant_conv=shared.opts.sdnq_quantize_conv_layers,
@@ -231,7 +238,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             self.weight = torch.nn.Parameter(weights_backup.to(device), requires_grad=False)
             if hasattr(self, "sdnq_dequantizer_backup"):
                 self.sdnq_dequantizer = self.sdnq_dequantizer_backup.to(device)
-                del self.sdnq_dequantizer_backup
+                self.scale = torch.nn.Parameter(self.sdnq_scale_backup.to(device), requires_grad=False)
+                if self.sdnq_zero_point_backup is not None:
+                    self.zero_point = torch.nn.Parameter(self.sdnq_zero_point_backup.to(device), requires_grad=False)
+                else:
+                    self.zero_point = None
+                del self.sdnq_dequantizer_backup, self.sdnq_scale_backup, self.sdnq_zero_point_backup
 
     if bias_backup is not None:
         self.bias = None
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index a84f48f2e..16fe6ec47 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -179,9 +179,15 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             if not use_tensorwise_fp8_matmul and not dtype_dict[weights_dtype]["is_integer"]:
                 scale = scale.to(torch.float32)
 
+        scale = scale.to(return_device, non_blocking=non_blocking)
+        layer.scale = torch.nn.Parameter(scale, requires_grad=False)
+        if zero_point is not None:
+            zero_point = zero_point.to(return_device, non_blocking=non_blocking)
+            layer.zero_point = torch.nn.Parameter(zero_point, requires_grad=False)
+        else:
+            layer.zero_point = None
+
         layer.sdnq_dequantizer = dequantizer_dict[weights_dtype](
-            scale=scale,
-            zero_point=zero_point,
             quantized_weight_shape=layer.weight.shape,
             result_dtype=torch_dtype,
             result_shape=result_shape,
@@ -191,7 +197,6 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             re_quantize_for_matmul=re_quantize_for_matmul,
         )
         layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
-        layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
 
         layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
         layer.forward = layer.forward.__get__(layer, layer.__class__)
diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index be68ab96d..d82849177 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -73,8 +73,6 @@ def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: tor
 class AsymmetricWeightsDequantizer(torch.nn.Module):
     def __init__(
         self,
-        scale: torch.FloatTensor,
-        zero_point: torch.FloatTensor,
         result_dtype: torch.dtype,
         result_shape: torch.Size,
         original_shape: torch.Size,
@@ -89,23 +87,20 @@ class AsymmetricWeightsDequantizer(torch.nn.Module):
         self.re_quantize_for_matmul = True
         self.result_dtype = result_dtype
         self.result_shape = result_shape
-        self.register_buffer("scale", scale)
-        self.register_buffer("zero_point", zero_point)
 
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return weight.to(dtype=dtype_dict[self.weights_dtype]["torch_dtype"])
 
-    def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_asymmetric_compiled(weight, self.scale, self.zero_point, self.result_shape)
+    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_asymmetric_compiled(weight, scale, zero_point, self.result_shape)
 
-    def forward(self, weight, **kwargs): # pylint: disable=unused-argument
-        return dequantize_asymmetric_compiled(weight, self.scale, self.zero_point, self.result_dtype, self.result_shape)
+    def forward(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
+        return dequantize_asymmetric_compiled(weight, scale, zero_point, self.result_dtype, self.result_shape)
 
 
 class SymmetricWeightsDequantizer(torch.nn.Module):
     def __init__(
         self,
-        scale: torch.FloatTensor,
         result_dtype: torch.dtype,
         result_shape: torch.Size,
         original_shape: torch.Size,
@@ -121,24 +116,21 @@ class SymmetricWeightsDequantizer(torch.nn.Module):
         self.re_quantize_for_matmul = re_quantize_for_matmul
         self.result_dtype = result_dtype
         self.result_shape = result_shape
-        self.register_buffer("scale", scale)
 
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return weight.to(dtype=dtype_dict[self.weights_dtype]["torch_dtype"])
 
-    def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_symmetric_compiled(weight, self.scale, self.result_shape)
+    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_symmetric_compiled(weight, scale, self.result_shape)
 
-    def forward(self, weight, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
+    def forward(self, weight, scale, zero_point, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
         skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul
-        return dequantize_symmetric_compiled(weight, self.scale, self.result_dtype, self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
+        return dequantize_symmetric_compiled(weight, scale, self.result_dtype, self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
 
 
 class PackedINTAsymmetricWeightsDequantizer(torch.nn.Module):
     def __init__(
         self,
-        scale: torch.FloatTensor,
-        zero_point: torch.FloatTensor,
         quantized_weight_shape: torch.Size,
         result_dtype: torch.dtype,
         result_shape: torch.Size,
@@ -155,23 +147,20 @@ class PackedINTAsymmetricWeightsDequantizer(torch.nn.Module):
         self.quantized_weight_shape = quantized_weight_shape
         self.result_dtype = result_dtype
         self.result_shape = result_shape
-        self.register_buffer("scale", scale)
-        self.register_buffer("zero_point", zero_point)
 
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return pack_int_asymetric(weight, self.weights_dtype)
 
-    def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_packed_int_asymmetric_compiled(weight, self.scale, self.zero_point, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
+    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
 
-    def forward(self, weight, **kwargs): # pylint: disable=unused-argument
-        return dequantize_packed_int_asymmetric_compiled(weight, self.scale, self.zero_point, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype)
+    def forward(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
+        return dequantize_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype)
 
 
 class PackedINTSymmetricWeightsDequantizer(torch.nn.Module):
     def __init__(
         self,
-        scale: torch.FloatTensor,
         quantized_weight_shape: torch.Size,
         result_dtype: torch.dtype,
         result_shape: torch.Size,
@@ -189,17 +178,16 @@ class PackedINTSymmetricWeightsDequantizer(torch.nn.Module):
         self.quantized_weight_shape = quantized_weight_shape
         self.result_dtype = result_dtype
         self.result_shape = result_shape
-        self.register_buffer("scale", scale)
 
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return pack_int_symetric(weight, self.weights_dtype)
 
-    def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_packed_int_symmetric_compiled(weight, self.scale, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
+    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
 
-    def forward(self, weight, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
+    def forward(self, weight, scale, zero_point, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
         skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul
-        return dequantize_packed_int_symmetric_compiled(weight, self.scale, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype, skip_quantized_matmul=skip_quantized_matmul)
+        return dequantize_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype, skip_quantized_matmul=skip_quantized_matmul)
 
 
 dequantizer_dict = {
diff --git a/modules/sdnq/layers/conv/conv_fp8.py b/modules/sdnq/layers/conv/conv_fp8.py
index 7a83bb3a8..47f214937 100644
--- a/modules/sdnq/layers/conv/conv_fp8.py
+++ b/modules/sdnq/layers/conv/conv_fp8.py
@@ -58,11 +58,10 @@ def conv_fp8_matmul(
 
 def quantized_conv_forward_fp8_matmul(self, input) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[2] < 32:
-        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
+        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
     conv_type, stride, padding, dilation = get_conv_args(input.ndim, self.stride, self.padding, self.dilation)
     return conv_fp8_matmul(
-        input, self.weight, self.bias,
-        self.sdnq_dequantizer.scale,
+        input, self.weight, self.bias, self.scale,
         self.sdnq_dequantizer.result_shape,
         self._reversed_padding_repeated_twice,
         self.padding_mode, conv_type,
diff --git a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
index e3b005622..592a512ee 100644
--- a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
+++ b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
@@ -53,11 +53,10 @@ def conv_fp8_matmul_tensorwise(
 
 def quantized_conv_forward_fp8_matmul_tensorwise(self, input) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[2] < 32:
-        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
+        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
     conv_type, stride, padding, dilation = get_conv_args(input.ndim, self.stride, self.padding, self.dilation)
     return conv_fp8_matmul_tensorwise(
-        input, self.weight, self.bias,
-        self.sdnq_dequantizer.scale,
+        input, self.weight, self.bias, self.scale,
         self.sdnq_dequantizer.result_shape,
         self._reversed_padding_repeated_twice,
         self.padding_mode, conv_type,
diff --git a/modules/sdnq/layers/conv/conv_int8.py b/modules/sdnq/layers/conv/conv_int8.py
index 260acd170..0ae26b8f5 100644
--- a/modules/sdnq/layers/conv/conv_int8.py
+++ b/modules/sdnq/layers/conv/conv_int8.py
@@ -57,14 +57,14 @@ def conv_int8_matmul(
 
 def quantized_conv_forward_int8_matmul(self, input) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[2] < 32:
-        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
+        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
     conv_type, stride, padding, dilation = get_conv_args(input.ndim, self.stride, self.padding, self.dilation)
     if self.sdnq_dequantizer.re_quantize_for_matmul:
-        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight)
+        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point)
         quantized_weight_shape = None
     else:
         weight = self.weight
-        scale = self.sdnq_dequantizer.scale
+        scale = self.scale
         quantized_weight_shape = getattr(self.sdnq_dequantizer, "quantized_weight_shape", None)
     return conv_int8_matmul(
         input, weight, self.bias,
diff --git a/modules/sdnq/layers/conv/forward.py b/modules/sdnq/layers/conv/forward.py
index 44c90b061..69237ea68 100644
--- a/modules/sdnq/layers/conv/forward.py
+++ b/modules/sdnq/layers/conv/forward.py
@@ -75,19 +75,19 @@ def process_conv_input(conv_type, input, reversed_padding_repeated_twice, paddin
 
 
 def quantized_conv_forward(self, input) -> torch.FloatTensor:
-    return self._conv_forward(input, self.sdnq_dequantizer(self.weight), self.bias)
+    return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias)
 
 
 def quantized_conv_transpose_1d_forward(self, input: torch.FloatTensor, output_size: Optional[list[int]] = None) -> torch.FloatTensor:
     output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size, 1, self.dilation)
-    return torch.nn.functional.conv_transpose1d(input, self.sdnq_dequantizer(self.weight), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
+    return torch.nn.functional.conv_transpose1d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
 
 
 def quantized_conv_transpose_2d_forward(self, input: torch.FloatTensor, output_size: Optional[list[int]] = None) -> torch.FloatTensor:
     output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size, 2, self.dilation)
-    return torch.nn.functional.conv_transpose2d(input, self.sdnq_dequantizer(self.weight), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
+    return torch.nn.functional.conv_transpose2d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
 
 
 def quantized_conv_transpose_3d_forward(self, input: torch.FloatTensor, output_size: Optional[list[int]] = None) -> torch.FloatTensor:
     output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size, 3, self.dilation)
-    return torch.nn.functional.conv_transpose3d(input, self.sdnq_dequantizer(self.weight), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
+    return torch.nn.functional.conv_transpose3d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index 6977d6566..fc5b2ce60 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -15,4 +15,4 @@ def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor,
 
 
 def quantized_linear_forward(self, input: torch.FloatTensor) -> torch.FloatTensor:
-    return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight), self.bias)
+    return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias)
diff --git a/modules/sdnq/layers/linear/linear_fp8.py b/modules/sdnq/layers/linear/linear_fp8.py
index d1845b1e7..3f2fb407c 100644
--- a/modules/sdnq/layers/linear/linear_fp8.py
+++ b/modules/sdnq/layers/linear/linear_fp8.py
@@ -32,8 +32,8 @@ def fp8_matmul(
 
 def quantized_linear_forward_fp8_matmul(self, input: torch.FloatTensor) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[-1] < 32:
-        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
-    return fp8_matmul(input, self.weight, self.bias, self.sdnq_dequantizer.scale)
+        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
+    return fp8_matmul(input, self.weight, self.bias, self.scale)
 
 
 fp8_matmul = compile_func(fp8_matmul)
diff --git a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
index a35eb0721..d39ea5519 100644
--- a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
+++ b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
@@ -37,8 +37,8 @@ def fp8_matmul_tensorwise(
 
 def quantized_linear_forward_fp8_matmul_tensorwise(self, input: torch.FloatTensor) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[-1] < 32:
-        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
-    return fp8_matmul_tensorwise(input, self.weight, self.bias, self.sdnq_dequantizer.scale)
+        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
+    return fp8_matmul_tensorwise(input, self.weight, self.bias, self.scale)
 
 
 fp8_matmul_tensorwise = compile_func(fp8_matmul_tensorwise)
diff --git a/modules/sdnq/layers/linear/linear_int8.py b/modules/sdnq/layers/linear/linear_int8.py
index 356179b97..bfca56ce2 100644
--- a/modules/sdnq/layers/linear/linear_int8.py
+++ b/modules/sdnq/layers/linear/linear_int8.py
@@ -41,13 +41,13 @@ def int8_matmul(
 
 def quantized_linear_forward_int8_matmul(self, input: torch.FloatTensor) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[-1] < 32:
-        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, skip_quantized_matmul=True), self.bias)
+        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
     if self.sdnq_dequantizer.re_quantize_for_matmul:
-        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight)
+        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point)
         quantized_weight_shape = None
     else:
         weight = self.weight
-        scale = self.sdnq_dequantizer.scale
+        scale = self.scale
         quantized_weight_shape = getattr(self.sdnq_dequantizer, "quantized_weight_shape", None)
     return int8_matmul(input, weight, self.bias, scale, quantized_weight_shape, self.sdnq_dequantizer.weights_dtype)
 

From a6108dd6df8a1a480c3a6c70466c54e81a3bf664 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 3 Oct 2025 11:36:12 -0400
Subject: [PATCH 123/230] add qwen pruning variants

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    |  6 ++++--
 html/reference.json             | 31 +++++++++++++++++++++++++++++--
 modules/mit_nunchaku.py         |  2 +-
 modules/modelloader.py          | 11 +++++++++--
 modules/sd_checkpoint.py        | 13 ++++++++++---
 pipelines/generic.py            |  2 +-
 pipelines/model_qwen.py         | 14 +++++++++-----
 pipelines/qwen/__init__.py      |  2 ++
 pipelines/qwen/qwen_nunchaku.py |  4 +++-
 pipelines/qwen/qwen_pruning.py  | 12 ++++++++++++
 wiki                            |  2 +-
 11 files changed, 81 insertions(+), 18 deletions(-)
 create mode 100644 pipelines/qwen/__init__.py
 create mode 100644 pipelines/qwen/qwen_pruning.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b85d076d0..b9cde65f2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,12 +1,14 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-02
+## Update for 2025-10-03
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
   - [Qwen Image Edit 2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) and [Nunchaku Qwen Image Edit 2509](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit-2509)  
     updated version of Qwen Image Edit with improved image consistency  
+  - [Qwen Image Pruning](https://huggingface.co/OPPOer/Qwen-Image-Pruning) and [Qwen Image Edit Pruning](https://huggingface.co/OPPOer/Qwen-Image-Edit-Pruning)  
+    pruned versions of Qwen with 13B params instead of 20B, with some quality tradeoff  
   - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
     updated version of E1 image editing model  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
@@ -15,7 +17,6 @@
     impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
-    *note*: sdxl support for nunchaku is not in released version of `nunchaku==1.0.0`, so you need to build [nunchaku](https://nunchaku.tech/docs/nunchaku/installation/installation.html) from source
 - **Features**
   - [Cache-DiT](https://github.com/vipshop/cache-dit)  
     cache-dit is a unified, flexible and training-free cache acceleration framework  
@@ -74,6 +75,7 @@
   - **attention** remove split-attention and add explicitly attention slicing enable/disable option  
     enable in *settings -> compute settings*  
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
+  - **nunchaku** update to `1.0.1`  
 - **Experimental**
   - `new` command line flag enables new `pydantic` and `albumentations` packages  
   - **modular pipelines**: enable in *settings -> model options*  
diff --git a/html/reference.json b/html/reference.json
index 8c5901381..ad1b7e333 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -183,7 +183,7 @@
   "Qwen-Image": {
     "path": "Qwen/Qwen-Image",
     "preview": "Qwen--Qwen-Image.jpg",
-    "desc": " Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing.",
+    "desc": "Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing.",
     "skip": true,
     "extras": "",
     "size": 56.1,
@@ -228,12 +228,39 @@
   "Qwen-Image-Lightning-Edit": {
     "path": "vladmandic/Qwen-Lightning-Edit",
     "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
-    "desc": " Qwen-Lightning-Edit is step-distilled from Qwen-Image-Edit to allow for generation in 8 steps.",
+    "desc": "Qwen-Lightning-Edit is step-distilled from Qwen-Image-Edit to allow for generation in 8 steps.",
     "skip": true,
     "extras": "steps: 8",
     "size": 56.1,
     "date": "2025 August"
   },
+  "Qwen-Image-Pruning": {
+    "path": "OPPOer/Qwen-Image-Pruning-13B",
+    "subfolder": "Qwen-Image-13B",
+    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
+    "skip": true,
+    "size": 56.1,
+    "date": "2025 Ocotober"
+  },
+  "Qwen-Image-Edit-Pruning": {
+    "path": "OPPOer/Qwen-Image-Edit-Pruning-13B",
+    "subfolder": "Qwen-Image-Edit-13B",
+    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
+    "skip": true,
+    "size": 56.1,
+    "date": "2025 Ocotober"
+  },
+  "Qwen-Image-Edit-2509-Pruning": {
+    "path": "OPPOer/Qwen-Image-Edit-Pruning-14B",
+    "subfolder": "Qwen-Image-Edit-2509-14B",
+    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
+    "skip": true,
+    "size": 56.1,
+    "date": "2025 Ocotober"
+  },
 
   "lodestones Chroma1 HD": {
     "path": "lodestones/Chroma1-HD",
diff --git a/modules/mit_nunchaku.py b/modules/mit_nunchaku.py
index a06e7cfda..b331f683a 100644
--- a/modules/mit_nunchaku.py
+++ b/modules/mit_nunchaku.py
@@ -4,7 +4,7 @@ from installer import log, pip
 from modules import devices
 
 
-ver = '1.0.0'
+ver = '1.0.1'
 ok = False
 
 
diff --git a/modules/modelloader.py b/modules/modelloader.py
index bc2e0b607..0502a367c 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -180,6 +180,13 @@ def find_diffuser(name: str, full=False):
     if len(repo) > 0:
         return [repo[0]['name']]
     hf_api = hf.HfApi()
+    suffix = ''
+    if len(name) > 3 and name.count('/') > 1:
+        parts = name.split('/')
+        name = '/'.join(parts[:2]) # only user/model
+        suffix = '/'.join(parts[2:]) # subfolder
+        if len(suffix) > 0:
+            suffix = '/' + suffix
     models = list(hf_api.list_models(model_name=name, library=['diffusers'], full=True, limit=20, sort="downloads", direction=-1))
     if len(models) == 0:
         models = list(hf_api.list_models(model_name=name, full=True, limit=20, sort="downloads", direction=-1)) # widen search
@@ -187,9 +194,9 @@ def find_diffuser(name: str, full=False):
     shared.log.debug(f'Search model: repo="{name}" {len(models) > 0}')
     if len(models) > 0:
         if not full:
-            return models[0].id
+            return models[0].id + suffix
         else:
-            return [m.id for m in models]
+            return [m.id + suffix for m in models]
     return None
 
 
diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py
index a1ea0d8ed..6870c740f 100644
--- a/modules/sd_checkpoint.py
+++ b/modules/sd_checkpoint.py
@@ -27,6 +27,7 @@ class CheckpointInfo:
         self.hash = sha
         self.filename = filename
         self.type = ''
+        self.subfolder = None
         relname = filename
         app_path = os.path.abspath(paths.script_path)
 
@@ -106,7 +107,7 @@ class CheckpointInfo:
         return self.shorthash
 
     def __str__(self):
-        return f"CheckpointInfo(name={self.name} filename={self.filename} hash={self.shorthash} type={self.type}"
+        return f'CheckpointInfo(name="{self.name}" filename="{self.filename}" hash={self.shorthash} type={self.type} title="{self.title}" path="{self.path}" subfolder="{self.subfolder}")'
 
 
 def setup_model():
@@ -231,7 +232,12 @@ def get_closet_checkpoint_match(s: str) -> CheckpointInfo:
         return checkpoint_info
 
     # huggingface search
-    if shared.opts.sd_checkpoint_autodownload and s.count('/') == 1:
+    if shared.opts.sd_checkpoint_autodownload and (s.count('/') == 1 or s.count('/') == 2):
+        if s.count('/') == 2:
+            subfolder = '/'.join(s.split('/')[2:]) # subfolder
+            s = '/'.join(s.split('/')[:2]) # only user/model
+        else:
+            subfolder = None
         modelloader.hf_login()
         found = modelloader.find_diffuser(s, full=True)
         if found is None:
@@ -241,6 +247,7 @@ def get_closet_checkpoint_match(s: str) -> CheckpointInfo:
         if found is not None and len(found) == 1:
             checkpoint_info = CheckpointInfo(s)
             checkpoint_info.type = 'huggingface'
+            checkpoint_info.subfolder = subfolder
             return checkpoint_info
 
     # civitai search
@@ -289,7 +296,7 @@ def select_checkpoint(op='model', sd_model_checkpoint=None):
         return None
     if model_checkpoint is not None:
         if model_checkpoint != 'model.safetensors' and model_checkpoint != 'stabilityai/stable-diffusion-xl-base-1.0':
-            shared.log.info(f'Load {op}: search="{model_checkpoint}" not found')
+            shared.log.error(f'Load {op}: search="{model_checkpoint}" not found')
         else:
             shared.log.info("Selecting first available checkpoint")
     else:
diff --git a/pipelines/generic.py b/pipelines/generic.py
index 2c5f0b847..0eb2c19c1 100644
--- a/pipelines/generic.py
+++ b/pipelines/generic.py
@@ -48,7 +48,7 @@ def load_transformer(repo_id, cls_name, load_config={}, subfolder="transformer",
                 **quant_args,
             )
         else:
-            shared.log.debug(f'Load model: transformer="{repo_id}" cls={cls_name.__name__} quant="{quant_type}" args={load_args}')
+            shared.log.debug(f'Load model: transformer="{repo_id}" cls={cls_name.__name__} subfolder={subfolder} quant="{quant_type}" args={load_args}')
             if dtype is not None:
                 load_args['torch_dtype'] = dtype
             if subfolder is not None:
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index f28313603..eedefd014 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -1,11 +1,12 @@
 import transformers
 import diffusers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
-from pipelines import generic
 
 
 def load_qwen(checkpoint_info, diffusers_load_config={}):
+    from pipelines import generic, qwen
     repo_id = sd_models.path_to_repo(checkpoint_info)
+    repo_subfolder = checkpoint_info.subfolder
     sd_models.hf_auth_check(checkpoint_info)
     transformer = None
 
@@ -29,8 +30,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
         diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageInpaintPipeline
 
     if model_quant.check_nunchaku('Model'):
-        from pipelines.qwen.qwen_nunchaku import load_qwen_nunchaku
-        transformer = load_qwen_nunchaku(repo_id)
+        transformer = qwen.load_qwen_nunchaku(repo_id)
 
     if 'Qwen-Image-Distill-Full' in repo_id:
         repo_transformer = repo_id
@@ -38,7 +38,10 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
         repo_id = 'Qwen/Qwen-Image'
     else:
         repo_transformer = repo_id
-        transformer_subfolder = "transformer"
+        if repo_subfolder is not None:
+            transformer_subfolder = repo_subfolder + '/transformer'
+        else:
+            transformer_subfolder = "transformer"
 
     if transformer is None:
         transformer = generic.load_transformer(
@@ -52,11 +55,12 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
     repo_te = 'Qwen/Qwen-Image'
     text_encoder = generic.load_text_encoder(repo_te, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
 
-    # NunchakuQwenImagePipeline
+    repo_id = qwen.check_qwen_pruning(repo_id)
     pipe = cls_name.from_pretrained(
         repo_id,
         transformer=transformer,
         text_encoder=text_encoder,
+        subfolder=repo_subfolder,
         cache_dir=shared.opts.diffusers_dir,
         **load_args,
     )
diff --git a/pipelines/qwen/__init__.py b/pipelines/qwen/__init__.py
new file mode 100644
index 000000000..e1455f988
--- /dev/null
+++ b/pipelines/qwen/__init__.py
@@ -0,0 +1,2 @@
+from pipelines.qwen.qwen_nunchaku import load_qwen_nunchaku
+from pipelines.qwen.qwen_pruning import check_qwen_pruning
diff --git a/pipelines/qwen/qwen_nunchaku.py b/pipelines/qwen/qwen_nunchaku.py
index 7c689c7a3..4c89b7b1c 100644
--- a/pipelines/qwen/qwen_nunchaku.py
+++ b/pipelines/qwen/qwen_nunchaku.py
@@ -11,7 +11,9 @@ def load_qwen_nunchaku(repo_id):
     except Exception:
         shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" low nunchaku version')
         return None
-    if repo_id.lower().endswith('qwen-image'):
+    if 'pruning' in repo_id.lower() or 'distill' in repo_id.lower():
+        return None
+    elif repo_id.lower().endswith('qwen-image'):
         nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image.safetensors" # r32 vs r128
     elif repo_id.lower().endswith('qwen-lightning'):
         nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image-lightningv1.1-8steps.safetensors" # 8-step variant
diff --git a/pipelines/qwen/qwen_pruning.py b/pipelines/qwen/qwen_pruning.py
new file mode 100644
index 000000000..971589958
--- /dev/null
+++ b/pipelines/qwen/qwen_pruning.py
@@ -0,0 +1,12 @@
+def check_qwen_pruning(repo_id):
+    from modules.shared import log
+    if 'pruning' not in repo_id.lower():
+        return repo_id
+    if '2509' in repo_id:
+        repo_id = "Qwen/Qwen-Image-Edit-2509"
+    elif 'Edit' in repo_id:
+        repo_id = "Qwen/Qwen-Image-Edit"
+    else:
+        repo_id = "Qwen/Qwen-Image"
+    log.debug(f'Load model: variant=pruning target="{repo_id}"')
+    return repo_id
diff --git a/wiki b/wiki
index f274489e4..dfcb801c5 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit f274489e42b97b57ea91b9e94087b8b39820a4ee
+Subproject commit dfcb801c592a493d48b7e22a0a46ab39340fd021

From 95a7da7e75350a0f9179f339fc06e36ece2d0b60 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Fri, 3 Oct 2025 18:54:58 +0300
Subject: [PATCH 124/230] SDNQ use non-contiguous re-quantize

---
 modules/sdnq/__init__.py    |  1 +
 modules/sdnq/dequantizer.py | 23 +++++++++++++++--------
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 16fe6ec47..c7d832bb9 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -197,6 +197,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             re_quantize_for_matmul=re_quantize_for_matmul,
         )
         layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
+        layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
 
         layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
         layer.forward = layer.forward.__get__(layer, layer.__class__)
diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index d82849177..22771b643 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -48,18 +48,25 @@ def quantize_fp8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.Tensor,
     return input, scale
 
 
+def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
+    if weight.ndim > 2: # convs
+        weight = weight.flatten(1,-1)
+    if weight.device.type == "xpu":
+        # return contiguous
+        weight, scale = quantize_int8(weight.t(), dim=0)
+    else:
+        # return non-contiguous
+        weight, scale = quantize_int8(weight, dim=-1)
+        weight, scale = weight.t_(), scale.t_()
+    return weight, scale
+
+
 def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
-    result = dequantize_asymmetric(weight, scale, zero_point, scale.dtype, result_shape)
-    if result.ndim > 2: # convs
-        result = result.flatten(1,-1)
-    return quantize_int8(result.t_(), dim=0)
+    return re_quantize_int8(dequantize_asymmetric(weight, scale, zero_point, scale.dtype, result_shape))
 
 
 def re_quantize_matmul_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
-    result = dequantize_symmetric(weight, scale, scale.dtype, result_shape)
-    if result.ndim > 2: # convs
-        result = result.flatten(1,-1)
-    return quantize_int8(result.t_(), dim=0)
+    return re_quantize_int8(dequantize_symmetric(weight, scale, scale.dtype, result_shape))
 
 
 def re_quantize_matmul_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:

From 54ae18a6110a3a83babf6047fd3c0d99807c8bc1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 3 Oct 2025 12:23:22 -0400
Subject: [PATCH 125/230] update nunchaku

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md            | 2 +-
 modules/mit_nunchaku.py | 6 +++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b9cde65f2..a34b0ec27 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -75,7 +75,7 @@
   - **attention** remove split-attention and add explicitly attention slicing enable/disable option  
     enable in *settings -> compute settings*  
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
-  - **nunchaku** update to `1.0.1`  
+  - **nunchaku** update to `1.0.1` and enhance installer    
 - **Experimental**
   - `new` command line flag enables new `pydantic` and `albumentations` packages  
   - **modular pipelines**: enable in *settings -> model options*  
diff --git a/modules/mit_nunchaku.py b/modules/mit_nunchaku.py
index b331f683a..9817c4e53 100644
--- a/modules/mit_nunchaku.py
+++ b/modules/mit_nunchaku.py
@@ -15,7 +15,11 @@ def check():
     try:
         import nunchaku
         import nunchaku.utils
-        log.info(f'Nunchaku: path={nunchaku.__path__} precision={nunchaku.utils.get_precision()}')
+        from nunchaku import __version__
+        log.info(f'Nunchaku: path={nunchaku.__path__} version={__version__.__version__} precision={nunchaku.utils.get_precision()}')
+        if __version__.__version__ != ver:
+            ok = False
+            return False
         ok = True
         return True
     except Exception as e:

From e5ef08d4901b9be716a3c6604a32f20a522f5068 Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Sat, 4 Oct 2025 12:18:17 +0300
Subject: [PATCH 126/230] SD1.5 and SD2.1 updated cover images

---
 ...stabilityai--stable-diffusion-2-1-base.jpg | Bin 58475 -> 49294 bytes
 .../stabilityai--stable-diffusion-2-1.jpg     | Bin 63076 -> 83198 bytes
 models/Reference/v1-5-pruned-fp16-emaonly.jpg | Bin 94318 -> 84740 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/models/Reference/stabilityai--stable-diffusion-2-1-base.jpg b/models/Reference/stabilityai--stable-diffusion-2-1-base.jpg
index ab0e655f2d571d50d4879684ce48561c74016b3b..9912ea894e3d42e637f6d58c5065ba9ea089e28a 100644
GIT binary patch
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From 99113947bf3ff83f705c47795ba344740490e855 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 4 Oct 2025 18:31:25 +0300
Subject: [PATCH 127/230] SDNQ add RDNA2 INT8 support via Triton

---
 modules/sdnq/__init__.py                  |   4 +-
 modules/sdnq/common.py                    |  48 +++++++--
 modules/sdnq/dequantizer.py               |   6 +-
 modules/sdnq/layers/conv/conv_int8.py     |   6 +-
 modules/sdnq/layers/linear/forward.py     |   4 +-
 modules/sdnq/layers/linear/linear_int8.py |   6 +-
 modules/sdnq/triton_mm.py                 | 122 ++++++++++++++++++++++
 7 files changed, 172 insertions(+), 24 deletions(-)
 create mode 100644 modules/sdnq/triton_mm.py

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index c7d832bb9..1009fd811 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -11,7 +11,7 @@ from diffusers.quantizers.quantization_config import QuantizationConfigMixin
 from diffusers.utils import get_module_from_name
 from modules import devices, shared
 
-from .common import dtype_dict, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types
+from .common import dtype_dict, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
 from .dequantizer import dequantizer_dict
 from .forward import get_forward_func
 
@@ -172,7 +172,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         if use_quantized_matmul and not re_quantize_for_matmul:
             scale.transpose_(0,1)
             layer.weight.transpose_(0,1)
-            if devices.backend == "ipex":
+            if use_contiguous_mm:
                 layer.weight.data = layer.weight.contiguous()
             elif layer.weight.is_contiguous():
                 layer.weight.data = layer.weight.t_().contiguous().t_()
diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index b70c7b06b..ed131423d 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -1,12 +1,10 @@
 # pylint: disable=redefined-builtin,no-member,protected-access
 
 import os
-from functools import partial
 import torch
 
 from modules import shared, devices
 
-torch_version = float(torch.__version__[:3])
 
 dtype_dict = {
     "int8": {"min": -128, "max": 127, "num_bits": 8, "target_dtype": torch.int8, "torch_dtype": torch.int8, "storage_dtype": torch.int8, "is_unsigned": False, "is_integer": True},
@@ -33,23 +31,51 @@ if hasattr(torch, "float8_e4m3fnuz"):
 if hasattr(torch, "float8_e5m2fnuz"):
     dtype_dict["float8_e5m2fnuz"] = {"min": -57344, "max": 57344, "num_bits": 8, "target_dtype": "fp8", "torch_dtype": torch.float8_e5m2fnuz, "storage_dtype": torch.float8_e5m2fnuz, "is_unsigned": False, "is_integer": False}
 
+linear_types = {"Linear"}
+conv_types = {"Conv1d", "Conv2d", "Conv3d"}
+conv_transpose_types = {"ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d"}
+allowed_types = set.union(linear_types, conv_types, conv_transpose_types)
+
 use_torch_compile = shared.opts.sdnq_dequantize_compile # this setting requires a full restart of the webui to apply
+is_rdna2 = bool(devices.backend == "rocm" and int(getattr(torch.cuda.get_device_properties(devices.device), "gcnArchName", "gfx0000")[3:]) < 1100)
 
-if devices.backend == "cuda" and os.environ.get("SDNQ_USE_TENSORWISE_FP8_MATMUL", None) is None:
-     # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
-    use_tensorwise_fp8_matmul = torch.cuda.get_device_capability(devices.device) < (9,0)
+
+if os.environ.get("SDNQ_USE_TENSORWISE_FP8_MM", None) is None:
+    # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
+    use_tensorwise_fp8_matmul = bool(devices.backend == "cuda" and torch.cuda.get_device_capability(devices.device) < (9,0))
 else:
-    use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MATMUL", "1").lower() not in {"0", "false", "no"}
+    use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MM", "1").lower() not in {"0", "false", "no"}
+
+if os.environ.get("SDNQ_USE_CONTIGUOUS_MM", None) is None:
+    use_contiguous_mm = bool(is_rdna2 or devices.backend in {"cpu", "ipex", "zluda"})
+else:
+    use_contiguous_mm = bool(os.environ.get("SDNQ_USE_CONTIGUOUS_MM", "0").lower() not in {"0", "false", "no"})
+
+if os.environ.get("SDNQ_USE_TRITON_MM", None) is None:
+    use_triton_mm = bool(is_rdna2 or devices.backend in {"cuda", "zluda"})
+else:
+    use_triton_mm = bool(os.environ.get("SDNQ_USE_TRITON_MM", "0").lower() not in {"0", "false", "no"})
+
+
+if use_triton_mm:
+    try:
+        from .triton_mm import int_mm
+        int_mm_func = int_mm
+    except ImportError:
+        int_mm_func = torch._int_mm
+else:
+    int_mm_func = torch._int_mm
 
-linear_types = ("Linear",)
-conv_types = ("Conv1d", "Conv2d", "Conv3d")
-conv_transpose_types = ("ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d")
-allowed_types = linear_types + conv_types + conv_transpose_types
 
 if use_torch_compile:
     torch._dynamo.config.cache_size_limit = max(8192, torch._dynamo.config.cache_size_limit)
     torch._dynamo.config.accumulated_recompile_limit = max(8192, torch._dynamo.config.accumulated_recompile_limit)
-    compile_func = partial(torch.compile, fullgraph=True, dynamic=False)
+    def compile_func(fn, **kwargs):
+        if kwargs.get("fullgraph", None) is None:
+            kwargs["fullgraph"] = True
+        if kwargs.get("dynamic", None) is None:
+            kwargs["dynamic"] = False
+        return torch.compile(fn, **kwargs)
 else:
     def compile_func(fn, **kwargs): # pylint: disable=unused-argument
         return fn
diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 22771b643..411ae236c 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from .common import dtype_dict, compile_func
+from .common import dtype_dict, compile_func, use_contiguous_mm
 from .packed_int import pack_int_symetric, unpack_int_symetric, pack_int_asymetric, unpack_int_asymetric
 
 
@@ -51,11 +51,9 @@ def quantize_fp8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.Tensor,
 def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
     if weight.ndim > 2: # convs
         weight = weight.flatten(1,-1)
-    if weight.device.type == "xpu":
-        # return contiguous
+    if use_contiguous_mm:
         weight, scale = quantize_int8(weight.t(), dim=0)
     else:
-        # return non-contiguous
         weight, scale = quantize_int8(weight, dim=-1)
         weight, scale = weight.t_(), scale.t_()
     return weight, scale
diff --git a/modules/sdnq/layers/conv/conv_int8.py b/modules/sdnq/layers/conv/conv_int8.py
index 0ae26b8f5..294f696b4 100644
--- a/modules/sdnq/layers/conv/conv_int8.py
+++ b/modules/sdnq/layers/conv/conv_int8.py
@@ -4,7 +4,7 @@ from typing import List
 
 import torch
 
-from ...common import compile_func # noqa: TID252
+from ...common import compile_func, int_mm_func # noqa: TID252
 from ...packed_int import unpack_int_symetric # noqa: TID252
 from ...dequantizer import dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 from ..linear.linear_int8 import quantize_int8_matmul_input # noqa: TID252
@@ -33,13 +33,13 @@ def conv_int8_matmul(
     input, weight = check_mats(input, weight)
 
     if groups == 1:
-        result = torch._int_mm(input, weight)
+        result = int_mm_func(input, weight)
     else:
         weight = weight.view(weight.shape[0], groups, weight.shape[1] // groups)
         input = input.view(input.shape[0], groups, input.shape[1] // groups)
         result = []
         for i in range(groups):
-            result.append(torch._int_mm(input[:, i], weight[:, i]))
+            result.append(int_mm_func(input[:, i], weight[:, i]))
         result = torch.cat(result, dim=-1)
     if bias is not None:
         result = dequantize_symmetric_with_bias(result, scale, bias, return_dtype, mm_output_shape)
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index fc5b2ce60..880b8ebc8 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -4,10 +4,12 @@ from typing import Tuple
 
 import torch
 
+from ...common import use_contiguous_mm # noqa: TID252
+
 
 def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     input = input.contiguous()
-    if weight.device.type == "xpu":
+    if use_contiguous_mm:
         weight = weight.contiguous()
     elif weight.is_contiguous():
         weight = weight.t().contiguous().t()
diff --git a/modules/sdnq/layers/linear/linear_int8.py b/modules/sdnq/layers/linear/linear_int8.py
index bfca56ce2..ae218a4fe 100644
--- a/modules/sdnq/layers/linear/linear_int8.py
+++ b/modules/sdnq/layers/linear/linear_int8.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from ...common import compile_func # noqa: TID252
+from ...common import compile_func, int_mm_func # noqa: TID252
 from ...packed_int import unpack_int_symetric # noqa: TID252
 from ...dequantizer import quantize_int8, dequantize_symmetric, dequantize_symmetric_with_bias # noqa: TID252
 from .forward import check_mats
@@ -34,9 +34,9 @@ def int8_matmul(
     input, scale = quantize_int8_matmul_input(input, scale)
     input, weight = check_mats(input, weight)
     if bias is not None:
-        return dequantize_symmetric_with_bias(torch._int_mm(input, weight), scale, bias, return_dtype, output_shape)
+        return dequantize_symmetric_with_bias(int_mm_func(input, weight), scale, bias, return_dtype, output_shape)
     else:
-        return dequantize_symmetric(torch._int_mm(input, weight), scale, return_dtype, output_shape)
+        return dequantize_symmetric(int_mm_func(input, weight), scale, return_dtype, output_shape)
 
 
 def quantized_linear_forward_int8_matmul(self, input: torch.FloatTensor) -> torch.FloatTensor:
diff --git a/modules/sdnq/triton_mm.py b/modules/sdnq/triton_mm.py
new file mode 100644
index 000000000..d15294c3e
--- /dev/null
+++ b/modules/sdnq/triton_mm.py
@@ -0,0 +1,122 @@
+"""
+Modified from Triton MatMul example.
+PyTorch torch._int_mm is broken on backward pass with Nvidia.
+AMD RDNA2 doesn't support torch._int_mm, so we use int_mm via Triton.
+"""
+
+import torch
+
+import triton
+import triton.language as tl
+
+
+def get_autotune_config():
+    if triton.runtime.driver.active.get_current_target().backend == "cuda":
+        return [
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  32, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N':  32, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+            triton.Config({'BLOCK_SIZE_M':  32, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+            #
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  32, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+            triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        ]
+    else:
+        return [
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  32, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N':  32, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=2),
+            triton.Config({'BLOCK_SIZE_M':  32, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K':  32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=2),
+            #
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M':  64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N':  32, 'BLOCK_SIZE_K':  64, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+            triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),
+            triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':  64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
+        ]
+
+
+@triton.autotune(configs=get_autotune_config(), key=['M', 'N', 'K', 'stride_bk'])
+@triton.jit
+def int_mm_kernel(
+        a_ptr, b_ptr, c_ptr,
+        M, N, K,
+        stride_am, stride_ak,
+        stride_bk, stride_bn,
+        stride_cm, stride_cn,
+        BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,  #
+        GROUP_SIZE_M: tl.constexpr
+):
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    tl.assume(pid_m >= 0)
+    tl.assume(pid_n >= 0)
+    tl.assume(stride_am > 0)
+    tl.assume(stride_ak > 0)
+    tl.assume(stride_bn > 0)
+    tl.assume(stride_bk > 0)
+    tl.assume(stride_cm > 0)
+    tl.assume(stride_cn > 0)
+
+    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
+    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
+    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
+
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.int32)
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
+        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
+        accumulator = tl.dot(a, b, accumulator, out_dtype=tl.int32)
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
+    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, accumulator, mask=c_mask)
+
+
+def int_mm(a, b):
+    assert a.shape[1] == b.shape[0], "Incompatible dimensions"
+    assert a.is_contiguous(), "Matrix A must be contiguous"
+    M, K = a.shape
+    K, N = b.shape
+    c = torch.empty((M, N), device=a.device, dtype=torch.int32)
+    def grid(META):
+        return (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
+    int_mm_kernel[grid](
+        a, b, c,
+        M, N, K,
+        a.stride(0), a.stride(1),
+        b.stride(0), b.stride(1),
+        c.stride(0), c.stride(1),
+    )
+    return c

From 5c5d7d5a86ac0d4602c959bdc761dd8101435a1d Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 4 Oct 2025 18:38:18 +0300
Subject: [PATCH 128/230] cleanup

---
 modules/sdnq/common.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index ed131423d..5bcd60ce9 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -44,7 +44,7 @@ if os.environ.get("SDNQ_USE_TENSORWISE_FP8_MM", None) is None:
     # row-wise FP8 only exist on H100 hardware, sdnq will use software row-wise with tensorwise hardware with this setting
     use_tensorwise_fp8_matmul = bool(devices.backend == "cuda" and torch.cuda.get_device_capability(devices.device) < (9,0))
 else:
-    use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MM", "1").lower() not in {"0", "false", "no"}
+    use_tensorwise_fp8_matmul = os.environ.get("SDNQ_USE_TENSORWISE_FP8_MM", "0").lower() not in {"0", "false", "no"}
 
 if os.environ.get("SDNQ_USE_CONTIGUOUS_MM", None) is None:
     use_contiguous_mm = bool(is_rdna2 or devices.backend in {"cpu", "ipex", "zluda"})

From df142afe81d1e1f12223efd7dcae73e9dde7a77e Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 4 Oct 2025 18:48:03 +0300
Subject: [PATCH 129/230] don't use triton mm for nvidia

---
 modules/sdnq/common.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 5bcd60ce9..8d0114e3e 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -52,7 +52,7 @@ else:
     use_contiguous_mm = bool(os.environ.get("SDNQ_USE_CONTIGUOUS_MM", "0").lower() not in {"0", "false", "no"})
 
 if os.environ.get("SDNQ_USE_TRITON_MM", None) is None:
-    use_triton_mm = bool(is_rdna2 or devices.backend in {"cuda", "zluda"})
+    use_triton_mm = bool(is_rdna2 or devices.backend == "zluda")
 else:
     use_triton_mm = bool(os.environ.get("SDNQ_USE_TRITON_MM", "0").lower() not in {"0", "false", "no"})
 

From f2e12a682ffc40ad7be52ed05e1954dce32bd0bc Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 4 Oct 2025 19:17:05 +0300
Subject: [PATCH 130/230] SDNQ remove use_contiguous_mm path in re_quant

---
 modules/sdnq/dequantizer.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 411ae236c..3881cfe34 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -4,7 +4,7 @@ from typing import Tuple
 
 import torch
 
-from .common import dtype_dict, compile_func, use_contiguous_mm
+from .common import dtype_dict, compile_func
 from .packed_int import pack_int_symetric, unpack_int_symetric, pack_int_asymetric, unpack_int_asymetric
 
 
@@ -51,11 +51,7 @@ def quantize_fp8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.Tensor,
 def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
     if weight.ndim > 2: # convs
         weight = weight.flatten(1,-1)
-    if use_contiguous_mm:
-        weight, scale = quantize_int8(weight.t(), dim=0)
-    else:
-        weight, scale = quantize_int8(weight, dim=-1)
-        weight, scale = weight.t_(), scale.t_()
+    weight, scale = quantize_int8(weight.t(), dim=0)
     return weight, scale
 
 

From 8b698ed67f3da4b0a41b1bfe5bb2d5b6834bb4f8 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 4 Oct 2025 15:48:42 -0400
Subject: [PATCH 131/230] upadte qwen pruning and allow hf models in subfolders

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 html/reference.json                      | 23 ++++++++++++++---------
 modules/extras.py                        | 14 +++++++-------
 modules/modelloader.py                   |  6 ++++--
 modules/modelstats.py                    |  2 +-
 modules/onnx_impl/ui.py                  |  4 ++--
 modules/sd_checkpoint.py                 | 23 +++++++++++++++++------
 modules/sd_models.py                     |  2 +-
 modules/sd_models_utils.py               |  4 +++-
 modules/ui_extra_networks_checkpoints.py |  4 +++-
 modules/ui_settings.py                   |  2 +-
 pipelines/model_qwen.py                  |  2 +-
 pipelines/qwen/qwen_pruning.py           | 16 ++++++++--------
 scripts/stablevideodiffusion.py          |  2 +-
 scripts/text2video.py                    |  2 +-
 scripts/xyz/xyz_grid_shared.py           |  4 ++--
 wiki                                     |  2 +-
 16 files changed, 67 insertions(+), 45 deletions(-)

diff --git a/html/reference.json b/html/reference.json
index ad1b7e333..2c3ca1d7e 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -234,31 +234,36 @@
     "size": 56.1,
     "date": "2025 August"
   },
-  "Qwen-Image-Pruning": {
-    "path": "OPPOer/Qwen-Image-Pruning-13B",
+  "Qwen-Image Pruning-13B": {
+    "path": "OPPOer/Qwen-Image-Pruning",
     "subfolder": "Qwen-Image-13B",
     "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
     "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
-    "size": 56.1,
     "date": "2025 Ocotober"
   },
-  "Qwen-Image-Edit-Pruning": {
-    "path": "OPPOer/Qwen-Image-Edit-Pruning-13B",
+  "Qwen-Image Pruning-12B": {
+    "path": "OPPOer/Qwen-Image-Pruning",
+    "subfolder": "Qwen-Image-12B-40steps",
+    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
+    "skip": true,
+    "date": "2025 Ocotober"
+  },
+  "Qwen-Image-Edit Pruning-13B": {
+    "path": "OPPOer/Qwen-Image-Edit-Pruning",
     "subfolder": "Qwen-Image-Edit-13B",
     "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
     "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
-    "size": 56.1,
     "date": "2025 Ocotober"
   },
-  "Qwen-Image-Edit-2509-Pruning": {
-    "path": "OPPOer/Qwen-Image-Edit-Pruning-14B",
+  "Qwen-Image-Edit-2509 Pruning-14B": {
+    "path": "OPPOer/Qwen-Image-Edit-Pruning",
     "subfolder": "Qwen-Image-Edit-2509-14B",
     "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
     "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
-    "size": 56.1,
     "date": "2025 Ocotober"
   },
 
diff --git a/modules/extras.py b/modules/extras.py
index 2914d6c1e..4f282653c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -39,24 +39,24 @@ def run_modelmerger(id_task, **kwargs):  # pylint: disable=unused-argument
         return [*[gr.update() for _ in range(4)], message]
 
     kwargs["models"] = {
-        "model_a": sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None)).filename,
-        "model_b": sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None)).filename,
+        "model_a": sd_models.get_closest_checkpoint_match(kwargs.get("primary_model_name", None)).filename,
+        "model_b": sd_models.get_closest_checkpoint_match(kwargs.get("secondary_model_name", None)).filename,
     }
 
     if kwargs.get("primary_model_name", None) in [None, 'None']:
         return fail("Failed: Merging requires a primary model.")
-    primary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None))
+    primary_model_info = sd_models.get_closest_checkpoint_match(kwargs.get("primary_model_name", None))
     if kwargs.get("secondary_model_name", None) in [None, 'None']:
         return fail("Failed: Merging requires a secondary model.")
-    secondary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None))
+    secondary_model_info = sd_models.get_closest_checkpoint_match(kwargs.get("secondary_model_name", None))
     if kwargs.get("tertiary_model_name", None) in [None, 'None'] and kwargs.get("merge_mode", None) in merge_utils.TRIPLE_METHODS:
         return fail(f"Failed: Interpolation method ({kwargs.get('merge_mode', None)}) requires a tertiary model.")
-    tertiary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)) if kwargs.get("merge_mode", None) in merge_utils.TRIPLE_METHODS else None
+    tertiary_model_info = sd_models.get_closest_checkpoint_match(kwargs.get("tertiary_model_name", None)) if kwargs.get("merge_mode", None) in merge_utils.TRIPLE_METHODS else None
 
     del kwargs["primary_model_name"]
     del kwargs["secondary_model_name"]
     if kwargs.get("tertiary_model_name", None) is not None:
-        kwargs["models"] |= {"model_c": sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)).filename}
+        kwargs["models"] |= {"model_c": sd_models.get_closest_checkpoint_match(kwargs.get("tertiary_model_name", None)).filename}
         del kwargs["tertiary_model_name"]
 
     if kwargs.get("alpha_base", None) and kwargs.get("alpha_in_blocks", None) and kwargs.get("alpha_mid_block", None) and kwargs.get("alpha_out_blocks", None):
@@ -204,7 +204,7 @@ def run_model_modules(model_type:str, model_name:str, custom_name:str,
     if len(custom_name) == 0:
         yield msg("output name is required", err=True)
         return
-    checkpoint_info = sd_models.get_closet_checkpoint_match(model_name)
+    checkpoint_info = sd_models.get_closest_checkpoint_match(model_name)
     if checkpoint_info is None:
         yield msg("input model not found", err=True)
         return
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 0502a367c..5018d5cb9 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -227,6 +227,8 @@ def get_reference_opts(name: str, quiet=False):
 
 
 def load_reference(name: str, variant: str = None, revision: str = None, mirror: str = None, custom_pipeline: str = None):
+    if '+' in name:
+        name = name.split('+')[0]
     found = [r for r in diffuser_repos if name == r['name'] or name == r['friendly'] or name == r['path']]
     if len(found) > 0: # already downloaded
         model_opts = get_reference_opts(found[0]['name'])
@@ -258,7 +260,7 @@ def load_reference(name: str, variant: str = None, revision: str = None, mirror:
 def load_civitai(model: str, url: str):
     from modules import sd_models
     name, _ext = os.path.splitext(model)
-    info = sd_models.get_closet_checkpoint_match(name)
+    info = sd_models.get_closest_checkpoint_match(name)
     if info is not None:
         _model_opts = get_reference_opts(info.model_name)
         return name # already downloaded
@@ -268,7 +270,7 @@ def load_civitai(model: str, url: str):
         download_civit_model_thread(model_name=model, model_url=url, model_path='', model_type='safetensors', token=shared.opts.civitai_token)
         shared.log.debug(f'Reference download complete: model="{name}"')
         sd_models.list_models()
-        info = sd_models.get_closet_checkpoint_match(name)
+        info = sd_models.get_closest_checkpoint_match(name)
         if info is not None:
             shared.log.debug(f'Reference: model="{name}"')
             return name # already downloaded
diff --git a/modules/modelstats.py b/modules/modelstats.py
index 2e5e7f447..2ab1a6b62 100644
--- a/modules/modelstats.py
+++ b/modules/modelstats.py
@@ -79,7 +79,7 @@ class Model():
             return
         self.cls = shared.sd_model.__class__.__name__
         self.type = shared.sd_model_type
-        self.info = sd_models.get_closet_checkpoint_match(name)
+        self.info = sd_models.get_closest_checkpoint_match(name)
         if self.info is not None:
             self.name = self.info.name or self.name
             self.hash = self.info.shorthash or ''
diff --git a/modules/onnx_impl/ui.py b/modules/onnx_impl/ui.py
index 0af6ba345..703392d82 100644
--- a/modules/onnx_impl/ui.py
+++ b/modules/onnx_impl/ui.py
@@ -15,7 +15,7 @@ def create_ui():
     from modules.ui_common import create_refresh_button
     from modules.ui_components import DropdownMulti
     from modules.shared import log, opts, cmd_opts, refresh_checkpoints
-    from modules.sd_models import checkpoint_titles, get_closet_checkpoint_match
+    from modules.sd_models import checkpoint_titles, get_closest_checkpoint_match
     from modules.paths import sd_configs_path
     from .execution_providers import ExecutionProvider, install_execution_provider
     from .utils import check_diffusers_cache
@@ -74,7 +74,7 @@ def create_ui():
                         cache_remove_optimized.click(fn=remove_cache_optimized, inputs=[cache_state_dirname, cache_optimized_selected,])
 
                     def cache_update_menus(query: str):
-                        checkpoint_info = get_closet_checkpoint_match(query)
+                        checkpoint_info = get_closest_checkpoint_match(query)
                         if checkpoint_info is None:
                             log.error(f"Could not find checkpoint object for '{query}'.")
                             return
diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py
index 6870c740f..4b0be2e97 100644
--- a/modules/sd_checkpoint.py
+++ b/modules/sd_checkpoint.py
@@ -22,12 +22,12 @@ warn_once = False
 
 
 class CheckpointInfo:
-    def __init__(self, filename, sha=None):
+    def __init__(self, filename, sha=None, subfolder=None):
         self.name = None
         self.hash = sha
         self.filename = filename
         self.type = ''
-        self.subfolder = None
+        self.subfolder = subfolder
         relname = filename
         app_path = os.path.abspath(paths.script_path)
 
@@ -197,13 +197,13 @@ def remove_hash(s):
     return re.sub(r'\s*\[.*?\]', '', s)
 
 
-def get_closet_checkpoint_match(s: str) -> CheckpointInfo:
+def get_closest_checkpoint_match(s: str) -> CheckpointInfo:
+    # direct hf url
     if s.startswith('https://huggingface.co/'):
         model_name = s.replace('https://huggingface.co/', '')
         checkpoint_info = CheckpointInfo(model_name) # create a virutal model info
         checkpoint_info.type = 'huggingface'
         return checkpoint_info
-
     if s.startswith('huggingface/'):
         model_name = s.replace('huggingface/', '')
         checkpoint_info = CheckpointInfo(model_name) # create a virutal model info
@@ -229,6 +229,16 @@ def get_closet_checkpoint_match(s: str) -> CheckpointInfo:
     # absolute path
     if s.endswith('.safetensors') and os.path.isfile(s):
         checkpoint_info = CheckpointInfo(s)
+        checkpoint_info.type = 'safetensors'
+        return checkpoint_info
+
+    # reference search
+    ref = [(k, v) for k, v in shared.reference_models.items() if f"{v.get('path', '')}+{v.get('subfolder', '')}" == s]
+    if ref and len(ref) > 0:
+        _name, info = ref[0]
+        checkpoint_info = CheckpointInfo(s)
+        checkpoint_info.subfolder = info.get('subfolder', None)
+        checkpoint_info.type = 'reference'
         return checkpoint_info
 
     # huggingface search
@@ -247,7 +257,8 @@ def get_closet_checkpoint_match(s: str) -> CheckpointInfo:
         if found is not None and len(found) == 1:
             checkpoint_info = CheckpointInfo(s)
             checkpoint_info.type = 'huggingface'
-            checkpoint_info.subfolder = subfolder
+            if subfolder is not None and len(subfolder) > 0:
+                checkpoint_info.subfolder = subfolder
             return checkpoint_info
 
     # civitai search
@@ -281,7 +292,7 @@ def select_checkpoint(op='model', sd_model_checkpoint=None):
     model_checkpoint = sd_model_checkpoint or (shared.opts.data.get('sd_model_refiner', None) if op == 'refiner' else shared.opts.data.get('sd_model_checkpoint', None))
     if model_checkpoint is None or model_checkpoint == 'None' or len(model_checkpoint) < 3:
         return None
-    checkpoint_info = get_closet_checkpoint_match(model_checkpoint)
+    checkpoint_info = get_closest_checkpoint_match(model_checkpoint)
     if checkpoint_info is not None:
         shared.log.info(f'Load {op}: select="{checkpoint_info.title if checkpoint_info is not None else None}"')
         return checkpoint_info
diff --git a/modules/sd_models.py b/modules/sd_models.py
index bb89f6df2..8e971e400 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -13,7 +13,7 @@ from installer import log
 from modules import timer, paths, shared, shared_items, modelloader, devices, script_callbacks, sd_vae, sd_unet, errors, sd_models_compile, sd_hijack_accelerate, sd_detect, model_quant, sd_hijack_te
 from modules.memstats import memory_stats
 from modules.modeldata import model_data
-from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closet_checkpoint_match, model_hash, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import
+from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closest_checkpoint_match, model_hash, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import
 from modules.sd_offload import disable_offload, set_diffuser_offload, apply_balanced_offload, set_accelerate # pylint: disable=unused-import
 from modules.sd_models_utils import NoWatermark, get_signature, get_call, path_to_repo, patch_diffuser_config, convert_to_faketensors, read_state_dict, get_state_dict_from_checkpoint, apply_function_to_model # pylint: disable=unused-import
 
diff --git a/modules/sd_models_utils.py b/modules/sd_models_utils.py
index 0d201779c..637f8c585 100644
--- a/modules/sd_models_utils.py
+++ b/modules/sd_models_utils.py
@@ -8,7 +8,7 @@ import torch
 import safetensors.torch
 
 from modules import paths, shared, errors
-from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closet_checkpoint_match, model_hash, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import
+from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closest_checkpoint_match, model_hash, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import
 from modules.sd_offload import disable_offload, set_diffuser_offload, apply_balanced_offload, set_accelerate # pylint: disable=unused-import
 
 
@@ -46,6 +46,8 @@ def path_to_repo(checkpoint_info):
     repo_id = repo_id.replace('--', '/')
     if repo_id.count('/') != 1:
         shared.log.warning(f'Model: repo="{repo_id}" repository not recognized')
+    if '+' in repo_id:
+        repo_id = repo_id.split('+')[0]
     return repo_id
 
 
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index ae4c98422..bd7bccc87 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -32,6 +32,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
 
         def reference_downloaded(url):
             url = url.split('@')[0] if '@' in url else 'Diffusers/' + url
+            url = url.split('+')[0] if '+' in url else url
             return any(model.endswith(url) for model in existing)
 
         if not shared.opts.sd_checkpoint_autodownload or not shared.opts.extra_network_reference_enable:
@@ -56,6 +57,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                     mtime = datetime.strptime(mtime, '%Y %B') # 2025 January
                 except Exception:
                     _size, mtime = modelstats.stat(preview_file)
+            path = f'{v.get("path", "")}+{v.get("subfolder", "")}'
             yield {
                 "type": 'Model',
                 "name": name,
@@ -63,7 +65,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                 "filename": url,
                 "preview": self.find_preview(os.path.join(paths.reference_path, preview)),
                 "local_preview": preview_file,
-                "onclick": '"' + html.escape(f"selectReference({json.dumps(url)})") + '"',
+                "onclick": '"' + html.escape(f"selectReference({json.dumps(path)})") + '"',
                 "hash": None,
                 "mtime": mtime,
                 "size": size,
diff --git a/modules/ui_settings.py b/modules/ui_settings.py
index 4cee32959..926b31762 100644
--- a/modules/ui_settings.py
+++ b/modules/ui_settings.py
@@ -24,7 +24,7 @@ def apply_setting(key, value):
     if key in shared.opts.disable_apply_metadata:
         gr.update()
     if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+        ckpt_info = sd_models.get_closest_checkpoint_match(value)
         if ckpt_info is not None:
             value = ckpt_info.title
         else:
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index eedefd014..6ed90bb15 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -55,7 +55,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
     repo_te = 'Qwen/Qwen-Image'
     text_encoder = generic.load_text_encoder(repo_te, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
 
-    repo_id = qwen.check_qwen_pruning(repo_id)
+    repo_id, repo_subfolder = qwen.check_qwen_pruning(repo_id, repo_subfolder)
     pipe = cls_name.from_pretrained(
         repo_id,
         transformer=transformer,
diff --git a/pipelines/qwen/qwen_pruning.py b/pipelines/qwen/qwen_pruning.py
index 971589958..e2a642af1 100644
--- a/pipelines/qwen/qwen_pruning.py
+++ b/pipelines/qwen/qwen_pruning.py
@@ -1,12 +1,12 @@
-def check_qwen_pruning(repo_id):
+def check_qwen_pruning(repo_id, subfolder):
     from modules.shared import log
     if 'pruning' not in repo_id.lower():
-        return repo_id
-    if '2509' in repo_id:
-        repo_id = "Qwen/Qwen-Image-Edit-2509"
-    elif 'Edit' in repo_id:
-        repo_id = "Qwen/Qwen-Image-Edit"
+        return repo_id, subfolder
+    if '2509' in (repo_id or '') or '2509' in (subfolder or ''):
+        repo_id, subfolder = "Qwen/Qwen-Image-Edit-2509", None
+    elif 'Edit' in (repo_id or '') or 'Edit' in (subfolder or ''):
+        repo_id, subfolder = "Qwen/Qwen-Image-Edit", None
     else:
-        repo_id = "Qwen/Qwen-Image"
+        repo_id, subfolder = "Qwen/Qwen-Image", None
     log.debug(f'Load model: variant=pruning target="{repo_id}"')
-    return repo_id
+    return repo_id, subfolder
diff --git a/scripts/stablevideodiffusion.py b/scripts/stablevideodiffusion.py
index 76aa08b22..a0b959824 100644
--- a/scripts/stablevideodiffusion.py
+++ b/scripts/stablevideodiffusion.py
@@ -53,7 +53,7 @@ class Script(scripts_manager.Script):
         # load/download model on-demand
         model_path = models[model]
         model_name = os.path.basename(model_path)
-        has_checkpoint = sd_models.get_closet_checkpoint_match(model_path)
+        has_checkpoint = sd_models.get_closest_checkpoint_match(model_path)
         if has_checkpoint is None:
             shared.log.error(f'SVD: no checkpoint for {model_name}')
             modelloader.load_reference(model_path, variant='fp16')
diff --git a/scripts/text2video.py b/scripts/text2video.py
index b69fd7605..f6880b10f 100644
--- a/scripts/text2video.py
+++ b/scripts/text2video.py
@@ -62,7 +62,7 @@ class Script(scripts_manager.Script):
         if model['path'] in shared.opts.sd_model_checkpoint:
             shared.log.debug(f'Text2Video cached: model={shared.opts.sd_model_checkpoint}')
         else:
-            checkpoint = sd_models.get_closet_checkpoint_match(model['path'])
+            checkpoint = sd_models.get_closest_checkpoint_match(model['path'])
             if checkpoint is None:
                 shared.log.debug(f'Text2Video downloading: model={model["path"]}')
                 checkpoint = modelloader.download_diffusers_model(hub_id=model['path'])
diff --git a/scripts/xyz/xyz_grid_shared.py b/scripts/xyz/xyz_grid_shared.py
index 76d4df769..dd454d3c2 100644
--- a/scripts/xyz/xyz_grid_shared.py
+++ b/scripts/xyz/xyz_grid_shared.py
@@ -164,7 +164,7 @@ def apply_sdnq_quant_te(p, x, xs):
 def apply_checkpoint(p, x, xs):
     if x == shared.opts.sd_model_checkpoint:
         return
-    info = sd_models.get_closet_checkpoint_match(x)
+    info = sd_models.get_closest_checkpoint_match(x)
     if info is None:
         shared.log.warning(f"XYZ grid: apply checkpoint unknown checkpoint: {x}")
     else:
@@ -178,7 +178,7 @@ def apply_refiner(p, x, xs):
         return
     if x == 'None':
         return
-    info = sd_models.get_closet_checkpoint_match(x)
+    info = sd_models.get_closest_checkpoint_match(x)
     if info is None:
         shared.log.warning(f"XYZ grid: apply refiner unknown checkpoint: {x}")
     else:
diff --git a/wiki b/wiki
index dfcb801c5..f0f550688 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit dfcb801c592a493d48b7e22a0a46ab39340fd021
+Subproject commit f0f5506886e1478d08081ce5296eb246291fc196

From 4257ed7c4000c4438c3acba930757bfc539d74df Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 4 Oct 2025 22:59:58 +0300
Subject: [PATCH 132/230] Update changelog

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a34b0ec27..45a1a05e3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -53,6 +53,10 @@
     main logo in top-left corner now indicates server connection status and hovering over it shows connection details  
   - separate guidance and detail sections  
   - networks ability to filter lora by base model version  
+- **SDNQ**
+    - make sdnq scales compatible with balanced offload  
+    - add int8 matmul support for rdna2 gpus via triton  
+    - improve int8 mamtul performance on intel gpus   
 - **Other**
   - server will note when restart is recommended due to package updates  
   - **interrrupt** will now show last known preview image  

From c530167cbe0403506f9ed1184645281feb21d8eb Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 4 Oct 2025 18:06:22 -0400
Subject: [PATCH 133/230] qwen multi-image edits

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                  |  9 ++++++++-
 javascript/control.js         |  8 ++------
 modules/control/run.py        |  1 +
 modules/processing_args.py    | 16 +++++++++-------
 modules/processing_class.py   |  4 +++-
 modules/ui_control.py         | 18 +++++++++---------
 modules/ui_control_helpers.py | 10 +++++-----
 7 files changed, 37 insertions(+), 29 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 45a1a05e3..9ba0cb443 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-03
+## Update for 2025-10-04
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -18,6 +18,11 @@
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
 - **Features**
+  - [Qwen Image-Edit] multi-image editing
+    requires qwen-image-edit-2509 or its variant as multi-image edits are not available in original qwen-image  
+    in ui control tab: inputs -> separate init image  
+    add image for *input media* and *control media*  
+    can be 
   - [Cache-DiT](https://github.com/vipshop/cache-dit)  
     cache-dit is a unified, flexible and training-free cache acceleration framework  
     compatible with many dit-based models such as FLUX.1, Qwen, HunyuanImage, Wan2.2, Chroma, etc.  
@@ -87,6 +92,8 @@
   - **modular guiders**: automatically used for compatible pipelines when *modular pipelines* is enabled  
     allows for using many different guidance methods:  
     *CFG, CFGZero, PAG, APG, SLG, SEG, TCFG, FDG*  
+- **Wiki**
+  - updates to *AMD-ROCm, ZLUDA, LoRA, DirectML, SDNQ* pages
 - **Fixes**
   - **Microsoft Florence 2** both base and large variants  
     *note* this will trigger download of the new variant of the model, feel free to delete older variant in `huggingface` folder  
diff --git a/javascript/control.js b/javascript/control.js
index 1c1c8b3cc..c94c61a67 100644
--- a/javascript/control.js
+++ b/javascript/control.js
@@ -2,13 +2,9 @@ function controlInputMode(inputMode, ...args) {
   const updateEl = gradioApp().getElementById('control_update');
   if (updateEl) updateEl.click();
   const tab = gradioApp().querySelector('#control-tab-input button.selected');
-  if (!tab) return ['Select', ...args];
+  if (!tab) return ['Image', ...args];
   inputMode = tab.innerText;
-  if (inputMode === 'Image') {
-    if (!gradioApp().getElementById('control_input_select').classList.contains('hidden')) inputMode = 'Select';
-    else if (!gradioApp().getElementById('control_input_resize').classList.contains('hidden')) inputMode = 'Outpaint';
-    else if (!gradioApp().getElementById('control_input_inpaint').classList.contains('hidden')) inputMode = 'Inpaint';
-  }
+  console.log('HERE0', tab, inputMode);
   return [inputMode, ...args];
 }
 
diff --git a/modules/control/run.py b/modules/control/run.py
index 3f6b0f7be..b62cad89f 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -388,6 +388,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
     )
     p.state = state
     p.is_tile = False
+    p.init_control = inits or []
     p.orig_init_images = inputs
 
     # TODO modernui: monkey-patch for missing tabs.select event
diff --git a/modules/processing_args.py b/modules/processing_args.py
index c1f0b9cdb..3e28e158a 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -123,9 +123,11 @@ def task_specific_kwargs(p, model):
         }
 
     # model specific args
-    if 'QwenImageEdit' in model_cls and len(getattr(p, 'init_images', [])) == 0:
+    if 'QwenImageEdit' in model_cls and (p.init_images is None or len(p.init_images) == 0):
         task_args['image'] = [Image.new('RGB', (p.width, p.height), (0, 0, 0))] # monkey-patch so qwen-image-edit pipeline does not error-out on t2i
-    if 'LatentConsistencyModelPipeline' in model_cls and hasattr(p, 'init_images') and len(p.init_images) > 0:
+    if 'QwenImageEditPlusPipeline' in model_cls and p.init_control is not None and len(p.init_control) > 0:
+        task_args['image'] += p.init_control
+    if 'LatentConsistencyModelPipeline' in model_cls and len(p.init_images) > 0:
         p.ops.append('lcm')
         init_latents = [processing_vae.vae_encode(image, model=shared.sd_model, vae_type=p.vae_type).squeeze(dim=0) for image in p.init_images]
         init_latent = torch.stack(init_latents, dim=0).to(shared.device)
@@ -133,11 +135,11 @@ def task_specific_kwargs(p, model):
         init_latent = (1 - p.denoising_strength) * init_latent + init_noise
         task_args = {
             'latents': init_latent.to(model.dtype),
-            'width': p.width if hasattr(p, 'width') else None,
-            'height': p.height if hasattr(p, 'height') else None,
+            'width': p.width,
+            'height': p.height,
         }
     if 'BlipDiffusionPipeline' in model_cls:
-        if len(getattr(p, 'init_images', [])) == 0:
+        if len(p.init_images) == 0:
             shared.log.error('BLiP diffusion requires init image')
             return task_args
         task_args = {
@@ -146,9 +148,9 @@ def task_specific_kwargs(p, model):
             'target_subject_category': getattr(p, 'prompt', '').split()[-1],
             'output_type': 'pil',
         }
-    if ('WanImageToVideoPipeline' in model_cls) and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
+    if ('WanImageToVideoPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
         task_args['image'] = p.init_images[0]
-    if ('WanVACEPipeline' in model_cls) and (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
+    if ('WanVACEPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
         task_args['reference_images'] = p.init_images
 
     if debug_enabled:
diff --git a/modules/processing_class.py b/modules/processing_class.py
index ddad4686e..8ac7286fc 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -78,7 +78,8 @@ class StableDiffusionProcessing:
                  hdr_color_picker: str = None,
                  hdr_tint_ratio: float = 0,
                  # img2img
-                 init_images: list = None,
+                 init_images: list = [],
+                 init_control: list = [],
                  denoising_strength: float = 0.3,
                  image_cfg_scale: float = None,
                  initial_noise_multiplier: float = None, # pylint: disable=unused-argument # a1111 compatibility
@@ -214,6 +215,7 @@ class StableDiffusionProcessing:
         self.detailer_resolution = detailer_resolution
         self.restore_faces = restore_faces
         self.init_images = init_images
+        self.init_control = init_control
         self.resize_mode = resize_mode
         self.resize_name = resize_name
         self.resize_context = resize_context
diff --git a/modules/ui_control.py b/modules/ui_control.py
index 3f43701af..5ac075d61 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -183,14 +183,14 @@ def create_ui(_blocks: gr.Blocks=None):
                 with gr.Column(scale=9, elem_id='control-input-column', visible=True) as column_input:
                     gr.HTML('<span id="control-input-button">Input</p>')
                     with gr.Tabs(elem_classes=['control-tabs'], elem_id='control-tab-input'):
+                        input_mode = gr.Label(value='select', visible=False)
                         with gr.Tab('Image', id='in-image') as tab_image:
-                            input_mode = gr.Label(value='select', visible=False)
-                            input_image = gr.Image(label="Input", show_label=False, type="pil", interactive=True, tool="editor", height=gr_height, visible=True, image_mode='RGB', elem_id='control_input_select', elem_classes=['control-image'])
-                            input_resize = gr.Image(label="Input", show_label=False, type="pil", interactive=True, tool="select", height=gr_height, visible=False, image_mode='RGB', elem_id='control_input_resize', elem_classes=['control-image'])
-                            input_inpaint = gr.Image(label="Input", show_label=False, type="pil", interactive=True, tool="sketch", height=gr_height, visible=False, image_mode='RGB', elem_id='control_input_inpaint', brush_radius=32, mask_opacity=0.6, elem_classes=['control-image'])
+                            input_image = gr.Image(label="Input", show_label=False, type="pil", interactive=True, tool="editor", height=gr_height, image_mode='RGB', elem_id='control_input_select', elem_classes=['control-image'])
                             btn_interrogate = ui_sections.create_interrogate_button('control', what='input')
-                            with gr.Row():
-                                input_buttons = [gr.Button('Select', visible=True, interactive=False), gr.Button('Inpaint', visible=True, interactive=True), gr.Button('Outpaint', visible=True, interactive=True)]
+                        with gr.Tab('Inpaint', id='in-inpaint') as _tab_inpaint:
+                            input_inpaint = gr.Image(label="Input", show_label=False, type="pil", interactive=True, tool="sketch", height=gr_height, image_mode='RGB', elem_id='control_input_inpaint', brush_radius=32, mask_opacity=0.6, elem_classes=['control-image'])
+                        with gr.Tab('Outpaint', id='in-outpaint') as _tab_outpaint:
+                            input_resize = gr.Image(label="Input", show_label=False, type="pil", interactive=True, tool="select", height=gr_height, image_mode='RGB', elem_id='control_input_resize', elem_classes=['control-image'])
                         with gr.Tab('Video', id='in-video') as tab_video:
                             input_video = gr.Video(label="Input", show_label=False, interactive=True, height=gr_height, elem_classes=['control-image'])
                         with gr.Tab('Batch', id='in-batch') as tab_batch:
@@ -231,9 +231,9 @@ def create_ui(_blocks: gr.Blocks=None):
                 input_script_args = scripts_manager.scripts_current.setup_ui(parent='control', accordion=True)
 
             # handlers
-            for btn in input_buttons:
-                btn.click(fn=helpers.copy_input, inputs=[input_mode, btn, input_image, input_resize, input_inpaint], outputs=[input_image, input_resize, input_inpaint], _js='controlInputMode')
-                btn.click(fn=helpers.transfer_input, inputs=[btn], outputs=[input_image, input_resize, input_inpaint] + input_buttons)
+            # for btn in input_buttons:
+            #     btn.click(fn=helpers.copy_input, inputs=[input_mode, btn, input_image, input_resize, input_inpaint], outputs=[input_image, input_resize, input_inpaint], _js='controlInputMode')
+            #     btn.click(fn=helpers.transfer_input, inputs=[btn], outputs=[input_image, input_resize, input_inpaint] + input_buttons)
 
             # hidden button to update gradio control values
             for u in units:
diff --git a/modules/ui_control_helpers.py b/modules/ui_control_helpers.py
index c5757c56f..05e31bf1c 100644
--- a/modules/ui_control_helpers.py
+++ b/modules/ui_control_helpers.py
@@ -77,7 +77,7 @@ def get_video(filepath: str):
 def select_input(input_mode, input_image, init_image, init_type, input_resize, input_inpaint, input_video, input_batch, input_folder):
     global busy, input_source, input_init, input_mask # pylint: disable=global-statement
     busy = True
-    if input_mode == 'Select':
+    if input_mode == 'Image':
         selected_input = input_image
     elif input_mode == 'Outpaint':
         selected_input = input_resize
@@ -159,16 +159,16 @@ def copy_input(mode_from, mode_to, input_image, input_resize, input_inpaint):
 
     if mode_from == mode_to:
         return [gr.update(), gr.update(), gr.update()]
-    elif mode_to == 'Select':
+    elif mode_to == 'Image':
         return [getimg(input_resize) if mode_from == 'Outpaint' else getimg(input_inpaint), None, None]
     elif mode_to == 'Inpaint':
-        return [None, None, getimg(input_image) if mode_from == 'Select' else getimg(input_resize)]
+        return [None, None, getimg(input_image) if mode_from == 'Image' else getimg(input_resize)]
     elif mode_to == 'Outpaint':
-        return [None, getimg(input_image) if mode_from == 'Select' else getimg(input_inpaint), None]
+        return [None, getimg(input_image) if mode_from == 'Image' else getimg(input_inpaint), None]
     else:
         shared.log.error(f'Control transfer unknown input: from={mode_from} to={mode_to}')
         return [gr.update(), gr.update(), gr.update()]
 
 
 def transfer_input(dst):
-    return [gr.update(visible=dst=='Select'), gr.update(visible=dst=='Outpaint'), gr.update(visible=dst=='Inpaint'), gr.update(interactive=dst!='Select'), gr.update(interactive=dst!='Inpaint'), gr.update(interactive=dst!='Outpaint')]
+    return [gr.update(visible=dst=='Image'), gr.update(visible=dst=='Outpaint'), gr.update(visible=dst=='Inpaint'), gr.update(interactive=dst!='Image'), gr.update(interactive=dst!='Inpaint'), gr.update(interactive=dst!='Outpaint')]

From f38de145839346550c1c00002f11ce9bf7b950cc Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 4 Oct 2025 18:06:47 -0400
Subject: [PATCH 134/230] update modernui

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui | 2 +-
 wiki                               | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index e56b4f695..165925a2c 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit e56b4f69549e6654c953346082ff3947256c9636
+Subproject commit 165925a2cc849595053ddb147eb0dc781b302d2b
diff --git a/wiki b/wiki
index f0f550688..f483ca713 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit f0f5506886e1478d08081ce5296eb246291fc196
+Subproject commit f483ca7138cd2e67822f980d2eb8dc677202c7ce

From 8325e886c722e64df7c33f0f2d016b873b151a63 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 4 Oct 2025 18:11:52 -0400
Subject: [PATCH 135/230] add typo to legacy compatibility options

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 TODO.md              | 1 +
 modules/sd_models.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/TODO.md b/TODO.md
index 1a5475c7d..01bcdad79 100644
--- a/TODO.md
+++ b/TODO.md
@@ -36,6 +36,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ### New models
 
+- [Ovi](https://github.com/character-ai/Ovi)
 - [Kandinsky 5](https://github.com/ai-forever/Kandinsky-5)
 - [Bytedance Lynx](https://github.com/bytedance/lynx)
 - [HunyuanImage](https://huggingface.co/tencent/HunyuanImage-2.1)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8e971e400..b8e3c0391 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -28,6 +28,7 @@ debug_move = log.trace if os.environ.get('SD_MOVE_DEBUG', None) is not None else
 debug_load = os.environ.get('SD_LOAD_DEBUG', None)
 debug_process = log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
 diffusers_version = int(diffusers.__version__.split('.')[1])
+get_closet_checkpoint_match = get_closest_checkpoint_match # legacy compatibility
 checkpoint_tiles = checkpoint_titles # legacy compatibility
 allow_post_quant = None
 pipe_switch_task_exclude = [

From 95217d48c1aeb3bd2bb1e838f779407aabc9f602 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 4 Oct 2025 18:32:56 -0400
Subject: [PATCH 136/230] xyz add guidance params

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 scripts/xyz/xyz_grid_classes.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/scripts/xyz/xyz_grid_classes.py b/scripts/xyz/xyz_grid_classes.py
index 0836adbbf..43dc9b2a3 100644
--- a/scripts/xyz/xyz_grid_classes.py
+++ b/scripts/xyz/xyz_grid_classes.py
@@ -201,8 +201,6 @@ axis_options = [
     AxisOption("[Param] Height", int, apply_field("height")),
     AxisOption("[Param] Seed", int, apply_seed),
     AxisOption("[Param] Steps", int, apply_field("steps")),
-    AxisOption("[Param] Guidance scale", float, apply_field("cfg_scale")),
-    AxisOption("[Param] Guidance end", float, apply_field("cfg_end")),
     AxisOption("[Param] Variation seed", int, apply_field("subseed")),
     AxisOption("[Param] Variation strength", float, apply_field("subseed_strength")),
     AxisOption("[Param] Clip skip", float, apply_clip_skip),
@@ -224,13 +222,15 @@ axis_options = [
     AxisOption("[Sampler] Shift", float, apply_setting("schedulers_shift")),
     AxisOption("[Sampler] eta delta", float, apply_setting("eta_noise_seed_delta")),
     AxisOption("[Sampler] eta multiplier", float, apply_setting("scheduler_eta")),
-    AxisOption("[Guidance] Guidance name", str, apply_guidance, choices=lambda: ['Default', 'CFG', 'Auto', 'Zero', 'PAG', 'APG', 'SLG', 'SEG', 'TCFG', 'FDG']),
+    AxisOption("[Guidance] scale", float, apply_field("cfg_scale")),
+    AxisOption("[Guidance] end", float, apply_field("cfg_end")),
+    AxisOption("[Guidance] image scale", float, apply_field("image_cfg_scale")),
+    AxisOption("[Guidance] rescale", float, apply_field("diffusers_guidance_rescale")),
+    AxisOption("[Guidance] modular name", str, apply_guidance, choices=lambda: ['Default', 'CFG', 'Auto', 'Zero', 'PAG', 'APG', 'SLG', 'SEG', 'TCFG', 'FDG']),
     AxisOption("[Refine] Upscaler", str, apply_field("hr_upscaler"), cost=0.3, choices=lambda: [x.name for x in shared.sd_upscalers]),
     AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
     AxisOption("[Refine] Denoising strength", float, apply_field("denoising_strength")),
     AxisOption("[Refine] Hires steps", int, apply_field("hr_second_pass_steps")),
-    AxisOption("[Refine] Guidance scale", float, apply_field("image_cfg_scale")),
-    AxisOption("[Refine] Guidance rescale", float, apply_field("diffusers_guidance_rescale")),
     AxisOption("[Refine] Refiner start", float, apply_field("refiner_start")),
     AxisOption("[Refine] Refiner steps", float, apply_field("refiner_steps")),
     AxisOption("[Postprocess] Upscaler", str, apply_upscaler, cost=0.4, choices=lambda: [x.name for x in shared.sd_upscalers]),

From 96e5e6f164413c13f1a23f7f4df6effcf5ad2bc1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 4 Oct 2025 18:33:26 -0400
Subject: [PATCH 137/230] update changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9ba0cb443..d722cc944 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -84,7 +84,8 @@
   - **attention** remove split-attention and add explicitly attention slicing enable/disable option  
     enable in *settings -> compute settings*  
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
-  - **nunchaku** update to `1.0.1` and enhance installer    
+  - **nunchaku** update to `1.0.1` and enhance installer  
+  - **xyz-grid** add guidance section  
 - **Experimental**
   - `new` command line flag enables new `pydantic` and `albumentations` packages  
   - **modular pipelines**: enable in *settings -> model options*  

From a164f3e0c282eb38f9cfd86e16434b3ebc6678f3 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 5 Oct 2025 03:12:05 +0300
Subject: [PATCH 138/230] SDNQ Improve UINT3 and below quant speed

---
 modules/sdnq/packed_int.py | 93 ++++++++++++++++----------------------
 1 file changed, 39 insertions(+), 54 deletions(-)

diff --git a/modules/sdnq/packed_int.py b/modules/sdnq/packed_int.py
index 31e8d9214..09a38efbc 100644
--- a/modules/sdnq/packed_int.py
+++ b/modules/sdnq/packed_int.py
@@ -219,79 +219,64 @@ def unpack_uint4(packed_tensor: torch.ByteTensor, shape: torch.Size) -> torch.By
 
 
 def unpack_uint3(packed_tensor: torch.ByteTensor, shape: torch.Size) -> torch.ByteTensor:
-    result = torch.cat(
-        (
-            torch.bitwise_and(
-                torch.cat(
-                    (
-                        packed_tensor[:, :3],
-                        torch.bitwise_right_shift(packed_tensor[:, :3], 3)
-                    ),
-                    dim=-1
-                ),
-                7
-            ),
-            torch.bitwise_or(
-                torch.bitwise_right_shift(packed_tensor[:, :2], 6),
-                torch.bitwise_and(
-                    torch.stack(
-                        (
-                            torch.bitwise_right_shift(packed_tensor[:, 2], 4),
-                            torch.bitwise_right_shift(packed_tensor[:, 2], 5),
+    result = torch.bitwise_and(
+        torch.cat(
+            (
+                packed_tensor[:, :3],
+                torch.bitwise_right_shift(packed_tensor[:, :3], 3),
+                torch.bitwise_or(
+                    torch.bitwise_right_shift(packed_tensor[:, :2], 6),
+                    torch.bitwise_and(
+                        torch.stack(
+                            (
+                                torch.bitwise_right_shift(packed_tensor[:, 2], 4),
+                                torch.bitwise_right_shift(packed_tensor[:, 2], 5),
+                            ),
+                            dim=-1
                         ),
-                        dim=-1
+                        4
                     ),
-                    4
                 ),
             ),
+            dim=-1
         ),
-        dim=-1
+        7
     ).view(shape)
     return result
 
 
 def unpack_uint2(packed_tensor: torch.ByteTensor, shape: torch.Size) -> torch.ByteTensor:
-    result = torch.cat(
-        (
-            torch.bitwise_and(
-                torch.stack(
-                    (
-                        packed_tensor,
-                        torch.bitwise_right_shift(packed_tensor, 2),
-                        torch.bitwise_right_shift(packed_tensor, 4)
-                    ),
-                    dim=-1
-                ),
-                3
+    result = torch.bitwise_and(
+        torch.stack(
+            (
+                packed_tensor,
+                torch.bitwise_right_shift(packed_tensor, 2),
+                torch.bitwise_right_shift(packed_tensor, 4),
+                torch.bitwise_right_shift(packed_tensor, 6),
             ),
-            torch.bitwise_right_shift(packed_tensor, 6).unsqueeze(-1),
+            dim=-1
         ),
-        dim=-1
+        3
     ).view(shape)
     return result
 
 
 def unpack_uint1(packed_tensor: torch.Tensor, shape: torch.Size) -> torch.Tensor:
-    result = torch.cat(
-        (
-            torch.bitwise_and(
-                torch.stack(
-                    (
-                        packed_tensor,
-                        torch.bitwise_right_shift(packed_tensor, 1),
-                        torch.bitwise_right_shift(packed_tensor, 2),
-                        torch.bitwise_right_shift(packed_tensor, 3),
-                        torch.bitwise_right_shift(packed_tensor, 4),
-                        torch.bitwise_right_shift(packed_tensor, 5),
-                        torch.bitwise_right_shift(packed_tensor, 6),
-                    ),
-                    dim=-1
-                ),
-                1
+    result = torch.bitwise_and(
+        torch.stack(
+            (
+                packed_tensor,
+                torch.bitwise_right_shift(packed_tensor, 1),
+                torch.bitwise_right_shift(packed_tensor, 2),
+                torch.bitwise_right_shift(packed_tensor, 3),
+                torch.bitwise_right_shift(packed_tensor, 4),
+                torch.bitwise_right_shift(packed_tensor, 5),
+                torch.bitwise_right_shift(packed_tensor, 6),
+                torch.bitwise_right_shift(packed_tensor, 7),
             ),
-            torch.bitwise_right_shift(packed_tensor, 7).unsqueeze(-1),
+            dim=-1
         ),
-        dim=-1
+        1
     ).view(shape)
     return result
 

From 428600613aa47881c5af8624f97612a8d69dc83b Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 5 Oct 2025 15:30:15 +0300
Subject: [PATCH 139/230] SDNQ fix new transformers again

---
 modules/sdnq/__init__.py | 36 ++++++++++++++++++++----------------
 1 file changed, 20 insertions(+), 16 deletions(-)

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 1009fd811..39077d917 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -1,6 +1,6 @@
 # pylint: disable=redefined-builtin,no-member,protected-access
 
-from typing import Any, Dict, List, Tuple, Optional, Union
+from typing import Dict, List, Tuple, Optional, Union
 from dataclasses import dataclass
 from enum import Enum
 
@@ -286,8 +286,7 @@ class SDNQQuantizer(DiffusersQuantizer):
         model,
         param_value: "torch.Tensor",
         param_name: str,
-        state_dict: Dict[str, Any], # pylint: disable=unused-argument
-        **kwargs, # pylint: disable=unused-argument
+        *args, **kwargs, # pylint: disable=unused-argument
     ):
         if param_name.endswith(".weight"):
             split_param_name = param_name.split(".")
@@ -303,8 +302,9 @@ class SDNQQuantizer(DiffusersQuantizer):
                             return True
                     else:
                         return True
-        with devices.inference_context():
-            param_value.data = param_value.clone() # safetensors is unable to release the cpu memory without this
+        if param_value is not None:
+            with devices.inference_context():
+                param_value.data = param_value.clone() # safetensors is unable to release the cpu memory without this
         return False
 
     def check_quantized_param(self, *args, **kwargs) -> bool:
@@ -313,6 +313,12 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return self.check_if_quantized_param(*args, **kwargs)
 
+    def param_needs_quantization(self, model, param_name: str, *args, **kwargs) -> bool:
+        """
+        needed for transformers compatibilty, returns self.check_if_quantized_param
+        """
+        return self.check_if_quantized_param(model, None, param_name, *args, **kwargs)
+
     @devices.inference_context()
     def create_quantized_param( # pylint: disable=arguments-differ
         self,
@@ -320,9 +326,7 @@ class SDNQQuantizer(DiffusersQuantizer):
         param_value: torch.FloatTensor,
         param_name: str,
         target_device: torch.device,
-        state_dict: Dict[str, Any], # pylint: disable=unused-argument
-        unexpected_keys: List[str], # pylint: disable=unused-argument
-        **kwargs, # pylint: disable=unused-argument
+        *args, **kwargs, # pylint: disable=unused-argument
     ):
         weights_dtype = self.quantization_config.weights_dtype
         if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
@@ -422,49 +426,49 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return self.get_accelerator_warm_up_factor()
 
-    def update_tp_plan(self, config):
+    def update_tp_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return config
 
-    def update_ep_plan(self, config):
+    def update_ep_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return config
 
-    def update_unexpected_keys(self, model, unexpected_keys: List[str], prefix: str) -> List[str]: # pylint: disable=unused-argument
+    def update_unexpected_keys(self, model, unexpected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return unexpected_keys
 
-    def update_missing_keys_after_loading(self, model, missing_keys: List[str], prefix: str) -> List[str]: # pylint: disable=unused-argument
+    def update_missing_keys_after_loading(self, model, missing_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return missing_keys
 
-    def update_state_dict_with_metadata(self, state_dict: dict, metadata: dict) -> dict: # pylint: disable=unused-argument
+    def update_state_dict_with_metadata(self, state_dict: dict, *args, **kwargs) -> dict: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return state_dict
 
-    def update_expected_keys(self, model, expected_keys: List[str], loaded_keys: List[str]) -> List[str]: # pylint: disable=unused-argument
+    def update_expected_keys(self, model, expected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return expected_keys
 
-    def update_param_name(self, param_name: str) -> str:
+    def update_param_name(self, param_name: str, *args, **kwargs) -> str: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """
         return param_name
 
-    def update_dtype(self, dtype: torch.dtype) -> torch.dtype:
+    def update_dtype(self, dtype: torch.dtype, *args, **kwargs) -> torch.dtype: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
         """

From 18e24e9861e52a93e8dcd8ec73410fba608cc61c Mon Sep 17 00:00:00 2001
From: -akku- <151517253+akku1139@users.noreply.github.com>
Date: Sun, 5 Oct 2025 22:36:30 +0900
Subject: [PATCH 140/230] fix typo: "Transfrer" -> "Transfer"

---
 html/locale_en.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/html/locale_en.json b/html/locale_en.json
index 056637d2b..42d81e431 100644
--- a/html/locale_en.json
+++ b/html/locale_en.json
@@ -126,7 +126,7 @@
   {"id":"","label":"➠ Sketch","localized":"","reload":"","hint":"Transfer image to sketch interface"},
   {"id":"","label":"➠ Composite","localized":"","reload":"","hint":"Transfer image to inpaint sketch interface"},
   {"id":"","label":"➠ Process","localized":"","reload":"","hint":"Transfer image to process interface"},
-  {"id":"","label":"➠ Control","localized":"","reload":"","hint":"Transfrer image to control interface"},
+  {"id":"","label":"➠ Control","localized":"","reload":"","hint":"Transfer image to control interface"},
   {"id":"","label":"➠ Caption","localized":"","reload":"","hint":"Transfer image to caption interface"}
 ],
 "generate": [

From 7cd2c6d87f1e1aa3d6701baf9422c92c4672a616 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 09:56:28 -0400
Subject: [PATCH 141/230] update diffusers

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 2 +-
 installer.py | 2 +-
 wiki         | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d722cc944..58e7aacb1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-04
+## Update for 2025-10-05
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
diff --git a/installer.py b/installer.py
index a4fea4002..63e678bef 100644
--- a/installer.py
+++ b/installer.py
@@ -608,7 +608,7 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = 'b4297967a04cca6ac4493202c02d81c30d0f9ee8' # diffusers commit hash
+    sha = '2b7deffe361b7b0e1d2665a1f9f0bd4daea4927c' # diffusers commit hash
     if args.use_rocm or args.use_zluda or args.use_directml:
         sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
diff --git a/wiki b/wiki
index f483ca713..c5321049f 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit f483ca7138cd2e67822f980d2eb8dc677202c7ce
+Subproject commit c5321049fbab6169c0ffa10accde4ad139639f2a

From 268798a24e82410a355b118cb957a59c78e69ecf Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 10:39:55 -0400
Subject: [PATCH 142/230] add framepack granular job tracking

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                          |  3 +++
 modules/framepack/framepack_vae.py    |  5 ++++-
 modules/framepack/framepack_worker.py | 15 ++++++++-------
 3 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 58e7aacb1..4d3d9a478 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -107,6 +107,9 @@
   - reference: fix download for sd15/sdxl reference models  
   - fix checks in init/mask image decode  
   - fix hf token with extra chars  
+  - **FramePack** job state tracking  
+  - **LTXVideo** with offloading  
+  - **WAN** with offloading  
 
 ## Update for 2025-09-15
 
diff --git a/modules/framepack/framepack_vae.py b/modules/framepack/framepack_vae.py
index da5144cf7..908378a8b 100644
--- a/modules/framepack/framepack_vae.py
+++ b/modules/framepack/framepack_vae.py
@@ -85,7 +85,10 @@ def vae_decode(latents, vae_type):
     elif vae_type == 'Remote':
         return vae_decode_remote(latents)
     else: # vae_type == 'Full'
-        return vae_decode_full(latents)
+        jobid = shared.state.begin('VAE Decode')
+        result = vae_decode_full(latents)
+        shared.state.end(jobid)
+        return result
 
 
 def vae_encode(image):
diff --git a/modules/framepack/framepack_worker.py b/modules/framepack/framepack_worker.py
index eedbad13b..cda5677f8 100644
--- a/modules/framepack/framepack_worker.py
+++ b/modules/framepack/framepack_worker.py
@@ -65,14 +65,13 @@ def worker(
     metadata['title'] = 'sdnext framepack'
     metadata['description'] = f'variant:{variant} seed:{seed} steps:{steps} scale:{cfg_scale} distilled:{cfg_distilled} rescale:{cfg_rescale} shift:{shift} start:{start_weight} end:{end_weight} vision:{vision_weight}'
 
-    shared.state.begin('Video')
+    videojob = shared.state.begin('Video')
     shared.state.job_count = 1
 
     text_encoder = shared.sd_model.text_encoder
     text_encoder_2 = shared.sd_model.text_encoder_2
     tokenizer = shared.sd_model.tokenizer
     tokenizer_2 = shared.sd_model.tokenizer_2
-    vae = shared.sd_model.vae
     feature_extractor = shared.sd_model.feature_extractor
     image_encoder = shared.sd_model.image_processor
     transformer = shared.sd_model.transformer
@@ -196,7 +195,6 @@ def worker(
             image_encoder_last_hidden_state = vision_encode(input_image, end_image)
 
             # Sample loop
-            shared.state.textinfo = 'Sample'
             stream.output_queue.push(('progress', (None, 'Start sampling...')))
             generator = torch.Generator("cpu").manual_seed(seed)
             if is_f1:
@@ -213,6 +211,7 @@ def worker(
                     llama_vec, llama_vec_n, llama_attention_mask, llama_attention_mask_n, clip_l_pooler, clip_l_pooler_n = text_encode(current_prompt, i=lattent_padding_loop+1)
                     last_prompt = current_prompt
 
+                sammplejob = shared.state.begin('Sample')
                 lattent_padding_loop += 1
                 # shared.log.trace(f'FramePack: op=sample section={lattent_padding_loop}/{len(latent_paddings)} frames={total_generated_frames}/{num_frames*len(latent_paddings)} window={latent_window_size} size={num_frames}')
                 if is_f1:
@@ -274,7 +273,6 @@ def worker(
                     dtype=devices.dtype,
                     callback=step_callback,
                 )
-                timer.process.add('sample', time.time()-t_sample)
 
                 if is_last_section:
                     generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2)
@@ -287,15 +285,18 @@ def worker(
                     history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2)
                     real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :]
 
-                t_vae = time.time()
                 sd_models.apply_balanced_offload(shared.sd_model)
+                timer.process.add('sample', time.time()-t_sample)
+                shared.state.end(sammplejob)
+
+                t_vae = time.time()
                 if history_pixels is None:
                     history_pixels = framepack_vae.vae_decode(real_history_latents, vae_type=vae_type).cpu()
                 else:
                     overlapped_frames = latent_window_size * 4 - 3
                     if is_f1:
                         section_latent_frames = latent_window_size * 2
-                        current_pixels = framepack_vae.vae_decode(real_history_latents[:, :, -section_latent_frames:], vae).cpu()
+                        current_pixels = framepack_vae.vae_decode(real_history_latents[:, :, -section_latent_frames:], vae_type=vae_type).cpu()
                         history_pixels = utils.soft_append_bcthw(history_pixels, current_pixels, overlapped_frames)
                     else:
                         section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)
@@ -321,4 +322,4 @@ def worker(
     stream.output_queue.push(('end', None))
     t1 = time.time()
     shared.log.info(f'Processed: frames={total_generated_frames} fps={total_generated_frames/(t1-t0):.2f} its={(shared.state.sampling_step)/(t1-t0):.2f} time={t1-t0:.2f} timers={timer.process.dct()} memory={memstats.memory_stats()}')
-    shared.state.end()
+    shared.state.end(videojob)

From 9e52d0c1fbf76f2f9a2cccc224f0cd3cb71703c4 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 5 Oct 2025 22:50:30 +0300
Subject: [PATCH 143/230] SDNQ add SVDQuant quantization method

---
 CHANGELOG.md                                  |  5 +-
 modules/lora/lora_apply.py                    | 53 +++++++++++--
 modules/model_quant.py                        | 10 ++-
 modules/sdnq/__init__.py                      | 75 ++++++++++++++----
 modules/sdnq/dequantizer.py                   | 78 +++++++++++--------
 modules/sdnq/layers/conv/conv_fp8.py          | 12 ++-
 .../sdnq/layers/conv/conv_fp8_tensorwise.py   | 13 +++-
 modules/sdnq/layers/conv/conv_int8.py         | 15 +++-
 modules/sdnq/layers/conv/forward.py           |  8 +-
 modules/sdnq/layers/linear/forward.py         |  2 +-
 modules/sdnq/layers/linear/linear_fp8.py      | 13 +++-
 .../layers/linear/linear_fp8_tensorwise.py    | 11 ++-
 modules/sdnq/layers/linear/linear_int8.py     | 13 +++-
 modules/shared.py                             |  2 +
 14 files changed, 229 insertions(+), 81 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4d3d9a478..55df207f5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -59,9 +59,10 @@
   - separate guidance and detail sections  
   - networks ability to filter lora by base model version  
 - **SDNQ**
+    - add `SVDQuant` quantization method support  
     - make sdnq scales compatible with balanced offload  
-    - add int8 matmul support for rdna2 gpus via triton  
-    - improve int8 mamtul performance on intel gpus   
+    - add int8 matmul support for RDNA2 GPUs via triton  
+    - improve int8 mamtul performance on Intel GPUs  
 - **Other**
   - server will note when restart is recommended due to package updates  
   - **interrrupt** will now show last known preview image  
diff --git a/modules/lora/lora_apply.py b/modules/lora/lora_apply.py
index 47b408446..d076efb97 100644
--- a/modules/lora/lora_apply.py
+++ b/modules/lora/lora_apply.py
@@ -52,6 +52,12 @@ def network_backup_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.n
                             self.sdnq_zero_point_backup = self.zero_point.clone().to(devices.cpu)
                         else:
                             self.sdnq_zero_point_backup = None
+                        if self.svd_up is not None:
+                            self.sdnq_svd_up_backup = self.svd_up.clone().to(devices.cpu)
+                            self.sdnq_svd_down_backup = self.svd_down.clone().to(devices.cpu)
+                        else:
+                            self.sdnq_svd_up_backup = None
+                            self.sdnq_svd_down_backup = None
 
         if bias_backup is None:
             if getattr(self, 'bias', None) is not None:
@@ -85,9 +91,23 @@ def network_calc_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.
         try:
             t0 = time.time()
             if hasattr(self, "sdnq_dequantizer_backup"):
-                weight = self.sdnq_dequantizer_backup.to(devices.device)(self.weight.to(devices.device), self.sdnq_scale_backup.to(devices.device), self.sdnq_zero_point_backup.to(devices.device) if self.sdnq_zero_point_backup is not None else None, skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul)
+                weight = self.sdnq_dequantizer_backup.to(devices.device)(
+                    self.weight.to(devices.device),
+                    self.sdnq_scale_backup.to(devices.device),
+                    self.sdnq_zero_point_backup.to(devices.device) if self.sdnq_zero_point_backup is not None else None,
+                    self.sdnq_svd_up_backup.to(devices.device) if self.sdnq_svd_up_backup is not None else None,
+                    self.sdnq_svd_down_backup.to(devices.device) if self.sdnq_svd_down_backup is not None else None,
+                    skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul
+                )
             elif hasattr(self, "sdnq_dequantizer"):
-                weight = self.sdnq_dequantizer.to(devices.device)(self.weight.to(devices.device), self.scale.to(devices.device), self.zero_point.to(devices.device) if self.zero_point is not None else None, skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul)
+                weight = self.sdnq_dequantizer.to(devices.device)(
+                    self.weight.to(devices.device),
+                    self.scale.to(devices.device),
+                    self.zero_point.to(devices.device) if self.zero_point is not None else None,
+                    self.svd_up.to(devices.device) if self.svd_up is not None else None,
+                    self.svd_down.to(devices.device) if self.svd_down is not None else None,
+                    skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul
+                )
             else:
                 weight = self.weight.to(devices.device) # must perform calc on gpu due to performance
             updown, ex_bias = module.calc_updown(weight)
@@ -148,19 +168,35 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
             from modules.sdnq import sdnq_quantize_layer
             if hasattr(self, "sdnq_dequantizer_backup"):
                 weights_dtype = self.sdnq_dequantizer_backup.weights_dtype
-                dequant_weight = self.sdnq_dequantizer_backup.to(devices.device)(model_weights.to(devices.device), self.sdnq_scale_backup.to(devices.device), self.sdnq_zero_point_backup.to(devices.device) if self.sdnq_zero_point_backup is not None else None, skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul)
+                dequant_weight = self.sdnq_dequantizer_backup.to(devices.device)(
+                    model_weights.to(devices.device),
+                    self.sdnq_scale_backup.to(devices.device),
+                    self.sdnq_zero_point_backup.to(devices.device) if self.sdnq_zero_point_backup is not None else None,
+                    self.sdnq_svd_up_backup.to(devices.device) if self.sdnq_svd_up_backup is not None else None,
+                    self.sdnq_svd_down_backup.to(devices.device) if self.sdnq_svd_down_backup is not None else None,
+                    skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul
+                )
             elif hasattr(self, "sdnq_dequantizer"):
                 weights_dtype = self.sdnq_dequantizer.weights_dtype
-                dequant_weight = self.sdnq_dequantizer.to(devices.device)(model_weights.to(devices.device), self.scale.to(devices.device), self.zero_point.to(devices.device) if self.zero_point is not None else None, skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul)
+                dequant_weight = self.sdnq_dequantizer.to(devices.device)(
+                    model_weights.to(devices.device),
+                    self.scale.to(devices.device),
+                    self.zero_point.to(devices.device) if self.zero_point is not None else None,
+                    self.svd_up.to(devices.device) if self.svd_up is not None else None,
+                    self.svd_down.to(devices.device) if self.svd_down is not None else None,
+                    skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul
+                )
 
             new_weight = dequant_weight.to(devices.device, dtype=torch.float32) + lora_weights.to(devices.device, dtype=torch.float32)
             self.weight = torch.nn.Parameter(new_weight, requires_grad=False)
-            del self.sdnq_dequantizer, self.scale, self.zero_point
+            del self.sdnq_dequantizer, self.scale, self.zero_point, self.svd_up, self.svd_down
             self = sdnq_quantize_layer(
                 self,
                 weights_dtype=weights_dtype,
                 torch_dtype=devices.dtype,
                 group_size=shared.opts.sdnq_quantize_weights_group_size,
+                svd_rank=shared.opts.sdnq_svd_rank,
+                use_svd=shared.opts.sdnq_use_svd,
                 quant_conv=shared.opts.sdnq_quantize_conv_layers,
                 use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
                 use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
@@ -243,7 +279,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
                     self.zero_point = torch.nn.Parameter(self.sdnq_zero_point_backup.to(device), requires_grad=False)
                 else:
                     self.zero_point = None
-                del self.sdnq_dequantizer_backup, self.sdnq_scale_backup, self.sdnq_zero_point_backup
+                if self.sdnq_svd_up_backup is not None:
+                    self.svd_up = torch.nn.Parameter(self.sdnq_svd_up_backup.to(device), requires_grad=False)
+                    self.svd_down = torch.nn.Parameter(self.sdnq_svd_down_backup.to(device), requires_grad=False)
+                else:
+                    self.svd_up, self.svd_down = None, None
+                del self.sdnq_dequantizer_backup, self.sdnq_scale_backup, self.sdnq_zero_point_backup, self.sdnq_svd_up_backup, self.sdnq_svd_down_backup
 
     if bias_backup is not None:
         self.bias = None
diff --git a/modules/model_quant.py b/modules/model_quant.py
index 19b7ae7a7..288d6e8ed 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -205,6 +205,8 @@ def create_sdnq_config(kwargs = None, allow: bool = True, module: str = 'Model',
         sdnq_config = SDNQConfig(
             weights_dtype=weights_dtype,
             group_size=shared.opts.sdnq_quantize_weights_group_size,
+            svd_rank=shared.opts.sdnq_svd_rank,
+            use_svd=shared.opts.sdnq_use_svd,
             quant_conv=shared.opts.sdnq_quantize_conv_layers,
             use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
             use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
@@ -215,7 +217,7 @@ def create_sdnq_config(kwargs = None, allow: bool = True, module: str = 'Model',
             modules_to_not_convert=modules_to_not_convert,
             modules_dtype_dict=modules_dtype_dict.copy(),
         )
-        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
+        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} use_svd={shared.opts.sdnq_use_svd} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
         if kwargs is None:
             return sdnq_config
         else:
@@ -533,6 +535,8 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         weights_dtype=weights_dtype,
         torch_dtype=devices.dtype,
         group_size=shared.opts.sdnq_quantize_weights_group_size,
+        svd_rank=shared.opts.sdnq_svd_rank,
+        use_svd=shared.opts.sdnq_use_svd,
         quant_conv=shared.opts.sdnq_quantize_conv_layers,
         use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
         use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
@@ -572,7 +576,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
     if do_gc:
         devices.torch_gc(force=True, reason='sdnq')
 
-    log.debug(f'Quantization: module="{op if op is not None else model.__class__}" type=sdnq mode=post dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
+    log.debug(f'Quantization: module="{op if op is not None else model.__class__}" type=sdnq mode=post dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} use_svd={shared.opts.sdnq_use_svd} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
     return model
 
 
@@ -580,7 +584,7 @@ def sdnq_quantize_weights(sd_model):
     try:
         t0 = time.time()
         from modules import shared, devices, sd_models
-        log.debug(f"Quantization: type=SDNQ modules={shared.opts.sdnq_quantize_weights} dtype={shared.opts.sdnq_quantize_weights_mode} dtype_te={shared.opts.sdnq_quantize_weights_mode_te} matmul={shared.opts.sdnq_use_quantized_matmul}  group_size={shared.opts.sdnq_quantize_weights_group_size} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} pre_forward={shared.opts.diffusers_offload_pre}")
+        log.debug(f"Quantization: type=SDNQ modules={shared.opts.sdnq_quantize_weights} dtype={shared.opts.sdnq_quantize_weights_mode} dtype_te={shared.opts.sdnq_quantize_weights_mode_te} matmul={shared.opts.sdnq_use_quantized_matmul} svd_rank={shared.opts.sdnq_svd_rank} use_svd={shared.opts.sdnq_use_svd} group_size={shared.opts.sdnq_quantize_weights_group_size} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} pre_forward={shared.opts.diffusers_offload_pre}")
         global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
 
         sd_model = sd_models.apply_function_to_model(sd_model, sdnq_quantize_model, shared.opts.sdnq_quantize_weights, op="sdnq")
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 39077d917..5e90e0cc1 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -48,8 +48,15 @@ def quantize_weight(weight: torch.FloatTensor, reduction_axes: Union[int, List[i
     return quantized_weight, scale, zero_point
 
 
+def apply_svdquant(weight: torch.FloatTensor, rank: int = 128) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+    U, S, svd_down = torch.svd_lowrank(weight, q=rank)
+    svd_up = torch.mul(U, S.unsqueeze(0))
+    svd_down = svd_down.t_()
+    return weight.sub_(torch.mm(svd_up, svd_down)), svd_up, svd_down
+
+
 @devices.inference_context()
-def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
+def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
     layer_class_name = layer.__class__.__name__
     if layer_class_name in allowed_types:
         num_of_groups = 1
@@ -103,13 +110,30 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
                     else:
                         use_quantized_matmul = output_channel_size % 16 == 0 and channel_size % 16 == 0
 
+        layer.weight.requires_grad = False
+        if return_device is None:
+            return_device = layer.weight.device
+        if quantization_device is not None:
+            layer.weight.data = layer.weight.to(quantization_device, non_blocking=non_blocking)
+        if layer.weight.dtype != torch.float32:
+            layer.weight.data = layer.weight.to(dtype=torch.float32)
+
+        if use_svd and is_linear_type:
+            layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank)
+            if use_quantized_matmul:
+                svd_up = svd_up.t_()
+                svd_down = svd_down.t_()
+        else:
+            use_svd = False
+            svd_up, svd_down = None, None
+
         if group_size == 0:
             if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] >= 6:
                 group_size = -1
             elif is_linear_type:
-                group_size = 2 ** (2 + dtype_dict[weights_dtype]["num_bits"])
+                group_size = 2 ** ((2 if not use_svd else 3) + dtype_dict[weights_dtype]["num_bits"])
             else:
-                group_size = 2 ** (1 + dtype_dict[weights_dtype]["num_bits"])
+                group_size = 2 ** ((1 if not use_svd else 2) + dtype_dict[weights_dtype]["num_bits"])
         elif use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
             group_size = -1 # override user value, re-quantizing 8bit into 8bit is pointless
         elif group_size != -1 and not is_linear_type:
@@ -154,24 +178,19 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
                     new_shape[last_dim_index - 1 : last_dim_index] = (num_of_groups, group_size)
                 layer.weight.data = layer.weight.reshape(new_shape)
 
-        layer.weight.requires_grad = False
-        if return_device is None:
-            return_device = layer.weight.device
-        if quantization_device is not None:
-            layer.weight.data = layer.weight.to(quantization_device, non_blocking=non_blocking)
-        if layer.weight.dtype != torch.float32:
-            layer.weight.data = layer.weight.to(dtype=torch.float32)
-
         layer.weight.data, scale, zero_point = quantize_weight(layer.weight, reduction_axes, weights_dtype)
         if not dequantize_fp32 and not (use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"] and not use_tensorwise_fp8_matmul):
-            scale = scale.to(torch_dtype)
+            scale = scale.to(dtype=torch_dtype)
             if zero_point is not None:
-                zero_point = zero_point.to(torch_dtype)
+                zero_point = zero_point.to(dtype=torch_dtype)
+            if svd_up is not None:
+                svd_up = svd_up.to(dtype=torch_dtype)
+                svd_down = svd_down.to(dtype=torch_dtype)
 
         re_quantize_for_matmul = (num_of_groups > 1 or zero_point is not None)
         if use_quantized_matmul and not re_quantize_for_matmul:
-            scale.transpose_(0,1)
-            layer.weight.transpose_(0,1)
+            scale.t_()
+            layer.weight.t_()
             if use_contiguous_mm:
                 layer.weight.data = layer.weight.contiguous()
             elif layer.weight.is_contiguous():
@@ -186,6 +205,13 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             layer.zero_point = torch.nn.Parameter(zero_point, requires_grad=False)
         else:
             layer.zero_point = None
+        if svd_up is not None:
+            svd_up = svd_up.to(return_device, non_blocking=non_blocking)
+            svd_down = svd_down.to(return_device, non_blocking=non_blocking)
+            layer.svd_up = torch.nn.Parameter(svd_up, requires_grad=False)
+            layer.svd_down = torch.nn.Parameter(svd_down, requires_grad=False)
+        else:
+            layer.svd_up, layer.svd_down = None, None
 
         layer.sdnq_dequantizer = dequantizer_dict[weights_dtype](
             quantized_weight_shape=layer.weight.shape,
@@ -204,7 +230,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
     return layer
 
 
-def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, op=None): # pylint: disable=unused-argument
+def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, op=None): # pylint: disable=unused-argument
     has_children = list(model.children())
     if not has_children:
         return model
@@ -238,6 +264,8 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
                 weights_dtype=weights_dtype,
                 torch_dtype=torch_dtype,
                 group_size=group_size,
+                svd_rank=svd_rank,
+                use_svd=use_svd,
                 quant_conv=quant_conv,
                 use_quantized_matmul=use_quantized_matmul,
                 use_quantized_matmul_conv=use_quantized_matmul_conv,
@@ -252,6 +280,8 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
                 weights_dtype=weights_dtype,
                 torch_dtype=torch_dtype,
                 group_size=group_size,
+                svd_rank=svd_rank,
+                use_svd=use_svd,
                 quant_conv=quant_conv,
                 use_quantized_matmul=use_quantized_matmul,
                 use_quantized_matmul_conv=use_quantized_matmul_conv,
@@ -371,6 +401,8 @@ class SDNQQuantizer(DiffusersQuantizer):
             weights_dtype=weights_dtype,
             torch_dtype=self.torch_dtype,
             group_size=self.quantization_config.group_size,
+            svd_rank=self.quantization_config.svd_rank,
+            use_svd=self.quantization_config.use_svd,
             quant_conv=self.quantization_config.quant_conv,
             use_quantized_matmul=self.quantization_config.use_quantized_matmul,
             use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
@@ -497,8 +529,13 @@ class SDNQConfig(QuantizationConfigMixin):
         weights_dtype (`str`, *optional*, defaults to `"int8"`):
             The target dtype for the weights after quantization. Supported values are:
             ("int8", "int7", "int6", "int5", "int4", "int3", "int2", "uint8", "uint7", "uint6", "uint5", "uint4", "uint3", "uint2", "uint1", "bool", "float8_e4m3fn", "float8_e4m3fnuz", "float8_e5m2", "float8_e5m2fnuz")
-        weights_dtype (`int`, *optional*, defaults to `0`):
+        group_size (`int`, *optional*, defaults to `0`):
             Used to decide how many elements of a tensor will share the same quantization group.
+            group_size = 0 will automatically select a group size based on weights_dtype.
+        svd_rank (`int`, *optional*, defaults to `128`):
+            The rank size used for the SVDQuant algorithm.
+        use_svd (`bool`, *optional*, defaults to `False`):
+            Enabling this option will use SVDQuant algorithm.
         quant_conv (`bool`, *optional*, defaults to `False`):
             Enabling this option will quantize the convolutional layers in UNet models too.
         use_quantized_matmul (`bool`, *optional*, defaults to `False`):
@@ -524,6 +561,8 @@ class SDNQConfig(QuantizationConfigMixin):
         self,
         weights_dtype: str = "int8",
         group_size: int = 0,
+        svd_rank: int = 128,
+        use_svd: bool = False,
         quant_conv: bool = False,
         use_quantized_matmul: bool = False,
         use_quantized_matmul_conv: bool = False,
@@ -538,6 +577,8 @@ class SDNQConfig(QuantizationConfigMixin):
         self.weights_dtype = weights_dtype
         self.quant_method = QuantizationMethod.SDNQ
         self.group_size = group_size
+        self.svd_rank = svd_rank
+        self.use_svd = use_svd
         self.quant_conv = quant_conv
         self.use_quantized_matmul = use_quantized_matmul
         self.use_quantized_matmul_conv = use_quantized_matmul_conv
diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 3881cfe34..72a058555 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -1,6 +1,6 @@
 # pylint: disable=redefined-builtin,no-member,protected-access
 
-from typing import Tuple
+from typing import Tuple, Optional
 
 import torch
 
@@ -8,19 +8,31 @@ from .common import dtype_dict, compile_func
 from .packed_int import pack_int_symetric, unpack_int_symetric, pack_int_asymetric, unpack_int_asymetric
 
 
-def dequantize_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, dtype: torch.dtype, result_shape: torch.Size) -> torch.FloatTensor:
-    result = torch.addcmul(zero_point, weight.to(dtype=scale.dtype), scale).to(dtype=dtype)
+def dequantize_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, dtype: torch.dtype, result_shape: torch.Size, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor:
+    result = torch.addcmul(zero_point, weight.to(dtype=scale.dtype), scale)
     if result_shape is not None:
         result = result.view(result_shape)
+    if svd_up is not None:
+        if skip_quantized_matmul:
+            svd_up, svd_down = svd_up.t(), svd_down.t()
+        result = torch.addmm(result, svd_up, svd_down)
+    if dtype is not None:
+        result = result.to(dtype=dtype)
     return result
 
 
-def dequantize_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, dtype: torch.dtype, result_shape: torch.Size, skip_quantized_matmul: bool = False) -> torch.FloatTensor:
-    result = weight.to(dtype=scale.dtype).mul_(scale).to(dtype=dtype)
+def dequantize_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, dtype: torch.dtype, result_shape: torch.Size, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor:
+    result = weight.to(dtype=scale.dtype).mul_(scale)
     if skip_quantized_matmul:
         result.t_()
     if result_shape is not None:
         result = result.view(result_shape)
+    if svd_up is not None:
+        if skip_quantized_matmul:
+            svd_up, svd_down = svd_up.t(), svd_down.t()
+        result = torch.addmm(result, svd_up, svd_down)
+    if dtype is not None:
+        result = result.to(dtype=dtype)
     return result
 
 
@@ -28,12 +40,12 @@ def dequantize_symmetric_with_bias(weight: torch.CharTensor, scale: torch.FloatT
     return torch.addcmul(bias, weight.to(dtype=scale.dtype), scale).to(dtype=dtype).view(result_shape)
 
 
-def dequantize_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, dtype: torch.dtype, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
-    return dequantize_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, dtype, result_shape)
+def dequantize_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, dtype: torch.dtype, result_shape: torch.Size, weights_dtype: str, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor:
+    return dequantize_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, dtype, result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul)
 
 
-def dequantize_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, dtype: torch.dtype, result_shape: torch.Size, weights_dtype: str, skip_quantized_matmul: bool = False) -> torch.FloatTensor:
-    return dequantize_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, dtype, result_shape, skip_quantized_matmul=skip_quantized_matmul)
+def dequantize_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, dtype: torch.dtype, result_shape: torch.Size, weights_dtype: str, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor:
+    return dequantize_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, dtype, result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul)
 
 
 def quantize_int8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.CharTensor, torch.FloatTensor]:
@@ -55,20 +67,20 @@ def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch
     return weight, scale
 
 
-def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
-    return re_quantize_int8(dequantize_asymmetric(weight, scale, zero_point, scale.dtype, result_shape))
+def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, result_shape: torch.Size, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]:
+    return re_quantize_int8(dequantize_asymmetric(weight, scale, zero_point, scale.dtype, result_shape, svd_up=svd_up, svd_down=svd_down))
 
 
-def re_quantize_matmul_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
-    return re_quantize_int8(dequantize_symmetric(weight, scale, scale.dtype, result_shape))
+def re_quantize_matmul_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, result_shape: torch.Size, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]:
+    return re_quantize_int8(dequantize_symmetric(weight, scale, scale.dtype, result_shape, svd_up=svd_up, svd_down=svd_down))
 
 
-def re_quantize_matmul_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
-    return re_quantize_matmul_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, result_shape)
+def re_quantize_matmul_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
+    return re_quantize_matmul_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, result_shape, svd_up=svd_up, svd_down=svd_down)
 
 
-def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
-    return re_quantize_matmul_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, result_shape)
+def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
+    return re_quantize_matmul_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, result_shape, svd_up=svd_up, svd_down=svd_down)
 
 
 class AsymmetricWeightsDequantizer(torch.nn.Module):
@@ -92,11 +104,11 @@ class AsymmetricWeightsDequantizer(torch.nn.Module):
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return weight.to(dtype=dtype_dict[self.weights_dtype]["torch_dtype"])
 
-    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_asymmetric_compiled(weight, scale, zero_point, self.result_shape)
+    def re_quantize_matmul(self, weight, scale, zero_point, svd_up, svd_down, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_asymmetric_compiled(weight, scale, zero_point, self.result_shape, svd_up=svd_up, svd_down=svd_down)
 
-    def forward(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
-        return dequantize_asymmetric_compiled(weight, scale, zero_point, self.result_dtype, self.result_shape)
+    def forward(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul=False): # pylint: disable=unused-argument
+        return dequantize_asymmetric_compiled(weight, scale, zero_point, self.result_dtype, self.result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul)
 
 
 class SymmetricWeightsDequantizer(torch.nn.Module):
@@ -121,12 +133,12 @@ class SymmetricWeightsDequantizer(torch.nn.Module):
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return weight.to(dtype=dtype_dict[self.weights_dtype]["torch_dtype"])
 
-    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_symmetric_compiled(weight, scale, self.result_shape)
+    def re_quantize_matmul(self, weight, scale, zero_point, svd_up, svd_down, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_symmetric_compiled(weight, scale, self.result_shape, svd_up=svd_up, svd_down=svd_down)
 
-    def forward(self, weight, scale, zero_point, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
+    def forward(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul=False): # pylint: disable=unused-argument
         skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul
-        return dequantize_symmetric_compiled(weight, scale, self.result_dtype, self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
+        return dequantize_symmetric_compiled(weight, scale, self.result_dtype, self.result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul)
 
 
 class PackedINTAsymmetricWeightsDequantizer(torch.nn.Module):
@@ -152,11 +164,11 @@ class PackedINTAsymmetricWeightsDequantizer(torch.nn.Module):
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return pack_int_asymetric(weight, self.weights_dtype)
 
-    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
+    def re_quantize_matmul(self, weight, scale, zero_point, svd_up, svd_down, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.result_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down)
 
-    def forward(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
-        return dequantize_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype)
+    def forward(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul=False): # pylint: disable=unused-argument
+        return dequantize_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul)
 
 
 class PackedINTSymmetricWeightsDequantizer(torch.nn.Module):
@@ -183,12 +195,12 @@ class PackedINTSymmetricWeightsDequantizer(torch.nn.Module):
     def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
         return pack_int_symetric(weight, self.weights_dtype)
 
-    def re_quantize_matmul(self, weight, scale, zero_point, **kwargs): # pylint: disable=unused-argument
-        return re_quantize_matmul_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
+    def re_quantize_matmul(self, weight, scale, zero_point, svd_up, svd_down, **kwargs): # pylint: disable=unused-argument
+        return re_quantize_matmul_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.result_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down)
 
-    def forward(self, weight, scale, zero_point, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
+    def forward(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul=False): # pylint: disable=unused-argument
         skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul
-        return dequantize_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype, skip_quantized_matmul=skip_quantized_matmul)
+        return dequantize_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul)
 
 
 dequantizer_dict = {
diff --git a/modules/sdnq/layers/conv/conv_fp8.py b/modules/sdnq/layers/conv/conv_fp8.py
index 47f214937..d700a6998 100644
--- a/modules/sdnq/layers/conv/conv_fp8.py
+++ b/modules/sdnq/layers/conv/conv_fp8.py
@@ -15,6 +15,8 @@ def conv_fp8_matmul(
     weight: torch.Tensor,
     bias: torch.FloatTensor,
     scale: torch.FloatTensor,
+    svd_up: torch.FloatTensor,
+    svd_down: torch.FloatTensor,
     result_shape: torch.Size,
     reversed_padding_repeated_twice: List[int],
     padding_mode: str, conv_type: int,
@@ -23,6 +25,9 @@ def conv_fp8_matmul(
 ) -> torch.FloatTensor:
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
+    if svd_up is not None:
+        svd_bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+
     input, input_scale = quantize_fp8_matmul_input(input)
     input, weight = check_mats(input, weight)
 
@@ -46,6 +51,8 @@ def conv_fp8_matmul(
             for i in range(groups):
                 result.append(torch._scaled_mm(input[:, i], weight[:, i], scale_a=input_scale[i], scale_b=scale[i], bias=None, out_dtype=torch.bfloat16))
         result = torch.cat(result, dim=-1).view(mm_output_shape).to(return_dtype)
+    if svd_up is not None:
+        result.add_(svd_bias)
 
     if conv_type == 1:
         result = result.transpose_(1,2)
@@ -58,10 +65,11 @@ def conv_fp8_matmul(
 
 def quantized_conv_forward_fp8_matmul(self, input) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[2] < 32:
-        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
+        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
     conv_type, stride, padding, dilation = get_conv_args(input.ndim, self.stride, self.padding, self.dilation)
     return conv_fp8_matmul(
-        input, self.weight, self.bias, self.scale,
+        input, self.weight, self.bias,
+        self.scale, self.svd_up, self.svd_down,
         self.sdnq_dequantizer.result_shape,
         self._reversed_padding_repeated_twice,
         self.padding_mode, conv_type,
diff --git a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
index 592a512ee..1893ad8a5 100644
--- a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
+++ b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
@@ -16,6 +16,8 @@ def conv_fp8_matmul_tensorwise(
     weight: torch.Tensor,
     bias: torch.FloatTensor,
     scale: torch.FloatTensor,
+    svd_up: torch.FloatTensor,
+    svd_down: torch.FloatTensor,
     result_shape: torch.Size,
     reversed_padding_repeated_twice: List[int],
     padding_mode: str, conv_type: int,
@@ -24,6 +26,12 @@ def conv_fp8_matmul_tensorwise(
 ) -> torch.FloatTensor:
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
+    if svd_up is not None:
+        if bias is not None:
+            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+        else:
+            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+
     input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
     input, weight = check_mats(input, weight)
     dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
@@ -53,10 +61,11 @@ def conv_fp8_matmul_tensorwise(
 
 def quantized_conv_forward_fp8_matmul_tensorwise(self, input) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[2] < 32:
-        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
+        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
     conv_type, stride, padding, dilation = get_conv_args(input.ndim, self.stride, self.padding, self.dilation)
     return conv_fp8_matmul_tensorwise(
-        input, self.weight, self.bias, self.scale,
+        input, self.weight, self.bias,
+        self.scale, self.svd_up, self.svd_down,
         self.sdnq_dequantizer.result_shape,
         self._reversed_padding_repeated_twice,
         self.padding_mode, conv_type,
diff --git a/modules/sdnq/layers/conv/conv_int8.py b/modules/sdnq/layers/conv/conv_int8.py
index 294f696b4..82bd51f95 100644
--- a/modules/sdnq/layers/conv/conv_int8.py
+++ b/modules/sdnq/layers/conv/conv_int8.py
@@ -17,6 +17,8 @@ def conv_int8_matmul(
     weight: torch.CharTensor,
     bias: torch.FloatTensor,
     scale: torch.FloatTensor,
+    svd_up: torch.FloatTensor,
+    svd_down: torch.FloatTensor,
     quantized_weight_shape: torch.Size,
     result_shape: torch.Size,
     weights_dtype: str,
@@ -27,6 +29,12 @@ def conv_int8_matmul(
 ) -> torch.FloatTensor:
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
+    if svd_up is not None:
+        if bias is not None:
+            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+        else:
+            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+
     input, scale = quantize_int8_matmul_input(input, scale)
     if quantized_weight_shape is not None:
         weight = unpack_int_symetric(weight, quantized_weight_shape, weights_dtype, dtype=torch.int8)
@@ -57,10 +65,10 @@ def conv_int8_matmul(
 
 def quantized_conv_forward_int8_matmul(self, input) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[2] < 32:
-        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
+        return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
     conv_type, stride, padding, dilation = get_conv_args(input.ndim, self.stride, self.padding, self.dilation)
     if self.sdnq_dequantizer.re_quantize_for_matmul:
-        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point)
+        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point, None, None)
         quantized_weight_shape = None
     else:
         weight = self.weight
@@ -68,7 +76,8 @@ def quantized_conv_forward_int8_matmul(self, input) -> torch.FloatTensor:
         quantized_weight_shape = getattr(self.sdnq_dequantizer, "quantized_weight_shape", None)
     return conv_int8_matmul(
         input, weight, self.bias,
-        scale, quantized_weight_shape,
+        scale, self.svd_up, self.svd_down,
+        quantized_weight_shape,
         self.sdnq_dequantizer.result_shape,
         self.sdnq_dequantizer.weights_dtype,
         self._reversed_padding_repeated_twice,
diff --git a/modules/sdnq/layers/conv/forward.py b/modules/sdnq/layers/conv/forward.py
index 69237ea68..2ed3d816f 100644
--- a/modules/sdnq/layers/conv/forward.py
+++ b/modules/sdnq/layers/conv/forward.py
@@ -75,19 +75,19 @@ def process_conv_input(conv_type, input, reversed_padding_repeated_twice, paddin
 
 
 def quantized_conv_forward(self, input) -> torch.FloatTensor:
-    return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias)
+    return self._conv_forward(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down), self.bias)
 
 
 def quantized_conv_transpose_1d_forward(self, input: torch.FloatTensor, output_size: Optional[list[int]] = None) -> torch.FloatTensor:
     output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size, 1, self.dilation)
-    return torch.nn.functional.conv_transpose1d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
+    return torch.nn.functional.conv_transpose1d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
 
 
 def quantized_conv_transpose_2d_forward(self, input: torch.FloatTensor, output_size: Optional[list[int]] = None) -> torch.FloatTensor:
     output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size, 2, self.dilation)
-    return torch.nn.functional.conv_transpose2d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
+    return torch.nn.functional.conv_transpose2d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
 
 
 def quantized_conv_transpose_3d_forward(self, input: torch.FloatTensor, output_size: Optional[list[int]] = None) -> torch.FloatTensor:
     output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size, 3, self.dilation)
-    return torch.nn.functional.conv_transpose3d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
+    return torch.nn.functional.conv_transpose3d(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down), self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
diff --git a/modules/sdnq/layers/linear/forward.py b/modules/sdnq/layers/linear/forward.py
index 880b8ebc8..2371b6abb 100644
--- a/modules/sdnq/layers/linear/forward.py
+++ b/modules/sdnq/layers/linear/forward.py
@@ -17,4 +17,4 @@ def check_mats(input: torch.Tensor, weight: torch.Tensor) -> Tuple[torch.Tensor,
 
 
 def quantized_linear_forward(self, input: torch.FloatTensor) -> torch.FloatTensor:
-    return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point), self.bias)
+    return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down), self.bias)
diff --git a/modules/sdnq/layers/linear/linear_fp8.py b/modules/sdnq/layers/linear/linear_fp8.py
index 3f2fb407c..205b141e8 100644
--- a/modules/sdnq/layers/linear/linear_fp8.py
+++ b/modules/sdnq/layers/linear/linear_fp8.py
@@ -20,20 +20,27 @@ def fp8_matmul(
     weight: torch.Tensor,
     bias: torch.FloatTensor,
     scale: torch.FloatTensor,
+    svd_up: torch.FloatTensor,
+    svd_down: torch.FloatTensor,
 ) -> torch.FloatTensor:
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
+    if svd_up is not None:
+        svd_bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
     input, input_scale = quantize_fp8_matmul_input(input)
     input, weight = check_mats(input, weight)
     if bias is not None and bias.dtype != torch.bfloat16:
         bias = bias.to(dtype=torch.bfloat16)
-    return torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16).view(output_shape).to(return_dtype)
+    result = torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16).view(output_shape).to(return_dtype)
+    if svd_up is not None:
+        result.add_(svd_bias)
+    return result
 
 
 def quantized_linear_forward_fp8_matmul(self, input: torch.FloatTensor) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[-1] < 32:
-        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
-    return fp8_matmul(input, self.weight, self.bias, self.scale)
+        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
+    return fp8_matmul(input, self.weight, self.bias, self.scale, self.svd_up, self.svd_down)
 
 
 fp8_matmul = compile_func(fp8_matmul)
diff --git a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
index d39ea5519..bd7a9b973 100644
--- a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
+++ b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
@@ -23,10 +23,17 @@ def fp8_matmul_tensorwise(
     weight: torch.Tensor,
     bias: torch.FloatTensor,
     scale: torch.FloatTensor,
+    svd_up: torch.FloatTensor,
+    svd_down: torch.FloatTensor,
 ) -> torch.FloatTensor:
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
     dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
+    if svd_up is not None:
+        if bias is not None:
+            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+        else:
+            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
     input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
     input, weight = check_mats(input, weight)
     if bias is not None:
@@ -37,8 +44,8 @@ def fp8_matmul_tensorwise(
 
 def quantized_linear_forward_fp8_matmul_tensorwise(self, input: torch.FloatTensor) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[-1] < 32:
-        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
-    return fp8_matmul_tensorwise(input, self.weight, self.bias, self.scale)
+        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
+    return fp8_matmul_tensorwise(input, self.weight, self.bias, self.scale, self.svd_up, self.svd_down)
 
 
 fp8_matmul_tensorwise = compile_func(fp8_matmul_tensorwise)
diff --git a/modules/sdnq/layers/linear/linear_int8.py b/modules/sdnq/layers/linear/linear_int8.py
index ae218a4fe..ce8bcf788 100644
--- a/modules/sdnq/layers/linear/linear_int8.py
+++ b/modules/sdnq/layers/linear/linear_int8.py
@@ -24,6 +24,8 @@ def int8_matmul(
     weight: torch.Tensor,
     bias: torch.FloatTensor,
     scale: torch.FloatTensor,
+    svd_up: torch.FloatTensor,
+    svd_down: torch.FloatTensor,
     quantized_weight_shape: torch.Size,
     weights_dtype: str,
 ) -> torch.FloatTensor:
@@ -31,6 +33,11 @@ def int8_matmul(
         weight = unpack_int_symetric(weight, quantized_weight_shape, weights_dtype, dtype=torch.int8)
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
+    if svd_up is not None:
+        if bias is not None:
+            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+        else:
+            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
     input, scale = quantize_int8_matmul_input(input, scale)
     input, weight = check_mats(input, weight)
     if bias is not None:
@@ -41,15 +48,15 @@ def int8_matmul(
 
 def quantized_linear_forward_int8_matmul(self, input: torch.FloatTensor) -> torch.FloatTensor:
     if torch.numel(input) / input.shape[-1] < 32:
-        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, skip_quantized_matmul=True), self.bias)
+        return torch.nn.functional.linear(input, self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=True), self.bias)
     if self.sdnq_dequantizer.re_quantize_for_matmul:
-        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point)
+        weight, scale = self.sdnq_dequantizer.re_quantize_matmul(self.weight, self.scale, self.zero_point, None, None)
         quantized_weight_shape = None
     else:
         weight = self.weight
         scale = self.scale
         quantized_weight_shape = getattr(self.sdnq_dequantizer, "quantized_weight_shape", None)
-    return int8_matmul(input, weight, self.bias, scale, quantized_weight_shape, self.sdnq_dequantizer.weights_dtype)
+    return int8_matmul(input, weight, self.bias, scale, self.svd_up, self.svd_down, quantized_weight_shape, self.sdnq_dequantizer.weights_dtype)
 
 
 int8_matmul = compile_func(int8_matmul)
diff --git a/modules/shared.py b/modules/shared.py
index fab234f94..339cac0f1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -201,6 +201,8 @@ options_templates.update(options_section(("quantization", "Model Quantization"),
     "sdnq_modules_to_not_convert": OptionInfo("", "Modules to not convert"),
     "sdnq_modules_dtype_dict": OptionInfo("{}", "Modules dtype dict"),
     "sdnq_quantize_weights_group_size": OptionInfo(0, "Group size", gr.Slider, {"minimum": -1, "maximum": 4096, "step": 1}),
+    "sdnq_svd_rank": OptionInfo(128, "SVDQuant Rank size", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
+    "sdnq_use_svd": OptionInfo(False, "Use SVDQuant quantization", gr.Checkbox),
     "sdnq_quantize_conv_layers": OptionInfo(False, "Quantize convolutional layers", gr.Checkbox),
     "sdnq_dequantize_compile": OptionInfo(devices.has_triton(), "Dequantize using torch.compile", gr.Checkbox),
     "sdnq_use_quantized_matmul": OptionInfo(False, "Use quantized MatMul", gr.Checkbox),

From 0acb571472563c34c27318b14783278569dad623 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 00:57:23 +0300
Subject: [PATCH 144/230] SDNQ ass load and save model funcs

---
 modules/sdnq/__init__.py  | 629 +------------------------------------
 modules/sdnq/loader.py    |  65 ++++
 modules/sdnq/quantizer.py | 635 ++++++++++++++++++++++++++++++++++++++
 3 files changed, 712 insertions(+), 617 deletions(-)
 create mode 100644 modules/sdnq/loader.py
 create mode 100644 modules/sdnq/quantizer.py

diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 5e90e0cc1..121db2c8d 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -1,617 +1,12 @@
-# pylint: disable=redefined-builtin,no-member,protected-access
-
-from typing import Dict, List, Tuple, Optional, Union
-from dataclasses import dataclass
-from enum import Enum
-
-import re
-import torch
-from diffusers.quantizers.base import DiffusersQuantizer
-from diffusers.quantizers.quantization_config import QuantizationConfigMixin
-from diffusers.utils import get_module_from_name
-from modules import devices, shared
-
-from .common import dtype_dict, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
-from .dequantizer import dequantizer_dict
-from .forward import get_forward_func
-
-
-class QuantizationMethod(str, Enum):
-    SDNQ = "sdnq"
-
-
-def get_scale_asymmetric(weight: torch.FloatTensor, reduction_axes: Union[int, List[int]], weights_dtype: str) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-    zero_point = torch.amin(weight, dim=reduction_axes, keepdims=True)
-    scale = torch.amax(weight, dim=reduction_axes, keepdims=True).sub_(zero_point).div_(dtype_dict[weights_dtype]["max"] - dtype_dict[weights_dtype]["min"])
-    if dtype_dict[weights_dtype]["min"] != 0:
-        zero_point.sub_(torch.mul(scale, dtype_dict[weights_dtype]["min"]))
-    return scale, zero_point
-
-
-def get_scale_symmetric(weight: torch.FloatTensor, reduction_axes: Union[int, List[int]], weights_dtype: str) -> torch.FloatTensor:
-    return torch.amax(weight.abs(), dim=reduction_axes, keepdims=True).div_(dtype_dict[weights_dtype]["max"])
-
-
-def quantize_weight(weight: torch.FloatTensor, reduction_axes: Union[int, List[int]], weights_dtype: str) -> Tuple[torch.Tensor, torch.FloatTensor, torch.FloatTensor]:
-    if dtype_dict[weights_dtype]["is_unsigned"]:
-        scale, zero_point = get_scale_asymmetric(weight, reduction_axes, weights_dtype)
-        quantized_weight = torch.sub(weight, zero_point).div_(scale)
-    else:
-        scale = get_scale_symmetric(weight, reduction_axes, weights_dtype)
-        quantized_weight = torch.div(weight, scale)
-        zero_point = None
-    if dtype_dict[weights_dtype]["is_integer"]:
-        quantized_weight.round_()
-    else:
-        quantized_weight.nan_to_num_()
-    quantized_weight = quantized_weight.clamp_(dtype_dict[weights_dtype]["min"], dtype_dict[weights_dtype]["max"]).to(dtype_dict[weights_dtype]["torch_dtype"])
-    return quantized_weight, scale, zero_point
-
-
-def apply_svdquant(weight: torch.FloatTensor, rank: int = 128) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
-    U, S, svd_down = torch.svd_lowrank(weight, q=rank)
-    svd_up = torch.mul(U, S.unsqueeze(0))
-    svd_down = svd_down.t_()
-    return weight.sub_(torch.mm(svd_up, svd_down)), svd_up, svd_down
-
-
-@devices.inference_context()
-def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
-    layer_class_name = layer.__class__.__name__
-    if layer_class_name in allowed_types:
-        num_of_groups = 1
-        is_conv_type = False
-        is_conv_transpose_type = False
-        is_linear_type = False
-        result_shape = None
-        original_shape = layer.weight.shape
-        if torch_dtype is None:
-            torch_dtype = devices.dtype
-
-        if layer_class_name in conv_types:
-            if not quant_conv:
-                return layer
-            if dtype_dict[weights_dtype]["num_bits"] < 4:
-                weights_dtype = "uint4"
-            is_conv_type = True
-            reduction_axes = 1
-            output_channel_size, channel_size = layer.weight.shape[:2]
-            group_channel_size = channel_size // layer.groups
-            use_quantized_matmul = False
-            if use_quantized_matmul_conv:
-                use_quantized_matmul = group_channel_size >= 32 and output_channel_size >= 32
-                if use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"]:
-                    use_quantized_matmul = output_channel_size % 16 == 0 and group_channel_size % 16 == 0
-            if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
-                result_shape = layer.weight.shape
-                layer.weight.data = layer.weight.flatten(1,-1)
-                reduction_axes = -1
-        elif layer_class_name in conv_transpose_types:
-            if not quant_conv:
-                return layer
-            if dtype_dict[weights_dtype]["num_bits"] < 4:
-                weights_dtype = "uint4"
-            is_conv_transpose_type = True
-            reduction_axes = 0
-            channel_size, output_channel_size = layer.weight.shape[:2]
-            use_quantized_matmul = False
-        else:
-            is_linear_type = True
-            reduction_axes = -1
-            try:
-                output_channel_size, channel_size = layer.weight.shape
-            except Exception as e:
-                raise ValueError(f"SDNQ: param_name={param_name} layer_class_name={layer_class_name} layer_weight_shape={layer.weight.shape} weights_dtype={weights_dtype} unsupported") from e
-            if use_quantized_matmul:
-                use_quantized_matmul = channel_size >= 32 and output_channel_size >= 32
-                if use_quantized_matmul:
-                    if dtype_dict[weights_dtype]["is_integer"]:
-                        use_quantized_matmul = output_channel_size % 8 == 0 and channel_size % 8 == 0
-                    else:
-                        use_quantized_matmul = output_channel_size % 16 == 0 and channel_size % 16 == 0
-
-        layer.weight.requires_grad = False
-        if return_device is None:
-            return_device = layer.weight.device
-        if quantization_device is not None:
-            layer.weight.data = layer.weight.to(quantization_device, non_blocking=non_blocking)
-        if layer.weight.dtype != torch.float32:
-            layer.weight.data = layer.weight.to(dtype=torch.float32)
-
-        if use_svd and is_linear_type:
-            layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank)
-            if use_quantized_matmul:
-                svd_up = svd_up.t_()
-                svd_down = svd_down.t_()
-        else:
-            use_svd = False
-            svd_up, svd_down = None, None
-
-        if group_size == 0:
-            if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] >= 6:
-                group_size = -1
-            elif is_linear_type:
-                group_size = 2 ** ((2 if not use_svd else 3) + dtype_dict[weights_dtype]["num_bits"])
-            else:
-                group_size = 2 ** ((1 if not use_svd else 2) + dtype_dict[weights_dtype]["num_bits"])
-        elif use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
-            group_size = -1 # override user value, re-quantizing 8bit into 8bit is pointless
-        elif group_size != -1 and not is_linear_type:
-            group_size = max(group_size // 2, 1)
-
-        if group_size > 0:
-            if group_size >= channel_size:
-                group_size = channel_size
-                num_of_groups = 1
-            else:
-                num_of_groups = channel_size // group_size
-                while num_of_groups * group_size != channel_size: # find something divisible
-                    num_of_groups -= 1
-                    if num_of_groups <= 1:
-                        group_size = channel_size
-                        num_of_groups = 1
-                        break
-                    group_size = channel_size // num_of_groups
-            group_size = int(group_size)
-            num_of_groups = int(num_of_groups)
-
-            if num_of_groups > 1:
-                if result_shape is None:
-                    result_shape = layer.weight.shape
-                new_shape = list(result_shape)
-                if is_conv_type:
-                    # output_channel_size, channel_size, X, X
-                    # output_channel_size, num_of_groups, group_size, X, X
-                    new_shape[1] = group_size
-                    new_shape.insert(1, num_of_groups)
-                    reduction_axes = 2
-                elif is_conv_transpose_type:
-                    #channel_size, output_channel_size, X, X
-                    #num_of_groups, group_size, output_channel_size, X, X
-                    new_shape[0] = group_size
-                    new_shape.insert(0, num_of_groups)
-                    reduction_axes = 1
-                elif is_linear_type:
-                    # output_channel_size, channel_size
-                    # output_channel_size, num_of_groups, group_size
-                    last_dim_index = layer.weight.ndim
-                    new_shape[last_dim_index - 1 : last_dim_index] = (num_of_groups, group_size)
-                layer.weight.data = layer.weight.reshape(new_shape)
-
-        layer.weight.data, scale, zero_point = quantize_weight(layer.weight, reduction_axes, weights_dtype)
-        if not dequantize_fp32 and not (use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"] and not use_tensorwise_fp8_matmul):
-            scale = scale.to(dtype=torch_dtype)
-            if zero_point is not None:
-                zero_point = zero_point.to(dtype=torch_dtype)
-            if svd_up is not None:
-                svd_up = svd_up.to(dtype=torch_dtype)
-                svd_down = svd_down.to(dtype=torch_dtype)
-
-        re_quantize_for_matmul = (num_of_groups > 1 or zero_point is not None)
-        if use_quantized_matmul and not re_quantize_for_matmul:
-            scale.t_()
-            layer.weight.t_()
-            if use_contiguous_mm:
-                layer.weight.data = layer.weight.contiguous()
-            elif layer.weight.is_contiguous():
-                layer.weight.data = layer.weight.t_().contiguous().t_()
-            if not use_tensorwise_fp8_matmul and not dtype_dict[weights_dtype]["is_integer"]:
-                scale = scale.to(torch.float32)
-
-        scale = scale.to(return_device, non_blocking=non_blocking)
-        layer.scale = torch.nn.Parameter(scale, requires_grad=False)
-        if zero_point is not None:
-            zero_point = zero_point.to(return_device, non_blocking=non_blocking)
-            layer.zero_point = torch.nn.Parameter(zero_point, requires_grad=False)
-        else:
-            layer.zero_point = None
-        if svd_up is not None:
-            svd_up = svd_up.to(return_device, non_blocking=non_blocking)
-            svd_down = svd_down.to(return_device, non_blocking=non_blocking)
-            layer.svd_up = torch.nn.Parameter(svd_up, requires_grad=False)
-            layer.svd_down = torch.nn.Parameter(svd_down, requires_grad=False)
-        else:
-            layer.svd_up, layer.svd_down = None, None
-
-        layer.sdnq_dequantizer = dequantizer_dict[weights_dtype](
-            quantized_weight_shape=layer.weight.shape,
-            result_dtype=torch_dtype,
-            result_shape=result_shape,
-            original_shape=original_shape,
-            weights_dtype=weights_dtype,
-            use_quantized_matmul=use_quantized_matmul,
-            re_quantize_for_matmul=re_quantize_for_matmul,
-        )
-        layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
-        layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
-
-        layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
-        layer.forward = layer.forward.__get__(layer, layer.__class__)
-    return layer
-
-
-def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, op=None): # pylint: disable=unused-argument
-    has_children = list(model.children())
-    if not has_children:
-        return model
-    if modules_to_not_convert is None:
-        modules_to_not_convert = []
-    if modules_dtype_dict is None:
-        modules_dtype_dict = {}
-    for param_name, module in model.named_children():
-        if param_name in modules_to_not_convert:
-            continue
-        if hasattr(module, "weight") and module.weight is not None:
-            if len(modules_dtype_dict.keys()) > 0:
-                for key, value in modules_dtype_dict.items():
-                    if param_name in value:
-                        key = key.lower()
-                        if key in {"8bit", "8bits"}:
-                            if dtype_dict[weights_dtype]["num_bits"] != 8:
-                                weights_dtype = "int8"
-                        elif key.startswith("minimum_"):
-                            minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
-                            minimum_bits = int(minimum_bits_str)
-                            if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
-                                weights_dtype = "int" + minimum_bits_str
-                                if minimum_bits <= 4:
-                                    weights_dtype = "u" + weights_dtype
-                        else:
-                            weights_dtype = key
-
-            module = sdnq_quantize_layer(
-                module,
-                weights_dtype=weights_dtype,
-                torch_dtype=torch_dtype,
-                group_size=group_size,
-                svd_rank=svd_rank,
-                use_svd=use_svd,
-                quant_conv=quant_conv,
-                use_quantized_matmul=use_quantized_matmul,
-                use_quantized_matmul_conv=use_quantized_matmul_conv,
-                dequantize_fp32=dequantize_fp32,
-                non_blocking=non_blocking,
-                quantization_device=quantization_device,
-                return_device=return_device,
-                param_name=param_name,
-            )
-        module = apply_sdnq_to_module(
-                module,
-                weights_dtype=weights_dtype,
-                torch_dtype=torch_dtype,
-                group_size=group_size,
-                svd_rank=svd_rank,
-                use_svd=use_svd,
-                quant_conv=quant_conv,
-                use_quantized_matmul=use_quantized_matmul,
-                use_quantized_matmul_conv=use_quantized_matmul_conv,
-                dequantize_fp32=dequantize_fp32,
-                non_blocking=non_blocking,
-                quantization_device=quantization_device,
-                return_device=return_device,
-                modules_to_not_convert=modules_to_not_convert,
-                modules_dtype_dict=modules_dtype_dict,
-                op=op,
-            )
-    return model
-
-
-class SDNQQuantizer(DiffusersQuantizer):
-    r"""
-    Diffusers Quantizer for SDNQ
-    """
-
-    requires_parameters_quantization = True
-    use_keep_in_fp32_modules = True
-    requires_calibration = False
-    required_packages = None
-    torch_dtype = None
-
-    def __init__(self, quantization_config, **kwargs):
-        super().__init__(quantization_config, **kwargs)
-        self.modules_to_not_convert = []
-
-    def check_if_quantized_param(
-        self,
-        model,
-        param_value: "torch.Tensor",
-        param_name: str,
-        *args, **kwargs, # pylint: disable=unused-argument
-    ):
-        if param_name.endswith(".weight"):
-            split_param_name = param_name.split(".")
-            if (
-                param_name not in self.modules_to_not_convert
-                and not any(param in split_param_name for param in self.modules_to_not_convert)
-                and not any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in self.modules_to_not_convert)
-            ):
-                layer_class_name = get_module_from_name(model, param_name)[0].__class__.__name__
-                if layer_class_name in allowed_types:
-                    if layer_class_name in conv_types or layer_class_name in conv_transpose_types:
-                        if self.quantization_config.quant_conv:
-                            return True
-                    else:
-                        return True
-        if param_value is not None:
-            with devices.inference_context():
-                param_value.data = param_value.clone() # safetensors is unable to release the cpu memory without this
-        return False
-
-    def check_quantized_param(self, *args, **kwargs) -> bool:
-        """
-        needed for transformers compatibilty, returns self.check_if_quantized_param
-        """
-        return self.check_if_quantized_param(*args, **kwargs)
-
-    def param_needs_quantization(self, model, param_name: str, *args, **kwargs) -> bool:
-        """
-        needed for transformers compatibilty, returns self.check_if_quantized_param
-        """
-        return self.check_if_quantized_param(model, None, param_name, *args, **kwargs)
-
-    @devices.inference_context()
-    def create_quantized_param( # pylint: disable=arguments-differ
-        self,
-        model,
-        param_value: torch.FloatTensor,
-        param_name: str,
-        target_device: torch.device,
-        *args, **kwargs, # pylint: disable=unused-argument
-    ):
-        weights_dtype = self.quantization_config.weights_dtype
-        if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
-            split_param_name = param_name.split(".")
-            for key, value in self.quantization_config.modules_dtype_dict.items():
-                if (
-                    param_name in value
-                    or any(param in split_param_name for param in value)
-                    or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in value)
-                ):
-                    key = key.lower()
-                    if key in {"8bit", "8bits"}:
-                        if dtype_dict[weights_dtype]["num_bits"] != 8:
-                            weights_dtype = "int8"
-                    elif key.startswith("minimum_"):
-                        minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
-                        minimum_bits = int(minimum_bits_str)
-                        if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
-                            weights_dtype = "int" + minimum_bits_str
-                            if minimum_bits <= 4:
-                                weights_dtype = "u" + weights_dtype
-                    else:
-                        weights_dtype = key
-
-        if self.quantization_config.return_device is not None:
-            return_device = self.quantization_config.return_device
-        else:
-            return_device = target_device
-
-        if self.quantization_config.quantization_device is not None:
-            target_device = self.quantization_config.quantization_device
-
-        if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
-            param_value = param_value.clone()
-        else:
-            param_value = param_value.to(target_device, non_blocking=self.quantization_config.non_blocking).to(dtype=torch.float32)
-
-        layer, _ = get_module_from_name(model, param_name)
-        layer.weight = torch.nn.Parameter(param_value, requires_grad=False)
-        layer = sdnq_quantize_layer(
-            layer,
-            weights_dtype=weights_dtype,
-            torch_dtype=self.torch_dtype,
-            group_size=self.quantization_config.group_size,
-            svd_rank=self.quantization_config.svd_rank,
-            use_svd=self.quantization_config.use_svd,
-            quant_conv=self.quantization_config.quant_conv,
-            use_quantized_matmul=self.quantization_config.use_quantized_matmul,
-            use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
-            dequantize_fp32=self.quantization_config.dequantize_fp32,
-            non_blocking=self.quantization_config.non_blocking,
-            quantization_device=None,
-            return_device=return_device,
-            param_name=param_name,
-        )
-
-    def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
-        max_memory = {key: val * 0.80 for key, val in max_memory.items()}
-        return max_memory
-
-    def adjust_target_dtype(self, target_dtype: torch.dtype) -> torch.dtype: # pylint: disable=unused-argument,arguments-renamed
-        return dtype_dict[self.quantization_config.weights_dtype]["target_dtype"]
-
-    def update_torch_dtype(self, torch_dtype: torch.dtype = None) -> torch.dtype:
-        if torch_dtype is None:
-            torch_dtype = devices.dtype
-        self.torch_dtype = torch_dtype
-        return torch_dtype
-
-    def _process_model_before_weight_loading( # pylint: disable=arguments-differ
-        self,
-        model,
-        device_map, # pylint: disable=unused-argument
-        keep_in_fp32_modules: List[str] = None,
-        **kwargs, # pylint: disable=unused-argument
-    ):
-        if keep_in_fp32_modules is not None:
-            self.modules_to_not_convert.extend(keep_in_fp32_modules)
-        elif getattr(model, "_keep_in_fp32_modules", None) is not None:
-            self.modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
-        if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
-            self.modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
-        self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
-        self.quantization_config.modules_to_not_convert = self.modules_to_not_convert
-        model.config.quantization_config = self.quantization_config
-
-    def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument
-        if shared.opts.diffusers_offload_mode != "none":
-            model = model.to(devices.cpu)
-        devices.torch_gc(force=True, reason="sdnq")
-        return model
-
-    def get_accelerator_warm_up_factor(self):
-        return 32 // dtype_dict[self.quantization_config.weights_dtype]["num_bits"]
-
-    def get_cuda_warm_up_factor(self):
-        """
-        needed for transformers compatibilty, returns self.get_accelerator_warm_up_factor
-        """
-        return self.get_accelerator_warm_up_factor()
-
-    def update_tp_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return config
-
-    def update_ep_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return config
-
-    def update_unexpected_keys(self, model, unexpected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return unexpected_keys
-
-    def update_missing_keys_after_loading(self, model, missing_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return missing_keys
-
-    def update_state_dict_with_metadata(self, state_dict: dict, *args, **kwargs) -> dict: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return state_dict
-
-    def update_expected_keys(self, model, expected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return expected_keys
-
-    def update_param_name(self, param_name: str, *args, **kwargs) -> str: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return param_name
-
-    def update_dtype(self, dtype: torch.dtype, *args, **kwargs) -> torch.dtype: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return dtype
-
-    @property
-    def is_trainable(self):
-        return False
-
-    @property
-    def is_serializable(self):
-        return True
-
-    @property
-    def is_compileable(self):
-        return True
-
-
-@dataclass
-class SDNQConfig(QuantizationConfigMixin):
-    """
-    This is a wrapper class about all possible attributes and features that you can play with a model that has been
-    loaded using `sdnq`.
-
-    Args:
-        weights_dtype (`str`, *optional*, defaults to `"int8"`):
-            The target dtype for the weights after quantization. Supported values are:
-            ("int8", "int7", "int6", "int5", "int4", "int3", "int2", "uint8", "uint7", "uint6", "uint5", "uint4", "uint3", "uint2", "uint1", "bool", "float8_e4m3fn", "float8_e4m3fnuz", "float8_e5m2", "float8_e5m2fnuz")
-        group_size (`int`, *optional*, defaults to `0`):
-            Used to decide how many elements of a tensor will share the same quantization group.
-            group_size = 0 will automatically select a group size based on weights_dtype.
-        svd_rank (`int`, *optional*, defaults to `128`):
-            The rank size used for the SVDQuant algorithm.
-        use_svd (`bool`, *optional*, defaults to `False`):
-            Enabling this option will use SVDQuant algorithm.
-        quant_conv (`bool`, *optional*, defaults to `False`):
-            Enabling this option will quantize the convolutional layers in UNet models too.
-        use_quantized_matmul (`bool`, *optional*, defaults to `False`):
-            Enabling this option will use quantized INT8 or FP8 MatMul instead of BF16 / FP16.
-        use_quantized_matmul_conv (`bool`, *optional*, defaults to `False`):
-            Same as use_quantized_matmul_conv but for the convolutional layers with UNets like SDXL.
-        dequantize_fp32 (`bool`, *optional*, defaults to `False`):
-            Enabling this option will use FP32 on the dequantization step.
-        non_blocking (`bool`, *optional*, defaults to `False`):
-            Enabling this option will use non blocking ops when moving layers between the quantization device and the return device.
-        quantization_device (`torch.device`, *optional*, defaults to `None`):
-            Used to set which device will be used for the quantization calculation on model load.
-        return_device (`torch.device`, *optional*, defaults to `None`):
-            Used to set which device will the quantized weights be sent back to.
-        modules_to_not_convert (`list`, *optional*, default to `None`):
-            The list of modules to not quantize, useful for quantizing models that explicitly require to have some
-            modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
-        modules_dtype_dict (`dict`, *optional*, default to `None`):
-            The dict of dtypes and list of modules, useful for quantizing some modules with a different dtype.
-    """
-
-    def __init__( # pylint: disable=super-init-not-called
-        self,
-        weights_dtype: str = "int8",
-        group_size: int = 0,
-        svd_rank: int = 128,
-        use_svd: bool = False,
-        quant_conv: bool = False,
-        use_quantized_matmul: bool = False,
-        use_quantized_matmul_conv: bool = False,
-        dequantize_fp32: bool = False,
-        non_blocking: bool = False,
-        quantization_device: Optional[torch.device] = None,
-        return_device: Optional[torch.device] = None,
-        modules_to_not_convert: Optional[List[str]] = None,
-        modules_dtype_dict: Optional[Dict[str, List[str]]] = None,
-        **kwargs, # pylint: disable=unused-argument
-    ):
-        self.weights_dtype = weights_dtype
-        self.quant_method = QuantizationMethod.SDNQ
-        self.group_size = group_size
-        self.svd_rank = svd_rank
-        self.use_svd = use_svd
-        self.quant_conv = quant_conv
-        self.use_quantized_matmul = use_quantized_matmul
-        self.use_quantized_matmul_conv = use_quantized_matmul_conv
-        self.dequantize_fp32 = dequantize_fp32
-        self.non_blocking = non_blocking
-        self.quantization_device = quantization_device
-        self.return_device = return_device
-        self.modules_to_not_convert = modules_to_not_convert
-        self.modules_dtype_dict = modules_dtype_dict
-        self.post_init()
-        self.is_integer = dtype_dict[self.weights_dtype]["is_integer"]
-
-    def post_init(self):
-        r"""
-        Safety checker that arguments are correct
-        """
-        accepted_weights = ["int8", "int7", "int6", "int5", "int4", "int3", "int2", "uint8", "uint7", "uint6", "uint5", "uint4", "uint3", "uint2", "uint1", "bool", "float8_e4m3fn", "float8_e4m3fnuz", "float8_e5m2", "float8_e5m2fnuz"]
-        if self.weights_dtype not in accepted_weights:
-            raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights_dtype}")
-
-        if self.modules_to_not_convert is None:
-            self.modules_to_not_convert = []
-        elif not isinstance(self.modules_to_not_convert, list):
-            self.modules_to_not_convert = [self.modules_to_not_convert]
-
-        if self.modules_dtype_dict is None:
-            self.modules_dtype_dict = {}
-
-
-import diffusers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
-diffusers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
-diffusers.quantizers.auto.AUTO_QUANTIZATION_CONFIG_MAPPING["sdnq"] = SDNQConfig
-
-import transformers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
-transformers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
-transformers.quantizers.auto.AUTO_QUANTIZATION_CONFIG_MAPPING["sdnq"] = SDNQConfig
+from .quantizer import SDNQConfig, SDNQQuantizer, apply_sdnq_to_module, sdnq_quantize_layer
+from .loader import save_sdnq_model, load_sdnq_model
+
+
+__all__ = [
+    "SDNQConfig",
+    "SDNQQuantizer",
+    "apply_sdnq_to_module",
+    "load_sdnq_model",
+    "save_sdnq_model",
+    "sdnq_quantize_layer",
+]
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
new file mode 100644
index 000000000..3473e70ad
--- /dev/null
+++ b/modules/sdnq/loader.py
@@ -0,0 +1,65 @@
+import os
+import json
+import torch
+from safetensors import safe_open
+from diffusers.models.modeling_utils import ModelMixin
+
+from .quantizer import SDNQConfig, apply_sdnq_to_module
+from .common import use_contiguous_mm, use_tensorwise_fp8_matmul
+from .dequantizer import dequantize_symmetric_compiled, quantize_fp8
+
+
+def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str) -> None:
+    model.save_pretrained(model_path)
+    sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
+
+
+def load_sdnq_model(model_cls: ModelMixin, model_path: str, use_quantized_matmul: bool = False) -> ModelMixin:
+    with torch.device("meta"):
+        with open(os.path.join(model_path, "quantization_config.json"), "r") as f:
+            quantization_config = json.load(f)
+        quantization_config.pop("is_integer", None)
+        quantization_config.pop("quant_method", None)
+        quantization_config.pop("quantization_device", None)
+        quantization_config.pop("return_device", None)
+        quantization_config.pop("non_blocking", None)
+        config = model_cls.load_config(model_path)
+        model = model_cls.from_config(config)
+        model = apply_sdnq_to_module(model, **quantization_config)
+
+    state_dict = {}
+    with safe_open(os.path.join(model_path, "diffusion_pytorch_model.safetensors"), framework="pt") as f:
+        for k in f.keys():
+            state_dict[k] = f.get_tensor(k)
+    model.load_state_dict(state_dict, assign=True)
+    del state_dict
+    if use_quantized_matmul and not quantization_config["use_quantized_matmul"]:
+        model = enable_quantized_mamtul(model)
+    return model
+
+
+def enable_quantized_mamtul(model):
+    has_children = list(model.children())
+    if not has_children:
+        return model
+    for module in model.children():
+        if hasattr(module, "sdnq_dequantizer"):
+            if not module.sdnq_dequantizer.use_quantized_matmul:
+                if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn"} and module.sdnq_dequantizer.result_shape != module.weight.shape:
+                    if module.sdnq_dequantizer.weights_dtype == "int8":
+                        module.weight.data, module.scale.data = module.sdnq_dequantizer.re_quantize_matmul(module.weight, module.scale, module.zero_point, None, None)
+                    elif module.sdnq_dequantizer.weights_dtype == "float8_e4m3fn":
+                        module.weight.data, module.scale.data = quantize_fp8(dequantize_symmetric_compiled(module.weight, module.scale, module.sdnq_dequantizer.result_dtype, module.sdnq_dequantizer.result_shape))
+                        module.weight.data, module.scale.data = module.weight.t_(), module.scale.t_()
+                        if not use_tensorwise_fp8_matmul:
+                            module.scale.data = module.scale.to(dtype=torch.float32)
+                    if use_contiguous_mm:
+                        module.weight.data = module.weight.contiguous()
+                    elif module.weight.is_contiguous():
+                        module.weight.data = module.weight.t_().contiguous().t_()
+                if module.svd_up is not None:
+                    module.svd_up.data = module.svd_up.t_()
+                    module.svd_down.data = module.svd_down.t_()
+                module.sdnq_dequantizer.use_quantized_matmul = True
+        module = enable_quantized_mamtul(module)
+    return model
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
new file mode 100644
index 000000000..135736d60
--- /dev/null
+++ b/modules/sdnq/quantizer.py
@@ -0,0 +1,635 @@
+# pylint: disable=redefined-builtin,no-member,protected-access
+
+from typing import Dict, List, Tuple, Optional, Union
+from dataclasses import dataclass
+from enum import Enum
+
+import re
+import torch
+from diffusers.quantizers.base import DiffusersQuantizer
+from diffusers.quantizers.quantization_config import QuantizationConfigMixin
+from diffusers.utils import get_module_from_name
+from modules import devices, shared
+
+from .common import dtype_dict, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
+from .dequantizer import dequantizer_dict
+from .forward import get_forward_func
+
+
+class QuantizationMethod(str, Enum):
+    SDNQ = "sdnq"
+
+
+def get_scale_asymmetric(weight: torch.FloatTensor, reduction_axes: Union[int, List[int]], weights_dtype: str) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+    zero_point = torch.amin(weight, dim=reduction_axes, keepdims=True)
+    scale = torch.amax(weight, dim=reduction_axes, keepdims=True).sub_(zero_point).div_(dtype_dict[weights_dtype]["max"] - dtype_dict[weights_dtype]["min"])
+    if dtype_dict[weights_dtype]["min"] != 0:
+        zero_point.sub_(torch.mul(scale, dtype_dict[weights_dtype]["min"]))
+    return scale, zero_point
+
+
+def get_scale_symmetric(weight: torch.FloatTensor, reduction_axes: Union[int, List[int]], weights_dtype: str) -> torch.FloatTensor:
+    return torch.amax(weight.abs(), dim=reduction_axes, keepdims=True).div_(dtype_dict[weights_dtype]["max"])
+
+
+def quantize_weight(weight: torch.FloatTensor, reduction_axes: Union[int, List[int]], weights_dtype: str) -> Tuple[torch.Tensor, torch.FloatTensor, torch.FloatTensor]:
+    if dtype_dict[weights_dtype]["is_unsigned"]:
+        scale, zero_point = get_scale_asymmetric(weight, reduction_axes, weights_dtype)
+        quantized_weight = torch.sub(weight, zero_point).div_(scale)
+    else:
+        scale = get_scale_symmetric(weight, reduction_axes, weights_dtype)
+        quantized_weight = torch.div(weight, scale)
+        zero_point = None
+    if dtype_dict[weights_dtype]["is_integer"]:
+        quantized_weight.round_()
+    else:
+        quantized_weight.nan_to_num_()
+    quantized_weight = quantized_weight.clamp_(dtype_dict[weights_dtype]["min"], dtype_dict[weights_dtype]["max"]).to(dtype_dict[weights_dtype]["torch_dtype"])
+    return quantized_weight, scale, zero_point
+
+
+def apply_svdquant(weight: torch.FloatTensor, rank: int = 128) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+    U, S, svd_down = torch.svd_lowrank(weight, q=rank)
+    svd_up = torch.mul(U, S.unsqueeze(0))
+    svd_down = svd_down.t_()
+    return weight.sub_(torch.mm(svd_up, svd_down)), svd_up, svd_down
+
+
+@devices.inference_context()
+def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
+    layer_class_name = layer.__class__.__name__
+    if layer_class_name in allowed_types:
+        num_of_groups = 1
+        is_conv_type = False
+        is_conv_transpose_type = False
+        is_linear_type = False
+        result_shape = None
+        original_shape = layer.weight.shape
+        if torch_dtype is None:
+            torch_dtype = devices.dtype
+
+        if layer_class_name in conv_types:
+            if not quant_conv:
+                return layer
+            if dtype_dict[weights_dtype]["num_bits"] < 4:
+                weights_dtype = "uint4"
+            is_conv_type = True
+            reduction_axes = 1
+            output_channel_size, channel_size = layer.weight.shape[:2]
+            group_channel_size = channel_size // layer.groups
+            use_quantized_matmul = False
+            if use_quantized_matmul_conv:
+                use_quantized_matmul = group_channel_size >= 32 and output_channel_size >= 32
+                if use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"]:
+                    use_quantized_matmul = output_channel_size % 16 == 0 and group_channel_size % 16 == 0
+            if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
+                result_shape = layer.weight.shape
+                layer.weight.data = layer.weight.flatten(1,-1)
+                reduction_axes = -1
+        elif layer_class_name in conv_transpose_types:
+            if not quant_conv:
+                return layer
+            if dtype_dict[weights_dtype]["num_bits"] < 4:
+                weights_dtype = "uint4"
+            is_conv_transpose_type = True
+            reduction_axes = 0
+            channel_size, output_channel_size = layer.weight.shape[:2]
+            use_quantized_matmul = False
+        else:
+            is_linear_type = True
+            reduction_axes = -1
+            try:
+                output_channel_size, channel_size = layer.weight.shape
+            except Exception as e:
+                raise ValueError(f"SDNQ: param_name={param_name} layer_class_name={layer_class_name} layer_weight_shape={layer.weight.shape} weights_dtype={weights_dtype} unsupported") from e
+            if use_quantized_matmul:
+                use_quantized_matmul = channel_size >= 32 and output_channel_size >= 32
+                if use_quantized_matmul:
+                    if dtype_dict[weights_dtype]["is_integer"]:
+                        use_quantized_matmul = output_channel_size % 8 == 0 and channel_size % 8 == 0
+                    else:
+                        use_quantized_matmul = output_channel_size % 16 == 0 and channel_size % 16 == 0
+
+        layer.weight.requires_grad = False
+        if return_device is None:
+            return_device = layer.weight.device
+        if quantization_device is not None:
+            layer.weight.data = layer.weight.to(quantization_device, non_blocking=non_blocking)
+        if layer.weight.dtype != torch.float32:
+            layer.weight.data = layer.weight.to(dtype=torch.float32)
+
+        if use_svd and is_linear_type:
+            layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank)
+            if use_quantized_matmul:
+                svd_up = svd_up.t_()
+                svd_down = svd_down.t_()
+        else:
+            use_svd = False
+            svd_up, svd_down = None, None
+
+        if group_size == 0:
+            if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] >= 6:
+                group_size = -1
+            elif is_linear_type:
+                group_size = 2 ** ((2 if not use_svd else 3) + dtype_dict[weights_dtype]["num_bits"])
+            else:
+                group_size = 2 ** ((1 if not use_svd else 2) + dtype_dict[weights_dtype]["num_bits"])
+        elif use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
+            group_size = -1 # override user value, re-quantizing 8bit into 8bit is pointless
+        elif group_size != -1 and not is_linear_type:
+            group_size = max(group_size // 2, 1)
+
+        if group_size > 0:
+            if group_size >= channel_size:
+                group_size = channel_size
+                num_of_groups = 1
+            else:
+                num_of_groups = channel_size // group_size
+                while num_of_groups * group_size != channel_size: # find something divisible
+                    num_of_groups -= 1
+                    if num_of_groups <= 1:
+                        group_size = channel_size
+                        num_of_groups = 1
+                        break
+                    group_size = channel_size // num_of_groups
+            group_size = int(group_size)
+            num_of_groups = int(num_of_groups)
+
+            if num_of_groups > 1:
+                if result_shape is None:
+                    result_shape = layer.weight.shape
+                new_shape = list(result_shape)
+                if is_conv_type:
+                    # output_channel_size, channel_size, X, X
+                    # output_channel_size, num_of_groups, group_size, X, X
+                    new_shape[1] = group_size
+                    new_shape.insert(1, num_of_groups)
+                    reduction_axes = 2
+                elif is_conv_transpose_type:
+                    #channel_size, output_channel_size, X, X
+                    #num_of_groups, group_size, output_channel_size, X, X
+                    new_shape[0] = group_size
+                    new_shape.insert(0, num_of_groups)
+                    reduction_axes = 1
+                elif is_linear_type:
+                    # output_channel_size, channel_size
+                    # output_channel_size, num_of_groups, group_size
+                    last_dim_index = layer.weight.ndim
+                    new_shape[last_dim_index - 1 : last_dim_index] = (num_of_groups, group_size)
+                layer.weight.data = layer.weight.reshape(new_shape)
+
+        layer.weight.data, scale, zero_point = quantize_weight(layer.weight, reduction_axes, weights_dtype)
+        if not dequantize_fp32 and not (use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"] and not use_tensorwise_fp8_matmul):
+            scale = scale.to(dtype=torch_dtype)
+            if zero_point is not None:
+                zero_point = zero_point.to(dtype=torch_dtype)
+            if svd_up is not None:
+                svd_up = svd_up.to(dtype=torch_dtype)
+                svd_down = svd_down.to(dtype=torch_dtype)
+
+        re_quantize_for_matmul = (num_of_groups > 1 or zero_point is not None)
+        if use_quantized_matmul and not re_quantize_for_matmul:
+            scale.t_()
+            layer.weight.t_()
+            if use_contiguous_mm:
+                layer.weight.data = layer.weight.contiguous()
+            elif layer.weight.is_contiguous():
+                layer.weight.data = layer.weight.t_().contiguous().t_()
+            if not use_tensorwise_fp8_matmul and not dtype_dict[weights_dtype]["is_integer"]:
+                scale = scale.to(dtype=torch.float32)
+
+        scale = scale.to(return_device, non_blocking=non_blocking)
+        layer.scale = torch.nn.Parameter(scale, requires_grad=False)
+        if zero_point is not None:
+            zero_point = zero_point.to(return_device, non_blocking=non_blocking)
+            layer.zero_point = torch.nn.Parameter(zero_point, requires_grad=False)
+        else:
+            layer.zero_point = None
+        if svd_up is not None:
+            svd_up = svd_up.to(return_device, non_blocking=non_blocking)
+            svd_down = svd_down.to(return_device, non_blocking=non_blocking)
+            layer.svd_up = torch.nn.Parameter(svd_up, requires_grad=False)
+            layer.svd_down = torch.nn.Parameter(svd_down, requires_grad=False)
+        else:
+            layer.svd_up, layer.svd_down = None, None
+
+        layer.sdnq_dequantizer = dequantizer_dict[weights_dtype](
+            quantized_weight_shape=layer.weight.shape,
+            result_dtype=torch_dtype,
+            result_shape=result_shape,
+            original_shape=original_shape,
+            weights_dtype=weights_dtype,
+            use_quantized_matmul=use_quantized_matmul,
+            re_quantize_for_matmul=re_quantize_for_matmul,
+        )
+        layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
+        layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
+
+        layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
+        layer.forward = layer.forward.__get__(layer, layer.__class__)
+    return layer
+
+
+def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, full_param_name="", op=None): # pylint: disable=unused-argument
+    has_children = list(model.children())
+    if not has_children:
+        return model
+    if modules_to_not_convert is None:
+        modules_to_not_convert = []
+    if modules_dtype_dict is None:
+        modules_dtype_dict = {}
+    for param_name, module in model.named_children():
+        if param_name == "sdnq_dequantizer":
+            continue
+        if full_param_name:
+            param_name = full_param_name + "." + param_name
+        if hasattr(module, "weight") and module.weight is not None:
+            param_name = param_name + ".weight"
+            split_param_name = param_name.split(".")
+            if (
+                param_name in modules_to_not_convert
+                or any(param in split_param_name for param in modules_to_not_convert)
+                or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in modules_to_not_convert)
+            ):
+                continue
+            else:
+                layer_class_name = module.__class__.__name__
+                if layer_class_name in allowed_types:
+                    if (layer_class_name in conv_types or layer_class_name in conv_transpose_types) and not quant_conv:
+                        continue
+                else:
+                    continue
+            if len(modules_dtype_dict.keys()) > 0:
+                for key, value in modules_dtype_dict.items():
+                    if param_name in value:
+                        key = key.lower()
+                        if key in {"8bit", "8bits"}:
+                            if dtype_dict[weights_dtype]["num_bits"] != 8:
+                                weights_dtype = "int8"
+                        elif key.startswith("minimum_"):
+                            minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
+                            minimum_bits = int(minimum_bits_str)
+                            if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
+                                weights_dtype = "int" + minimum_bits_str
+                                if minimum_bits <= 4:
+                                    weights_dtype = "u" + weights_dtype
+                        else:
+                            weights_dtype = key
+
+            module = sdnq_quantize_layer(
+                module,
+                weights_dtype=weights_dtype,
+                torch_dtype=torch_dtype,
+                group_size=group_size,
+                svd_rank=svd_rank,
+                use_svd=use_svd,
+                quant_conv=quant_conv,
+                use_quantized_matmul=use_quantized_matmul,
+                use_quantized_matmul_conv=use_quantized_matmul_conv,
+                dequantize_fp32=dequantize_fp32,
+                non_blocking=non_blocking,
+                quantization_device=quantization_device,
+                return_device=return_device,
+                param_name=param_name,
+            )
+        module = apply_sdnq_to_module(
+                module,
+                weights_dtype=weights_dtype,
+                torch_dtype=torch_dtype,
+                group_size=group_size,
+                svd_rank=svd_rank,
+                use_svd=use_svd,
+                quant_conv=quant_conv,
+                use_quantized_matmul=use_quantized_matmul,
+                use_quantized_matmul_conv=use_quantized_matmul_conv,
+                dequantize_fp32=dequantize_fp32,
+                non_blocking=non_blocking,
+                quantization_device=quantization_device,
+                return_device=return_device,
+                modules_to_not_convert=modules_to_not_convert,
+                modules_dtype_dict=modules_dtype_dict,
+                full_param_name=param_name,
+                op=op,
+            )
+    return model
+
+
+class SDNQQuantizer(DiffusersQuantizer):
+    r"""
+    Diffusers Quantizer for SDNQ
+    """
+
+    requires_parameters_quantization = True
+    use_keep_in_fp32_modules = True
+    requires_calibration = False
+    required_packages = None
+    torch_dtype = None
+
+    def __init__(self, quantization_config, **kwargs):
+        super().__init__(quantization_config, **kwargs)
+        self.modules_to_not_convert = []
+
+    def check_if_quantized_param(
+        self,
+        model,
+        param_value: "torch.Tensor",
+        param_name: str,
+        *args, **kwargs, # pylint: disable=unused-argument
+    ):
+        if param_name.endswith(".weight"):
+            split_param_name = param_name.split(".")
+            if (
+                param_name not in self.modules_to_not_convert
+                and not any(param in split_param_name for param in self.modules_to_not_convert)
+                and not any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in self.modules_to_not_convert)
+            ):
+                layer_class_name = get_module_from_name(model, param_name)[0].__class__.__name__
+                if layer_class_name in allowed_types:
+                    if layer_class_name in conv_types or layer_class_name in conv_transpose_types:
+                        if self.quantization_config.quant_conv:
+                            return True
+                    else:
+                        return True
+        if param_value is not None:
+            with devices.inference_context():
+                param_value.data = param_value.clone() # safetensors is unable to release the cpu memory without this
+        return False
+
+    def check_quantized_param(self, *args, **kwargs) -> bool:
+        """
+        needed for transformers compatibilty, returns self.check_if_quantized_param
+        """
+        return self.check_if_quantized_param(*args, **kwargs)
+
+    def param_needs_quantization(self, model, param_name: str, *args, **kwargs) -> bool:
+        """
+        needed for transformers compatibilty, returns self.check_if_quantized_param
+        """
+        return self.check_if_quantized_param(model, None, param_name, *args, **kwargs)
+
+    @devices.inference_context()
+    def create_quantized_param( # pylint: disable=arguments-differ
+        self,
+        model,
+        param_value: torch.FloatTensor,
+        param_name: str,
+        target_device: torch.device,
+        *args, **kwargs, # pylint: disable=unused-argument
+    ):
+        weights_dtype = self.quantization_config.weights_dtype
+        if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
+            split_param_name = param_name.split(".")
+            for key, value in self.quantization_config.modules_dtype_dict.items():
+                if (
+                    param_name in value
+                    or any(param in split_param_name for param in value)
+                    or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in value)
+                ):
+                    key = key.lower()
+                    if key in {"8bit", "8bits"}:
+                        if dtype_dict[weights_dtype]["num_bits"] != 8:
+                            weights_dtype = "int8"
+                    elif key.startswith("minimum_"):
+                        minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
+                        minimum_bits = int(minimum_bits_str)
+                        if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
+                            weights_dtype = "int" + minimum_bits_str
+                            if minimum_bits <= 4:
+                                weights_dtype = "u" + weights_dtype
+                    else:
+                        weights_dtype = key
+
+        if self.quantization_config.return_device is not None:
+            return_device = self.quantization_config.return_device
+        else:
+            return_device = target_device
+
+        if self.quantization_config.quantization_device is not None:
+            target_device = self.quantization_config.quantization_device
+
+        if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
+            param_value = param_value.clone()
+        else:
+            param_value = param_value.to(target_device, non_blocking=self.quantization_config.non_blocking).to(dtype=torch.float32)
+
+        layer, _ = get_module_from_name(model, param_name)
+        layer.weight = torch.nn.Parameter(param_value, requires_grad=False)
+        layer = sdnq_quantize_layer(
+            layer,
+            weights_dtype=weights_dtype,
+            torch_dtype=self.torch_dtype,
+            group_size=self.quantization_config.group_size,
+            svd_rank=self.quantization_config.svd_rank,
+            use_svd=self.quantization_config.use_svd,
+            quant_conv=self.quantization_config.quant_conv,
+            use_quantized_matmul=self.quantization_config.use_quantized_matmul,
+            use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
+            dequantize_fp32=self.quantization_config.dequantize_fp32,
+            non_blocking=self.quantization_config.non_blocking,
+            quantization_device=None,
+            return_device=return_device,
+            param_name=param_name,
+        )
+
+    def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
+        max_memory = {key: val * 0.80 for key, val in max_memory.items()}
+        return max_memory
+
+    def adjust_target_dtype(self, target_dtype: torch.dtype) -> torch.dtype: # pylint: disable=unused-argument,arguments-renamed
+        return dtype_dict[self.quantization_config.weights_dtype]["target_dtype"]
+
+    def update_torch_dtype(self, torch_dtype: torch.dtype = None) -> torch.dtype:
+        if torch_dtype is None:
+            torch_dtype = devices.dtype
+        self.torch_dtype = torch_dtype
+        return torch_dtype
+
+    def _process_model_before_weight_loading( # pylint: disable=arguments-differ
+        self,
+        model,
+        device_map, # pylint: disable=unused-argument
+        keep_in_fp32_modules: List[str] = None,
+        **kwargs, # pylint: disable=unused-argument
+    ):
+        if keep_in_fp32_modules is not None:
+            self.modules_to_not_convert.extend(keep_in_fp32_modules)
+        elif getattr(model, "_keep_in_fp32_modules", None) is not None:
+            self.modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
+        if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
+            self.modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
+        self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
+        self.quantization_config.modules_to_not_convert = self.modules_to_not_convert
+        model.config.quantization_config = self.quantization_config
+
+    def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument
+        if shared.opts.diffusers_offload_mode != "none":
+            model = model.to(devices.cpu)
+        devices.torch_gc(force=True, reason="sdnq")
+        return model
+
+    def get_accelerator_warm_up_factor(self):
+        return 32 // dtype_dict[self.quantization_config.weights_dtype]["num_bits"]
+
+    def get_cuda_warm_up_factor(self):
+        """
+        needed for transformers compatibilty, returns self.get_accelerator_warm_up_factor
+        """
+        return self.get_accelerator_warm_up_factor()
+
+    def update_tp_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return config
+
+    def update_ep_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return config
+
+    def update_unexpected_keys(self, model, unexpected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return unexpected_keys
+
+    def update_missing_keys_after_loading(self, model, missing_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return missing_keys
+
+    def update_state_dict_with_metadata(self, state_dict: dict, *args, **kwargs) -> dict: # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return state_dict
+
+    def update_expected_keys(self, model, expected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return expected_keys
+
+    def update_param_name(self, param_name: str, *args, **kwargs) -> str: # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return param_name
+
+    def update_dtype(self, dtype: torch.dtype, *args, **kwargs) -> torch.dtype: # pylint: disable=unused-argument
+        """
+        needed for transformers compatibilty, no-op function
+        """
+        return dtype
+
+    @property
+    def is_trainable(self):
+        return False
+
+    @property
+    def is_serializable(self):
+        return True
+
+    @property
+    def is_compileable(self):
+        return True
+
+
+@dataclass
+class SDNQConfig(QuantizationConfigMixin):
+    """
+    This is a wrapper class about all possible attributes and features that you can play with a model that has been
+    loaded using `sdnq`.
+
+    Args:
+        weights_dtype (`str`, *optional*, defaults to `"int8"`):
+            The target dtype for the weights after quantization. Supported values are:
+            ("int8", "int7", "int6", "int5", "int4", "int3", "int2", "uint8", "uint7", "uint6", "uint5", "uint4", "uint3", "uint2", "uint1", "bool", "float8_e4m3fn", "float8_e4m3fnuz", "float8_e5m2", "float8_e5m2fnuz")
+        group_size (`int`, *optional*, defaults to `0`):
+            Used to decide how many elements of a tensor will share the same quantization group.
+            group_size = 0 will automatically select a group size based on weights_dtype.
+        svd_rank (`int`, *optional*, defaults to `128`):
+            The rank size used for the SVDQuant algorithm.
+        use_svd (`bool`, *optional*, defaults to `False`):
+            Enabling this option will use SVDQuant algorithm.
+        quant_conv (`bool`, *optional*, defaults to `False`):
+            Enabling this option will quantize the convolutional layers in UNet models too.
+        use_quantized_matmul (`bool`, *optional*, defaults to `False`):
+            Enabling this option will use quantized INT8 or FP8 MatMul instead of BF16 / FP16.
+        use_quantized_matmul_conv (`bool`, *optional*, defaults to `False`):
+            Same as use_quantized_matmul_conv but for the convolutional layers with UNets like SDXL.
+        dequantize_fp32 (`bool`, *optional*, defaults to `False`):
+            Enabling this option will use FP32 on the dequantization step.
+        non_blocking (`bool`, *optional*, defaults to `False`):
+            Enabling this option will use non blocking ops when moving layers between the quantization device and the return device.
+        quantization_device (`torch.device`, *optional*, defaults to `None`):
+            Used to set which device will be used for the quantization calculation on model load.
+        return_device (`torch.device`, *optional*, defaults to `None`):
+            Used to set which device will the quantized weights be sent back to.
+        modules_to_not_convert (`list`, *optional*, default to `None`):
+            The list of modules to not quantize, useful for quantizing models that explicitly require to have some
+            modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
+        modules_dtype_dict (`dict`, *optional*, default to `None`):
+            The dict of dtypes and list of modules, useful for quantizing some modules with a different dtype.
+    """
+
+    def __init__( # pylint: disable=super-init-not-called
+        self,
+        weights_dtype: str = "int8",
+        group_size: int = 0,
+        svd_rank: int = 128,
+        use_svd: bool = False,
+        quant_conv: bool = False,
+        use_quantized_matmul: bool = False,
+        use_quantized_matmul_conv: bool = False,
+        dequantize_fp32: bool = False,
+        non_blocking: bool = False,
+        quantization_device: Optional[torch.device] = None,
+        return_device: Optional[torch.device] = None,
+        modules_to_not_convert: Optional[List[str]] = None,
+        modules_dtype_dict: Optional[Dict[str, List[str]]] = None,
+        **kwargs, # pylint: disable=unused-argument
+    ):
+        self.weights_dtype = weights_dtype
+        self.quant_method = QuantizationMethod.SDNQ
+        self.group_size = group_size
+        self.svd_rank = svd_rank
+        self.use_svd = use_svd
+        self.quant_conv = quant_conv
+        self.use_quantized_matmul = use_quantized_matmul
+        self.use_quantized_matmul_conv = use_quantized_matmul_conv
+        self.dequantize_fp32 = dequantize_fp32
+        self.non_blocking = non_blocking
+        self.quantization_device = quantization_device
+        self.return_device = return_device
+        self.modules_to_not_convert = modules_to_not_convert
+        self.modules_dtype_dict = modules_dtype_dict
+        self.post_init()
+        self.is_integer = dtype_dict[self.weights_dtype]["is_integer"]
+
+    def post_init(self):
+        r"""
+        Safety checker that arguments are correct
+        """
+        accepted_weights = ["int8", "int7", "int6", "int5", "int4", "int3", "int2", "uint8", "uint7", "uint6", "uint5", "uint4", "uint3", "uint2", "uint1", "bool", "float8_e4m3fn", "float8_e4m3fnuz", "float8_e5m2", "float8_e5m2fnuz"]
+        if self.weights_dtype not in accepted_weights:
+            raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights_dtype}")
+
+        if self.modules_to_not_convert is None:
+            self.modules_to_not_convert = []
+        elif not isinstance(self.modules_to_not_convert, list):
+            self.modules_to_not_convert = [self.modules_to_not_convert]
+
+        if self.modules_dtype_dict is None:
+            self.modules_dtype_dict = {}
+
+
+import diffusers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
+diffusers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
+diffusers.quantizers.auto.AUTO_QUANTIZATION_CONFIG_MAPPING["sdnq"] = SDNQConfig
+
+import transformers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
+transformers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
+transformers.quantizers.auto.AUTO_QUANTIZATION_CONFIG_MAPPING["sdnq"] = SDNQConfig

From ebb26ac123c548981c5a9b7d38d739a3e217da5a Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 01:04:00 +0300
Subject: [PATCH 145/230] SDNQ make load file name configurable

---
 modules/sdnq/loader.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 3473e70ad..fc2ed831c 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -14,7 +14,7 @@ def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str)
     sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
 
 
-def load_sdnq_model(model_cls: ModelMixin, model_path: str, use_quantized_matmul: bool = False) -> ModelMixin:
+def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", use_quantized_matmul: bool = False) -> ModelMixin:
     with torch.device("meta"):
         with open(os.path.join(model_path, "quantization_config.json"), "r") as f:
             quantization_config = json.load(f)
@@ -28,7 +28,7 @@ def load_sdnq_model(model_cls: ModelMixin, model_path: str, use_quantized_matmul
         model = apply_sdnq_to_module(model, **quantization_config)
 
     state_dict = {}
-    with safe_open(os.path.join(model_path, "diffusion_pytorch_model.safetensors"), framework="pt") as f:
+    with safe_open(os.path.join(model_path, file_name), framework="pt") as f:
         for k in f.keys():
             state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)

From 1f81a37e8e8dea43f2497d26d1b1d32475294cb9 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 01:27:29 +0300
Subject: [PATCH 146/230] Set the default svd rank to 32

---
 modules/sdnq/loader.py    |  4 ++--
 modules/sdnq/quantizer.py | 10 +++++-----
 modules/shared.py         |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index fc2ed831c..6a625ba17 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -9,8 +9,8 @@ from .common import use_contiguous_mm, use_tensorwise_fp8_matmul
 from .dequantizer import dequantize_symmetric_compiled, quantize_fp8
 
 
-def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str) -> None:
-    model.save_pretrained(model_path)
+def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str, max_shard_size: str = "10GB") -> None:
+    model.save_pretrained(model_path, max_shard_size=max_shard_size)
     sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
 
 
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 135736d60..a92cb455a 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -48,7 +48,7 @@ def quantize_weight(weight: torch.FloatTensor, reduction_axes: Union[int, List[i
     return quantized_weight, scale, zero_point
 
 
-def apply_svdquant(weight: torch.FloatTensor, rank: int = 128) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+def apply_svdquant(weight: torch.FloatTensor, rank: int = 32) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
     U, S, svd_down = torch.svd_lowrank(weight, q=rank)
     svd_up = torch.mul(U, S.unsqueeze(0))
     svd_down = svd_down.t_()
@@ -56,7 +56,7 @@ def apply_svdquant(weight: torch.FloatTensor, rank: int = 128) -> Tuple[torch.Fl
 
 
 @devices.inference_context()
-def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
+def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
     layer_class_name = layer.__class__.__name__
     if layer_class_name in allowed_types:
         num_of_groups = 1
@@ -230,7 +230,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
     return layer
 
 
-def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=128, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, full_param_name="", op=None): # pylint: disable=unused-argument
+def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, full_param_name="", op=None): # pylint: disable=unused-argument
     has_children = list(model.children())
     if not has_children:
         return model
@@ -550,7 +550,7 @@ class SDNQConfig(QuantizationConfigMixin):
         group_size (`int`, *optional*, defaults to `0`):
             Used to decide how many elements of a tensor will share the same quantization group.
             group_size = 0 will automatically select a group size based on weights_dtype.
-        svd_rank (`int`, *optional*, defaults to `128`):
+        svd_rank (`int`, *optional*, defaults to `32`):
             The rank size used for the SVDQuant algorithm.
         use_svd (`bool`, *optional*, defaults to `False`):
             Enabling this option will use SVDQuant algorithm.
@@ -579,7 +579,7 @@ class SDNQConfig(QuantizationConfigMixin):
         self,
         weights_dtype: str = "int8",
         group_size: int = 0,
-        svd_rank: int = 128,
+        svd_rank: int = 32,
         use_svd: bool = False,
         quant_conv: bool = False,
         use_quantized_matmul: bool = False,
diff --git a/modules/shared.py b/modules/shared.py
index 339cac0f1..16009b1e0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -201,7 +201,7 @@ options_templates.update(options_section(("quantization", "Model Quantization"),
     "sdnq_modules_to_not_convert": OptionInfo("", "Modules to not convert"),
     "sdnq_modules_dtype_dict": OptionInfo("{}", "Modules dtype dict"),
     "sdnq_quantize_weights_group_size": OptionInfo(0, "Group size", gr.Slider, {"minimum": -1, "maximum": 4096, "step": 1}),
-    "sdnq_svd_rank": OptionInfo(128, "SVDQuant Rank size", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
+    "sdnq_svd_rank": OptionInfo(32, "SVDQuant Rank size", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
     "sdnq_use_svd": OptionInfo(False, "Use SVDQuant quantization", gr.Checkbox),
     "sdnq_quantize_conv_layers": OptionInfo(False, "Quantize convolutional layers", gr.Checkbox),
     "sdnq_dequantize_compile": OptionInfo(devices.has_triton(), "Dequantize using torch.compile", gr.Checkbox),

From 23f2deaa584e7e5a494936766129045be18e8d5e Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 02:04:28 +0300
Subject: [PATCH 147/230] fix enable_quantized_mamtul

---
 modules/sdnq/dequantizer.py | 11 ++++++++++-
 modules/sdnq/loader.py      | 18 +++++++++---------
 2 files changed, 19 insertions(+), 10 deletions(-)

diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 72a058555..38f09f0a1 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -4,7 +4,7 @@ from typing import Tuple, Optional
 
 import torch
 
-from .common import dtype_dict, compile_func
+from .common import dtype_dict, compile_func, use_tensorwise_fp8_matmul
 from .packed_int import pack_int_symetric, unpack_int_symetric, pack_int_asymetric, unpack_int_asymetric
 
 
@@ -67,6 +67,15 @@ def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch
     return weight, scale
 
 
+def re_quantize_fp8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
+    if weight.ndim > 2: # convs
+        weight = weight.flatten(1,-1)
+    weight, scale = quantize_fp8(weight.t(), dim=0)
+    if not use_tensorwise_fp8_matmul:
+        scale = scale.to(dtype=torch.float32)
+    return weight, scale
+
+
 def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, result_shape: torch.Size, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]:
     return re_quantize_int8(dequantize_asymmetric(weight, scale, zero_point, scale.dtype, result_shape, svd_up=svd_up, svd_down=svd_down))
 
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 6a625ba17..aabd2496c 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -4,9 +4,9 @@ import torch
 from safetensors import safe_open
 from diffusers.models.modeling_utils import ModelMixin
 
+from .common import use_contiguous_mm
 from .quantizer import SDNQConfig, apply_sdnq_to_module
-from .common import use_contiguous_mm, use_tensorwise_fp8_matmul
-from .dequantizer import dequantize_symmetric_compiled, quantize_fp8
+from .dequantizer import dequantize_symmetric_compiled, re_quantize_int8, re_quantize_fp8
 
 
 def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str, max_shard_size: str = "10GB") -> None:
@@ -45,14 +45,14 @@ def enable_quantized_mamtul(model):
     for module in model.children():
         if hasattr(module, "sdnq_dequantizer"):
             if not module.sdnq_dequantizer.use_quantized_matmul:
-                if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn"} and module.sdnq_dequantizer.result_shape != module.weight.shape:
-                    if module.sdnq_dequantizer.weights_dtype == "int8":
-                        module.weight.data, module.scale.data = module.sdnq_dequantizer.re_quantize_matmul(module.weight, module.scale, module.zero_point, None, None)
-                    elif module.sdnq_dequantizer.weights_dtype == "float8_e4m3fn":
-                        module.weight.data, module.scale.data = quantize_fp8(dequantize_symmetric_compiled(module.weight, module.scale, module.sdnq_dequantizer.result_dtype, module.sdnq_dequantizer.result_shape))
+                if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn"}:
+                    if module.sdnq_dequantizer.re_quantize_for_matmul:
+                        if module.sdnq_dequantizer.weights_dtype == "int8":
+                            module.weight.data, module.scale.data = re_quantize_int8(dequantize_symmetric_compiled(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
+                        else:
+                            module.weight.data, module.scale.data = re_quantize_fp8(dequantize_symmetric_compiled(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
+                    else:
                         module.weight.data, module.scale.data = module.weight.t_(), module.scale.t_()
-                        if not use_tensorwise_fp8_matmul:
-                            module.scale.data = module.scale.to(dtype=torch.float32)
                     if use_contiguous_mm:
                         module.weight.data = module.weight.contiguous()
                     elif module.weight.is_contiguous():

From 50c3385cf96b5ae2110cea1ec17e7148c22fbf81 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 11:59:58 -0400
Subject: [PATCH 148/230] fix ltx model selection

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                     |  7 ++++---
 modules/ltx/ltx_process.py       |  4 +++-
 modules/ltx/ltx_ui.py            | 14 +-------------
 modules/ltx/ltx_util.py          |  9 ++++++++-
 modules/video_models/video_ui.py |  3 ++-
 5 files changed, 18 insertions(+), 19 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 55df207f5..105788420 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -108,9 +108,10 @@
   - reference: fix download for sd15/sdxl reference models  
   - fix checks in init/mask image decode  
   - fix hf token with extra chars  
-  - **FramePack** job state tracking  
-  - **LTXVideo** with offloading  
-  - **WAN** with offloading  
+  - **FramePack** add job state tracking  
+  - **LTXVideo** fix model selection in ltx tab
+  - **LTXVideo** fix run with offloading  
+  - **WAN** fix run with offloading  
 
 ## Update for 2025-09-15
 
diff --git a/modules/ltx/ltx_process.py b/modules/ltx/ltx_process.py
index 79837f1fa..4de9084a5 100644
--- a/modules/ltx/ltx_process.py
+++ b/modules/ltx/ltx_process.py
@@ -13,7 +13,7 @@ from modules.ltx.ltx_util import get_bucket, get_frames, load_model, load_upsamp
 
 
 debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
-engine, model = 'LTX Video', 'LTXVideo 0.9.7 13B'
+# engine, model = 'LTX Video', 'LTXVideo 0.9.7 13B'
 upsample_repo_id = "a-r-r-o-w/LTX-Video-0.9.7-Latent-Spatial-Upsampler-diffusers"
 upsample_pipe = None
 queue_lock = threading.Lock()
@@ -21,6 +21,7 @@ queue_lock = threading.Lock()
 
 def run_ltx(task_id,
             _ui_state,
+            model:str,
             prompt:str,
             negative:str,
             styles:list[str],
@@ -73,6 +74,7 @@ def run_ltx(task_id,
         memstats.reset_stats()
         timer.process.reset()
         yield None, 'LTX: Loading...'
+        engine = 'LTX Video'
         load_model(engine, model)
 
         shared.state.begin('Video', task_id=task_id)
diff --git a/modules/ltx/ltx_ui.py b/modules/ltx/ltx_ui.py
index e0626bbe4..04cea1d1e 100644
--- a/modules/ltx/ltx_ui.py
+++ b/modules/ltx/ltx_ui.py
@@ -10,18 +10,6 @@ from modules.ltx import ltx_process
 debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
-def load_model(model_name):
-    if model_name is None or model_name == 'None':
-        shared.log.info('LTX model unload')
-        from modules import sd_models
-        sd_models.unload_model_weights()
-        return
-    else:
-        model_instance = [m for m in models['LTX Video'] if m.name == model_name][0]
-        from modules.video_models import video_load
-        video_load.load_model(model_instance)
-
-
 def create_ui(prompt, negative, styles, overrides):
     with gr.Row():
         with gr.Column(variant='compact', elem_id="ltx_settings", elem_classes=['settings-column'], scale=1):
@@ -30,7 +18,6 @@ def create_ui(prompt, negative, styles, overrides):
             with gr.Row():
                 ltx_models = [m.name for m in models['LTX Video']]
                 model = gr.Dropdown(label='LTX model', choices=ltx_models, value=ltx_models[0])
-                model.change(fn=load_model, inputs=[model], outputs=[], show_progress=True)
             with gr.Accordion(open=True, label="LTX size", elem_id='ltx_generate_accordion'):
                 with gr.Row():
                     width, height = ui_sections.create_resolution_inputs('ltx', default_width=832, default_height=480)
@@ -90,6 +77,7 @@ def create_ui(prompt, negative, styles, overrides):
     state_inputs = [task_id, ui_state]
 
     video_inputs = [
+        model,
         prompt, negative, styles,
         width, height, frames,
         steps, sampler_index, seed,
diff --git a/modules/ltx/ltx_util.py b/modules/ltx/ltx_util.py
index a07434734..9cca59306 100644
--- a/modules/ltx/ltx_util.py
+++ b/modules/ltx/ltx_util.py
@@ -4,6 +4,9 @@ from PIL import Image
 from modules import devices, shared, sd_models, timer, extra_networks
 
 
+loaded_model: str = None
+
+
 def get_bucket(size: int):
     return int(size) - (int(size) % shared.sd_model.vae_temporal_compression_ratio)
 
@@ -13,11 +16,15 @@ def get_frames(frames: int):
 
 
 def load_model(engine: str, model: str):
+    global loaded_model # pylint: disable=global-statement
+    if loaded_model == model:
+        return
     t0 = time.time()
     from modules.video_models import models_def, video_load
     selected: models_def.Model = [m for m in models_def.models[engine] if m.name == model][0]
-    shared.log.info(f'Video load: cls={selected.repo_cls.__name__} repo="{selected.repo}"')
+    shared.log.info(f'Video load: engine="{engine}" selected="{model}" {selected}')
     video_load.load_model(selected)
+    loaded_model = model
     t1 = time.time()
     shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
     t2 = time.time()
diff --git a/modules/video_models/video_ui.py b/modules/video_models/video_ui.py
index 5b103d023..910f665a4 100644
--- a/modules/video_models/video_ui.py
+++ b/modules/video_models/video_ui.py
@@ -1,6 +1,6 @@
 import os
 import gradio as gr
-from modules import shared, sd_models, ui_common, ui_sections, ui_symbols, call_queue
+from modules import shared, sd_models, ui_common, ui_sections, ui_symbols, ui_video_vlm, call_queue
 from modules.ui_components import ToolButton
 from modules.video_models import models_def, video_utils
 from modules.video_models import video_run
@@ -112,6 +112,7 @@ def create_ui(prompt, negative, styles, overrides):
                 init_image = gr.Image(elem_id="video_image", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
                 gr.HTML("<br>&nbsp Last image")
                 last_image = gr.Image(elem_id="video_last", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
+            vlm_enhance, vlm_model, vlm_system_prompt = ui_video_vlm.create_ui(prompt_element=prompt, image_element=init_image)
             with gr.Accordion(open=False, label="Output", elem_id='video_output_accordion'):
                 with gr.Row():
                     save_frames = gr.Checkbox(label='Save image frames', value=False, elem_id="video_save_frames")

From 7dad90b385cb00aff983626dbf3973d8ddaa08d2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 12:15:15 -0400
Subject: [PATCH 149/230] video use shared t5

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 10 ++++++----
 modules/video_models/video_load.py | 15 +++++++++++++--
 2 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 105788420..ddd8b3a5d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -87,6 +87,12 @@
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
   - **nunchaku** update to `1.0.1` and enhance installer  
   - **xyz-grid** add guidance section  
+- **Video**
+  - use shared T5 text encoder for video models when possible  
+  - **FramePack** add job state tracking  
+  - **LTXVideo** fix model selection in ltx tab
+  - **LTXVideo** fix run with offloading  
+  - **WAN** fix run with offloading  
 - **Experimental**
   - `new` command line flag enables new `pydantic` and `albumentations` packages  
   - **modular pipelines**: enable in *settings -> model options*  
@@ -108,10 +114,6 @@
   - reference: fix download for sd15/sdxl reference models  
   - fix checks in init/mask image decode  
   - fix hf token with extra chars  
-  - **FramePack** add job state tracking  
-  - **LTXVideo** fix model selection in ltx tab
-  - **LTXVideo** fix run with offloading  
-  - **WAN** fix run with offloading  
 
 ## Update for 2025-09-15
 
diff --git a/modules/video_models/video_load.py b/modules/video_models/video_load.py
index 874df0231..51a17d2ce 100644
--- a/modules/video_models/video_load.py
+++ b/modules/video_models/video_load.py
@@ -25,6 +25,10 @@ def load_model(selected: models_def.Model):
     # text encoder
     try:
         load_args, quant_args = model_quant.get_dit_args({}, module='TE', device_map=True)
+        if selected.te_cls.__name__ == 'T5EncoderModel' and shared.opts.te_shared_t5:
+            selected.te = 'Disty0/t5-xxl'
+            selected.te_folder = ''
+            selected.te_revision = None
         shared.log.debug(f'Video load: module=te repo="{selected.te or selected.repo}" folder="{selected.te_folder}" cls={selected.te_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
         kwargs["text_encoder"] = selected.te_cls.from_pretrained(
             pretrained_model_name_or_path=selected.te or selected.repo,
@@ -84,17 +88,23 @@ def load_model(selected: models_def.Model):
     shared.sd_model.sd_model_hash = None
     sd_models.set_diffuser_options(shared.sd_model, offload=False)
 
+    decode, text, image, slicing, tiling = False, False, False, False, False
     if selected.vae_hijack and hasattr(shared.sd_model.vae, 'decode'):
         sd_hijack_vae.init_hijack(shared.sd_model)
+        decode = True
     if selected.te_hijack and hasattr(shared.sd_model, 'encode_prompt'):
         sd_hijack_te.init_hijack(shared.sd_model)
+        text = True
     if selected.image_hijack and hasattr(shared.sd_model, 'encode_image'):
         shared.sd_model.orig_encode_image = shared.sd_model.encode_image
         shared.sd_model.encode_image = video_utils.hijack_encode_image
-    if hasattr(shared.sd_model.vae, 'enable_slicing'):
+        image = True
+    if hasattr(shared.sd_model, 'vae') and hasattr(shared.sd_model.vae, 'enable_slicing'):
         shared.sd_model.vae.enable_slicing()
-    if hasattr(shared.sd_model.vae, 'enable_tiling'):
+        slicing = True
+    if hasattr(shared.sd_model, 'vae') and hasattr(shared.sd_model.vae, 'enable_tiling'):
         shared.sd_model.vae.enable_tiling()
+        tiling = True
     if hasattr(shared.sd_model, "set_progress_bar_config"):
         shared.sd_model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m', ncols=80, colour='#327fba')
 
@@ -104,5 +114,6 @@ def load_model(selected: models_def.Model):
     loaded_model = selected.name
     msg = f'Video load: cls={shared.sd_model.__class__.__name__} model="{selected.name}" time={t1-t0:.2f}'
     shared.log.info(msg)
+    shared.log.debug(f'Video hijacks: decode={decode} text={text} image={image} slicing={slicing} tiling={tiling}')
     shared.state.end(jobid)
     return msg

From d7d86ed28663211b52694a72693b14a0b9e823ff Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 12:23:33 -0400
Subject: [PATCH 150/230] ltx job tracking

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ltx/ltx_process.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/modules/ltx/ltx_process.py b/modules/ltx/ltx_process.py
index 4de9084a5..46374d77d 100644
--- a/modules/ltx/ltx_process.py
+++ b/modules/ltx/ltx_process.py
@@ -77,7 +77,7 @@ def run_ltx(task_id,
         engine = 'LTX Video'
         load_model(engine, model)
 
-        shared.state.begin('Video', task_id=task_id)
+        videojob = shared.state.begin('Video', task_id=task_id)
         shared.state.job_count = 1
 
         p = processing.StableDiffusionProcessingVideo(
@@ -107,7 +107,7 @@ def run_ltx(task_id,
         prompt, negative, networks = get_prompts(prompt, negative, styles)
         sampler_name = processing.get_sampler_name(sampler_index)
         sd_samplers.create_sampler(sampler_name, shared.sd_model)
-        shared.log.debug(f'Video: cls={shared.sd_model.__class__.__name__} op=init prompt="{prompt}" negative="{negative}" styles={styles} networks={networks} sampler={shared.sd_model.scheduler.__class__.__name__}')
+        shared.log.debug(f'Video: cls={shared.sd_model.__class__.__name__} op=init styles={styles} networks={networks} sampler={shared.sd_model.scheduler.__class__.__name__}')
         extra_networks.activate(p, networks)
 
         t0 = time.time()
@@ -129,6 +129,7 @@ def run_ltx(task_id,
         if len(conditions) > 0:
             base_args["conditions"] = conditions
         yield None, 'LTX: Generate in progress...'
+        samplejob = shared.state.begin('Sample')
         try:
             latents = shared.sd_model(**base_args).frames[0]
         except AssertionError as e:
@@ -143,10 +144,11 @@ def run_ltx(task_id,
         timer.process.add('offload', t1 - t0)
         timer.process.add('base', t2 - t1)
         timer.process.add('offload', t3 - t2)
+        shared.state.end(samplejob)
 
         if upsample_enable:
             t4 = time.time()
-            shared.state.begin('Upsample')
+            upsamplejob = shared.state.begin('Upsample')
             global upsample_pipe # pylint: disable=global-statement
             upsample_pipe = load_upsample(upsample_pipe, upsample_repo_id)
             upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe)
@@ -174,11 +176,11 @@ def run_ltx(task_id,
             t6 = time.time()
             timer.process.add('upsample', t5 - t4)
             timer.process.add('offload', t6 - t5)
-            shared.state.end()
+            shared.state.end(upsamplejob)
 
         if refine_enable:
             t7 = time.time()
-            shared.state.begin('Refine')
+            refinejob = shared.state.begin('Refine')
             shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
             refine_args = {
                 "prompt": prompt,
@@ -213,7 +215,7 @@ def run_ltx(task_id,
             t9 = time.time()
             timer.process.add('refine', t8 - t7)
             timer.process.add('offload', t9 - t8)
-            shared.state.end()
+            shared.state.end(refinejob)
 
         extra_networks.deactivate(p)
 
@@ -252,7 +254,7 @@ def run_ltx(task_id,
         fps = f'{num_frames/(t_end-t0):.2f}'
         its = f'{(steps)/(t_end-t0):.2f}'
 
-        shared.state.end()
+        shared.state.end(videojob)
         progress.finish_task(task_id)
 
         shared.log.info(f'Processed: fn="{video_file}" frames={num_frames} fps={fps} its={its} resolution={resolution} time={t_end-t0:.2f} timers={timer.process.dct()} memory={memstats.memory_stats()}')

From 58b0ab9da6db4807719ed4ae165e756e95b9ca22 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 13:30:22 -0400
Subject: [PATCH 151/230] unified video save

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       |  9 +++++----
 javascript/control.js              |  1 -
 javascript/history.js              |  2 +-
 modules/video.py                   |  4 ++--
 modules/video_models/video_run.py  | 27 +++++++++++++++++++++++----
 modules/video_models/video_save.py | 23 +++++++++++++++++++++--
 modules/video_models/video_ui.py   | 24 +++++++++++++++++-------
 7 files changed, 69 insertions(+), 21 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ddd8b3a5d..0b2124b1e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -89,10 +89,11 @@
   - **xyz-grid** add guidance section  
 - **Video**
   - use shared T5 text encoder for video models when possible  
-  - **FramePack** add job state tracking  
-  - **LTXVideo** fix model selection in ltx tab
-  - **LTXVideo** fix run with offloading  
-  - **WAN** fix run with offloading  
+  - unified video save code across all video models  
+    also avoids creation of temporary files for each frame unless user wants to save them  
+  - add job state tracking for video generation  
+  - improve offloading for **ltx** and **wan**
+  - fix model selection in ltx tab
 - **Experimental**
   - `new` command line flag enables new `pydantic` and `albumentations` packages  
   - **modular pipelines**: enable in *settings -> model options*  
diff --git a/javascript/control.js b/javascript/control.js
index c94c61a67..e0bb550ba 100644
--- a/javascript/control.js
+++ b/javascript/control.js
@@ -4,7 +4,6 @@ function controlInputMode(inputMode, ...args) {
   const tab = gradioApp().querySelector('#control-tab-input button.selected');
   if (!tab) return ['Image', ...args];
   inputMode = tab.innerText;
-  console.log('HERE0', tab, inputMode);
   return [inputMode, ...args];
 }
 
diff --git a/javascript/history.js b/javascript/history.js
index ff2db4831..2a40b0859 100644
--- a/javascript/history.js
+++ b/javascript/history.js
@@ -45,7 +45,7 @@ function refreshHistory() {
           if (inferenceTypes.some((type) => entry.job.toLowerCase().startsWith(type))) entry.type = 'inference';
           else if (ioTypes.some((type) => entry.job.toLowerCase().startsWith(type))) entry.type = 'io';
           else entry.type = 'default';
-          ts.push({ start, end: end.timestamp, label: entry.job, type: entry.type });
+          if (start && end.timestamp) ts.push({ start, end: end.timestamp, label: entry.job, type: entry.type });
         }
       }
       if (!ts.length) return;
diff --git a/modules/video.py b/modules/video.py
index fe7d88c36..b95ae7aa1 100644
--- a/modules/video.py
+++ b/modules/video.py
@@ -29,7 +29,7 @@ def save_video_atomic(images, filename, video_type: str = 'none', duration: floa
     except Exception as e:
         shared.log.error(f'Save video: cv2: {e}')
         return
-    jobid = shared.state.begin('Save video')
+    savejob = shared.state.begin('Save video')
     os.makedirs(os.path.dirname(filename), exist_ok=True)
     if video_type.lower() in ['gif', 'png']:
         append = images.copy()
@@ -57,7 +57,7 @@ def save_video_atomic(images, filename, video_type: str = 'none', duration: floa
             video_writer.write(img)
         size = os.path.getsize(filename)
         shared.log.info(f'Save video: file="{filename}" frames={len(frames)} duration={duration} fourcc={fourcc} size={size}')
-    shared.state.end(jobid)
+    shared.state.end(savejob)
 
 
 def save_video(p, images, filename = None, video_type: str = 'none', duration: float = 2.0, loop: bool = False, interpolate: int = 0, scale: float = 1.0, pad: int = 1, change: float = 0.3, sync: bool = False):
diff --git a/modules/video_models/video_run.py b/modules/video_models/video_run.py
index 33d4f5d13..1dccc3611 100644
--- a/modules/video_models/video_run.py
+++ b/modules/video_models/video_run.py
@@ -1,16 +1,17 @@
 import os
 import time
 from modules import shared, errors, sd_models, processing, devices, images, ui_common
-from modules.video_models import models_def, video_utils, video_load, video_vae, video_overrides
+from modules.video_models import models_def, video_utils, video_load, video_vae, video_overrides, video_save
 
 
 debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
 def generate(*args, **kwargs):
-    task_id, ui_state, engine, model, prompt, negative, styles, width, height, frames, steps, sampler_index, sampler_shift, dynamic_shift, seed, guidance_scale, guidance_true, init_image, init_strength, last_image, vae_type, vae_tile_frames, save_frames, video_type, video_duration, video_loop, video_pad, video_interpolate, override_settings = args
+    task_id, ui_state, engine, model, prompt, negative, styles, width, height, frames, steps, sampler_index, sampler_shift, dynamic_shift, seed, guidance_scale, guidance_true, init_image, init_strength, last_image, vae_type, vae_tile_frames, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, override_settings = args
     if engine is None or model is None or engine == 'None' or model == 'None':
         return video_utils.queue_err('model not selected')
+    # videojob = shared.state.begin('Video')
     found = [model.name for model in models_def.models.get(engine, [])]
     selected: models_def.Model = [m for m in models_def.models[engine] if m.name == model][0] if len(found) > 0 else None
     if not shared.sd_loaded:
@@ -51,7 +52,7 @@ def generate(*args, **kwargs):
     p.script_args = None
     p.state = ui_state
     p.do_not_save_grid = True
-    p.do_not_save_samples = not save_frames
+    p.do_not_save_samples = not mp4_frames
     p.outpath_samples = shared.opts.outdir_samples or shared.opts.outdir_video
     if 'T2V' in model:
         if init_image is not None:
@@ -119,6 +120,24 @@ def generate(*args, **kwargs):
     if processed is None or len(processed.images) == 0:
         return video_utils.queue_err('processing failed')
     shared.log.info(f'Video: name="{selected.name}" cls={shared.sd_model.__class__.__name__} frames={len(processed.images)} time={t1-t0:.2f}')
-    video_file = images.save_video(p, filename=None, images=processed.images, video_type=video_type, duration=video_duration, loop=video_loop, pad=video_pad, interpolate=video_interpolate)
+
+    # video_file = images.save_video(p, filename=None, images=processed.images, video_type=video_type, duration=video_duration, loop=video_loop, pad=video_pad, interpolate=video_interpolate) # legacy video save from list of images
+    pixels = video_save.images_to_tensor(processed.images)
+    _num_frames, video_file = video_save.save_video(
+        pixels=pixels,
+        mp4_fps=mp4_fps,
+        mp4_codec=mp4_codec,
+        mp4_opt=mp4_opt,
+        mp4_ext=mp4_ext,
+        mp4_sf=mp4_sf,
+        mp4_video=mp4_video,
+        mp4_frames=mp4_frames,
+        mp4_interpolate=mp4_interpolate,
+        metadata={},
+    )
+    if not mp4_frames:
+        processed.images = []
+
     generation_info_js = processed.js() if processed is not None else ''
+    # shared.state.end(videojob)
     return processed.images, video_file, generation_info_js, processed.info, ui_common.plaintext_to_html(processed.comments)
diff --git a/modules/video_models/video_save.py b/modules/video_models/video_save.py
index 2ce71f0af..41095922f 100644
--- a/modules/video_models/video_save.py
+++ b/modules/video_models/video_save.py
@@ -2,6 +2,7 @@ import os
 import time
 import datetime
 import cv2
+import numpy as np
 import torch
 import einops
 from modules import shared, errors ,timer, rife
@@ -13,6 +14,18 @@ def get_video_filename(frames:int, codec:str):
     return output_filename
 
 
+def images_to_tensor(images):
+    if images is None or len(images) == 0:
+        return None
+    array = [torch.from_numpy(np.array(image)) for image in images]
+    tensor = torch.stack(array, dim=0) # n h w c
+    tensor = tensor.unsqueeze(0) # 1, n, h, w, c
+    tensor = tensor.permute(0, 4, 1, 2, 3).contiguous() # 1, c, n, h, w
+    tensor = (tensor.float() / 127.5) - 1.0 # from [0,255] to [-1,1]
+    # shared.log.debug(f'Video output: images={len(images)} tensor={tensor.shape}')
+    return tensor
+
+
 def atomic_save_video(filename, tensor:torch.Tensor, fps:float=24, codec:str='libx264', pix_fmt:str='yuv420p', options:str='', metadata:dict={}, pbar=None):
     try:
         import av
@@ -21,6 +34,7 @@ def atomic_save_video(filename, tensor:torch.Tensor, fps:float=24, codec:str='li
         shared.log.error(f'Video: {e}')
         return
 
+    savejob = shared.state.begin('Save video')
     frames, height, width, _channels = tensor.shape
     rate = round(fps)
     options_str = options
@@ -54,6 +68,7 @@ def atomic_save_video(filename, tensor:torch.Tensor, fps:float=24, codec:str='li
         for packet in stream.encode(): # flush
             container.mux(packet)
     shared.state.outputs(filename)
+    shared.state.end(savejob)
 
 
 def save_video(
@@ -73,13 +88,16 @@ def save_video(
     output_video = None
     if pixels is None:
         return 0, output_video
+    if not torch.is_tensor(pixels):
+        shared.log.error(f'Video: type={type(pixels)} not a tensor')
+        return 0, output_video
     t_save = time.time()
     n, _c, t, h, w = pixels.shape
     size = pixels.element_size() * pixels.numel()
     shared.log.debug(f'Video: video={mp4_video} export={mp4_frames} safetensors={mp4_sf} interpolate={mp4_interpolate}')
     shared.log.debug(f'Video: encode={t} raw={size} latent={pixels.shape} fps={mp4_fps} codec={mp4_codec} ext={mp4_ext} options="{mp4_opt}"')
-    jobid = shared.state.begin('Save video')
     try:
+        preparejob = shared.state.begin('Prepare video')
         if stream is not None:
             stream.output_queue.push(('progress', (None, 'Saving video...')))
         if mp4_interpolate > 0:
@@ -111,6 +129,8 @@ def save_video(
                 shared.state.outputs(fn)
                 cv2.imwrite(fn, image)
 
+        shared.state.end(preparejob)
+
         if mp4_video and (mp4_codec != 'none'):
             output_video = f'{output_filename}.{mp4_ext}'
             atomic_save_video(output_video, tensor=x, fps=mp4_fps, codec=mp4_codec, options=mp4_opt, metadata=metadata, pbar=pbar)
@@ -125,5 +145,4 @@ def save_video(
         shared.log.error(f'Video save: raw={size} {e}')
         errors.display(e, 'video')
     timer.process.add('save', time.time()-t_save)
-    shared.state.end(jobid)
     return t, output_video
diff --git a/modules/video_models/video_ui.py b/modules/video_models/video_ui.py
index 910f665a4..faea8b6ef 100644
--- a/modules/video_models/video_ui.py
+++ b/modules/video_models/video_ui.py
@@ -112,20 +112,31 @@ def create_ui(prompt, negative, styles, overrides):
                 init_image = gr.Image(elem_id="video_image", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
                 gr.HTML("<br>&nbsp Last image")
                 last_image = gr.Image(elem_id="video_last", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
+
             vlm_enhance, vlm_model, vlm_system_prompt = ui_video_vlm.create_ui(prompt_element=prompt, image_element=init_image)
-            with gr.Accordion(open=False, label="Output", elem_id='video_output_accordion'):
+
+            with gr.Accordion(label="Video", open=False, elem_id='video_output_accordion'):
                 with gr.Row():
-                    save_frames = gr.Checkbox(label='Save image frames', value=False, elem_id="video_save_frames")
+                    mp4_fps = gr.Slider(label="FPS", minimum=1, maximum=60, value=24, step=1)
+                    mp4_interpolate = gr.Slider(label="Video interpolation", minimum=0, maximum=10, value=0, step=1)
                 with gr.Row():
-                    video_type, video_duration, video_loop, video_pad, video_interpolate = ui_sections.create_video_inputs(tab='video', show_always=True)
+                    mp4_codec = gr.Dropdown(label="Video codec", choices=['none', 'libx264'], value='libx264', type='value')
+                    ui_common.create_refresh_button(mp4_codec, video_utils.get_codecs, elem_id="framepack_mp4_codec_refresh")
+                    mp4_ext = gr.Textbox(label="Video format", value='mp4', elem_id="framepack_mp4_ext")
+                    mp4_opt = gr.Textbox(label="Video options", value='crf:16', elem_id="framepack_mp4_ext")
+                with gr.Row():
+                    mp4_video = gr.Checkbox(label='Video save video', value=True, elem_id="framepack_mp4_video")
+                    mp4_frames = gr.Checkbox(label='Video save frames', value=False, elem_id="framepack_mp4_frames")
+                    mp4_sf = gr.Checkbox(label='Video save safetensors', value=False, elem_id="framepack_mp4_sf")
+
 
         # output panel with gallery and video tabs
         with gr.Column(elem_id='video-output-column', scale=2) as _column_output:
             with gr.Tabs(elem_classes=['video-output-tabs'], elem_id='video-output-tabs'):
-                with gr.Tab('Frames', id='out-gallery'):
-                    gallery, gen_info, html_info, _html_info_formatted, html_log = ui_common.create_output_panel("video", prompt=prompt, preview=False, transfer=False, scale=2)
                 with gr.Tab('Video', id='out-video'):
                     video = gr.Video(label="Output", show_label=False, elem_id='control_output_video', elem_classes=['control-image'], height=512, autoplay=False)
+                with gr.Tab('Frames', id='out-gallery'):
+                    gallery, gen_info, html_info, _html_info_formatted, html_log = ui_common.create_output_panel("video", prompt=prompt, preview=False, transfer=False, scale=2)
 
     # connect reuse seed button
     ui_common.connect_reuse_seed(seed, reuse_seed, gen_info, is_subseed=False)
@@ -151,8 +162,7 @@ def create_ui(prompt, negative, styles, overrides):
         guidance_scale, guidance_true,
         init_image, init_strength, last_image,
         vae_type, vae_tile_frames,
-        save_frames,
-        video_type, video_duration, video_loop, video_pad, video_interpolate,
+        mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf,
         overrides,
     ]
     video_outputs = [

From 3e47f3dd9a91052d43f4397e412cc28abd027e9b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 13:56:10 -0400
Subject: [PATCH 152/230] video prompt enhance

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                         |  1 +
 modules/interrogate/vqa.py           | 17 ++++++++---------
 modules/ui_video_vlm.py              |  1 -
 modules/video_models/video_prompt.py | 21 +++++++++++++++++++++
 modules/video_models/video_run.py    |  6 ++++--
 modules/video_models/video_ui.py     |  1 +
 6 files changed, 35 insertions(+), 12 deletions(-)
 create mode 100644 modules/video_models/video_prompt.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0b2124b1e..8ad0b182a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -91,6 +91,7 @@
   - use shared T5 text encoder for video models when possible  
   - unified video save code across all video models  
     also avoids creation of temporary files for each frame unless user wants to save them  
+  - unified prompt enhance code across all video models  
   - add job state tracking for video generation  
   - improve offloading for **ltx** and **wan**
   - fix model selection in ltx tab
diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index baebb62c2..0821d5c4f 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -636,12 +636,11 @@ def interrogate(question:str='', system_prompt:str=None, prompt:str=None, image:
         image = image[0] if len(image) > 0 else None
     if isinstance(image, dict) and 'name' in image:
         image = Image.open(image['name'])
-    if image is None:
-        return ''
-    if image.width > 768 or image.height > 768:
-        image.thumbnail((768, 768), Image.Resampling.LANCZOS)
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
+    if isinstance(image, Image.Image):
+        if image.width > 768 or image.height > 768:
+            image.thumbnail((768, 768), Image.Resampling.LANCZOS)
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
     if prompt is not None and len(prompt) > 0:
         question = prompt
     if len(question) < 2:
@@ -664,9 +663,9 @@ def interrogate(question:str='', system_prompt:str=None, prompt:str=None, image:
         if vqa_model is None:
             shared.log.error(f'Interrogate: type=vlm model="{model_name}" unknown')
             return ''
-        if image is None:
-            shared.log.error(f'Interrogate: type=vlm model="{model_name}" no input image')
-            return ''
+        # if image is None:
+        #     shared.log.error(f'Interrogate: type=vlm model="{model_name}" no input image')
+        #     return ''
 
         if 'git' in vqa_model.lower():
             answer = git(question, image, vqa_model)
diff --git a/modules/ui_video_vlm.py b/modules/ui_video_vlm.py
index 685c6d8d8..c7ce3a229 100644
--- a/modules/ui_video_vlm.py
+++ b/modules/ui_video_vlm.py
@@ -46,7 +46,6 @@ def enhance_prompt(enable:bool, model:str=None, image=None, prompt:str='', syste
         system_prompt += system_prompts['nsfw_ok'] if nsfw else system_prompts['nsfw_no']
         system_prompt += f" {system_prompts['suffix']} {system_prompts['example']}"
     shared.log.debug(f'Video prompt enhance: model="{model}" image={image} nsfw={nsfw} prompt="{prompt}"')
-    # shared.log.trace(f'Video prompt enhance: system="{system_prompt}"')
     answer = vqa.interrogate(question='', prompt=prompt, system_prompt=system_prompt, image=image, model_name=model, quiet=False)
     shared.log.debug(f'Video prompt enhance: answer="{answer}"')
     return answer
diff --git a/modules/video_models/video_prompt.py b/modules/video_models/video_prompt.py
new file mode 100644
index 000000000..bbb29c409
--- /dev/null
+++ b/modules/video_models/video_prompt.py
@@ -0,0 +1,21 @@
+from modules import shared, extra_networks, ui_video_vlm
+
+
+def prepare_prompt(p, init_image, prompt:str, vlm_enhance:bool, vlm_model:str, vlm_system_prompt:str):
+    p.prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)
+    p.negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
+    shared.prompt_styles.apply_styles_to_extra(p)
+    p.prompts, p.network_data = extra_networks.parse_prompts([p.prompt])
+    extra_networks.activate(p)
+    prompt = p.prompts[0]
+
+    new_prompt = ui_video_vlm.enhance_prompt(
+        enable=vlm_enhance,
+        model=vlm_model,
+        image=init_image,
+        prompt=prompt,
+        system_prompt=vlm_system_prompt,
+    )
+    if new_prompt is not None and len(new_prompt) > 0:
+        prompt = new_prompt
+    return prompt
diff --git a/modules/video_models/video_run.py b/modules/video_models/video_run.py
index 1dccc3611..40f7e154f 100644
--- a/modules/video_models/video_run.py
+++ b/modules/video_models/video_run.py
@@ -1,14 +1,14 @@
 import os
 import time
 from modules import shared, errors, sd_models, processing, devices, images, ui_common
-from modules.video_models import models_def, video_utils, video_load, video_vae, video_overrides, video_save
+from modules.video_models import models_def, video_utils, video_load, video_vae, video_overrides, video_save, video_prompt
 
 
 debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
 def generate(*args, **kwargs):
-    task_id, ui_state, engine, model, prompt, negative, styles, width, height, frames, steps, sampler_index, sampler_shift, dynamic_shift, seed, guidance_scale, guidance_true, init_image, init_strength, last_image, vae_type, vae_tile_frames, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, override_settings = args
+    task_id, ui_state, engine, model, prompt, negative, styles, width, height, frames, steps, sampler_index, sampler_shift, dynamic_shift, seed, guidance_scale, guidance_true, init_image, init_strength, last_image, vae_type, vae_tile_frames, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, vlm_enhance, vlm_model, vlm_system_prompt, override_settings = args
     if engine is None or model is None or engine == 'None' or model == 'None':
         return video_utils.queue_err('model not selected')
     # videojob = shared.state.begin('Video')
@@ -81,6 +81,8 @@ def generate(*args, **kwargs):
     shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
     devices.torch_gc(force=True, reason='video')
 
+    prompt = video_prompt.prepare_prompt(p, init_image, prompt, vlm_enhance, vlm_model, vlm_system_prompt)
+
     # set args
     processing.fix_seed(p)
     video_vae.set_vae_params(p)
diff --git a/modules/video_models/video_ui.py b/modules/video_models/video_ui.py
index faea8b6ef..cd78f45a2 100644
--- a/modules/video_models/video_ui.py
+++ b/modules/video_models/video_ui.py
@@ -163,6 +163,7 @@ def create_ui(prompt, negative, styles, overrides):
         init_image, init_strength, last_image,
         vae_type, vae_tile_frames,
         mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf,
+        vlm_enhance, vlm_model, vlm_system_prompt,
         overrides,
     ]
     video_outputs = [

From d48fe5adab93ceac10a6f1c7d11e154d7da4625b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 14:14:38 -0400
Subject: [PATCH 153/230] update modernui

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 1 +
 extensions-builtin/sdnext-modernui | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8ad0b182a..110b6ae69 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -58,6 +58,7 @@
     main logo in top-left corner now indicates server connection status and hovering over it shows connection details  
   - separate guidance and detail sections  
   - networks ability to filter lora by base model version  
+  - add interrogate button to input images  
 - **SDNQ**
     - add `SVDQuant` quantization method support  
     - make sdnq scales compatible with balanced offload  
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 165925a2c..e58d96e26 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 165925a2cc849595053ddb147eb0dc781b302d2b
+Subproject commit e58d96e26290960e1df35d0e012c5e50b4e6a606

From 28e3ae048080a2ae50ad93bb7fb8f700242dd3a6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 20:17:30 -0400
Subject: [PATCH 154/230] experimental xomni

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                    |   1 +
 .ruff.toml                                   |   1 +
 CHANGELOG.md                                 |  11 +-
 TODO.md                                      |   1 -
 html/reference.json                          |  10 +
 models/Reference/X-Omni--X-Omni-SFT.jpg      | Bin 0 -> 77817 bytes
 modules/modeldata.py                         |   2 +
 modules/sd_checkpoint.py                     |   2 +
 modules/sd_detect.py                         |   2 +
 modules/sd_models.py                         |   5 +
 modules/sd_offload.py                        |   4 +-
 modules/shared_items.py                      |   1 +
 modules/ui_extra_networks_checkpoints.py     |   5 +-
 modules/video_models/video_load.py           |  33 +-
 pipelines/model_xomni.py                     | 113 +++
 pipelines/xomni/__init__.py                  |   0
 pipelines/xomni/configuration_xomni.py       |  25 +
 pipelines/xomni/modeling_siglip_flux.py      | 841 +++++++++++++++++++
 pipelines/xomni/modeling_siglip_tokenizer.py | 231 +++++
 pipelines/xomni/modeling_vit.py              | 699 +++++++++++++++
 pipelines/xomni/modeling_xomni.py            | 317 +++++++
 21 files changed, 2282 insertions(+), 22 deletions(-)
 create mode 100644 models/Reference/X-Omni--X-Omni-SFT.jpg
 create mode 100644 pipelines/model_xomni.py
 create mode 100644 pipelines/xomni/__init__.py
 create mode 100644 pipelines/xomni/configuration_xomni.py
 create mode 100644 pipelines/xomni/modeling_siglip_flux.py
 create mode 100644 pipelines/xomni/modeling_siglip_tokenizer.py
 create mode 100644 pipelines/xomni/modeling_vit.py
 create mode 100644 pipelines/xomni/modeling_xomni.py

diff --git a/.pylintrc b/.pylintrc
index 7b6cdb7cb..5b4e398dc 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -42,6 +42,7 @@ ignore-paths=/usr/lib/.*$,
              pipelines/meissonic,
              pipelines/omnigen2,
              pipelines/segmoe,
+             pipelines/xomni,
              scripts/consistory,
              scripts/ctrlx,
              scripts/daam,
diff --git a/.ruff.toml b/.ruff.toml
index 297515402..7433d76da 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -22,6 +22,7 @@ exclude = [
     "pipelines/omnigen2",
     "pipelines/hdm",
     "pipelines/segmoe",
+    "pipelines/xomni",
 
     "scripts/lbm",
     "scripts/daam",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 110b6ae69..ceb6ff539 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,14 +9,16 @@
     updated version of Qwen Image Edit with improved image consistency  
   - [Qwen Image Pruning](https://huggingface.co/OPPOer/Qwen-Image-Pruning) and [Qwen Image Edit Pruning](https://huggingface.co/OPPOer/Qwen-Image-Edit-Pruning)  
     pruned versions of Qwen with 13B params instead of 20B, with some quality tradeoff  
-  - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
-    updated version of E1 image editing model  
   - [Tencent FLUX.1 Dev SRPO](https://huggingface.co/tencent/SRPO)  
     SRPO is trained by Tencent with specific technique: directly aligning the full diffusion trajectory with fine-grained human preference  
   - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
     impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
+  - [X-Omni SFT](https://x-omni-team.github.io/)  
+    *experimental*: X-omni is a transformer-only discrete autoregressive image generative model trained with reinforcement learning  
+  - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
+    *experimental*: updated version of E1 image editing model  
 - **Features**
   - [Qwen Image-Edit] multi-image editing
     requires qwen-image-edit-2509 or its variant as multi-image edits are not available in original qwen-image  
@@ -94,8 +96,9 @@
     also avoids creation of temporary files for each frame unless user wants to save them  
   - unified prompt enhance code across all video models  
   - add job state tracking for video generation  
-  - improve offloading for **ltx** and **wan**
-  - fix model selection in ltx tab
+  - fix quantization not being applied on load for some models  
+  - improve offloading for **ltx** and **wan**  
+  - fix model selection in **ltx** tab  
 - **Experimental**
   - `new` command line flag enables new `pydantic` and `albumentations` packages  
   - **modular pipelines**: enable in *settings -> model options*  
diff --git a/TODO.md b/TODO.md
index 01bcdad79..adb84fbd8 100644
--- a/TODO.md
+++ b/TODO.md
@@ -16,7 +16,6 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 ### Under Consideration
 
 - [Inf-DiT](https://github.com/zai-org/Inf-DiT)
-- [X-Omni](https://github.com/X-Omni-Team/X-Omni/blob/main/README.md)
 - [DiffSynth Studio](https://github.com/modelscope/DiffSynth-Studio)
 - [IPAdapter negative guidance](https://github.com/huggingface/diffusers/discussions/7167)  
 - [IPAdapter composition](https://huggingface.co/ostris/ip-composition-adapter)  
diff --git a/html/reference.json b/html/reference.json
index 2c3ca1d7e..45f2bd2d5 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -501,6 +501,16 @@
     "date": "2025 June"
   },
 
+  "X-Omni SFT": {
+    "path": "X-Omni/X-Omni-SFT",
+    "desc": "X-Omni: Reinforcement learning makes discrete autoregressive image generative models great again",
+    "preview": "X-Omni--X-Omni-SFT.jpg",
+    "skip": true,
+    "size": 0,
+    "date": "2024 September",
+    "experimental": true
+  },
+
   "VectorSpaceLab OmniGen v1": {
     "path": "Shitao/OmniGen-v1-diffusers",
     "desc": "OmniGen is a unified image generation model that can generate a wide range of images from multi-modal prompts. It is designed to be simple, flexible and easy to use.",
diff --git a/models/Reference/X-Omni--X-Omni-SFT.jpg b/models/Reference/X-Omni--X-Omni-SFT.jpg
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HcmV?d00001

diff --git a/modules/modeldata.py b/modules/modeldata.py
index c56190ad8..881a5fba0 100644
--- a/modules/modeldata.py
+++ b/modules/modeldata.py
@@ -62,6 +62,8 @@ def get_model_type(pipe):
         model_type = 'qwen'
     elif 'NextStep' in name:
         model_type = 'nextstep'
+    elif 'X-Omni' in name:
+        model_type = 'x-omni'
     # video models
     elif "CogVideo" in name:
         model_type = 'cogvideo'
diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py
index 4b0be2e97..8857b3ccf 100644
--- a/modules/sd_checkpoint.py
+++ b/modules/sd_checkpoint.py
@@ -234,6 +234,8 @@ def get_closest_checkpoint_match(s: str) -> CheckpointInfo:
 
     # reference search
     ref = [(k, v) for k, v in shared.reference_models.items() if f"{v.get('path', '')}+{v.get('subfolder', '')}" == s]
+    if len(ref) == 0:
+        ref = [(k, v) for k, v in shared.reference_models.items() if v.get('path', '') == s]
     if ref and len(ref) > 0:
         _name, info = ref[0]
         checkpoint_info = CheckpointInfo(s)
diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 838c68770..0cd3cdb86 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -115,6 +115,8 @@ def guess_by_name(fn, current_guess):
         return 'Kandinsky 3.0'
     elif 'hunyuanimage' in fn.lower():
         return 'HunyuanImage'
+    elif 'x-omni' in fn.lower():
+        return 'X-Omni'
     elif 'sdxl-turbo' in fn.lower() or 'stable-diffusion-xl' in fn.lower():
         return 'Stable Diffusion XL'
     return current_guess
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b8e3c0391..426a7ca3d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -47,6 +47,7 @@ pipe_switch_task_exclude = [
     'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
     'StableDiffusionReferencePipeline',
     'StableDiffusionXLInstantIDPipeline',
+    'XOmniPipeline',
 ]
 i2i_pipes = [
     'LEditsPPPipelineStableDiffusion',
@@ -399,6 +400,10 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='
             from pipelines.model_hyimage import load_hyimage
             sd_model = load_hyimage(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none
             allow_post_quant = False
+        elif model_type in ['X-Omni']:
+            from pipelines.model_xomni import load_xomni
+            sd_model = load_xomni(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none
+            allow_post_quant = False
     except Exception as e:
         shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
         if debug_load:
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index 19e59de3d..54446abdf 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -13,7 +13,7 @@ from modules.timer import process as process_timer
 
 debug = os.environ.get('SD_MOVE_DEBUG', None) is not None
 debug_move = log.trace if debug else lambda *args, **kwargs: None
-offload_warn = ['sc', 'sd3', 'f1', 'h1', 'hunyuandit', 'auraflow', 'omnigen', 'omnigen2', 'cogview4', 'cosmos', 'chroma']
+offload_warn = ['sc', 'sd3', 'f1', 'h1', 'hunyuandit', 'auraflow', 'omnigen', 'omnigen2', 'cogview4', 'cosmos', 'chroma', 'x-omni']
 offload_post = ['h1']
 offload_hook_instance = None
 balanced_offload_exclude = ['CogView4Pipeline', 'MeissonicPipeline']
@@ -224,7 +224,7 @@ class OffloadHook(accelerate.hooks.ModelHook):
                     for module_name in get_module_names(pipe):
                         module_instance = getattr(pipe, module_name, None)
                         module_cls = module_instance.__class__.__name__
-                        if (_id != id(module_instance)) and (module_cls not in self.offload_never) and (not devices.same_device(module_instance.device, devices.cpu)):
+                        if (module_instance is not None) and (_id != id(module_instance)) and (module_cls not in self.offload_never) and (not devices.same_device(module_instance.device, devices.cpu)):
                             apply_balanced_offload_to_module(module_instance, op='pre')
                 self.last_cls = module.__class__.__name__
                 process_timer.add('offload', time.time() - t0)
diff --git a/modules/shared_items.py b/modules/shared_items.py
index f4d98e955..f09df5769 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -56,6 +56,7 @@ pipelines = {
     'Bria': getattr(diffusers, 'DiffusionPipeline', None),
     'hdm': getattr(diffusers, 'DiffusionPipeline', None),
     'HunyuanImage': getattr(diffusers, 'DiffusionPipeline', None),
+    'X-Omni': getattr(diffusers, 'DiffusionPipeline', None),
 }
 
 
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index bd7bccc87..6e6fcc197 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -57,7 +57,10 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                     mtime = datetime.strptime(mtime, '%Y %B') # 2025 January
                 except Exception:
                     _size, mtime = modelstats.stat(preview_file)
-            path = f'{v.get("path", "")}+{v.get("subfolder", "")}'
+            if len(v.get("subfolder", "")) > 0:
+                path = f'{v.get("path", "")}+{v.get("subfolder", "")}'
+            else:
+                path = f'{v.get("path", "")}'
             yield {
                 "type": 'Model',
                 "name": name,
diff --git a/modules/video_models/video_load.py b/modules/video_models/video_load.py
index 51a17d2ce..55e57647f 100644
--- a/modules/video_models/video_load.py
+++ b/modules/video_models/video_load.py
@@ -44,24 +44,29 @@ def load_model(selected: models_def.Model):
 
     # transformer
     try:
+        def load_dit_folder(dit_folder):
+            if dit_folder is not None and dit_folder not in kwargs:
+                # get a new quant arg on every loop to prevent the quant config classes getting entangled
+                load_args, quant_args = model_quant.get_dit_args({}, module='Model', device_map=True)
+                shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" module="{dit_folder}" folder="{dit_folder}" cls={selected.dit_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
+                kwargs[dit_folder] = selected.dit_cls.from_pretrained(
+                    pretrained_model_name_or_path=selected.dit or selected.repo,
+                    subfolder=dit_folder,
+                    revision=selected.dit_revision or selected.repo_revision,
+                    cache_dir=shared.opts.hfcache_dir,
+                    **load_args,
+                    **quant_args
+                )
+            else:
+                shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" module="{dit_folder}" folder="{dit_folder}" cls={selected.dit_cls.__name__} skip')
+
         if selected.dit_folder is None:
             selected.dit_folder = ['transformer']
         if isinstance(selected.dit_folder, list) or isinstance(selected.dit_folder, tuple):
             for dit_folder in selected.dit_folder: # wan a14b has transformer and transformer_2
-                if dit_folder is not None and dit_folder not in kwargs:
-                    # get a new quant arg on every loop to prevent the quant config classes getting entangled
-                    load_args, quant_args = model_quant.get_dit_args({}, module='Model', device_map=True)
-                    shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" module="{dit_folder}" folder="{dit_folder}" cls={selected.dit_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
-                    kwargs[dit_folder] = selected.dit_cls.from_pretrained(
-                        pretrained_model_name_or_path=selected.dit or selected.repo,
-                        subfolder=dit_folder,
-                        revision=selected.dit_revision or selected.repo_revision,
-                        cache_dir=shared.opts.hfcache_dir,
-                        **load_args,
-                        **quant_args
-                    )
-                else:
-                    shared.log.debug(f'Video load: module=transformer repo="{selected.dit or selected.repo}" module="{dit_folder}" folder="{dit_folder}" cls={selected.dit_cls.__name__} skip')
+                load_dit_folder(dit_folder)
+        else:
+            load_dit_folder(selected.dit_folder)
     except Exception as e:
         shared.log.error(f'video load: module=transformer cls={selected.dit_cls.__name__} {e}')
         errors.display(e, 'video')
diff --git a/pipelines/model_xomni.py b/pipelines/model_xomni.py
new file mode 100644
index 000000000..838a0c0b3
--- /dev/null
+++ b/pipelines/model_xomni.py
@@ -0,0 +1,113 @@
+import torch
+import transformers
+import diffusers
+from modules import shared, devices, sd_models, model_quant
+
+
+class XOmniPipeline(diffusers.DiffusionPipeline):
+    def __init__(
+            self,
+            tokenizer=None,
+            model=None,
+        ):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.model = model
+        self.register_modules(
+            tokenizer=tokenizer,
+            model=model,
+        )
+
+    def load(
+            self,
+            repo_id,
+            load_config: dict = {},
+        ):
+        from pipelines.xomni import modeling_xomni
+        load_args, quant_args = model_quant.get_dit_args(load_config, module='Model', device_map=True)
+        shared.log.debug(f'Load model: cls=XOmniPipeline module=tokenizer repo_id="{repo_id}"')
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+            repo_id,
+            use_fast=True,
+        )
+        shared.log.debug(f'Load model: cls=XOmniPipeline module=transformer repo_id="{repo_id}" args={load_args}')
+        # self.model = transformers.AutoModelForCausalLM.from_pretrained(
+        self.model = modeling_xomni.XOmniForCausalLM.from_pretrained(
+            repo_id,
+            # trust_remote_code=True,
+            cache_dir=shared.opts.hfcache_dir,
+            **load_args,
+            **quant_args,
+        )
+        flux_repo_id = "black-forest-labs/FLUX.1-dev"
+        shared.log.debug(f'Load model: cls=XOmniPipeline module=vision repo_id="{flux_repo_id}"')
+        self.model.init_vision(
+            flux_repo_id,
+            **quant_args,
+        )
+        self.model.set_generation_mode('image')
+
+    def __call__(
+            self,
+            prompt: str = "",
+            width: int = 1024,
+            height: int = 1024,
+            seed: int = -1,
+            temperature: float = 1.0,
+            downsample_size: int = 16,
+            min_p: float = 0.03,
+            top_p: float = 1.0,
+            cfg_scale: float = 1.0,
+        ):
+
+        if isinstance(prompt, list):
+            prompt = prompt[0]
+        token_h, token_w = height // downsample_size, width // downsample_size
+        image_prefix = f'<SOM>{token_h} {token_w}<IMAGE>'
+        generation_config = transformers.generation.GenerationConfig(
+            max_new_tokens=token_h * token_w,
+            do_sample=True,
+            temperature=temperature,
+            min_p=min_p,
+            top_p=top_p,
+            guidance_scale=cfg_scale,
+            suppress_tokens=self.tokenizer.convert_tokens_to_ids(self.model.config.mm_special_tokens),
+        )
+
+        # Sample inputs:
+        tokens = self.tokenizer(
+            [prompt + image_prefix],
+            return_tensors='pt',
+            padding='longest',
+            padding_side='left',
+        )
+        input_ids = tokens.input_ids.to(devices.device)
+        attention_mask = tokens.attention_mask.to(devices.device)
+        negative_ids = self.tokenizer.encode(
+            image_prefix,
+            add_special_tokens=False,
+            return_tensors='pt',
+        ).to(devices.device).expand(1, -1)
+
+        torch.manual_seed(seed)
+        tokens = self.model.generate(
+            inputs=input_ids,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            negative_prompt_ids=negative_ids,
+        )
+
+        tokens = torch.nn.functional.pad(tokens, (0, 1), value=self.tokenizer.convert_tokens_to_ids('<EOM>'))
+        torch.manual_seed(seed)
+        _, images = self.model.mmdecode(self.tokenizer, tokens[0], skip_special_tokens=False)
+        images[0].save('/tmp/xomni_out.png')
+        return images
+
+
+def load_xomni(checkpoint_info, diffusers_load_config={}):
+    repo_id = sd_models.path_to_repo(checkpoint_info)
+    sd_models.hf_auth_check(checkpoint_info)
+
+    pipe = XOmniPipeline()
+    pipe.load(repo_id, load_config=diffusers_load_config)
+    return pipe
diff --git a/pipelines/xomni/__init__.py b/pipelines/xomni/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pipelines/xomni/configuration_xomni.py b/pipelines/xomni/configuration_xomni.py
new file mode 100644
index 000000000..dc22747f7
--- /dev/null
+++ b/pipelines/xomni/configuration_xomni.py
@@ -0,0 +1,25 @@
+from transformers import AutoConfig, Qwen2Config
+from typing import Tuple
+
+
+class XOmniConfig(Qwen2Config):
+    model_type = "x-omni"
+
+    def __init__(
+        self,
+        num_mm_adap_layers: int = 4,
+        num_mm_head_layers: int = 4,
+        mm_vocab_size: int = 16448,
+        image_vocab_size: int = 16384,
+        mm_special_tokens: Tuple[str] = ('<SOM>', '<EOM>', '<IMAGE>'),
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.num_mm_adap_layers = num_mm_adap_layers
+        self.num_mm_head_layers = num_mm_head_layers
+        self.mm_vocab_size = mm_vocab_size
+        self.image_vocab_size = image_vocab_size
+        self.mm_special_tokens = mm_special_tokens
+
+
+AutoConfig.register("x-omni", XOmniConfig)
diff --git a/pipelines/xomni/modeling_siglip_flux.py b/pipelines/xomni/modeling_siglip_flux.py
new file mode 100644
index 000000000..8db60374a
--- /dev/null
+++ b/pipelines/xomni/modeling_siglip_flux.py
@@ -0,0 +1,841 @@
+import torch
+import numpy as np
+
+from typing import Any, Callable, Dict, Tuple, List, Optional, Union
+from diffusers import FluxTransformer2DModel
+from diffusers.configuration_utils import register_to_config
+from diffusers.utils import logging, USE_PEFT_BACKEND, scale_lora_layers, unscale_lora_layers
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def drop_token(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0], x.shape[1], 1)
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class FluxTransformer2DModelWithSigLIP(FluxTransformer2DModel):
+    @register_to_config
+    def __init__(
+            self,
+            patch_size: int = 1,
+            in_channels: int = 64,
+            out_channels: Optional[int] = None,
+            num_layers: int = 19,
+            num_single_layers: int = 38,
+            attention_head_dim: int = 128,
+            num_attention_heads: int = 24,
+            joint_attention_dim: int = 4096,
+            pooled_projection_dim: int = 768,
+            guidance_embeds: bool = False,
+            axes_dims_rope: Tuple[int] = (16, 56, 56),
+            siglip_channels: Optional[int] = None,
+            drop_token_prob: float = 0.,
+    ):
+        super().__init__(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            num_layers=num_layers,
+            num_single_layers=num_single_layers,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            joint_attention_dim=joint_attention_dim,
+            pooled_projection_dim=pooled_projection_dim,
+            guidance_embeds=guidance_embeds,
+            axes_dims_rope=axes_dims_rope,
+        )
+        self.drop_token_prob = drop_token_prob
+        if siglip_channels is not None:
+            self.init_siglip_embed(siglip_channels)
+
+    def init_siglip_embed(self, siglip_channels):
+        self.siglip_embed = torch.nn.Linear(siglip_channels, self.inner_dim, bias=False)
+        torch.nn.init.zeros_(self.siglip_embed.weight)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            encoder_hidden_states: torch.Tensor = None,
+            pooled_projections: torch.Tensor = None,
+            timestep: torch.LongTensor = None,
+            img_ids: torch.Tensor = None,
+            txt_ids: torch.Tensor = None,
+            guidance: torch.Tensor = None,
+            siglip_tensor: Optional[torch.Tensor] = None,
+            joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+            controlnet_block_samples=None,
+            controlnet_single_block_samples=None,
+            return_dict: bool = True,
+            controlnet_blocks_repeat: bool = False,
+    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.Tensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        temb = (
+            self.time_text_embed(timestep, pooled_projections)
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            logger.warning(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            logger.warning(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+
+        if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
+            ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
+            ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+            joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                            hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+        if siglip_tensor is not None:
+            siglip_tensor = drop_token(siglip_tensor, self.drop_token_prob, training=self.training)
+            hidden_states = hidden_states + self.siglip_embed(siglip_tensor)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1]:, ...] = (
+                        hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+                        + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+
+
+def teacache_forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        controlnet_blocks_repeat: bool = False,
+        siglip_tensor: Optional[torch.Tensor] = None,
+) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    """
+    The [`FluxTransformer2DModel`] forward method.
+
+    Args:
+        hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+            Input `hidden_states`.
+        encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+        pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+            from the embeddings of input conditions.
+        timestep ( `torch.LongTensor`):
+            Used to indicate denoising step.
+        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+            A list of tensors that if specified are added to the residuals of transformer blocks.
+        joint_attention_kwargs (`dict`, *optional*):
+            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+            `self.processor` in
+            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+        return_dict (`bool`, *optional*, defaults to `True`):
+            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+            tuple.
+
+    Returns:
+        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+        `tuple` where the first element is the sample tensor.
+    """
+    if joint_attention_kwargs is not None:
+        joint_attention_kwargs = joint_attention_kwargs.copy()
+        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+    else:
+        lora_scale = 1.0
+
+    if USE_PEFT_BACKEND:
+        # weight the lora layers by setting `lora_scale` for each PEFT layer
+        scale_lora_layers(self, lora_scale)
+    else:
+        if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+            logger.warning(
+                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+            )
+
+    batch_size, seq_len, channels = hidden_states.shape
+    device, dtype = hidden_states.device, hidden_states.dtype
+    hidden_states = self.x_embedder(hidden_states)
+
+    timestep = timestep.to(hidden_states.dtype) * 1000
+    if guidance is not None:
+        guidance = guidance.to(hidden_states.dtype) * 1000
+    else:
+        guidance = None
+
+    temb = (
+        self.time_text_embed(timestep, pooled_projections)
+        if guidance is None
+        else self.time_text_embed(timestep, guidance, pooled_projections)
+    )
+    encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+    if txt_ids.ndim == 3:
+        logger.warning(
+            "Passing `txt_ids` 3d torch.Tensor is deprecated."
+            "Please remove the batch dimension and pass it as a 2d torch Tensor"
+        )
+        txt_ids = txt_ids[0]
+    if img_ids.ndim == 3:
+        logger.warning(
+            "Passing `img_ids` 3d torch.Tensor is deprecated."
+            "Please remove the batch dimension and pass it as a 2d torch Tensor"
+        )
+        img_ids = img_ids[0]
+
+    ids = torch.cat((txt_ids, img_ids), dim=0)
+    image_rotary_emb = self.pos_embed(ids)
+
+    if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
+        ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
+        ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+        joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+    if self.enable_teacache:
+        inp = hidden_states.clone()
+        temb_ = temb.clone()
+        modulated_inp, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.transformer_blocks[0].norm1(inp, emb=temb_)
+        if self.cnt == 0 or self.cnt == self.num_steps - 1:
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else:
+            coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01]
+            rescale_func = np.poly1d(coefficients)
+            # rescale_func = Polynomial(coefficients.reverse())
+            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp - self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
+            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                should_calc = False
+            else:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = modulated_inp
+        self.cnt += 1
+        if self.cnt == self.num_steps:
+            self.cnt = 0
+
+    if self.enable_teacache:
+        if not should_calc:
+            hidden_states += self.previous_residual
+        else:
+            ori_hidden_states = hidden_states.clone()
+            for index_block, block in enumerate(self.transformer_blocks):
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                        block,
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        image_rotary_emb,
+                    )
+
+                else:
+                    encoder_hidden_states, hidden_states = block(
+                        hidden_states=hidden_states,
+                        encoder_hidden_states=encoder_hidden_states,
+                        temb=temb,
+                        image_rotary_emb=image_rotary_emb,
+                        joint_attention_kwargs=joint_attention_kwargs,
+                    )
+
+                # controlnet residual
+                if controlnet_block_samples is not None:
+                    interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                    interval_control = int(np.ceil(interval_control))
+                    # For Xlabs ControlNet.
+                    if controlnet_blocks_repeat:
+                        hidden_states = (
+                                hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                        )
+                    else:
+                        hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+            if siglip_tensor is not None:
+                siglip_tensor = drop_token(siglip_tensor, self.drop_token_prob, training=self.training)
+                hidden_states = hidden_states + self.siglip_embed(siglip_tensor)
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+            for index_block, block in enumerate(self.single_transformer_blocks):
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    hidden_states = self._gradient_checkpointing_func(
+                        block,
+                        hidden_states,
+                        temb,
+                        image_rotary_emb,
+                    )
+
+                else:
+                    hidden_states = block(
+                        hidden_states=hidden_states,
+                        temb=temb,
+                        image_rotary_emb=image_rotary_emb,
+                        joint_attention_kwargs=joint_attention_kwargs,
+                    )
+
+                # controlnet residual
+                if controlnet_single_block_samples is not None:
+                    interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                    interval_control = int(np.ceil(interval_control))
+                    hidden_states[:, encoder_hidden_states.shape[1]:, ...] = (
+                            hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+                            + controlnet_single_block_samples[index_block // interval_control]
+                    )
+
+            hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+            self.previous_residual = hidden_states - ori_hidden_states
+    else:
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                            hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+        if siglip_tensor is not None:
+            siglip_tensor = drop_token(siglip_tensor, self.drop_token_prob, training=self.training)
+            hidden_states = hidden_states + self.siglip_embed(siglip_tensor)
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1]:, ...] = (
+                        hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+                        + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+
+    hidden_states = self.norm_out(hidden_states, temb)
+    output = self.proj_out(hidden_states)
+
+    if USE_PEFT_BACKEND:
+        # remove `lora_scale` from each PEFT layer
+        unscale_lora_layers(self, lora_scale)
+
+    if not return_dict:
+        return (output,)
+
+    return Transformer2DModelOutput(sample=output)
+
+
+class FluxPipelineWithSigLIP(FluxPipeline):
+   
+    @torch.no_grad()
+    def __call__(
+            self,
+            siglip_tensor: torch.Tensor,
+            prompt: Union[str, List[str]] = None,
+            prompt_2: Optional[Union[str, List[str]]] = None,
+            negative_prompt: Union[str, List[str]] = None,
+            negative_prompt_2: Optional[Union[str, List[str]]] = None,
+            true_cfg_scale: float = 1.0,
+            true_cfg_scale_2: float = 1.0,
+            height: Optional[int] = None,
+            width: Optional[int] = None,
+            num_inference_steps: int = 28,
+            sigmas: Optional[List[float]] = None,
+            guidance_scale: float = 3.5,
+            num_images_per_prompt: Optional[int] = 1,
+            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+            latents: Optional[torch.FloatTensor] = None,
+            prompt_embeds: Optional[torch.FloatTensor] = None,
+            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            ip_adapter_image: Optional[PipelineImageInput] = None,
+            ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+            negative_ip_adapter_image: Optional[PipelineImageInput] = None,
+            negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            output_type: Optional[str] = "pil",
+            return_dict: bool = True,
+            joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+            callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+            max_sequence_length: int = 512,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 3.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            negative_ip_adapter_image:
+                (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+        assert true_cfg_scale == true_cfg_scale_2
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        has_neg_prompt = negative_prompt is not None or (
+                negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
+        )
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+        assert do_true_cfg
+        (
+            negative_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            _,
+        ) = self.encode_prompt(
+            prompt=negative_prompt,
+            prompt_2=negative_prompt_2,
+            prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0] * 2)
+        else:
+            guidance = None
+
+        if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and (
+                negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None
+        ):
+            negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
+            negative_ip_adapter_image = [negative_ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
+
+        elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and (
+                negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None
+        ):
+            ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
+            ip_adapter_image = [ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
+
+        if self.joint_attention_kwargs is None:
+            self._joint_attention_kwargs = {}
+
+        image_embeds = None
+        negative_image_embeds = None
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+            )
+        if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None:
+            negative_image_embeds = self.prepare_ip_adapter_image_embeds(
+                negative_ip_adapter_image,
+                negative_ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+            )
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                if image_embeds is not None:
+                    self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0] * 2).to(latents.dtype)
+
+                batch_noise_pred = self.transformer(
+                    hidden_states=torch.cat([latents, latents], dim=0),
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=torch.cat([pooled_prompt_embeds, negative_pooled_prompt_embeds.expand_as(pooled_prompt_embeds)], dim=0),
+                    encoder_hidden_states=torch.cat([prompt_embeds, negative_prompt_embeds.expand_as(prompt_embeds)], dim=0),
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    siglip_tensor=torch.cat([siglip_tensor, torch.zeros_like(siglip_tensor)], dim=0),
+                    return_dict=False,
+                )[0]
+                noise_pred, neg_noise_pred = batch_noise_pred.chunk(2)
+                noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        self._current_timestep = None
+
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
diff --git a/pipelines/xomni/modeling_siglip_tokenizer.py b/pipelines/xomni/modeling_siglip_tokenizer.py
new file mode 100644
index 000000000..26e67571d
--- /dev/null
+++ b/pipelines/xomni/modeling_siglip_tokenizer.py
@@ -0,0 +1,231 @@
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import einsum
+from torchvision import transforms
+
+from PIL import Image
+from einops import rearrange
+
+from .modeling_vit import create_siglip_vit
+
+
+def create_anyres_preprocess(
+    short_size=384, 
+    long_size=1152, 
+    patch_size=16, 
+    random_ratio=None, 
+    min_short_size=128, 
+    max_aspect_ratio=3., 
+    filtering=True
+):
+
+    def resize_and_filtering(pil_image):
+        pil_image = pil_image.convert('RGB')
+        width, height = pil_image.size
+        ss, ls = min(width, height), max(width, height)
+        aspect_ratio = ls / ss
+        if filtering and (ss < min_short_size or aspect_ratio > max_aspect_ratio):
+            return None
+        target_width, target_height = width, height
+        if random_ratio is not None:
+            log_ratio = torch.log(torch.tensor(random_ratio))
+            sqrt_ratio = torch.exp(0.5 * torch.empty(1).uniform_(log_ratio[0], log_ratio[1])).item()
+            target_width = int(round(target_width * sqrt_ratio))
+            target_height = int(round(target_height / sqrt_ratio))
+        
+        ss = min(target_width, target_height)
+        if ss < short_size:
+            target_width = target_width * (short_size / ss)
+            target_height = target_height * (short_size / ss)
+        
+        ls = max(target_width, target_height)
+        if ls > long_size:
+            target_width = target_width * (long_size / ls)
+            target_height = target_height * (long_size / ls)
+        
+        target_width = int(round(target_width / patch_size)) * patch_size
+        target_height = int(round(target_height / patch_size)) * patch_size
+        pil_image = pil_image.resize((target_width, target_height), resample=Image.BICUBIC)
+        
+        to_tensor = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ])
+        return to_tensor(pil_image)
+
+    transform = transforms.Lambda(resize_and_filtering)
+    return transform
+
+
+class IBQ(nn.Module):
+    def __init__(self, n_e, e_dim, skip_quantization_prob=0.0, quantization_temp=2.0, beta=0.25, sane_index_shape=False, l2_norm=True):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.quantization_temp = quantization_temp
+        self.skip_quantization_prob = skip_quantization_prob
+        self.beta = beta
+        self.sane_index_shape = sane_index_shape
+        self.l2_norm = l2_norm
+
+        self.embedding = nn.Embedding(self.n_e, self.e_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+        if self.l2_norm:
+            self.embedding.weight.data = F.normalize(self.embedding.weight.data, p=2, dim=-1)
+    
+    def forward(self, z, temp=None, rescale_logits=False, return_logits=False, **kwargs):
+        assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
+        assert rescale_logits == False, "Only for interface compatible with Gumbel"
+        assert return_logits == False, "Only for interface compatible with Gumbel"
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = rearrange(z, 'b c h w -> b h w c').contiguous()
+        assert z.shape[-1] == self.e_dim
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        if self.l2_norm:
+            z = F.normalize(z, p=2, dim=-1)
+            z_flattened = F.normalize(z_flattened, p=2, dim=-1)
+            embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
+        else:
+            embedding = self.embedding.weight
+
+        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
+            torch.sum(embedding**2, dim=1) - 2 * \
+            torch.einsum('bd,dn->bn', z_flattened, torch.einsum('n d -> d n', embedding))
+        
+        if self.training:
+            logits = -d / self.quantization_temp
+            soft_one_hot = F.softmax(logits, dim=1)
+            min_encoding_indices = soft_one_hot.max(1, keepdim=True)[1]
+            hard_one_hot = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(1, min_encoding_indices, 1.0)
+            one_hot = hard_one_hot - soft_one_hot.detach() + soft_one_hot
+
+            z_q = einsum('b n, n d -> b d', one_hot, self.embedding.weight).view(z.shape)
+            z_q_2 = einsum('b n, n d -> b d', hard_one_hot, self.embedding.weight).view(z.shape)
+
+            # compute loss for embedding
+            commit_loss = torch.mean((z_q - z) ** 2) + torch.mean((z_q_2.detach() - z) ** 2) + self.beta * \
+                        torch.mean((z_q_2 - z.detach()) ** 2)
+        else:
+            min_encoding_indices = torch.argmin(d, dim=1)
+            z_q = embedding[min_encoding_indices].view(z.shape)
+            commit_loss = None
+        
+        if self.training and self.skip_quantization_prob > 0.0:
+            z_q = torch.where(
+                torch.rand_like(z_q[:, 0:1, 0:1, 0:1]).expand_as(z_q) <= self.skip_quantization_prob,
+                z, z_q,
+            )
+        
+        # reshape back to match original input shape
+        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
+
+        if self.sane_index_shape:
+            min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
+
+        return (z_q, None, min_encoding_indices), commit_loss
+
+    def get_codebook_entry(self, indices, bhwc):
+        # shape specifying (batch, height, width, channel)
+        # get quantized latent vectors
+        z_q = self.embedding(indices)
+
+        if bhwc is not None:
+            z_q = z_q.view(bhwc)
+            # reshape back to match original input shape
+            z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels, num_groups=32):
+        super().__init__()
+        self.conv1 = nn.Conv2d(channels, channels, 3, padding='same')
+        self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=channels)
+        self.activate = nn.GELU()
+        self.conv2 = nn.Conv2d(channels, channels, 3, padding='same')
+        self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=channels)
+    
+    def forward(self, x):
+        res = x
+        x = self.norm1(x)
+        x = self.activate(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.activate(x)
+        x = self.conv2(x)
+        return x + res
+
+
+class VQConvProjector(nn.Module):
+    def __init__(
+        self, 
+        z_channels=1536, 
+        codebook_size=16384, 
+        codebook_dim=2048, 
+        conv_layers=2,
+        with_norm=True,
+        skip_quant_prob=0.1,
+    ):
+        super().__init__()
+        self.quant_conv = nn.Conv2d(z_channels, codebook_dim, 1)
+        self.quantize = IBQ(codebook_size, codebook_dim, skip_quant_prob, sane_index_shape=True)
+        self.post_quant_conv = nn.Conv2d(codebook_dim, z_channels, 1)
+        block = ResidualBlock
+        self.post_conv = nn.Sequential(*[block(z_channels) for _ in range(conv_layers)])
+    
+    def forward(self, x, h, w):
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        z = self.quant_conv(x)
+        (z_q, _, _), codebook_loss = self.quantize(z)
+        z = self.post_quant_conv(z_q)
+        z = self.post_conv(z)
+        z = rearrange(z, 'b c h w -> b (h w) c')
+        return z, codebook_loss
+    
+    def encode(self, x, h, w):
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        z = self.quant_conv(x)
+        (_, _, tokens), _ = self.quantize(z)
+        return tokens
+    
+    def decode(self, tokens, bhwc):
+        z_q = self.quantize.get_codebook_entry(tokens, bhwc)
+        z = self.post_quant_conv(z_q)
+        z = self.post_conv(z)        
+        return z
+
+
+class SiglipTokenizer(nn.Module):
+    def __init__(
+        self, 
+        siglip_name, 
+        siglip_path, 
+        projector_path, 
+        z_channels=1536, 
+        codebook_size=16384, 
+        codebook_dim=2048, 
+        with_norm=True
+    ):
+        super().__init__()
+        self.vit = create_siglip_vit(model_name=siglip_name, path=siglip_path)
+        self.vqproj = VQConvProjector(
+            z_channels=z_channels, 
+            codebook_size=codebook_size, 
+            codebook_dim=codebook_dim, 
+            with_norm=with_norm
+        )
+        self.vqproj.load_state_dict(torch.load(projector_path, map_location='cpu'), strict=True)
+
+    def encode(self, x):
+        features, (h, w), _ = self.vit(x)
+        tokens = self.vqproj.encode(features, h, w)
+        return tokens
+    
+    def decode(self, tokens, bhwc):
+        return self.vqproj.decode(tokens, bhwc)
diff --git a/pipelines/xomni/modeling_vit.py b/pipelines/xomni/modeling_vit.py
new file mode 100644
index 000000000..722851a41
--- /dev/null
+++ b/pipelines/xomni/modeling_vit.py
@@ -0,0 +1,699 @@
+import math
+import warnings
+from dataclasses import dataclass
+from functools import partial
+from typing import (
+    Callable, Dict, Final, List, Literal, Optional,
+    Sequence, Set, Tuple, Type, Union,
+)
+
+from torch.utils.checkpoint import checkpoint
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from timm.layers import (
+    DropPath, LayerType, Mlp, PatchDropout,
+    PatchEmbed, resample_abs_pos_embed,
+)
+from timm.models._manipulate import checkpoint_seq, named_apply
+
+from flash_attn import flash_attn_func, flash_attn_varlen_func
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)  # noqa: E741
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
+    r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
+    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its orignal dtype.
+    Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
+    from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+
+    with torch.no_grad():
+        dtype = tensor.dtype
+        tensor_fp32 = tensor.float()
+        tensor_fp32 = _no_grad_trunc_normal_(tensor_fp32, mean, std, a, b)
+        tensor_dtype = tensor_fp32.to(dtype=dtype)
+        tensor.copy_(tensor_dtype)
+
+
+def init_weights(self):
+    if self.pos_embed is not None:
+        trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
+    trunc_normal_(self.latent, std=self.latent_dim**-0.5)
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif hasattr(module, "init_weights"):
+        module.init_weights()
+
+
+class Attention(nn.Module):
+    fused_attn: Final[bool]
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        norm_layer: nn.Module = nn.LayerNorm,
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        # self.fused_attn = use_fused_attn()
+        self.fused_attn = True
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor, cu_slens=None) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, self.head_dim)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if cu_slens is not None:
+            q = q.permute(0, 2, 1, 3)   # B, num_heads, N, C -> B, N, num_heads, C
+            k = k.permute(0, 2, 1, 3)
+            v = v.permute(0, 2, 1, 3)
+            max_seqlen = torch.max(cu_slens[1:] - cu_slens[:-1]).item()
+            x = flash_attn_varlen_func(
+                q.squeeze(0),
+                k.squeeze(0),
+                v.squeeze(0),
+                cu_seqlens_q=cu_slens,
+                cu_seqlens_k=cu_slens,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                softmax_scale=self.scale,
+                causal=False,
+                )
+
+            x = x.reshape(B, N, -1)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+
+        else:
+            q = q.permute(0, 2, 1, 3)   # B, num_heads, N, C -> B, N, num_heads, C
+            k = k.permute(0, 2, 1, 3)
+            v = v.permute(0, 2, 1, 3)
+            x = flash_attn_func(q, k, v, softmax_scale=self.scale) # -> b, n, h, c
+
+            x = x.reshape(B, N, -1)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+        return x
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: float = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        proj_drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values: Optional[float] = None,
+        drop_path: float = 0.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.LayerNorm,
+        mlp_layer: nn.Module = Mlp,
+    ) -> None:
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+        self.ls1 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = mlp_layer(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+        )
+        self.ls2 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor, cu_slens=None) -> torch.Tensor:
+        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), cu_slens=cu_slens)))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+    """
+
+    dynamic_img_size: Final[bool]
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        num_classes: int = 1000,
+        global_pool: Literal["", "avg", "token", "map"] = "token",
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        init_values: Optional[float] = None,
+        class_token: bool = True,
+        no_embed_class: bool = False,
+        reg_tokens: int = 0,
+        pre_norm: bool = False,
+        fc_norm: Optional[bool] = None,
+        dynamic_img_size: bool = False,
+        dynamic_img_pad: bool = False,
+        drop_rate: float = 0.0,
+        pos_drop_rate: float = 0.0,
+        patch_drop_rate: float = 0.0,
+        proj_drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        weight_init: Literal["skip", "jax", "jax_nlhb", "moco", ""] = "",
+        embed_layer: Callable = PatchEmbed,
+        norm_layer: Optional[LayerType] = None,
+        act_layer: Optional[LayerType] = None,
+        strict_img_size: bool = False,
+        block_fn: Type[nn.Module] = Block,
+        mlp_layer: Type[nn.Module] = Mlp,
+        ignore_head: bool = False,
+    ) -> None:
+        """
+        Args:
+            img_size: Input image size.
+            patch_size: Patch size.
+            in_chans: Number of image input channels.
+            num_classes: Mumber of classes for classification head.
+            global_pool: Type of global pooling for final sequence (default: 'token').
+            embed_dim: Transformer embedding dimension.
+            depth: Depth of transformer.
+            num_heads: Number of attention heads.
+            mlp_ratio: Ratio of mlp hidden dim to embedding dim.
+            qkv_bias: Enable bias for qkv projections if True.
+            init_values: Layer-scale init values (layer-scale enabled if not None).
+            class_token: Use class token.
+            no_embed_class: Don't include position embeddings for class (or reg) tokens.
+            reg_tokens: Number of register tokens.
+            fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
+            drop_rate: Head dropout rate.
+            pos_drop_rate: Position embedding dropout rate.
+            attn_drop_rate: Attention dropout rate.
+            drop_path_rate: Stochastic depth rate.
+            weight_init: Weight initialization scheme.
+            embed_layer: Patch embedding layer.
+            norm_layer: Normalization layer.
+            act_layer: MLP activation layer.
+            block_fn: Transformer block layer.
+        """
+        super().__init__()
+        assert global_pool in ("", "avg", "token", "map")
+        assert class_token or global_pool != "token"
+        use_fc_norm = global_pool == "avg" if fc_norm is None else fc_norm
+        # norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
+        # act_layer = get_act_layer(act_layer) or nn.GELU
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        act_layer = nn.GELU
+
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.num_features = self.embed_dim = (
+            embed_dim  # num_features for consistency with other models
+        )
+        self.num_prefix_tokens = 1 if class_token else 0
+        self.num_prefix_tokens += reg_tokens
+        self.num_reg_tokens = reg_tokens
+        self.has_class_token = class_token
+        self.no_embed_class = (
+            no_embed_class  # don't embed prefix positions (includes reg)
+        )
+        self.dynamic_img_size = dynamic_img_size
+        self.grad_checkpointing = False
+        self.ignore_head = ignore_head
+
+        embed_args = {}
+        if dynamic_img_size:
+            # flatten deferred until after pos embed
+            embed_args.update(dict(strict_img_size=False, output_fmt="NHWC"))
+        self.patch_embed = embed_layer(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
+            dynamic_img_pad=dynamic_img_pad,
+            strict_img_size=strict_img_size,
+            **embed_args,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = (
+            nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
+        )
+        self.reg_token = (
+            nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
+        )
+        embed_len = (
+            num_patches if no_embed_class else num_patches + self.num_prefix_tokens
+        )
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
+        self.pos_drop = nn.Dropout(p=pos_drop_rate)
+        if patch_drop_rate > 0:
+            self.patch_drop = PatchDropout(
+                patch_drop_rate,
+                num_prefix_tokens=self.num_prefix_tokens,
+            )
+        else:
+            self.patch_drop = nn.Identity()
+        self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
+
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(
+            *[
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_norm=qk_norm,
+                    init_values=init_values,
+                    proj_drop=proj_drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    mlp_layer=mlp_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+    def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = "") -> None:
+        assert mode in ("jax", "jax_nlhb", "moco", "")
+        # head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
+        trunc_normal_(self.pos_embed, std=0.02)
+        if self.cls_token is not None:
+            nn.init.normal_(self.cls_token, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self) -> Set:
+        return {"pos_embed", "cls_token", "dist_token"}
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict:
+        return dict(
+            stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
+            blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True) -> None:
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> nn.Module:
+        return self.head
+
+    def reset_classifier(self, num_classes: int, global_pool=None) -> None:
+        self.num_classes = num_classes
+        if global_pool is not None:
+            assert global_pool in ("", "avg", "token", "map")
+            if global_pool == "map" and self.attn_pool is None:
+                assert (
+                    False
+                ), "Cannot currently add attention pooling in reset_classifier()."
+            elif global_pool != "map " and self.attn_pool is not None:
+                self.attn_pool = None  # remove attention pooling
+            self.global_pool = global_pool
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+    def rescale_positional_embedding(self, out_size):
+        h, w = out_size
+        pos_embed_shape = int((self.pos_embed.shape[1]) ** 0.5)
+        if (h, w) == (pos_embed_shape, pos_embed_shape):
+            return self.pos_embed
+        rescaled_positional_embedding = \
+            self.pos_embed.new_zeros(1, h*w, self.pos_embed.shape[2])
+        pe_2d = self.pos_embed[0].T.contiguous().view(1, -1, pos_embed_shape, pos_embed_shape)
+        pe_2d = F.interpolate(pe_2d, out_size, mode='bilinear', align_corners=False).view(-1, h*w)
+        rescaled_positional_embedding[0] = pe_2d.T.contiguous()
+        return rescaled_positional_embedding
+
+    def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dynamic_img_size:
+            B, H, W, C = x.shape
+            pos_embed = resample_abs_pos_embed(
+                self.pos_embed,
+                (H, W),
+                num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
+            )
+            x = x.view(B, -1, C)
+        else:
+            pos_embed = self.pos_embed
+
+        to_cat = []
+        if self.cls_token is not None:
+            to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
+        if self.reg_token is not None:
+            to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
+
+        if self.no_embed_class:
+            # deit-3, updated JAX (big vision)
+            # position embedding does not overlap with class token, add then concat
+            x = x + pos_embed
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+        else:
+            # original timm, JAX, and deit vit impl
+            # pos_embed has entry for class token, concat then add
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+            x = x + pos_embed
+
+        return self.pos_drop(x)
+
+    def _intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+    ) -> List[torch.Tensor]:
+        outputs, num_blocks = [], len(self.blocks)
+        take_indices = set(
+            range(num_blocks - n, num_blocks) if isinstance(n, int) else n
+        )
+
+        # forward pass
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in take_indices:
+                outputs.append(x)
+
+        return outputs
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+        reshape: bool = False,
+        return_prefix_tokens: bool = False,
+        norm: bool = False,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        """Intermediate layer accessor (NOTE: This is a WIP experiment).
+        Inspired by DINO / DINOv2 interface
+        """
+        # take last n blocks if n is an int, if in is a sequence, select by matching indices
+        outputs = self._intermediate_layers(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        prefix_tokens = [out[:, 0 : self.num_prefix_tokens] for out in outputs]
+        outputs = [out[:, self.num_prefix_tokens :] for out in outputs]
+
+        if reshape:
+            grid_size = self.patch_embed.grid_size
+            outputs = [
+                out.reshape(x.shape[0], grid_size[0], grid_size[1], -1)
+                .permute(0, 3, 1, 2)
+                .contiguous()
+                for out in outputs
+            ]
+
+        if return_prefix_tokens:
+            return tuple(zip(outputs, prefix_tokens))
+        return tuple(outputs)
+
+    def forward_features_list(self, x_list):
+        x_all = []
+        image_sizes = []
+        for x in x_list:
+            bs, _, h, w = x.shape
+
+            # fix patch size=14 in datasets 
+            pad_h = (self.patch_embed.patch_size[0] - h % self.patch_embed.patch_size[0]) % self.patch_embed.patch_size[0]
+            pad_w = (self.patch_embed.patch_size[1] - w % self.patch_embed.patch_size[1]) % self.patch_embed.patch_size[1]
+            x = F.pad(x, (0, pad_w, 0, pad_h))
+
+            bs, _, h, w = x.shape
+
+            h = h // self.patch_embed.patch_size[0]
+            w = w // self.patch_embed.patch_size[1]
+
+            x = self.patch_embed(x)
+            x = x + self.rescale_positional_embedding(out_size=(h, w))
+            x = self.patch_drop(x)
+            x = self.norm_pre(x)
+            x_all.append(x)
+            image_sizes.append((h, w))
+
+        slen = [xi.size(1) for xi in x_all]
+        x = torch.cat(x_all, dim=1)
+
+        cu_indices = [0, ]
+        for i in slen:
+            cu_indices.append(cu_indices[-1] + i)
+
+        cu_slens = torch.tensor(cu_indices, dtype=torch.int32).to(x.device)
+        for idx, blk in enumerate(self.blocks):
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint(blk, x, cu_slens, use_reentrant=True)
+            else:
+                x = blk(x, cu_slens=cu_slens)
+        feats = x.split(slen, dim=1) #[(1, slen, c)]
+        return feats, image_sizes
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        bs, _, h, w = x.shape
+        h = h // self.patch_embed.patch_size[0]
+        w = w // self.patch_embed.patch_size[1]
+        
+        x = self.patch_embed(x)
+        # x = self._pos_embed(x)
+        x = x + self.rescale_positional_embedding(out_size=(h, w))
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        return x, (h, w)
+
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
+        x = self.norm(x)
+        if self.attn_pool is not None:
+            x = self.attn_pool(x)
+        elif self.global_pool == "avg":
+            x = x[:, self.num_prefix_tokens :].mean(dim=1)
+        elif self.global_pool:
+            x = x[:, 0]  # class token
+        x = self.fc_norm(x)
+        x = self.head_drop(x)
+        return x if pre_logits else self.head(x)
+
+    def forward(self, x, cal_attn_pool=False):
+        if type(x) is list:
+            x, image_sizes = self.forward_features_list(x)
+            return x, image_sizes, None
+        else:
+            x, image_sizes = self.forward_features(x)
+            return x, image_sizes, None
+
+@dataclass
+class SigLIPVisionCfg:
+    width: int = 1152
+    layers: Union[Tuple[int, int, int, int], int] = 27
+    heads: int = 16
+    patch_size: int = 14
+    image_size: Union[Tuple[int, int], int] = 336
+    global_pool: str = "map"
+    mlp_ratio: float = 3.7362
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+
+
+SigLIP_MODEL_CONFIG = {
+    "siglip_so400m_patch16_384": {
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip2_giant_patch16_384":{
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1536,
+        "layers": 40,
+        "heads": 16,
+        "mlp_ratio": 4,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+}
+
+
+def resize_evaclip_pos_embed(model: VisionTransformer, interpolation: str = 'bicubic'):
+    # interpolate position embedding
+    orig_size = 24
+    new_size = 128
+    pos_tokens = model.pos_embed
+    pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, model.embed_dim).permute(0, 3, 1, 2)
+    pos_tokens = torch.nn.functional.interpolate(
+        pos_tokens, size=(new_size, new_size), mode=interpolation, align_corners=False)
+    pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+    model.pos_embed = nn.Parameter(pos_tokens, requires_grad=True)
+    return model 
+
+
+def create_siglip_vit(
+    model_name: str = "siglip_so400m_patch14_384",
+    select_layer: int = -1,
+    path: str = "",
+    gradient_checkpointing: bool = False,
+    **kwargs,
+):
+    vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])
+
+    if select_layer <= 0:
+        layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
+    else:
+        layers = min(vision_cfg.layers, select_layer)
+
+    model = VisionTransformer(
+        img_size=2048,
+        patch_size=16,
+        embed_dim=vision_cfg.width,
+        depth=layers,
+        num_heads=vision_cfg.heads,
+        mlp_ratio=vision_cfg.mlp_ratio,
+        class_token=vision_cfg.class_token,
+        global_pool=vision_cfg.global_pool,
+        dynamic_img_pad=False,
+        strict_img_size=False,
+        ignore_head=kwargs.get("ignore_head", False),
+        weight_init=kwargs.get("weight_init", "skip"),
+        num_classes=0
+    )
+    model.config = vision_cfg
+    state_dict = torch.load(path, map_location="cpu")
+    model.load_state_dict(state_dict, strict=False)
+
+    if gradient_checkpointing:
+        model.set_grad_checkpointing(True)
+    return model
diff --git a/pipelines/xomni/modeling_xomni.py b/pipelines/xomni/modeling_xomni.py
new file mode 100644
index 000000000..2e05dec0e
--- /dev/null
+++ b/pipelines/xomni/modeling_xomni.py
@@ -0,0 +1,317 @@
+import os
+from types import SimpleNamespace
+from typing import Tuple, List, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from huggingface_hub import hf_hub_download
+from transformers import Qwen2ForCausalLM, AutoModel, AutoModelForCausalLM
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm, Qwen2RotaryEmbedding, Qwen2DecoderLayer, Qwen2Model, Qwen2PreTrainedModel
+
+from .configuration_xomni import XOmniConfig
+from .modeling_siglip_tokenizer import create_anyres_preprocess, SiglipTokenizer
+from .modeling_siglip_flux import FluxTransformer2DModelWithSigLIP, FluxPipelineWithSigLIP
+from .modeling_vit import create_siglip_vit
+
+
+class XOmniDecoderLayer(Qwen2DecoderLayer):
+    def __init__(self, config: XOmniConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.layer_idx = layer_idx
+        self.is_lm_layer = config.num_mm_adap_layers <= layer_idx < config.num_hidden_layers - config.num_mm_head_layers
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        hidden_states, multimodal_mask = torch.split(hidden_states, hidden_states.shape[-1] // 2, dim=-1)
+        if self.is_lm_layer:
+            output_hidden_states, *others = super().forward(hidden_states, **kwargs)
+            output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
+            return output_hidden_states, *others
+        
+        # mm_hidden_states = torch.where(multimodal_mask.bool(), hidden_states, torch.zeros_like(hidden_states))
+        output_hidden_states, *others = super().forward(hidden_states, **kwargs)
+        output_hidden_states = torch.where(multimodal_mask.bool(), output_hidden_states, hidden_states)
+        output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
+        return output_hidden_states, *others
+
+
+class XOmniModel(Qwen2Model, Qwen2PreTrainedModel):
+    model_type = "x-omni"
+    config_class = XOmniConfig
+
+    def __init__(self, config: XOmniConfig):
+        Qwen2PreTrainedModel.__init__(self, config)
+        self.padding_idx = -1
+        self.vocab_size = config.vocab_size
+        
+        self.lm_embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.mm_embed_tokens = nn.Embedding(config.mm_vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList(
+            [XOmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.lm_norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mm_norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.lm_embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.lm_embed_tokens = value
+
+    def embed_tokens(self, input_ids):
+        (B, L), C = input_ids.shape, self.config.hidden_size
+        multimodal_mask = input_ids >= self.config.vocab_size
+        lm_input_ids = input_ids[~multimodal_mask][None, :]
+        mm_input_ids = input_ids[multimodal_mask][None, :] - self.config.vocab_size
+        lm_embeds = self.lm_embed_tokens(lm_input_ids)
+        mm_embeds = self.mm_embed_tokens(mm_input_ids)
+
+        inputs_embeds = lm_embeds.new_empty((B, L, C))
+        multimodal_mask = multimodal_mask[:, :, None].expand_as(inputs_embeds)
+        inputs_embeds[~multimodal_mask] = lm_embeds.reshape(-1)
+        inputs_embeds[multimodal_mask] = mm_embeds.reshape(-1)
+
+        inputs_embeds = torch.cat([inputs_embeds, multimodal_mask.to(inputs_embeds.dtype)], dim=-1)
+        return inputs_embeds
+
+    def norm(self, hidden_states):
+        hidden_states, multimodal_mask = torch.split(hidden_states, hidden_states.shape[-1] // 2, dim=-1)
+        return torch.where(multimodal_mask.bool(), self.mm_norm(hidden_states), self.lm_norm(hidden_states))
+
+
+class XOmniForCausalLM(Qwen2ForCausalLM):
+    model_type = "x-omni"
+    config_class = XOmniConfig
+    
+    _keys_to_ignore_on_load_missing = r'image_tokenizer\.*'
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = XOmniModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.mm_head = nn.Linear(config.hidden_size, config.mm_vocab_size, bias=False)
+        
+        self.generation_mode = 'text'
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @property
+    def device(self):
+        return next(iter(self.parameters())).device
+    
+    def init_vision(self, flux_pipe_path, **kwargs):
+        self.som_token = self.config.mm_special_tokens[0]
+        self.eom_token = self.config.mm_special_tokens[1]
+        self.img_token = self.config.mm_special_tokens[2]
+
+        self.vision_config = SimpleNamespace(**self.config.vision_config)
+        self.transform_config = SimpleNamespace(**self.vision_config.transform)
+        self.encoder_config = SimpleNamespace(**self.vision_config.encoder)
+        self.decoder_config = SimpleNamespace(**self.vision_config.decoder)
+
+        dtype_map = {'float32': torch.float32, 'float16': torch.float16, 'bfloat16': torch.bfloat16}
+        self.vision_dtype = dtype_map[self.vision_config.dtype]
+
+        self.image_transform = create_anyres_preprocess(**self.vision_config.transform)
+        
+        self.encoder_config.siglip_path = os.path.join(self.name_or_path, self.encoder_config.siglip_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.siglip_path)
+        self.encoder_config.projector_path = os.path.join(self.name_or_path, self.encoder_config.projector_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.projector_path)
+
+        self.image_tokenizer = SiglipTokenizer(**vars(self.encoder_config))
+        self.image_tokenizer.to(self.device, self.vision_dtype)
+
+        transformer = FluxTransformer2DModelWithSigLIP.from_pretrained(
+            self.name_or_path, 
+            siglip_channels=self.encoder_config.z_channels, 
+            torch_dtype=self.vision_dtype,
+            subfolder=self.decoder_config.model_path,
+            **kwargs,
+        )
+
+        self.decoder_pipe = FluxPipelineWithSigLIP.from_pretrained(
+            flux_pipe_path, 
+            transformer=transformer,
+            torch_dtype=self.vision_dtype,
+        )
+        self.decoder_pipe.set_progress_bar_config(disable=True)
+
+    def set_generation_mode(self, mode):
+        assert mode in ('text', 'image'), f'Invalid generation mode: {mode}'
+        self.generation_mode = mode
+
+    def mmencode(self, tokenizer, texts=None, images=None, **kwargs):
+        texts = texts or []
+        images = images or []
+        doc = ''
+        while len(texts) > 0 or len(images) > 0:
+            if len(texts) > 0:
+                doc += texts.pop(0)
+            if len(images) > 0:
+                doc += self.tokenize_image(images.pop(0))
+        return tokenizer.encode(doc, **kwargs)
+    
+    def mmdecode(self, tokenizer, token_ids, force_text=None, **kwargs):
+        force_text = force_text or []
+        if isinstance(token_ids, torch.Tensor):
+            if len(token_ids.shape) == 2:
+                assert token_ids.shape[0] == 1
+                token_ids = token_ids[0]
+            assert len(token_ids.shape) == 1
+        else:
+            if not isinstance(token_ids[0], int):
+                assert len(token_ids) == 1
+                token_ids = token_ids[0]
+            assert isinstance(token_ids[0], int)
+        
+        doc = tokenizer.decode(token_ids, **kwargs)
+        doc = doc.replace(tokenizer.pad_token, '')
+        doc = doc.replace('<SEP>', '')
+        texts, images = [], []
+        text_image_chunks = doc.split(self.eom_token)
+        for chunk in text_image_chunks:
+            text, image_str = chunk.split(self.som_token) \
+                if self.som_token in chunk else (chunk, '')
+            texts.append(text)
+            if self.img_token in image_str:
+                image_meta, token_str = image_str.split(self.img_token)
+                H, W = tuple(map(int, image_meta.split(' ')))
+                token_ids = list(map(
+                    lambda x: int(x.split('>')[0]),
+                    token_str.split('<MM-Token-')[1:H*W+1],
+                ))
+                if len(force_text) > 0:
+                    image = self.detokenize_image([force_text.pop(0)], images, token_ids, (H, W))
+                else:
+                    image = self.detokenize_image(texts, images, token_ids, (H, W))
+                images.append(image)
+        return texts, images
+    
+    @torch.no_grad()
+    def tokenize_image(self, image):
+        assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
+
+        image_str = self.som_token
+        image = self.image_transform(image)
+        assert image is not None, f'Unsupported image aspect ratio (max {self.transform_config.max_aspect_ratio}) or image resolution is too low (min {self.transform_config.min_short_size})'
+
+        image = image[None, ...].to(self.device, self.vision_dtype)
+        tokens = self.image_tokenizer.encode(image)
+        B, H, W = tokens.shape
+        tokens = tokens.view(B, -1).cpu().tolist()[0]
+        token_str = ''.join(map(lambda x: '<MM-Token-{token_id}>'.format(token_id=x), tokens))
+        image_str = f'{self.som_token}{H} {W}{self.img_token}{token_str}{self.eom_token}'
+        return image_str
+    
+    @torch.no_grad()
+    def detokenize_image(self, texts, images, token_ids, shape):
+        assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
+        assert len(texts) == 1 and len(images) == 0, 'Only support one image per sample.'
+        H, W = shape
+        tokens = torch.tensor(token_ids, device=self.device, dtype=torch.long)
+        latents = self.image_tokenizer.decode(tokens, (1, H, W, self.encoder_config.codebook_dim))
+        upscale_factor = self.decoder_config.upscale_factor
+        latents = latents.reshape(*latents.shape[:2], -1).transpose(1, 2).contiguous()
+        image = self.decoder_pipe(
+            latents,
+            [texts[0]],
+            negative_prompt=[''],
+            height=H * upscale_factor, width=W * upscale_factor,
+            num_inference_steps=self.decoder_config.num_inference_steps, 
+            guidance_scale=1.0,
+            true_cfg_scale=self.decoder_config.cfg_scale,
+            true_cfg_scale_2=self.decoder_config.cfg_scale_2,
+        ).images[0]
+
+
+        return image
+    
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        self.model.has_sliding_layers = False
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = hidden_states[:, -num_logits_to_keep:, :]
+        logits = hidden_states.new_full(
+            (*hidden_states.shape[:-1], self.config.vocab_size + self.config.mm_vocab_size), 
+            torch.finfo(hidden_states.dtype).min
+        )
+        if self.generation_mode == 'text':
+            logits[:, :, :self.config.vocab_size] = self.lm_head(hidden_states)
+        else:
+            logits[:, :, self.config.vocab_size:self.config.vocab_size + self.config.image_vocab_size] = self.mm_head(hidden_states)[:, :, :self.config.image_vocab_size]
+        
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+AutoModel.register(XOmniConfig, XOmniModel)
+AutoModelForCausalLM.register(XOmniConfig, XOmniForCausalLM)

From a315a004e96bb5571205af9e2d6e7e7e34c654c7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 5 Oct 2025 20:25:33 -0400
Subject: [PATCH 155/230] linting

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/lora/lora_apply.py                   |  2 +
 modules/sdnq/loader.py                       |  2 +-
 modules/sdnq/quantizer.py                    | 11 ++--
 pipelines/xomni/modeling_siglip_flux.py      |  2 +-
 pipelines/xomni/modeling_siglip_tokenizer.py | 68 ++++++++++----------
 pipelines/xomni/modeling_vit.py              |  6 +-
 pipelines/xomni/modeling_xomni.py            | 34 +++++-----
 7 files changed, 63 insertions(+), 62 deletions(-)

diff --git a/modules/lora/lora_apply.py b/modules/lora/lora_apply.py
index d076efb97..51be0a74f 100644
--- a/modules/lora/lora_apply.py
+++ b/modules/lora/lora_apply.py
@@ -186,6 +186,8 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
                     self.svd_down.to(devices.device) if self.svd_down is not None else None,
                     skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul
                 )
+            else:
+                weights_dtype = devices.dtype
 
             new_weight = dequant_weight.to(devices.device, dtype=torch.float32) + lora_weights.to(devices.device, dtype=torch.float32)
             self.weight = torch.nn.Parameter(new_weight, requires_grad=False)
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index aabd2496c..a7a7f3ae6 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -16,7 +16,7 @@ def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str,
 
 def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", use_quantized_matmul: bool = False) -> ModelMixin:
     with torch.device("meta"):
-        with open(os.path.join(model_path, "quantization_config.json"), "r") as f:
+        with open(os.path.join(model_path, "quantization_config.json"), "r", encoding="utf-8") as f:
             quantization_config = json.load(f)
         quantization_config.pop("is_integer", None)
         quantization_config.pop("quant_method", None)
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index a92cb455a..d4769821e 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -252,13 +252,12 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
                 or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in modules_to_not_convert)
             ):
                 continue
-            else:
-                layer_class_name = module.__class__.__name__
-                if layer_class_name in allowed_types:
-                    if (layer_class_name in conv_types or layer_class_name in conv_transpose_types) and not quant_conv:
-                        continue
-                else:
+            layer_class_name = module.__class__.__name__
+            if layer_class_name in allowed_types:
+                if (layer_class_name in conv_types or layer_class_name in conv_transpose_types) and not quant_conv:
                     continue
+            else:
+                continue
             if len(modules_dtype_dict.keys()) > 0:
                 for key, value in modules_dtype_dict.items():
                     if param_name in value:
diff --git a/pipelines/xomni/modeling_siglip_flux.py b/pipelines/xomni/modeling_siglip_flux.py
index 8db60374a..18b7b3463 100644
--- a/pipelines/xomni/modeling_siglip_flux.py
+++ b/pipelines/xomni/modeling_siglip_flux.py
@@ -497,7 +497,7 @@ def teacache_forward(
 
 
 class FluxPipelineWithSigLIP(FluxPipeline):
-   
+
     @torch.no_grad()
     def __call__(
             self,
diff --git a/pipelines/xomni/modeling_siglip_tokenizer.py b/pipelines/xomni/modeling_siglip_tokenizer.py
index 26e67571d..5e99d4ff8 100644
--- a/pipelines/xomni/modeling_siglip_tokenizer.py
+++ b/pipelines/xomni/modeling_siglip_tokenizer.py
@@ -13,12 +13,12 @@ from .modeling_vit import create_siglip_vit
 
 
 def create_anyres_preprocess(
-    short_size=384, 
-    long_size=1152, 
-    patch_size=16, 
-    random_ratio=None, 
-    min_short_size=128, 
-    max_aspect_ratio=3., 
+    short_size=384,
+    long_size=1152,
+    patch_size=16,
+    random_ratio=None,
+    min_short_size=128,
+    max_aspect_ratio=3.,
     filtering=True
 ):
 
@@ -35,21 +35,21 @@ def create_anyres_preprocess(
             sqrt_ratio = torch.exp(0.5 * torch.empty(1).uniform_(log_ratio[0], log_ratio[1])).item()
             target_width = int(round(target_width * sqrt_ratio))
             target_height = int(round(target_height / sqrt_ratio))
-        
+
         ss = min(target_width, target_height)
         if ss < short_size:
             target_width = target_width * (short_size / ss)
             target_height = target_height * (short_size / ss)
-        
+
         ls = max(target_width, target_height)
         if ls > long_size:
             target_width = target_width * (long_size / ls)
             target_height = target_height * (long_size / ls)
-        
+
         target_width = int(round(target_width / patch_size)) * patch_size
         target_height = int(round(target_height / patch_size)) * patch_size
         pil_image = pil_image.resize((target_width, target_height), resample=Image.BICUBIC)
-        
+
         to_tensor = transforms.Compose([
             transforms.ToTensor(),
             transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
@@ -75,7 +75,7 @@ class IBQ(nn.Module):
         self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
         if self.l2_norm:
             self.embedding.weight.data = F.normalize(self.embedding.weight.data, p=2, dim=-1)
-    
+
     def forward(self, z, temp=None, rescale_logits=False, return_logits=False, **kwargs):
         assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
         assert rescale_logits == False, "Only for interface compatible with Gumbel"
@@ -96,7 +96,7 @@ class IBQ(nn.Module):
         d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
             torch.sum(embedding**2, dim=1) - 2 * \
             torch.einsum('bd,dn->bn', z_flattened, torch.einsum('n d -> d n', embedding))
-        
+
         if self.training:
             logits = -d / self.quantization_temp
             soft_one_hot = F.softmax(logits, dim=1)
@@ -114,13 +114,13 @@ class IBQ(nn.Module):
             min_encoding_indices = torch.argmin(d, dim=1)
             z_q = embedding[min_encoding_indices].view(z.shape)
             commit_loss = None
-        
+
         if self.training and self.skip_quantization_prob > 0.0:
             z_q = torch.where(
                 torch.rand_like(z_q[:, 0:1, 0:1, 0:1]).expand_as(z_q) <= self.skip_quantization_prob,
                 z, z_q,
             )
-        
+
         # reshape back to match original input shape
         z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
 
@@ -150,7 +150,7 @@ class ResidualBlock(nn.Module):
         self.activate = nn.GELU()
         self.conv2 = nn.Conv2d(channels, channels, 3, padding='same')
         self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=channels)
-    
+
     def forward(self, x):
         res = x
         x = self.norm1(x)
@@ -164,10 +164,10 @@ class ResidualBlock(nn.Module):
 
 class VQConvProjector(nn.Module):
     def __init__(
-        self, 
-        z_channels=1536, 
-        codebook_size=16384, 
-        codebook_dim=2048, 
+        self,
+        z_channels=1536,
+        codebook_size=16384,
+        codebook_dim=2048,
         conv_layers=2,
         with_norm=True,
         skip_quant_prob=0.1,
@@ -178,7 +178,7 @@ class VQConvProjector(nn.Module):
         self.post_quant_conv = nn.Conv2d(codebook_dim, z_channels, 1)
         block = ResidualBlock
         self.post_conv = nn.Sequential(*[block(z_channels) for _ in range(conv_layers)])
-    
+
     def forward(self, x, h, w):
         x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
         z = self.quant_conv(x)
@@ -187,37 +187,37 @@ class VQConvProjector(nn.Module):
         z = self.post_conv(z)
         z = rearrange(z, 'b c h w -> b (h w) c')
         return z, codebook_loss
-    
+
     def encode(self, x, h, w):
         x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
         z = self.quant_conv(x)
         (_, _, tokens), _ = self.quantize(z)
         return tokens
-    
+
     def decode(self, tokens, bhwc):
         z_q = self.quantize.get_codebook_entry(tokens, bhwc)
         z = self.post_quant_conv(z_q)
-        z = self.post_conv(z)        
+        z = self.post_conv(z)
         return z
 
 
 class SiglipTokenizer(nn.Module):
     def __init__(
-        self, 
-        siglip_name, 
-        siglip_path, 
-        projector_path, 
-        z_channels=1536, 
-        codebook_size=16384, 
-        codebook_dim=2048, 
+        self,
+        siglip_name,
+        siglip_path,
+        projector_path,
+        z_channels=1536,
+        codebook_size=16384,
+        codebook_dim=2048,
         with_norm=True
     ):
         super().__init__()
         self.vit = create_siglip_vit(model_name=siglip_name, path=siglip_path)
         self.vqproj = VQConvProjector(
-            z_channels=z_channels, 
-            codebook_size=codebook_size, 
-            codebook_dim=codebook_dim, 
+            z_channels=z_channels,
+            codebook_size=codebook_size,
+            codebook_dim=codebook_dim,
             with_norm=with_norm
         )
         self.vqproj.load_state_dict(torch.load(projector_path, map_location='cpu'), strict=True)
@@ -226,6 +226,6 @@ class SiglipTokenizer(nn.Module):
         features, (h, w), _ = self.vit(x)
         tokens = self.vqproj.encode(features, h, w)
         return tokens
-    
+
     def decode(self, tokens, bhwc):
         return self.vqproj.decode(tokens, bhwc)
diff --git a/pipelines/xomni/modeling_vit.py b/pipelines/xomni/modeling_vit.py
index 722851a41..150571c1d 100644
--- a/pipelines/xomni/modeling_vit.py
+++ b/pipelines/xomni/modeling_vit.py
@@ -541,7 +541,7 @@ class VisionTransformer(nn.Module):
         for x in x_list:
             bs, _, h, w = x.shape
 
-            # fix patch size=14 in datasets 
+            # fix patch size=14 in datasets
             pad_h = (self.patch_embed.patch_size[0] - h % self.patch_embed.patch_size[0]) % self.patch_embed.patch_size[0]
             pad_w = (self.patch_embed.patch_size[1] - w % self.patch_embed.patch_size[1]) % self.patch_embed.patch_size[1]
             x = F.pad(x, (0, pad_w, 0, pad_h))
@@ -578,7 +578,7 @@ class VisionTransformer(nn.Module):
         bs, _, h, w = x.shape
         h = h // self.patch_embed.patch_size[0]
         w = w // self.patch_embed.patch_size[1]
-        
+
         x = self.patch_embed(x)
         # x = self._pos_embed(x)
         x = x + self.rescale_positional_embedding(out_size=(h, w))
@@ -658,7 +658,7 @@ def resize_evaclip_pos_embed(model: VisionTransformer, interpolation: str = 'bic
         pos_tokens, size=(new_size, new_size), mode=interpolation, align_corners=False)
     pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
     model.pos_embed = nn.Parameter(pos_tokens, requires_grad=True)
-    return model 
+    return model
 
 
 def create_siglip_vit(
diff --git a/pipelines/xomni/modeling_xomni.py b/pipelines/xomni/modeling_xomni.py
index 2e05dec0e..7e46f781c 100644
--- a/pipelines/xomni/modeling_xomni.py
+++ b/pipelines/xomni/modeling_xomni.py
@@ -32,7 +32,7 @@ class XOmniDecoderLayer(Qwen2DecoderLayer):
             output_hidden_states, *others = super().forward(hidden_states, **kwargs)
             output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
             return output_hidden_states, *others
-        
+
         # mm_hidden_states = torch.where(multimodal_mask.bool(), hidden_states, torch.zeros_like(hidden_states))
         output_hidden_states, *others = super().forward(hidden_states, **kwargs)
         output_hidden_states = torch.where(multimodal_mask.bool(), output_hidden_states, hidden_states)
@@ -48,7 +48,7 @@ class XOmniModel(Qwen2Model, Qwen2PreTrainedModel):
         Qwen2PreTrainedModel.__init__(self, config)
         self.padding_idx = -1
         self.vocab_size = config.vocab_size
-        
+
         self.lm_embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         self.mm_embed_tokens = nn.Embedding(config.mm_vocab_size, config.hidden_size, self.padding_idx)
 
@@ -94,7 +94,7 @@ class XOmniModel(Qwen2Model, Qwen2PreTrainedModel):
 class XOmniForCausalLM(Qwen2ForCausalLM):
     model_type = "x-omni"
     config_class = XOmniConfig
-    
+
     _keys_to_ignore_on_load_missing = r'image_tokenizer\.*'
 
     def __init__(self, config):
@@ -102,7 +102,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
         self.model = XOmniModel(config)
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
         self.mm_head = nn.Linear(config.hidden_size, config.mm_vocab_size, bias=False)
-        
+
         self.generation_mode = 'text'
         # Initialize weights and apply final processing
         self.post_init()
@@ -110,7 +110,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
     @property
     def device(self):
         return next(iter(self.parameters())).device
-    
+
     def init_vision(self, flux_pipe_path, **kwargs):
         self.som_token = self.config.mm_special_tokens[0]
         self.eom_token = self.config.mm_special_tokens[1]
@@ -125,7 +125,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
         self.vision_dtype = dtype_map[self.vision_config.dtype]
 
         self.image_transform = create_anyres_preprocess(**self.vision_config.transform)
-        
+
         self.encoder_config.siglip_path = os.path.join(self.name_or_path, self.encoder_config.siglip_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.siglip_path)
         self.encoder_config.projector_path = os.path.join(self.name_or_path, self.encoder_config.projector_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.projector_path)
 
@@ -133,15 +133,15 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
         self.image_tokenizer.to(self.device, self.vision_dtype)
 
         transformer = FluxTransformer2DModelWithSigLIP.from_pretrained(
-            self.name_or_path, 
-            siglip_channels=self.encoder_config.z_channels, 
+            self.name_or_path,
+            siglip_channels=self.encoder_config.z_channels,
             torch_dtype=self.vision_dtype,
             subfolder=self.decoder_config.model_path,
             **kwargs,
         )
 
         self.decoder_pipe = FluxPipelineWithSigLIP.from_pretrained(
-            flux_pipe_path, 
+            flux_pipe_path,
             transformer=transformer,
             torch_dtype=self.vision_dtype,
         )
@@ -161,7 +161,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
             if len(images) > 0:
                 doc += self.tokenize_image(images.pop(0))
         return tokenizer.encode(doc, **kwargs)
-    
+
     def mmdecode(self, tokenizer, token_ids, force_text=None, **kwargs):
         force_text = force_text or []
         if isinstance(token_ids, torch.Tensor):
@@ -174,7 +174,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
                 assert len(token_ids) == 1
                 token_ids = token_ids[0]
             assert isinstance(token_ids[0], int)
-        
+
         doc = tokenizer.decode(token_ids, **kwargs)
         doc = doc.replace(tokenizer.pad_token, '')
         doc = doc.replace('<SEP>', '')
@@ -197,7 +197,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
                     image = self.detokenize_image(texts, images, token_ids, (H, W))
                 images.append(image)
         return texts, images
-    
+
     @torch.no_grad()
     def tokenize_image(self, image):
         assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
@@ -213,7 +213,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
         token_str = ''.join(map(lambda x: '<MM-Token-{token_id}>'.format(token_id=x), tokens))
         image_str = f'{self.som_token}{H} {W}{self.img_token}{token_str}{self.eom_token}'
         return image_str
-    
+
     @torch.no_grad()
     def detokenize_image(self, texts, images, token_ids, shape):
         assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
@@ -228,7 +228,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
             [texts[0]],
             negative_prompt=[''],
             height=H * upscale_factor, width=W * upscale_factor,
-            num_inference_steps=self.decoder_config.num_inference_steps, 
+            num_inference_steps=self.decoder_config.num_inference_steps,
             guidance_scale=1.0,
             true_cfg_scale=self.decoder_config.cfg_scale,
             true_cfg_scale_2=self.decoder_config.cfg_scale_2,
@@ -236,7 +236,7 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
 
 
         return image
-    
+
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -275,14 +275,14 @@ class XOmniForCausalLM(Qwen2ForCausalLM):
         hidden_states = outputs[0]
         hidden_states = hidden_states[:, -num_logits_to_keep:, :]
         logits = hidden_states.new_full(
-            (*hidden_states.shape[:-1], self.config.vocab_size + self.config.mm_vocab_size), 
+            (*hidden_states.shape[:-1], self.config.vocab_size + self.config.mm_vocab_size),
             torch.finfo(hidden_states.dtype).min
         )
         if self.generation_mode == 'text':
             logits[:, :, :self.config.vocab_size] = self.lm_head(hidden_states)
         else:
             logits[:, :, self.config.vocab_size:self.config.vocab_size + self.config.image_vocab_size] = self.mm_head(hidden_states)[:, :, :self.config.image_vocab_size]
-        
+
         logits = logits.float()
 
         loss = None

From 5c042c5fb88e52a93ace4c860d824d28a3f27abf Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 11:30:26 +0300
Subject: [PATCH 156/230] cleanup

---
 modules/lora/lora_apply.py | 4 +---
 modules/sdnq/loader.py     | 6 +++++-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/modules/lora/lora_apply.py b/modules/lora/lora_apply.py
index 51be0a74f..2221fbf3e 100644
--- a/modules/lora/lora_apply.py
+++ b/modules/lora/lora_apply.py
@@ -176,7 +176,7 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
                     self.sdnq_svd_down_backup.to(devices.device) if self.sdnq_svd_down_backup is not None else None,
                     skip_quantized_matmul=self.sdnq_dequantizer_backup.use_quantized_matmul
                 )
-            elif hasattr(self, "sdnq_dequantizer"):
+            else:
                 weights_dtype = self.sdnq_dequantizer.weights_dtype
                 dequant_weight = self.sdnq_dequantizer.to(devices.device)(
                     model_weights.to(devices.device),
@@ -186,8 +186,6 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
                     self.svd_down.to(devices.device) if self.svd_down is not None else None,
                     skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul
                 )
-            else:
-                weights_dtype = devices.dtype
 
             new_weight = dequant_weight.to(devices.device, dtype=torch.float32) + lora_weights.to(devices.device, dtype=torch.float32)
             self.weight = torch.nn.Parameter(new_weight, requires_grad=False)
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index a7a7f3ae6..700c497e5 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -4,7 +4,7 @@ import torch
 from safetensors import safe_open
 from diffusers.models.modeling_utils import ModelMixin
 
-from .common import use_contiguous_mm
+from .common import use_tensorwise_fp8_matmul, use_contiguous_mm
 from .quantizer import SDNQConfig, apply_sdnq_to_module
 from .dequantizer import dequantize_symmetric_compiled, re_quantize_int8, re_quantize_fp8
 
@@ -47,10 +47,14 @@ def enable_quantized_mamtul(model):
             if not module.sdnq_dequantizer.use_quantized_matmul:
                 if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn"}:
                     if module.sdnq_dequantizer.re_quantize_for_matmul:
+                        return_dtype = module.scale.dtype
                         if module.sdnq_dequantizer.weights_dtype == "int8":
                             module.weight.data, module.scale.data = re_quantize_int8(dequantize_symmetric_compiled(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
+                            module.scale.data = module.scale.to(dtype=return_dtype)
                         else:
                             module.weight.data, module.scale.data = re_quantize_fp8(dequantize_symmetric_compiled(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
+                            if use_tensorwise_fp8_matmul:
+                                module.scale.data = module.scale.to(dtype=return_dtype)
                     else:
                         module.weight.data, module.scale.data = module.weight.t_(), module.scale.t_()
                     if use_contiguous_mm:

From c931bf9efab69ecdc91d342d6f7adbbdeef10b35 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 17:44:52 +0300
Subject: [PATCH 157/230] SDNQ add dtype casting to loader

---
 modules/sdnq/dequantizer.py | 12 ++++----
 modules/sdnq/loader.py      | 58 ++++++++++++++++++++++++-------------
 2 files changed, 45 insertions(+), 25 deletions(-)

diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 38f09f0a1..0d7b0a83e 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -54,9 +54,11 @@ def quantize_int8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.CharTe
     return input, scale
 
 
-def quantize_fp8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.Tensor, torch.FloatTensor]:
-    scale = torch.amax(input.abs(), dim=dim, keepdims=True).div_(448)
-    input = torch.div(input, scale).nan_to_num_().clamp_(-448, 448).to(dtype=torch.float8_e4m3fn)
+def quantize_fp8(input: torch.FloatTensor, dim: int = -1, is_e5: bool = False) -> Tuple[torch.Tensor, torch.FloatTensor]:
+    max_range = 57344 if is_e5 else 448
+    fp8_dtype = torch.float8_e5m2 if is_e5 else torch.float8_e4m3fn
+    scale = torch.amax(input.abs(), dim=dim, keepdims=True).div_(max_range)
+    input = torch.div(input, scale).nan_to_num_().clamp_(-max_range, max_range).to(dtype=fp8_dtype)
     return input, scale
 
 
@@ -67,10 +69,10 @@ def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch
     return weight, scale
 
 
-def re_quantize_fp8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
+def re_quantize_fp8(weight: torch.FloatTensor, is_e5: bool = False) -> Tuple[torch.CharTensor, torch.FloatTensor]:
     if weight.ndim > 2: # convs
         weight = weight.flatten(1,-1)
-    weight, scale = quantize_fp8(weight.t(), dim=0)
+    weight, scale = quantize_fp8(weight.t(), dim=0, is_e5=is_e5)
     if not use_tensorwise_fp8_matmul:
         scale = scale.to(dtype=torch.float32)
     return weight, scale
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 700c497e5..de02f0145 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -6,7 +6,7 @@ from diffusers.models.modeling_utils import ModelMixin
 
 from .common import use_tensorwise_fp8_matmul, use_contiguous_mm
 from .quantizer import SDNQConfig, apply_sdnq_to_module
-from .dequantizer import dequantize_symmetric_compiled, re_quantize_int8, re_quantize_fp8
+from .dequantizer import dequantize_symmetric, re_quantize_int8, re_quantize_fp8
 
 
 def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str, max_shard_size: str = "10GB") -> None:
@@ -14,7 +14,7 @@ def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str,
     sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
 
 
-def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", use_quantized_matmul: bool = False) -> ModelMixin:
+def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = False) -> ModelMixin:
     with torch.device("meta"):
         with open(os.path.join(model_path, "quantization_config.json"), "r", encoding="utf-8") as f:
             quantization_config = json.load(f)
@@ -33,37 +33,55 @@ def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "di
             state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)
     del state_dict
-    if use_quantized_matmul and not quantization_config["use_quantized_matmul"]:
-        model = enable_quantized_mamtul(model)
+    model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
     return model
 
 
-def enable_quantized_mamtul(model):
+def apply_options_to_model(model, dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = False):
     has_children = list(model.children())
     if not has_children:
         return model
     for module in model.children():
         if hasattr(module, "sdnq_dequantizer"):
-            if not module.sdnq_dequantizer.use_quantized_matmul:
-                if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn"}:
-                    if module.sdnq_dequantizer.re_quantize_for_matmul:
-                        return_dtype = module.scale.dtype
+            if dtype is not None:
+                module.sdnq_dequantizer.result_dtype = dtype
+
+            current_scale_dtype = module.svd_up.dtype if module.svd_up is not None else module.scale.dtype
+            scale_dtype = torch.float32 if dequantize_fp32 is None and current_scale_dtype == torch.float32 else torch.float32 if dequantize_fp32 else module.sdnq_dequantizer.result_dtype
+            upcast_scale = bool(use_quantized_matmul and use_tensorwise_fp8_matmul and module.sdnq_dequantizer.weights_dtype in {"float8_e4m3fn", "float8_e5m2"})
+
+            if upcast_scale:
+                module.scale.data = module.scale.to(dtype=torch.float32)
+            else:
+                module.scale.data = module.scale.to(dtype=scale_dtype)
+            if module.zero_point is not None:
+                module.zero_point.data = module.zero_point.to(dtype=scale_dtype)
+            if module.svd_up is not None:
+                module.svd_up.data = module.svd_up.to(dtype=scale_dtype)
+                module.svd_down.data = module.svd_down.to(dtype=scale_dtype)
+
+            if use_quantized_matmul != module.sdnq_dequantizer.use_quantized_matmul:
+                if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn", "float8_e5m2"}:
+                    if use_quantized_matmul and module.sdnq_dequantizer.re_quantize_for_matmul:
+                        scale_dtype = module.scale.dtype
                         if module.sdnq_dequantizer.weights_dtype == "int8":
-                            module.weight.data, module.scale.data = re_quantize_int8(dequantize_symmetric_compiled(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
-                            module.scale.data = module.scale.to(dtype=return_dtype)
+                            module.weight.data, module.scale.data = re_quantize_int8(dequantize_symmetric(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
+                            module.scale.data = module.scale.to(dtype=scale_dtype)
                         else:
-                            module.weight.data, module.scale.data = re_quantize_fp8(dequantize_symmetric_compiled(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape))
+                            is_e5 = bool(module.sdnq_dequantizer.weights_dtype == "float8_e5m2")
+                            module.weight.data, module.scale.data = re_quantize_fp8(dequantize_symmetric(module.weight, module.scale, torch.float32, module.sdnq_dequantizer.result_shape), is_e5=is_e5)
                             if use_tensorwise_fp8_matmul:
-                                module.scale.data = module.scale.to(dtype=return_dtype)
-                    else:
+                                module.scale.data = module.scale.to(dtype=scale_dtype)
+                    elif not module.sdnq_dequantizer.re_quantize_for_matmul:
                         module.weight.data, module.scale.data = module.weight.t_(), module.scale.t_()
-                    if use_contiguous_mm:
-                        module.weight.data = module.weight.contiguous()
-                    elif module.weight.is_contiguous():
-                        module.weight.data = module.weight.t_().contiguous().t_()
+                    if use_quantized_matmul:
+                        if use_contiguous_mm:
+                            module.weight.data = module.weight.contiguous()
+                        elif module.weight.is_contiguous():
+                            module.weight.data = module.weight.t_().contiguous().t_()
                 if module.svd_up is not None:
                     module.svd_up.data = module.svd_up.t_()
                     module.svd_down.data = module.svd_down.t_()
-                module.sdnq_dequantizer.use_quantized_matmul = True
-        module = enable_quantized_mamtul(module)
+                module.sdnq_dequantizer.use_quantized_matmul = use_quantized_matmul
+        module = apply_options_to_model(module, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
     return model

From be91bbff753c862c4088f6faefec48b08a5d756e Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 18:26:42 +0300
Subject: [PATCH 158/230] SDNQ add SVD support for Convs

---
 modules/sdnq/dequantizer.py              | 10 ++++++++--
 modules/sdnq/layers/conv/conv_fp8.py     |  5 +++--
 modules/sdnq/layers/linear/linear_fp8.py |  3 ++-
 modules/sdnq/quantizer.py                | 13 ++++++++++---
 4 files changed, 23 insertions(+), 8 deletions(-)

diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 0d7b0a83e..0f2ee676e 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -15,7 +15,10 @@ def dequantize_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, ze
     if svd_up is not None:
         if skip_quantized_matmul:
             svd_up, svd_down = svd_up.t(), svd_down.t()
-        result = torch.addmm(result, svd_up, svd_down)
+        if result.ndim > 2 and weight.ndim > 2: # convs
+            result = result.add_(torch.mm(svd_up, svd_down).unflatten(-1, (*result.shape[1:],)))
+        else:
+            result = result.addmm_(svd_up, svd_down)
     if dtype is not None:
         result = result.to(dtype=dtype)
     return result
@@ -30,7 +33,10 @@ def dequantize_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, dty
     if svd_up is not None:
         if skip_quantized_matmul:
             svd_up, svd_down = svd_up.t(), svd_down.t()
-        result = torch.addmm(result, svd_up, svd_down)
+        if result.ndim > 2 and weight.ndim > 2: # convs
+            result = result.add_(torch.mm(svd_up, svd_down).unflatten(-1, (*result.shape[1:],)))
+        else:
+            result = result.addmm_(svd_up, svd_down)
     if dtype is not None:
         result = result.to(dtype=dtype)
     return result
diff --git a/modules/sdnq/layers/conv/conv_fp8.py b/modules/sdnq/layers/conv/conv_fp8.py
index d700a6998..b07196ea7 100644
--- a/modules/sdnq/layers/conv/conv_fp8.py
+++ b/modules/sdnq/layers/conv/conv_fp8.py
@@ -34,7 +34,7 @@ def conv_fp8_matmul(
     if groups == 1:
         if bias is not None and bias.dtype != torch.bfloat16:
             bias = bias.to(dtype=torch.bfloat16)
-        result = torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16).view(mm_output_shape).to(return_dtype)
+        result = torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16)
     else:
         scale = scale.view(groups, 1, scale.shape[1] // groups)
         input_scale = input_scale.view(groups, input_scale.shape[0] // groups, 1)
@@ -50,9 +50,10 @@ def conv_fp8_matmul(
         else:
             for i in range(groups):
                 result.append(torch._scaled_mm(input[:, i], weight[:, i], scale_a=input_scale[i], scale_b=scale[i], bias=None, out_dtype=torch.bfloat16))
-        result = torch.cat(result, dim=-1).view(mm_output_shape).to(return_dtype)
+        result = torch.cat(result, dim=-1)
     if svd_up is not None:
         result.add_(svd_bias)
+    result = result.view(mm_output_shape).to(return_dtype)
 
     if conv_type == 1:
         result = result.transpose_(1,2)
diff --git a/modules/sdnq/layers/linear/linear_fp8.py b/modules/sdnq/layers/linear/linear_fp8.py
index 205b141e8..0c8c26341 100644
--- a/modules/sdnq/layers/linear/linear_fp8.py
+++ b/modules/sdnq/layers/linear/linear_fp8.py
@@ -31,9 +31,10 @@ def fp8_matmul(
     input, weight = check_mats(input, weight)
     if bias is not None and bias.dtype != torch.bfloat16:
         bias = bias.to(dtype=torch.bfloat16)
-    result = torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16).view(output_shape).to(return_dtype)
+    result = torch._scaled_mm(input, weight, scale_a=input_scale, scale_b=scale, bias=bias, out_dtype=torch.bfloat16)
     if svd_up is not None:
         result.add_(svd_bias)
+    result = result.view(output_shape).to(return_dtype)
     return result
 
 
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index d4769821e..7261d2373 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -49,10 +49,18 @@ def quantize_weight(weight: torch.FloatTensor, reduction_axes: Union[int, List[i
 
 
 def apply_svdquant(weight: torch.FloatTensor, rank: int = 32) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+    reshape_weight = False
+    if weight.ndim > 2: # convs
+        reshape_weight = True
+        weight_shape = weight.shape
+        weight = weight.flatten(1,-1)
     U, S, svd_down = torch.svd_lowrank(weight, q=rank)
     svd_up = torch.mul(U, S.unsqueeze(0))
     svd_down = svd_down.t_()
-    return weight.sub_(torch.mm(svd_up, svd_down)), svd_up, svd_down
+    weight = weight.sub_(torch.mm(svd_up, svd_down))
+    if reshape_weight:
+        weight = weight.unflatten(-1, (*weight_shape[1:],))
+    return weight, svd_up, svd_down
 
 
 @devices.inference_context()
@@ -118,13 +126,12 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         if layer.weight.dtype != torch.float32:
             layer.weight.data = layer.weight.to(dtype=torch.float32)
 
-        if use_svd and is_linear_type:
+        if use_svd:
             layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank)
             if use_quantized_matmul:
                 svd_up = svd_up.t_()
                 svd_down = svd_down.t_()
         else:
-            use_svd = False
             svd_up, svd_down = None, None
 
         if group_size == 0:

From 5306376b2a0025d3cdd712c153496259aafc0f01 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 6 Oct 2025 19:05:46 +0300
Subject: [PATCH 159/230] improve contiguous mm performance

---
 modules/sdnq/dequantizer.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 0f2ee676e..1181776c8 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -4,7 +4,7 @@ from typing import Tuple, Optional
 
 import torch
 
-from .common import dtype_dict, compile_func, use_tensorwise_fp8_matmul
+from .common import dtype_dict, compile_func, use_contiguous_mm, use_tensorwise_fp8_matmul
 from .packed_int import pack_int_symetric, unpack_int_symetric, pack_int_asymetric, unpack_int_asymetric
 
 
@@ -71,7 +71,10 @@ def quantize_fp8(input: torch.FloatTensor, dim: int = -1, is_e5: bool = False) -
 def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]:
     if weight.ndim > 2: # convs
         weight = weight.flatten(1,-1)
-    weight, scale = quantize_int8(weight.t(), dim=0)
+    weight = weight.t()
+    if use_contiguous_mm:
+        weight = weight.contiguous()
+    weight, scale = quantize_int8(weight, dim=0)
     return weight, scale
 
 

From aa0c10440f9225002fd157eefd512194ac99b005 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 7 Oct 2025 00:15:23 +0300
Subject: [PATCH 160/230] SDNQ make the loader don't touch the model options by
 default

---
 modules/sdnq/loader.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index de02f0145..79cd26054 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -14,7 +14,7 @@ def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str,
     sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
 
 
-def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = False) -> ModelMixin:
+def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = None) -> ModelMixin:
     with torch.device("meta"):
         with open(os.path.join(model_path, "quantization_config.json"), "r", encoding="utf-8") as f:
             quantization_config = json.load(f)
@@ -33,11 +33,12 @@ def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "di
             state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)
     del state_dict
-    model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
+    if dtype is not None or dequantize_fp32 is not None or use_quantized_matmul is not None:
+        model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
     return model
 
 
-def apply_options_to_model(model, dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = False):
+def apply_options_to_model(model, dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = None):
     has_children = list(model.children())
     if not has_children:
         return model
@@ -48,7 +49,7 @@ def apply_options_to_model(model, dtype: torch.dtype = None, dequantize_fp32: bo
 
             current_scale_dtype = module.svd_up.dtype if module.svd_up is not None else module.scale.dtype
             scale_dtype = torch.float32 if dequantize_fp32 is None and current_scale_dtype == torch.float32 else torch.float32 if dequantize_fp32 else module.sdnq_dequantizer.result_dtype
-            upcast_scale = bool(use_quantized_matmul and use_tensorwise_fp8_matmul and module.sdnq_dequantizer.weights_dtype in {"float8_e4m3fn", "float8_e5m2"})
+            upcast_scale = bool(use_tensorwise_fp8_matmul and module.sdnq_dequantizer.weights_dtype in {"float8_e4m3fn", "float8_e5m2"} and (use_quantized_matmul or (use_quantized_matmul is None and module.sdnq_dequantizer.use_quantized_matmul)))
 
             if upcast_scale:
                 module.scale.data = module.scale.to(dtype=torch.float32)
@@ -60,7 +61,7 @@ def apply_options_to_model(model, dtype: torch.dtype = None, dequantize_fp32: bo
                 module.svd_up.data = module.svd_up.to(dtype=scale_dtype)
                 module.svd_down.data = module.svd_down.to(dtype=scale_dtype)
 
-            if use_quantized_matmul != module.sdnq_dequantizer.use_quantized_matmul:
+            if use_quantized_matmul is not None and use_quantized_matmul != module.sdnq_dequantizer.use_quantized_matmul:
                 if module.sdnq_dequantizer.weights_dtype in {"int8", "float8_e4m3fn", "float8_e5m2"}:
                     if use_quantized_matmul and module.sdnq_dequantizer.re_quantize_for_matmul:
                         scale_dtype = module.scale.dtype

From 18f6ebb3f16e76a4cb619f7ff6fb04c0788c2da6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 6 Oct 2025 17:39:42 -0400
Subject: [PATCH 161/230] update modernui

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index e58d96e26..85ab79ed0 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit e58d96e26290960e1df35d0e012c5e50b4e6a606
+Subproject commit 85ab79ed0f8a9c8484e2b2372d745527ebc84e7d

From d012ad5556fc63bf322955a9e52b5279b7bf1fe8 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 6 Oct 2025 17:59:38 -0400
Subject: [PATCH 162/230] image viewer refocus on close

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 1 +
 extensions-builtin/sdnext-modernui | 2 +-
 javascript/imageViewer.js          | 9 +++++++++
 3 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ceb6ff539..b2d4023e3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -120,6 +120,7 @@
   - reference: fix download for sd15/sdxl reference models  
   - fix checks in init/mask image decode  
   - fix hf token with extra chars  
+  - image viewer refocus on gallery after returning from full screen mode  
 
 ## Update for 2025-09-15
 
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 85ab79ed0..39277a468 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 85ab79ed0f8a9c8484e2b2372d745527ebc84e7d
+Subproject commit 39277a4688473e1e5fdb8a6173c8ded28c0439e1
diff --git a/javascript/imageViewer.js b/javascript/imageViewer.js
index 5631c173b..e7eaca7a8 100644
--- a/javascript/imageViewer.js
+++ b/javascript/imageViewer.js
@@ -15,11 +15,20 @@ function cycleImageFit() {
   log('cycleImageFit', current, next);
 }
 
+function isInViewport(element) {
+  const rect = element.getBoundingClientRect();
+  return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || document.documentElement.clientHeight) && rect.right <= (window.innerWidth || document.documentElement.clientWidth);
+}
+
 function closeModal(evt, force = false) {
   if (force) gradioApp().getElementById('lightboxModal').style.display = 'none';
   if (previewDrag) return;
   if (evt?.button !== 0) return;
   gradioApp().getElementById('lightboxModal').style.display = 'none';
+  let thumbnails = Array.from(gradioApp().querySelectorAll('.thumbnails .thumbnail-item'));
+  thumbnails = thumbnails.filter((el) => el.checkVisibility());
+  if (thumbnails.length === 0) return;
+  thumbnails[0].focus();
 }
 
 function modalImageSwitch(offset) {

From 1cd7b6d63a14b98f057694b7c66c302d83984e11 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 7 Oct 2025 01:27:49 +0300
Subject: [PATCH 163/230] fix upcast scale check

---
 modules/sdnq/loader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 79cd26054..92f784aaa 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -49,7 +49,7 @@ def apply_options_to_model(model, dtype: torch.dtype = None, dequantize_fp32: bo
 
             current_scale_dtype = module.svd_up.dtype if module.svd_up is not None else module.scale.dtype
             scale_dtype = torch.float32 if dequantize_fp32 is None and current_scale_dtype == torch.float32 else torch.float32 if dequantize_fp32 else module.sdnq_dequantizer.result_dtype
-            upcast_scale = bool(use_tensorwise_fp8_matmul and module.sdnq_dequantizer.weights_dtype in {"float8_e4m3fn", "float8_e5m2"} and (use_quantized_matmul or (use_quantized_matmul is None and module.sdnq_dequantizer.use_quantized_matmul)))
+            upcast_scale = bool(not use_tensorwise_fp8_matmul and module.sdnq_dequantizer.weights_dtype in {"float8_e4m3fn", "float8_e5m2"} and (use_quantized_matmul or (use_quantized_matmul is None and module.sdnq_dequantizer.use_quantized_matmul)))
 
             if upcast_scale:
                 module.scale.data = module.scale.to(dtype=torch.float32)

From 3fe1d090e4f0b5fe9aca88cda37acf616b8732df Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 6 Oct 2025 19:13:29 -0400
Subject: [PATCH 164/230] add configurable layers to taehv

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md            |  9 +++++----
 modules/sd_vae_taesd.py |  4 +++-
 modules/taesd/taehv.py  | 34 +++++++++++++++++++++++++++-------
 3 files changed, 35 insertions(+), 12 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b2d4023e3..c4d78920c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -62,10 +62,10 @@
   - networks ability to filter lora by base model version  
   - add interrogate button to input images  
 - **SDNQ**
-    - add `SVDQuant` quantization method support  
-    - make sdnq scales compatible with balanced offload  
-    - add int8 matmul support for RDNA2 GPUs via triton  
-    - improve int8 mamtul performance on Intel GPUs  
+  - add `SVDQuant` quantization method support  
+  - make sdnq scales compatible with balanced offload  
+  - add int8 matmul support for RDNA2 GPUs via triton  
+  - improve int8 mamtul performance on Intel GPUs  
 - **Other**
   - server will note when restart is recommended due to package updates  
   - **interrrupt** will now show last known preview image  
@@ -90,6 +90,7 @@
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  
   - **nunchaku** update to `1.0.1` and enhance installer  
   - **xyz-grid** add guidance section  
+  - **preview** implement configurable layers for WAN, Qwen, HV  
 - **Video**
   - use shared T5 text encoder for video models when possible  
   - unified video save code across all video models  
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index aed995f1b..510215855 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -79,6 +79,7 @@ def get_model(model_type = 'decoder', variant = None):
                 uri += '/tae' + model_cls + '_' + model_type + '.pth'
             try:
                 torch.hub.download_url_to_file(uri, fn)
+                shared.log.print() # new line
                 shared.log.info(f'Decode: type="taesd" variant="{variant}": uri="{uri}" fn="{fn}" download')
             except Exception as e:
                 warn_once(f'download uri={uri} {e}', variant=variant)
@@ -86,7 +87,8 @@ def get_model(model_type = 'decoder', variant = None):
             prev_cls = model_cls
             prev_type = model_type
             prev_model = variant
-            shared.log.debug(f'Decode: type="taesd" variant="{variant}" fn="{fn}" load')
+            shared.log.print() # new line
+            shared.log.debug(f'Decode: type="taesd" variant="{variant}" fn="{fn}" layers={shared.opts.taesd_layers} load')
             vae = None
             if 'TAE HunyuanVideo' in variant:
                 from modules.taesd.taehv import TAEHV
diff --git a/modules/taesd/taehv.py b/modules/taesd/taehv.py
index 0f1828e45..bfeced7a7 100644
--- a/modules/taesd/taehv.py
+++ b/modules/taesd/taehv.py
@@ -159,6 +159,7 @@ class TAEHV(nn.Module):
             decoder_space_upscale: whether spatial upsampling is enabled for each block. upsampling can be disabled for a cheaper preview.
         """
         super().__init__()
+        from modules import shared
         self.encoder = nn.Sequential(
             conv(TAEHV.image_channels, 64), nn.ReLU(inplace=True),
             TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64),
@@ -168,13 +169,32 @@ class TAEHV(nn.Module):
         )
         n_f = [256, 128, 64, 64]
         self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
-        self.decoder = nn.Sequential(
-            Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True),
-            MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
-            MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
-            MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
-            nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
-        )
+
+        if shared.opts.taesd_layers == 1:
+            self.decoder = nn.Sequential(
+                Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True),
+                MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
+                MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Identity(), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
+                MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), nn.Identity(), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
+                nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
+            )
+        elif shared.opts.taesd_layers == 2:
+            self.decoder = nn.Sequential(
+                Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True),
+                MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
+                MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
+                MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), nn.Identity(), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
+                nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
+            )
+        else:
+            self.decoder = nn.Sequential(
+                Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True),
+                MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
+                MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
+                MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
+                nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
+            )
+
         if checkpoint_path is not None:
             self.load_state_dict(self.patch_tgrow_layers(torch.load(checkpoint_path, map_location="cpu", weights_only=True)))
 

From fc472f3f3a90de9b3c6fce3390f402ea973d9a18 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 6 Oct 2025 20:20:46 -0400
Subject: [PATCH 165/230] reinstall if torch-cpu and use-cuda or use-rocm

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md |  1 +
 installer.py | 14 ++++++++++++--
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c4d78920c..a707ffb6a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -39,6 +39,7 @@
     `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
   - command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible  
     previously sdnext was performing a fallback to `torch-cpu` which is not desired  
+  - if `--use-cuda` or `--use-rocm` are specified and `torch-cpu` is installed, installer will attempt to reinstall correct torch package  
   - **installer**: warn if cuda or rocm are available and `torch-cpu` is installed  
 - **Extensions**  
   - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
diff --git a/installer.py b/installer.py
index 63e678bef..55b198ff8 100644
--- a/installer.py
+++ b/installer.py
@@ -953,9 +953,19 @@ def check_torch():
             pass
         if 'cpu' in torch.__version__:
             if is_cuda_available:
-                log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is available - consider reinstalling')
+                if args.use_cuda:
+                    log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is selected - reinstalling')
+                    uninstall(['torch', 'torchvision'], quiet=True)
+                    install(torch_command, 'torch torchvision', quiet=True) # foce reinstall
+                else:
+                    log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is available - consider reinstalling')
             elif is_rocm_available:
-                log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is available - consider reinstalling')
+                if args.use_rocm:
+                    log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is selected - reinstalling')
+                    uninstall(['torch', 'torchvision'], quiet=True)
+                    install(torch_command, 'torch torchvision', quiet=True) # foce reinstall
+                else:
+                    log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is available - consider reinstalling')
         if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
             if shutil.which('icpx') is not None:
                 log.info(f'{os.popen("icpx --version").read().rstrip()}')

From 0889d7227ec6de2c23e4f1bec61a5657f390e2fe Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 6 Oct 2025 20:31:20 -0400
Subject: [PATCH 166/230] add force to pip flags

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/installer.py b/installer.py
index 55b198ff8..95a007198 100644
--- a/installer.py
+++ b/installer.py
@@ -955,15 +955,13 @@ def check_torch():
             if is_cuda_available:
                 if args.use_cuda:
                     log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is selected - reinstalling')
-                    uninstall(['torch', 'torchvision'], quiet=True)
-                    install(torch_command, 'torch torchvision', quiet=True) # foce reinstall
+                    install(torch_command, 'torch torchvision', quiet=True, reinstall=True, force=True) # foce reinstall
                 else:
                     log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is available - consider reinstalling')
             elif is_rocm_available:
                 if args.use_rocm:
                     log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is selected - reinstalling')
-                    uninstall(['torch', 'torchvision'], quiet=True)
-                    install(torch_command, 'torch torchvision', quiet=True) # foce reinstall
+                    install(torch_command, 'torch torchvision', quiet=True, reinstall=True, force=True) # foce reinstall
                 else:
                     log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is available - consider reinstalling')
         if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:

From 8e7ff0981134ec0a281b6df951332a88af5f4765 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 6 Oct 2025 20:54:48 -0400
Subject: [PATCH 167/230] fix sm dependency mess

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 6ca4153e9..f41e599e2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -51,6 +51,7 @@ pandas==2.3.1
 numba==0.61.2
 protobuf==4.25.3
 pytorch_lightning==2.5.5
+PyWavelets==1.9.0
 urllib3==1.26.19
 Pillow==10.4.0
 timm==1.0.16

From fe41d7da2ac455083b60633e6b1e9acfb3e8f839 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 7 Oct 2025 08:14:11 -0400
Subject: [PATCH 168/230] use shared llama

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 3 ++-
 modules/video_models/video_load.py | 7 +++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a707ffb6a..5032207d4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -93,7 +93,8 @@
   - **xyz-grid** add guidance section  
   - **preview** implement configurable layers for WAN, Qwen, HV  
 - **Video**
-  - use shared T5 text encoder for video models when possible  
+  - use shared **T5** text encoder for video models when possible  
+  - use shared **LLama** text encoder for video models when possible  
   - unified video save code across all video models  
     also avoids creation of temporary files for each frame unless user wants to save them  
   - unified prompt enhance code across all video models  
diff --git a/modules/video_models/video_load.py b/modules/video_models/video_load.py
index 55e57647f..e8d616c69 100644
--- a/modules/video_models/video_load.py
+++ b/modules/video_models/video_load.py
@@ -25,10 +25,17 @@ def load_model(selected: models_def.Model):
     # text encoder
     try:
         load_args, quant_args = model_quant.get_dit_args({}, module='TE', device_map=True)
+
+        # loader deduplication of text-encoder models
         if selected.te_cls.__name__ == 'T5EncoderModel' and shared.opts.te_shared_t5:
             selected.te = 'Disty0/t5-xxl'
             selected.te_folder = ''
             selected.te_revision = None
+        if selected.te_cls.__name__ == 'LlamaModel' and shared.opts.te_shared_t5:
+            selected.te = 'hunyuanvideo-community/HunyuanVideo'
+            selected.te_folder = 'text_encoder'
+            selected.te_revision = None
+
         shared.log.debug(f'Video load: module=te repo="{selected.te or selected.repo}" folder="{selected.te_folder}" cls={selected.te_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
         kwargs["text_encoder"] = selected.te_cls.from_pretrained(
             pretrained_model_name_or_path=selected.te or selected.repo,

From 25e28050c370061eec2bc7040aef4e5a6e8145ee Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 7 Oct 2025 08:21:05 -0400
Subject: [PATCH 169/230] update swagger docs

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md        |     1 +
 html/swagger.css    | 11351 ++++++++++++++++++++++++++++++++++++++----
 modules/api/docs.py |    25 +-
 3 files changed, 10504 insertions(+), 873 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5032207d4..d4f1ed331 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -92,6 +92,7 @@
   - **nunchaku** update to `1.0.1` and enhance installer  
   - **xyz-grid** add guidance section  
   - **preview** implement configurable layers for WAN, Qwen, HV  
+  - update swagger `/docs` endpoint style  
 - **Video**
   - use shared **T5** text encoder for video models when possible  
   - use shared **LLama** text encoder for video models when possible  
diff --git a/html/swagger.css b/html/swagger.css
index 77a578923..ea5bc261b 100644
--- a/html/swagger.css
+++ b/html/swagger.css
@@ -1,868 +1,10499 @@
-.opblock {
-  border-width: 0 !important;
-}
-.opblock-summary-operation-id {
-  display: none !important;
-}
-.swagger-ui .models .json-schema-2020-12:not(.json-schema-2020-12--embedded)>.json-schema-2020-12-head .json-schema-2020-12__title:first-of-type {
-  font-size: 14px;
-  color: white;
+html {
+  background-color: #aaa;
 }
 
-.swagger-ui .json-schema-2020-12-keyword__name--primary {
-  color: aqua;
+.swagger-ui {
+    color: #3b4151;
+    font-family: system-ui, sans-serif;
+}
+
+.swagger-ui body {
+    margin: 0;
+}
+
+.swagger-ui article,.swagger-ui aside,.swagger-ui footer,.swagger-ui header,.swagger-ui nav,.swagger-ui section {
+    display: block
+}
+
+.swagger-ui h1 {
+    font-size: 2em;
+    margin: .67em 0
+}
+
+.swagger-ui figcaption,.swagger-ui figure,.swagger-ui main {
+    display: block
+}
+
+.swagger-ui figure {
+    margin: 1em 40px
+}
+
+.swagger-ui hr {
+    box-sizing: content-box;
+    height: 0;
+    overflow: visible
+}
+
+.swagger-ui pre {
+    font-family: monospace,monospace;
+    font-size: 1em
+}
+
+.swagger-ui a {
+    background-color: transparent;
+    -webkit-text-decoration-skip: objects
+}
+
+.swagger-ui abbr[title] {
+    text-decoration: underline;
+    -webkit-text-decoration: underline dotted;
+    text-decoration: underline dotted
+}
+
+.swagger-ui b,.swagger-ui strong {
+    font-weight: inherit;
+    font-weight: bolder
+}
+
+.swagger-ui code,.swagger-ui kbd,.swagger-ui samp {
+    font-family: monospace,monospace;
+    font-size: 1em
+}
+
+.swagger-ui dfn {
+    font-style: italic
+}
+
+.swagger-ui mark {
+    background-color: #ff0;
+    color: #000
+}
+
+.swagger-ui small {
+    font-size: 80%
+}
+
+.swagger-ui sub,.swagger-ui sup {
+    font-size: 75%;
+    line-height: 0;
+    position: relative;
+    vertical-align: baseline
+}
+
+.swagger-ui sub {
+    bottom: -.25em
+}
+
+.swagger-ui sup {
+    top: -.5em
+}
+
+.swagger-ui audio,.swagger-ui video {
+    display: inline-block
+}
+
+.swagger-ui audio:not([controls]) {
+    display: none;
+    height: 0
+}
+
+.swagger-ui svg:not(:root) {
+    overflow: hidden
+}
+
+.swagger-ui button,.swagger-ui input,.swagger-ui optgroup,.swagger-ui select,.swagger-ui textarea {
+    font-family: sans-serif;
+    font-size: 100%;
+    line-height: 1.15;
+    margin: 0
+}
+
+.swagger-ui button,.swagger-ui input {
+    overflow: visible
+}
+
+.swagger-ui button,.swagger-ui select {
+    text-transform: none
+}
+
+.swagger-ui [type=reset],.swagger-ui [type=submit],.swagger-ui button,.swagger-ui html [type=button] {
+    -webkit-appearance: button
+}
+
+.swagger-ui [type=button]::-moz-focus-inner,.swagger-ui [type=reset]::-moz-focus-inner,.swagger-ui [type=submit]::-moz-focus-inner,.swagger-ui button::-moz-focus-inner {
+    padding: 0
+}
+
+.swagger-ui [type=button]:-moz-focusring,.swagger-ui [type=reset]:-moz-focusring,.swagger-ui [type=submit]:-moz-focusring,.swagger-ui button:-moz-focusring {
+    outline: 1px dotted ButtonText
+}
+
+.swagger-ui fieldset {
+    padding: .35em .75em .625em
+}
+
+.swagger-ui legend {
+    box-sizing: border-box;
+    color: inherit;
+    display: table;
+    max-width: 100%;
+    padding: 0;
+    white-space: normal
+}
+
+.swagger-ui progress {
+    display: inline-block;
+    vertical-align: baseline
+}
+
+.swagger-ui textarea {
+    overflow: auto
+}
+
+.swagger-ui [type=checkbox],.swagger-ui [type=radio] {
+    box-sizing: border-box;
+    padding: 0
+}
+
+.swagger-ui [type=number]::-webkit-inner-spin-button,.swagger-ui [type=number]::-webkit-outer-spin-button {
+    height: auto
+}
+
+.swagger-ui [type=search] {
+    -webkit-appearance: textfield;
+    outline-offset: -2px
+}
+
+.swagger-ui [type=search]::-webkit-search-cancel-button,.swagger-ui [type=search]::-webkit-search-decoration {
+    -webkit-appearance: none
+}
+
+.swagger-ui ::-webkit-file-upload-button {
+    -webkit-appearance: button;
+    font: inherit
+}
+
+.swagger-ui details,.swagger-ui menu {
+    display: block
+}
+
+.swagger-ui summary {
+    display: list-item
+}
+
+.swagger-ui canvas {
+    display: inline-block
+}
+
+.swagger-ui [hidden],.swagger-ui template {
+    display: none
+}
+
+.swagger-ui .debug * {
+    outline: 1px solid gold
+}
+
+.swagger-ui .debug-white * {
+    outline: 1px solid #fff
+}
+
+.swagger-ui .debug-black * {
+    outline: 1px solid #000
+}
+
+.swagger-ui .debug-grid {
+    background: transparent url(data:image/png;base64,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) repeat 0 0
+}
+
+.swagger-ui .debug-grid-16 {
+    background: transparent url(data:image/png;base64,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) repeat 0 0
+}
+
+.swagger-ui .debug-grid-8-solid {
+    background: #fff url(data:image/jpeg;base64,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) repeat 0 0
+}
+
+.swagger-ui .debug-grid-16-solid {
+    background: #fff url(data:image/png;base64,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) repeat 0 0
+}
+
+.swagger-ui .border-box,.swagger-ui a,.swagger-ui article,.swagger-ui body,.swagger-ui code,.swagger-ui dd,.swagger-ui div,.swagger-ui dl,.swagger-ui dt,.swagger-ui fieldset,.swagger-ui footer,.swagger-ui form,.swagger-ui h1,.swagger-ui h2,.swagger-ui h3,.swagger-ui h4,.swagger-ui h5,.swagger-ui h6,.swagger-ui header,.swagger-ui html,.swagger-ui input[type=email],.swagger-ui input[type=number],.swagger-ui input[type=password],.swagger-ui input[type=tel],.swagger-ui input[type=text],.swagger-ui input[type=url],.swagger-ui legend,.swagger-ui li,.swagger-ui main,.swagger-ui ol,.swagger-ui p,.swagger-ui pre,.swagger-ui section,.swagger-ui table,.swagger-ui td,.swagger-ui textarea,.swagger-ui th,.swagger-ui tr,.swagger-ui ul {
+    box-sizing: border-box
+}
+
+.swagger-ui .aspect-ratio {
+    height: 0;
+    position: relative
+}
+
+.swagger-ui .aspect-ratio--16x9 {
+    padding-bottom: 56.25%
+}
+
+.swagger-ui .aspect-ratio--9x16 {
+    padding-bottom: 177.77%
+}
+
+.swagger-ui .aspect-ratio--4x3 {
+    padding-bottom: 75%
+}
+
+.swagger-ui .aspect-ratio--3x4 {
+    padding-bottom: 133.33%
+}
+
+.swagger-ui .aspect-ratio--6x4 {
+    padding-bottom: 66.6%
+}
+
+.swagger-ui .aspect-ratio--4x6 {
+    padding-bottom: 150%
+}
+
+.swagger-ui .aspect-ratio--8x5 {
+    padding-bottom: 62.5%
+}
+
+.swagger-ui .aspect-ratio--5x8 {
+    padding-bottom: 160%
+}
+
+.swagger-ui .aspect-ratio--7x5 {
+    padding-bottom: 71.42%
+}
+
+.swagger-ui .aspect-ratio--5x7 {
+    padding-bottom: 140%
+}
+
+.swagger-ui .aspect-ratio--1x1 {
+    padding-bottom: 100%
+}
+
+.swagger-ui .aspect-ratio--object {
+    bottom: 0;
+    height: 100%;
+    left: 0;
+    position: absolute;
+    right: 0;
+    top: 0;
+    width: 100%;
+    z-index: 100
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .aspect-ratio-ns {
+        height:0;
+        position: relative
+    }
+
+    .swagger-ui .aspect-ratio--16x9-ns {
+        padding-bottom: 56.25%
+    }
+
+    .swagger-ui .aspect-ratio--9x16-ns {
+        padding-bottom: 177.77%
+    }
+
+    .swagger-ui .aspect-ratio--4x3-ns {
+        padding-bottom: 75%
+    }
+
+    .swagger-ui .aspect-ratio--3x4-ns {
+        padding-bottom: 133.33%
+    }
+
+    .swagger-ui .aspect-ratio--6x4-ns {
+        padding-bottom: 66.6%
+    }
+
+    .swagger-ui .aspect-ratio--4x6-ns {
+        padding-bottom: 150%
+    }
+
+    .swagger-ui .aspect-ratio--8x5-ns {
+        padding-bottom: 62.5%
+    }
+
+    .swagger-ui .aspect-ratio--5x8-ns {
+        padding-bottom: 160%
+    }
+
+    .swagger-ui .aspect-ratio--7x5-ns {
+        padding-bottom: 71.42%
+    }
+
+    .swagger-ui .aspect-ratio--5x7-ns {
+        padding-bottom: 140%
+    }
+
+    .swagger-ui .aspect-ratio--1x1-ns {
+        padding-bottom: 100%
+    }
+
+    .swagger-ui .aspect-ratio--object-ns {
+        bottom: 0;
+        height: 100%;
+        left: 0;
+        position: absolute;
+        right: 0;
+        top: 0;
+        width: 100%;
+        z-index: 100
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .aspect-ratio-m {
+        height:0;
+        position: relative
+    }
+
+    .swagger-ui .aspect-ratio--16x9-m {
+        padding-bottom: 56.25%
+    }
+
+    .swagger-ui .aspect-ratio--9x16-m {
+        padding-bottom: 177.77%
+    }
+
+    .swagger-ui .aspect-ratio--4x3-m {
+        padding-bottom: 75%
+    }
+
+    .swagger-ui .aspect-ratio--3x4-m {
+        padding-bottom: 133.33%
+    }
+
+    .swagger-ui .aspect-ratio--6x4-m {
+        padding-bottom: 66.6%
+    }
+
+    .swagger-ui .aspect-ratio--4x6-m {
+        padding-bottom: 150%
+    }
+
+    .swagger-ui .aspect-ratio--8x5-m {
+        padding-bottom: 62.5%
+    }
+
+    .swagger-ui .aspect-ratio--5x8-m {
+        padding-bottom: 160%
+    }
+
+    .swagger-ui .aspect-ratio--7x5-m {
+        padding-bottom: 71.42%
+    }
+
+    .swagger-ui .aspect-ratio--5x7-m {
+        padding-bottom: 140%
+    }
+
+    .swagger-ui .aspect-ratio--1x1-m {
+        padding-bottom: 100%
+    }
+
+    .swagger-ui .aspect-ratio--object-m {
+        bottom: 0;
+        height: 100%;
+        left: 0;
+        position: absolute;
+        right: 0;
+        top: 0;
+        width: 100%;
+        z-index: 100
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .aspect-ratio-l {
+        height:0;
+        position: relative
+    }
+
+    .swagger-ui .aspect-ratio--16x9-l {
+        padding-bottom: 56.25%
+    }
+
+    .swagger-ui .aspect-ratio--9x16-l {
+        padding-bottom: 177.77%
+    }
+
+    .swagger-ui .aspect-ratio--4x3-l {
+        padding-bottom: 75%
+    }
+
+    .swagger-ui .aspect-ratio--3x4-l {
+        padding-bottom: 133.33%
+    }
+
+    .swagger-ui .aspect-ratio--6x4-l {
+        padding-bottom: 66.6%
+    }
+
+    .swagger-ui .aspect-ratio--4x6-l {
+        padding-bottom: 150%
+    }
+
+    .swagger-ui .aspect-ratio--8x5-l {
+        padding-bottom: 62.5%
+    }
+
+    .swagger-ui .aspect-ratio--5x8-l {
+        padding-bottom: 160%
+    }
+
+    .swagger-ui .aspect-ratio--7x5-l {
+        padding-bottom: 71.42%
+    }
+
+    .swagger-ui .aspect-ratio--5x7-l {
+        padding-bottom: 140%
+    }
+
+    .swagger-ui .aspect-ratio--1x1-l {
+        padding-bottom: 100%
+    }
+
+    .swagger-ui .aspect-ratio--object-l {
+        bottom: 0;
+        height: 100%;
+        left: 0;
+        position: absolute;
+        right: 0;
+        top: 0;
+        width: 100%;
+        z-index: 100
+    }
+}
+
+.swagger-ui img {
+    max-width: 100%
+}
+
+.swagger-ui .cover {
+    background-size: cover!important
+}
+
+.swagger-ui .contain {
+    background-size: contain!important
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .cover-ns {
+        background-size:cover!important
+    }
+
+    .swagger-ui .contain-ns {
+        background-size: contain!important
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .cover-m {
+        background-size:cover!important
+    }
+
+    .swagger-ui .contain-m {
+        background-size: contain!important
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .cover-l {
+        background-size:cover!important
+    }
+
+    .swagger-ui .contain-l {
+        background-size: contain!important
+    }
+}
+
+.swagger-ui .bg-center {
+    background-position: 50%;
+    background-repeat: no-repeat
+}
+
+.swagger-ui .bg-top {
+    background-position: top;
+    background-repeat: no-repeat
+}
+
+.swagger-ui .bg-right {
+    background-position: 100%;
+    background-repeat: no-repeat
+}
+
+.swagger-ui .bg-bottom {
+    background-position: bottom;
+    background-repeat: no-repeat
+}
+
+.swagger-ui .bg-left {
+    background-position: 0;
+    background-repeat: no-repeat
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .bg-center-ns {
+        background-position:50%;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-top-ns {
+        background-position: top;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-right-ns {
+        background-position: 100%;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-bottom-ns {
+        background-position: bottom;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-left-ns {
+        background-position: 0;
+        background-repeat: no-repeat
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .bg-center-m {
+        background-position:50%;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-top-m {
+        background-position: top;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-right-m {
+        background-position: 100%;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-bottom-m {
+        background-position: bottom;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-left-m {
+        background-position: 0;
+        background-repeat: no-repeat
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .bg-center-l {
+        background-position:50%;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-top-l {
+        background-position: top;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-right-l {
+        background-position: 100%;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-bottom-l {
+        background-position: bottom;
+        background-repeat: no-repeat
+    }
+
+    .swagger-ui .bg-left-l {
+        background-position: 0;
+        background-repeat: no-repeat
+    }
+}
+
+.swagger-ui .pre {
+    overflow-x: auto;
+    overflow-y: hidden;
+    overflow: scroll
+}
+
+.swagger-ui .top-0 {
+    top: 0
+}
+
+.swagger-ui .right-0 {
+    right: 0
+}
+
+.swagger-ui .bottom-0 {
+    bottom: 0
+}
+
+.swagger-ui .left-0 {
+    left: 0
+}
+
+.swagger-ui .top-1 {
+    top: 1rem
+}
+
+.swagger-ui .right-1 {
+    right: 1rem
+}
+
+.swagger-ui .bottom-1 {
+    bottom: 1rem
+}
+
+.swagger-ui .left-1 {
+    left: 1rem
+}
+
+.swagger-ui .top-2 {
+    top: 2rem
+}
+
+.swagger-ui .right-2 {
+    right: 2rem
+}
+
+.swagger-ui .bottom-2 {
+    bottom: 2rem
+}
+
+.swagger-ui .left-2 {
+    left: 2rem
+}
+
+.swagger-ui .top--1 {
+    top: -1rem
+}
+
+.swagger-ui .right--1 {
+    right: -1rem
+}
+
+.swagger-ui .bottom--1 {
+    bottom: -1rem
+}
+
+.swagger-ui .left--1 {
+    left: -1rem
+}
+
+.swagger-ui .top--2 {
+    top: -2rem
+}
+
+.swagger-ui .right--2 {
+    right: -2rem
+}
+
+.swagger-ui .bottom--2 {
+    bottom: -2rem
+}
+
+.swagger-ui .left--2 {
+    left: -2rem
+}
+
+.swagger-ui .absolute--fill {
+    bottom: 0;
+    left: 0;
+    right: 0;
+    top: 0
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .top-0-ns {
+        top:0
+    }
+
+    .swagger-ui .left-0-ns {
+        left: 0
+    }
+
+    .swagger-ui .right-0-ns {
+        right: 0
+    }
+
+    .swagger-ui .bottom-0-ns {
+        bottom: 0
+    }
+
+    .swagger-ui .top-1-ns {
+        top: 1rem
+    }
+
+    .swagger-ui .left-1-ns {
+        left: 1rem
+    }
+
+    .swagger-ui .right-1-ns {
+        right: 1rem
+    }
+
+    .swagger-ui .bottom-1-ns {
+        bottom: 1rem
+    }
+
+    .swagger-ui .top-2-ns {
+        top: 2rem
+    }
+
+    .swagger-ui .left-2-ns {
+        left: 2rem
+    }
+
+    .swagger-ui .right-2-ns {
+        right: 2rem
+    }
+
+    .swagger-ui .bottom-2-ns {
+        bottom: 2rem
+    }
+
+    .swagger-ui .top--1-ns {
+        top: -1rem
+    }
+
+    .swagger-ui .right--1-ns {
+        right: -1rem
+    }
+
+    .swagger-ui .bottom--1-ns {
+        bottom: -1rem
+    }
+
+    .swagger-ui .left--1-ns {
+        left: -1rem
+    }
+
+    .swagger-ui .top--2-ns {
+        top: -2rem
+    }
+
+    .swagger-ui .right--2-ns {
+        right: -2rem
+    }
+
+    .swagger-ui .bottom--2-ns {
+        bottom: -2rem
+    }
+
+    .swagger-ui .left--2-ns {
+        left: -2rem
+    }
+
+    .swagger-ui .absolute--fill-ns {
+        bottom: 0;
+        left: 0;
+        right: 0;
+        top: 0
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .top-0-m {
+        top:0
+    }
+
+    .swagger-ui .left-0-m {
+        left: 0
+    }
+
+    .swagger-ui .right-0-m {
+        right: 0
+    }
+
+    .swagger-ui .bottom-0-m {
+        bottom: 0
+    }
+
+    .swagger-ui .top-1-m {
+        top: 1rem
+    }
+
+    .swagger-ui .left-1-m {
+        left: 1rem
+    }
+
+    .swagger-ui .right-1-m {
+        right: 1rem
+    }
+
+    .swagger-ui .bottom-1-m {
+        bottom: 1rem
+    }
+
+    .swagger-ui .top-2-m {
+        top: 2rem
+    }
+
+    .swagger-ui .left-2-m {
+        left: 2rem
+    }
+
+    .swagger-ui .right-2-m {
+        right: 2rem
+    }
+
+    .swagger-ui .bottom-2-m {
+        bottom: 2rem
+    }
+
+    .swagger-ui .top--1-m {
+        top: -1rem
+    }
+
+    .swagger-ui .right--1-m {
+        right: -1rem
+    }
+
+    .swagger-ui .bottom--1-m {
+        bottom: -1rem
+    }
+
+    .swagger-ui .left--1-m {
+        left: -1rem
+    }
+
+    .swagger-ui .top--2-m {
+        top: -2rem
+    }
+
+    .swagger-ui .right--2-m {
+        right: -2rem
+    }
+
+    .swagger-ui .bottom--2-m {
+        bottom: -2rem
+    }
+
+    .swagger-ui .left--2-m {
+        left: -2rem
+    }
+
+    .swagger-ui .absolute--fill-m {
+        bottom: 0;
+        left: 0;
+        right: 0;
+        top: 0
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .top-0-l {
+        top:0
+    }
+
+    .swagger-ui .left-0-l {
+        left: 0
+    }
+
+    .swagger-ui .right-0-l {
+        right: 0
+    }
+
+    .swagger-ui .bottom-0-l {
+        bottom: 0
+    }
+
+    .swagger-ui .top-1-l {
+        top: 1rem
+    }
+
+    .swagger-ui .left-1-l {
+        left: 1rem
+    }
+
+    .swagger-ui .right-1-l {
+        right: 1rem
+    }
+
+    .swagger-ui .bottom-1-l {
+        bottom: 1rem
+    }
+
+    .swagger-ui .top-2-l {
+        top: 2rem
+    }
+
+    .swagger-ui .left-2-l {
+        left: 2rem
+    }
+
+    .swagger-ui .right-2-l {
+        right: 2rem
+    }
+
+    .swagger-ui .bottom-2-l {
+        bottom: 2rem
+    }
+
+    .swagger-ui .top--1-l {
+        top: -1rem
+    }
+
+    .swagger-ui .right--1-l {
+        right: -1rem
+    }
+
+    .swagger-ui .bottom--1-l {
+        bottom: -1rem
+    }
+
+    .swagger-ui .left--1-l {
+        left: -1rem
+    }
+
+    .swagger-ui .top--2-l {
+        top: -2rem
+    }
+
+    .swagger-ui .right--2-l {
+        right: -2rem
+    }
+
+    .swagger-ui .bottom--2-l {
+        bottom: -2rem
+    }
+
+    .swagger-ui .left--2-l {
+        left: -2rem
+    }
+
+    .swagger-ui .absolute--fill-l {
+        bottom: 0;
+        left: 0;
+        right: 0;
+        top: 0
+    }
+}
+
+.swagger-ui .cf:after,.swagger-ui .cf:before {
+    content: " ";
+    display: table
+}
+
+.swagger-ui .cf:after {
+    clear: both
+}
+
+.swagger-ui .cf {
+    zoom:1}
+
+.swagger-ui .cl {
+    clear: left
+}
+
+.swagger-ui .cr {
+    clear: right
+}
+
+.swagger-ui .cb {
+    clear: both
+}
+
+.swagger-ui .cn {
+    clear: none
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .cl-ns {
+        clear:left
+    }
+
+    .swagger-ui .cr-ns {
+        clear: right
+    }
+
+    .swagger-ui .cb-ns {
+        clear: both
+    }
+
+    .swagger-ui .cn-ns {
+        clear: none
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .cl-m {
+        clear:left
+    }
+
+    .swagger-ui .cr-m {
+        clear: right
+    }
+
+    .swagger-ui .cb-m {
+        clear: both
+    }
+
+    .swagger-ui .cn-m {
+        clear: none
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .cl-l {
+        clear:left
+    }
+
+    .swagger-ui .cr-l {
+        clear: right
+    }
+
+    .swagger-ui .cb-l {
+        clear: both
+    }
+
+    .swagger-ui .cn-l {
+        clear: none
+    }
+}
+
+.swagger-ui .flex {
+    display: flex
+}
+
+.swagger-ui .inline-flex {
+    display: inline-flex
+}
+
+.swagger-ui .flex-auto {
+    flex: 1 1 auto;
+    min-height: 0;
+    min-width: 0
+}
+
+.swagger-ui .flex-none {
+    flex: none
+}
+
+.swagger-ui .flex-column {
+    flex-direction: column
+}
+
+.swagger-ui .flex-row {
+    flex-direction: row
+}
+
+.swagger-ui .flex-wrap {
+    flex-wrap: wrap
+}
+
+.swagger-ui .flex-nowrap {
+    flex-wrap: nowrap
+}
+
+.swagger-ui .flex-wrap-reverse {
+    flex-wrap: wrap-reverse
+}
+
+.swagger-ui .flex-column-reverse {
+    flex-direction: column-reverse
+}
+
+.swagger-ui .flex-row-reverse {
+    flex-direction: row-reverse
+}
+
+.swagger-ui .items-start {
+    align-items: flex-start
+}
+
+.swagger-ui .items-end {
+    align-items: flex-end
+}
+
+.swagger-ui .items-center {
+    align-items: center
+}
+
+.swagger-ui .items-baseline {
+    align-items: baseline
+}
+
+.swagger-ui .items-stretch {
+    align-items: stretch
+}
+
+.swagger-ui .self-start {
+    align-self: flex-start
+}
+
+.swagger-ui .self-end {
+    align-self: flex-end
+}
+
+.swagger-ui .self-center {
+    align-self: center
+}
+
+.swagger-ui .self-baseline {
+    align-self: baseline
+}
+
+.swagger-ui .self-stretch {
+    align-self: stretch
+}
+
+.swagger-ui .justify-start {
+    justify-content: flex-start
+}
+
+.swagger-ui .justify-end {
+    justify-content: flex-end
+}
+
+.swagger-ui .justify-center {
+    justify-content: center
+}
+
+.swagger-ui .justify-between {
+    justify-content: space-between
+}
+
+.swagger-ui .justify-around {
+    justify-content: space-around
+}
+
+.swagger-ui .content-start {
+    align-content: flex-start
+}
+
+.swagger-ui .content-end {
+    align-content: flex-end
+}
+
+.swagger-ui .content-center {
+    align-content: center
+}
+
+.swagger-ui .content-between {
+    align-content: space-between
+}
+
+.swagger-ui .content-around {
+    align-content: space-around
+}
+
+.swagger-ui .content-stretch {
+    align-content: stretch
+}
+
+.swagger-ui .order-0 {
+    order: 0
+}
+
+.swagger-ui .order-1 {
+    order: 1
+}
+
+.swagger-ui .order-2 {
+    order: 2
+}
+
+.swagger-ui .order-3 {
+    order: 3
+}
+
+.swagger-ui .order-4 {
+    order: 4
+}
+
+.swagger-ui .order-5 {
+    order: 5
+}
+
+.swagger-ui .order-6 {
+    order: 6
+}
+
+.swagger-ui .order-7 {
+    order: 7
+}
+
+.swagger-ui .order-8 {
+    order: 8
+}
+
+.swagger-ui .order-last {
+    order: 99999
+}
+
+.swagger-ui .flex-grow-0 {
+    flex-grow: 0
+}
+
+.swagger-ui .flex-grow-1 {
+    flex-grow: 1
+}
+
+.swagger-ui .flex-shrink-0 {
+    flex-shrink: 0
+}
+
+.swagger-ui .flex-shrink-1 {
+    flex-shrink: 1
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .flex-ns {
+        display:flex
+    }
+
+    .swagger-ui .inline-flex-ns {
+        display: inline-flex
+    }
+
+    .swagger-ui .flex-auto-ns {
+        flex: 1 1 auto;
+        min-height: 0;
+        min-width: 0
+    }
+
+    .swagger-ui .flex-none-ns {
+        flex: none
+    }
+
+    .swagger-ui .flex-column-ns {
+        flex-direction: column
+    }
+
+    .swagger-ui .flex-row-ns {
+        flex-direction: row
+    }
+
+    .swagger-ui .flex-wrap-ns {
+        flex-wrap: wrap
+    }
+
+    .swagger-ui .flex-nowrap-ns {
+        flex-wrap: nowrap
+    }
+
+    .swagger-ui .flex-wrap-reverse-ns {
+        flex-wrap: wrap-reverse
+    }
+
+    .swagger-ui .flex-column-reverse-ns {
+        flex-direction: column-reverse
+    }
+
+    .swagger-ui .flex-row-reverse-ns {
+        flex-direction: row-reverse
+    }
+
+    .swagger-ui .items-start-ns {
+        align-items: flex-start
+    }
+
+    .swagger-ui .items-end-ns {
+        align-items: flex-end
+    }
+
+    .swagger-ui .items-center-ns {
+        align-items: center
+    }
+
+    .swagger-ui .items-baseline-ns {
+        align-items: baseline
+    }
+
+    .swagger-ui .items-stretch-ns {
+        align-items: stretch
+    }
+
+    .swagger-ui .self-start-ns {
+        align-self: flex-start
+    }
+
+    .swagger-ui .self-end-ns {
+        align-self: flex-end
+    }
+
+    .swagger-ui .self-center-ns {
+        align-self: center
+    }
+
+    .swagger-ui .self-baseline-ns {
+        align-self: baseline
+    }
+
+    .swagger-ui .self-stretch-ns {
+        align-self: stretch
+    }
+
+    .swagger-ui .justify-start-ns {
+        justify-content: flex-start
+    }
+
+    .swagger-ui .justify-end-ns {
+        justify-content: flex-end
+    }
+
+    .swagger-ui .justify-center-ns {
+        justify-content: center
+    }
+
+    .swagger-ui .justify-between-ns {
+        justify-content: space-between
+    }
+
+    .swagger-ui .justify-around-ns {
+        justify-content: space-around
+    }
+
+    .swagger-ui .content-start-ns {
+        align-content: flex-start
+    }
+
+    .swagger-ui .content-end-ns {
+        align-content: flex-end
+    }
+
+    .swagger-ui .content-center-ns {
+        align-content: center
+    }
+
+    .swagger-ui .content-between-ns {
+        align-content: space-between
+    }
+
+    .swagger-ui .content-around-ns {
+        align-content: space-around
+    }
+
+    .swagger-ui .content-stretch-ns {
+        align-content: stretch
+    }
+
+    .swagger-ui .order-0-ns {
+        order: 0
+    }
+
+    .swagger-ui .order-1-ns {
+        order: 1
+    }
+
+    .swagger-ui .order-2-ns {
+        order: 2
+    }
+
+    .swagger-ui .order-3-ns {
+        order: 3
+    }
+
+    .swagger-ui .order-4-ns {
+        order: 4
+    }
+
+    .swagger-ui .order-5-ns {
+        order: 5
+    }
+
+    .swagger-ui .order-6-ns {
+        order: 6
+    }
+
+    .swagger-ui .order-7-ns {
+        order: 7
+    }
+
+    .swagger-ui .order-8-ns {
+        order: 8
+    }
+
+    .swagger-ui .order-last-ns {
+        order: 99999
+    }
+
+    .swagger-ui .flex-grow-0-ns {
+        flex-grow: 0
+    }
+
+    .swagger-ui .flex-grow-1-ns {
+        flex-grow: 1
+    }
+
+    .swagger-ui .flex-shrink-0-ns {
+        flex-shrink: 0
+    }
+
+    .swagger-ui .flex-shrink-1-ns {
+        flex-shrink: 1
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .flex-m {
+        display:flex
+    }
+
+    .swagger-ui .inline-flex-m {
+        display: inline-flex
+    }
+
+    .swagger-ui .flex-auto-m {
+        flex: 1 1 auto;
+        min-height: 0;
+        min-width: 0
+    }
+
+    .swagger-ui .flex-none-m {
+        flex: none
+    }
+
+    .swagger-ui .flex-column-m {
+        flex-direction: column
+    }
+
+    .swagger-ui .flex-row-m {
+        flex-direction: row
+    }
+
+    .swagger-ui .flex-wrap-m {
+        flex-wrap: wrap
+    }
+
+    .swagger-ui .flex-nowrap-m {
+        flex-wrap: nowrap
+    }
+
+    .swagger-ui .flex-wrap-reverse-m {
+        flex-wrap: wrap-reverse
+    }
+
+    .swagger-ui .flex-column-reverse-m {
+        flex-direction: column-reverse
+    }
+
+    .swagger-ui .flex-row-reverse-m {
+        flex-direction: row-reverse
+    }
+
+    .swagger-ui .items-start-m {
+        align-items: flex-start
+    }
+
+    .swagger-ui .items-end-m {
+        align-items: flex-end
+    }
+
+    .swagger-ui .items-center-m {
+        align-items: center
+    }
+
+    .swagger-ui .items-baseline-m {
+        align-items: baseline
+    }
+
+    .swagger-ui .items-stretch-m {
+        align-items: stretch
+    }
+
+    .swagger-ui .self-start-m {
+        align-self: flex-start
+    }
+
+    .swagger-ui .self-end-m {
+        align-self: flex-end
+    }
+
+    .swagger-ui .self-center-m {
+        align-self: center
+    }
+
+    .swagger-ui .self-baseline-m {
+        align-self: baseline
+    }
+
+    .swagger-ui .self-stretch-m {
+        align-self: stretch
+    }
+
+    .swagger-ui .justify-start-m {
+        justify-content: flex-start
+    }
+
+    .swagger-ui .justify-end-m {
+        justify-content: flex-end
+    }
+
+    .swagger-ui .justify-center-m {
+        justify-content: center
+    }
+
+    .swagger-ui .justify-between-m {
+        justify-content: space-between
+    }
+
+    .swagger-ui .justify-around-m {
+        justify-content: space-around
+    }
+
+    .swagger-ui .content-start-m {
+        align-content: flex-start
+    }
+
+    .swagger-ui .content-end-m {
+        align-content: flex-end
+    }
+
+    .swagger-ui .content-center-m {
+        align-content: center
+    }
+
+    .swagger-ui .content-between-m {
+        align-content: space-between
+    }
+
+    .swagger-ui .content-around-m {
+        align-content: space-around
+    }
+
+    .swagger-ui .content-stretch-m {
+        align-content: stretch
+    }
+
+    .swagger-ui .order-0-m {
+        order: 0
+    }
+
+    .swagger-ui .order-1-m {
+        order: 1
+    }
+
+    .swagger-ui .order-2-m {
+        order: 2
+    }
+
+    .swagger-ui .order-3-m {
+        order: 3
+    }
+
+    .swagger-ui .order-4-m {
+        order: 4
+    }
+
+    .swagger-ui .order-5-m {
+        order: 5
+    }
+
+    .swagger-ui .order-6-m {
+        order: 6
+    }
+
+    .swagger-ui .order-7-m {
+        order: 7
+    }
+
+    .swagger-ui .order-8-m {
+        order: 8
+    }
+
+    .swagger-ui .order-last-m {
+        order: 99999
+    }
+
+    .swagger-ui .flex-grow-0-m {
+        flex-grow: 0
+    }
+
+    .swagger-ui .flex-grow-1-m {
+        flex-grow: 1
+    }
+
+    .swagger-ui .flex-shrink-0-m {
+        flex-shrink: 0
+    }
+
+    .swagger-ui .flex-shrink-1-m {
+        flex-shrink: 1
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .flex-l {
+        display:flex
+    }
+
+    .swagger-ui .inline-flex-l {
+        display: inline-flex
+    }
+
+    .swagger-ui .flex-auto-l {
+        flex: 1 1 auto;
+        min-height: 0;
+        min-width: 0
+    }
+
+    .swagger-ui .flex-none-l {
+        flex: none
+    }
+
+    .swagger-ui .flex-column-l {
+        flex-direction: column
+    }
+
+    .swagger-ui .flex-row-l {
+        flex-direction: row
+    }
+
+    .swagger-ui .flex-wrap-l {
+        flex-wrap: wrap
+    }
+
+    .swagger-ui .flex-nowrap-l {
+        flex-wrap: nowrap
+    }
+
+    .swagger-ui .flex-wrap-reverse-l {
+        flex-wrap: wrap-reverse
+    }
+
+    .swagger-ui .flex-column-reverse-l {
+        flex-direction: column-reverse
+    }
+
+    .swagger-ui .flex-row-reverse-l {
+        flex-direction: row-reverse
+    }
+
+    .swagger-ui .items-start-l {
+        align-items: flex-start
+    }
+
+    .swagger-ui .items-end-l {
+        align-items: flex-end
+    }
+
+    .swagger-ui .items-center-l {
+        align-items: center
+    }
+
+    .swagger-ui .items-baseline-l {
+        align-items: baseline
+    }
+
+    .swagger-ui .items-stretch-l {
+        align-items: stretch
+    }
+
+    .swagger-ui .self-start-l {
+        align-self: flex-start
+    }
+
+    .swagger-ui .self-end-l {
+        align-self: flex-end
+    }
+
+    .swagger-ui .self-center-l {
+        align-self: center
+    }
+
+    .swagger-ui .self-baseline-l {
+        align-self: baseline
+    }
+
+    .swagger-ui .self-stretch-l {
+        align-self: stretch
+    }
+
+    .swagger-ui .justify-start-l {
+        justify-content: flex-start
+    }
+
+    .swagger-ui .justify-end-l {
+        justify-content: flex-end
+    }
+
+    .swagger-ui .justify-center-l {
+        justify-content: center
+    }
+
+    .swagger-ui .justify-between-l {
+        justify-content: space-between
+    }
+
+    .swagger-ui .justify-around-l {
+        justify-content: space-around
+    }
+
+    .swagger-ui .content-start-l {
+        align-content: flex-start
+    }
+
+    .swagger-ui .content-end-l {
+        align-content: flex-end
+    }
+
+    .swagger-ui .content-center-l {
+        align-content: center
+    }
+
+    .swagger-ui .content-between-l {
+        align-content: space-between
+    }
+
+    .swagger-ui .content-around-l {
+        align-content: space-around
+    }
+
+    .swagger-ui .content-stretch-l {
+        align-content: stretch
+    }
+
+    .swagger-ui .order-0-l {
+        order: 0
+    }
+
+    .swagger-ui .order-1-l {
+        order: 1
+    }
+
+    .swagger-ui .order-2-l {
+        order: 2
+    }
+
+    .swagger-ui .order-3-l {
+        order: 3
+    }
+
+    .swagger-ui .order-4-l {
+        order: 4
+    }
+
+    .swagger-ui .order-5-l {
+        order: 5
+    }
+
+    .swagger-ui .order-6-l {
+        order: 6
+    }
+
+    .swagger-ui .order-7-l {
+        order: 7
+    }
+
+    .swagger-ui .order-8-l {
+        order: 8
+    }
+
+    .swagger-ui .order-last-l {
+        order: 99999
+    }
+
+    .swagger-ui .flex-grow-0-l {
+        flex-grow: 0
+    }
+
+    .swagger-ui .flex-grow-1-l {
+        flex-grow: 1
+    }
+
+    .swagger-ui .flex-shrink-0-l {
+        flex-shrink: 0
+    }
+
+    .swagger-ui .flex-shrink-1-l {
+        flex-shrink: 1
+    }
+}
+
+.swagger-ui .dn {
+    display: none
+}
+
+.swagger-ui .di {
+    display: inline
+}
+
+.swagger-ui .db {
+    display: block
+}
+
+.swagger-ui .dib {
+    display: inline-block
+}
+
+.swagger-ui .dit {
+    display: inline-table
+}
+
+.swagger-ui .dt {
+    display: table
+}
+
+.swagger-ui .dtc {
+    display: table-cell
+}
+
+.swagger-ui .dt-row {
+    display: table-row
+}
+
+.swagger-ui .dt-row-group {
+    display: table-row-group
+}
+
+.swagger-ui .dt-column {
+    display: table-column
+}
+
+.swagger-ui .dt-column-group {
+    display: table-column-group
+}
+
+.swagger-ui .dt--fixed {
+    table-layout: fixed;
+    width: 100%
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .dn-ns {
+        display:none
+    }
+
+    .swagger-ui .di-ns {
+        display: inline
+    }
+
+    .swagger-ui .db-ns {
+        display: block
+    }
+
+    .swagger-ui .dib-ns {
+        display: inline-block
+    }
+
+    .swagger-ui .dit-ns {
+        display: inline-table
+    }
+
+    .swagger-ui .dt-ns {
+        display: table
+    }
+
+    .swagger-ui .dtc-ns {
+        display: table-cell
+    }
+
+    .swagger-ui .dt-row-ns {
+        display: table-row
+    }
+
+    .swagger-ui .dt-row-group-ns {
+        display: table-row-group
+    }
+
+    .swagger-ui .dt-column-ns {
+        display: table-column
+    }
+
+    .swagger-ui .dt-column-group-ns {
+        display: table-column-group
+    }
+
+    .swagger-ui .dt--fixed-ns {
+        table-layout: fixed;
+        width: 100%
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .dn-m {
+        display:none
+    }
+
+    .swagger-ui .di-m {
+        display: inline
+    }
+
+    .swagger-ui .db-m {
+        display: block
+    }
+
+    .swagger-ui .dib-m {
+        display: inline-block
+    }
+
+    .swagger-ui .dit-m {
+        display: inline-table
+    }
+
+    .swagger-ui .dt-m {
+        display: table
+    }
+
+    .swagger-ui .dtc-m {
+        display: table-cell
+    }
+
+    .swagger-ui .dt-row-m {
+        display: table-row
+    }
+
+    .swagger-ui .dt-row-group-m {
+        display: table-row-group
+    }
+
+    .swagger-ui .dt-column-m {
+        display: table-column
+    }
+
+    .swagger-ui .dt-column-group-m {
+        display: table-column-group
+    }
+
+    .swagger-ui .dt--fixed-m {
+        table-layout: fixed;
+        width: 100%
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .dn-l {
+        display:none
+    }
+
+    .swagger-ui .di-l {
+        display: inline
+    }
+
+    .swagger-ui .db-l {
+        display: block
+    }
+
+    .swagger-ui .dib-l {
+        display: inline-block
+    }
+
+    .swagger-ui .dit-l {
+        display: inline-table
+    }
+
+    .swagger-ui .dt-l {
+        display: table
+    }
+
+    .swagger-ui .dtc-l {
+        display: table-cell
+    }
+
+    .swagger-ui .dt-row-l {
+        display: table-row
+    }
+
+    .swagger-ui .dt-row-group-l {
+        display: table-row-group
+    }
+
+    .swagger-ui .dt-column-l {
+        display: table-column
+    }
+
+    .swagger-ui .dt-column-group-l {
+        display: table-column-group
+    }
+
+    .swagger-ui .dt--fixed-l {
+        table-layout: fixed;
+        width: 100%
+    }
+}
+
+.swagger-ui .fl {
+    _display: inline;
+    float: left
+}
+
+.swagger-ui .fr {
+    _display: inline;
+    float: right
+}
+
+.swagger-ui .fn {
+    float: none
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .fl-ns {
+        _display:inline;
+        float: left
+    }
+
+    .swagger-ui .fr-ns {
+        _display: inline;
+        float: right
+    }
+
+    .swagger-ui .fn-ns {
+        float: none
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .fl-m {
+        _display:inline;
+        float: left
+    }
+
+    .swagger-ui .fr-m {
+        _display: inline;
+        float: right
+    }
+
+    .swagger-ui .fn-m {
+        float: none
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .fl-l {
+        _display:inline;
+        float: left
+    }
+
+    .swagger-ui .fr-l {
+        _display: inline;
+        float: right
+    }
+
+    .swagger-ui .fn-l {
+        float: none
+    }
+}
+
+.swagger-ui .sans-serif {
+    font-family: -apple-system,BlinkMacSystemFont,avenir next,avenir,helvetica,helvetica neue,ubuntu,roboto,noto,segoe ui,arial,sans-serif
+}
+
+.swagger-ui .serif {
+    font-family: georgia,serif
+}
+
+.swagger-ui .system-sans-serif {
+    font-family: sans-serif
+}
+
+.swagger-ui .system-serif {
+    font-family: serif
+}
+
+.swagger-ui .code,.swagger-ui code {
+    font-family: Consolas,monaco,monospace
+}
+
+.swagger-ui .courier {
+    font-family: Courier Next,courier,monospace
+}
+
+.swagger-ui .helvetica {
+    font-family: helvetica neue,helvetica,sans-serif
+}
+
+.swagger-ui .avenir {
+    font-family: avenir next,avenir,sans-serif
+}
+
+.swagger-ui .athelas {
+    font-family: athelas,georgia,serif
+}
+
+.swagger-ui .georgia {
+    font-family: georgia,serif
+}
+
+.swagger-ui .times {
+    font-family: times,serif
+}
+
+.swagger-ui .bodoni {
+    font-family: Bodoni MT,serif
+}
+
+.swagger-ui .calisto {
+    font-family: Calisto MT,serif
+}
+
+.swagger-ui .garamond {
+    font-family: garamond,serif
+}
+
+.swagger-ui .baskerville {
+    font-family: baskerville,serif
+}
+
+.swagger-ui .i {
+    font-style: italic
+}
+
+.swagger-ui .fs-normal {
+    font-style: normal
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .i-ns {
+        font-style:italic
+    }
+
+    .swagger-ui .fs-normal-ns {
+        font-style: normal
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .i-m {
+        font-style:italic
+    }
+
+    .swagger-ui .fs-normal-m {
+        font-style: normal
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .i-l {
+        font-style:italic
+    }
+
+    .swagger-ui .fs-normal-l {
+        font-style: normal
+    }
+}
+
+.swagger-ui .normal {
+    font-weight: 400
+}
+
+.swagger-ui .b {
+    font-weight: 700
+}
+
+.swagger-ui .fw1 {
+    font-weight: 100
+}
+
+.swagger-ui .fw2 {
+    font-weight: 200
+}
+
+.swagger-ui .fw3 {
+    font-weight: 300
+}
+
+.swagger-ui .fw4 {
+    font-weight: 400
+}
+
+.swagger-ui .fw5 {
+    font-weight: 500
+}
+
+.swagger-ui .fw6 {
+    font-weight: 600
+}
+
+.swagger-ui .fw7 {
+    font-weight: 700
+}
+
+.swagger-ui .fw8 {
+    font-weight: 800
+}
+
+.swagger-ui .fw9 {
+    font-weight: 900
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .normal-ns {
+        font-weight:400
+    }
+
+    .swagger-ui .b-ns {
+        font-weight: 700
+    }
+
+    .swagger-ui .fw1-ns {
+        font-weight: 100
+    }
+
+    .swagger-ui .fw2-ns {
+        font-weight: 200
+    }
+
+    .swagger-ui .fw3-ns {
+        font-weight: 300
+    }
+
+    .swagger-ui .fw4-ns {
+        font-weight: 400
+    }
+
+    .swagger-ui .fw5-ns {
+        font-weight: 500
+    }
+
+    .swagger-ui .fw6-ns {
+        font-weight: 600
+    }
+
+    .swagger-ui .fw7-ns {
+        font-weight: 700
+    }
+
+    .swagger-ui .fw8-ns {
+        font-weight: 800
+    }
+
+    .swagger-ui .fw9-ns {
+        font-weight: 900
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .normal-m {
+        font-weight:400
+    }
+
+    .swagger-ui .b-m {
+        font-weight: 700
+    }
+
+    .swagger-ui .fw1-m {
+        font-weight: 100
+    }
+
+    .swagger-ui .fw2-m {
+        font-weight: 200
+    }
+
+    .swagger-ui .fw3-m {
+        font-weight: 300
+    }
+
+    .swagger-ui .fw4-m {
+        font-weight: 400
+    }
+
+    .swagger-ui .fw5-m {
+        font-weight: 500
+    }
+
+    .swagger-ui .fw6-m {
+        font-weight: 600
+    }
+
+    .swagger-ui .fw7-m {
+        font-weight: 700
+    }
+
+    .swagger-ui .fw8-m {
+        font-weight: 800
+    }
+
+    .swagger-ui .fw9-m {
+        font-weight: 900
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .normal-l {
+        font-weight:400
+    }
+
+    .swagger-ui .b-l {
+        font-weight: 700
+    }
+
+    .swagger-ui .fw1-l {
+        font-weight: 100
+    }
+
+    .swagger-ui .fw2-l {
+        font-weight: 200
+    }
+
+    .swagger-ui .fw3-l {
+        font-weight: 300
+    }
+
+    .swagger-ui .fw4-l {
+        font-weight: 400
+    }
+
+    .swagger-ui .fw5-l {
+        font-weight: 500
+    }
+
+    .swagger-ui .fw6-l {
+        font-weight: 600
+    }
+
+    .swagger-ui .fw7-l {
+        font-weight: 700
+    }
+
+    .swagger-ui .fw8-l {
+        font-weight: 800
+    }
+
+    .swagger-ui .fw9-l {
+        font-weight: 900
+    }
+}
+
+.swagger-ui .input-reset {
+    -webkit-appearance: none;
+    -moz-appearance: none
+}
+
+.swagger-ui .button-reset::-moz-focus-inner,.swagger-ui .input-reset::-moz-focus-inner {
+    padding: 0
+}
+
+.swagger-ui .h1 {
+    height: 1rem
+}
+
+.swagger-ui .h2 {
+    height: 2rem
+}
+
+.swagger-ui .h3 {
+    height: 4rem
+}
+
+.swagger-ui .h4 {
+    height: 8rem
+}
+
+.swagger-ui .h5 {
+    height: 16rem
+}
+
+.swagger-ui .h-25 {
+    height: 25%
+}
+
+.swagger-ui .h-50 {
+    height: 50%
+}
+
+.swagger-ui .h-75 {
+    height: 75%
+}
+
+.swagger-ui .h-100 {
+    height: 100%
+}
+
+.swagger-ui .min-h-100 {
+    min-height: 100%
+}
+
+.swagger-ui .vh-25 {
+    height: 25vh
+}
+
+.swagger-ui .vh-50 {
+    height: 50vh
+}
+
+.swagger-ui .vh-75 {
+    height: 75vh
+}
+
+.swagger-ui .vh-100 {
+    height: 100vh
+}
+
+.swagger-ui .min-vh-100 {
+    min-height: 100vh
+}
+
+.swagger-ui .h-auto {
+    height: auto
+}
+
+.swagger-ui .h-inherit {
+    height: inherit
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .h1-ns {
+        height:1rem
+    }
+
+    .swagger-ui .h2-ns {
+        height: 2rem
+    }
+
+    .swagger-ui .h3-ns {
+        height: 4rem
+    }
+
+    .swagger-ui .h4-ns {
+        height: 8rem
+    }
+
+    .swagger-ui .h5-ns {
+        height: 16rem
+    }
+
+    .swagger-ui .h-25-ns {
+        height: 25%
+    }
+
+    .swagger-ui .h-50-ns {
+        height: 50%
+    }
+
+    .swagger-ui .h-75-ns {
+        height: 75%
+    }
+
+    .swagger-ui .h-100-ns {
+        height: 100%
+    }
+
+    .swagger-ui .min-h-100-ns {
+        min-height: 100%
+    }
+
+    .swagger-ui .vh-25-ns {
+        height: 25vh
+    }
+
+    .swagger-ui .vh-50-ns {
+        height: 50vh
+    }
+
+    .swagger-ui .vh-75-ns {
+        height: 75vh
+    }
+
+    .swagger-ui .vh-100-ns {
+        height: 100vh
+    }
+
+    .swagger-ui .min-vh-100-ns {
+        min-height: 100vh
+    }
+
+    .swagger-ui .h-auto-ns {
+        height: auto
+    }
+
+    .swagger-ui .h-inherit-ns {
+        height: inherit
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .h1-m {
+        height:1rem
+    }
+
+    .swagger-ui .h2-m {
+        height: 2rem
+    }
+
+    .swagger-ui .h3-m {
+        height: 4rem
+    }
+
+    .swagger-ui .h4-m {
+        height: 8rem
+    }
+
+    .swagger-ui .h5-m {
+        height: 16rem
+    }
+
+    .swagger-ui .h-25-m {
+        height: 25%
+    }
+
+    .swagger-ui .h-50-m {
+        height: 50%
+    }
+
+    .swagger-ui .h-75-m {
+        height: 75%
+    }
+
+    .swagger-ui .h-100-m {
+        height: 100%
+    }
+
+    .swagger-ui .min-h-100-m {
+        min-height: 100%
+    }
+
+    .swagger-ui .vh-25-m {
+        height: 25vh
+    }
+
+    .swagger-ui .vh-50-m {
+        height: 50vh
+    }
+
+    .swagger-ui .vh-75-m {
+        height: 75vh
+    }
+
+    .swagger-ui .vh-100-m {
+        height: 100vh
+    }
+
+    .swagger-ui .min-vh-100-m {
+        min-height: 100vh
+    }
+
+    .swagger-ui .h-auto-m {
+        height: auto
+    }
+
+    .swagger-ui .h-inherit-m {
+        height: inherit
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .h1-l {
+        height:1rem
+    }
+
+    .swagger-ui .h2-l {
+        height: 2rem
+    }
+
+    .swagger-ui .h3-l {
+        height: 4rem
+    }
+
+    .swagger-ui .h4-l {
+        height: 8rem
+    }
+
+    .swagger-ui .h5-l {
+        height: 16rem
+    }
+
+    .swagger-ui .h-25-l {
+        height: 25%
+    }
+
+    .swagger-ui .h-50-l {
+        height: 50%
+    }
+
+    .swagger-ui .h-75-l {
+        height: 75%
+    }
+
+    .swagger-ui .h-100-l {
+        height: 100%
+    }
+
+    .swagger-ui .min-h-100-l {
+        min-height: 100%
+    }
+
+    .swagger-ui .vh-25-l {
+        height: 25vh
+    }
+
+    .swagger-ui .vh-50-l {
+        height: 50vh
+    }
+
+    .swagger-ui .vh-75-l {
+        height: 75vh
+    }
+
+    .swagger-ui .vh-100-l {
+        height: 100vh
+    }
+
+    .swagger-ui .min-vh-100-l {
+        min-height: 100vh
+    }
+
+    .swagger-ui .h-auto-l {
+        height: auto
+    }
+
+    .swagger-ui .h-inherit-l {
+        height: inherit
+    }
+}
+
+.swagger-ui .tracked {
+    letter-spacing: .1em
+}
+
+.swagger-ui .tracked-tight {
+    letter-spacing: -.05em
+}
+
+.swagger-ui .tracked-mega {
+    letter-spacing: .25em
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .tracked-ns {
+        letter-spacing:.1em
+    }
+
+    .swagger-ui .tracked-tight-ns {
+        letter-spacing: -.05em
+    }
+
+    .swagger-ui .tracked-mega-ns {
+        letter-spacing: .25em
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .tracked-m {
+        letter-spacing:.1em
+    }
+
+    .swagger-ui .tracked-tight-m {
+        letter-spacing: -.05em
+    }
+
+    .swagger-ui .tracked-mega-m {
+        letter-spacing: .25em
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .tracked-l {
+        letter-spacing:.1em
+    }
+
+    .swagger-ui .tracked-tight-l {
+        letter-spacing: -.05em
+    }
+
+    .swagger-ui .tracked-mega-l {
+        letter-spacing: .25em
+    }
+}
+
+.swagger-ui .lh-solid {
+    line-height: 1
+}
+
+.swagger-ui .lh-title {
+    line-height: 1.25
+}
+
+.swagger-ui .lh-copy {
+    line-height: 1.5
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .lh-solid-ns {
+        line-height:1
+    }
+
+    .swagger-ui .lh-title-ns {
+        line-height: 1.25
+    }
+
+    .swagger-ui .lh-copy-ns {
+        line-height: 1.5
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .lh-solid-m {
+        line-height:1
+    }
+
+    .swagger-ui .lh-title-m {
+        line-height: 1.25
+    }
+
+    .swagger-ui .lh-copy-m {
+        line-height: 1.5
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .lh-solid-l {
+        line-height:1
+    }
+
+    .swagger-ui .lh-title-l {
+        line-height: 1.25
+    }
+
+    .swagger-ui .lh-copy-l {
+        line-height: 1.5
+    }
+}
+
+.swagger-ui .link {
+    -webkit-text-decoration: none;
+    text-decoration: none
+}
+
+.swagger-ui .link,.swagger-ui .link:active,.swagger-ui .link:focus,.swagger-ui .link:hover,.swagger-ui .link:link,.swagger-ui .link:visited {
+    transition: color .15s ease-in
+}
+
+.swagger-ui .link:focus {
+    outline: 1px dotted currentColor
+}
+
+.swagger-ui .list {
+    list-style-type: none
+}
+
+.swagger-ui .mw-100 {
+    max-width: 100%
+}
+
+.swagger-ui .mw1 {
+    max-width: 1rem
+}
+
+.swagger-ui .mw2 {
+    max-width: 2rem
+}
+
+.swagger-ui .mw3 {
+    max-width: 4rem
+}
+
+.swagger-ui .mw4 {
+    max-width: 8rem
+}
+
+.swagger-ui .mw5 {
+    max-width: 16rem
+}
+
+.swagger-ui .mw6 {
+    max-width: 32rem
+}
+
+.swagger-ui .mw7 {
+    max-width: 48rem
+}
+
+.swagger-ui .mw8 {
+    max-width: 64rem
+}
+
+.swagger-ui .mw9 {
+    max-width: 96rem
+}
+
+.swagger-ui .mw-none {
+    max-width: none
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .mw-100-ns {
+        max-width:100%
+    }
+
+    .swagger-ui .mw1-ns {
+        max-width: 1rem
+    }
+
+    .swagger-ui .mw2-ns {
+        max-width: 2rem
+    }
+
+    .swagger-ui .mw3-ns {
+        max-width: 4rem
+    }
+
+    .swagger-ui .mw4-ns {
+        max-width: 8rem
+    }
+
+    .swagger-ui .mw5-ns {
+        max-width: 16rem
+    }
+
+    .swagger-ui .mw6-ns {
+        max-width: 32rem
+    }
+
+    .swagger-ui .mw7-ns {
+        max-width: 48rem
+    }
+
+    .swagger-ui .mw8-ns {
+        max-width: 64rem
+    }
+
+    .swagger-ui .mw9-ns {
+        max-width: 96rem
+    }
+
+    .swagger-ui .mw-none-ns {
+        max-width: none
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .mw-100-m {
+        max-width:100%
+    }
+
+    .swagger-ui .mw1-m {
+        max-width: 1rem
+    }
+
+    .swagger-ui .mw2-m {
+        max-width: 2rem
+    }
+
+    .swagger-ui .mw3-m {
+        max-width: 4rem
+    }
+
+    .swagger-ui .mw4-m {
+        max-width: 8rem
+    }
+
+    .swagger-ui .mw5-m {
+        max-width: 16rem
+    }
+
+    .swagger-ui .mw6-m {
+        max-width: 32rem
+    }
+
+    .swagger-ui .mw7-m {
+        max-width: 48rem
+    }
+
+    .swagger-ui .mw8-m {
+        max-width: 64rem
+    }
+
+    .swagger-ui .mw9-m {
+        max-width: 96rem
+    }
+
+    .swagger-ui .mw-none-m {
+        max-width: none
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .mw-100-l {
+        max-width:100%
+    }
+
+    .swagger-ui .mw1-l {
+        max-width: 1rem
+    }
+
+    .swagger-ui .mw2-l {
+        max-width: 2rem
+    }
+
+    .swagger-ui .mw3-l {
+        max-width: 4rem
+    }
+
+    .swagger-ui .mw4-l {
+        max-width: 8rem
+    }
+
+    .swagger-ui .mw5-l {
+        max-width: 16rem
+    }
+
+    .swagger-ui .mw6-l {
+        max-width: 32rem
+    }
+
+    .swagger-ui .mw7-l {
+        max-width: 48rem
+    }
+
+    .swagger-ui .mw8-l {
+        max-width: 64rem
+    }
+
+    .swagger-ui .mw9-l {
+        max-width: 96rem
+    }
+
+    .swagger-ui .mw-none-l {
+        max-width: none
+    }
+}
+
+.swagger-ui .w1 {
+    width: 1rem
+}
+
+.swagger-ui .w2 {
+    width: 2rem
+}
+
+.swagger-ui .w3 {
+    width: 4rem
+}
+
+.swagger-ui .w4 {
+    width: 8rem
+}
+
+.swagger-ui .w5 {
+    width: 16rem
+}
+
+.swagger-ui .w-10 {
+    width: 10%
+}
+
+.swagger-ui .w-20 {
+    width: 20%
+}
+
+.swagger-ui .w-25 {
+    width: 25%
+}
+
+.swagger-ui .w-30 {
+    width: 30%
+}
+
+.swagger-ui .w-33 {
+    width: 33%
+}
+
+.swagger-ui .w-34 {
+    width: 34%
+}
+
+.swagger-ui .w-40 {
+    width: 40%
+}
+
+.swagger-ui .w-50 {
+    width: 50%
+}
+
+.swagger-ui .w-60 {
+    width: 60%
+}
+
+.swagger-ui .w-70 {
+    width: 70%
+}
+
+.swagger-ui .w-75 {
+    width: 75%
+}
+
+.swagger-ui .w-80 {
+    width: 80%
+}
+
+.swagger-ui .w-90 {
+    width: 90%
+}
+
+.swagger-ui .w-100 {
+    width: 100%
+}
+
+.swagger-ui .w-third {
+    width: 33.3333333333%
+}
+
+.swagger-ui .w-two-thirds {
+    width: 66.6666666667%
+}
+
+.swagger-ui .w-auto {
+    width: auto
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .w1-ns {
+        width:1rem
+    }
+
+    .swagger-ui .w2-ns {
+        width: 2rem
+    }
+
+    .swagger-ui .w3-ns {
+        width: 4rem
+    }
+
+    .swagger-ui .w4-ns {
+        width: 8rem
+    }
+
+    .swagger-ui .w5-ns {
+        width: 16rem
+    }
+
+    .swagger-ui .w-10-ns {
+        width: 10%
+    }
+
+    .swagger-ui .w-20-ns {
+        width: 20%
+    }
+
+    .swagger-ui .w-25-ns {
+        width: 25%
+    }
+
+    .swagger-ui .w-30-ns {
+        width: 30%
+    }
+
+    .swagger-ui .w-33-ns {
+        width: 33%
+    }
+
+    .swagger-ui .w-34-ns {
+        width: 34%
+    }
+
+    .swagger-ui .w-40-ns {
+        width: 40%
+    }
+
+    .swagger-ui .w-50-ns {
+        width: 50%
+    }
+
+    .swagger-ui .w-60-ns {
+        width: 60%
+    }
+
+    .swagger-ui .w-70-ns {
+        width: 70%
+    }
+
+    .swagger-ui .w-75-ns {
+        width: 75%
+    }
+
+    .swagger-ui .w-80-ns {
+        width: 80%
+    }
+
+    .swagger-ui .w-90-ns {
+        width: 90%
+    }
+
+    .swagger-ui .w-100-ns {
+        width: 100%
+    }
+
+    .swagger-ui .w-third-ns {
+        width: 33.3333333333%
+    }
+
+    .swagger-ui .w-two-thirds-ns {
+        width: 66.6666666667%
+    }
+
+    .swagger-ui .w-auto-ns {
+        width: auto
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .w1-m {
+        width:1rem
+    }
+
+    .swagger-ui .w2-m {
+        width: 2rem
+    }
+
+    .swagger-ui .w3-m {
+        width: 4rem
+    }
+
+    .swagger-ui .w4-m {
+        width: 8rem
+    }
+
+    .swagger-ui .w5-m {
+        width: 16rem
+    }
+
+    .swagger-ui .w-10-m {
+        width: 10%
+    }
+
+    .swagger-ui .w-20-m {
+        width: 20%
+    }
+
+    .swagger-ui .w-25-m {
+        width: 25%
+    }
+
+    .swagger-ui .w-30-m {
+        width: 30%
+    }
+
+    .swagger-ui .w-33-m {
+        width: 33%
+    }
+
+    .swagger-ui .w-34-m {
+        width: 34%
+    }
+
+    .swagger-ui .w-40-m {
+        width: 40%
+    }
+
+    .swagger-ui .w-50-m {
+        width: 50%
+    }
+
+    .swagger-ui .w-60-m {
+        width: 60%
+    }
+
+    .swagger-ui .w-70-m {
+        width: 70%
+    }
+
+    .swagger-ui .w-75-m {
+        width: 75%
+    }
+
+    .swagger-ui .w-80-m {
+        width: 80%
+    }
+
+    .swagger-ui .w-90-m {
+        width: 90%
+    }
+
+    .swagger-ui .w-100-m {
+        width: 100%
+    }
+
+    .swagger-ui .w-third-m {
+        width: 33.3333333333%
+    }
+
+    .swagger-ui .w-two-thirds-m {
+        width: 66.6666666667%
+    }
+
+    .swagger-ui .w-auto-m {
+        width: auto
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .w1-l {
+        width:1rem
+    }
+
+    .swagger-ui .w2-l {
+        width: 2rem
+    }
+
+    .swagger-ui .w3-l {
+        width: 4rem
+    }
+
+    .swagger-ui .w4-l {
+        width: 8rem
+    }
+
+    .swagger-ui .w5-l {
+        width: 16rem
+    }
+
+    .swagger-ui .w-10-l {
+        width: 10%
+    }
+
+    .swagger-ui .w-20-l {
+        width: 20%
+    }
+
+    .swagger-ui .w-25-l {
+        width: 25%
+    }
+
+    .swagger-ui .w-30-l {
+        width: 30%
+    }
+
+    .swagger-ui .w-33-l {
+        width: 33%
+    }
+
+    .swagger-ui .w-34-l {
+        width: 34%
+    }
+
+    .swagger-ui .w-40-l {
+        width: 40%
+    }
+
+    .swagger-ui .w-50-l {
+        width: 50%
+    }
+
+    .swagger-ui .w-60-l {
+        width: 60%
+    }
+
+    .swagger-ui .w-70-l {
+        width: 70%
+    }
+
+    .swagger-ui .w-75-l {
+        width: 75%
+    }
+
+    .swagger-ui .w-80-l {
+        width: 80%
+    }
+
+    .swagger-ui .w-90-l {
+        width: 90%
+    }
+
+    .swagger-ui .w-100-l {
+        width: 100%
+    }
+
+    .swagger-ui .w-third-l {
+        width: 33.3333333333%
+    }
+
+    .swagger-ui .w-two-thirds-l {
+        width: 66.6666666667%
+    }
+
+    .swagger-ui .w-auto-l {
+        width: auto
+    }
+}
+
+.swagger-ui .overflow-visible {
+    overflow: visible
+}
+
+.swagger-ui .overflow-hidden {
+    overflow: hidden
+}
+
+.swagger-ui .overflow-scroll {
+    overflow: scroll
+}
+
+.swagger-ui .overflow-auto {
+    overflow: auto
+}
+
+.swagger-ui .overflow-x-visible {
+    overflow-x: visible
+}
+
+.swagger-ui .overflow-x-hidden {
+    overflow-x: hidden
+}
+
+.swagger-ui .overflow-x-scroll {
+    overflow-x: scroll
+}
+
+.swagger-ui .overflow-x-auto {
+    overflow-x: auto
+}
+
+.swagger-ui .overflow-y-visible {
+    overflow-y: visible
+}
+
+.swagger-ui .overflow-y-hidden {
+    overflow-y: hidden
+}
+
+.swagger-ui .overflow-y-scroll {
+    overflow-y: scroll
+}
+
+.swagger-ui .overflow-y-auto {
+    overflow-y: auto
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .overflow-visible-ns {
+        overflow:visible
+    }
+
+    .swagger-ui .overflow-hidden-ns {
+        overflow: hidden
+    }
+
+    .swagger-ui .overflow-scroll-ns {
+        overflow: scroll
+    }
+
+    .swagger-ui .overflow-auto-ns {
+        overflow: auto
+    }
+
+    .swagger-ui .overflow-x-visible-ns {
+        overflow-x: visible
+    }
+
+    .swagger-ui .overflow-x-hidden-ns {
+        overflow-x: hidden
+    }
+
+    .swagger-ui .overflow-x-scroll-ns {
+        overflow-x: scroll
+    }
+
+    .swagger-ui .overflow-x-auto-ns {
+        overflow-x: auto
+    }
+
+    .swagger-ui .overflow-y-visible-ns {
+        overflow-y: visible
+    }
+
+    .swagger-ui .overflow-y-hidden-ns {
+        overflow-y: hidden
+    }
+
+    .swagger-ui .overflow-y-scroll-ns {
+        overflow-y: scroll
+    }
+
+    .swagger-ui .overflow-y-auto-ns {
+        overflow-y: auto
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .overflow-visible-m {
+        overflow:visible
+    }
+
+    .swagger-ui .overflow-hidden-m {
+        overflow: hidden
+    }
+
+    .swagger-ui .overflow-scroll-m {
+        overflow: scroll
+    }
+
+    .swagger-ui .overflow-auto-m {
+        overflow: auto
+    }
+
+    .swagger-ui .overflow-x-visible-m {
+        overflow-x: visible
+    }
+
+    .swagger-ui .overflow-x-hidden-m {
+        overflow-x: hidden
+    }
+
+    .swagger-ui .overflow-x-scroll-m {
+        overflow-x: scroll
+    }
+
+    .swagger-ui .overflow-x-auto-m {
+        overflow-x: auto
+    }
+
+    .swagger-ui .overflow-y-visible-m {
+        overflow-y: visible
+    }
+
+    .swagger-ui .overflow-y-hidden-m {
+        overflow-y: hidden
+    }
+
+    .swagger-ui .overflow-y-scroll-m {
+        overflow-y: scroll
+    }
+
+    .swagger-ui .overflow-y-auto-m {
+        overflow-y: auto
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .overflow-visible-l {
+        overflow:visible
+    }
+
+    .swagger-ui .overflow-hidden-l {
+        overflow: hidden
+    }
+
+    .swagger-ui .overflow-scroll-l {
+        overflow: scroll
+    }
+
+    .swagger-ui .overflow-auto-l {
+        overflow: auto
+    }
+
+    .swagger-ui .overflow-x-visible-l {
+        overflow-x: visible
+    }
+
+    .swagger-ui .overflow-x-hidden-l {
+        overflow-x: hidden
+    }
+
+    .swagger-ui .overflow-x-scroll-l {
+        overflow-x: scroll
+    }
+
+    .swagger-ui .overflow-x-auto-l {
+        overflow-x: auto
+    }
+
+    .swagger-ui .overflow-y-visible-l {
+        overflow-y: visible
+    }
+
+    .swagger-ui .overflow-y-hidden-l {
+        overflow-y: hidden
+    }
+
+    .swagger-ui .overflow-y-scroll-l {
+        overflow-y: scroll
+    }
+
+    .swagger-ui .overflow-y-auto-l {
+        overflow-y: auto
+    }
+}
+
+.swagger-ui .static {
+    position: static
+}
+
+.swagger-ui .relative {
+    position: relative
+}
+
+.swagger-ui .absolute {
+    position: absolute
+}
+
+.swagger-ui .fixed {
+    position: fixed
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .static-ns {
+        position:static
+    }
+
+    .swagger-ui .relative-ns {
+        position: relative
+    }
+
+    .swagger-ui .absolute-ns {
+        position: absolute
+    }
+
+    .swagger-ui .fixed-ns {
+        position: fixed
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .static-m {
+        position:static
+    }
+
+    .swagger-ui .relative-m {
+        position: relative
+    }
+
+    .swagger-ui .absolute-m {
+        position: absolute
+    }
+
+    .swagger-ui .fixed-m {
+        position: fixed
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .static-l {
+        position:static
+    }
+
+    .swagger-ui .relative-l {
+        position: relative
+    }
+
+    .swagger-ui .absolute-l {
+        position: absolute
+    }
+
+    .swagger-ui .fixed-l {
+        position: fixed
+    }
+}
+
+.swagger-ui .o-100 {
+    opacity: 1
+}
+
+.swagger-ui .o-90 {
+    opacity: .9
+}
+
+.swagger-ui .o-80 {
+    opacity: .8
+}
+
+.swagger-ui .o-70 {
+    opacity: .7
+}
+
+.swagger-ui .o-60 {
+    opacity: .6
+}
+
+.swagger-ui .o-50 {
+    opacity: .5
+}
+
+.swagger-ui .o-40 {
+    opacity: .4
+}
+
+.swagger-ui .o-30 {
+    opacity: .3
+}
+
+.swagger-ui .o-20 {
+    opacity: .2
+}
+
+.swagger-ui .o-10 {
+    opacity: .1
+}
+
+.swagger-ui .o-05 {
+    opacity: .05
+}
+
+.swagger-ui .o-025 {
+    opacity: .025
+}
+
+.swagger-ui .o-0 {
+    opacity: 0
+}
+
+.swagger-ui .rotate-45 {
+    transform: rotate(45deg)
+}
+
+.swagger-ui .rotate-90 {
+    transform: rotate(90deg)
+}
+
+.swagger-ui .rotate-135 {
+    transform: rotate(135deg)
+}
+
+.swagger-ui .rotate-180 {
+    transform: rotate(180deg)
+}
+
+.swagger-ui .rotate-225 {
+    transform: rotate(225deg)
+}
+
+.swagger-ui .rotate-270 {
+    transform: rotate(270deg)
+}
+
+.swagger-ui .rotate-315 {
+    transform: rotate(315deg)
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .rotate-45-ns {
+        transform:rotate(45deg)
+    }
+
+    .swagger-ui .rotate-90-ns {
+        transform: rotate(90deg)
+    }
+
+    .swagger-ui .rotate-135-ns {
+        transform: rotate(135deg)
+    }
+
+    .swagger-ui .rotate-180-ns {
+        transform: rotate(180deg)
+    }
+
+    .swagger-ui .rotate-225-ns {
+        transform: rotate(225deg)
+    }
+
+    .swagger-ui .rotate-270-ns {
+        transform: rotate(270deg)
+    }
+
+    .swagger-ui .rotate-315-ns {
+        transform: rotate(315deg)
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .rotate-45-m {
+        transform:rotate(45deg)
+    }
+
+    .swagger-ui .rotate-90-m {
+        transform: rotate(90deg)
+    }
+
+    .swagger-ui .rotate-135-m {
+        transform: rotate(135deg)
+    }
+
+    .swagger-ui .rotate-180-m {
+        transform: rotate(180deg)
+    }
+
+    .swagger-ui .rotate-225-m {
+        transform: rotate(225deg)
+    }
+
+    .swagger-ui .rotate-270-m {
+        transform: rotate(270deg)
+    }
+
+    .swagger-ui .rotate-315-m {
+        transform: rotate(315deg)
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .rotate-45-l {
+        transform:rotate(45deg)
+    }
+
+    .swagger-ui .rotate-90-l {
+        transform: rotate(90deg)
+    }
+
+    .swagger-ui .rotate-135-l {
+        transform: rotate(135deg)
+    }
+
+    .swagger-ui .rotate-180-l {
+        transform: rotate(180deg)
+    }
+
+    .swagger-ui .rotate-225-l {
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
+
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+}
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+}
+
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+}
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+}
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+}
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+    background-color: #357edd
+}
+
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+}
+
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+}
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+}
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+}
+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+    padding-top: .25rem
+}
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+    padding-top: .5rem
+}
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+    padding-top: 1rem
+}
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+    padding-top: 2rem
+}
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+    padding-top: 4rem
+}
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+    padding-top: 8rem
+}
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+    padding-top: 16rem
+}
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+    padding-right: 0
+}
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+}
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+}
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+    padding-right: 1rem
+}
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+    padding-right: 2rem
+}
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+}
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+    padding-right: 16rem
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+    margin-top: .5rem
+}
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+}
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+}
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+}
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+}
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+}
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+    margin-top: 0
+}
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+    margin-top: .25rem
+}
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+    margin-top: .5rem
+}
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+    margin-top: 1rem
+}
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+    margin-top: 2rem
+}
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+    margin-top: 4rem
+}
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+    margin-top: 8rem
+}
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+    margin-top: 16rem
+}
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+    margin-right: 0
+}
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+    margin-right: .25rem
+}
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+    margin-right: .5rem
+}
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+    margin-right: 1rem
+}
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+    margin-right: 2rem
+}
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+}
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+    margin-right: 8rem
+}
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+    margin-right: 16rem
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .pa0-ns {
+        padding:0
+    }
+
+    .swagger-ui .pa1-ns {
+        padding: .25rem
+    }
+
+    .swagger-ui .pa2-ns {
+        padding: .5rem
+    }
+
+    .swagger-ui .pa3-ns {
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+    }
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+    .swagger-ui .pa4-ns {
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+    }
+
+    .swagger-ui .pa5-ns {
+        padding: 4rem
+    }
+
+    .swagger-ui .pa6-ns {
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+    }
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+    .swagger-ui .pa7-ns {
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+    }
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+    .swagger-ui .pl0-ns {
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+    }
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+    .swagger-ui .pl1-ns {
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+    }
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+    .swagger-ui .pl2-ns {
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+    .swagger-ui .pl3-ns {
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+    }
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+    .swagger-ui .pl4-ns {
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+    }
+
+    .swagger-ui .pl5-ns {
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+    .swagger-ui .pl6-ns {
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+    .swagger-ui .pl7-ns {
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+    }
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+    .swagger-ui .pr0-ns {
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+    }
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+    .swagger-ui .pr1-ns {
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+    }
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+    .swagger-ui .pr2-ns {
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+    .swagger-ui .pr3-ns {
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+    }
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+    .swagger-ui .pr4-ns {
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+    .swagger-ui .pr5-ns {
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+    }
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+    .swagger-ui .pr6-ns {
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+    .swagger-ui .pr7-ns {
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+    }
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+    .swagger-ui .pb0-ns {
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+    }
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+    .swagger-ui .pb1-ns {
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+    }
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+    .swagger-ui .pb2-ns {
+        padding-bottom: .5rem
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+    .swagger-ui .pb3-ns {
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+    .swagger-ui .pb4-ns {
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+    .swagger-ui .pb5-ns {
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+    .swagger-ui .pb6-ns {
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+    }
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+    .swagger-ui .pb7-ns {
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+    }
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+    .swagger-ui .pt0-ns {
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+    .swagger-ui .pt1-ns {
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+    .swagger-ui .pt2-ns {
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+    .swagger-ui .pt3-ns {
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+    }
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+    .swagger-ui .pt4-ns {
+        padding-top: 2rem
+    }
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+    .swagger-ui .pt5-ns {
+        padding-top: 4rem
+    }
+
+    .swagger-ui .pt6-ns {
+        padding-top: 8rem
+    }
+
+    .swagger-ui .pt7-ns {
+        padding-top: 16rem
+    }
+
+    .swagger-ui .pv0-ns {
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+        padding-top: 0
+    }
+
+    .swagger-ui .pv1-ns {
+        padding-bottom: .25rem;
+        padding-top: .25rem
+    }
+
+    .swagger-ui .pv2-ns {
+        padding-bottom: .5rem;
+        padding-top: .5rem
+    }
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+    .swagger-ui .pv3-ns {
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+        padding-top: 1rem
+    }
+
+    .swagger-ui .pv4-ns {
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+        padding-top: 2rem
+    }
+
+    .swagger-ui .pv5-ns {
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+        padding-top: 4rem
+    }
+
+    .swagger-ui .pv6-ns {
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+        padding-top: 8rem
+    }
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+    .swagger-ui .pv7-ns {
+        padding-bottom: 16rem;
+        padding-top: 16rem
+    }
+
+    .swagger-ui .ph0-ns {
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+        padding-right: 0
+    }
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+    .swagger-ui .ph1-ns {
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+        padding-right: .25rem
+    }
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+    .swagger-ui .ph2-ns {
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+        padding-right: .5rem
+    }
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+    .swagger-ui .ph3-ns {
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+        padding-right: 1rem
+    }
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+    .swagger-ui .ph4-ns {
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+        padding-right: 2rem
+    }
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+    .swagger-ui .ph5-ns {
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+        padding-right: 4rem
+    }
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+    .swagger-ui .ph6-ns {
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+        padding-right: 8rem
+    }
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+    .swagger-ui .ph7-ns {
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+        padding-right: 16rem
+    }
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+    .swagger-ui .ma0-ns {
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+    }
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+    .swagger-ui .ma1-ns {
+        margin: .25rem
+    }
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+    .swagger-ui .ma2-ns {
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+    .swagger-ui .ma3-ns {
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+    .swagger-ui .ma4-ns {
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+    .swagger-ui .ma5-ns {
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+    }
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+    .swagger-ui .ma6-ns {
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+    .swagger-ui .ma7-ns {
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+    .swagger-ui .ml0-ns {
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+    .swagger-ui .ml1-ns {
+        margin-left: .25rem
+    }
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+    .swagger-ui .ml2-ns {
+        margin-left: .5rem
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+    .swagger-ui .ml3-ns {
+        margin-left: 1rem
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+    .swagger-ui .ml4-ns {
+        margin-left: 2rem
+    }
+
+    .swagger-ui .ml5-ns {
+        margin-left: 4rem
+    }
+
+    .swagger-ui .ml6-ns {
+        margin-left: 8rem
+    }
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+    .swagger-ui .ml7-ns {
+        margin-left: 16rem
+    }
+
+    .swagger-ui .mr0-ns {
+        margin-right: 0
+    }
+
+    .swagger-ui .mr1-ns {
+        margin-right: .25rem
+    }
+
+    .swagger-ui .mr2-ns {
+        margin-right: .5rem
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+    .swagger-ui .mr3-ns {
+        margin-right: 1rem
+    }
+
+    .swagger-ui .mr4-ns {
+        margin-right: 2rem
+    }
+
+    .swagger-ui .mr5-ns {
+        margin-right: 4rem
+    }
+
+    .swagger-ui .mr6-ns {
+        margin-right: 8rem
+    }
+
+    .swagger-ui .mr7-ns {
+        margin-right: 16rem
+    }
+
+    .swagger-ui .mb0-ns {
+        margin-bottom: 0
+    }
+
+    .swagger-ui .mb1-ns {
+        margin-bottom: .25rem
+    }
+
+    .swagger-ui .mb2-ns {
+        margin-bottom: .5rem
+    }
+
+    .swagger-ui .mb3-ns {
+        margin-bottom: 1rem
+    }
+
+    .swagger-ui .mb4-ns {
+        margin-bottom: 2rem
+    }
+
+    .swagger-ui .mb5-ns {
+        margin-bottom: 4rem
+    }
+
+    .swagger-ui .mb6-ns {
+        margin-bottom: 8rem
+    }
+
+    .swagger-ui .mb7-ns {
+        margin-bottom: 16rem
+    }
+
+    .swagger-ui .mt0-ns {
+        margin-top: 0
+    }
+
+    .swagger-ui .mt1-ns {
+        margin-top: .25rem
+    }
+
+    .swagger-ui .mt2-ns {
+        margin-top: .5rem
+    }
+
+    .swagger-ui .mt3-ns {
+        margin-top: 1rem
+    }
+
+    .swagger-ui .mt4-ns {
+        margin-top: 2rem
+    }
+
+    .swagger-ui .mt5-ns {
+        margin-top: 4rem
+    }
+
+    .swagger-ui .mt6-ns {
+        margin-top: 8rem
+    }
+
+    .swagger-ui .mt7-ns {
+        margin-top: 16rem
+    }
+
+    .swagger-ui .mv0-ns {
+        margin-bottom: 0;
+        margin-top: 0
+    }
+
+    .swagger-ui .mv1-ns {
+        margin-bottom: .25rem;
+        margin-top: .25rem
+    }
+
+    .swagger-ui .mv2-ns {
+        margin-bottom: .5rem;
+        margin-top: .5rem
+    }
+
+    .swagger-ui .mv3-ns {
+        margin-bottom: 1rem;
+        margin-top: 1rem
+    }
+
+    .swagger-ui .mv4-ns {
+        margin-bottom: 2rem;
+        margin-top: 2rem
+    }
+
+    .swagger-ui .mv5-ns {
+        margin-bottom: 4rem;
+        margin-top: 4rem
+    }
+
+    .swagger-ui .mv6-ns {
+        margin-bottom: 8rem;
+        margin-top: 8rem
+    }
+
+    .swagger-ui .mv7-ns {
+        margin-bottom: 16rem;
+        margin-top: 16rem
+    }
+
+    .swagger-ui .mh0-ns {
+        margin-left: 0;
+        margin-right: 0
+    }
+
+    .swagger-ui .mh1-ns {
+        margin-left: .25rem;
+        margin-right: .25rem
+    }
+
+    .swagger-ui .mh2-ns {
+        margin-left: .5rem;
+        margin-right: .5rem
+    }
+
+    .swagger-ui .mh3-ns {
+        margin-left: 1rem;
+        margin-right: 1rem
+    }
+
+    .swagger-ui .mh4-ns {
+        margin-left: 2rem;
+        margin-right: 2rem
+    }
+
+    .swagger-ui .mh5-ns {
+        margin-left: 4rem;
+        margin-right: 4rem
+    }
+
+    .swagger-ui .mh6-ns {
+        margin-left: 8rem;
+        margin-right: 8rem
+    }
+
+    .swagger-ui .mh7-ns {
+        margin-left: 16rem;
+        margin-right: 16rem
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .pa0-m {
+        padding:0
+    }
+
+    .swagger-ui .pa1-m {
+        padding: .25rem
+    }
+
+    .swagger-ui .pa2-m {
+        padding: .5rem
+    }
+
+    .swagger-ui .pa3-m {
+        padding: 1rem
+    }
+
+    .swagger-ui .pa4-m {
+        padding: 2rem
+    }
+
+    .swagger-ui .pa5-m {
+        padding: 4rem
+    }
+
+    .swagger-ui .pa6-m {
+        padding: 8rem
+    }
+
+    .swagger-ui .pa7-m {
+        padding: 16rem
+    }
+
+    .swagger-ui .pl0-m {
+        padding-left: 0
+    }
+
+    .swagger-ui .pl1-m {
+        padding-left: .25rem
+    }
+
+    .swagger-ui .pl2-m {
+        padding-left: .5rem
+    }
+
+    .swagger-ui .pl3-m {
+        padding-left: 1rem
+    }
+
+    .swagger-ui .pl4-m {
+        padding-left: 2rem
+    }
+
+    .swagger-ui .pl5-m {
+        padding-left: 4rem
+    }
+
+    .swagger-ui .pl6-m {
+        padding-left: 8rem
+    }
+
+    .swagger-ui .pl7-m {
+        padding-left: 16rem
+    }
+
+    .swagger-ui .pr0-m {
+        padding-right: 0
+    }
+
+    .swagger-ui .pr1-m {
+        padding-right: .25rem
+    }
+
+    .swagger-ui .pr2-m {
+        padding-right: .5rem
+    }
+
+    .swagger-ui .pr3-m {
+        padding-right: 1rem
+    }
+
+    .swagger-ui .pr4-m {
+        padding-right: 2rem
+    }
+
+    .swagger-ui .pr5-m {
+        padding-right: 4rem
+    }
+
+    .swagger-ui .pr6-m {
+        padding-right: 8rem
+    }
+
+    .swagger-ui .pr7-m {
+        padding-right: 16rem
+    }
+
+    .swagger-ui .pb0-m {
+        padding-bottom: 0
+    }
+
+    .swagger-ui .pb1-m {
+        padding-bottom: .25rem
+    }
+
+    .swagger-ui .pb2-m {
+        padding-bottom: .5rem
+    }
+
+    .swagger-ui .pb3-m {
+        padding-bottom: 1rem
+    }
+
+    .swagger-ui .pb4-m {
+        padding-bottom: 2rem
+    }
+
+    .swagger-ui .pb5-m {
+        padding-bottom: 4rem
+    }
+
+    .swagger-ui .pb6-m {
+        padding-bottom: 8rem
+    }
+
+    .swagger-ui .pb7-m {
+        padding-bottom: 16rem
+    }
+
+    .swagger-ui .pt0-m {
+        padding-top: 0
+    }
+
+    .swagger-ui .pt1-m {
+        padding-top: .25rem
+    }
+
+    .swagger-ui .pt2-m {
+        padding-top: .5rem
+    }
+
+    .swagger-ui .pt3-m {
+        padding-top: 1rem
+    }
+
+    .swagger-ui .pt4-m {
+        padding-top: 2rem
+    }
+
+    .swagger-ui .pt5-m {
+        padding-top: 4rem
+    }
+
+    .swagger-ui .pt6-m {
+        padding-top: 8rem
+    }
+
+    .swagger-ui .pt7-m {
+        padding-top: 16rem
+    }
+
+    .swagger-ui .pv0-m {
+        padding-bottom: 0;
+        padding-top: 0
+    }
+
+    .swagger-ui .pv1-m {
+        padding-bottom: .25rem;
+        padding-top: .25rem
+    }
+
+    .swagger-ui .pv2-m {
+        padding-bottom: .5rem;
+        padding-top: .5rem
+    }
+
+    .swagger-ui .pv3-m {
+        padding-bottom: 1rem;
+        padding-top: 1rem
+    }
+
+    .swagger-ui .pv4-m {
+        padding-bottom: 2rem;
+        padding-top: 2rem
+    }
+
+    .swagger-ui .pv5-m {
+        padding-bottom: 4rem;
+        padding-top: 4rem
+    }
+
+    .swagger-ui .pv6-m {
+        padding-bottom: 8rem;
+        padding-top: 8rem
+    }
+
+    .swagger-ui .pv7-m {
+        padding-bottom: 16rem;
+        padding-top: 16rem
+    }
+
+    .swagger-ui .ph0-m {
+        padding-left: 0;
+        padding-right: 0
+    }
+
+    .swagger-ui .ph1-m {
+        padding-left: .25rem;
+        padding-right: .25rem
+    }
+
+    .swagger-ui .ph2-m {
+        padding-left: .5rem;
+        padding-right: .5rem
+    }
+
+    .swagger-ui .ph3-m {
+        padding-left: 1rem;
+        padding-right: 1rem
+    }
+
+    .swagger-ui .ph4-m {
+        padding-left: 2rem;
+        padding-right: 2rem
+    }
+
+    .swagger-ui .ph5-m {
+        padding-left: 4rem;
+        padding-right: 4rem
+    }
+
+    .swagger-ui .ph6-m {
+        padding-left: 8rem;
+        padding-right: 8rem
+    }
+
+    .swagger-ui .ph7-m {
+        padding-left: 16rem;
+        padding-right: 16rem
+    }
+
+    .swagger-ui .ma0-m {
+        margin: 0
+    }
+
+    .swagger-ui .ma1-m {
+        margin: .25rem
+    }
+
+    .swagger-ui .ma2-m {
+        margin: .5rem
+    }
+
+    .swagger-ui .ma3-m {
+        margin: 1rem
+    }
+
+    .swagger-ui .ma4-m {
+        margin: 2rem
+    }
+
+    .swagger-ui .ma5-m {
+        margin: 4rem
+    }
+
+    .swagger-ui .ma6-m {
+        margin: 8rem
+    }
+
+    .swagger-ui .ma7-m {
+        margin: 16rem
+    }
+
+    .swagger-ui .ml0-m {
+        margin-left: 0
+    }
+
+    .swagger-ui .ml1-m {
+        margin-left: .25rem
+    }
+
+    .swagger-ui .ml2-m {
+        margin-left: .5rem
+    }
+
+    .swagger-ui .ml3-m {
+        margin-left: 1rem
+    }
+
+    .swagger-ui .ml4-m {
+        margin-left: 2rem
+    }
+
+    .swagger-ui .ml5-m {
+        margin-left: 4rem
+    }
+
+    .swagger-ui .ml6-m {
+        margin-left: 8rem
+    }
+
+    .swagger-ui .ml7-m {
+        margin-left: 16rem
+    }
+
+    .swagger-ui .mr0-m {
+        margin-right: 0
+    }
+
+    .swagger-ui .mr1-m {
+        margin-right: .25rem
+    }
+
+    .swagger-ui .mr2-m {
+        margin-right: .5rem
+    }
+
+    .swagger-ui .mr3-m {
+        margin-right: 1rem
+    }
+
+    .swagger-ui .mr4-m {
+        margin-right: 2rem
+    }
+
+    .swagger-ui .mr5-m {
+        margin-right: 4rem
+    }
+
+    .swagger-ui .mr6-m {
+        margin-right: 8rem
+    }
+
+    .swagger-ui .mr7-m {
+        margin-right: 16rem
+    }
+
+    .swagger-ui .mb0-m {
+        margin-bottom: 0
+    }
+
+    .swagger-ui .mb1-m {
+        margin-bottom: .25rem
+    }
+
+    .swagger-ui .mb2-m {
+        margin-bottom: .5rem
+    }
+
+    .swagger-ui .mb3-m {
+        margin-bottom: 1rem
+    }
+
+    .swagger-ui .mb4-m {
+        margin-bottom: 2rem
+    }
+
+    .swagger-ui .mb5-m {
+        margin-bottom: 4rem
+    }
+
+    .swagger-ui .mb6-m {
+        margin-bottom: 8rem
+    }
+
+    .swagger-ui .mb7-m {
+        margin-bottom: 16rem
+    }
+
+    .swagger-ui .mt0-m {
+        margin-top: 0
+    }
+
+    .swagger-ui .mt1-m {
+        margin-top: .25rem
+    }
+
+    .swagger-ui .mt2-m {
+        margin-top: .5rem
+    }
+
+    .swagger-ui .mt3-m {
+        margin-top: 1rem
+    }
+
+    .swagger-ui .mt4-m {
+        margin-top: 2rem
+    }
+
+    .swagger-ui .mt5-m {
+        margin-top: 4rem
+    }
+
+    .swagger-ui .mt6-m {
+        margin-top: 8rem
+    }
+
+    .swagger-ui .mt7-m {
+        margin-top: 16rem
+    }
+
+    .swagger-ui .mv0-m {
+        margin-bottom: 0;
+        margin-top: 0
+    }
+
+    .swagger-ui .mv1-m {
+        margin-bottom: .25rem;
+        margin-top: .25rem
+    }
+
+    .swagger-ui .mv2-m {
+        margin-bottom: .5rem;
+        margin-top: .5rem
+    }
+
+    .swagger-ui .mv3-m {
+        margin-bottom: 1rem;
+        margin-top: 1rem
+    }
+
+    .swagger-ui .mv4-m {
+        margin-bottom: 2rem;
+        margin-top: 2rem
+    }
+
+    .swagger-ui .mv5-m {
+        margin-bottom: 4rem;
+        margin-top: 4rem
+    }
+
+    .swagger-ui .mv6-m {
+        margin-bottom: 8rem;
+        margin-top: 8rem
+    }
+
+    .swagger-ui .mv7-m {
+        margin-bottom: 16rem;
+        margin-top: 16rem
+    }
+
+    .swagger-ui .mh0-m {
+        margin-left: 0;
+        margin-right: 0
+    }
+
+    .swagger-ui .mh1-m {
+        margin-left: .25rem;
+        margin-right: .25rem
+    }
+
+    .swagger-ui .mh2-m {
+        margin-left: .5rem;
+        margin-right: .5rem
+    }
+
+    .swagger-ui .mh3-m {
+        margin-left: 1rem;
+        margin-right: 1rem
+    }
+
+    .swagger-ui .mh4-m {
+        margin-left: 2rem;
+        margin-right: 2rem
+    }
+
+    .swagger-ui .mh5-m {
+        margin-left: 4rem;
+        margin-right: 4rem
+    }
+
+    .swagger-ui .mh6-m {
+        margin-left: 8rem;
+        margin-right: 8rem
+    }
+
+    .swagger-ui .mh7-m {
+        margin-left: 16rem;
+        margin-right: 16rem
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .pa0-l {
+        padding:0
+    }
+
+    .swagger-ui .pa1-l {
+        padding: .25rem
+    }
+
+    .swagger-ui .pa2-l {
+        padding: .5rem
+    }
+
+    .swagger-ui .pa3-l {
+        padding: 1rem
+    }
+
+    .swagger-ui .pa4-l {
+        padding: 2rem
+    }
+
+    .swagger-ui .pa5-l {
+        padding: 4rem
+    }
+
+    .swagger-ui .pa6-l {
+        padding: 8rem
+    }
+
+    .swagger-ui .pa7-l {
+        padding: 16rem
+    }
+
+    .swagger-ui .pl0-l {
+        padding-left: 0
+    }
+
+    .swagger-ui .pl1-l {
+        padding-left: .25rem
+    }
+
+    .swagger-ui .pl2-l {
+        padding-left: .5rem
+    }
+
+    .swagger-ui .pl3-l {
+        padding-left: 1rem
+    }
+
+    .swagger-ui .pl4-l {
+        padding-left: 2rem
+    }
+
+    .swagger-ui .pl5-l {
+        padding-left: 4rem
+    }
+
+    .swagger-ui .pl6-l {
+        padding-left: 8rem
+    }
+
+    .swagger-ui .pl7-l {
+        padding-left: 16rem
+    }
+
+    .swagger-ui .pr0-l {
+        padding-right: 0
+    }
+
+    .swagger-ui .pr1-l {
+        padding-right: .25rem
+    }
+
+    .swagger-ui .pr2-l {
+        padding-right: .5rem
+    }
+
+    .swagger-ui .pr3-l {
+        padding-right: 1rem
+    }
+
+    .swagger-ui .pr4-l {
+        padding-right: 2rem
+    }
+
+    .swagger-ui .pr5-l {
+        padding-right: 4rem
+    }
+
+    .swagger-ui .pr6-l {
+        padding-right: 8rem
+    }
+
+    .swagger-ui .pr7-l {
+        padding-right: 16rem
+    }
+
+    .swagger-ui .pb0-l {
+        padding-bottom: 0
+    }
+
+    .swagger-ui .pb1-l {
+        padding-bottom: .25rem
+    }
+
+    .swagger-ui .pb2-l {
+        padding-bottom: .5rem
+    }
+
+    .swagger-ui .pb3-l {
+        padding-bottom: 1rem
+    }
+
+    .swagger-ui .pb4-l {
+        padding-bottom: 2rem
+    }
+
+    .swagger-ui .pb5-l {
+        padding-bottom: 4rem
+    }
+
+    .swagger-ui .pb6-l {
+        padding-bottom: 8rem
+    }
+
+    .swagger-ui .pb7-l {
+        padding-bottom: 16rem
+    }
+
+    .swagger-ui .pt0-l {
+        padding-top: 0
+    }
+
+    .swagger-ui .pt1-l {
+        padding-top: .25rem
+    }
+
+    .swagger-ui .pt2-l {
+        padding-top: .5rem
+    }
+
+    .swagger-ui .pt3-l {
+        padding-top: 1rem
+    }
+
+    .swagger-ui .pt4-l {
+        padding-top: 2rem
+    }
+
+    .swagger-ui .pt5-l {
+        padding-top: 4rem
+    }
+
+    .swagger-ui .pt6-l {
+        padding-top: 8rem
+    }
+
+    .swagger-ui .pt7-l {
+        padding-top: 16rem
+    }
+
+    .swagger-ui .pv0-l {
+        padding-bottom: 0;
+        padding-top: 0
+    }
+
+    .swagger-ui .pv1-l {
+        padding-bottom: .25rem;
+        padding-top: .25rem
+    }
+
+    .swagger-ui .pv2-l {
+        padding-bottom: .5rem;
+        padding-top: .5rem
+    }
+
+    .swagger-ui .pv3-l {
+        padding-bottom: 1rem;
+        padding-top: 1rem
+    }
+
+    .swagger-ui .pv4-l {
+        padding-bottom: 2rem;
+        padding-top: 2rem
+    }
+
+    .swagger-ui .pv5-l {
+        padding-bottom: 4rem;
+        padding-top: 4rem
+    }
+
+    .swagger-ui .pv6-l {
+        padding-bottom: 8rem;
+        padding-top: 8rem
+    }
+
+    .swagger-ui .pv7-l {
+        padding-bottom: 16rem;
+        padding-top: 16rem
+    }
+
+    .swagger-ui .ph0-l {
+        padding-left: 0;
+        padding-right: 0
+    }
+
+    .swagger-ui .ph1-l {
+        padding-left: .25rem;
+        padding-right: .25rem
+    }
+
+    .swagger-ui .ph2-l {
+        padding-left: .5rem;
+        padding-right: .5rem
+    }
+
+    .swagger-ui .ph3-l {
+        padding-left: 1rem;
+        padding-right: 1rem
+    }
+
+    .swagger-ui .ph4-l {
+        padding-left: 2rem;
+        padding-right: 2rem
+    }
+
+    .swagger-ui .ph5-l {
+        padding-left: 4rem;
+        padding-right: 4rem
+    }
+
+    .swagger-ui .ph6-l {
+        padding-left: 8rem;
+        padding-right: 8rem
+    }
+
+    .swagger-ui .ph7-l {
+        padding-left: 16rem;
+        padding-right: 16rem
+    }
+
+    .swagger-ui .ma0-l {
+        margin: 0
+    }
+
+    .swagger-ui .ma1-l {
+        margin: .25rem
+    }
+
+    .swagger-ui .ma2-l {
+        margin: .5rem
+    }
+
+    .swagger-ui .ma3-l {
+        margin: 1rem
+    }
+
+    .swagger-ui .ma4-l {
+        margin: 2rem
+    }
+
+    .swagger-ui .ma5-l {
+        margin: 4rem
+    }
+
+    .swagger-ui .ma6-l {
+        margin: 8rem
+    }
+
+    .swagger-ui .ma7-l {
+        margin: 16rem
+    }
+
+    .swagger-ui .ml0-l {
+        margin-left: 0
+    }
+
+    .swagger-ui .ml1-l {
+        margin-left: .25rem
+    }
+
+    .swagger-ui .ml2-l {
+        margin-left: .5rem
+    }
+
+    .swagger-ui .ml3-l {
+        margin-left: 1rem
+    }
+
+    .swagger-ui .ml4-l {
+        margin-left: 2rem
+    }
+
+    .swagger-ui .ml5-l {
+        margin-left: 4rem
+    }
+
+    .swagger-ui .ml6-l {
+        margin-left: 8rem
+    }
+
+    .swagger-ui .ml7-l {
+        margin-left: 16rem
+    }
+
+    .swagger-ui .mr0-l {
+        margin-right: 0
+    }
+
+    .swagger-ui .mr1-l {
+        margin-right: .25rem
+    }
+
+    .swagger-ui .mr2-l {
+        margin-right: .5rem
+    }
+
+    .swagger-ui .mr3-l {
+        margin-right: 1rem
+    }
+
+    .swagger-ui .mr4-l {
+        margin-right: 2rem
+    }
+
+    .swagger-ui .mr5-l {
+        margin-right: 4rem
+    }
+
+    .swagger-ui .mr6-l {
+        margin-right: 8rem
+    }
+
+    .swagger-ui .mr7-l {
+        margin-right: 16rem
+    }
+
+    .swagger-ui .mb0-l {
+        margin-bottom: 0
+    }
+
+    .swagger-ui .mb1-l {
+        margin-bottom: .25rem
+    }
+
+    .swagger-ui .mb2-l {
+        margin-bottom: .5rem
+    }
+
+    .swagger-ui .mb3-l {
+        margin-bottom: 1rem
+    }
+
+    .swagger-ui .mb4-l {
+        margin-bottom: 2rem
+    }
+
+    .swagger-ui .mb5-l {
+        margin-bottom: 4rem
+    }
+
+    .swagger-ui .mb6-l {
+        margin-bottom: 8rem
+    }
+
+    .swagger-ui .mb7-l {
+        margin-bottom: 16rem
+    }
+
+    .swagger-ui .mt0-l {
+        margin-top: 0
+    }
+
+    .swagger-ui .mt1-l {
+        margin-top: .25rem
+    }
+
+    .swagger-ui .mt2-l {
+        margin-top: .5rem
+    }
+
+    .swagger-ui .mt3-l {
+        margin-top: 1rem
+    }
+
+    .swagger-ui .mt4-l {
+        margin-top: 2rem
+    }
+
+    .swagger-ui .mt5-l {
+        margin-top: 4rem
+    }
+
+    .swagger-ui .mt6-l {
+        margin-top: 8rem
+    }
+
+    .swagger-ui .mt7-l {
+        margin-top: 16rem
+    }
+
+    .swagger-ui .mv0-l {
+        margin-bottom: 0;
+        margin-top: 0
+    }
+
+    .swagger-ui .mv1-l {
+        margin-bottom: .25rem;
+        margin-top: .25rem
+    }
+
+    .swagger-ui .mv2-l {
+        margin-bottom: .5rem;
+        margin-top: .5rem
+    }
+
+    .swagger-ui .mv3-l {
+        margin-bottom: 1rem;
+        margin-top: 1rem
+    }
+
+    .swagger-ui .mv4-l {
+        margin-bottom: 2rem;
+        margin-top: 2rem
+    }
+
+    .swagger-ui .mv5-l {
+        margin-bottom: 4rem;
+        margin-top: 4rem
+    }
+
+    .swagger-ui .mv6-l {
+        margin-bottom: 8rem;
+        margin-top: 8rem
+    }
+
+    .swagger-ui .mv7-l {
+        margin-bottom: 16rem;
+        margin-top: 16rem
+    }
+
+    .swagger-ui .mh0-l {
+        margin-left: 0;
+        margin-right: 0
+    }
+
+    .swagger-ui .mh1-l {
+        margin-left: .25rem;
+        margin-right: .25rem
+    }
+
+    .swagger-ui .mh2-l {
+        margin-left: .5rem;
+        margin-right: .5rem
+    }
+
+    .swagger-ui .mh3-l {
+        margin-left: 1rem;
+        margin-right: 1rem
+    }
+
+    .swagger-ui .mh4-l {
+        margin-left: 2rem;
+        margin-right: 2rem
+    }
+
+    .swagger-ui .mh5-l {
+        margin-left: 4rem;
+        margin-right: 4rem
+    }
+
+    .swagger-ui .mh6-l {
+        margin-left: 8rem;
+        margin-right: 8rem
+    }
+
+    .swagger-ui .mh7-l {
+        margin-left: 16rem;
+        margin-right: 16rem
+    }
+}
+
+.swagger-ui .na1 {
+    margin: -.25rem
+}
+
+.swagger-ui .na2 {
+    margin: -.5rem
+}
+
+.swagger-ui .na3 {
+    margin: -1rem
+}
+
+.swagger-ui .na4 {
+    margin: -2rem
+}
+
+.swagger-ui .na5 {
+    margin: -4rem
+}
+
+.swagger-ui .na6 {
+    margin: -8rem
+}
+
+.swagger-ui .na7 {
+    margin: -16rem
+}
+
+.swagger-ui .nl1 {
+    margin-left: -.25rem
+}
+
+.swagger-ui .nl2 {
+    margin-left: -.5rem
+}
+
+.swagger-ui .nl3 {
+    margin-left: -1rem
+}
+
+.swagger-ui .nl4 {
+    margin-left: -2rem
+}
+
+.swagger-ui .nl5 {
+    margin-left: -4rem
+}
+
+.swagger-ui .nl6 {
+    margin-left: -8rem
+}
+
+.swagger-ui .nl7 {
+    margin-left: -16rem
+}
+
+.swagger-ui .nr1 {
+    margin-right: -.25rem
+}
+
+.swagger-ui .nr2 {
+    margin-right: -.5rem
+}
+
+.swagger-ui .nr3 {
+    margin-right: -1rem
+}
+
+.swagger-ui .nr4 {
+    margin-right: -2rem
+}
+
+.swagger-ui .nr5 {
+    margin-right: -4rem
+}
+
+.swagger-ui .nr6 {
+    margin-right: -8rem
+}
+
+.swagger-ui .nr7 {
+    margin-right: -16rem
+}
+
+.swagger-ui .nb1 {
+    margin-bottom: -.25rem
+}
+
+.swagger-ui .nb2 {
+    margin-bottom: -.5rem
+}
+
+.swagger-ui .nb3 {
+    margin-bottom: -1rem
+}
+
+.swagger-ui .nb4 {
+    margin-bottom: -2rem
+}
+
+.swagger-ui .nb5 {
+    margin-bottom: -4rem
+}
+
+.swagger-ui .nb6 {
+    margin-bottom: -8rem
+}
+
+.swagger-ui .nb7 {
+    margin-bottom: -16rem
+}
+
+.swagger-ui .nt1 {
+    margin-top: -.25rem
+}
+
+.swagger-ui .nt2 {
+    margin-top: -.5rem
+}
+
+.swagger-ui .nt3 {
+    margin-top: -1rem
+}
+
+.swagger-ui .nt4 {
+    margin-top: -2rem
+}
+
+.swagger-ui .nt5 {
+    margin-top: -4rem
+}
+
+.swagger-ui .nt6 {
+    margin-top: -8rem
+}
+
+.swagger-ui .nt7 {
+    margin-top: -16rem
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .na1-ns {
+        margin:-.25rem
+    }
+
+    .swagger-ui .na2-ns {
+        margin: -.5rem
+    }
+
+    .swagger-ui .na3-ns {
+        margin: -1rem
+    }
+
+    .swagger-ui .na4-ns {
+        margin: -2rem
+    }
+
+    .swagger-ui .na5-ns {
+        margin: -4rem
+    }
+
+    .swagger-ui .na6-ns {
+        margin: -8rem
+    }
+
+    .swagger-ui .na7-ns {
+        margin: -16rem
+    }
+
+    .swagger-ui .nl1-ns {
+        margin-left: -.25rem
+    }
+
+    .swagger-ui .nl2-ns {
+        margin-left: -.5rem
+    }
+
+    .swagger-ui .nl3-ns {
+        margin-left: -1rem
+    }
+
+    .swagger-ui .nl4-ns {
+        margin-left: -2rem
+    }
+
+    .swagger-ui .nl5-ns {
+        margin-left: -4rem
+    }
+
+    .swagger-ui .nl6-ns {
+        margin-left: -8rem
+    }
+
+    .swagger-ui .nl7-ns {
+        margin-left: -16rem
+    }
+
+    .swagger-ui .nr1-ns {
+        margin-right: -.25rem
+    }
+
+    .swagger-ui .nr2-ns {
+        margin-right: -.5rem
+    }
+
+    .swagger-ui .nr3-ns {
+        margin-right: -1rem
+    }
+
+    .swagger-ui .nr4-ns {
+        margin-right: -2rem
+    }
+
+    .swagger-ui .nr5-ns {
+        margin-right: -4rem
+    }
+
+    .swagger-ui .nr6-ns {
+        margin-right: -8rem
+    }
+
+    .swagger-ui .nr7-ns {
+        margin-right: -16rem
+    }
+
+    .swagger-ui .nb1-ns {
+        margin-bottom: -.25rem
+    }
+
+    .swagger-ui .nb2-ns {
+        margin-bottom: -.5rem
+    }
+
+    .swagger-ui .nb3-ns {
+        margin-bottom: -1rem
+    }
+
+    .swagger-ui .nb4-ns {
+        margin-bottom: -2rem
+    }
+
+    .swagger-ui .nb5-ns {
+        margin-bottom: -4rem
+    }
+
+    .swagger-ui .nb6-ns {
+        margin-bottom: -8rem
+    }
+
+    .swagger-ui .nb7-ns {
+        margin-bottom: -16rem
+    }
+
+    .swagger-ui .nt1-ns {
+        margin-top: -.25rem
+    }
+
+    .swagger-ui .nt2-ns {
+        margin-top: -.5rem
+    }
+
+    .swagger-ui .nt3-ns {
+        margin-top: -1rem
+    }
+
+    .swagger-ui .nt4-ns {
+        margin-top: -2rem
+    }
+
+    .swagger-ui .nt5-ns {
+        margin-top: -4rem
+    }
+
+    .swagger-ui .nt6-ns {
+        margin-top: -8rem
+    }
+
+    .swagger-ui .nt7-ns {
+        margin-top: -16rem
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .na1-m {
+        margin:-.25rem
+    }
+
+    .swagger-ui .na2-m {
+        margin: -.5rem
+    }
+
+    .swagger-ui .na3-m {
+        margin: -1rem
+    }
+
+    .swagger-ui .na4-m {
+        margin: -2rem
+    }
+
+    .swagger-ui .na5-m {
+        margin: -4rem
+    }
+
+    .swagger-ui .na6-m {
+        margin: -8rem
+    }
+
+    .swagger-ui .na7-m {
+        margin: -16rem
+    }
+
+    .swagger-ui .nl1-m {
+        margin-left: -.25rem
+    }
+
+    .swagger-ui .nl2-m {
+        margin-left: -.5rem
+    }
+
+    .swagger-ui .nl3-m {
+        margin-left: -1rem
+    }
+
+    .swagger-ui .nl4-m {
+        margin-left: -2rem
+    }
+
+    .swagger-ui .nl5-m {
+        margin-left: -4rem
+    }
+
+    .swagger-ui .nl6-m {
+        margin-left: -8rem
+    }
+
+    .swagger-ui .nl7-m {
+        margin-left: -16rem
+    }
+
+    .swagger-ui .nr1-m {
+        margin-right: -.25rem
+    }
+
+    .swagger-ui .nr2-m {
+        margin-right: -.5rem
+    }
+
+    .swagger-ui .nr3-m {
+        margin-right: -1rem
+    }
+
+    .swagger-ui .nr4-m {
+        margin-right: -2rem
+    }
+
+    .swagger-ui .nr5-m {
+        margin-right: -4rem
+    }
+
+    .swagger-ui .nr6-m {
+        margin-right: -8rem
+    }
+
+    .swagger-ui .nr7-m {
+        margin-right: -16rem
+    }
+
+    .swagger-ui .nb1-m {
+        margin-bottom: -.25rem
+    }
+
+    .swagger-ui .nb2-m {
+        margin-bottom: -.5rem
+    }
+
+    .swagger-ui .nb3-m {
+        margin-bottom: -1rem
+    }
+
+    .swagger-ui .nb4-m {
+        margin-bottom: -2rem
+    }
+
+    .swagger-ui .nb5-m {
+        margin-bottom: -4rem
+    }
+
+    .swagger-ui .nb6-m {
+        margin-bottom: -8rem
+    }
+
+    .swagger-ui .nb7-m {
+        margin-bottom: -16rem
+    }
+
+    .swagger-ui .nt1-m {
+        margin-top: -.25rem
+    }
+
+    .swagger-ui .nt2-m {
+        margin-top: -.5rem
+    }
+
+    .swagger-ui .nt3-m {
+        margin-top: -1rem
+    }
+
+    .swagger-ui .nt4-m {
+        margin-top: -2rem
+    }
+
+    .swagger-ui .nt5-m {
+        margin-top: -4rem
+    }
+
+    .swagger-ui .nt6-m {
+        margin-top: -8rem
+    }
+
+    .swagger-ui .nt7-m {
+        margin-top: -16rem
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .na1-l {
+        margin:-.25rem
+    }
+
+    .swagger-ui .na2-l {
+        margin: -.5rem
+    }
+
+    .swagger-ui .na3-l {
+        margin: -1rem
+    }
+
+    .swagger-ui .na4-l {
+        margin: -2rem
+    }
+
+    .swagger-ui .na5-l {
+        margin: -4rem
+    }
+
+    .swagger-ui .na6-l {
+        margin: -8rem
+    }
+
+    .swagger-ui .na7-l {
+        margin: -16rem
+    }
+
+    .swagger-ui .nl1-l {
+        margin-left: -.25rem
+    }
+
+    .swagger-ui .nl2-l {
+        margin-left: -.5rem
+    }
+
+    .swagger-ui .nl3-l {
+        margin-left: -1rem
+    }
+
+    .swagger-ui .nl4-l {
+        margin-left: -2rem
+    }
+
+    .swagger-ui .nl5-l {
+        margin-left: -4rem
+    }
+
+    .swagger-ui .nl6-l {
+        margin-left: -8rem
+    }
+
+    .swagger-ui .nl7-l {
+        margin-left: -16rem
+    }
+
+    .swagger-ui .nr1-l {
+        margin-right: -.25rem
+    }
+
+    .swagger-ui .nr2-l {
+        margin-right: -.5rem
+    }
+
+    .swagger-ui .nr3-l {
+        margin-right: -1rem
+    }
+
+    .swagger-ui .nr4-l {
+        margin-right: -2rem
+    }
+
+    .swagger-ui .nr5-l {
+        margin-right: -4rem
+    }
+
+    .swagger-ui .nr6-l {
+        margin-right: -8rem
+    }
+
+    .swagger-ui .nr7-l {
+        margin-right: -16rem
+    }
+
+    .swagger-ui .nb1-l {
+        margin-bottom: -.25rem
+    }
+
+    .swagger-ui .nb2-l {
+        margin-bottom: -.5rem
+    }
+
+    .swagger-ui .nb3-l {
+        margin-bottom: -1rem
+    }
+
+    .swagger-ui .nb4-l {
+        margin-bottom: -2rem
+    }
+
+    .swagger-ui .nb5-l {
+        margin-bottom: -4rem
+    }
+
+    .swagger-ui .nb6-l {
+        margin-bottom: -8rem
+    }
+
+    .swagger-ui .nb7-l {
+        margin-bottom: -16rem
+    }
+
+    .swagger-ui .nt1-l {
+        margin-top: -.25rem
+    }
+
+    .swagger-ui .nt2-l {
+        margin-top: -.5rem
+    }
+
+    .swagger-ui .nt3-l {
+        margin-top: -1rem
+    }
+
+    .swagger-ui .nt4-l {
+        margin-top: -2rem
+    }
+
+    .swagger-ui .nt5-l {
+        margin-top: -4rem
+    }
+
+    .swagger-ui .nt6-l {
+        margin-top: -8rem
+    }
+
+    .swagger-ui .nt7-l {
+        margin-top: -16rem
+    }
+}
+
+.swagger-ui .collapse {
+    border-collapse: collapse;
+    border-spacing: 0
+}
+
+.swagger-ui .striped--light-silver:nth-child(odd) {
+    background-color: #aaa
+}
+
+.swagger-ui .striped--moon-gray:nth-child(odd) {
+    background-color: #ccc
+}
+
+.swagger-ui .striped--light-gray:nth-child(odd) {
+    background-color: #eee
+}
+
+.swagger-ui .striped--near-white:nth-child(odd) {
+    background-color: #f4f4f4
+}
+
+.swagger-ui .stripe-light:nth-child(odd) {
+    background-color: hsla(0,0%,100%,.1)
+}
+
+.swagger-ui .stripe-dark:nth-child(odd) {
+    background-color: rgba(0,0,0,.1)
+}
+
+.swagger-ui .strike {
+    -webkit-text-decoration: line-through;
+    text-decoration: line-through
+}
+
+.swagger-ui .underline {
+    -webkit-text-decoration: underline;
+    text-decoration: underline
+}
+
+.swagger-ui .no-underline {
+    -webkit-text-decoration: none;
+    text-decoration: none
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .strike-ns {
+        -webkit-text-decoration:line-through;
+        text-decoration: line-through
+    }
+
+    .swagger-ui .underline-ns {
+        -webkit-text-decoration: underline;
+        text-decoration: underline
+    }
+
+    .swagger-ui .no-underline-ns {
+        -webkit-text-decoration: none;
+        text-decoration: none
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .strike-m {
+        -webkit-text-decoration:line-through;
+        text-decoration: line-through
+    }
+
+    .swagger-ui .underline-m {
+        -webkit-text-decoration: underline;
+        text-decoration: underline
+    }
+
+    .swagger-ui .no-underline-m {
+        -webkit-text-decoration: none;
+        text-decoration: none
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .strike-l {
+        -webkit-text-decoration:line-through;
+        text-decoration: line-through
+    }
+
+    .swagger-ui .underline-l {
+        -webkit-text-decoration: underline;
+        text-decoration: underline
+    }
+
+    .swagger-ui .no-underline-l {
+        -webkit-text-decoration: none;
+        text-decoration: none
+    }
+}
+
+.swagger-ui .tl {
+    text-align: left
+}
+
+.swagger-ui .tr {
+    text-align: right
+}
+
+.swagger-ui .tc {
+    text-align: center
+}
+
+.swagger-ui .tj {
+    text-align: justify
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .tl-ns {
+        text-align:left
+    }
+
+    .swagger-ui .tr-ns {
+        text-align: right
+    }
+
+    .swagger-ui .tc-ns {
+        text-align: center
+    }
+
+    .swagger-ui .tj-ns {
+        text-align: justify
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .tl-m {
+        text-align:left
+    }
+
+    .swagger-ui .tr-m {
+        text-align: right
+    }
+
+    .swagger-ui .tc-m {
+        text-align: center
+    }
+
+    .swagger-ui .tj-m {
+        text-align: justify
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .tl-l {
+        text-align:left
+    }
+
+    .swagger-ui .tr-l {
+        text-align: right
+    }
+
+    .swagger-ui .tc-l {
+        text-align: center
+    }
+
+    .swagger-ui .tj-l {
+        text-align: justify
+    }
+}
+
+.swagger-ui .ttc {
+    text-transform: capitalize
+}
+
+.swagger-ui .ttl {
+    text-transform: lowercase
+}
+
+.swagger-ui .ttu {
+    text-transform: uppercase
+}
+
+.swagger-ui .ttn {
+    text-transform: none
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .ttc-ns {
+        text-transform:capitalize
+    }
+
+    .swagger-ui .ttl-ns {
+        text-transform: lowercase
+    }
+
+    .swagger-ui .ttu-ns {
+        text-transform: uppercase
+    }
+
+    .swagger-ui .ttn-ns {
+        text-transform: none
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .ttc-m {
+        text-transform:capitalize
+    }
+
+    .swagger-ui .ttl-m {
+        text-transform: lowercase
+    }
+
+    .swagger-ui .ttu-m {
+        text-transform: uppercase
+    }
+
+    .swagger-ui .ttn-m {
+        text-transform: none
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .ttc-l {
+        text-transform:capitalize
+    }
+
+    .swagger-ui .ttl-l {
+        text-transform: lowercase
+    }
+
+    .swagger-ui .ttu-l {
+        text-transform: uppercase
+    }
+
+    .swagger-ui .ttn-l {
+        text-transform: none
+    }
+}
+
+.swagger-ui .f-6,.swagger-ui .f-headline {
+    font-size: 6rem
+}
+
+.swagger-ui .f-5,.swagger-ui .f-subheadline {
+    font-size: 5rem
+}
+
+.swagger-ui .f1 {
+    font-size: 3rem
+}
+
+.swagger-ui .f2 {
+    font-size: 2.25rem
+}
+
+.swagger-ui .f3 {
+    font-size: 1.5rem
+}
+
+.swagger-ui .f4 {
+    font-size: 1.25rem
+}
+
+.swagger-ui .f5 {
+    font-size: 1rem
+}
+
+.swagger-ui .f6 {
+    font-size: .875rem
+}
+
+.swagger-ui .f7 {
+    font-size: .75rem
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .f-6-ns,.swagger-ui .f-headline-ns {
+        font-size:6rem
+    }
+
+    .swagger-ui .f-5-ns,.swagger-ui .f-subheadline-ns {
+        font-size: 5rem
+    }
+
+    .swagger-ui .f1-ns {
+        font-size: 3rem
+    }
+
+    .swagger-ui .f2-ns {
+        font-size: 2.25rem
+    }
+
+    .swagger-ui .f3-ns {
+        font-size: 1.5rem
+    }
+
+    .swagger-ui .f4-ns {
+        font-size: 1.25rem
+    }
+
+    .swagger-ui .f5-ns {
+        font-size: 1rem
+    }
+
+    .swagger-ui .f6-ns {
+        font-size: .875rem
+    }
+
+    .swagger-ui .f7-ns {
+        font-size: .75rem
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .f-6-m,.swagger-ui .f-headline-m {
+        font-size:6rem
+    }
+
+    .swagger-ui .f-5-m,.swagger-ui .f-subheadline-m {
+        font-size: 5rem
+    }
+
+    .swagger-ui .f1-m {
+        font-size: 3rem
+    }
+
+    .swagger-ui .f2-m {
+        font-size: 2.25rem
+    }
+
+    .swagger-ui .f3-m {
+        font-size: 1.5rem
+    }
+
+    .swagger-ui .f4-m {
+        font-size: 1.25rem
+    }
+
+    .swagger-ui .f5-m {
+        font-size: 1rem
+    }
+
+    .swagger-ui .f6-m {
+        font-size: .875rem
+    }
+
+    .swagger-ui .f7-m {
+        font-size: .75rem
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .f-6-l,.swagger-ui .f-headline-l {
+        font-size:6rem
+    }
+
+    .swagger-ui .f-5-l,.swagger-ui .f-subheadline-l {
+        font-size: 5rem
+    }
+
+    .swagger-ui .f1-l {
+        font-size: 3rem
+    }
+
+    .swagger-ui .f2-l {
+        font-size: 2.25rem
+    }
+
+    .swagger-ui .f3-l {
+        font-size: 1.5rem
+    }
+
+    .swagger-ui .f4-l {
+        font-size: 1.25rem
+    }
+
+    .swagger-ui .f5-l {
+        font-size: 1rem
+    }
+
+    .swagger-ui .f6-l {
+        font-size: .875rem
+    }
+
+    .swagger-ui .f7-l {
+        font-size: .75rem
+    }
+}
+
+.swagger-ui .measure {
+    max-width: 30em
+}
+
+.swagger-ui .measure-wide {
+    max-width: 34em
+}
+
+.swagger-ui .measure-narrow {
+    max-width: 20em
+}
+
+.swagger-ui .indent {
+    margin-bottom: 0;
+    margin-top: 0;
+    text-indent: 1em
+}
+
+.swagger-ui .small-caps {
+    font-feature-settings: "smcp";
+    font-variant: small-caps
+}
+
+.swagger-ui .truncate {
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .measure-ns {
+        max-width:30em
+    }
+
+    .swagger-ui .measure-wide-ns {
+        max-width: 34em
+    }
+
+    .swagger-ui .measure-narrow-ns {
+        max-width: 20em
+    }
+
+    .swagger-ui .indent-ns {
+        margin-bottom: 0;
+        margin-top: 0;
+        text-indent: 1em
+    }
+
+    .swagger-ui .small-caps-ns {
+        font-feature-settings: "smcp";
+        font-variant: small-caps
+    }
+
+    .swagger-ui .truncate-ns {
+        overflow: hidden;
+        text-overflow: ellipsis;
+        white-space: nowrap
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .measure-m {
+        max-width:30em
+    }
+
+    .swagger-ui .measure-wide-m {
+        max-width: 34em
+    }
+
+    .swagger-ui .measure-narrow-m {
+        max-width: 20em
+    }
+
+    .swagger-ui .indent-m {
+        margin-bottom: 0;
+        margin-top: 0;
+        text-indent: 1em
+    }
+
+    .swagger-ui .small-caps-m {
+        font-feature-settings: "smcp";
+        font-variant: small-caps
+    }
+
+    .swagger-ui .truncate-m {
+        overflow: hidden;
+        text-overflow: ellipsis;
+        white-space: nowrap
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .measure-l {
+        max-width:30em
+    }
+
+    .swagger-ui .measure-wide-l {
+        max-width: 34em
+    }
+
+    .swagger-ui .measure-narrow-l {
+        max-width: 20em
+    }
+
+    .swagger-ui .indent-l {
+        margin-bottom: 0;
+        margin-top: 0;
+        text-indent: 1em
+    }
+
+    .swagger-ui .small-caps-l {
+        font-feature-settings: "smcp";
+        font-variant: small-caps
+    }
+
+    .swagger-ui .truncate-l {
+        overflow: hidden;
+        text-overflow: ellipsis;
+        white-space: nowrap
+    }
+}
+
+.swagger-ui .overflow-container {
+    overflow-y: scroll
+}
+
+.swagger-ui .center {
+    margin-left: auto;
+    margin-right: auto
+}
+
+.swagger-ui .mr-auto {
+    margin-right: auto
+}
+
+.swagger-ui .ml-auto {
+    margin-left: auto
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .center-ns {
+        margin-left:auto;
+        margin-right: auto
+    }
+
+    .swagger-ui .mr-auto-ns {
+        margin-right: auto
+    }
+
+    .swagger-ui .ml-auto-ns {
+        margin-left: auto
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .center-m {
+        margin-left:auto;
+        margin-right: auto
+    }
+
+    .swagger-ui .mr-auto-m {
+        margin-right: auto
+    }
+
+    .swagger-ui .ml-auto-m {
+        margin-left: auto
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .center-l {
+        margin-left:auto;
+        margin-right: auto
+    }
+
+    .swagger-ui .mr-auto-l {
+        margin-right: auto
+    }
+
+    .swagger-ui .ml-auto-l {
+        margin-left: auto
+    }
+}
+
+.swagger-ui .clip {
+    position: fixed!important;
+    _position: absolute!important;
+    clip: rect(1px 1px 1px 1px);
+    clip: rect(1px,1px,1px,1px)
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .clip-ns {
+        position:fixed!important;
+        _position: absolute!important;
+        clip: rect(1px 1px 1px 1px);
+        clip: rect(1px,1px,1px,1px)
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .clip-m {
+        position:fixed!important;
+        _position: absolute!important;
+        clip: rect(1px 1px 1px 1px);
+        clip: rect(1px,1px,1px,1px)
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .clip-l {
+        position:fixed!important;
+        _position: absolute!important;
+        clip: rect(1px 1px 1px 1px);
+        clip: rect(1px,1px,1px,1px)
+    }
+}
+
+.swagger-ui .ws-normal {
+    white-space: normal
+}
+
+.swagger-ui .nowrap {
+    white-space: nowrap
+}
+
+.swagger-ui .pre {
+    white-space: pre
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .ws-normal-ns {
+        white-space:normal
+    }
+
+    .swagger-ui .nowrap-ns {
+        white-space: nowrap
+    }
+
+    .swagger-ui .pre-ns {
+        white-space: pre
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .ws-normal-m {
+        white-space:normal
+    }
+
+    .swagger-ui .nowrap-m {
+        white-space: nowrap
+    }
+
+    .swagger-ui .pre-m {
+        white-space: pre
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .ws-normal-l {
+        white-space:normal
+    }
+
+    .swagger-ui .nowrap-l {
+        white-space: nowrap
+    }
+
+    .swagger-ui .pre-l {
+        white-space: pre
+    }
+}
+
+.swagger-ui .v-base {
+    vertical-align: baseline
+}
+
+.swagger-ui .v-mid {
+    vertical-align: middle
+}
+
+.swagger-ui .v-top {
+    vertical-align: top
+}
+
+.swagger-ui .v-btm {
+    vertical-align: bottom
+}
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .v-base-ns {
+        vertical-align:baseline
+    }
+
+    .swagger-ui .v-mid-ns {
+        vertical-align: middle
+    }
+
+    .swagger-ui .v-top-ns {
+        vertical-align: top
+    }
+
+    .swagger-ui .v-btm-ns {
+        vertical-align: bottom
+    }
+}
+
+@media screen and (min-width: 30em)and (max-width:60em) {
+    .swagger-ui .v-base-m {
+        vertical-align:baseline
+    }
+
+    .swagger-ui .v-mid-m {
+        vertical-align: middle
+    }
+
+    .swagger-ui .v-top-m {
+        vertical-align: top
+    }
+
+    .swagger-ui .v-btm-m {
+        vertical-align: bottom
+    }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .v-base-l {
+        vertical-align:baseline
+    }
+
+    .swagger-ui .v-mid-l {
+        vertical-align: middle
+    }
+
+    .swagger-ui .v-top-l {
+        vertical-align: top
+    }
+
+    .swagger-ui .v-btm-l {
+        vertical-align: bottom
+    }
+}
+
+.swagger-ui .dim {
+    opacity: 1;
+    transition: opacity .15s ease-in
+}
+
+.swagger-ui .dim:focus,.swagger-ui .dim:hover {
+    opacity: .5;
+    transition: opacity .15s ease-in
+}
+
+.swagger-ui .dim:active {
+    opacity: .8;
+    transition: opacity .15s ease-out
+}
+
+.swagger-ui .glow {
+    transition: opacity .15s ease-in
+}
+
+.swagger-ui .glow:focus,.swagger-ui .glow:hover {
+    opacity: 1;
+    transition: opacity .15s ease-in
+}
+
+.swagger-ui .hide-child .child {
+    opacity: 0;
+    transition: opacity .15s ease-in
+}
+
+.swagger-ui .hide-child:active .child,.swagger-ui .hide-child:focus .child,.swagger-ui .hide-child:hover .child {
+    opacity: 1;
+    transition: opacity .15s ease-in
+}
+
+.swagger-ui .underline-hover:focus,.swagger-ui .underline-hover:hover {
+    -webkit-text-decoration: underline;
+    text-decoration: underline
+}
+
+.swagger-ui .grow {
+    -moz-osx-font-smoothing: grayscale;
+    backface-visibility: hidden;
+    transform: translateZ(0);
+    transition: transform .25s ease-out
+}
+
+.swagger-ui .grow:focus,.swagger-ui .grow:hover {
+    transform: scale(1.05)
+}
+
+.swagger-ui .grow:active {
+    transform: scale(.9)
+}
+
+.swagger-ui .grow-large {
+    -moz-osx-font-smoothing: grayscale;
+    backface-visibility: hidden;
+    transform: translateZ(0);
+    transition: transform .25s ease-in-out
+}
+
+.swagger-ui .grow-large:focus,.swagger-ui .grow-large:hover {
+    transform: scale(1.2)
+}
+
+.swagger-ui .grow-large:active {
+    transform: scale(.95)
+}
+
+.swagger-ui .pointer:hover {
+    cursor: pointer
+}
+
+.swagger-ui .shadow-hover {
+    cursor: pointer;
+    position: relative;
+    transition: all .5s cubic-bezier(.165,.84,.44,1)
+}
+
+.swagger-ui .shadow-hover:after {
+    content: "";
+    height: 100%;
+    left: 0;
+    opacity: 0;
+    position: absolute;
+    top: 0;
+    transition: opacity .5s cubic-bezier(.165,.84,.44,1);
+    width: 100%;
+    z-index: -1
+}
+
+.swagger-ui .shadow-hover:focus:after,.swagger-ui .shadow-hover:hover:after {
+    opacity: 1
+}
+
+.swagger-ui .bg-animate,.swagger-ui .bg-animate:focus,.swagger-ui .bg-animate:hover {
+    transition: background-color .15s ease-in-out
+}
+
+.swagger-ui .z-0 {
+    z-index: 0
+}
+
+.swagger-ui .z-1 {
+    z-index: 1
+}
+
+.swagger-ui .z-2 {
+    z-index: 2
+}
+
+.swagger-ui .z-3 {
+    z-index: 3
+}
+
+.swagger-ui .z-4 {
+    z-index: 4
+}
+
+.swagger-ui .z-5 {
+    z-index: 5
+}
+
+.swagger-ui .z-999 {
+    z-index: 999
+}
+
+.swagger-ui .z-9999 {
+    z-index: 9999
+}
+
+.swagger-ui .z-max {
+    z-index: 2147483647
+}
+
+.swagger-ui .z-inherit {
+    z-index: inherit
+}
+
+.swagger-ui .z-initial,.swagger-ui .z-unset {
+    z-index: auto
+}
+
+.swagger-ui .nested-copy-line-height ol,.swagger-ui .nested-copy-line-height p,.swagger-ui .nested-copy-line-height ul {
+    line-height: 1.5
+}
+
+.swagger-ui .nested-headline-line-height h1,.swagger-ui .nested-headline-line-height h2,.swagger-ui .nested-headline-line-height h3,.swagger-ui .nested-headline-line-height h4,.swagger-ui .nested-headline-line-height h5,.swagger-ui .nested-headline-line-height h6 {
+    line-height: 1.25
+}
+
+.swagger-ui .nested-list-reset ol,.swagger-ui .nested-list-reset ul {
+    list-style-type: none;
+    margin-left: 0;
+    padding-left: 0
+}
+
+.swagger-ui .nested-copy-indent p+p {
+    margin-bottom: 0;
+    margin-top: 0;
+    text-indent: .1em
+}
+
+.swagger-ui .nested-copy-seperator p+p {
+    margin-top: 1.5em
+}
+
+.swagger-ui .nested-img img {
+    display: block;
+    max-width: 100%;
+    width: 100%
+}
+
+.swagger-ui .nested-links a {
+    color: #357edd;
+    transition: color .15s ease-in
+}
+
+.swagger-ui .nested-links a:focus,.swagger-ui .nested-links a:hover {
+    color: #96ccff;
+    transition: color .15s ease-in
+}
+
+.swagger-ui .wrapper {
+    box-sizing: border-box;
+    margin: 0 auto;
+    max-width: 1460px;
+    padding: 0 20px;
+    width: 100%
+}
+
+.swagger-ui .opblock-tag-section {
+    display: flex;
+    flex-direction: column
+}
+
+.swagger-ui .try-out.btn-group {
+    display: flex;
+    flex: .1 2 auto;
+    padding: 0
+}
+
+.swagger-ui .try-out__btn {
+    margin-left: 1.25rem
+}
+
+.swagger-ui .opblock-tag {
+    align-items: center;
+    cursor: pointer;
+    display: none;
+    padding: 10px 20px 10px 10px;
+    transition: all .2s
+}
+
+.swagger-ui .opblock-tag:hover {
+    background: rgba(0,0,0,.02)
+}
+
+.swagger-ui .opblock-tag {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 24px;
+    margin: 0 0 5px
+}
+
+.swagger-ui .opblock-tag.no-desc span {
+    flex: 1
+}
+
+.swagger-ui .opblock-tag svg {
+    transition: all .4s
+}
+
+.swagger-ui .opblock-tag small {
+    color: #3b4151;
+    flex: 2;
+    font-family: sans-serif;
+    font-size: 14px;
+    font-weight: 400;
+    padding: 0 10px
+}
+
+.swagger-ui .opblock-tag>div {
+    flex: 1 1 150px;
+    font-weight: 400;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap
+}
+
+@media(max-width: 640px) {
+    .swagger-ui .opblock-tag small,.swagger-ui .opblock-tag>div {
+        flex:1
+    }
+}
+
+.swagger-ui .opblock-tag .info__externaldocs {
+    text-align: right
+}
+
+.swagger-ui .parameter__type {
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 600;
+    padding: 5px 0
+}
+
+.swagger-ui .parameter-controls {
+    margin-top: .75em
+}
+
+.swagger-ui .examples__title {
+    display: block;
+    font-size: 1.1em;
+    font-weight: 700;
+    margin-bottom: .75em
+}
+
+.swagger-ui .examples__section {
+    margin-top: 1.5em
+}
+
+.swagger-ui .examples__section-header {
+    font-size: .9rem;
+    font-weight: 700;
+    margin-bottom: .5rem
+}
+
+.swagger-ui .examples-select {
+    display: inline-block;
+    margin-bottom: .75em
+}
+
+.swagger-ui .examples-select .examples-select-element {
+    width: 100%
+}
+
+.swagger-ui .examples-select__section-label {
+    font-size: .9rem;
+    font-weight: 700;
+    margin-right: .5rem
+}
+
+.swagger-ui .example__section {
+    margin-top: 1.5em
+}
+
+.swagger-ui .example__section-header {
+    font-size: .9rem;
+    font-weight: 700;
+    margin-bottom: .5rem
+}
+
+.swagger-ui .view-line-link {
+    cursor: pointer;
+    margin: 0 5px;
+    position: relative;
+    top: 3px;
+    transition: all .5s;
+    width: 20px
+}
+
+.swagger-ui .opblock .tab-header {
+    display: flex;
+    flex: 1
+}
+
+.swagger-ui .opblock .tab-header .tab-item {
+    cursor: pointer;
+    padding: 0 40px
+}
+
+.swagger-ui .opblock .tab-header .tab-item:first-of-type {
+    padding: 0 40px 0 0
+}
+
+.swagger-ui .opblock .tab-header .tab-item.active h4 span {
+    position: relative
+}
+
+.swagger-ui .opblock .tab-header .tab-item.active h4 span:after {
+    background: grey;
+    bottom: -15px;
+    content: "";
+    height: 4px;
+    left: 50%;
+    position: absolute;
+    transform: translateX(-50%);
+    width: 120%
+}
+
+.swagger-ui .opblock .opblock-section-header {
+    align-items: center;
+    background: #ccc;
+    display: flex;
+    min-height: 50px;
+    padding: 8px 20px
+}
+
+.swagger-ui .opblock .opblock-section-header>label {
+    align-items: center;
+    color: #3b4151;
+    display: flex;
+    font-family: sans-serif;
+    font-size: 12px;
+    font-weight: 700;
+    margin: 0 0 0 auto
+}
+
+.swagger-ui .opblock .opblock-section-header>label>span {
+    padding: 0 10px 0 0
+}
+
+.swagger-ui .opblock .opblock-section-header h4 {
+    color: #3b4151;
+    flex: 1;
+    font-family: sans-serif;
+    font-size: 14px;
+    margin: 0
+}
+
+.swagger-ui .opblock .opblock-summary-method {
+    background: #000;
+    color: #fff;
+    font-family: sans-serif;
+    font-size: 14px;
+    font-weight: 700;
+    min-width: 80px;
+    padding: 6px 0;
+    text-align: center;
+    text-shadow: 0 1px 0 rgba(0,0,0,.1)
+}
+
+@media(max-width: 768px) {
+    .swagger-ui .opblock .opblock-summary-method {
+        font-size:12px
+    }
+}
+
+.swagger-ui .opblock .opblock-summary-operation-id,.swagger-ui .opblock .opblock-summary-path,.swagger-ui .opblock .opblock-summary-path__deprecated {
+    align-items: center;
+    color: #3b4151;
+    display: flex;
+    font-family: monospace;
+    font-size: 16px;
+    font-weight: 600;
+    word-break: break-word
+}
+
+@media(max-width: 768px) {
+    .swagger-ui .opblock .opblock-summary-operation-id,.swagger-ui .opblock .opblock-summary-path,.swagger-ui .opblock .opblock-summary-path__deprecated {
+        font-size:12px
+    }
+}
+
+.swagger-ui .opblock .opblock-summary-path {
+    flex-shrink: 1
+}
+
+@media(max-width: 640px) {
+    .swagger-ui .opblock .opblock-summary-path {
+        max-width:100%
+    }
+}
+
+.swagger-ui .opblock .opblock-summary-path__deprecated {
+    -webkit-text-decoration: line-through;
+    text-decoration: line-through
+}
+
+.swagger-ui .opblock .opblock-summary-operation-id {
+    font-size: 14px
+}
+
+.swagger-ui .opblock .opblock-summary-description {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 13px;
+    word-break: break-word
+}
+
+.swagger-ui .opblock .opblock-summary-path-description-wrapper {
+    align-items: center;
+    display: flex;
+    flex-direction: row;
+    flex-grow: 1;
+    flex-wrap: wrap;
+    gap: 0 10px;
+    padding: 0 10px
+}
+
+@media(max-width: 550px) {
+    .swagger-ui .opblock .opblock-summary-path-description-wrapper {
+        align-items:flex-start;
+        flex-direction: column
+    }
+}
+
+.swagger-ui .opblock .opblock-summary {
+    align-items: center;
+    cursor: pointer;
+    display: flex;
+    padding: 5px
+}
+
+.swagger-ui .opblock .opblock-summary .view-line-link {
+    cursor: pointer;
+    margin: 0;
+    position: relative;
+    top: 2px;
+    transition: all .5s;
+    width: 0
+}
+
+.swagger-ui .opblock .opblock-summary:hover .view-line-link {
+    margin: 0 5px;
+    width: 18px
+}
+
+.swagger-ui .opblock .opblock-summary:hover .view-line-link.copy-to-clipboard {
+    width: 24px
+}
+
+.swagger-ui .opblock.opblock-post {
+    background: rgba(73,204,144,.1);
+}
+
+.swagger-ui .opblock.opblock-post .opblock-summary-method {
+    background: darkmagenta;
+    box-shadow: 4px 4px 8px #444
+}
+
+.swagger-ui .opblock.opblock-post .tab-header .tab-item.active h4 span:after {
+    background: #49cc90
+}
+
+.swagger-ui .opblock.opblock-put {
+    background: rgba(252,161,48,.1);
+}
+
+.swagger-ui .opblock.opblock-put .opblock-summary-method {
+    background: #fca130
+}
+
+.swagger-ui .opblock.opblock-put .tab-header .tab-item.active h4 span:after {
+    background: #fca130
+}
+
+.swagger-ui .opblock.opblock-delete {
+    background: rgba(249,62,62,.1);
+}
+
+.swagger-ui .opblock.opblock-delete .opblock-summary-method {
+    background: #f93e3e
+}
+
+.swagger-ui .opblock.opblock-delete .tab-header .tab-item.active h4 span:after {
+    background: #f93e3e
+}
+
+.swagger-ui .opblock.opblock-get {
+    background: rgba(97,175,254,.1);
+}
+
+.swagger-ui .opblock.opblock-get .opblock-summary-method {
+    background: teal;
+    box-shadow: 4px 4px 8px #444;
+}
+
+.swagger-ui .opblock.opblock-get .tab-header .tab-item.active h4 span:after {
+    background: #61affe
+}
+
+.swagger-ui .opblock.opblock-patch {
+    background: rgba(80,227,194,.1);
+}
+
+.swagger-ui .opblock.opblock-patch .opblock-summary-method {
+    background: #50e3c2
+}
+
+.swagger-ui .opblock.opblock-patch .tab-header .tab-item.active h4 span:after {
+    background: #50e3c2
+}
+
+.swagger-ui .opblock.opblock-head .opblock-summary-method {
+    background: #9012fe;
+    box-shadow: 4px 4px 8px #444;
+}
+
+.swagger-ui .opblock.opblock-head .tab-header .tab-item.active h4 span:after {
+    background: #9012fe
+}
+
+.swagger-ui .opblock.opblock-options {
+    background: rgba(13,90,167,.1);
+}
+
+.swagger-ui .opblock.opblock-options .opblock-summary-method {
+    background: #0d5aa7
+}
+
+.swagger-ui .opblock.opblock-options .tab-header .tab-item.active h4 span:after {
+    background: #0d5aa7
+}
+
+.swagger-ui .opblock.opblock-deprecated {
+    background: hsla(0,0%,92%,.1);
+    opacity: .6
+}
+
+.swagger-ui .opblock.opblock-deprecated .opblock-summary-method {
+    background: #ebebeb
+}
+
+.swagger-ui .opblock.opblock-deprecated .tab-header .tab-item.active h4 span:after {
+    background: #ebebeb
+}
+
+.swagger-ui .opblock .opblock-schemes {
+    padding: 8px 20px
+}
+
+.swagger-ui .opblock .opblock-schemes .schemes-title {
+    padding: 0 10px 0 0
+}
+
+.swagger-ui .filter .operation-filter-input {
+    margin: 20px 0;
+    padding: 10px;
+    width: 100%
+}
+
+.swagger-ui .download-url-wrapper .failed,.swagger-ui .filter .failed {
+    color: red
+}
+
+.swagger-ui .download-url-wrapper .loading,.swagger-ui .filter .loading {
+    color: #aaa
+}
+
+.swagger-ui .model-example {
+    margin-top: 1em
+}
+
+.swagger-ui .model-example .model-container {
+    overflow-x: auto;
+    width: 100%
+}
+
+.swagger-ui .model-example .model-container .model-hint:not(.model-hint--embedded) {
+    top: -1.15em
+}
+
+.swagger-ui .tab {
+    display: flex;
+    list-style: none;
+    padding: 0;
+    margin: 0.1em;
+}
+
+.swagger-ui .tab li {
+    color: #3b4151;
+    cursor: pointer;
+    font-family: sans-serif;
+    font-size: 12px;
+    min-width: 60px;
+    padding: 0
+}
+
+.swagger-ui .tab li:first-of-type {
+    padding-left: 0;
+    padding-right: 12px;
+    position: relative
+}
+
+.swagger-ui .tab li:first-of-type:after {
+    background: rgba(0,0,0,.2);
+    content: "";
+    height: 100%;
+    position: absolute;
+    right: 6px;
+    top: 0;
+    width: 1px
+}
+
+.swagger-ui .tab li.active {
+    font-weight: 700
+}
+
+.swagger-ui .tab li button.tablinks {
+    background: none;
+    color: inherit;
+    font-family: inherit;
+    font-weight: inherit;
+    padding: 0
+}
+
+.swagger-ui .opblock-description-wrapper,.swagger-ui .opblock-external-docs-wrapper,.swagger-ui .opblock-title_normal {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    margin: 0 0 5px;
+    padding: 15px 20px
+}
+
+.swagger-ui .opblock-description-wrapper h4,.swagger-ui .opblock-external-docs-wrapper h4,.swagger-ui .opblock-title_normal h4 {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    margin: 0 0 5px
+}
+
+.swagger-ui .opblock-description-wrapper p,.swagger-ui .opblock-external-docs-wrapper p,.swagger-ui .opblock-title_normal p {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 14px;
+    margin: 0
+}
+
+.swagger-ui .opblock-external-docs-wrapper h4 {
+    padding-left: 0
+}
+
+.swagger-ui .execute-wrapper {
+    padding: 20px;
+    text-align: right
+}
+
+.swagger-ui .execute-wrapper .btn {
+    padding: 8px 40px;
+    width: 100%
+}
+
+.swagger-ui .body-param-options {
+    display: flex;
+    flex-direction: column
+}
+
+.swagger-ui .body-param-options .body-param-edit {
+    padding: 10px 0
+}
+
+.swagger-ui .body-param-options label {
+    padding: 8px 0
+}
+
+.swagger-ui .body-param-options label select {
+    margin: 3px 0 0
+}
+
+.swagger-ui .responses-inner {
+    padding-left: 20px;
+}
+
+.swagger-ui .responses-inner h4,.swagger-ui .responses-inner h5 {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    margin: 10px 0 5px
+}
+
+.swagger-ui .responses-inner .curl {
+    max-height: 400px;
+    min-height: 6em;
+    overflow-y: auto
+}
+
+.swagger-ui .response-col_status {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 14px
+}
+
+.swagger-ui .response-col_status .response-undocumented {
+    color: #909090;
+    font-family: monospace;
+    font-size: 11px;
+    font-weight: 600
+}
+
+.swagger-ui .response-col_links {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 14px;
+    max-width: 40em;
+    padding-left: 2em
+}
+
+.swagger-ui .response-col_links .response-undocumented {
+    color: #909090;
+    font-family: monospace;
+    font-size: 11px;
+    font-weight: 600
+}
+
+.swagger-ui .response-col_links .operation-link {
+    margin-bottom: 1.5em
+}
+
+.swagger-ui .response-col_links .operation-link .description {
+    margin-bottom: .5em
+}
+
+.swagger-ui .opblock-body .opblock-loading-animation {
+    display: block;
+    margin: 3em auto
+}
+
+.swagger-ui .opblock-body pre.microlight {
+    background: #333;
+    font-size: 12px;
+    hyphens: auto;
+    margin: 0;
+    padding: 10px;
+    white-space: pre-wrap;
+    word-break: break-all;
+    word-break: break-word;
+    word-wrap: break-word;
+    color: #fff;
+    font-family: monospace;
+    font-weight: 600
+}
+
+.swagger-ui .opblock-body pre.microlight .headerline {
+    display: block
+}
+
+.swagger-ui .highlight-code {
+    position: relative
+}
+
+.swagger-ui .highlight-code>.microlight {
+    max-height: 400px;
+    min-height: 6em;
+    overflow-y: auto
+}
+
+.swagger-ui .highlight-code>.microlight code {
+    white-space: pre-wrap!important;
+    word-break: break-all
+}
+
+.swagger-ui .curl-command {
+    position: relative
+}
+
+.swagger-ui .download-contents {
+    align-items: center;
+    background: #7d8293;
+    bottom: 10px;
+    color: #fff;
+    display: flex;
+    font-family: sans-serif;
+    font-size: 14px;
+    font-weight: 600;
+    height: 30px;
+    justify-content: center;
+    padding: 5px;
+    position: absolute;
+    right: 10px;
+    text-align: center
+}
+
+.swagger-ui .scheme-container {
+    background: #fff;
+    margin: 0 0 20px;
+    padding: 30px 0
+}
+
+.swagger-ui .scheme-container .schemes {
+    align-items: flex-end;
+    display: flex;
+    flex-wrap: wrap;
+    gap: 10px;
+    justify-content: space-between
+}
+
+.swagger-ui .scheme-container .schemes>.schemes-server-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 10px
+}
+
+.swagger-ui .scheme-container .schemes>.schemes-server-container>label {
+    color: #3b4151;
+    display: flex;
+    flex-direction: column;
+    font-family: sans-serif;
+    font-size: 12px;
+    font-weight: 700;
+    margin: -20px 15px 0 0
+}
+
+.swagger-ui .scheme-container .schemes>.schemes-server-container>label select {
+    min-width: 130px;
+    text-transform: uppercase
+}
+
+.swagger-ui .scheme-container .schemes:not(:has(.schemes-server-container)) {
+    justify-content: flex-end
+}
+
+.swagger-ui .scheme-container .schemes .auth-wrapper {
+    flex: none;
+    justify-content: start
+}
+
+.swagger-ui .scheme-container .schemes .auth-wrapper .authorize {
+    display: flex;
+    flex-wrap: nowrap;
+    margin: 0;
+    padding-right: 20px
+}
+
+.swagger-ui .loading-container {
+    align-items: center;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    margin-top: 1em;
+    min-height: 1px;
+    padding: 40px 0 60px
+}
+
+.swagger-ui .loading-container .loading {
+    position: relative
+}
+
+.swagger-ui .loading-container .loading:after {
+    color: #3b4151;
+    content: "loading";
+    font-family: sans-serif;
+    font-size: 10px;
+    font-weight: 700;
+    left: 50%;
+    position: absolute;
+    text-transform: uppercase;
+    top: 50%;
+    transform: translate(-50%,-50%)
+}
+
+.swagger-ui .loading-container .loading:before {
+    animation: rotation 1s linear infinite,opacity .5s;
+    backface-visibility: hidden;
+    content: "";
+    display: block;
+    height: 60px;
+    left: 50%;
+    margin: -30px;
+    opacity: 1;
+    position: absolute;
+    top: 50%;
+    width: 60px
+}
+
+@keyframes rotation {
+    to {
+        transform: rotate(1turn)
+    }
+}
+
+.swagger-ui .response-controls {
+    display: none;
+}
+
+.swagger-ui .response-control-media-type {
+    margin-right: 1em
+}
+
+.swagger-ui .response-control-media-type__accept-message {
+    color: green;
+    font-size: .7em
+}
+
+.swagger-ui .response-control-examples__title,.swagger-ui .response-control-media-type__title {
+    display: block;
+    font-size: .7em;
+    margin-bottom: .2em
+}
+
+@keyframes blinker {
+    50% {
+        opacity: 0
+    }
+}
+
+.swagger-ui .hidden {
+    display: none
+}
+
+.swagger-ui .no-margin {
+    height: auto;
+    margin: 0;
+    padding: 0
+}
+
+.swagger-ui .float-right {
+    float: right
+}
+
+.swagger-ui .svg-assets {
+    height: 0;
+    position: absolute;
+    width: 0
+}
+
+.swagger-ui section h3 {
+    color: #3b4151;
+    font-family: sans-serif
+}
+
+.swagger-ui a.nostyle {
+    display: inline
+}
+
+.swagger-ui a.nostyle,.swagger-ui a.nostyle:visited {
+    color: inherit;
+    cursor: pointer;
+    text-decoration: inherit
+}
+
+.swagger-ui .fallback {
+    color: #aaa;
+    padding: 1em
+}
+
+.swagger-ui .version-pragma {
+    height: 100%;
+    padding: 5em 0
+}
+
+.swagger-ui .version-pragma__message {
+    display: flex;
+    font-size: 1.2em;
+    height: 100%;
+    justify-content: center;
+    line-height: 1.5em;
+    padding: 0 .6em;
+    text-align: center
+}
+
+.swagger-ui .version-pragma__message>div {
+    flex: 1;
+    max-width: 55ch
+}
+
+.swagger-ui .version-pragma__message code {
+    background-color: #dedede;
+    padding: 4px 4px 2px;
+    white-space: pre
+}
+
+.swagger-ui .opblock-link {
+    font-weight: 400
+}
+
+.swagger-ui .opblock-link.shown {
+    font-weight: 700
+}
+
+.swagger-ui span.token-string {
+    color: #555
+}
+
+.swagger-ui span.token-not-formatted {
+    color: #555;
+    font-weight: 700
+}
+
+.swagger-ui .btn {
+    background: transparent;
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 14px;
+    font-weight: 700;
+    padding: 5px 23px;
+    transition: all .3s;
+    box-shadow: 4px 4px 8px #444
+}
+
+.swagger-ui .btn.btn-sm {
+    font-size: 12px;
+    padding: 4px 23px
+}
+
+.swagger-ui .btn[disabled] {
+    cursor: not-allowed;
+    opacity: .3
+}
+
+.swagger-ui .btn.cancel {
+    background-color: transparent;
+    color: #ff6060;
+    font-family: sans-serif
+}
+
+.swagger-ui .btn.authorize {
+    background-color: transparent;
+    color: #49cc90;
+    display: inline;
+    line-height: 1
+}
+
+.swagger-ui .btn.authorize span {
+    float: left;
+    padding: 4px 20px 0 0
+}
+
+.swagger-ui .btn.authorize svg {
+    fill: #49cc90
+}
+
+.swagger-ui .btn.execute {
+    background-color: #4990e2;
+    color: #fff
+}
+
+.swagger-ui .btn-group {
+    display: flex;
+    padding: 10px
+}
+
+.swagger-ui .btn-group .btn {
+    flex: 1
+}
+
+.swagger-ui .authorization__btn {
+    background: none;
+    padding: 0 0 0 10px
+}
+
+.swagger-ui .authorization__btn .locked {
+    opacity: 1
+}
+
+.swagger-ui .authorization__btn .unlocked {
+    opacity: .4
+}
+
+.swagger-ui .model-box-control,.swagger-ui .models-control,.swagger-ui .opblock-summary-control {
+    all: inherit;
+    cursor: pointer;
+    flex: 1;
+    padding: 0
+}
+
+.swagger-ui .model-box-control:focus,.swagger-ui .models-control:focus,.swagger-ui .opblock-summary-control:focus {
+    outline: auto
+}
+
+.swagger-ui .expand-methods,.swagger-ui .expand-operation {
+    background: none;
+}
+
+.swagger-ui .expand-methods svg,.swagger-ui .expand-operation svg {
+    height: 20px;
+    width: 20px
+}
+
+.swagger-ui .expand-methods {
+    padding: 0 10px
+}
+
+.swagger-ui .expand-methods:hover svg {
+    fill: #404040
+}
+
+.swagger-ui .expand-methods svg {
+    transition: all .3s;
+    fill: #707070
+}
+
+.swagger-ui button {
+    cursor: pointer;
+    border: none;
+    box-shadow: 2px 2px 8px #aaa;
+}
+
+.swagger-ui button.invalid {
+    animation: shake .4s 1;
+    background: #feebeb;
+}
+
+.swagger-ui .copy-to-clipboard {
+    align-items: center;
+    background: #7d8293;
+    bottom: 10px;
+    display: flex;
+    height: 30px;
+    justify-content: center;
+    position: absolute;
+    right: 100px;
+    width: 30px
+}
+
+.swagger-ui .copy-to-clipboard button {
+    background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"16\" height=\"15\" aria-hidden=\"true\"><path fill=\"%23fff\" fill-rule=\"evenodd\" d=\"M4 12h4v1H4zm5-6H4v1h5zm2 3V7l-3 3 3 3v-2h5V9zM6.5 8H4v1h2.5zM4 11h2.5v-1H4zm9 1h1v2c-.02.28-.11.52-.3.7s-.42.28-.7.3H3c-.55 0-1-.45-1-1V3c0-.55.45-1 1-1h3c0-1.11.89-2 2-2s2 .89 2 2h3c.55 0 1 .45 1 1v5h-1V5H3v9h10zM4 4h8c0-.55-.45-1-1-1h-1c-.55 0-1-.45-1-1s-.45-1-1-1-1 .45-1 1-.45 1-1 1H5c-.55 0-1 .45-1 1\"/></svg>") 50% no-repeat;
+    flex-grow: 1;
+    flex-shrink: 1;
+    height: 25px
+}
+
+.swagger-ui .copy-to-clipboard:active {
+    background: #5e626f
+}
+
+.swagger-ui .opblock-control-arrow {
+    background: none;
+    text-align: center
+}
+
+.swagger-ui .curl-command .copy-to-clipboard {
+    bottom: 5px;
+    height: 20px;
+    right: 10px;
+    width: 20px
+}
+
+.swagger-ui .curl-command .copy-to-clipboard button {
+    height: 18px
+}
+
+.swagger-ui .opblock .opblock-summary .view-line-link.copy-to-clipboard {
+    height: 26px;
+    position: static
+}
+
+.swagger-ui select {
+    -webkit-appearance: none;
+    -moz-appearance: none;
+    appearance: none;
+    background: #f7f7f7 url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 20 20\"><path d=\"M13.418 7.859a.695.695 0 0 1 .978 0 .68.68 0 0 1 0 .969l-3.908 3.83a.697.697 0 0 1-.979 0l-3.908-3.83a.68.68 0 0 1 0-.969.695.695 0 0 1 .978 0L10 11z\"/></svg>") right 10px center no-repeat;
+    background-size: 20px;
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 14px;
+    font-weight: 700;
+    padding: 5px 40px 5px 10px;
+    border: none;
+}
+
+.swagger-ui select[multiple] {
+    background: #f7f7f7;
+    margin: 5px 0;
+    padding: 5px
+}
+
+.swagger-ui select.invalid {
+    animation: shake .4s 1;
+    background: #feebeb;
+}
+
+.swagger-ui .opblock-body select {
+    min-width: 230px
+}
+
+@media(max-width: 768px) {
+    .swagger-ui .opblock-body select {
+        min-width:180px
+    }
+}
+
+@media(max-width: 640px) {
+    .swagger-ui .opblock-body select {
+        min-width:100%;
+        width: 100%
+    }
+}
+
+.swagger-ui label {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    font-weight: 700;
+    margin: 0 0 5px
+}
+
+.swagger-ui input[type=email],.swagger-ui input[type=file],.swagger-ui input[type=password],.swagger-ui input[type=search],.swagger-ui input[type=text] {
+    line-height: 1
+}
+
+@media(max-width: 768px) {
+    .swagger-ui input[type=email],.swagger-ui input[type=file],.swagger-ui input[type=password],.swagger-ui input[type=search],.swagger-ui input[type=text] {
+        max-width:175px
+    }
+}
+
+.swagger-ui input[type=email],.swagger-ui input[type=file],.swagger-ui input[type=password],.swagger-ui input[type=search],.swagger-ui input[type=text],.swagger-ui textarea {
+    background: #fff;
+    margin: 5px 0;
+    min-width: 100px;
+    padding: 8px 10px
+}
+
+.swagger-ui input[type=email].invalid,.swagger-ui input[type=file].invalid,.swagger-ui input[type=password].invalid,.swagger-ui input[type=search].invalid,.swagger-ui input[type=text].invalid,.swagger-ui textarea.invalid {
+    animation: shake .4s 1;
+    background: #feebeb;
+}
+
+.swagger-ui input[disabled],.swagger-ui select[disabled],.swagger-ui textarea[disabled] {
+    background-color: #fafafa;
+    color: #888;
+    cursor: not-allowed
+}
+
+.swagger-ui textarea[disabled] {
+    background-color: #41444e;
+    color: #fff
+}
+
+@keyframes shake {
+    10%,90% {
+        transform: translate3d(-1px,0,0)
+    }
+
+    20%,80% {
+        transform: translate3d(2px,0,0)
+    }
+
+    30%,50%,70% {
+        transform: translate3d(-4px,0,0)
+    }
+
+    40%,60% {
+        transform: translate3d(4px,0,0)
+    }
+}
+
+.swagger-ui textarea {
+    background: hsla(0,0%,100%,.8);
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 600;
+    min-height: 280px;
+    outline: none;
+    padding: 10px;
+    width: 100%
+}
+
+.swagger-ui textarea.curl {
+    background: #41444e;
+    color: #fff;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 600;
+    margin: 0;
+    min-height: 100px;
+    padding: 10px;
+    resize: none
+}
+
+.swagger-ui .checkbox {
+    color: #303030;
+    padding: 5px 0 10px;
+    transition: opacity .5s
+}
+
+.swagger-ui .checkbox label {
+    display: flex
+}
+
+.swagger-ui .checkbox p {
+    color: #3b4151;
+    font-family: monospace;
+    font-style: italic;
+    font-weight: 400!important;
+    font-weight: 600;
+    margin: 0!important
+}
+
+.swagger-ui .checkbox input[type=checkbox] {
+    display: none
+}
+
+.swagger-ui .checkbox input[type=checkbox]+label>.item {
+    background: #e8e8e8;
+    cursor: pointer;
+    display: inline-block;
+    flex: none;
+    height: 16px;
+    margin: 0 8px 0 0;
+    padding: 5px;
+    position: relative;
+    top: 3px;
+    width: 16px
+}
+
+.swagger-ui .checkbox input[type=checkbox]+label>.item:active {
+    transform: scale(.9)
+}
+
+.swagger-ui .checkbox input[type=checkbox]:checked+label>.item {
+    background: #e8e8e8 url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"10\" height=\"8\" viewBox=\"3 7 10 8\"><path fill=\"%2341474E\" fill-rule=\"evenodd\" d=\"M6.333 15 3 11.667l1.333-1.334 2 2L11.667 7 13 8.333z\"/></svg>") 50% no-repeat
+}
+
+.swagger-ui .dialog-ux {
+    bottom: 0;
+    left: 0;
+    position: fixed;
+    right: 0;
+    top: 0;
+    z-index: 9999
+}
+
+.swagger-ui .dialog-ux .backdrop-ux {
+    background: rgba(0,0,0,.8);
+    bottom: 0;
+    left: 0;
+    position: fixed;
+    right: 0;
+    top: 0
+}
+
+.swagger-ui .dialog-ux .modal-ux {
+    background: #fff;
+    left: 50%;
+    max-width: 650px;
+    min-width: 300px;
+    position: absolute;
+    top: 50%;
+    transform: translate(-50%,-50%);
+    width: 100%;
+    z-index: 9999
+}
+
+.swagger-ui .dialog-ux .modal-ux-content {
+    max-height: 540px;
+    overflow-y: auto;
+    padding: 20px
+}
+
+.swagger-ui .dialog-ux .modal-ux-content p {
+    color: #41444e;
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    margin: 0 0 5px
+}
+
+.swagger-ui .dialog-ux .modal-ux-content h4 {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 18px;
+    font-weight: 600;
+    margin: 15px 0 0
+}
+
+.swagger-ui .dialog-ux .modal-ux-header {
+    align-items: center;
+    display: flex;
+    padding: 12px 0
+}
+
+.swagger-ui .dialog-ux .modal-ux-header .close-modal {
+    -webkit-appearance: none;
+    -moz-appearance: none;
+    appearance: none;
+    background: none;
+    padding: 0 10px
+}
+
+.swagger-ui .dialog-ux .modal-ux-header h3 {
+    color: #3b4151;
+    flex: 1;
+    font-family: sans-serif;
+    font-size: 20px;
+    font-weight: 600;
+    margin: 0;
+    padding: 0 20px
+}
+
+.swagger-ui .model {
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 300;
+    font-weight: 600
+}
+
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+    color: #a0a0a0!important
+}
+
+.swagger-ui .model .deprecated>td:first-of-type {
+    -webkit-text-decoration: line-through;
+    text-decoration: line-through
+}
+
+.swagger-ui .model-toggle {
+    cursor: pointer;
+    display: inline-block;
+    font-size: 10px;
+    margin: auto .3em;
+    position: relative;
+    top: 6px;
+    transform: rotate(90deg);
+    transform-origin: 50% 50%;
+    transition: transform .15s ease-in
+}
+
+.swagger-ui .model-toggle.collapsed {
+    transform: rotate(0deg)
+}
+
+.swagger-ui .model-toggle:after {
+    background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"24\" height=\"24\" viewBox=\"0 0 24 24\"><path d=\"M10 6 8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z\"/></svg>") 50% no-repeat;
+    background-size: 100%;
+    content: "";
+    display: block;
+    height: 20px;
+    width: 20px
+}
+
+.swagger-ui .model-jump-to-path {
+    cursor: pointer;
+    position: relative
+}
+
+.swagger-ui .model-jump-to-path .view-line-link {
+    cursor: pointer;
+    position: absolute;
+    top: -.4em
+}
+
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+    position: relative
+}
+
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+    display: block
+}
+
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+    background: rgba(0,0,0,.7);
+    color: #ebebeb;
+    display: none;
+    padding: .1em .5em;
+    position: absolute;
+    top: -1.8em;
+    white-space: nowrap
+}
+
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+    margin: 0 0 1em
+}
+
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+    font-style: italic
+}
+
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+    color: #6b6b6b
+}
+
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+    display: block
+}
+
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+    padding-left: 0;
+    padding-right: 0;
+    width: auto
+}
+
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+    content: ": "
+}
+
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+    color: #666;
+    font-weight: 400
+}
+
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+    font-weight: 700
+}
+
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+    vertical-align: top
+}
+
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+    padding-right: .2em
+}
+
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+}
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+}
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+    vertical-align: top
+}
+
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+    font-weight: 700
+}
+
+.swagger-ui table.model tr .renderedMarkdown p:first-child {
+    margin-top: 0
+}
+
+.swagger-ui section.models {
+    margin: 30px 0
+}
+
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+    cursor: pointer
+}
+
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+    padding: 0 0 20px
+}
+
+.swagger-ui section.models.is-open h4 {
+    margin: 0 0 5px
+}
+
+.swagger-ui section.models h4 {
+    align-items: center;
+    color: #606060;
+    cursor: pointer;
+    display: flex;
+    font-family: sans-serif;
+    font-size: 16px;
+    margin: 0;
+    padding: 10px 20px 10px 10px;
+    transition: all .2s
+}
+
+.swagger-ui section.models h4 svg {
+    transition: all .4s
+}
+
+.swagger-ui section.models h4 span {
+    flex: 1
+}
+
+.swagger-ui section.models h4:hover {
+    background: rgba(0,0,0,.02)
+}
+
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+    color: #707070;
+    font-family: sans-serif;
+    font-size: 16px;
+    margin: 0 0 10px
+}
+
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+    position: relative;
+    top: 5px
+}
+
+.swagger-ui section.models .model-container {
+    background: rgba(0,0,0,.05);
+    margin: 0 20px 15px;
+    position: relative;
+    transition: all .5s
+}
+
+.swagger-ui section.models .model-container:hover {
+    background: rgba(0,0,0,.07)
+}
+
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+    margin: 20px
+}
+
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+    margin: 0 20px
+}
+
+.swagger-ui section.models .model-container .models-jump-to-path {
+    opacity: .65;
+    position: absolute;
+    right: 5px;
+    top: 8px
+}
+
+.swagger-ui section.models .model-box {
+    background: none
+}
+
+.swagger-ui section.models .model-box:has(.model-box) {
+    overflow-x: auto;
+    width: 100%
+}
+
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+    background: rgba(0,0,0,.1);
+    display: inline-block;
+    padding: 10px
+}
+
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+    position: relative;
+    top: 4px
+}
+
+.swagger-ui .model-box.deprecated {
+    opacity: .5
+}
+
+.swagger-ui .model-title {
+    color: #505050;
+    font-family: sans-serif;
+    font-size: 16px
+}
+
+.swagger-ui .model-title img {
+    bottom: 0;
+    margin-left: 1em;
+    position: relative
+}
+
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+    color: #f93e3e;
+    font-family: sans-serif;
+    font-size: 16px;
+    font-weight: 600;
+    margin-right: 1em
+}
+
+.swagger-ui span>span.model .brace-close {
+    padding: 0 0 0 10px
+}
+
+.swagger-ui .prop-name {
+    display: inline-block;
+    margin-right: 1em
+}
+
+.swagger-ui .prop-type {
+    color: #55a
+}
+
+.swagger-ui .prop-enum {
+    display: block
+}
+
+.swagger-ui .prop-format {
+    color: #606060
+}
+
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+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    margin: -20px 15px 0 0
+}
+
+.swagger-ui .servers>label select {
+    max-width: 100%;
+    min-width: 130px;
+    width: 100%
+}
+
+.swagger-ui .servers h4.message {
+    padding-bottom: 2em
+}
+
+.swagger-ui .servers table tr {
+    width: 30em
+}
+
+.swagger-ui .servers table td {
+    display: inline-block;
+    max-width: 15em;
+    padding-bottom: 10px;
+    padding-top: 10px;
+    vertical-align: middle
+}
+
+.swagger-ui .servers table td:first-of-type {
+    padding-right: 1em
+}
+
+.swagger-ui .servers table td input {
+    height: 100%;
+    width: 100%
+}
+
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+    margin: 2em 0
+}
+
+.swagger-ui .servers .computed-url code {
+    display: inline-block;
+    font-size: 16px;
+    margin: 0 1em;
+    padding: 4px
+}
+
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+    font-size: 12px;
+    font-weight: 700
+}
+
+.swagger-ui .operation-servers h4.message {
+    margin-bottom: 2em
+}
+
+.swagger-ui table {
+    border-collapse: collapse;
+    padding: 0 10px;
+    width: 100%
+}
+
+.swagger-ui table.model tbody tr td {
+    padding: 0 0 0 1em;
+    vertical-align: top
+}
+
+.swagger-ui table.model tbody tr td:first-of-type {
+    padding: 0 0 0 2em;
+    width: 174px
+}
+
+.swagger-ui table.headers td {
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 300;
+    font-weight: 600;
+    vertical-align: middle
+}
+
+.swagger-ui table.headers .header-example {
+    color: #999;
+    font-style: italic
+}
+
+.swagger-ui table tbody tr td {
+    vertical-align: top
+}
+
+.swagger-ui table tbody tr td:first-of-type {
+    min-width: 6em;
+    padding: 4px;
+}
+
+.swagger-ui table tbody tr td:has(.model-box) {
+    max-width: 1px
+}
+
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+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 12px;
+    font-weight: 700;
+    padding: 12px 0;
+    text-align: left
+}
+
+.swagger-ui .parameters-col_description {
+    margin-bottom: 2em;
+    width: 99%
+}
+
+.swagger-ui .parameters-col_description input {
+    max-width: 340px;
+    width: 100%;
+    border: none;
+    background-color: #eee;
+}
+
+.swagger-ui .parameters-col_description .markdown:first-child p:first-child,.swagger-ui .parameters-col_description .renderedMarkdown:first-child p:first-child {
+    margin: 0
+}
+
+.swagger-ui .parameter__name {
+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 16px;
+    font-weight: 400;
+    margin-right: .75em
+}
+
+.swagger-ui .parameter__name.required {
+    font-weight: 700
+}
+
+.swagger-ui .parameter__name.required span {
+    color: red
+}
+
+.swagger-ui .parameter__name.required:after {
+    color: rgba(255,0,0,.6);
+    content: "required";
+    font-size: 10px;
+    padding: 5px;
+    position: relative;
+    top: -6px
+}
+
+.swagger-ui .parameter__extension,.swagger-ui .parameter__in {
+    color: grey;
+    font-family: monospace;
+    font-size: 12px;
+    font-style: italic;
+    font-weight: 600
+}
+
+.swagger-ui .parameter__deprecated {
+    color: red;
+    font-family: monospace;
+    font-size: 12px;
+    font-style: italic;
+    font-weight: 600
+}
+
+.swagger-ui .parameter__empty_value_toggle {
+    display: block;
+    font-size: 13px;
+    padding-bottom: 12px;
+    padding-top: 5px
+}
+
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+    margin-right: 7px;
+    width: auto
+}
+
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+}
+
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+    padding-left: 20px
+}
+
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+    width: 99%
+}
+
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+    margin: 0
+}
+
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+    margin-bottom: 0
+}
+
+.swagger-ui .response-col_links {
+    min-width: 6em
+}
+
+.swagger-ui .response__extension {
+    color: grey;
+    font-family: monospace;
+    font-size: 12px;
+    font-style: italic;
+    font-weight: 600
+}
+
+.swagger-ui .topbar {
+    background-color: #1b1b1b;
+    padding: 10px 0
+}
+
+.swagger-ui .topbar .topbar-wrapper {
+    align-items: center;
+    display: flex;
+    flex-wrap: wrap;
+    gap: 10px
+}
+
+@media(max-width: 550px) {
+    .swagger-ui .topbar .topbar-wrapper {
+        align-items:start;
+        flex-direction: column
+    }
+}
+
+.swagger-ui .topbar a {
+    align-items: center;
+    color: #fff;
+    display: flex;
+    flex: 1;
+    font-family: sans-serif;
+    font-size: 1.5em;
+    font-weight: 700;
+    max-width: 300px;
+    -webkit-text-decoration: none;
+    text-decoration: none
+}
+
+.swagger-ui .topbar a span {
+    margin: 0;
+    padding: 0 10px
+}
+
+.swagger-ui .topbar .download-url-wrapper {
+    display: flex;
+    flex: 3;
+    justify-content: flex-end
+}
+
+.swagger-ui .topbar .download-url-wrapper input[type=text] {
+    margin: 0;
+    max-width: 100%;
+    outline: none;
+    width: 100%
+}
+
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+    align-items: center;
+    color: #f0f0f0;
+    display: flex;
+    margin: 0;
+    max-width: 600px;
+    width: 100%
+}
+
+.swagger-ui .topbar .download-url-wrapper .select-label span {
+    flex: 1;
+    font-size: 16px;
+    padding: 0 10px 0 0;
+    text-align: right
+}
+
+.swagger-ui .topbar .download-url-wrapper .select-label select {
+    flex: 2;
+    outline: none;
+    width: 100%
+}
+
+.swagger-ui .topbar .download-url-wrapper .download-url-button {
+    background: #62a03f;
+    color: #fff;
+    font-family: sans-serif;
+    font-size: 16px;
+    font-weight: 700;
+    padding: 4px 30px
+}
+
+@media(max-width: 550px) {
+    .swagger-ui .topbar .download-url-wrapper {
+        width:100%
+    }
+}
+
+.swagger-ui .info {
+    margin: 20px 0;
+}
+
+.swagger-ui .info.failed-config {
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 880px;
+    text-align: center
+}
+
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+    font-size: 12px
+}
+
+.swagger-ui .info li,.swagger-ui .info p,.swagger-ui .info pre,.swagger-ui .info table {
+    font-size: 14px
+}
+
+.swagger-ui .info h1,.swagger-ui .info h2,.swagger-ui .info h3,.swagger-ui .info h4,.swagger-ui .info h5,.swagger-ui .info li,.swagger-ui .info p,.swagger-ui .info table {
+    color: #3b4151;
+    font-family: sans-serif
+}
+
+.swagger-ui .info a {
+    color: #4990e2;
+    font-family: sans-serif;
+    font-size: 14px;
+    transition: all .4s;
+    display: none;
+}
+
+.swagger-ui .info a:hover {
+    color: #1f69c0
+}
+
+.swagger-ui .info>div {
+    margin: 0 0 5px
+}
+
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+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 300!important;
+    font-weight: 600;
+    margin: 0
+}
+
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+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 36px;
+    margin: 0
+}
+
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+    background: #7d8492;
+    display: inline-block;
+    font-size: 10px;
+    margin: 0 0 0 5px;
+    padding: 2px 4px;
+    position: relative;
+    top: -5px;
+    vertical-align: super
+}
+
+.swagger-ui .info .title small.version-stamp {
+    background-color: #89bf04
+}
+
+.swagger-ui .info .title small pre {
+    color: #fff;
+    font-family: sans-serif;
+    margin: 0;
+    padding: 0
+}
+
+.swagger-ui .auth-btn-wrapper {
+    display: flex;
+    justify-content: center;
+    padding: 10px 0
+}
+
+.swagger-ui .auth-btn-wrapper .btn-done {
+    margin-right: 1em
+}
+
+.swagger-ui .auth-wrapper {
+    display: flex;
+    flex: 1;
+    justify-content: flex-end
+}
+
+.swagger-ui .auth-wrapper .authorize {
+    margin-left: 10px;
+    margin-right: 10px;
+    padding-right: 20px
+}
+
+.swagger-ui .auth-container {
+    margin: 0 0 10px;
+    padding: 10px 20px
+}
+
+.swagger-ui .auth-container:last-of-type {
+    margin: 0;
+    padding: 10px 20px
+}
+
+.swagger-ui .auth-container h4 {
+    margin: 5px 0 15px!important
+}
+
+.swagger-ui .auth-container .wrapper {
+    margin: 0;
+    padding: 0
+}
+
+.swagger-ui .auth-container input[type=password],.swagger-ui .auth-container input[type=text] {
+    min-width: 230px
+}
+
+.swagger-ui .auth-container .errors {
+    background-color: #fee;
+    color: red;
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 600;
+    margin: 1em;
+    padding: 10px
+}
+
+.swagger-ui .auth-container .errors b {
+    margin-right: 1em;
+    text-transform: capitalize
+}
+
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+    color: #3b4151;
+    font-family: sans-serif;
+    font-size: 14px
+}
+
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+    color: #4990e2;
+    cursor: pointer;
+    font-size: 12px;
+    padding-left: 10px;
+    -webkit-text-decoration: underline;
+    text-decoration: underline
+}
+
+.swagger-ui .scope-def {
+    padding: 0 0 20px
+}
+
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+    animation: scaleUp .5s;
+    background: rgba(249,62,62,.1);
+    margin: 20px;
+    padding: 10px 20px
+}
+
+.swagger-ui .errors-wrapper .error-wrapper {
+    margin: 0 0 10px
+}
+
+.swagger-ui .errors-wrapper .errors h4 {
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 14px;
+    font-weight: 600;
+    margin: 0
+}
+
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+    color: #606060
+}
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+    white-space: pre-line
+}
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+}
+
+.swagger-ui .errors-wrapper .errors .error-line {
+    cursor: pointer;
+    -webkit-text-decoration: underline;
+    text-decoration: underline
+}
+
+.swagger-ui .errors-wrapper hgroup {
+    align-items: center;
+    display: flex
+}
+
+.swagger-ui .errors-wrapper hgroup h4 {
+    color: #3b4151;
+    flex: 1;
+    font-family: sans-serif;
+    font-size: 20px;
+    margin: 0
+}
+
+@keyframes scaleUp {
+    0% {
+        opacity: 0;
+        transform: scale(.8)
+    }
+
+    to {
+        opacity: 1;
+        transform: scale(1)
+    }
+}
+
+.swagger-ui .Resizer.vertical.disabled {
+    display: none
+}
+
+.swagger-ui .markdown p,.swagger-ui .markdown pre,.swagger-ui .renderedMarkdown p,.swagger-ui .renderedMarkdown pre {
+    margin: 1em auto;
+    word-break: break-all;
+    word-break: break-word
+}
+
+.swagger-ui .markdown pre,.swagger-ui .renderedMarkdown pre {
+    background: none;
+    color: #000;
+    font-weight: 400;
+    padding: 0;
+    white-space: pre-wrap
+}
+
+.swagger-ui .markdown code,.swagger-ui .renderedMarkdown code {
+    background: rgba(0,0,0,.05);
+    color: #9012fe;
+    font-family: monospace;
+    font-size: 14px;
+    font-weight: 600;
+    padding: 5px 7px
+}
+
+.swagger-ui .markdown pre>code,.swagger-ui .renderedMarkdown pre>code {
+    display: block
+}
+
+.swagger-ui .json-schema-2020-12-keyword--\$vocabulary ul {
+    margin: 0 0 0 20px
+}
+
+.swagger-ui .json-schema-2020-12-\$vocabulary-uri {
+    margin-left: 35px
+}
+
+.swagger-ui .json-schema-2020-12-\$vocabulary-uri--disabled {
+    -webkit-text-decoration: line-through;
+    text-decoration: line-through
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+
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+    color: #3b4151;
+    font-style: normal
+}
+
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+    color: #3b4151;
+    color: #fff;
+    font-family: monospace;
+    font-weight: 600;
+    line-height: 1.5;
+    margin-left: 10px;
+    padding: 1px 3px
+}
+
+.swagger-ui .json-schema-2020-12__constraint--string {
+    background-color: #d69e2e;
+    color: #fff
+}
+
+.swagger-ui .json-schema-2020-12-keyword--default .json-schema-2020-12-json-viewer__name,.swagger-ui .json-schema-2020-12-keyword--default .json-schema-2020-12-json-viewer__value {
+    color: #3b4151;
+    font-style: normal
+}
+
+.swagger-ui .json-schema-2020-12-keyword--dependentRequired>ul {
+    display: inline-block;
+    margin: 0;
+    padding: 0
+}
+
+.swagger-ui .json-schema-2020-12-keyword--dependentRequired>ul li {
+    display: inline;
+    list-style-type: none
+}
+
+.swagger-ui .json-schema-2020-12-keyword--description {
+    color: #6b6b6b;
+    font-size: 12px;
+    margin-left: 20px
+}
+
+.swagger-ui .json-schema-2020-12-keyword--description p {
+    margin: 0
+}
+
+.swagger-ui .json-schema-2020-12-keyword--enum .json-schema-2020-12-json-viewer__name,.swagger-ui .json-schema-2020-12-keyword--enum .json-schema-2020-12-json-viewer__value,.swagger-ui .json-schema-2020-12-keyword--examples .json-schema-2020-12-json-viewer__name,.swagger-ui .json-schema-2020-12-keyword--examples .json-schema-2020-12-json-viewer__value {
+    color: #3b4151;
+    font-style: normal
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer-extension-keyword .json-schema-2020-12-json-viewer__name,.swagger-ui .json-schema-2020-12-json-viewer-extension-keyword .json-schema-2020-12-json-viewer__value {
+    color: #929292;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-keyword--patternProperties ul {
+    margin: 0;
+    padding: 0
+}
+
+.swagger-ui .json-schema-2020-12-keyword--patternProperties .json-schema-2020-12__title:first-of-type:after,.swagger-ui .json-schema-2020-12-keyword--patternProperties .json-schema-2020-12__title:first-of-type:before {
+    color: #55a;
+    content: "/"
+}
+
+.swagger-ui .json-schema-2020-12-keyword--properties>ul {
+    margin: 0;
+    padding: 0
+}
+
+.swagger-ui .json-schema-2020-12-property {
+    list-style-type: none
+}
+
+.swagger-ui .json-schema-2020-12-property--required>.json-schema-2020-12:first-of-type>.json-schema-2020-12-head .json-schema-2020-12__title:after {
+    color: red;
+    content: "*";
+    font-weight: 700
+}
+
+.swagger-ui .json-schema-2020-12__title {
+    color: #505050;
+    display: inline-block;
+    font-family: sans-serif;
+    font-size: 12px;
+    font-weight: 700;
+    line-height: normal
+}
+
+.swagger-ui .json-schema-2020-12__title .json-schema-2020-12-keyword__name {
+    margin: 0
+}
+
+.swagger-ui .json-schema-2020-12-property {
+    margin: 7px 0
 }
 
 .swagger-ui .json-schema-2020-12-property .json-schema-2020-12__title {
-  color: aqua;
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    font-weight: 600;
+    vertical-align: middle
 }
 
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-      overflow-y: scroll;
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-  }
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-      background-color: #1f1f1f;
-      color: #bfbfbf;
-  }
-
-  font, html { color: #bfbfbf; }
-
-  .swagger-ui, .swagger-ui section h3 { color: #b5bac9; }
-
-  .swagger-ui a { background-color: transparent; }
-
-  .swagger-ui mark {
-      background-color: #664b00;
-      color: #bfbfbf;
-  }
-
-  .swagger-ui legend { color: inherit; }
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-  .swagger-ui .debug * { outline: #e6da99 solid 1px; }
-
-  .swagger-ui .debug-white * { outline: #fff solid 1px; }
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-
-  .swagger-ui .debug-grid-8-solid { background: url(data:image/jpeg;base64,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) 0 0 #1c1c21; }
-
-  .swagger-ui .debug-grid-16-solid { background: url(data:image/png;base64,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) 0 0 #1c1c21; }
-
-  .swagger-ui .b--black { border-color: #000; }
-
-  .swagger-ui .b--near-black { border-color: #121212; }
-
-  .swagger-ui .b--dark-gray { border-color: #333; }
-
-  .swagger-ui .b--mid-gray { border-color: #545454; }
-
-  .swagger-ui .b--gray { border-color: #787878; }
-
-  .swagger-ui .b--silver { border-color: #999; }
-
-  .swagger-ui .b--light-silver { border-color: #6e6e6e; }
-
-  .swagger-ui .b--moon-gray { border-color: #4d4d4d; }
-
-  .swagger-ui .b--light-gray { border-color: #2b2b2b; }
-
-  .swagger-ui .b--near-white { border-color: #242424; }
-
-  .swagger-ui .b--white { border-color: #1c1c21; }
-
-  .swagger-ui .b--white-90 { border-color: rgba(28, 28, 33, .9); }
-
-  .swagger-ui .b--white-80 { border-color: rgba(28, 28, 33, .8); }
-
-  .swagger-ui .b--white-70 { border-color: rgba(28, 28, 33, .7); }
-
-  .swagger-ui .b--white-60 { border-color: rgba(28, 28, 33, .6); }
-
-  .swagger-ui .b--white-50 { border-color: rgba(28, 28, 33, .5); }
-
-  .swagger-ui .b--white-40 { border-color: rgba(28, 28, 33, .4); }
-
-  .swagger-ui .b--white-30 { border-color: rgba(28, 28, 33, .3); }
-
-  .swagger-ui .b--white-20 { border-color: rgba(28, 28, 33, .2); }
-
-  .swagger-ui .b--white-10 { border-color: rgba(28, 28, 33, .1); }
-
-  .swagger-ui .b--white-05 { border-color: rgba(28, 28, 33, .05); }
-
-  .swagger-ui .b--white-025 { border-color: rgba(28, 28, 33, .024); }
-
-  .swagger-ui .b--white-0125 { border-color: rgba(28, 28, 33, .01); }
-
-  .swagger-ui .b--black-90 { border-color: rgba(0, 0, 0, .9); }
-
-  .swagger-ui .b--black-80 { border-color: rgba(0, 0, 0, .8); }
-
-  .swagger-ui .b--black-70 { border-color: rgba(0, 0, 0, .7); }
-
-  .swagger-ui .b--black-60 { border-color: rgba(0, 0, 0, .6); }
-
-  .swagger-ui .b--black-50 { border-color: rgba(0, 0, 0, .5); }
-
-  .swagger-ui .b--black-40 { border-color: rgba(0, 0, 0, .4); }
-
-  .swagger-ui .b--black-30 { border-color: rgba(0, 0, 0, .3); }
-
-  .swagger-ui .b--black-20 { border-color: rgba(0, 0, 0, .2); }
-
-  .swagger-ui .b--black-10 { border-color: rgba(0, 0, 0, .1); }
-
-  .swagger-ui .b--black-05 { border-color: rgba(0, 0, 0, .05); }
-
-  .swagger-ui .b--black-025 { border-color: rgba(0, 0, 0, .024); }
-
-  .swagger-ui .b--black-0125 { border-color: rgba(0, 0, 0, .01); }
-
-  .swagger-ui .b--dark-red { border-color: #bc2f36; }
-
-  .swagger-ui .b--red { border-color: #c83932; }
-
-  .swagger-ui .b--light-red { border-color: #ab3c2b; }
-
-  .swagger-ui .b--orange { border-color: #cc6e33; }
-
-  .swagger-ui .b--purple { border-color: #5e2ca5; }
-
-  .swagger-ui .b--light-purple { border-color: #672caf; }
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-  .swagger-ui .b--dark-pink { border-color: #ab2b81; }
-
-  .swagger-ui .b--hot-pink { border-color: #c03086; }
-
-  .swagger-ui .b--pink { border-color: #8f2464; }
-
-  .swagger-ui .b--light-pink { border-color: #721d4d; }
-
-  .swagger-ui .b--dark-green { border-color: #1c6e50; }
-
-  .swagger-ui .b--green { border-color: #279b70; }
-
-  .swagger-ui .b--light-green { border-color: #228762; }
-
-  .swagger-ui .b--navy { border-color: #0d1d35; }
-
-  .swagger-ui .b--dark-blue { border-color: #20497e; }
-
-  .swagger-ui .b--blue { border-color: #4380d0; }
-
-  .swagger-ui .b--light-blue { border-color: #20517e; }
-
-  .swagger-ui .b--lightest-blue { border-color: #143a52; }
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-  .swagger-ui .b--washed-blue { border-color: #0c312d; }
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-  .swagger-ui .b--washed-green { border-color: #0f3d2c; }
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-  .swagger-ui .b--washed-red { border-color: #411010; }
-
-  .swagger-ui .b--transparent { border-color: transparent; }
-
-  .swagger-ui .b--gold, .swagger-ui .b--light-yellow, .swagger-ui .b--washed-yellow, .swagger-ui .b--yellow { border-color: #664b00; }
-
-  .swagger-ui .shadow-1 { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
-
-  .swagger-ui .shadow-2 { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
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-  .swagger-ui .shadow-3 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
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-  .swagger-ui .shadow-4 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
-
-  .swagger-ui .shadow-5 { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
-
-  @media screen and (min-width: 30em) {
-      .swagger-ui .shadow-1-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
-
-      .swagger-ui .shadow-2-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
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-      .swagger-ui .shadow-3-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
-
-      .swagger-ui .shadow-4-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
-
-      .swagger-ui .shadow-5-ns { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
-  }
-
-  @media screen and (max-width: 60em) and (min-width: 30em) {
-      .swagger-ui .shadow-1-m { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
-
-      .swagger-ui .shadow-2-m { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
-
-      .swagger-ui .shadow-3-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
-
-      .swagger-ui .shadow-4-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
-
-      .swagger-ui .shadow-5-m { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
-  }
-
-  @media screen and (min-width: 60em) {
-      .swagger-ui .shadow-1-l { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
-
-      .swagger-ui .shadow-2-l { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
-
-      .swagger-ui .shadow-3-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
-
-      .swagger-ui .shadow-4-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
-
-      .swagger-ui .shadow-5-l { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
-  }
-
-  .swagger-ui .black-05 { color: rgba(191, 191, 191, .05); }
-
-  .swagger-ui .bg-black-05 { background-color: rgba(0, 0, 0, .05); }
-
-  .swagger-ui .black-90, .swagger-ui .hover-black-90:focus, .swagger-ui .hover-black-90:hover { color: rgba(191, 191, 191, .9); }
-
-  .swagger-ui .black-80, .swagger-ui .hover-black-80:focus, .swagger-ui .hover-black-80:hover { color: rgba(191, 191, 191, .8); }
-
-  .swagger-ui .black-70, .swagger-ui .hover-black-70:focus, .swagger-ui .hover-black-70:hover { color: rgba(191, 191, 191, .7); }
-
-  .swagger-ui .black-60, .swagger-ui .hover-black-60:focus, .swagger-ui .hover-black-60:hover { color: rgba(191, 191, 191, .6); }
-
-  .swagger-ui .black-50, .swagger-ui .hover-black-50:focus, .swagger-ui .hover-black-50:hover { color: rgba(191, 191, 191, .5); }
-
-  .swagger-ui .black-40, .swagger-ui .hover-black-40:focus, .swagger-ui .hover-black-40:hover { color: rgba(191, 191, 191, .4); }
-
-  .swagger-ui .black-30, .swagger-ui .hover-black-30:focus, .swagger-ui .hover-black-30:hover { color: rgba(191, 191, 191, .3); }
-
-  .swagger-ui .black-20, .swagger-ui .hover-black-20:focus, .swagger-ui .hover-black-20:hover { color: rgba(191, 191, 191, .2); }
-
-  .swagger-ui .black-10, .swagger-ui .hover-black-10:focus, .swagger-ui .hover-black-10:hover { color: rgba(191, 191, 191, .1); }
-
-  .swagger-ui .hover-white-90:focus, .swagger-ui .hover-white-90:hover, .swagger-ui .white-90 { color: rgba(255, 255, 255, .9); }
-
-  .swagger-ui .hover-white-80:focus, .swagger-ui .hover-white-80:hover, .swagger-ui .white-80 { color: rgba(255, 255, 255, .8); }
-
-  .swagger-ui .hover-white-70:focus, .swagger-ui .hover-white-70:hover, .swagger-ui .white-70 { color: rgba(255, 255, 255, .7); }
-
-  .swagger-ui .hover-white-60:focus, .swagger-ui .hover-white-60:hover, .swagger-ui .white-60 { color: rgba(255, 255, 255, .6); }
-
-  .swagger-ui .hover-white-50:focus, .swagger-ui .hover-white-50:hover, .swagger-ui .white-50 { color: rgba(255, 255, 255, .5); }
-
-  .swagger-ui .hover-white-40:focus, .swagger-ui .hover-white-40:hover, .swagger-ui .white-40 { color: rgba(255, 255, 255, .4); }
-
-  .swagger-ui .hover-white-30:focus, .swagger-ui .hover-white-30:hover, .swagger-ui .white-30 { color: rgba(255, 255, 255, .3); }
-
-  .swagger-ui .hover-white-20:focus, .swagger-ui .hover-white-20:hover, .swagger-ui .white-20 { color: rgba(255, 255, 255, .2); }
-
-  .swagger-ui .hover-white-10:focus, .swagger-ui .hover-white-10:hover, .swagger-ui .white-10 { color: rgba(255, 255, 255, .1); }
-
-  .swagger-ui .hover-moon-gray:focus, .swagger-ui .hover-moon-gray:hover, .swagger-ui .moon-gray { color: #ccc; }
-
-  .swagger-ui .hover-light-gray:focus, .swagger-ui .hover-light-gray:hover, .swagger-ui .light-gray { color: #ededed; }
-
-  .swagger-ui .hover-near-white:focus, .swagger-ui .hover-near-white:hover, .swagger-ui .near-white { color: #f5f5f5; }
-
-  .swagger-ui .dark-red, .swagger-ui .hover-dark-red:focus, .swagger-ui .hover-dark-red:hover { color: #e6999d; }
-
-  .swagger-ui .hover-red:focus, .swagger-ui .hover-red:hover, .swagger-ui .red { color: #e69d99; }
-
-  .swagger-ui .hover-light-red:focus, .swagger-ui .hover-light-red:hover, .swagger-ui .light-red { color: #e6a399; }
-
-  .swagger-ui .hover-orange:focus, .swagger-ui .hover-orange:hover, .swagger-ui .orange { color: #e6b699; }
-
-  .swagger-ui .gold, .swagger-ui .hover-gold:focus, .swagger-ui .hover-gold:hover { color: #e6d099; }
-
-  .swagger-ui .hover-yellow:focus, .swagger-ui .hover-yellow:hover, .swagger-ui .yellow { color: #e6da99; }
-
-  .swagger-ui .hover-light-yellow:focus, .swagger-ui .hover-light-yellow:hover, .swagger-ui .light-yellow { color: #ede6b6; }
-
-  .swagger-ui .hover-purple:focus, .swagger-ui .hover-purple:hover, .swagger-ui .purple { color: #b99ae4; }
-
-  .swagger-ui .hover-light-purple:focus, .swagger-ui .hover-light-purple:hover, .swagger-ui .light-purple { color: #bb99e6; }
-
-  .swagger-ui .dark-pink, .swagger-ui .hover-dark-pink:focus, .swagger-ui .hover-dark-pink:hover { color: #e699cc; }
-
-  .swagger-ui .hot-pink, .swagger-ui .hover-hot-pink:focus, .swagger-ui .hover-hot-pink:hover, .swagger-ui .hover-pink:focus, .swagger-ui .hover-pink:hover, .swagger-ui .pink { color: #e699c7; }
-
-  .swagger-ui .hover-light-pink:focus, .swagger-ui .hover-light-pink:hover, .swagger-ui .light-pink { color: #edb6d5; }
-
-  .swagger-ui .dark-green, .swagger-ui .green, .swagger-ui .hover-dark-green:focus, .swagger-ui .hover-dark-green:hover, .swagger-ui .hover-green:focus, .swagger-ui .hover-green:hover { color: #99e6c9; }
-
-  .swagger-ui .hover-light-green:focus, .swagger-ui .hover-light-green:hover, .swagger-ui .light-green { color: #a1e8ce; }
-
-  .swagger-ui .hover-navy:focus, .swagger-ui .hover-navy:hover, .swagger-ui .navy { color: #99b8e6; }
-
-  .swagger-ui .blue, .swagger-ui .dark-blue, .swagger-ui .hover-blue:focus, .swagger-ui .hover-blue:hover, .swagger-ui .hover-dark-blue:focus, .swagger-ui .hover-dark-blue:hover { color: #99bae6; }
-
-  .swagger-ui .hover-light-blue:focus, .swagger-ui .hover-light-blue:hover, .swagger-ui .light-blue { color: #a9cbea; }
-
-  .swagger-ui .hover-lightest-blue:focus, .swagger-ui .hover-lightest-blue:hover, .swagger-ui .lightest-blue { color: #d6e9f5; }
-
-  .swagger-ui .hover-washed-blue:focus, .swagger-ui .hover-washed-blue:hover, .swagger-ui .washed-blue { color: #f7fdfc; }
-
-  .swagger-ui .hover-washed-green:focus, .swagger-ui .hover-washed-green:hover, .swagger-ui .washed-green { color: #ebfaf4; }
-
-  .swagger-ui .hover-washed-yellow:focus, .swagger-ui .hover-washed-yellow:hover, .swagger-ui .washed-yellow { color: #fbf9ef; }
-
-  .swagger-ui .hover-washed-red:focus, .swagger-ui .hover-washed-red:hover, .swagger-ui .washed-red { color: #f9e7e7; }
-
-  .swagger-ui .color-inherit, .swagger-ui .hover-inherit:focus, .swagger-ui .hover-inherit:hover { color: inherit; }
-
-  .swagger-ui .bg-black-90, .swagger-ui .hover-bg-black-90:focus, .swagger-ui .hover-bg-black-90:hover { background-color: rgba(0, 0, 0, .9); }
-
-  .swagger-ui .bg-black-80, .swagger-ui .hover-bg-black-80:focus, .swagger-ui .hover-bg-black-80:hover { background-color: rgba(0, 0, 0, .8); }
-
-  .swagger-ui .bg-black-70, .swagger-ui .hover-bg-black-70:focus, .swagger-ui .hover-bg-black-70:hover { background-color: rgba(0, 0, 0, .7); }
-
-  .swagger-ui .bg-black-60, .swagger-ui .hover-bg-black-60:focus, .swagger-ui .hover-bg-black-60:hover { background-color: rgba(0, 0, 0, .6); }
-
-  .swagger-ui .bg-black-50, .swagger-ui .hover-bg-black-50:focus, .swagger-ui .hover-bg-black-50:hover { background-color: rgba(0, 0, 0, .5); }
-
-  .swagger-ui .bg-black-40, .swagger-ui .hover-bg-black-40:focus, .swagger-ui .hover-bg-black-40:hover { background-color: rgba(0, 0, 0, .4); }
-
-  .swagger-ui .bg-black-30, .swagger-ui .hover-bg-black-30:focus, .swagger-ui .hover-bg-black-30:hover { background-color: rgba(0, 0, 0, .3); }
-
-  .swagger-ui .bg-black-20, .swagger-ui .hover-bg-black-20:focus, .swagger-ui .hover-bg-black-20:hover { background-color: rgba(0, 0, 0, .2); }
-
-  .swagger-ui .bg-white-90, .swagger-ui .hover-bg-white-90:focus, .swagger-ui .hover-bg-white-90:hover { background-color: rgba(28, 28, 33, .9); }
-
-  .swagger-ui .bg-white-80, .swagger-ui .hover-bg-white-80:focus, .swagger-ui .hover-bg-white-80:hover { background-color: rgba(28, 28, 33, .8); }
-
-  .swagger-ui .bg-white-70, .swagger-ui .hover-bg-white-70:focus, .swagger-ui .hover-bg-white-70:hover { background-color: rgba(28, 28, 33, .7); }
-
-  .swagger-ui .bg-white-60, .swagger-ui .hover-bg-white-60:focus, .swagger-ui .hover-bg-white-60:hover { background-color: rgba(28, 28, 33, .6); }
-
-  .swagger-ui .bg-white-50, .swagger-ui .hover-bg-white-50:focus, .swagger-ui .hover-bg-white-50:hover { background-color: rgba(28, 28, 33, .5); }
-
-  .swagger-ui .bg-white-40, .swagger-ui .hover-bg-white-40:focus, .swagger-ui .hover-bg-white-40:hover { background-color: rgba(28, 28, 33, .4); }
-
-  .swagger-ui .bg-white-30, .swagger-ui .hover-bg-white-30:focus, .swagger-ui .hover-bg-white-30:hover { background-color: rgba(28, 28, 33, .3); }
-
-  .swagger-ui .bg-white-20, .swagger-ui .hover-bg-white-20:focus, .swagger-ui .hover-bg-white-20:hover { background-color: rgba(28, 28, 33, .2); }
-
-  .swagger-ui .bg-black, .swagger-ui .hover-bg-black:focus, .swagger-ui .hover-bg-black:hover { background-color: #000; }
-
-  .swagger-ui .bg-near-black, .swagger-ui .hover-bg-near-black:focus, .swagger-ui .hover-bg-near-black:hover { background-color: #121212; }
-
-  .swagger-ui .bg-dark-gray, .swagger-ui .hover-bg-dark-gray:focus, .swagger-ui .hover-bg-dark-gray:hover { background-color: #333; }
-
-  .swagger-ui .bg-mid-gray, .swagger-ui .hover-bg-mid-gray:focus, .swagger-ui .hover-bg-mid-gray:hover { background-color: #545454; }
-
-  .swagger-ui .bg-gray, .swagger-ui .hover-bg-gray:focus, .swagger-ui .hover-bg-gray:hover { background-color: #787878; }
-
-  .swagger-ui .bg-silver, .swagger-ui .hover-bg-silver:focus, .swagger-ui .hover-bg-silver:hover { background-color: #999; }
-
-  .swagger-ui .bg-white, .swagger-ui .hover-bg-white:focus, .swagger-ui .hover-bg-white:hover { background-color: #1c1c21; }
-
-  .swagger-ui .bg-transparent, .swagger-ui .hover-bg-transparent:focus, .swagger-ui .hover-bg-transparent:hover { background-color: transparent; }
-
-  .swagger-ui .bg-dark-red, .swagger-ui .hover-bg-dark-red:focus, .swagger-ui .hover-bg-dark-red:hover { background-color: #bc2f36; }
-
-  .swagger-ui .bg-red, .swagger-ui .hover-bg-red:focus, .swagger-ui .hover-bg-red:hover { background-color: #c83932; }
-
-  .swagger-ui .bg-light-red, .swagger-ui .hover-bg-light-red:focus, .swagger-ui .hover-bg-light-red:hover { background-color: #ab3c2b; }
-
-  .swagger-ui .bg-orange, .swagger-ui .hover-bg-orange:focus, .swagger-ui .hover-bg-orange:hover { background-color: #cc6e33; }
-
-  .swagger-ui .bg-gold, .swagger-ui .bg-light-yellow, .swagger-ui .bg-washed-yellow, .swagger-ui .bg-yellow, .swagger-ui .hover-bg-gold:focus, .swagger-ui .hover-bg-gold:hover, .swagger-ui .hover-bg-light-yellow:focus, .swagger-ui .hover-bg-light-yellow:hover, .swagger-ui .hover-bg-washed-yellow:focus, .swagger-ui .hover-bg-washed-yellow:hover, .swagger-ui .hover-bg-yellow:focus, .swagger-ui .hover-bg-yellow:hover { background-color: #664b00; }
-
-  .swagger-ui .bg-purple, .swagger-ui .hover-bg-purple:focus, .swagger-ui .hover-bg-purple:hover { background-color: #5e2ca5; }
-
-  .swagger-ui .bg-light-purple, .swagger-ui .hover-bg-light-purple:focus, .swagger-ui .hover-bg-light-purple:hover { background-color: #672caf; }
-
-  .swagger-ui .bg-dark-pink, .swagger-ui .hover-bg-dark-pink:focus, .swagger-ui .hover-bg-dark-pink:hover { background-color: #ab2b81; }
-
-  .swagger-ui .bg-hot-pink, .swagger-ui .hover-bg-hot-pink:focus, .swagger-ui .hover-bg-hot-pink:hover { background-color: #c03086; }
-
-  .swagger-ui .bg-pink, .swagger-ui .hover-bg-pink:focus, .swagger-ui .hover-bg-pink:hover { background-color: #8f2464; }
-
-  .swagger-ui .bg-light-pink, .swagger-ui .hover-bg-light-pink:focus, .swagger-ui .hover-bg-light-pink:hover { background-color: #721d4d; }
-
-  .swagger-ui .bg-dark-green, .swagger-ui .hover-bg-dark-green:focus, .swagger-ui .hover-bg-dark-green:hover { background-color: #1c6e50; }
-
-  .swagger-ui .bg-green, .swagger-ui .hover-bg-green:focus, .swagger-ui .hover-bg-green:hover { background-color: #279b70; }
-
-  .swagger-ui .bg-light-green, .swagger-ui .hover-bg-light-green:focus, .swagger-ui .hover-bg-light-green:hover { background-color: #228762; }
-
-  .swagger-ui .bg-navy, .swagger-ui .hover-bg-navy:focus, .swagger-ui .hover-bg-navy:hover { background-color: #0d1d35; }
-
-  .swagger-ui .bg-dark-blue, .swagger-ui .hover-bg-dark-blue:focus, .swagger-ui .hover-bg-dark-blue:hover { background-color: #20497e; }
-
-  .swagger-ui .bg-blue, .swagger-ui .hover-bg-blue:focus, .swagger-ui .hover-bg-blue:hover { background-color: #4380d0; }
-
-  .swagger-ui .bg-light-blue, .swagger-ui .hover-bg-light-blue:focus, .swagger-ui .hover-bg-light-blue:hover { background-color: #20517e; }
-
-  .swagger-ui .bg-lightest-blue, .swagger-ui .hover-bg-lightest-blue:focus, .swagger-ui .hover-bg-lightest-blue:hover { background-color: #143a52; }
-
-  .swagger-ui .bg-washed-blue, .swagger-ui .hover-bg-washed-blue:focus, .swagger-ui .hover-bg-washed-blue:hover { background-color: #0c312d; }
-
-  .swagger-ui .bg-washed-green, .swagger-ui .hover-bg-washed-green:focus, .swagger-ui .hover-bg-washed-green:hover { background-color: #0f3d2c; }
-
-  .swagger-ui .bg-washed-red, .swagger-ui .hover-bg-washed-red:focus, .swagger-ui .hover-bg-washed-red:hover { background-color: #411010; }
-
-  .swagger-ui .bg-inherit, .swagger-ui .hover-bg-inherit:focus, .swagger-ui .hover-bg-inherit:hover { background-color: inherit; }
-
-  .swagger-ui .shadow-hover { transition: all .5s cubic-bezier(.165, .84, .44, 1) 0s; }
-
-  .swagger-ui .shadow-hover::after {
-      border-radius: inherit;
-      box-shadow: rgba(0, 0, 0, .2) 0 0 16px 2px;
-      content: "";
-      height: 100%;
-      left: 0;
-      opacity: 0;
-      position: absolute;
-      top: 0;
-      transition: opacity .5s cubic-bezier(.165, .84, .44, 1) 0s;
-      width: 100%;
-      z-index: -1;
-  }
-
-  .swagger-ui .bg-animate, .swagger-ui .bg-animate:focus, .swagger-ui .bg-animate:hover { transition: background-color .15s ease-in-out 0s; }
-
-  .swagger-ui .nested-links a {
-      color: #99bae6;
-      transition: color .15s ease-in 0s;
-  }
-
-  .swagger-ui .nested-links a:focus, .swagger-ui .nested-links a:hover {
-      color: #a9cbea;
-      transition: color .15s ease-in 0s;
-  }
-
-  .swagger-ui .opblock-tag {
-      border-bottom: 1px solid rgba(58, 64, 80, .3);
-      color: #b5bac9;
-      transition: all .2s ease 0s;
-  }
-
-  .swagger-ui .opblock-tag svg, .swagger-ui section.models h4 svg { transition: all .4s ease 0s; }
-
-  .swagger-ui .opblock {
-      border: 1px solid #000;
-      border-radius: 4px;
-      box-shadow: rgba(0, 0, 0, .19) 0 0 3px;
-      margin: 0 0 15px;
-  }
-
-  .swagger-ui .opblock .tab-header .tab-item.active h4 span::after { background: gray; }
-
-  .swagger-ui .opblock.is-open .opblock-summary { border-bottom: 1px solid #000; }
-
-  .swagger-ui .opblock .opblock-section-header {
-      background: rgba(28, 28, 33, .8);
-      box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
-  }
-
-  .swagger-ui .opblock .opblock-section-header > label > span { padding: 0 10px 0 0; }
-
-  .swagger-ui .opblock .opblock-summary-method {
-      background: #000;
-      color: #fff;
-      text-shadow: rgba(0, 0, 0, .1) 0 1px 0;
-  }
-
-  .swagger-ui .opblock.opblock-post {
-      background: rgba(72, 203, 144, .1);
-      border-color: #48cb90;
-  }
-
-  .swagger-ui .opblock.opblock-post .opblock-summary-method, .swagger-ui .opblock.opblock-post .tab-header .tab-item.active h4 span::after { background: #48cb90; }
-
-  .swagger-ui .opblock.opblock-post .opblock-summary { border-color: #48cb90; }
-
-  .swagger-ui .opblock.opblock-put {
-      background: rgba(213, 157, 88, .1);
-      border-color: #d59d58;
-  }
-
-  .swagger-ui .opblock.opblock-put .opblock-summary-method, .swagger-ui .opblock.opblock-put .tab-header .tab-item.active h4 span::after { background: #d59d58; }
-
-  .swagger-ui .opblock.opblock-put .opblock-summary { border-color: #d59d58; }
-
-  .swagger-ui .opblock.opblock-delete {
-      background: rgba(200, 50, 50, .1);
-      border-color: #c83232;
-  }
-
-  .swagger-ui .opblock.opblock-delete .opblock-summary-method, .swagger-ui .opblock.opblock-delete .tab-header .tab-item.active h4 span::after { background: #c83232; }
-
-  .swagger-ui .opblock.opblock-delete .opblock-summary { border-color: #c83232; }
-
-  .swagger-ui .opblock.opblock-get {
-      background: rgba(42, 105, 167, .1);
-      border-color: #2a69a7;
-  }
-
-  .swagger-ui .opblock.opblock-get .opblock-summary-method, .swagger-ui .opblock.opblock-get .tab-header .tab-item.active h4 span::after { background: #2a69a7; }
-
-  .swagger-ui .opblock.opblock-get .opblock-summary { border-color: #2a69a7; }
-
-  .swagger-ui .opblock.opblock-patch {
-      background: rgba(92, 214, 188, .1);
-      border-color: #5cd6bc;
-  }
-
-  .swagger-ui .opblock.opblock-patch .opblock-summary-method, .swagger-ui .opblock.opblock-patch .tab-header .tab-item.active h4 span::after { background: #5cd6bc; }
-
-  .swagger-ui .opblock.opblock-patch .opblock-summary { border-color: #5cd6bc; }
-
-  .swagger-ui .opblock.opblock-head {
-      background: rgba(140, 63, 207, .1);
-      border-color: #8c3fcf;
-  }
-
-  .swagger-ui .opblock.opblock-head .opblock-summary-method, .swagger-ui .opblock.opblock-head .tab-header .tab-item.active h4 span::after { background: #8c3fcf; }
-
-  .swagger-ui .opblock.opblock-head .opblock-summary { border-color: #8c3fcf; }
-
-  .swagger-ui .opblock.opblock-options {
-      background: rgba(36, 89, 143, .1);
-      border-color: #24598f;
-  }
-
-  .swagger-ui .opblock.opblock-options .opblock-summary-method, .swagger-ui .opblock.opblock-options .tab-header .tab-item.active h4 span::after { background: #24598f; }
-
-  .swagger-ui .opblock.opblock-options .opblock-summary { border-color: #24598f; }
-
-  .swagger-ui .opblock.opblock-deprecated {
-      background: rgba(46, 46, 46, .1);
-      border-color: #2e2e2e;
-      opacity: .6;
-  }
-
-  .swagger-ui .opblock.opblock-deprecated .opblock-summary-method, .swagger-ui .opblock.opblock-deprecated .tab-header .tab-item.active h4 span::after { background: #2e2e2e; }
-
-  .swagger-ui .opblock.opblock-deprecated .opblock-summary { border-color: #2e2e2e; }
-
-  .swagger-ui .filter .operation-filter-input { border: 2px solid #2b3446; }
-
-  .swagger-ui .tab li:first-of-type::after { background: rgba(0, 0, 0, .2); }
-
-  .swagger-ui .download-contents {
-      background: #7c8192;
-      color: #fff;
-  }
-
-  .swagger-ui .scheme-container {
-      background: #1c1c21;
-      box-shadow: rgba(0, 0, 0, .15) 0 1px 2px 0;
-  }
-
-  .swagger-ui .loading-container .loading::before {
-      animation: 1s linear 0s infinite normal none running rotation, .5s ease 0s 1 normal none running opacity;
-      border-color: rgba(0, 0, 0, .6) rgba(84, 84, 84, .1) rgba(84, 84, 84, .1);
-  }
-
-  .swagger-ui .response-control-media-type--accept-controller select { border-color: #196619; }
-
-  .swagger-ui .response-control-media-type__accept-message { color: #99e699; }
-
-  .swagger-ui .version-pragma__message code { background-color: #3b3b3b; }
-
-  .swagger-ui .btn {
-      background: 0 0;
-      border: 2px solid gray;
-      box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
-      color: #b5bac9;
-  }
-
-  .swagger-ui .btn:hover { box-shadow: rgba(0, 0, 0, .3) 0 0 5px; }
-
-  .swagger-ui .btn.authorize, .swagger-ui .btn.cancel {
-      background-color: transparent;
-      border-color: #a72a2a;
-      color: #e69999;
-  }
-
-  .swagger-ui .btn.cancel:hover {
-      background-color: #a72a2a;
-      color: #fff;
-  }
-
-  .swagger-ui .btn.authorize {
-      border-color: #48cb90;
-      color: #9ce3c3;
-  }
-
-  .swagger-ui .btn.authorize svg { fill: #9ce3c3; }
-
-  .btn.authorize.unlocked:hover {
-      background-color: #48cb90;
-      color: #fff;
-  }
-
-  .btn.authorize.unlocked:hover svg {
-      fill: #fbfbfb;
-  }
-
-  .swagger-ui .btn.execute {
-      background-color: #5892d5;
-      border-color: #5892d5;
-      color: #fff;
-  }
-
-  .swagger-ui .copy-to-clipboard { background: #7c8192; }
-
-  .swagger-ui .copy-to-clipboard button { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"16\" height=\"16\" aria-hidden=\"true\"><path fill=\"%23fff\" fill-rule=\"evenodd\" d=\"M2 13h4v1H2v-1zm5-6H2v1h5V7zm2 3V8l-3 3 3 3v-2h5v-2H9zM4.5 9H2v1h2.5V9zM2 12h2.5v-1H2v1zm9 1h1v2c-.02.28-.11.52-.3.7-.19.18-.42.28-.7.3H1c-.55 0-1-.45-1-1V4c0-.55.45-1 1-1h3c0-1.11.89-2 2-2 1.11 0 2 .89 2 2h3c.55 0 1 .45 1 1v5h-1V6H1v9h10v-2zM2 5h8c0-.55-.45-1-1-1H8c-.55 0-1-.45-1-1s-.45-1-1-1-1 .45-1 1-.45 1-1 1H3c-.55 0-1 .45-1 1z\"/></svg>") 50% center no-repeat; }
-
-  .swagger-ui select {
-      background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 20 20\"><path d=\"M13.418 7.859a.695.695 0 01.978 0 .68.68 0 010 .969l-3.908 3.83a.697.697 0 01-.979 0l-3.908-3.83a.68.68 0 010-.969.695.695 0 01.978 0L10 11l3.418-3.141z\"/></svg>") right 10px center/20px no-repeat #212121;
-      background: url(data:image/svg+xml;base64,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) right 10px center/20px no-repeat #1c1c21;
-      border: 2px solid #41444e;
-  }
-
-  .swagger-ui select[multiple] { background: #212121; }
-
-  .swagger-ui button.invalid, .swagger-ui input[type=email].invalid, .swagger-ui input[type=file].invalid, .swagger-ui input[type=password].invalid, .swagger-ui input[type=search].invalid, .swagger-ui input[type=text].invalid, .swagger-ui select.invalid, .swagger-ui textarea.invalid {
-      background: #390e0e;
-      border-color: #c83232;
-  }
-
-  .swagger-ui input[type=email], .swagger-ui input[type=file], .swagger-ui input[type=password], .swagger-ui input[type=search], .swagger-ui input[type=text], .swagger-ui textarea {
-      background: #1c1c21;
-      border: 1px solid #404040;
-  }
-
-  .swagger-ui textarea {
-      background: rgba(28, 28, 33, .8);
-      color: #b5bac9;
-  }
-
-  .swagger-ui input[disabled], .swagger-ui select[disabled] {
-      background-color: #1f1f1f;
-      color: #bfbfbf;
-  }
-
-  .swagger-ui textarea[disabled] {
-      background-color: #41444e;
-      color: #fff;
-  }
-
-  .swagger-ui select[disabled] { border-color: #878787; }
-
-  .swagger-ui textarea:focus { border: 2px solid #2a69a7; }
-
-  .swagger-ui .checkbox input[type=checkbox] + label > .item {
-      background: #303030;
-      box-shadow: #303030 0 0 0 2px;
-  }
-
-  .swagger-ui .checkbox input[type=checkbox]:checked + label > .item { background: url("data:image/svg+xml;charset=utf-8,<svg width=\"10\" height=\"8\" viewBox=\"3 7 10 8\" xmlns=\"http://www.w3.org/2000/svg\"><path fill=\"%2341474E\" fill-rule=\"evenodd\" d=\"M6.333 15L3 11.667l1.333-1.334 2 2L11.667 7 13 8.333z\"/></svg>") 50% center no-repeat #303030; }
-
-  .swagger-ui .dialog-ux .backdrop-ux { background: rgba(0, 0, 0, .8); }
-
-  .swagger-ui .dialog-ux .modal-ux {
-      background: #1c1c21;
-      border: 1px solid #2e2e2e;
-      box-shadow: rgba(0, 0, 0, .2) 0 10px 30px 0;
-  }
-
-  .swagger-ui .dialog-ux .modal-ux-header .close-modal { background: 0 0; }
-
-  .swagger-ui .model .deprecated span, .swagger-ui .model .deprecated td { color: #bfbfbf !important; }
-
-  .swagger-ui .model-toggle::after { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"24\" height=\"24\"><path d=\"M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z\"/></svg>") 50% center/100% no-repeat; }
-
-  .swagger-ui .model-hint {
-      background: rgba(0, 0, 0, .7);
-      color: #ebebeb;
-  }
-
-  .swagger-ui section.models { border: 1px solid rgba(58, 64, 80, .3); }
-
-  .swagger-ui section.models.is-open h4 { border-bottom: 1px solid rgba(58, 64, 80, .3); }
-
-  .swagger-ui section.models .model-container { background: rgba(0, 0, 0, .05); }
-
-  .swagger-ui section.models .model-container:hover { background: rgba(0, 0, 0, .07); }
-
-  .swagger-ui .model-box { background: rgba(0, 0, 0, .1); }
-
-  .swagger-ui .prop-type { color: #aaaad4; }
-
-  .swagger-ui table thead tr td, .swagger-ui table thead tr th {
-      border-bottom: 1px solid rgba(58, 64, 80, .2);
-      color: #b5bac9;
-  }
-
-  .swagger-ui .parameter__name.required::after { color: rgba(230, 153, 153, .6); }
-
-  .swagger-ui .topbar .download-url-wrapper .select-label { color: #f0f0f0; }
-
-  .swagger-ui .topbar .download-url-wrapper .download-url-button {
-      background: #63a040;
-      color: #fff;
-  }
-
-  .swagger-ui .info .title small { background: #7c8492; }
-
-  .swagger-ui .info .title small.version-stamp { background-color: #7a9b27; }
-
-  .swagger-ui .auth-container .errors {
-      background-color: #350d0d;
-      color: #b5bac9;
-  }
-
-  .swagger-ui .errors-wrapper {
-      background: rgba(200, 50, 50, .1);
-      border: 2px solid #c83232;
-  }
-
-  .swagger-ui .markdown code, .swagger-ui .renderedmarkdown code {
-      background: rgba(0, 0, 0, .05);
-      color: #c299e6;
-  }
-
-  .swagger-ui .model-toggle:after { background: url(data:image/svg+xml;base64,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) 50% no-repeat; }
-
-  /* arrows for each operation and request are now white */
-  .arrow, #large-arrow-up { fill: #fff; }
-
-  #unlocked { fill: #fff; }
-
-  ::-webkit-scrollbar-track { background-color: #646464 !important; }
-
-  ::-webkit-scrollbar-thumb {
-      background-color: #242424 !important;
-      border: 2px solid #3e4346 !important;
-  }
-
-  ::-webkit-scrollbar-button:vertical:start:decrement {
-      background: linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%), linear-gradient(230deg, #696969 40%, transparent 41%), linear-gradient(0deg, #696969 40%, transparent 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button:vertical:end:increment {
-      background: linear-gradient(310deg, #696969 40%, transparent 41%), linear-gradient(50deg, #696969 40%, transparent 41%), linear-gradient(180deg, #696969 40%, transparent 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button:horizontal:end:increment {
-      background: linear-gradient(210deg, #696969 40%, transparent 41%), linear-gradient(330deg, #696969 40%, transparent 41%), linear-gradient(90deg, #696969 30%, transparent 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button:horizontal:start:decrement {
-      background: linear-gradient(30deg, #696969 40%, transparent 41%), linear-gradient(150deg, #696969 40%, transparent 41%), linear-gradient(270deg, #696969 30%, transparent 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button, ::-webkit-scrollbar-track-piece { background-color: #3e4346 !important; }
-
-  .swagger-ui .black, .swagger-ui .checkbox, .swagger-ui .dark-gray, .swagger-ui .download-url-wrapper .loading, .swagger-ui .errors-wrapper .errors small, .swagger-ui .fallback, .swagger-ui .filter .loading, .swagger-ui .gray, .swagger-ui .hover-black:focus, .swagger-ui .hover-black:hover, .swagger-ui .hover-dark-gray:focus, .swagger-ui .hover-dark-gray:hover, .swagger-ui .hover-gray:focus, .swagger-ui .hover-gray:hover, .swagger-ui .hover-light-silver:focus, .swagger-ui .hover-light-silver:hover, .swagger-ui .hover-mid-gray:focus, .swagger-ui .hover-mid-gray:hover, .swagger-ui .hover-near-black:focus, .swagger-ui .hover-near-black:hover, .swagger-ui .hover-silver:focus, .swagger-ui .hover-silver:hover, .swagger-ui .light-silver, .swagger-ui .markdown pre, .swagger-ui .mid-gray, .swagger-ui .model .property, .swagger-ui .model .property.primitive, .swagger-ui .model-title, .swagger-ui .near-black, .swagger-ui .parameter__extension, .swagger-ui .parameter__in, .swagger-ui .prop-format, .swagger-ui .renderedmarkdown pre, .swagger-ui .response-col_links .response-undocumented, .swagger-ui .response-col_status .response-undocumented, .swagger-ui .silver, .swagger-ui section.models h4, .swagger-ui section.models h5, .swagger-ui span.token-not-formatted, .swagger-ui span.token-string, .swagger-ui table.headers .header-example, .swagger-ui table.model tr.description, .swagger-ui table.model tr.extension { color: #bfbfbf; }
-
-  .swagger-ui .hover-white:focus, .swagger-ui .hover-white:hover, .swagger-ui .info .title small pre, .swagger-ui .topbar a, .swagger-ui .white { color: #fff; }
-
-  .swagger-ui .bg-black-10, .swagger-ui .hover-bg-black-10:focus, .swagger-ui .hover-bg-black-10:hover, .swagger-ui .stripe-dark:nth-child(2n + 1) { background-color: rgba(0, 0, 0, .1); }
-
-  .swagger-ui .bg-white-10, .swagger-ui .hover-bg-white-10:focus, .swagger-ui .hover-bg-white-10:hover, .swagger-ui .stripe-light:nth-child(2n + 1) { background-color: rgba(28, 28, 33, .1); }
-
-  .swagger-ui .bg-light-silver, .swagger-ui .hover-bg-light-silver:focus, .swagger-ui .hover-bg-light-silver:hover, .swagger-ui .striped--light-silver:nth-child(2n + 1) { background-color: #6e6e6e; }
-
-  .swagger-ui .bg-moon-gray, .swagger-ui .hover-bg-moon-gray:focus, .swagger-ui .hover-bg-moon-gray:hover, .swagger-ui .striped--moon-gray:nth-child(2n + 1) { background-color: #4d4d4d; }
-
-  .swagger-ui .bg-light-gray, .swagger-ui .hover-bg-light-gray:focus, .swagger-ui .hover-bg-light-gray:hover, .swagger-ui .striped--light-gray:nth-child(2n + 1) { background-color: #2b2b2b; }
-
-  .swagger-ui .bg-near-white, .swagger-ui .hover-bg-near-white:focus, .swagger-ui .hover-bg-near-white:hover, .swagger-ui .striped--near-white:nth-child(2n + 1) { background-color: #242424; }
-
-  .swagger-ui .opblock-tag:hover, .swagger-ui section.models h4:hover { background: rgba(0, 0, 0, .02); }
-
-  .swagger-ui .checkbox p, .swagger-ui .dialog-ux .modal-ux-content h4, .swagger-ui .dialog-ux .modal-ux-content p, .swagger-ui .dialog-ux .modal-ux-header h3, .swagger-ui .errors-wrapper .errors h4, .swagger-ui .errors-wrapper hgroup h4, .swagger-ui .info .base-url, .swagger-ui .info .title, .swagger-ui .info h1, .swagger-ui .info h2, .swagger-ui .info h3, .swagger-ui .info h4, .swagger-ui .info h5, .swagger-ui .info li, .swagger-ui .info p, .swagger-ui .info table, .swagger-ui .loading-container .loading::after, .swagger-ui .model, .swagger-ui .opblock .opblock-section-header h4, .swagger-ui .opblock .opblock-section-header > label, .swagger-ui .opblock .opblock-summary-description, .swagger-ui .opblock .opblock-summary-operation-id, .swagger-ui .opblock .opblock-summary-path, .swagger-ui .opblock .opblock-summary-path__deprecated, .swagger-ui .opblock-description-wrapper, .swagger-ui .opblock-description-wrapper h4, .swagger-ui .opblock-description-wrapper p, .swagger-ui .opblock-external-docs-wrapper, .swagger-ui .opblock-external-docs-wrapper h4, .swagger-ui .opblock-external-docs-wrapper p, .swagger-ui .opblock-tag small, .swagger-ui .opblock-title_normal, .swagger-ui .opblock-title_normal h4, .swagger-ui .opblock-title_normal p, .swagger-ui .parameter__name, .swagger-ui .parameter__type, .swagger-ui .response-col_links, .swagger-ui .response-col_status, .swagger-ui .responses-inner h4, .swagger-ui .responses-inner h5, .swagger-ui .scheme-container .schemes > label, .swagger-ui .scopes h2, .swagger-ui .servers > label, .swagger-ui .tab li, .swagger-ui label, .swagger-ui select, .swagger-ui table.headers td { color: #b5bac9; }
-
-  .swagger-ui .download-url-wrapper .failed, .swagger-ui .filter .failed, .swagger-ui .model-deprecated-warning, .swagger-ui .parameter__deprecated, .swagger-ui .parameter__name.required span, .swagger-ui table.model tr.property-row .star { color: #e69999; }
-
-  .swagger-ui .opblock-body pre.microlight, .swagger-ui textarea.curl {
-      background: #41444e;
-      border-radius: 4px;
-      color: #fff;
-  }
-
-  .swagger-ui .expand-methods svg, .swagger-ui .expand-methods:hover svg { fill: #bfbfbf; }
-
-  .swagger-ui .auth-container, .swagger-ui .dialog-ux .modal-ux-header { border-bottom: 1px solid #2e2e2e; }
-
-  .swagger-ui .topbar .download-url-wrapper .select-label select, .swagger-ui .topbar .download-url-wrapper input[type=text] { border: 2px solid #63a040; }
-
-  .swagger-ui .info a, .swagger-ui .info a:hover, .swagger-ui .scopes h2 a { color: #99bde6; }
-
-  /* Dark Scrollbar */
-  ::-webkit-scrollbar {
-      width: 14px;
-      height: 14px;
-  }
-
-  ::-webkit-scrollbar-button {
-      background-color: #3e4346 !important;
-  }
-
-  ::-webkit-scrollbar-track {
-      background-color: #646464 !important;
-  }
-
-  ::-webkit-scrollbar-track-piece {
-      background-color: #3e4346 !important;
-  }
-
-  ::-webkit-scrollbar-thumb {
-      height: 50px;
-      background-color: #242424 !important;
-      border: 2px solid #3e4346 !important;
-  }
-
-  ::-webkit-scrollbar-corner {}
-
-  ::-webkit-resizer {}
-
-  ::-webkit-scrollbar-button:vertical:start:decrement {
-      background:
-          linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%),
-          linear-gradient(230deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(0deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button:vertical:end:increment {
-      background:
-          linear-gradient(310deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(50deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(180deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button:horizontal:end:increment {
-      background:
-          linear-gradient(210deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(330deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(90deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
-      background-color: #b6b6b6;
-  }
-
-  ::-webkit-scrollbar-button:horizontal:start:decrement {
-      background:
-          linear-gradient(30deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(150deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
-          linear-gradient(270deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
-      background-color: #b6b6b6;
-  }
+.swagger-ui .json-schema-2020-12-keyword {
+    margin: 5px 0
+}
+
+.swagger-ui .json-schema-2020-12-keyword__children {
+    margin: 0 0 0 20px;
+    padding: 0
+}
+
+.swagger-ui .json-schema-2020-12-keyword__children--collapsed {
+    display: none
+}
+
+.swagger-ui .json-schema-2020-12-keyword__name {
+    font-size: 12px;
+    font-weight: 700;
+    margin-left: 20px
+}
+
+.swagger-ui .json-schema-2020-12-keyword__name--primary {
+    color: #3b4151;
+    font-style: normal
+}
+
+.swagger-ui .json-schema-2020-12-keyword__name--secondary {
+    color: #6b6b6b;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-keyword__name--extension {
+    color: #929292;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-keyword__value {
+    color: #6b6b6b;
+    font-size: 12px;
+    font-style: italic;
+    font-weight: 400
+}
+
+.swagger-ui .json-schema-2020-12-keyword__value--primary {
+    color: #3b4151;
+    font-style: normal
+}
+
+.swagger-ui .json-schema-2020-12-keyword__value--secondary {
+    color: #6b6b6b;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-keyword__value--extension {
+    color: #929292;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-keyword__value--warning {
+    color: #3b4151;
+    color: red;
+    display: inline-block;
+    font-family: monospace;
+    font-style: normal;
+    font-weight: 600;
+    line-height: 1.5;
+    margin-left: 10px;
+    padding: 1px 4px
+}
+
+.swagger-ui .json-schema-2020-12-keyword__name--secondary+.json-schema-2020-12-keyword__value--secondary:before {
+    content: "="
+}
+
+.swagger-ui .json-schema-2020-12__attribute {
+    color: #3b4151;
+    font-family: monospace;
+    font-size: 12px;
+    padding-left: 10px;
+    text-transform: lowercase
+}
+
+.swagger-ui .json-schema-2020-12__attribute--primary {
+    color: #55a
+}
+
+.swagger-ui .json-schema-2020-12__attribute--muted {
+    color: gray
+}
+
+.swagger-ui .json-schema-2020-12__attribute--warning {
+    color: red
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer {
+    margin: 5px 0
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__children {
+    margin: 0 0 0 20px;
+    padding: 0
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__children--collapsed {
+    display: none
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__name {
+    font-size: 12px;
+    font-weight: 700;
+    margin-left: 20px
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__name--primary {
+    color: #3b4151;
+    font-style: normal
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__name--secondary {
+    color: #6b6b6b;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__name--extension {
+    color: #929292;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__value {
+    color: #6b6b6b;
+    font-size: 12px;
+    font-style: italic;
+    font-weight: 400
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__value--primary {
+    color: #3b4151;
+    font-style: normal
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__value--secondary {
+    color: #6b6b6b;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__value--extension {
+    color: #929292;
+    font-style: italic
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__value--warning {
+    color: #3b4151;
+    color: red;
+    display: inline-block;
+    font-family: monospace;
+    font-style: normal;
+    font-weight: 600;
+    line-height: 1.5;
+    margin-left: 10px;
+    padding: 1px 4px
+}
+
+.swagger-ui .json-schema-2020-12-json-viewer__name--secondary+.json-schema-2020-12-json-viewer__value--secondary:before {
+    content: "="
+}
+
+.swagger-ui .json-schema-2020-12 {
+    background-color: rgba(0,0,0,.05);
+    padding: 12px 0 12px 20px
+}
+
+.swagger-ui .json-schema-2020-12--embedded {
+    background-color: inherit;
+    padding-bottom: 0;
+    padding-left: inherit;
+    padding-right: inherit;
+    padding-top: 0
+}
+
+.swagger-ui .json-schema-2020-12-body {
+    margin: 2px 0
+}
+
+.swagger-ui .json-schema-2020-12-body--collapsed {
+    display: none
+}
+
+.swagger-ui .json-schema-2020-12-accordion {
+    outline: none;
+    padding-left: 0;
+    border: none;
+    background-color: transparent;
+}
+
+.swagger-ui .json-schema-2020-12-accordion__children {
+    display: inline-block
+}
+
+.swagger-ui .json-schema-2020-12-accordion__icon {
+    display: inline-block;
+    height: 18px;
+    vertical-align: bottom;
+    width: 18px
+}
+
+.swagger-ui .json-schema-2020-12-accordion__icon--expanded {
+    transform: rotate(-90deg);
+    transform-origin: 50% 50%;
+    transition: transform .15s ease-in
+}
+
+.swagger-ui .json-schema-2020-12-accordion__icon--collapsed {
+    transform: rotate(0deg);
+    transform-origin: 50% 50%;
+    transition: transform .15s ease-in
+}
+
+.swagger-ui .json-schema-2020-12-accordion__icon svg {
+    height: 20px;
+    width: 20px
+}
+
+.swagger-ui .json-schema-2020-12-expand-deep-button {
+    color: #505050;
+    color: #55a;
+    font-family: sans-serif;
+    font-size: 12px;
+    padding-right: 0;
+    border: none;
+    background-color: transparent;
+}
+
+.swagger-ui .model-box .json-schema-2020-12:not(.json-schema-2020-12--embedded)>.json-schema-2020-12-head .json-schema-2020-12__title:first-of-type {
+    font-size: 16px
+}
+
+.swagger-ui .model-box>.json-schema-2020-12 {
+    margin: 0
+}
+
+.swagger-ui .model-box .json-schema-2020-12 {
+    background-color: transparent;
+    padding: 0
+}
+
+.swagger-ui .model-box .json-schema-2020-12-accordion,.swagger-ui .model-box .json-schema-2020-12-expand-deep-button {
+    background-color: transparent
+}
+
+.swagger-ui .models .json-schema-2020-12:not(.json-schema-2020-12--embedded)>.json-schema-2020-12-head .json-schema-2020-12__title:first-of-type {
+    font-size: 16px
+}
+
+.swagger-ui .models .json-schema-2020-12:not(.json-schema-2020-12--embedded) {
+    overflow-x: auto;
+    width: calc(100% - 40px)
+}
+
+.response-col_description__inner {
+    font-weight: 500;
+    font-size: 1.2em;
 }
diff --git a/modules/api/docs.py b/modules/api/docs.py
index f384a1328..1eac8226e 100644
--- a/modules/api/docs.py
+++ b/modules/api/docs.py
@@ -41,20 +41,18 @@ def get_swagger_ui_html(*,
     if oauth2_redirect_url:
         html += f"oauth2RedirectUrl: window.location.origin + '{oauth2_redirect_url}',"
     html += """
-    presets: [
-        SwaggerUIBundle.presets.apis,
-        SwaggerUIBundle.SwaggerUIStandalonePreset
-        ],
-    })"""
+        presets: [
+            SwaggerUIBundle.presets.apis,
+            SwaggerUIBundle.SwaggerUIStandalonePreset
+            ],
+        })"""
     if init_oauth:
-        html += f"""
-        ui.initOAuth({json.dumps(jsonable_encoder(init_oauth))})
-        """
+        html += f"ui.initOAuth({json.dumps(jsonable_encoder(init_oauth))})"
     html += """
-    </script>
-    </body>
-    </html>
-    """
+        </script>
+        </body>
+        </html>
+        """
     return HTMLResponse(html)
 
 
@@ -74,8 +72,9 @@ def create_docs(app: FastAPI):
             title=f'{app.title}: Swagger UI',
             openapi_url=app.openapi_url,
             swagger_favicon_url='/file=html/favicon.svg',
+            swagger_css_url='/file=html/swagger.css',
             swagger_ui_parameters=swagger_ui_parameters,
-            swagger_extra_css_url='file=html/swagger.css',
+            # swagger_extra_css_url='file=html/swagger.css',
         )
         # res = inject_css(html.content, 'html/swagger.css')
         return res

From 7fdc880a7370eeb5814368d2475cefea4a451484 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 7 Oct 2025 09:43:34 -0400
Subject: [PATCH 170/230] sdnq patches

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md              |  2 +-
 modules/sdnq/loader.py    | 21 ++++++++++++++++++---
 modules/sdnq/quantizer.py | 21 +++++++++++++++------
 pipelines/generic.py      |  7 +++++++
 4 files changed, 41 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d4f1ed331..036717542 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-05
+## Update for 2025-10-07
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 92f784aaa..86f67d372 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -9,9 +9,24 @@ from .quantizer import SDNQConfig, apply_sdnq_to_module
 from .dequantizer import dequantize_symmetric, re_quantize_int8, re_quantize_fp8
 
 
-def save_sdnq_model(model: ModelMixin, sdnq_config: SDNQConfig, model_path: str, max_shard_size: str = "10GB") -> None:
-    model.save_pretrained(model_path, max_shard_size=max_shard_size)
-    sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
+def get_module_names(model: ModelMixin) -> list:
+    modules_names = model._internal_dict.keys() # pylint: disable=protected-access
+    modules_names = [m for m in modules_names if not m.startswith('_')]
+    modules_names = [m for m in modules_names if isinstance(getattr(model, m, None), torch.nn.Module)]
+    modules_names = sorted(set(modules_names))
+    return modules_names
+
+
+def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", sdnq_config: SDNQConfig = None) -> None:
+    model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
+
+    if sdnq_config is not None: # if provided, save global config
+        sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
+
+    for module_name in get_module_names(model): # save per-module config if available
+        module = getattr(model, module_name, None)
+        if (module is not None) and hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
+            module.quantization_config.to_json_file(os.path.join(model_path, f"{module_name}_quantization_config.json"))
 
 
 def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = None) -> ModelMixin:
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 7261d2373..b3b853e97 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -6,6 +6,7 @@ from enum import Enum
 
 import re
 import torch
+from transformers.quantizers import HfQuantizer
 from diffusers.quantizers.base import DiffusersQuantizer
 from diffusers.quantizers.quantization_config import QuantizationConfigMixin
 from diffusers.utils import get_module_from_name
@@ -59,7 +60,7 @@ def apply_svdquant(weight: torch.FloatTensor, rank: int = 32) -> Tuple[torch.Flo
     svd_down = svd_down.t_()
     weight = weight.sub_(torch.mm(svd_up, svd_down))
     if reshape_weight:
-        weight = weight.unflatten(-1, (*weight_shape[1:],))
+        weight = weight.unflatten(-1, (*weight_shape[1:],)) # pylint: disable=possibly-used-before-assignment
     return weight, svd_up, svd_down
 
 
@@ -320,7 +321,7 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
     return model
 
 
-class SDNQQuantizer(DiffusersQuantizer):
+class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
     r"""
     Diffusers Quantizer for SDNQ
     """
@@ -530,14 +531,16 @@ class SDNQQuantizer(DiffusersQuantizer):
         """
         return dtype
 
+    def is_serializable(self, *args, **kwargs) -> bool:  # pylint: disable=unused-argument, invalid-overridden-method
+        """
+        needed for transformers compatibilty, returns True
+        """
+        return True
+
     @property
     def is_trainable(self):
         return False
 
-    @property
-    def is_serializable(self):
-        return True
-
     @property
     def is_compileable(self):
         return True
@@ -631,6 +634,12 @@ class SDNQConfig(QuantizationConfigMixin):
         if self.modules_dtype_dict is None:
             self.modules_dtype_dict = {}
 
+    def to_dict(self):
+        dct = self.__dict__.copy() # make serializable
+        dct["quantization_device"] = str(dct["quantization_device"]) if dct["quantization_device"] is not None else None
+        dct["return_device"] = str(dct["return_device"]) if dct["return_device"] is not None else None
+        return dct
+
 
 import diffusers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
 diffusers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
diff --git a/pipelines/generic.py b/pipelines/generic.py
index 0eb2c19c1..f14f88ab8 100644
--- a/pipelines/generic.py
+++ b/pipelines/generic.py
@@ -61,9 +61,13 @@ def load_transformer(repo_id, cls_name, load_config={}, subfolder="transformer",
                 **load_args,
                 **quant_args,
             )
+
         sd_models.allow_post_quant = False # we already handled it
         if shared.opts.diffusers_offload_mode != 'none' and transformer is not None:
             sd_models.move_model(transformer, devices.cpu)
+
+        if (transformer is not None) and (quant_type is not None) and (quant_args.get('quantization_config', None) is not None): # attach quantization_config
+            transformer.quantization_config = quant_args.get('quantization_config', None)
     except Exception as e:
         shared.log.error(f'Load model: transformer="{repo_id}" cls={cls_name.__name__} {e}')
         errors.display(e, 'Load:')
@@ -157,6 +161,9 @@ def load_text_encoder(repo_id, cls_name, load_config={}, subfolder="text_encoder
         sd_models.allow_post_quant = False # we already handled it
         if shared.opts.diffusers_offload_mode != 'none' and text_encoder is not None:
             sd_models.move_model(text_encoder, devices.cpu)
+
+        if (text_encoder is not None) and (quant_type is not None) and (quant_args.get('quantization_config', None) is not None): # attach quantization_config
+            text_encoder.quantization_config = quant_args.get('quantization_config', None)
     except Exception as e:
         shared.log.error(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} {e}')
         errors.display(e, 'Load:')

From 0092a8b86b36d07238c703e8b0be901ed9dcfd41 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 7 Oct 2025 10:14:31 -0400
Subject: [PATCH 171/230] add quantization_config for post-load

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/model_quant.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/modules/model_quant.py b/modules/model_quant.py
index 288d6e8ed..f5baec12a 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -530,6 +530,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         backup_embeddings = copy.deepcopy(model.get_input_embeddings())
 
     t0 = time.time()
+
     model = apply_sdnq_to_module(
         model,
         weights_dtype=weights_dtype,
@@ -548,6 +549,24 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         modules_dtype_dict=modules_dtype_dict.copy(),
         op=op,
     )
+
+    from modules.sdnq import SDNQConfig
+    model.quantization_config = SDNQConfig(
+        weights_dtype=weights_dtype,
+        group_size=shared.opts.sdnq_quantize_weights_group_size,
+        svd_rank=shared.opts.sdnq_svd_rank,
+        use_svd=shared.opts.sdnq_use_svd,
+        quant_conv=shared.opts.sdnq_quantize_conv_layers,
+        use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
+        use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
+        dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
+        non_blocking=shared.opts.diffusers_offload_nonblocking,
+        quantization_device=quantization_device,
+        return_device=return_device,
+        modules_to_not_convert=modules_to_not_convert,
+        modules_dtype_dict=modules_dtype_dict.copy(),
+    )
+
     t1 = time.time()
     timer.load.add('sdnq', t1 - t0)
     model.quantization_method = 'SDNQ'

From e4120bd4d6b6412007278018d01db5b9dffc27e0 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 7 Oct 2025 10:57:17 -0400
Subject: [PATCH 172/230] detect sdnq saved model

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_detect.py |  5 +++++
 modules/sd_models.py | 11 +++++++++++
 2 files changed, 16 insertions(+)

diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 0cd3cdb86..059b081a4 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -124,6 +124,8 @@ def guess_by_name(fn, current_guess):
 
 def guess_by_diffusers(fn, current_guess):
     exclude_by_name = ['ostris/Flex.2-preview'] # pipeline may be misleading
+    if not os.path.isdir(fn):
+        return current_guess, None
     index = os.path.join(fn, 'model_index.json')
     if os.path.exists(index) and os.path.isfile(index):
         index = shared.readfile(index, silent=True)
@@ -136,9 +138,12 @@ def guess_by_diffusers(fn, current_guess):
             if pipeline is None:
                 pipeline = cls
         if callable(pipeline):
+            is_quant = any(f for f in os.listdir(fn) if f.endswith('quantization_config.json'))
             pipelines = shared_items.get_pipelines()
             for k, v in pipelines.items():
                 if v is not None and v.__name__ == pipeline.__name__:
+                    if is_quant:
+                        k = f'{k} SDNQ'
                     return k, v
     return current_guess, None
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 426a7ca3d..449e26772 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -541,6 +541,11 @@ def load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_con
     return sd_model
 
 
+def load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op):
+    shared.log.error(f'Load {op}: model="{checkpoint_info.name}" cls={pipeline.__name__} args={diffusers_load_config} SDNQ pre-quant not supported')
+    return None
+
+
 def set_overrides(sd_model, checkpoint_info):
     checkpoint_info_name = checkpoint_info.name.lower()
     if 'bigaspv25' in checkpoint_info_name or ('flow' in checkpoint_info_name and 'flower' not in checkpoint_info_name):
@@ -657,6 +662,12 @@ def load_diffuser(checkpoint_info=None, op='model', revision=None): # pylint: di
                 shared.log.error(f'Load {op}: type="{model_type}" pipeline="{pipeline}" not loaded')
                 return
 
+        # load sdnq-prequantized model
+        if sd_model is None:
+            if model_type.endswith('SDNQ'):
+                allow_post_quant = False
+                sd_model = load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op)
+
         # load from hf folder-style
         if sd_model is None:
             if os.path.isdir(checkpoint_info.path) or checkpoint_info.type == 'huggingface' or checkpoint_info.type == 'transformer':

From 962cb7115d33ab863caf3b12ad4caab6d4abfdcf Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 7 Oct 2025 14:30:45 -0400
Subject: [PATCH 173/230] infra for full-model load/save with quant

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/prompt_parser_diffusers.py |  3 ++
 modules/sd_models.py               | 28 ++++++++++++++---
 modules/sd_offload.py              |  1 +
 modules/sdnq/loader.py             | 49 +++++++++++++++++++++++++-----
 4 files changed, 70 insertions(+), 11 deletions(-)

diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 31947ee7b..571bf70d1 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -589,6 +589,9 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
         negative_weights.pop(0)
 
     embedding_providers = prepare_embedding_providers(pipe, clip_skip)
+    if len(embedding_providers) == 0:
+        shared.log.error("Prompt encode: cannot find text encoder in model")
+        return None, None, None, None, None, None
     empty_embedding_providers = None
     if 'StableCascade' in pipe.__class__.__name__:
         empty_embedding_providers = [embedding_providers[1]]
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 449e26772..c8f78c1ae 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -542,8 +542,27 @@ def load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_con
 
 
 def load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op):
-    shared.log.error(f'Load {op}: model="{checkpoint_info.name}" cls={pipeline.__name__} args={diffusers_load_config} SDNQ pre-quant not supported')
-    return None
+    from modules import sdnq
+    modules = {}
+    for f in os.listdir(checkpoint_info.path):
+        if not f.endswith('quantization_config.json'):
+            continue
+        module_name = f.replace('_quantization_config.json', '')
+        quantization_config = shared.readfile(os.path.join(checkpoint_info.path, f), silent=True)
+        shared.log.debug(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" prequant=sdnq')
+        module_path = os.path.join(checkpoint_info.path, module_name)
+        modules[module_name] = sdnq.load_sdnq_model(
+            model_path=module_path,
+            quantization_config=quantization_config,
+        )
+        modules[module_name] = modules[module_name].to(device=devices.device)
+    sd_model = pipeline.from_pretrained(
+        checkpoint_info.path,
+        cache_dir=shared.opts.diffusers_dir,
+        **modules,
+        **diffusers_load_config,
+    )
+    return sd_model
 
 
 def set_overrides(sd_model, checkpoint_info):
@@ -665,8 +684,9 @@ def load_diffuser(checkpoint_info=None, op='model', revision=None): # pylint: di
         # load sdnq-prequantized model
         if sd_model is None:
             if model_type.endswith('SDNQ'):
-                allow_post_quant = False
                 sd_model = load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op)
+                allow_post_quant = False
+                model_type = model_type.replace(' SDNQ', '')
 
         # load from hf folder-style
         if sd_model is None:
@@ -1056,7 +1076,7 @@ def set_diffusers_attention(pipe, quiet:bool=False):
         if attention is None:
             return
         # other models uses their own attention processor
-        if pipe.__class__.__name__.startswith("StableDiffusion") and hasattr(pipe, "unet"):
+        if pipe.__class__.__name__.startswith("StableDiffusion") and getattr(pipe, "unet", None) is not None and hasattr(pipe.unet, "set_attn_processor"):
             try:
                 pipe.unet.set_attn_processor(attention)
             except Exception as e:
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index 54446abdf..c52220ccb 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -434,6 +434,7 @@ def apply_balanced_offload(sd_model=None, exclude=[]):
             module.offload_dir = os.path.join(shared.opts.accelerate_offload_path, checkpoint_name, module_name)
             apply_balanced_offload_to_module(module, op='apply')
             report_model_stats(module_name, module)
+
     set_accelerate(sd_model)
     t = time.time() - t0
     process_timer.add('offload', t)
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 86f67d372..4ba68a69f 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -29,24 +29,59 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "1
             module.quantization_config.to_json_file(os.path.join(model_path, f"{module_name}_quantization_config.json"))
 
 
-def load_sdnq_model(model_cls: ModelMixin, model_path: str, file_name: str = "diffusion_pytorch_model.safetensors", dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = None) -> ModelMixin:
-    with torch.device("meta"):
-        with open(os.path.join(model_path, "quantization_config.json"), "r", encoding="utf-8") as f:
-            quantization_config = json.load(f)
+def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:
+    with torch.device("cpu"):
+        if quantization_config is None:
+            try:
+                with open(os.path.join(model_path, "quantization_config.json"), "r", encoding="utf-8") as f:
+                    quantization_config = json.load(f)
+            except Exception:
+                quantization_config = {}
+        if model_config is None:
+            try:
+                with open(os.path.join(model_path, 'config.json'), "r", encoding="utf-8") as f:
+                    model_config = json.load(f)
+            except Exception:
+                model_config = {}
+        if model_cls is None:
+            import transformers
+            import diffusers
+            class_name = model_config.get("_class_name", None) or model_config.get("architectures", None)
+            if isinstance(class_name, list):
+                class_name = class_name[0]
+            if class_name is not None:
+                model_cls = getattr(diffusers, class_name, None) or getattr(transformers, class_name, None)
+        if model_cls is None:
+            raise ValueError(f"Cannot determine model class for {model_path}, please provide model_cls argument")
         quantization_config.pop("is_integer", None)
         quantization_config.pop("quant_method", None)
         quantization_config.pop("quantization_device", None)
         quantization_config.pop("return_device", None)
         quantization_config.pop("non_blocking", None)
-        config = model_cls.load_config(model_path)
-        model = model_cls.from_config(config)
+        if hasattr(model_cls, "load_config"):
+            config = model_cls.load_config(model_path)
+            model = model_cls.from_config(config)
+        elif hasattr(model_cls, "_from_config"):
+            config = transformers.PretrainedConfig.from_dict(model_config)
+            model = model_cls._from_config(config) # pylint: disable=protected-access
+        else:
+            raise ValueError(f"Dont know how to load model for {model_cls}")
         model = apply_sdnq_to_module(model, **quantization_config)
 
     state_dict = {}
-    with safe_open(os.path.join(model_path, file_name), framework="pt") as f:
+    if file_name:
+        fn = os.path.join(model_path, file_name)
+    elif os.path.exists(os.path.join(model_path, "diffusion_pytorch_model.safetensors")):
+        fn = os.path.join(model_path, "diffusion_pytorch_model.safetensors")
+    elif os.path.exists(os.path.join(model_path, "model.safetensors")):
+        fn = os.path.join(model_path, "model.safetensors")
+    else:
+        raise ValueError(f"Cannot find safetensors file in {model_path}, please provide file_name argument")
+    with safe_open(fn, framework="pt") as f:
         for k in f.keys():
             state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)
+    model.quantization_method = "sdnq"
     del state_dict
     if dtype is not None or dequantize_fp32 is not None or use_quantized_matmul is not None:
         model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)

From df03ea9ba8626c315cd1a7325ee6cb8fbf6e8e33 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 8 Oct 2025 00:29:36 +0300
Subject: [PATCH 174/230] SDNQ add sdnq_post_load_quant and update Qwen keys

---
 modules/model_quant.py    | 43 ++++---------------------
 modules/sdnq/__init__.py  |  4 ++-
 modules/sdnq/loader.py    |  5 ++-
 modules/sdnq/quantizer.py | 68 ++++++++++++++++++++++++++++++++++++---
 pipelines/model_qwen.py   |  3 +-
 5 files changed, 77 insertions(+), 46 deletions(-)

diff --git a/modules/model_quant.py b/modules/model_quant.py
index f5baec12a..c3d2439ec 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -479,7 +479,7 @@ def apply_layerwise(sd_model, quiet:bool=False):
 def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weights_dtype: str = None, modules_to_not_convert: list = None, modules_dtype_dict: dict = None):
     global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
     from modules import devices, shared, timer
-    from modules.sdnq import apply_sdnq_to_module
+    from modules.sdnq import sdnq_post_load_quant
 
     if weights_dtype is None:
         if op is not None and ("text_encoder" in op or op in {"TE", "LLM"}) and shared.opts.sdnq_quantize_weights_mode_te not in {"Same as model", "default"}:
@@ -497,13 +497,6 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
     if modules_dtype_dict is None:
         modules_dtype_dict = {}
 
-    if getattr(model, "_keep_in_fp32_modules", None) is not None:
-        modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
-    if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
-        modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
-    if model.__class__.__name__ == "ChromaTransformer2DModel":
-        modules_to_not_convert.append("distilled_guidance_layer")
-
     sdnq_modules_to_not_convert = [m.strip() for m in re.split(';|,| ', shared.opts.sdnq_modules_to_not_convert) if len(m.strip()) > 1]
     if len(sdnq_modules_to_not_convert) > 0:
         modules_to_not_convert.extend(sdnq_modules_to_not_convert)
@@ -524,14 +517,9 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
     except Exception as e:
         log.warning(f'Quantization: SDNQ failed to parse sdnq_modules_dtype_dict: {e}')
 
-    model.eval()
-    backup_embeddings = None
-    if hasattr(model, "get_input_embeddings"):
-        backup_embeddings = copy.deepcopy(model.get_input_embeddings())
-
     t0 = time.time()
 
-    model = apply_sdnq_to_module(
+    model = sdnq_post_load_quant(
         model,
         weights_dtype=weights_dtype,
         torch_dtype=devices.dtype,
@@ -550,29 +538,8 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         op=op,
     )
 
-    from modules.sdnq import SDNQConfig
-    model.quantization_config = SDNQConfig(
-        weights_dtype=weights_dtype,
-        group_size=shared.opts.sdnq_quantize_weights_group_size,
-        svd_rank=shared.opts.sdnq_svd_rank,
-        use_svd=shared.opts.sdnq_use_svd,
-        quant_conv=shared.opts.sdnq_quantize_conv_layers,
-        use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
-        use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
-        dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
-        non_blocking=shared.opts.diffusers_offload_nonblocking,
-        quantization_device=quantization_device,
-        return_device=return_device,
-        modules_to_not_convert=modules_to_not_convert,
-        modules_dtype_dict=modules_dtype_dict.copy(),
-    )
-
     t1 = time.time()
     timer.load.add('sdnq', t1 - t0)
-    model.quantization_method = 'SDNQ'
-
-    if hasattr(model, "set_input_embeddings") and backup_embeddings is not None:
-        model.set_input_embeddings(backup_embeddings)
 
     if op is not None and shared.opts.sdnq_quantize_shuffle_weights:
         if quant_last_model_name is not None:
@@ -628,12 +595,14 @@ def optimum_quanto_model(model, op=None, sd_model=None, weights=None, activation
     from modules import devices, shared
     quanto = load_quanto('Quantize model: type=Optimum Quanto')
     global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
-    if sd_model is not None and ("Flux" in sd_model.__class__.__name__ or "Chroma" in sd_model.__class__.__name__): # LayerNorm is not supported
+    if model.__class__.__name__ in {"FluxTransformer2DModel", "ChromaTransformer2DModel"}: # LayerNorm is not supported
         exclude_list = ["transformer_blocks.*.norm1.norm", "transformer_blocks.*.norm2", "transformer_blocks.*.norm1_context.norm", "transformer_blocks.*.norm2_context", "single_transformer_blocks.*.norm.norm", "norm_out.norm"]
-        if "Chroma" in sd_model.__class__.__name__:
+        if model.__class__.__name__ == "ChromaTransformer2DModel":
             # we ignore the distilled guidance layer because it degrades quality too much
             # see: https://github.com/huggingface/diffusers/pull/11698#issuecomment-2969717180 for more details
             exclude_list.append("distilled_guidance_layer.*")
+    elif model.__class__.__name__ == "QwenImageTransformer2DModel":
+        exclude_list = ["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"]
     else:
         exclude_list = None
     weights = getattr(quanto, weights) if weights is not None else getattr(quanto, shared.opts.optimum_quanto_weights_type)
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 121db2c8d..c67bf0db7 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -1,12 +1,14 @@
-from .quantizer import SDNQConfig, SDNQQuantizer, apply_sdnq_to_module, sdnq_quantize_layer
+from .quantizer import QuantizationMethod, SDNQConfig, SDNQQuantizer, sdnq_post_load_quant, apply_sdnq_to_module, sdnq_quantize_layer
 from .loader import save_sdnq_model, load_sdnq_model
 
 
 __all__ = [
+    "QuantizationMethod",
     "SDNQConfig",
     "SDNQQuantizer",
     "apply_sdnq_to_module",
     "load_sdnq_model",
     "save_sdnq_model",
+    "sdnq_post_load_quant",
     "sdnq_quantize_layer",
 ]
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 4ba68a69f..582c6b4b3 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -5,7 +5,7 @@ from safetensors import safe_open
 from diffusers.models.modeling_utils import ModelMixin
 
 from .common import use_tensorwise_fp8_matmul, use_contiguous_mm
-from .quantizer import SDNQConfig, apply_sdnq_to_module
+from .quantizer import SDNQConfig, sdnq_post_load_quant
 from .dequantizer import dequantize_symmetric, re_quantize_int8, re_quantize_fp8
 
 
@@ -66,7 +66,7 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             model = model_cls._from_config(config) # pylint: disable=protected-access
         else:
             raise ValueError(f"Dont know how to load model for {model_cls}")
-        model = apply_sdnq_to_module(model, **quantization_config)
+        model = sdnq_post_load_quant(model, **quantization_config)
 
     state_dict = {}
     if file_name:
@@ -81,7 +81,6 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
         for k in f.keys():
             state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)
-    model.quantization_method = "sdnq"
     del state_dict
     if dtype is not None or dequantize_fp32 is not None or use_quantized_matmul is not None:
         model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index b3b853e97..3fcf33ea7 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -75,7 +75,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
         result_shape = None
         original_shape = layer.weight.shape
         if torch_dtype is None:
-            torch_dtype = devices.dtype
+            torch_dtype = layer.weight.dtype
 
         if layer_class_name in conv_types:
             if not quant_conv:
@@ -321,6 +321,66 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
     return model
 
 
+def sdnq_post_load_quant(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, op=None): # pylint: disable=unused-argument
+    model.eval()
+    if modules_to_not_convert is None:
+        modules_to_not_convert = []
+    if modules_dtype_dict is None:
+        modules_dtype_dict = {}
+
+    if getattr(model, "_keep_in_fp32_modules", None) is not None:
+        modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
+    if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
+        modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
+    if model.__class__.__name__ == "ChromaTransformer2DModel":
+        modules_to_not_convert.append("distilled_guidance_layer")
+    elif model.__class__.__name__ == "QwenImageTransformer2DModel":
+        modules_to_not_convert.extend(["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"])
+        if "minimum_6bit" not in modules_dtype_dict.keys():
+            modules_dtype_dict["minimum_6bit"] = ["img_mod"]
+        else:
+            modules_dtype_dict["minimum_6bit"].append("img_mod")
+
+    model = apply_sdnq_to_module(
+        model,
+        weights_dtype=weights_dtype,
+        torch_dtype=torch_dtype,
+        group_size=group_size,
+        svd_rank=svd_rank,
+        use_svd=use_svd,
+        quant_conv=quant_conv,
+        use_quantized_matmul=use_quantized_matmul,
+        use_quantized_matmul_conv=use_quantized_matmul_conv,
+        dequantize_fp32=dequantize_fp32,
+        non_blocking=non_blocking,
+        quantization_device=quantization_device,
+        return_device=return_device,
+        modules_to_not_convert=modules_to_not_convert,
+        modules_dtype_dict=modules_dtype_dict.copy(),
+        op=op,
+    )
+
+    model.quantization_config = SDNQConfig(
+        weights_dtype=weights_dtype,
+        group_size=group_size,
+        svd_rank=svd_rank,
+        use_svd=use_svd,
+        quant_conv=quant_conv,
+        use_quantized_matmul=use_quantized_matmul,
+        use_quantized_matmul_conv=use_quantized_matmul_conv,
+        dequantize_fp32=dequantize_fp32,
+        non_blocking=non_blocking,
+        quantization_device=quantization_device,
+        return_device=return_device,
+        modules_to_not_convert=modules_to_not_convert,
+        modules_dtype_dict=modules_dtype_dict.copy(),
+    )
+
+    model.quantization_method = QuantizationMethod.SDNQ
+
+    return model
+
+
 class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
     r"""
     Diffusers Quantizer for SDNQ
@@ -384,6 +444,8 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         *args, **kwargs, # pylint: disable=unused-argument
     ):
         weights_dtype = self.quantization_config.weights_dtype
+        torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
+
         if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
             split_param_name = param_name.split(".")
             for key, value in self.quantization_config.modules_dtype_dict.items():
@@ -424,7 +486,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         layer = sdnq_quantize_layer(
             layer,
             weights_dtype=weights_dtype,
-            torch_dtype=self.torch_dtype,
+            torch_dtype=torch_dtype,
             group_size=self.quantization_config.group_size,
             svd_rank=self.quantization_config.svd_rank,
             use_svd=self.quantization_config.use_svd,
@@ -446,8 +508,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         return dtype_dict[self.quantization_config.weights_dtype]["target_dtype"]
 
     def update_torch_dtype(self, torch_dtype: torch.dtype = None) -> torch.dtype:
-        if torch_dtype is None:
-            torch_dtype = devices.dtype
         self.torch_dtype = torch_dtype
         return torch_dtype
 
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index 6ed90bb15..d9a98d9be 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -49,7 +49,8 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
             subfolder=transformer_subfolder,
             cls_name=diffusers.QwenImageTransformer2DModel,
             load_config=diffusers_load_config,
-            modules_dtype_dict={"minimum_8bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "img_mod", "transformer_blocks.0.img_mod.1.weight"]},
+            modules_dtype_dict={"minimum_6bit": ["img_mod"]},
+            modules_to_not_convert=["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"],
         )
 
     repo_te = 'Qwen/Qwen-Image'

From 7fdf400e8bdcec24679901099e2dcce7db5f9103 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 8 Oct 2025 00:41:04 +0300
Subject: [PATCH 175/230] cleanup

---
 modules/sdnq/loader.py    |  2 +-
 modules/sdnq/quantizer.py | 29 +++++++++++++++--------------
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 582c6b4b3..d4d4e8ab8 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -66,7 +66,7 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             model = model_cls._from_config(config) # pylint: disable=protected-access
         else:
             raise ValueError(f"Dont know how to load model for {model_cls}")
-        model = sdnq_post_load_quant(model, **quantization_config)
+        model = sdnq_post_load_quant(model, add_skip_keys=False, **quantization_config)
 
     state_dict = {}
     if file_name:
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 3fcf33ea7..9a9d9c368 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -321,26 +321,27 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
     return model
 
 
-def sdnq_post_load_quant(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, op=None): # pylint: disable=unused-argument
-    model.eval()
+def sdnq_post_load_quant(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, add_skip_keys=True, op=None): # pylint: disable=unused-argument
     if modules_to_not_convert is None:
         modules_to_not_convert = []
     if modules_dtype_dict is None:
         modules_dtype_dict = {}
 
-    if getattr(model, "_keep_in_fp32_modules", None) is not None:
-        modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
-    if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
-        modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
-    if model.__class__.__name__ == "ChromaTransformer2DModel":
-        modules_to_not_convert.append("distilled_guidance_layer")
-    elif model.__class__.__name__ == "QwenImageTransformer2DModel":
-        modules_to_not_convert.extend(["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"])
-        if "minimum_6bit" not in modules_dtype_dict.keys():
-            modules_dtype_dict["minimum_6bit"] = ["img_mod"]
-        else:
-            modules_dtype_dict["minimum_6bit"].append("img_mod")
+    if add_skip_keys:
+        if getattr(model, "_keep_in_fp32_modules", None) is not None:
+            modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
+        if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
+            modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
+        if model.__class__.__name__ == "ChromaTransformer2DModel":
+            modules_to_not_convert.append("distilled_guidance_layer")
+        elif model.__class__.__name__ == "QwenImageTransformer2DModel":
+            modules_to_not_convert.extend(["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"])
+            if "minimum_6bit" not in modules_dtype_dict.keys():
+                modules_dtype_dict["minimum_6bit"] = ["img_mod"]
+            else:
+                modules_dtype_dict["minimum_6bit"].append("img_mod")
 
+    model.eval()
     model = apply_sdnq_to_module(
         model,
         weights_dtype=weights_dtype,

From bdcd07f71318cb3393848185a4097a1c482cb7d7 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 8 Oct 2025 01:11:40 +0300
Subject: [PATCH 176/230] Add add_module_skip_keys to pre-load quant too

---
 modules/sdnq/loader.py    |  1 +
 modules/sdnq/quantizer.py | 71 ++++++++++++++++++++++++++++-----------
 2 files changed, 52 insertions(+), 20 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index d4d4e8ab8..967254070 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -58,6 +58,7 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
         quantization_config.pop("quantization_device", None)
         quantization_config.pop("return_device", None)
         quantization_config.pop("non_blocking", None)
+        quantization_config.pop("add_skip_keys", None)
         if hasattr(model_cls, "load_config"):
             config = model_cls.load_config(model_path)
             model = model_cls.from_config(config)
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 9a9d9c368..4d7fdc3e2 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -321,25 +321,48 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
     return model
 
 
-def sdnq_post_load_quant(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, add_skip_keys=True, op=None): # pylint: disable=unused-argument
+def add_module_skip_keys(model, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]]):
+    if getattr(model, "_keep_in_fp32_modules", None) is not None:
+        modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
+    if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
+        modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
+    if model.__class__.__name__ == "ChromaTransformer2DModel":
+        modules_to_not_convert.append("distilled_guidance_layer")
+    elif model.__class__.__name__ == "QwenImageTransformer2DModel":
+        modules_to_not_convert.extend(["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"])
+        if "minimum_6bit" not in modules_dtype_dict.keys():
+            modules_dtype_dict["minimum_6bit"] = ["img_mod"]
+        else:
+            modules_dtype_dict["minimum_6bit"].append("img_mod")
+    return model, modules_to_not_convert, modules_dtype_dict
+
+
+def sdnq_post_load_quant(
+    model,
+    weights_dtype="int8",
+    torch_dtype: torch.dtype = None,
+    group_size: int = 0,
+    svd_rank: int = 32,
+    use_svd: bool = False,
+    quant_conv: bool = False,
+    use_quantized_matmul: bool = False,
+    use_quantized_matmul_conv: bool = False,
+    dequantize_fp32: bool = False,
+    non_blocking: bool = False,
+    add_skip_keys:bool = True,
+    quantization_device: torch.device = None,
+    return_device: torch.device = None,
+    modules_to_not_convert: List[str] = None,
+    modules_dtype_dict: Dict[str, List[str]] = None,
+    op=None,
+):
     if modules_to_not_convert is None:
         modules_to_not_convert = []
     if modules_dtype_dict is None:
         modules_dtype_dict = {}
 
     if add_skip_keys:
-        if getattr(model, "_keep_in_fp32_modules", None) is not None:
-            modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
-        if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
-            modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
-        if model.__class__.__name__ == "ChromaTransformer2DModel":
-            modules_to_not_convert.append("distilled_guidance_layer")
-        elif model.__class__.__name__ == "QwenImageTransformer2DModel":
-            modules_to_not_convert.extend(["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"])
-            if "minimum_6bit" not in modules_dtype_dict.keys():
-                modules_dtype_dict["minimum_6bit"] = ["img_mod"]
-            else:
-                modules_dtype_dict["minimum_6bit"].append("img_mod")
+        model, modules_to_not_convert, modules_dtype_dict = add_module_skip_keys(model, modules_to_not_convert, modules_dtype_dict)
 
     model.eval()
     model = apply_sdnq_to_module(
@@ -377,6 +400,8 @@ def sdnq_post_load_quant(model, weights_dtype="int8", torch_dtype=None, group_si
         modules_dtype_dict=modules_dtype_dict.copy(),
     )
 
+    if hasattr(model, "config"):
+        model.config.quantization_config = model.quantization_config
     model.quantization_method = QuantizationMethod.SDNQ
 
     return model
@@ -519,15 +544,17 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         keep_in_fp32_modules: List[str] = None,
         **kwargs, # pylint: disable=unused-argument
     ):
-        if keep_in_fp32_modules is not None:
-            self.modules_to_not_convert.extend(keep_in_fp32_modules)
-        elif getattr(model, "_keep_in_fp32_modules", None) is not None:
-            self.modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
-        if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
-            self.modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
+        self.quantization_config.add_skip_keys:
+            if keep_in_fp32_modules is not None:
+                self.modules_to_not_convert.extend(keep_in_fp32_modules)
+            model, self.modules_to_not_convert, self.quantization_config.modules_dtype_dict = add_module_skip_keys(
+                model, self.modules_to_not_convert, self.quantization_config.modules_dtype_dict
+            )
         self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
         self.quantization_config.modules_to_not_convert = self.modules_to_not_convert
-        model.config.quantization_config = self.quantization_config
+        if hasattr(model, "config"):
+            model.config.quantization_config = self.quantization_config
+        model.quantization_config = self.quantization_config
 
     def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument
         if shared.opts.diffusers_offload_mode != "none":
@@ -634,6 +661,8 @@ class SDNQConfig(QuantizationConfigMixin):
             Enabling this option will use FP32 on the dequantization step.
         non_blocking (`bool`, *optional*, defaults to `False`):
             Enabling this option will use non blocking ops when moving layers between the quantization device and the return device.
+        add_skip_keys (`bool`, *optional*, defaults to `True`):
+            Disabling this option won't add model specific modules_to_not_convert and modules_dtype_dict keys.
         quantization_device (`torch.device`, *optional*, defaults to `None`):
             Used to set which device will be used for the quantization calculation on model load.
         return_device (`torch.device`, *optional*, defaults to `None`):
@@ -656,6 +685,7 @@ class SDNQConfig(QuantizationConfigMixin):
         use_quantized_matmul_conv: bool = False,
         dequantize_fp32: bool = False,
         non_blocking: bool = False,
+        add_skip_keys: bool = True,
         quantization_device: Optional[torch.device] = None,
         return_device: Optional[torch.device] = None,
         modules_to_not_convert: Optional[List[str]] = None,
@@ -672,6 +702,7 @@ class SDNQConfig(QuantizationConfigMixin):
         self.use_quantized_matmul_conv = use_quantized_matmul_conv
         self.dequantize_fp32 = dequantize_fp32
         self.non_blocking = non_blocking
+        self.add_skip_keys = add_skip_keys
         self.quantization_device = quantization_device
         self.return_device = return_device
         self.modules_to_not_convert = modules_to_not_convert

From 25303bb182af9162330ffcc319e5d4805079ff27 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 8 Oct 2025 01:16:25 +0300
Subject: [PATCH 177/230] cleanup

---
 modules/sdnq/quantizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 4d7fdc3e2..190ff096f 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -321,7 +321,7 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
     return model
 
 
-def add_module_skip_keys(model, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]]):
+def add_module_skip_keys(model, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None):
     if getattr(model, "_keep_in_fp32_modules", None) is not None:
         modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
     if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:

From 9c16e2234a83dd68b0b97fccb9afe345d59b878f Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 8 Oct 2025 01:18:12 +0300
Subject: [PATCH 178/230] cleanup

---
 modules/sdnq/quantizer.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 190ff096f..50d4b49f3 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -322,6 +322,10 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
 
 
 def add_module_skip_keys(model, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None):
+    if modules_to_not_convert is None:
+        modules_to_not_convert = []
+    if modules_dtype_dict is None:
+        modules_dtype_dict = {}
     if getattr(model, "_keep_in_fp32_modules", None) is not None:
         modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
     if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
@@ -356,11 +360,6 @@ def sdnq_post_load_quant(
     modules_dtype_dict: Dict[str, List[str]] = None,
     op=None,
 ):
-    if modules_to_not_convert is None:
-        modules_to_not_convert = []
-    if modules_dtype_dict is None:
-        modules_dtype_dict = {}
-
     if add_skip_keys:
         model, modules_to_not_convert, modules_dtype_dict = add_module_skip_keys(model, modules_to_not_convert, modules_dtype_dict)
 

From 35277a79d37b5c58a0b67eaecafe463dac368b42 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Wed, 8 Oct 2025 01:21:11 +0300
Subject: [PATCH 179/230] cleanup x3

---
 modules/sdnq/quantizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 50d4b49f3..a2922ec80 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -543,7 +543,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         keep_in_fp32_modules: List[str] = None,
         **kwargs, # pylint: disable=unused-argument
     ):
-        self.quantization_config.add_skip_keys:
+        if self.quantization_config.add_skip_keys:
             if keep_in_fp32_modules is not None:
                 self.modules_to_not_convert.extend(keep_in_fp32_modules)
             model, self.modules_to_not_convert, self.quantization_config.modules_dtype_dict = add_module_skip_keys(

From 6907fcd320a97f4ca852a6a2a1cf892272bf06fb Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 8 Oct 2025 13:47:34 -0400
Subject: [PATCH 180/230] speedup prequant model load

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_models.py   |  3 ++-
 modules/sdnq/loader.py | 15 ++++++++-------
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index c8f78c1ae..008125edb 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -549,11 +549,12 @@ def load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op):
             continue
         module_name = f.replace('_quantization_config.json', '')
         quantization_config = shared.readfile(os.path.join(checkpoint_info.path, f), silent=True)
-        shared.log.debug(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" prequant=sdnq')
+        shared.log.debug(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" direct={shared.opts.diffusers_to_gpu} prequant=sdnq')
         module_path = os.path.join(checkpoint_info.path, module_name)
         modules[module_name] = sdnq.load_sdnq_model(
             model_path=module_path,
             quantization_config=quantization_config,
+            device=devices.device if shared.opts.diffusers_to_gpu else devices.cpu,
         )
         modules[module_name] = modules[module_name].to(device=devices.device)
     sd_model = pipeline.from_pretrained(
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 967254070..75e45db97 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -1,9 +1,7 @@
 import os
 import json
 import torch
-from safetensors import safe_open
 from diffusers.models.modeling_utils import ModelMixin
-
 from .common import use_tensorwise_fp8_matmul, use_contiguous_mm
 from .quantizer import SDNQConfig, sdnq_post_load_quant
 from .dequantizer import dequantize_symmetric, re_quantize_int8, re_quantize_fp8
@@ -29,8 +27,10 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "1
             module.quantization_config.to_json_file(os.path.join(model_path, f"{module_name}_quantization_config.json"))
 
 
-def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:
-    with torch.device("cpu"):
+def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, device: torch.device = 'cpu', dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:
+    from accelerate import init_empty_weights
+    from safetensors.torch import safe_open
+    with init_empty_weights():
         if quantization_config is None:
             try:
                 with open(os.path.join(model_path, "quantization_config.json"), "r", encoding="utf-8") as f:
@@ -64,7 +64,8 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             model = model_cls.from_config(config)
         elif hasattr(model_cls, "_from_config"):
             config = transformers.PretrainedConfig.from_dict(model_config)
-            model = model_cls._from_config(config) # pylint: disable=protected-access
+            # model = model_cls._from_config(config) # pylint: disable=protected-access
+            model = model_cls(config)
         else:
             raise ValueError(f"Dont know how to load model for {model_cls}")
         model = sdnq_post_load_quant(model, add_skip_keys=False, **quantization_config)
@@ -78,12 +79,12 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
         fn = os.path.join(model_path, "model.safetensors")
     else:
         raise ValueError(f"Cannot find safetensors file in {model_path}, please provide file_name argument")
-    with safe_open(fn, framework="pt") as f:
+    with safe_open(fn, framework="pt", device=str(device)) as f:
         for k in f.keys():
             state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)
     del state_dict
-    if dtype is not None or dequantize_fp32 is not None or use_quantized_matmul is not None:
+    if (dtype is not None) or (dequantize_fp32 is not None) or (use_quantized_matmul is not None):
         model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
     return model
 

From 3c16b58e0df107a531226fa8d47aa73fca4066c4 Mon Sep 17 00:00:00 2001
From: Oleksandr Liutyi <liutyi@gmail.com>
Date: Wed, 8 Oct 2025 23:02:47 +0300
Subject: [PATCH 181/230] Qwen prunning models cover images

---
 html/reference.json                           |   8 ++++----
 models/Reference/Qwen-Image-12B.jpg           | Bin 0 -> 71565 bytes
 models/Reference/Qwen-Image-13B.jpg           | Bin 0 -> 176594 bytes
 models/Reference/Qwen-Image-Edit-13B.jpg      | Bin 0 -> 73538 bytes
 models/Reference/Qwen-Image-Edit-2509-14B.jpg | Bin 0 -> 90410 bytes
 5 files changed, 4 insertions(+), 4 deletions(-)
 create mode 100644 models/Reference/Qwen-Image-12B.jpg
 create mode 100644 models/Reference/Qwen-Image-13B.jpg
 create mode 100644 models/Reference/Qwen-Image-Edit-13B.jpg
 create mode 100644 models/Reference/Qwen-Image-Edit-2509-14B.jpg

diff --git a/html/reference.json b/html/reference.json
index 45f2bd2d5..2408364cc 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -237,7 +237,7 @@
   "Qwen-Image Pruning-13B": {
     "path": "OPPOer/Qwen-Image-Pruning",
     "subfolder": "Qwen-Image-13B",
-    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "preview": "Qwen-Image-13B.jpg",
     "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
@@ -245,7 +245,7 @@
   "Qwen-Image Pruning-12B": {
     "path": "OPPOer/Qwen-Image-Pruning",
     "subfolder": "Qwen-Image-12B-40steps",
-    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "preview": "Qwen-Image-12B.jpg",
     "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
@@ -253,7 +253,7 @@
   "Qwen-Image-Edit Pruning-13B": {
     "path": "OPPOer/Qwen-Image-Edit-Pruning",
     "subfolder": "Qwen-Image-Edit-13B",
-    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "preview": "Qwen-Image-Edit-13B.jpg",
     "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
@@ -261,7 +261,7 @@
   "Qwen-Image-Edit-2509 Pruning-14B": {
     "path": "OPPOer/Qwen-Image-Edit-Pruning",
     "subfolder": "Qwen-Image-Edit-2509-14B",
-    "preview": "vladmandic--Qwen-Lightning-Edit.jpg",
+    "preview": "Qwen-Image-Edit-2509-14B.jpg",
     "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
diff --git a/models/Reference/Qwen-Image-12B.jpg b/models/Reference/Qwen-Image-12B.jpg
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HcmV?d00001


From 9c4780a5e7da5c0c24171dc8833e9f7a91fa3550 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 9 Oct 2025 09:25:49 -0400
Subject: [PATCH 182/230] fix guidance metadata

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md               | 3 ++-
 modules/pag/__init__.py    | 4 ++--
 modules/processing_info.py | 3 ++-
 modules/ui_control.py      | 4 ++--
 modules/ui_img2img.py      | 4 ++--
 modules/ui_txt2img.py      | 4 ++--
 6 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 036717542..c8c9d9745 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-07
+## Update for 2025-10-09
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -125,6 +125,7 @@
   - fix checks in init/mask image decode  
   - fix hf token with extra chars  
   - image viewer refocus on gallery after returning from full screen mode  
+  - fix attention guidance metadata save/restore  
 
 ## Update for 2025-09-15
 
diff --git a/modules/pag/__init__.py b/modules/pag/__init__.py
index e67f5b086..4d92f689b 100644
--- a/modules/pag/__init__.py
+++ b/modules/pag/__init__.py
@@ -42,8 +42,8 @@ def apply(p: processing.StableDiffusionProcessing): # pylint: disable=arguments-
     pag_applied_layers_index = pag_applied_layers.split() if len(pag_applied_layers) > 0 else []
     pag_applied_layers_index = [p.strip() for p in pag_applied_layers_index]
     p.task_args['pag_applied_layers_index'] = pag_applied_layers_index if len(pag_applied_layers_index) > 0 else ['m0'] # Available layers: d[0-5], m[0], u[0-8]
-    p.extra_generation_params["PAG scale"] = p.pag_scale
-    p.extra_generation_params["PAG adaptive"] = p.pag_adaptive
+    p.extra_generation_params["CFG true"] = p.pag_scale
+    p.extra_generation_params["CFG adaptive"] = p.pag_adaptive
     # shared.log.debug(f'{c}: args={p.task_args}')
 
 
diff --git a/modules/processing_info.py b/modules/processing_info.py
index e1d4d6379..cb1050349 100644
--- a/modules/processing_info.py
+++ b/modules/processing_info.py
@@ -50,7 +50,8 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
         "CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else None,
         "CFG rescale": p.diffusers_guidance_rescale if p.diffusers_guidance_rescale > 0 else None,
         "CFG end": p.cfg_end if p.cfg_end < 1.0 else None,
-        "CFG true": p.pag_scale if p.pag_scale > 1 else None,
+        "CFG true": p.pag_scale if p.pag_scale > 0 else None,
+        "CFG adaptive": p.pag_adaptive if p.pag_adaptive != 0.5 else None,
         "Clip skip": p.clip_skip if p.clip_skip > 1 else None,
         "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None,
         "Refiner prompt": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
diff --git a/modules/ui_control.py b/modules/ui_control.py
index 5ac075d61..af5de9882 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -400,8 +400,8 @@ def create_ui(_blocks: gr.Blocks=None):
                 (refiner_prompt, "Refiner prompt"),
                 (refiner_negative, "Refiner negative"),
                 # pag
-                (pag_scale, "PAG scale"),
-                (pag_adaptive, "PAG adaptive"),
+                (pag_scale, "CFG true"),
+                (pag_adaptive, "CFG adaptive"),
                 # hidden
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index cf1324484..a22e66901 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -298,8 +298,8 @@ def create_ui():
                 (refiner_prompt, "refiner prompt"),
                 (refiner_negative, "Refiner negative"),
                 # pag
-                (pag_scale, "PAG scale"),
-                (pag_adaptive, "PAG adaptive"),
+                (pag_scale, "CFG true"),
+                (pag_adaptive, "CFG adaptive"),
                 # inpaint
                 (mask_blur, "Mask blur"),
                 (mask_alpha, "Mask alpha"),
diff --git a/modules/ui_txt2img.py b/modules/ui_txt2img.py
index 6eccbdd82..f3e108666 100644
--- a/modules/ui_txt2img.py
+++ b/modules/ui_txt2img.py
@@ -150,8 +150,8 @@ def create_ui():
                 (refiner_prompt, "refiner prompt"),
                 (refiner_negative, "Refiner negative"),
                 # pag
-                (pag_scale, "PAG scale"),
-                (pag_adaptive, "PAG adaptive"),
+                (pag_scale, "CFG true"),
+                (pag_adaptive, "CFG adaptive"),
                 # hidden
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),

From 620ad5acef2ffbabb06b46c0c95ed0b377b7c1d8 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 9 Oct 2025 09:38:07 -0400
Subject: [PATCH 183/230] update oppoer models

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 html/reference.json                           |  26 ++++++------------
 ... OPPOer--Qwen-Image-Edit-2509-Pruning.jpg} | Bin
 ...pg => OPPOer--Qwen-Image-Edit-Pruning.jpg} | Bin
 ...12B.jpg => OPPOer--Qwen-Image-Pruning.jpg} | Bin
 4 files changed, 9 insertions(+), 17 deletions(-)
 rename models/Reference/{Qwen-Image-Edit-2509-14B.jpg => OPPOer--Qwen-Image-Edit-2509-Pruning.jpg} (100%)
 rename models/Reference/{Qwen-Image-Edit-13B.jpg => OPPOer--Qwen-Image-Edit-Pruning.jpg} (100%)
 rename models/Reference/{Qwen-Image-12B.jpg => OPPOer--Qwen-Image-Pruning.jpg} (100%)

diff --git a/html/reference.json b/html/reference.json
index 2408364cc..8202631a2 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -234,34 +234,26 @@
     "size": 56.1,
     "date": "2025 August"
   },
-  "Qwen-Image Pruning-13B": {
-    "path": "OPPOer/Qwen-Image-Pruning",
-    "subfolder": "Qwen-Image-13B",
-    "preview": "Qwen-Image-13B.jpg",
-    "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
-    "skip": true,
-    "date": "2025 Ocotober"
-  },
   "Qwen-Image Pruning-12B": {
     "path": "OPPOer/Qwen-Image-Pruning",
-    "subfolder": "Qwen-Image-12B-40steps",
-    "preview": "Qwen-Image-12B.jpg",
-    "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
+    "subfolder": "Qwen-Image-12B-8steps",
+    "preview": "OPPOer--Qwen-Image-Pruning.jpg",
+    "desc": "This open-source project is based on Qwen-Image and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 12B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
   },
   "Qwen-Image-Edit Pruning-13B": {
     "path": "OPPOer/Qwen-Image-Edit-Pruning",
-    "subfolder": "Qwen-Image-Edit-13B",
-    "preview": "Qwen-Image-Edit-13B.jpg",
+    "subfolder": "Qwen-Image-Edit-13B-4steps",
+    "preview": "OPPOer--Qwen-Image-Edit-Pruning.jpg",
     "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
   },
-  "Qwen-Image-Edit-2509 Pruning-14B": {
-    "path": "OPPOer/Qwen-Image-Edit-Pruning",
-    "subfolder": "Qwen-Image-Edit-2509-14B",
-    "preview": "Qwen-Image-Edit-2509-14B.jpg",
+  "Qwen-Image-Edit-2509 Pruning-13B": {
+    "path": "OPPOer/Qwen-Image-Edit-2509-Pruning",
+    "subfolder": "Qwen-Image-Edit-2509-13B-4steps",
+    "preview": "OPPOer--Qwen-Image-Edit-2509-Pruning.jpg",
     "desc": "This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters.",
     "skip": true,
     "date": "2025 Ocotober"
diff --git a/models/Reference/Qwen-Image-Edit-2509-14B.jpg b/models/Reference/OPPOer--Qwen-Image-Edit-2509-Pruning.jpg
similarity index 100%
rename from models/Reference/Qwen-Image-Edit-2509-14B.jpg
rename to models/Reference/OPPOer--Qwen-Image-Edit-2509-Pruning.jpg
diff --git a/models/Reference/Qwen-Image-Edit-13B.jpg b/models/Reference/OPPOer--Qwen-Image-Edit-Pruning.jpg
similarity index 100%
rename from models/Reference/Qwen-Image-Edit-13B.jpg
rename to models/Reference/OPPOer--Qwen-Image-Edit-Pruning.jpg
diff --git a/models/Reference/Qwen-Image-12B.jpg b/models/Reference/OPPOer--Qwen-Image-Pruning.jpg
similarity index 100%
rename from models/Reference/Qwen-Image-12B.jpg
rename to models/Reference/OPPOer--Qwen-Image-Pruning.jpg

From 70defe6d06c70bee3925ab5491d7307206e5e979 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 9 Oct 2025 11:29:36 -0400
Subject: [PATCH 184/230] handle load shards

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py           |  6 +++---
 modules/loader.py      |  7 ++++++-
 modules/sd_models.py   | 46 ++++++++++++++++++++++++++++++++++++------
 modules/sdnq/loader.py | 18 ++++++++---------
 modules/ui_models.py   | 19 +++++++++++++----
 5 files changed, 72 insertions(+), 24 deletions(-)

diff --git a/installer.py b/installer.py
index 95a007198..9129d2620 100644
--- a/installer.py
+++ b/installer.py
@@ -608,9 +608,9 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = '2b7deffe361b7b0e1d2665a1f9f0bd4daea4927c' # diffusers commit hash
-    if args.use_rocm or args.use_zluda or args.use_directml:
-        sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
+    sha = 'a9df12ab451be65060f6f69e201e3a6cb2ee15e8' # diffusers commit hash
+    # if args.use_rocm or args.use_zluda or args.use_directml:
+    #     sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else -1)
     cur = opts.get('diffusers_version', '') if minor > -1 else ''
diff --git a/modules/loader.py b/modules/loader.py
index 7ee969962..a224c05c7 100644
--- a/modules/loader.py
+++ b/modules/loader.py
@@ -51,10 +51,15 @@ if torch.__version__.startswith('2.5.0'):
     errors.log.warning(f'Disabling cuDNN for SDP on torch={torch.__version__}')
     torch.backends.cuda.enable_cudnn_sdp(False)
 try:
-    import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+    import intel_extension_for_pytorch as ipex # pylint: disable=import-error,unused-import
     errors.log.debug(f'Load IPEX=={ipex.__version__}')
 except Exception:
     pass
+try:
+    import torch.distributed.distributed_c10d as _c10d # pylint: disable=unused-import,ungrouped-imports
+except Exception:
+    errors.log.warning('Loader: torch is not built with distributed support')
+
 
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 008125edb..b0606dd76 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -551,12 +551,16 @@ def load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op):
         quantization_config = shared.readfile(os.path.join(checkpoint_info.path, f), silent=True)
         shared.log.debug(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" direct={shared.opts.diffusers_to_gpu} prequant=sdnq')
         module_path = os.path.join(checkpoint_info.path, module_name)
-        modules[module_name] = sdnq.load_sdnq_model(
-            model_path=module_path,
-            quantization_config=quantization_config,
-            device=devices.device if shared.opts.diffusers_to_gpu else devices.cpu,
-        )
-        modules[module_name] = modules[module_name].to(device=devices.device)
+        try:
+            modules[module_name] = sdnq.load_sdnq_model(
+                model_path=module_path,
+                quantization_config=quantization_config,
+                device=devices.device if shared.opts.diffusers_to_gpu else devices.cpu,
+            )
+            modules[module_name] = modules[module_name].to(device=devices.device)
+        except Exception as e:
+            shared.log.error(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" {e}')
+            errors.display(e, 'Load')
     sd_model = pipeline.from_pretrained(
         checkpoint_info.path,
         cache_dir=shared.opts.diffusers_dir,
@@ -1265,3 +1269,33 @@ def hf_auth_check(checkpoint_info, force:bool=False):
     except Exception as e:
         shared.log.error(f'Load model: repo="{repo_id}" login={login} {e}')
         return False
+
+
+def save_model(name: str, path: str = None, shard: str = None, overwrite: bool = False):
+    if (name is None) or len(name.strip()) == 0:
+        shared.log.error('Save model: invalid model name')
+        return 'Invalid model name'
+    if not shared.sd_loaded:
+        shared.log.error('Save model: model not loaded')
+        return 'Model not loaded'
+    from modules.sdnq import save_sdnq_model
+    if path is None:
+        path = shared.opts.diffusers_dir
+    model_name = os.path.join(path.strip(), name.strip())
+    if os.path.exists(model_name) and not overwrite:
+        shared.log.error(f'Save model: path="{model_name}" exists')
+        return f'Path exists: {model_name}'
+    try:
+        t0 = time.time()
+        save_sdnq_model(
+            model=shared.sd_model,
+            model_path=model_name,
+            max_shard_size=shard,
+        )
+        t1 = time.time()
+        shared.log.info(f'Save model: path="{model_name}" cls={shared.sd_model.__class__.__name__} time={t1 - t0:.2f}')
+        return f'Saved: {model_name}'
+    except Exception as e:
+        shared.log.error(f'Save model: path="{model_name}" {e}')
+        errors.display(e, 'Save model')
+        return f'Error: {e}'
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 75e45db97..8ed2761ef 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -64,24 +64,22 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             model = model_cls.from_config(config)
         elif hasattr(model_cls, "_from_config"):
             config = transformers.PretrainedConfig.from_dict(model_config)
-            # model = model_cls._from_config(config) # pylint: disable=protected-access
             model = model_cls(config)
         else:
             raise ValueError(f"Dont know how to load model for {model_cls}")
         model = sdnq_post_load_quant(model, add_skip_keys=False, **quantization_config)
 
     state_dict = {}
+    files = []
     if file_name:
-        fn = os.path.join(model_path, file_name)
-    elif os.path.exists(os.path.join(model_path, "diffusion_pytorch_model.safetensors")):
-        fn = os.path.join(model_path, "diffusion_pytorch_model.safetensors")
-    elif os.path.exists(os.path.join(model_path, "model.safetensors")):
-        fn = os.path.join(model_path, "model.safetensors")
+        files.append(os.path.join(model_path, file_name))
     else:
-        raise ValueError(f"Cannot find safetensors file in {model_path}, please provide file_name argument")
-    with safe_open(fn, framework="pt", device=str(device)) as f:
-        for k in f.keys():
-            state_dict[k] = f.get_tensor(k)
+        all_files = os.listdir(model_path)
+        files = sorted([os.path.join(model_path, f) for f in all_files if f.endswith(".safetensors")])
+    for fn in files:
+        with safe_open(fn, framework="pt", device=str(device)) as f:
+            for k in f.keys():
+                state_dict[k] = f.get_tensor(k)
     model.load_state_dict(state_dict, assign=True)
     del state_dict
     if (dtype is not None) or (dequantize_fp32 is not None) or (use_quantized_matmul is not None):
diff --git a/modules/ui_models.py b/modules/ui_models.py
index 34563e891..89e5697ab 100644
--- a/modules/ui_models.py
+++ b/modules/ui_models.py
@@ -65,12 +65,23 @@ def create_ui():
                     return [html, meta]
 
                 with gr.Row():
-                    gr.HTML('<h2>Analyze currently loaded model<br></h2>')
-                with gr.Row():
-                    model_analyze = gr.Button(value="Analyze", variant='primary')
+                    model_analyze = gr.Button(value="Analyze model", variant='primary')
                 with gr.Row():
                     model_desc = gr.HTML(value="", elem_id="model_desc")
-                with gr.Row():
+                with gr.Accordion(label="Save model", open=False):
+                    with gr.Row():
+                        save_name = gr.Textbox(label="Model name", placeholder="Model name to save as")
+                    with gr.Row():
+                        save_path = gr.Textbox(label="Model base path", placeholder="Path to save model to", value=opts.diffusers_dir)
+                    with gr.Row():
+                        save_shard = gr.Textbox(label="Max shard size", placeholder="Maximum shard size", value="10GB")
+                        save_overwrite = gr.Checkbox(label="Overwrite existing", value=False)
+                    with gr.Row():
+                        save_result = gr.HTML(value="", elem_id="model_save_outcome")
+                    with gr.Row():
+                        model_save = gr.Button(value="Save model", variant='primary')
+                        model_save.click(fn=sd_models.save_model, inputs=[save_name, save_path, save_shard, save_overwrite], outputs=[save_result])
+                with gr.Accordion(label="Metadata", open=False):
                     model_meta = gr.JSON(label="Metadata", value={}, elem_id="model_meta")
 
                 model_analyze.click(fn=analyze, inputs=[], outputs=[model_desc, model_meta])

From ee3e0aa978ad0ad3008824d6e678bc7b88504bda Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 9 Oct 2025 12:22:55 -0400
Subject: [PATCH 185/230] add epoch to namegens and disable spellchecks

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md              | 2 ++
 modules/images_namegen.py | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c8c9d9745..214136cfe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -62,6 +62,7 @@
   - separate guidance and detail sections  
   - networks ability to filter lora by base model version  
   - add interrogate button to input images  
+  - disable spellchecks on all text inputs  
 - **SDNQ**
   - add `SVDQuant` quantization method support  
   - make sdnq scales compatible with balanced offload  
@@ -93,6 +94,7 @@
   - **xyz-grid** add guidance section  
   - **preview** implement configurable layers for WAN, Qwen, HV  
   - update swagger `/docs` endpoint style  
+  - add `[epoch]` to filename template  
 - **Video**
   - use shared **T5** text encoder for video models when possible  
   - use shared **LLama** text encoder for video models when possible  
diff --git a/modules/images_namegen.py b/modules/images_namegen.py
index c8971eff2..b50d2ae97 100644
--- a/modules/images_namegen.py
+++ b/modules/images_namegen.py
@@ -1,5 +1,6 @@
 import re
 import os
+import time
 import uuid
 import string
 import hashlib
@@ -33,6 +34,7 @@ class FilenameGenerator:
         'hash': lambda self: self.image_hash(),
         'image_hash': lambda self: self.image_hash(),
         'timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+        'epoch': lambda self: int(time.time()),
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
 
         'model': lambda self: shared.sd_model.sd_checkpoint_info.title if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',

From 04b5a1a9e770c5cbcf0a11c51aa103e610a29522 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 9 Oct 2025 12:23:07 -0400
Subject: [PATCH 186/230] update modernui

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 39277a468..8451ed225 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 39277a4688473e1e5fdb8a6173c8ded28c0439e1
+Subproject commit 8451ed225e63e25db8627b57958a9cba2103f1fe

From e19fb2d8337b8e7fb4fce409c66ea9f6b7e18d60 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 9 Oct 2025 19:34:48 +0300
Subject: [PATCH 187/230] SDNQ keep the quant configs inside the module
 subfolder, add dtype cast and don't send to GPU

---
 modules/sd_detect.py   | 14 +++++++++++++-
 modules/sd_models.py   | 15 ++++++++-------
 modules/sdnq/loader.py | 19 ++++++++++++++-----
 3 files changed, 35 insertions(+), 13 deletions(-)

diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 059b081a4..7d3e20c2e 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -138,7 +138,19 @@ def guess_by_diffusers(fn, current_guess):
             if pipeline is None:
                 pipeline = cls
         if callable(pipeline):
-            is_quant = any(f for f in os.listdir(fn) if f.endswith('quantization_config.json'))
+            is_quant = False
+            for folder in os.listdir(fn):
+                folder = os.path.join(fn, folder)
+                if is_quant:
+                    break
+                if folder.endswith('quantization_config.json'):
+                    is_quant = True
+                    break
+                elif os.path.isdir(folder):
+                    for f in os.listdir(folder):
+                        if f.endswith('quantization_config.json'):
+                            is_quant = True
+                            break
             pipelines = shared_items.get_pipelines()
             for k, v in pipelines.items():
                 if v is not None and v.__name__ == pipeline.__name__:
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b0606dd76..4400a0aa6 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -544,20 +544,20 @@ def load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_con
 def load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op):
     from modules import sdnq
     modules = {}
-    for f in os.listdir(checkpoint_info.path):
-        if not f.endswith('quantization_config.json'):
+    for module_name in os.listdir(checkpoint_info.path):
+        quantization_config_path = os.path.join(checkpoint_info.path, module_name, 'quantization_config.json')
+        if not os.path.exists(quantization_config_path):
             continue
-        module_name = f.replace('_quantization_config.json', '')
-        quantization_config = shared.readfile(os.path.join(checkpoint_info.path, f), silent=True)
+        model_path = os.path.join(checkpoint_info.path, module_name)
+        quantization_config = shared.readfile(quantization_config_path, silent=True)
         shared.log.debug(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" direct={shared.opts.diffusers_to_gpu} prequant=sdnq')
-        module_path = os.path.join(checkpoint_info.path, module_name)
         try:
             modules[module_name] = sdnq.load_sdnq_model(
-                model_path=module_path,
+                model_path=model_path,
                 quantization_config=quantization_config,
                 device=devices.device if shared.opts.diffusers_to_gpu else devices.cpu,
+                dtype=devices.dtype,
             )
-            modules[module_name] = modules[module_name].to(device=devices.device)
         except Exception as e:
             shared.log.error(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" {e}')
             errors.display(e, 'Load')
@@ -1291,6 +1291,7 @@ def save_model(name: str, path: str = None, shard: str = None, overwrite: bool =
             model=shared.sd_model,
             model_path=model_name,
             max_shard_size=shard,
+            is_pipeline=True,
         )
         t1 = time.time()
         shared.log.info(f'Save model: path="{model_name}" cls={shared.sd_model.__class__.__name__} time={t1 - t0:.2f}')
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 8ed2761ef..242f1e83b 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -15,16 +15,25 @@ def get_module_names(model: ModelMixin) -> list:
     return modules_names
 
 
-def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", sdnq_config: SDNQConfig = None) -> None:
+def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", is_pipeline: bool = False, sdnq_config: SDNQConfig = None) -> None:
     model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
 
     if sdnq_config is not None: # if provided, save global config
         sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
 
-    for module_name in get_module_names(model): # save per-module config if available
-        module = getattr(model, module_name, None)
-        if (module is not None) and hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
-            module.quantization_config.to_json_file(os.path.join(model_path, f"{module_name}_quantization_config.json"))
+    if is_pipeline:
+        for module_name in get_module_names(model): # save per-module config if available
+            module = getattr(model, module_name, None)
+            if (module is not None) and hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
+                module.quantization_config.to_json_file(os.path.join(model_path, module_name, "quantization_config.json"))
+    elif sdnq_config is None:
+        quantization_config = None
+        if hasattr(model, "quantization_config"):
+            quantization_config = model.quantization_config
+        elif hasattr(model, "config") and hasattr(model.config, "quantization_config"):
+            quantization_config = model.config.quantization_config
+        if quantization_config is not None:
+            quantization_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
 
 
 def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, device: torch.device = 'cpu', dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:

From a9de8ef1528223fe2b81fe0257e83efd147472e0 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 9 Oct 2025 19:58:57 +0300
Subject: [PATCH 188/230] cleanup

---
 modules/sdnq/loader.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 242f1e83b..e50a83977 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -18,22 +18,25 @@ def get_module_names(model: ModelMixin) -> list:
 def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", is_pipeline: bool = False, sdnq_config: SDNQConfig = None) -> None:
     model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
 
+    quantization_config_path = os.path.join(model_path, "quantization_config.json")
     if sdnq_config is not None: # if provided, save global config
-        sdnq_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
+        sdnq_config.to_json_file(quantization_config_path)
 
     if is_pipeline:
         for module_name in get_module_names(model): # save per-module config if available
             module = getattr(model, module_name, None)
-            if (module is not None) and hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
-                module.quantization_config.to_json_file(os.path.join(model_path, module_name, "quantization_config.json"))
+            if module is None:
+                continue
+            module_quantization_config_path = os.path.join(model_path, module_name, "quantization_config.json")
+            if hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
+                module.quantization_config.to_json_file(module_quantization_config_path)
+            elif hasattr(module, "config"), hasattr(module.config, "quantization_config") and isinstance(module.config.quantization_config, SDNQConfig):
+                module.config.quantization_config.to_json_file(module_quantization_config_path)
     elif sdnq_config is None:
-        quantization_config = None
-        if hasattr(model, "quantization_config"):
-            quantization_config = model.quantization_config
-        elif hasattr(model, "config") and hasattr(model.config, "quantization_config"):
-            quantization_config = model.config.quantization_config
-        if quantization_config is not None:
-            quantization_config.to_json_file(os.path.join(model_path, "quantization_config.json"))
+        if hasattr(model, "quantization_config") and isinstance(model.quantization_config, SDNQConfig):
+            model.quantization_config.to_json_file(quantization_config_path)
+        elif hasattr(model, "config") and hasattr(model.config, "quantization_config") and isinstance(model.config.quantization_config, SDNQConfig):
+            model.config.quantization_config.to_json_file(quantization_config_path)
 
 
 def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, device: torch.device = 'cpu', dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:

From 612df3abbbca036d4a506afca1156ceef0e78331 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 9 Oct 2025 20:09:34 +0300
Subject: [PATCH 189/230] cleanup

---
 modules/sdnq/loader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index e50a83977..de94a2a16 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -30,7 +30,7 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "1
             module_quantization_config_path = os.path.join(model_path, module_name, "quantization_config.json")
             if hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
                 module.quantization_config.to_json_file(module_quantization_config_path)
-            elif hasattr(module, "config"), hasattr(module.config, "quantization_config") and isinstance(module.config.quantization_config, SDNQConfig):
+            elif hasattr(module, "config") and hasattr(module.config, "quantization_config") and isinstance(module.config.quantization_config, SDNQConfig):
                 module.config.quantization_config.to_json_file(module_quantization_config_path)
     elif sdnq_config is None:
         if hasattr(model, "quantization_config") and isinstance(model.quantization_config, SDNQConfig):

From 624e525fd192d1c8c8e327677026cdd453d30796 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 9 Oct 2025 20:30:23 +0300
Subject: [PATCH 190/230] Fix VAE config not being read correctly with SDNQ
 pre-quants

---
 modules/sd_detect.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 7d3e20c2e..9996537c1 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -214,6 +214,7 @@ def detect_pipeline(f: str, op: str = 'model'):
 
 
 def get_load_config(model_file, model_type, config_type='yaml'):
+    model_type = model_type.removesuffix(' SDNQ')
     if config_type == 'yaml':
         yaml = os.path.splitext(model_file)[0] + '.yaml'
         if os.path.exists(yaml):

From 6995d8c3c6ad140775d75e3a9ff55474034d4abb Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 9 Oct 2025 22:42:20 +0300
Subject: [PATCH 191/230] SDNQ fix T5 loading

---
 modules/sdnq/loader.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index de94a2a16..a66e767a5 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -42,6 +42,7 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "1
 def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, device: torch.device = 'cpu', dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:
     from accelerate import init_empty_weights
     from safetensors.torch import safe_open
+
     with init_empty_weights():
         if quantization_config is None:
             try:
@@ -49,12 +50,14 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
                     quantization_config = json.load(f)
             except Exception:
                 quantization_config = {}
+
         if model_config is None:
             try:
                 with open(os.path.join(model_path, 'config.json'), "r", encoding="utf-8") as f:
                     model_config = json.load(f)
             except Exception:
                 model_config = {}
+
         if model_cls is None:
             import transformers
             import diffusers
@@ -65,12 +68,14 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
                 model_cls = getattr(diffusers, class_name, None) or getattr(transformers, class_name, None)
         if model_cls is None:
             raise ValueError(f"Cannot determine model class for {model_path}, please provide model_cls argument")
+
         quantization_config.pop("is_integer", None)
         quantization_config.pop("quant_method", None)
         quantization_config.pop("quantization_device", None)
         quantization_config.pop("return_device", None)
         quantization_config.pop("non_blocking", None)
         quantization_config.pop("add_skip_keys", None)
+
         if hasattr(model_cls, "load_config"):
             config = model_cls.load_config(model_path)
             model = model_cls.from_config(config)
@@ -79,6 +84,7 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             model = model_cls(config)
         else:
             raise ValueError(f"Dont know how to load model for {model_cls}")
+
         model = sdnq_post_load_quant(model, add_skip_keys=False, **quantization_config)
 
     state_dict = {}
@@ -88,12 +94,18 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
     else:
         all_files = os.listdir(model_path)
         files = sorted([os.path.join(model_path, f) for f in all_files if f.endswith(".safetensors")])
+
     for fn in files:
         with safe_open(fn, framework="pt", device=str(device)) as f:
             for k in f.keys():
                 state_dict[k] = f.get_tensor(k)
+
+    if model.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"}:
+        state_dict["encoder.embed_tokens.weight"] = state_dict["shared.weight"]
+
     model.load_state_dict(state_dict, assign=True)
     del state_dict
+
     if (dtype is not None) or (dequantize_fp32 is not None) or (use_quantized_matmul is not None):
         model = apply_options_to_model(model, dtype=dtype, dequantize_fp32=dequantize_fp32, use_quantized_matmul=use_quantized_matmul)
     return model

From 2a3deaa06434f44aa506fa20c06aead6a8f152a0 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 9 Oct 2025 22:46:27 +0300
Subject: [PATCH 192/230] Check T5 keys before override

---
 modules/sdnq/loader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index a66e767a5..375a336b6 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -100,7 +100,7 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             for k in f.keys():
                 state_dict[k] = f.get_tensor(k)
 
-    if model.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"}:
+    if model.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"} and "encoder.embed_tokens.weight" not in state_dict.keys():
         state_dict["encoder.embed_tokens.weight"] = state_dict["shared.weight"]
 
     model.load_state_dict(state_dict, assign=True)

From 5db54ffb55d2a4f909dae7ab9d5ea8b18aedc45b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 9 Oct 2025 16:11:47 -0400
Subject: [PATCH 193/230] set unique guidance labels

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui |  2 +-
 modules/ui_guidance.py             | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 8451ed225..81d8682e9 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 8451ed225e63e25db8627b57958a9cba2103f1fe
+Subproject commit 81d8682e99ce1b1543e3b1d079015e1caca285db
diff --git a/modules/ui_guidance.py b/modules/ui_guidance.py
index ae0506771..a6ea46391 100644
--- a/modules/ui_guidance.py
+++ b/modules/ui_guidance.py
@@ -15,11 +15,11 @@ def create_guidance_inputs(tab):
                 guidance_btn = ui_components.ToolButton(value=ui_symbols.book, elem_id=f"{tab}_guider_docs")
                 guidance_btn.click(fn=None, _js='getGuidanceDocs', inputs=[guidance_name], outputs=[])
             with gr.Row(visible=shared.opts.model_modular_enable):
-                guidance_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_guidance_scale")
-                guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance rescale', value=0.0, elem_id=f"{tab}_guidance_rescale")
+                guidance_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='_Guidance scale', value=6.0, elem_id=f"{tab}_guidance_scale")
+                guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='_Guidance rescale', value=0.0, elem_id=f"{tab}_guidance_rescale")
             with gr.Row(visible=shared.opts.model_modular_enable):
-                guidance_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance start', value=0.0, elem_id=f"{tab}_guidance_start")
-                guidance_stop = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance stop', value=1.0, elem_id=f"{tab}_guidance_stop")
+                guidance_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='_Guidance start', value=0.0, elem_id=f"{tab}_guidance_start")
+                guidance_stop = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='_Guidance stop', value=1.0, elem_id=f"{tab}_guidance_stop")
             guidance_args = [guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop]
 
             lsc_group = gr.Accordion(open=False, label='Layer skip guidance', elem_classes=["small-accordion"], visible=shared.opts.model_modular_enable)
@@ -117,8 +117,8 @@ def create_guidance_inputs(tab):
                 cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
                 cfg_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance end', value=1.0, elem_id=f"{tab}_cfg_end")
             with gr.Row():
+                diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance rescale', value=0.0, elem_id=f"{tab}_image_cfg_rescale")
                 image_cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Refine guidance', value=6.0, elem_id=f"{tab}_image_cfg_scale")
-                diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Rescale guidance', value=0.0, elem_id=f"{tab}_image_cfg_rescale")
             with gr.Row():
                 diffusers_pag_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='Attention guidance', value=0.0, elem_id=f"{tab}_pag_scale")
                 diffusers_pag_adaptive = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Adaptive scaling', value=0.5, elem_id=f"{tab}_pag_adaptive")

From 53672ab9ede508b4eae19e9f8678585877d15a67 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 10 Oct 2025 14:19:16 -0400
Subject: [PATCH 194/230] add model save notes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 9 ++++++++-
 extensions-builtin/sdnext-modernui | 2 +-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 214136cfe..16faa4108 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,7 +20,14 @@
   - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
     *experimental*: updated version of E1 image editing model  
 - **Features**
-  - [Qwen Image-Edit] multi-image editing
+  - **Model save**: ability to save currently loaded model as a new standalone model  
+    why? SD.Next always prefers to start with full model and quantize on-demand during load  
+    however, when you find your exact preferred quantization settings that work well for you,  
+    saving such model as a new model allows for faster loads and reduced disk space usage  
+    so its best of both worlds: you can experiment and test different quantizations and once you find the one that works for you, save it as a new model  
+    saved models appear in network tab as normal models and can be loaded as such  
+    available in *models* tab  
+  - [Qwen Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) multi-image editing
     requires qwen-image-edit-2509 or its variant as multi-image edits are not available in original qwen-image  
     in ui control tab: inputs -> separate init image  
     add image for *input media* and *control media*  
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 81d8682e9..638a07d70 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 81d8682e99ce1b1543e3b1d079015e1caca285db
+Subproject commit 638a07d70998cc4ffc1fd9fe8a51de180c1c9e08

From c7aba8589b46c075df9438c004eed91db61b898e Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 11 Oct 2025 00:05:09 +0300
Subject: [PATCH 195/230] SDNQ fix Qwen loading

---
 modules/sdnq/layers/conv/conv_fp8.py              |  3 ++-
 modules/sdnq/layers/conv/conv_fp8_tensorwise.py   |  5 +++--
 modules/sdnq/layers/conv/conv_int8.py             |  5 +++--
 modules/sdnq/layers/linear/linear_fp8.py          |  3 ++-
 .../sdnq/layers/linear/linear_fp8_tensorwise.py   |  7 ++++---
 modules/sdnq/layers/linear/linear_int8.py         |  5 +++--
 modules/sdnq/loader.py                            | 15 ++++++++++++---
 modules/sdnq/quantizer.py                         |  6 +++++-
 8 files changed, 34 insertions(+), 15 deletions(-)

diff --git a/modules/sdnq/layers/conv/conv_fp8.py b/modules/sdnq/layers/conv/conv_fp8.py
index b07196ea7..bdaf27062 100644
--- a/modules/sdnq/layers/conv/conv_fp8.py
+++ b/modules/sdnq/layers/conv/conv_fp8.py
@@ -26,7 +26,8 @@ def conv_fp8_matmul(
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
     if svd_up is not None:
-        svd_bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+        input = input.flatten(0,-2)
+        svd_bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
 
     input, input_scale = quantize_fp8_matmul_input(input)
     input, weight = check_mats(input, weight)
diff --git a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
index 1893ad8a5..2dc9fbda3 100644
--- a/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
+++ b/modules/sdnq/layers/conv/conv_fp8_tensorwise.py
@@ -27,10 +27,11 @@ def conv_fp8_matmul_tensorwise(
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
     if svd_up is not None:
+        input = input.flatten(0,-2)
         if bias is not None:
-            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
         else:
-            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
 
     input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
     input, weight = check_mats(input, weight)
diff --git a/modules/sdnq/layers/conv/conv_int8.py b/modules/sdnq/layers/conv/conv_int8.py
index 82bd51f95..3332537d1 100644
--- a/modules/sdnq/layers/conv/conv_int8.py
+++ b/modules/sdnq/layers/conv/conv_int8.py
@@ -30,10 +30,11 @@ def conv_int8_matmul(
     return_dtype = input.dtype
     input, mm_output_shape = process_conv_input(conv_type, input, reversed_padding_repeated_twice, padding_mode, result_shape, stride, padding, dilation)
     if svd_up is not None:
+        input = input.flatten(0,-2)
         if bias is not None:
-            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
         else:
-            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
 
     input, scale = quantize_int8_matmul_input(input, scale)
     if quantized_weight_shape is not None:
diff --git a/modules/sdnq/layers/linear/linear_fp8.py b/modules/sdnq/layers/linear/linear_fp8.py
index 0c8c26341..2ddd137c1 100644
--- a/modules/sdnq/layers/linear/linear_fp8.py
+++ b/modules/sdnq/layers/linear/linear_fp8.py
@@ -26,7 +26,8 @@ def fp8_matmul(
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
     if svd_up is not None:
-        svd_bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+        input = input.flatten(0,-2)
+        svd_bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
     input, input_scale = quantize_fp8_matmul_input(input)
     input, weight = check_mats(input, weight)
     if bias is not None and bias.dtype != torch.bfloat16:
diff --git a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
index bd7a9b973..c7978ef0a 100644
--- a/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
+++ b/modules/sdnq/layers/linear/linear_fp8_tensorwise.py
@@ -28,12 +28,13 @@ def fp8_matmul_tensorwise(
 ) -> torch.FloatTensor:
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
-    dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
     if svd_up is not None:
+        input.flatten(0,-2)
         if bias is not None:
-            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
         else:
-            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
+    dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
     input, scale = quantize_fp8_matmul_input_tensorwise(input, scale)
     input, weight = check_mats(input, weight)
     if bias is not None:
diff --git a/modules/sdnq/layers/linear/linear_int8.py b/modules/sdnq/layers/linear/linear_int8.py
index ce8bcf788..6d7f6f2b8 100644
--- a/modules/sdnq/layers/linear/linear_int8.py
+++ b/modules/sdnq/layers/linear/linear_int8.py
@@ -34,10 +34,11 @@ def int8_matmul(
     return_dtype = input.dtype
     output_shape = (*input.shape[:-1], weight.shape[-1])
     if svd_up is not None:
+        input = input.flatten(0,-2)
         if bias is not None:
-            bias = torch.addmm(bias, torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
         else:
-            bias = torch.mm(torch.mm(input.flatten(0,-2).to(dtype=svd_down.dtype), svd_down), svd_up)
+            bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
     input, scale = quantize_int8_matmul_input(input, scale)
     input, weight = check_mats(input, weight)
     if bias is not None:
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 375a336b6..04fc5e9a5 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -1,4 +1,5 @@
 import os
+import re
 import json
 import torch
 from diffusers.models.modeling_utils import ModelMixin
@@ -80,15 +81,17 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
             config = model_cls.load_config(model_path)
             model = model_cls.from_config(config)
         elif hasattr(model_cls, "_from_config"):
-            config = transformers.PretrainedConfig.from_dict(model_config)
+            config = transformers.AutoConfig.from_pretrained(model_path)
             model = model_cls(config)
         else:
             raise ValueError(f"Dont know how to load model for {model_cls}")
 
         model = sdnq_post_load_quant(model, add_skip_keys=False, **quantization_config)
 
+    key_mapping = getattr(model, "_checkpoint_conversion_mapping", None)
     state_dict = {}
     files = []
+
     if file_name:
         files.append(os.path.join(model_path, file_name))
     else:
@@ -97,8 +100,14 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
 
     for fn in files:
         with safe_open(fn, framework="pt", device=str(device)) as f:
-            for k in f.keys():
-                state_dict[k] = f.get_tensor(k)
+            for key in f.keys():
+                new_key = key
+                if key_mapping:
+                    for pattern, replacement in key_mapping.items():
+                        new_key, n_replace = re.subn(pattern, replacement, new_key)
+                        if n_replace > 0:
+                            break
+                state_dict[new_key] = f.get_tensor(key)
 
     if model.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"} and "encoder.embed_tokens.weight" not in state_dict.keys():
         state_dict["encoder.embed_tokens.weight"] = state_dict["shared.weight"]
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index a2922ec80..d39523e5b 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -268,7 +268,11 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
                 continue
             if len(modules_dtype_dict.keys()) > 0:
                 for key, value in modules_dtype_dict.items():
-                    if param_name in value:
+                    if (
+                        param_name in value
+                        or any(param in split_param_name for param in value)
+                        or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in value)
+                    ):
                         key = key.lower()
                         if key in {"8bit", "8bits"}:
                             if dtype_dict[weights_dtype]["num_bits"] != 8:

From 1d2775103fc9611b7d2fbe49ed4716690cefb7eb Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 11 Oct 2025 00:55:22 +0300
Subject: [PATCH 196/230] Fix Qwen VAE

---
 modules/processing_vae.py | 17 ++++++++++++-----
 modules/taesd/taehv.py    |  2 ++
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index 933af425f..00ffbb450 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -117,8 +117,10 @@ def full_vae_decode(latents, model):
     scaling_factor = model.vae.config.get("scaling_factor", 1.0)
     shift_factor = model.vae.config.get("shift_factor", None)
     if latents_mean and latents_std:
-        latents_mean = (torch.tensor(latents_mean).view(1, -1, 1, 1).to(latents.device, latents.dtype))
-        latents_std = (torch.tensor(latents_std).view(1, -1, 1, 1).to(latents.device, latents.dtype))
+        broadcast_shape = [1 for _ in range(latents.ndim)]
+        broadcast_shape[1] = -1
+        latents_mean = (torch.tensor(latents_mean).view(*broadcast_shape).to(latents.device, latents.dtype))
+        latents_std = (torch.tensor(latents_std).view(*broadcast_shape).to(latents.device, latents.dtype))
         latents = ((latents * latents_std) / scaling_factor) + latents_mean
     else:
         latents = latents / scaling_factor
@@ -237,13 +239,15 @@ def vae_postprocess(tensor, model, output_type='np'):
         if isinstance(tensor, list) and len(tensor) > 0 and torch.is_tensor(tensor[0]):
             tensor = torch.stack(tensor)
         if torch.is_tensor(tensor):
-            if len(tensor.shape) == 3 and tensor.shape[0] == 3:
+            if tensor.ndim == 3 and tensor.shape[0] == 3:
                 tensor = tensor.unsqueeze(0)
             if hasattr(model, 'video_processor'):
-                if len(tensor.shape) == 6 and tensor.shape[1] == 1:
+                if tensor.ndim == 6 and tensor.shape[1] == 1:
                     tensor = tensor.squeeze(0)
                 images = model.video_processor.postprocess_video(tensor, output_type='pil')
             elif hasattr(model, 'image_processor'):
+                if tensor.ndim == 5 and tensor.shape[1] == 3: # Qwen Image
+                    tensor = tensor[:, :, 0]
                 images = model.image_processor.postprocess(tensor, output_type=output_type)
             elif hasattr(model, "vqgan"):
                 images = tensor.permute(0, 2, 3, 1).cpu().float().numpy()
@@ -252,6 +256,8 @@ def vae_postprocess(tensor, model, output_type='np'):
             else:
                 from diffusers.image_processor import VaeImageProcessor
                 model.image_processor = VaeImageProcessor()
+                if tensor.ndim == 5 and tensor.shape[1] == 3: # Qwen Image
+                    tensor = tensor[:, :, 0]
                 images = model.image_processor.postprocess(tensor, output_type=output_type)
         else:
             images = tensor if isinstance(tensor, list) or isinstance(tensor, np.ndarray) else [tensor]
@@ -291,8 +297,9 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
         latent_num_frames = (frames - 1) // model.vae_temporal_compression_ratio + 1
         latents = model._unpack_latents(latents.unsqueeze(0), latent_num_frames, height // 32, width // 32, model.transformer_spatial_patch_size, model.transformer_temporal_patch_size) # pylint: disable=protected-access
         latents = model._denormalize_latents(latents, model.vae.latents_mean, model.vae.latents_std, model.vae.config.scaling_factor) # pylint: disable=protected-access
-    if hasattr(model, '_unpack_latents') and hasattr(model, "vae_scale_factor") and width is not None and height is not None and latents.ndim == 3: # FLUX
+    elif hasattr(model, '_unpack_latents') and hasattr(model, "vae_scale_factor") and width is not None and height is not None and latents.ndim == 3: # FLUX
         latents = model._unpack_latents(latents, height, width, model.vae_scale_factor) # pylint: disable=protected-access
+
     if latents.ndim == 3: # lost a batch dim in hires
         latents = latents.unsqueeze(0)
     if latents.shape[-1] <= 4: # not a latent, likely an image
diff --git a/modules/taesd/taehv.py b/modules/taesd/taehv.py
index bfeced7a7..a8ba17469 100644
--- a/modules/taesd/taehv.py
+++ b/modules/taesd/taehv.py
@@ -62,6 +62,8 @@ def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
     if x.ndim == 4:
         x = x.unsqueeze(0)
     assert x.ndim == 5, f"TAEHV operates on NTCHW tensors, but got {x.ndim}-dim tensor"
+    if x.shape[1] == 16 and x.shape[2] != 16:
+        x = x.transpose(1,2) # NCTHW to NTCHW
     N, T, C, H, W = x.shape
     if parallel:
         x = x.reshape(N*T, C, H, W)

From 0f785880eebcec5c190e40fd65e7ac84b73cec44 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 11 Oct 2025 02:38:49 +0300
Subject: [PATCH 197/230] SDNQ fix a singular bias not getting offloaded

---
 modules/sdnq/quantizer.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index d39523e5b..b069d470b 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -230,8 +230,11 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             use_quantized_matmul=use_quantized_matmul,
             re_quantize_for_matmul=re_quantize_for_matmul,
         )
+
         layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
         layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
+        if layer.bias is not None and layer.bias.device != "meta":
+            layer.bias.data = layer.bias.to(return_device, non_blocking=non_blocking)
 
         layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
         layer.forward = layer.forward.__get__(layer, layer.__class__)

From 6bc83bc29606b18b2c4ca548c779962d91ea7fc0 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 11 Oct 2025 03:19:30 +0300
Subject: [PATCH 198/230] Prevent accelerate from splitting Linear and Conv
 layers and causing device mismatch errors

---
 modules/sd_offload.py     | 11 ++++++-----
 modules/sdnq/quantizer.py |  2 --
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index c52220ccb..d20fb0371 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -12,11 +12,13 @@ from modules.timer import process as process_timer
 
 
 debug = os.environ.get('SD_MOVE_DEBUG', None) is not None
+verbose = os.environ.get('SD_MOVE_VERBOSE', None) is not None
 debug_move = log.trace if debug else lambda *args, **kwargs: None
 offload_warn = ['sc', 'sd3', 'f1', 'h1', 'hunyuandit', 'auraflow', 'omnigen', 'omnigen2', 'cogview4', 'cosmos', 'chroma', 'x-omni']
 offload_post = ['h1']
 offload_hook_instance = None
 balanced_offload_exclude = ['CogView4Pipeline', 'MeissonicPipeline']
+no_split_module_classes = ["Linear", "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d"]
 accelerate_dtype_byte_size = None
 move_stream = None
 
@@ -237,13 +239,12 @@ class OffloadHook(accelerate.hooks.ModelHook):
             max_memory = { device_index: self.gpu, "cpu": self.cpu }
             device_map = getattr(module, "balanced_offload_device_map", None)
             if (device_map is None) or (max_memory != getattr(module, "balanced_offload_max_memory", None)):
-                device_map = accelerate.infer_auto_device_map(module, max_memory=max_memory)
+                device_map = accelerate.infer_auto_device_map(module, max_memory=max_memory, no_split_module_classes=no_split_module_classes, verbose=verbose)
             offload_dir = getattr(module, "offload_dir", os.path.join(shared.opts.accelerate_offload_path, module.__class__.__name__))
             if devices.backend == "directml":
-                keys = device_map.keys()
-                for v in keys:
-                    if isinstance(device_map[v], int):
-                        device_map[v] = f"{devices.device.type}:{device_map[v]}" # int implies CUDA or XPU device, but it will break DirectML backend so we add type
+                for k, v in device_map.items():
+                    if isinstance(v, int):
+                        device_map[k] = f"{devices.device.type}:{v}" # int implies CUDA or XPU device, but it will break DirectML backend so we add type
             if device_map is not None:
                 module = accelerate.dispatch_model(module, device_map=device_map, offload_dir=offload_dir)
             module._hf_hook.execution_device = torch.device(devices.device) # pylint: disable=protected-access
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index b069d470b..58083079a 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -233,8 +233,6 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
 
         layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
         layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
-        if layer.bias is not None and layer.bias.device != "meta":
-            layer.bias.data = layer.bias.to(return_device, non_blocking=non_blocking)
 
         layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
         layer.forward = layer.forward.__get__(layer, layer.__class__)

From f7286c90d57a0f06aa3bdd09bd1334c9391be32c Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 11 Oct 2025 16:19:11 +0300
Subject: [PATCH 199/230] SDNQ add native pre-quant loader support to
 from_pretrained

---
 modules/sdnq/quantizer.py | 171 ++++++++++++++++++++++++++++----------
 1 file changed, 129 insertions(+), 42 deletions(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 58083079a..501f70c62 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -1,6 +1,6 @@
 # pylint: disable=redefined-builtin,no-member,protected-access
 
-from typing import Dict, List, Tuple, Optional, Union
+from typing import Any, Dict, List, Tuple, Optional, Union
 from dataclasses import dataclass
 from enum import Enum
 
@@ -230,9 +230,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             use_quantized_matmul=use_quantized_matmul,
             re_quantize_for_matmul=re_quantize_for_matmul,
         )
-
         layer.weight.data = layer.sdnq_dequantizer.pack_weight(layer.weight).to(return_device, non_blocking=non_blocking)
-        layer.sdnq_dequantizer = layer.sdnq_dequantizer.to(return_device, non_blocking=non_blocking)
 
         layer.forward = get_forward_func(layer_class_name, use_quantized_matmul, dtype_dict[weights_dtype]["is_integer"], use_tensorwise_fp8_matmul)
         layer.forward = layer.forward.__get__(layer, layer.__class__)
@@ -425,6 +423,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
     def __init__(self, quantization_config, **kwargs):
         super().__init__(quantization_config, **kwargs)
         self.modules_to_not_convert = []
+        self.updated_expected_keys = False
 
     def check_if_quantized_param(
         self,
@@ -433,6 +432,8 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         param_name: str,
         *args, **kwargs, # pylint: disable=unused-argument
     ):
+        if self.pre_quantized and self.updated_expected_keys and (param_name.endswith(".scale") or param_name.endswith(".zero_point") or param_name.endswith(".svd_up") or param_name.endswith(".svd_down")):
+            return True
         if param_name.endswith(".weight"):
             split_param_name = param_name.split(".")
             if (
@@ -471,11 +472,22 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         param_value: torch.FloatTensor,
         param_name: str,
         target_device: torch.device,
+        state_dict: Dict[str, Any],
         *args, **kwargs, # pylint: disable=unused-argument
     ):
-        weights_dtype = self.quantization_config.weights_dtype
-        torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
+        if self.pre_quantized and self.updated_expected_keys and (param_name.endswith(".scale") or param_name.endswith(".zero_point") or param_name.endswith(".svd_up") or param_name.endswith(".svd_down")):
+            layer, tensor_name = get_module_from_name(model, param_name)
+            return_dtype = torch.float32 if self.quantization_config.dequantize_fp32 else self.torch_dtype if self.torch_dtype is not None else param_value.dtype
+            if param_value is not None:
+                if param_value.dtype == return_dtype and devices.same_device(param_value.device, target_device):
+                    param_value = param_value.clone()
+                else:
+                    param_value = param_value.to(target_device, dtype=return_dtype)
+                param_value = torch.nn.Parameter(param_value, requires_grad=False)
+            setattr(layer, tensor_name, param_value)
+            return
 
+        weights_dtype = self.quantization_config.weights_dtype
         if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
             split_param_name = param_name.split(".")
             for key, value in self.quantization_config.modules_dtype_dict.items():
@@ -506,29 +518,88 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         if self.quantization_config.quantization_device is not None:
             target_device = self.quantization_config.quantization_device
 
-        if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
-            param_value = param_value.clone()
-        else:
-            param_value = param_value.to(target_device, non_blocking=self.quantization_config.non_blocking).to(dtype=torch.float32)
+        if not self.pre_quantized:
+            torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
+            if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
+                param_value = param_value.clone()
+            else:
+                param_value = param_value.to(target_device, non_blocking=self.quantization_config.non_blocking).to(dtype=torch.float32)
 
-        layer, _ = get_module_from_name(model, param_name)
-        layer.weight = torch.nn.Parameter(param_value, requires_grad=False)
-        layer = sdnq_quantize_layer(
-            layer,
-            weights_dtype=weights_dtype,
-            torch_dtype=torch_dtype,
-            group_size=self.quantization_config.group_size,
-            svd_rank=self.quantization_config.svd_rank,
-            use_svd=self.quantization_config.use_svd,
-            quant_conv=self.quantization_config.quant_conv,
-            use_quantized_matmul=self.quantization_config.use_quantized_matmul,
-            use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
-            dequantize_fp32=self.quantization_config.dequantize_fp32,
-            non_blocking=self.quantization_config.non_blocking,
-            quantization_device=None,
-            return_device=return_device,
-            param_name=param_name,
-        )
+            layer, _ = get_module_from_name(model, param_name)
+            layer.weight = torch.nn.Parameter(param_value, requires_grad=False)
+            layer = sdnq_quantize_layer(
+                layer,
+                weights_dtype=weights_dtype,
+                torch_dtype=torch_dtype,
+                group_size=self.quantization_config.group_size,
+                svd_rank=self.quantization_config.svd_rank,
+                use_svd=self.quantization_config.use_svd,
+                quant_conv=self.quantization_config.quant_conv,
+                use_quantized_matmul=self.quantization_config.use_quantized_matmul,
+                use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
+                dequantize_fp32=self.quantization_config.dequantize_fp32,
+                non_blocking=self.quantization_config.non_blocking,
+                quantization_device=None,
+                return_device=return_device,
+                param_name=param_name,
+            )
+        else:
+            from accelerate import init_empty_weights
+            layer, _ = get_module_from_name(model, param_name)
+            torch_dtype = layer.weight.dtype if self.torch_dtype is None else self.torch_dtype
+
+            if self.updated_expected_keys: # prevent overwrite to meta
+                scale, zero_point, svd_up, svd_down = None, None, None, None
+                if hasattr(layer, "scale") and layer.scale.device.type != "meta":
+                    scale = layer.scale
+                    del layer.scale
+                if hasattr(layer, "zero_point") and layer.zero_point.device.type != "meta":
+                    zero_point = layer.zero_point
+                    del layer.zero_point
+                if hasattr(layer, "svd_up") and layer.svd_up.device.type != "meta":
+                    svd_up = layer.svd_up
+                    del layer.svd_up
+                if hasattr(layer, "svd_down") and layer.svd_down.device.type != "meta":
+                    svd_down = layer.svd_down
+                    del layer.svd_down
+
+            with init_empty_weights():
+                # add sdnq_dequantizer
+                layer = sdnq_quantize_layer(
+                    layer,
+                    weights_dtype=weights_dtype,
+                    torch_dtype=torch_dtype,
+                    group_size=self.quantization_config.group_size,
+                    svd_rank=self.quantization_config.svd_rank,
+                    use_svd=self.quantization_config.use_svd,
+                    quant_conv=self.quantization_config.quant_conv,
+                    use_quantized_matmul=self.quantization_config.use_quantized_matmul,
+                    use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
+                    dequantize_fp32=self.quantization_config.dequantize_fp32,
+                    non_blocking=self.quantization_config.non_blocking,
+                    quantization_device="meta",
+                    return_device="meta",
+                    param_name=param_name,
+                )
+
+            layer.weight = torch.nn.Parameter(param_value.clone().to(target_device), requires_grad=False)
+            if self.updated_expected_keys: # Transformers
+                if scale is not None:
+                    layer.scale = torch.nn.Parameter(scale, requires_grad=False)
+                if zero_point is not None:
+                    layer.zero_point = torch.nn.Parameter(zero_point, requires_grad=False)
+                if svd_up is not None:
+                    layer.svd_up = torch.nn.Parameter(svd_up, requires_grad=False)
+                if svd_down is not None:
+                    layer.svd_down = torch.nn.Parameter(svd_down, requires_grad=False)
+            else: # Diffusers doesn't have the API for updating expected keys
+                layer_key = param_name.removesuffix(".weight")
+                layer.scale = torch.nn.Parameter(state_dict[layer_key + ".scale"].clone().to(target_device), requires_grad=False)
+                if layer.zero_point is not None:
+                    layer.zero_point = torch.nn.Parameter(state_dict[layer_key + ".zero_point"].clone().to(target_device), requires_grad=False)
+                if layer.svd_up is not None:
+                    layer.svd_up = torch.nn.Parameter(state_dict[layer_key + ".svd_up"].clone().to(target_device), requires_grad=False)
+                    layer.svd_down = torch.nn.Parameter(state_dict[layer_key + ".svd_down"].clone().to(target_device), requires_grad=False)
 
     def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
         max_memory = {key: val * 0.80 for key, val in max_memory.items()}
@@ -560,6 +631,19 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
             model.config.quantization_config = self.quantization_config
         model.quantization_config = self.quantization_config
 
+        if self.pre_quantized and hasattr(model, "get_parameter_or_buffer"):
+            from functools import wraps
+            @wraps(model.get_parameter_or_buffer)
+            def get_parameter_or_buffer(self, target: str):
+                try:
+                    return self.original_get_parameter_or_buffer(target)
+                except Exception as e:
+                    if target.endswith(".scale") or target.endswith(".zero_point") or target.endswith(".svd_up") or target.endswith(".svd_down"):
+                        return None
+                    raise e
+            model.original_get_parameter_or_buffer = model.get_parameter_or_buffer
+            model.get_parameter_or_buffer = get_parameter_or_buffer.__get__(model, model.__class__)
+
     def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument
         if shared.opts.diffusers_offload_mode != "none":
             model = model.to(devices.cpu)
@@ -575,6 +659,24 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         """
         return self.get_accelerator_warm_up_factor()
 
+    def update_unexpected_keys(self, model, unexpected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
+        if not self.pre_quantized:
+            return unexpected_keys
+        new_unexpected_keys = []
+        for key in unexpected_keys:
+            if not (key.endswith(".scale") or key.endswith(".zero_point") or key.endswith(".svd_up") or key.endswith(".svd_down")):
+                new_unexpected_keys.append(key)
+        return new_unexpected_keys
+
+    def update_expected_keys(self, model, expected_keys: List[str], loaded_keys: list[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
+        if not self.pre_quantized:
+            return expected_keys
+        self.updated_expected_keys = True
+        for key in loaded_keys:
+            if key.endswith(".scale") or key.endswith(".zero_point") or key.endswith(".svd_up") or key.endswith(".svd_down"):
+                expected_keys.append(key)
+        return expected_keys
+
     def update_tp_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
@@ -587,12 +689,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         """
         return config
 
-    def update_unexpected_keys(self, model, unexpected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return unexpected_keys
-
     def update_missing_keys_after_loading(self, model, missing_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
@@ -605,12 +701,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         """
         return state_dict
 
-    def update_expected_keys(self, model, expected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return expected_keys
-
     def update_param_name(self, param_name: str, *args, **kwargs) -> str: # pylint: disable=unused-argument
         """
         needed for transformers compatibilty, no-op function
@@ -624,9 +714,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         return dtype
 
     def is_serializable(self, *args, **kwargs) -> bool:  # pylint: disable=unused-argument, invalid-overridden-method
-        """
-        needed for transformers compatibilty, returns True
-        """
         return True
 
     @property

From c0600ae9609878a762a33b08379e89432c9c06c7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 11 Oct 2025 09:32:20 -0400
Subject: [PATCH 200/230] update seq method

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md              |  3 ++-
 modules/images_namegen.py | 15 ++++++++++++++-
 wiki                      |  2 +-
 3 files changed, 17 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 16faa4108..88b39bdf2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-09
+## Update for 2025-10-11
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -102,6 +102,7 @@
   - **preview** implement configurable layers for WAN, Qwen, HV  
   - update swagger `/docs` endpoint style  
   - add `[epoch]` to filename template  
+  - starting `[seq]` for filename template is now higher of largest previous sequence or number of files in folder  
 - **Video**
   - use shared **T5** text encoder for video models when possible  
   - use shared **LLama** text encoder for video models when possible  
diff --git a/modules/images_namegen.py b/modules/images_namegen.py
index b50d2ae97..849450d5b 100644
--- a/modules/images_namegen.py
+++ b/modules/images_namegen.py
@@ -16,6 +16,7 @@ re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 re_attention = re.compile(r'[\(*\[*](\w+)(:\d+(\.\d+))?[\)*\]*]|')
 re_network = re.compile(r'\<\w+:(\w+)(:\d+(\.\d+))?\>|')
 re_brackets = re.compile(r'[\([{})\]]')
+re_leading_seq = re.compile(r'^(0*\d+)(?=[-_.\s]|$)')
 seq = 0
 NOTHING = object()
 
@@ -185,12 +186,24 @@ class FilenameGenerator:
         debug(f'Filename sanitize: input="{filename}" parts={parts} output="{fn}" ext={ext} max={max_length} len={len(fn)}')
         return fn
 
+    def safe_int(self, s):
+        try:
+            return int(s)
+        except (ValueError, TypeError):
+            return 0
+
     def sequence(self, fn):
         global seq # pylint: disable=global-statement
         x = fn
         dirname = os.path.dirname(fn)
         if seq == 0:
-            seq = len(os.listdir(dirname)) if os.path.exists(dirname) and os.path.isdir(dirname) else 0
+            files = os.listdir(dirname) if os.path.exists(dirname) and os.path.isdir(dirname) else []
+            files = [f for f in files if os.path.isfile(os.path.join(dirname, f))]
+            seq_files = len(files)
+            seq_nums = [re_leading_seq.match(f) for f in files]
+            seq_nums = [self.safe_int(m.group(1)) for m in seq_nums if m is not None]
+            seq_num = max(seq_nums) if len(seq_nums) > 0 else 0
+            seq = max(seq_files, seq_num)
         if shared.opts.save_images_add_number or '[seq]' in fn:
             if '[seq]' not in fn:
                 fn = os.path.join(os.path.dirname(fn), f"[seq]-{os.path.basename(fn)}")
diff --git a/wiki b/wiki
index c5321049f..be337fb3e 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit c5321049fbab6169c0ffa10accde4ad139639f2a
+Subproject commit be337fb3e02048113e97515aceced6b3c67eb3d9

From 0ae4decadc859297888b3d690ace8b330b4ca8dd Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 11 Oct 2025 11:15:15 -0400
Subject: [PATCH 201/230] hidream-e1.1

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md               |  4 ++--
 TODO.md                    |  1 -
 html/reference.json        |  3 +--
 modules/sd_models.py       | 14 +++++---------
 pipelines/model_hidream.py |  4 ++++
 5 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 88b39bdf2..2e429d98d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,10 +15,10 @@
     impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
     note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
+  - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
+    updated version of E1 image editing model  
   - [X-Omni SFT](https://x-omni-team.github.io/)  
     *experimental*: X-omni is a transformer-only discrete autoregressive image generative model trained with reinforcement learning  
-  - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
-    *experimental*: updated version of E1 image editing model  
 - **Features**
   - **Model save**: ability to save currently loaded model as a new standalone model  
     why? SD.Next always prefers to start with full model and quantize on-demand during load  
diff --git a/TODO.md b/TODO.md
index adb84fbd8..5bdc45124 100644
--- a/TODO.md
+++ b/TODO.md
@@ -22,7 +22,6 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 - [STG](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#spatiotemporal-skip-guidance)  
 - [SmoothCache](https://github.com/huggingface/diffusers/issues/11135)  
 - [MagCache](https://github.com/lllyasviel/FramePack/pull/673/files)  
-- [Nunchaku PulID](https://github.com/mit-han-lab/nunchaku/pull/274)  
 - [Dream0 guidance](https://huggingface.co/ByteDance/DreamO)  
 - [SUPIR upscaler](https://github.com/Fanghua-Yu/SUPIR)  
 - [ByteDance OneReward](https://github.com/bytedance/OneReward)
diff --git a/html/reference.json b/html/reference.json
index 8202631a2..fced58542 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -700,8 +700,7 @@
     "desc": "HiDream-E1 is an image editing model built on HiDream-I1.",
     "preview": "HiDream-ai--HiDream-E1-Full.jpg",
     "skip": true,
-    "extras": "sampler: Default",
-    "experimental": true
+    "extras": "sampler: Default"
   },
 
   "Kwai Kolors": {
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4400a0aa6..8a133599e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -37,12 +37,10 @@ pipe_switch_task_exclude = [
     'FluxFillPipeline',
     'InstantIRPipeline',
     'LTXConditionPipeline',
-    'OmniGenPipeline',
-    'OmniGen2Pipeline',
+    'OmniGenPipeline', 'OmniGen2Pipeline',
     'PhotoMakerStableDiffusionXLPipeline',
     'PixelSmithXLPipeline',
     'StableDiffusion3ControlNetPipeline',
-    'StableDiffusionAdapterPipeline',
     'StableDiffusionAdapterPipeline', 'StableDiffusionXLAdapterPipeline',
     'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
     'StableDiffusionReferencePipeline',
@@ -50,10 +48,8 @@ pipe_switch_task_exclude = [
     'XOmniPipeline',
 ]
 i2i_pipes = [
-    'LEditsPPPipelineStableDiffusion',
-    'LEditsPPPipelineStableDiffusionXL',
-    'OmniGenPipeline',
-    'OmniGen2Pipeline',
+    'LEditsPPPipelineStableDiffusion', 'LEditsPPPipelineStableDiffusionXL',
+    'OmniGenPipeline', 'OmniGen2Pipeline',
     'StableDiffusionAdapterPipeline', 'StableDiffusionXLAdapterPipeline',
     'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
 ]
@@ -548,12 +544,12 @@ def load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op):
         quantization_config_path = os.path.join(checkpoint_info.path, module_name, 'quantization_config.json')
         if not os.path.exists(quantization_config_path):
             continue
-        model_path = os.path.join(checkpoint_info.path, module_name)
+        model_name = os.path.join(checkpoint_info.path, module_name)
         quantization_config = shared.readfile(quantization_config_path, silent=True)
         shared.log.debug(f'Load {op}: model="{checkpoint_info.name}" module="{module_name}" direct={shared.opts.diffusers_to_gpu} prequant=sdnq')
         try:
             modules[module_name] = sdnq.load_sdnq_model(
-                model_path=model_path,
+                model_path=model_name,
                 quantization_config=quantization_config,
                 device=devices.device if shared.opts.diffusers_to_gpu else devices.cpu,
                 dtype=devices.dtype,
diff --git a/pipelines/model_hidream.py b/pipelines/model_hidream.py
index 7f49997fc..120a89479 100644
--- a/pipelines/model_hidream.py
+++ b/pipelines/model_hidream.py
@@ -52,6 +52,10 @@ def load_hidream(checkpoint_info, diffusers_load_config={}):
         diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["hidream-e1"] = diffusers.HiDreamImagePipeline
         diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["hidream-e1"] = HiDreamImageEditingPipeline
         diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["hidream-e1"] = HiDreamImageEditingPipeline
+        if transformer and 'E1-1' in repo_id:
+            transformer.max_seq = 8192
+        elif transformer and 'E1' in repo_id:
+            transformer.max_seq = 4608
     else:
         shared.log.error(f'Load model: type=HiDream model="{checkpoint_info.name}" repo="{repo_id}" not recognized')
         return False

From 9a8ba0fc908924670debaf9b0d6c91131191f527 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sat, 11 Oct 2025 19:24:09 +0300
Subject: [PATCH 202/230] SDNQ unset device specific configs on save

---
 modules/sdnq/loader.py | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 04fc5e9a5..e2ecce092 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -17,10 +17,39 @@ def get_module_names(model: ModelMixin) -> list:
 
 
 def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", is_pipeline: bool = False, sdnq_config: SDNQConfig = None) -> None:
+    if is_pipeline:
+        for module_name in get_module_names(model):
+            module = getattr(model, module_name, None)
+            if hasattr(module, "config") and hasattr(module.config, "quantization_config") and isinstance(module.config.quantization_config, SDNQConfig):
+                module.config.quantization_config.quantization_device = None
+                module.config.quantization_config.return_device = None
+                module.config.quantization_config.non_blocking = False
+                module.config.quantization_config.add_skip_keys = False
+            if hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
+                module.quantization_config.quantization_device = None
+                module.quantization_config.return_device = None
+                module.quantization_config.non_blocking = False
+                module.quantization_config.add_skip_keys = False
+    else:
+        if hasattr(model, "config") and hasattr(model.config, "quantization_config") and isinstance(model.config.quantization_config, SDNQConfig):
+            model.config.quantization_config.quantization_device = None
+            model.config.quantization_config.return_device = None
+            model.config.quantization_config.non_blocking = False
+            model.config.quantization_config.add_skip_keys = False
+        if hasattr(model, "quantization_config") and isinstance(model.quantization_config, SDNQConfig):
+            model.quantization_config.quantization_device = None
+            model.quantization_config.return_device = None
+            model.quantization_config.non_blocking = False
+            model.quantization_config.add_skip_keys = False
+
     model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
 
     quantization_config_path = os.path.join(model_path, "quantization_config.json")
     if sdnq_config is not None: # if provided, save global config
+        sdnq_config.quantization_device = None
+        sdnq_config.return_device = None
+        sdnq_config.non_blocking = False
+        sdnq_config.add_skip_keys = False
         sdnq_config.to_json_file(quantization_config_path)
 
     if is_pipeline:

From 9206d9443e68b8a6fc91ac487533e81a48c02a6c Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 12 Oct 2025 00:00:53 +0300
Subject: [PATCH 203/230] SDNQ add dequantize model

---
 modules/sdnq/dequantizer.py | 17 +++++++++++++++++
 modules/sdnq/quantizer.py   | 12 +++++++++++-
 2 files changed, 28 insertions(+), 1 deletion(-)

diff --git a/modules/sdnq/dequantizer.py b/modules/sdnq/dequantizer.py
index 1181776c8..2b9998adb 100644
--- a/modules/sdnq/dequantizer.py
+++ b/modules/sdnq/dequantizer.py
@@ -103,6 +103,23 @@ def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: tor
     return re_quantize_matmul_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, result_shape, svd_up=svd_up, svd_down=svd_down)
 
 
+def dequantize_sdnq_model(model):
+    if hasattr(model, "sdnq_dequantizer"):
+        model.weight = torch.nn.Parameter(model.sdnq_dequantizer(model.weight, model.scale, model.zero_point, model.svd_up, model.svd_down))
+        del model.sdnq_dequantizer, model.scale, model.zero_point, model.svd_up, model.svd_down
+        return model
+    has_children = list(model.children())
+    if not has_children:
+        return model
+    for module in model.children():
+        if hasattr(module, "sdnq_dequantizer"):
+            module.weight = torch.nn.Parameter(module.sdnq_dequantizer(module.weight, module.scale, module.zero_point, module.svd_up, module.svd_down))
+            del module.sdnq_dequantizer, module.scale, module.zero_point, module.svd_up, module.svd_down
+        else:
+            module = dequantize_sdnq_model(module)
+    return model
+
+
 class AsymmetricWeightsDequantizer(torch.nn.Module):
     def __init__(
         self,
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 501f70c62..e56ecb43c 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -13,7 +13,7 @@ from diffusers.utils import get_module_from_name
 from modules import devices, shared
 
 from .common import dtype_dict, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
-from .dequantizer import dequantizer_dict
+from .dequantizer import dequantizer_dict, dequantize_sdnq_model
 from .forward import get_forward_func
 
 
@@ -713,6 +713,16 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         """
         return dtype
 
+    def _dequantize(self, model):
+        model = dequantize_sdnq_model(model)
+        if hasattr(model, "quantization_method"):
+            del model.quantization_method
+        if hasattr(model, "quantization_config"):
+            del model.quantization_config
+        if hasattr(model, "config") and hasattr(model.config, "quantization_config"):
+            del model.config.quantization_config
+        return model
+
     def is_serializable(self, *args, **kwargs) -> bool:  # pylint: disable=unused-argument, invalid-overridden-method
         return True
 

From a376f89fd62258f2af60010002e1b0cf9a550b4b Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 12 Oct 2025 01:02:47 +0300
Subject: [PATCH 204/230] Add type checking to SDNQConfig

---
 modules/sdnq/common.py    |  1 +
 modules/sdnq/quantizer.py | 27 +++++++++++++++++++++------
 2 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 8d0114e3e..57a4f0aaf 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -35,6 +35,7 @@ linear_types = {"Linear"}
 conv_types = {"Conv1d", "Conv2d", "Conv3d"}
 conv_transpose_types = {"ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d"}
 allowed_types = set.union(linear_types, conv_types, conv_transpose_types)
+accepted_weights = set(dtype_dict.keys())
 
 use_torch_compile = shared.opts.sdnq_dequantize_compile # this setting requires a full restart of the webui to apply
 is_rdna2 = bool(devices.backend == "rocm" and int(getattr(torch.cuda.get_device_properties(devices.device), "gcnArchName", "gfx0000")[3:]) < 1100)
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index e56ecb43c..56c6c6d7f 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -12,7 +12,7 @@ from diffusers.quantizers.quantization_config import QuantizationConfigMixin
 from diffusers.utils import get_module_from_name
 from modules import devices, shared
 
-from .common import dtype_dict, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
+from .common import dtype_dict, accepted_weights, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
 from .dequantizer import dequantizer_dict, dequantize_sdnq_model
 from .forward import get_forward_func
 
@@ -411,7 +411,7 @@ def sdnq_post_load_quant(
 
 class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
     r"""
-    Diffusers Quantizer for SDNQ
+    Diffusers and Transformers Quantizer for SDNQ
     """
 
     requires_parameters_quantization = True
@@ -751,7 +751,7 @@ class SDNQConfig(QuantizationConfigMixin):
         svd_rank (`int`, *optional*, defaults to `32`):
             The rank size used for the SVDQuant algorithm.
         use_svd (`bool`, *optional*, defaults to `False`):
-            Enabling this option will use SVDQuant algorithm.
+            Enabling this option will use SVDQuant algorithm on top of SDNQ quantization.
         quant_conv (`bool`, *optional*, defaults to `False`):
             Enabling this option will quantize the convolutional layers in UNet models too.
         use_quantized_matmul (`bool`, *optional*, defaults to `False`):
@@ -815,17 +815,32 @@ class SDNQConfig(QuantizationConfigMixin):
         r"""
         Safety checker that arguments are correct
         """
-        accepted_weights = ["int8", "int7", "int6", "int5", "int4", "int3", "int2", "uint8", "uint7", "uint6", "uint5", "uint4", "uint3", "uint2", "uint1", "bool", "float8_e4m3fn", "float8_e4m3fnuz", "float8_e5m2", "float8_e5m2fnuz"]
         if self.weights_dtype not in accepted_weights:
-            raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights_dtype}")
+            raise ValueError(f"SDNQ only support weights in {accepted_weights} but found {self.weights_dtype}")
 
         if self.modules_to_not_convert is None:
             self.modules_to_not_convert = []
-        elif not isinstance(self.modules_to_not_convert, list):
+        elif isinstance(self.modules_to_not_convert, str):
             self.modules_to_not_convert = [self.modules_to_not_convert]
+        elif isinstance(self.modules_to_not_convert, tuple):
+            self.modules_to_not_convert = list(self.modules_to_not_convert)
+        elif not isinstance(self.modules_to_not_convert, list):
+            raise ValueError(f"modules_to_not_convert must be a list but got {type(self.modules_to_not_convert)}")
 
         if self.modules_dtype_dict is None:
             self.modules_dtype_dict = {}
+        elif not isinstance(self.modules_dtype_dict, dict):
+            raise ValueError(f"modules_dtype_dict must be a dict but got {type(self.modules_dtype_dict)}")
+        elif len(self.modules_dtype_dict.keys()) > 0:
+            for key, value in self.modules_dtype_dict.items():
+                if isinstance(value, str):
+                    value = [value]
+                    self.modules_dtype_dict[key] = value
+                elif isinstance(value, tuple):
+                    value = list(value)
+                    self.modules_dtype_dict[key] = value
+                if not isinstance(key, str) or not isinstance(value, list):
+                    raise ValueError(f"modules_dtype_dict must be a dictionary of strings and lists but got {type(key)} and {type(value)}")
 
     def to_dict(self):
         dct = self.__dict__.copy() # make serializable

From eaa7dc119bd7d7598acf00cb98f934aeb66646e6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 11 Oct 2025 18:35:40 -0400
Subject: [PATCH 205/230] prototype seedvr

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                     |    1 +
 .ruff.toml                                    |    1 +
 CHANGELOG.md                                  |    7 +-
 installer.py                                  |    1 +
 modules/postprocess/seedvr_model.py           |   76 +
 modules/seedvr/__init__.py                    |    0
 modules/seedvr/config_3b.yaml                 |   97 ++
 modules/seedvr/config_7b.yaml                 |   94 ++
 modules/seedvr/src/__init__.py                |   37 +
 modules/seedvr/src/common/__init__.py         |    0
 modules/seedvr/src/common/cache.py            |   33 +
 modules/seedvr/src/common/config.py           |  128 ++
 modules/seedvr/src/common/decorators.py       |  126 ++
 .../seedvr/src/common/diffusion/__init__.py   |   56 +
 modules/seedvr/src/common/diffusion/config.py |   71 +
 .../src/common/diffusion/samplers/base.py     |  108 ++
 .../src/common/diffusion/samplers/euler.py    |  107 ++
 .../src/common/diffusion/schedules/base.py    |  131 ++
 .../src/common/diffusion/schedules/lerp.py    |   55 +
 .../src/common/diffusion/timesteps/base.py    |   72 +
 .../diffusion/timesteps/sampling/trailing.py  |   49 +
 modules/seedvr/src/common/diffusion/types.py  |   59 +
 modules/seedvr/src/common/diffusion/utils.py  |   84 +
 .../seedvr/src/common/distributed/__init__.py |   35 +
 .../seedvr/src/common/distributed/advanced.py |  208 +++
 .../seedvr/src/common/distributed/basic.py    |   67 +
 .../src/common/distributed/meta_init_utils.py |   41 +
 modules/seedvr/src/common/distributed/ops.py  |  494 ++++++
 .../seedvr/src/common/half_precision_fixes.py |   61 +
 modules/seedvr/src/common/logger.py           |   44 +
 modules/seedvr/src/common/partition.py        |   59 +
 modules/seedvr/src/common/seed.py             |   30 +
 modules/seedvr/src/core/__init__.py           |   47 +
 modules/seedvr/src/core/generation.py         |  365 +++++
 modules/seedvr/src/core/infer.py              |  357 +++++
 modules/seedvr/src/core/model_manager.py      |   61 +
 .../src/data/image/transforms/area_resize.py  |  131 ++
 .../data/image/transforms/divisible_crop.py   |   40 +
 .../src/data/image/transforms/na_resize.py    |   50 +
 .../src/data/image/transforms/side_resize.py  |   54 +
 modules/seedvr/src/models/dit/attention.py    |   94 ++
 .../seedvr/src/models/dit/blocks/__init__.py  |   25 +
 .../models/dit/blocks/mmdit_window_block.py   |  233 +++
 modules/seedvr/src/models/dit/embedding.py    |   62 +
 modules/seedvr/src/models/dit/mlp.py          |   62 +
 modules/seedvr/src/models/dit/mm.py           |   67 +
 modules/seedvr/src/models/dit/modulation.py   |   97 ++
 modules/seedvr/src/models/dit/na.py           |  241 +++
 .../src/models/dit/nablocks/__init__.py       |   25 +
 .../src/models/dit/nablocks/mmsr_block.py     |  248 +++
 modules/seedvr/src/models/dit/nadit.py        |  350 +++++
 .../seedvr/src/models/dit/normalization.py    |  131 ++
 modules/seedvr/src/models/dit/patch.py        |  112 ++
 modules/seedvr/src/models/dit/rope.py         |  102 ++
 modules/seedvr/src/models/dit/window.py       |   83 +
 modules/seedvr/src/models/dit_v2/attention.py |   93 ++
 modules/seedvr/src/models/dit_v2/embedding.py |   62 +
 modules/seedvr/src/models/dit_v2/mlp.py       |   62 +
 modules/seedvr/src/models/dit_v2/mm.py        |   74 +
 .../seedvr/src/models/dit_v2/modulation.py    |  118 ++
 modules/seedvr/src/models/dit_v2/na.py        |  241 +++
 .../src/models/dit_v2/nablocks/__init__.py    |   26 +
 .../dit_v2/nablocks/attention/__init__.py     |   25 +
 .../dit_v2/nablocks/attention/mmattn.py       |  267 ++++
 .../src/models/dit_v2/nablocks/mmsr_block.py  |  126 ++
 modules/seedvr/src/models/dit_v2/nadit.py     |  246 +++
 .../seedvr/src/models/dit_v2/normalization.py |  147 ++
 .../src/models/dit_v2/patch/__init__.py       |   19 +
 .../src/models/dit_v2/patch/patch_v1.py       |  127 ++
 modules/seedvr/src/models/dit_v2/rope.py      |  155 ++
 modules/seedvr/src/models/dit_v2/window.py    |   83 +
 .../video_vae_v3/modules/attn_video_vae.py    | 1345 +++++++++++++++++
 .../modules/causal_inflation_lib.py           |  437 ++++++
 .../modules/context_parallel_lib.py           |   66 +
 .../video_vae_v3/modules/global_config.py     |   28 +
 .../video_vae_v3/modules/inflated_layers.py   |  106 ++
 .../video_vae_v3/modules/inflated_lib.py      |  156 ++
 .../src/models/video_vae_v3/modules/types.py  |   76 +
 .../models/video_vae_v3/modules/video_vae.py  |  956 ++++++++++++
 .../video_vae_v3/s8_c16_t4_inflation_sd3.yaml |   28 +
 modules/seedvr/src/optimization/__init__.py   |    0
 .../seedvr/src/optimization/memory_manager.py |  131 ++
 .../seedvr/src/optimization/performance.py    |  160 ++
 modules/seedvr/src/utils/__init__.py          |    0
 modules/seedvr/src/utils/color_fix.py         |  138 ++
 modules/seedvr/test.py                        |   58 +
 modules/upscaler.py                           |    4 +-
 87 files changed, 11024 insertions(+), 5 deletions(-)
 create mode 100644 modules/postprocess/seedvr_model.py
 create mode 100644 modules/seedvr/__init__.py
 create mode 100644 modules/seedvr/config_3b.yaml
 create mode 100644 modules/seedvr/config_7b.yaml
 create mode 100644 modules/seedvr/src/__init__.py
 create mode 100644 modules/seedvr/src/common/__init__.py
 create mode 100644 modules/seedvr/src/common/cache.py
 create mode 100644 modules/seedvr/src/common/config.py
 create mode 100644 modules/seedvr/src/common/decorators.py
 create mode 100644 modules/seedvr/src/common/diffusion/__init__.py
 create mode 100644 modules/seedvr/src/common/diffusion/config.py
 create mode 100644 modules/seedvr/src/common/diffusion/samplers/base.py
 create mode 100644 modules/seedvr/src/common/diffusion/samplers/euler.py
 create mode 100644 modules/seedvr/src/common/diffusion/schedules/base.py
 create mode 100644 modules/seedvr/src/common/diffusion/schedules/lerp.py
 create mode 100644 modules/seedvr/src/common/diffusion/timesteps/base.py
 create mode 100644 modules/seedvr/src/common/diffusion/timesteps/sampling/trailing.py
 create mode 100644 modules/seedvr/src/common/diffusion/types.py
 create mode 100644 modules/seedvr/src/common/diffusion/utils.py
 create mode 100644 modules/seedvr/src/common/distributed/__init__.py
 create mode 100644 modules/seedvr/src/common/distributed/advanced.py
 create mode 100644 modules/seedvr/src/common/distributed/basic.py
 create mode 100644 modules/seedvr/src/common/distributed/meta_init_utils.py
 create mode 100644 modules/seedvr/src/common/distributed/ops.py
 create mode 100644 modules/seedvr/src/common/half_precision_fixes.py
 create mode 100644 modules/seedvr/src/common/logger.py
 create mode 100644 modules/seedvr/src/common/partition.py
 create mode 100644 modules/seedvr/src/common/seed.py
 create mode 100644 modules/seedvr/src/core/__init__.py
 create mode 100644 modules/seedvr/src/core/generation.py
 create mode 100644 modules/seedvr/src/core/infer.py
 create mode 100644 modules/seedvr/src/core/model_manager.py
 create mode 100644 modules/seedvr/src/data/image/transforms/area_resize.py
 create mode 100644 modules/seedvr/src/data/image/transforms/divisible_crop.py
 create mode 100644 modules/seedvr/src/data/image/transforms/na_resize.py
 create mode 100644 modules/seedvr/src/data/image/transforms/side_resize.py
 create mode 100644 modules/seedvr/src/models/dit/attention.py
 create mode 100644 modules/seedvr/src/models/dit/blocks/__init__.py
 create mode 100644 modules/seedvr/src/models/dit/blocks/mmdit_window_block.py
 create mode 100644 modules/seedvr/src/models/dit/embedding.py
 create mode 100644 modules/seedvr/src/models/dit/mlp.py
 create mode 100644 modules/seedvr/src/models/dit/mm.py
 create mode 100644 modules/seedvr/src/models/dit/modulation.py
 create mode 100644 modules/seedvr/src/models/dit/na.py
 create mode 100644 modules/seedvr/src/models/dit/nablocks/__init__.py
 create mode 100644 modules/seedvr/src/models/dit/nablocks/mmsr_block.py
 create mode 100644 modules/seedvr/src/models/dit/nadit.py
 create mode 100644 modules/seedvr/src/models/dit/normalization.py
 create mode 100644 modules/seedvr/src/models/dit/patch.py
 create mode 100644 modules/seedvr/src/models/dit/rope.py
 create mode 100644 modules/seedvr/src/models/dit/window.py
 create mode 100644 modules/seedvr/src/models/dit_v2/attention.py
 create mode 100644 modules/seedvr/src/models/dit_v2/embedding.py
 create mode 100644 modules/seedvr/src/models/dit_v2/mlp.py
 create mode 100644 modules/seedvr/src/models/dit_v2/mm.py
 create mode 100644 modules/seedvr/src/models/dit_v2/modulation.py
 create mode 100644 modules/seedvr/src/models/dit_v2/na.py
 create mode 100644 modules/seedvr/src/models/dit_v2/nablocks/__init__.py
 create mode 100644 modules/seedvr/src/models/dit_v2/nablocks/attention/__init__.py
 create mode 100644 modules/seedvr/src/models/dit_v2/nablocks/attention/mmattn.py
 create mode 100644 modules/seedvr/src/models/dit_v2/nablocks/mmsr_block.py
 create mode 100644 modules/seedvr/src/models/dit_v2/nadit.py
 create mode 100644 modules/seedvr/src/models/dit_v2/normalization.py
 create mode 100644 modules/seedvr/src/models/dit_v2/patch/__init__.py
 create mode 100644 modules/seedvr/src/models/dit_v2/patch/patch_v1.py
 create mode 100644 modules/seedvr/src/models/dit_v2/rope.py
 create mode 100644 modules/seedvr/src/models/dit_v2/window.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/global_config.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/types.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/modules/video_vae.py
 create mode 100644 modules/seedvr/src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml
 create mode 100644 modules/seedvr/src/optimization/__init__.py
 create mode 100644 modules/seedvr/src/optimization/memory_manager.py
 create mode 100644 modules/seedvr/src/optimization/performance.py
 create mode 100644 modules/seedvr/src/utils/__init__.py
 create mode 100644 modules/seedvr/src/utils/color_fix.py
 create mode 100644 modules/seedvr/test.py

diff --git a/.pylintrc b/.pylintrc
index 5b4e398dc..ff0af298f 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -29,6 +29,7 @@ ignore-paths=/usr/lib/.*$,
              modules/postprocess/aurasr_arch.py,
              modules/prompt_parser_xhinker.py,
              modules/ras,
+             modules/seedvr,
              modules/rife,
              modules/schedulers,
              modules/taesd,
diff --git a/.ruff.toml b/.ruff.toml
index 7433d76da..de4e0f7a5 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -12,6 +12,7 @@ exclude = [
     "modules/pag",
     "modules/schedulers",
     "modules/teacache",
+    "modules/seedvr",
 
     "modules/control/proc",
     "modules/control/units",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2e429d98d..4f06fff89 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -42,12 +42,13 @@
   - **ROCm** for Windows  
     support for both official torch preview release of `torch-rocm` for windows and **TheRock** unoffical `torch-rocm` builds for windows  
     note that rocm for windows is still in preview and has limited gpu support, please check rocm docs for details  
-  - **DirectML** warn as end-of-life  
+  - **DirectML** warn as *end-of-life*  
     `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
   - command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible  
     previously sdnext was performing a fallback to `torch-cpu` which is not desired  
-  - if `--use-cuda` or `--use-rocm` are specified and `torch-cpu` is installed, installer will attempt to reinstall correct torch package  
-  - **installer**: warn if cuda or rocm are available and `torch-cpu` is installed  
+  - **installer** if `--use-cuda` or `--use-rocm` are specified and `torch-cpu` is installed, installer will attempt to reinstall correct torch package  
+  - **installer** warn if *cuda* or *rocm* are available and `torch-cpu` is installed  
+  - support for `torch==2.10-nightly` with `cuda==13.0`  
 - **Extensions**  
   - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
     was a high-value built-in extension, but it has not been maintained for 1.5 years  
diff --git a/installer.py b/installer.py
index 9129d2620..e668f3c1a 100644
--- a/installer.py
+++ b/installer.py
@@ -1353,6 +1353,7 @@ def set_environment():
         allocator += ',backend:cudaMallocAsync'
     if opts.get("torch_expandable_segments", False):
         allocator += ',expandable_segments:True'
+    os.environ.setdefault('PYTORCH_ALLOC_CONF', allocator)
     os.environ.setdefault('PYTORCH_CUDA_ALLOC_CONF', allocator)
     os.environ.setdefault('PYTORCH_HIP_ALLOC_CONF', allocator)
     log.debug(f'Torch allocator: "{allocator}"')
diff --git a/modules/postprocess/seedvr_model.py b/modules/postprocess/seedvr_model.py
new file mode 100644
index 000000000..1f833cbd5
--- /dev/null
+++ b/modules/postprocess/seedvr_model.py
@@ -0,0 +1,76 @@
+import time
+import numpy as np
+import torch
+from PIL import Image
+from torchvision.transforms import ToPILImage
+from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, TimeElapsedColumn
+from modules import devices
+from modules.shared import opts, log
+from modules.upscaler import Upscaler, UpscalerData
+
+
+MODELS_MAP = {
+    "SeedVR2 3B": "seedvr2_ema_3b_fp16.safetensors",
+    "SeedVR2 7B": "seedvr2_ema_7b_fp16.safetensors",
+    "SeedVR2 7B Sharp": "seedvr2_ema_7b_sharp_fp16.safetensors",
+}
+to_pil = ToPILImage()
+
+
+class UpscalerSeedVR(Upscaler):
+    def __init__(self, dirname=None):
+        self.name = "SeedVR"
+        super().__init__()
+        self.scalers = [
+            UpscalerData(name="SeedVR2 3B", path=None, upscaler=self, model=None, scale=1),
+            UpscalerData(name="SeedVR2 7B", path=None, upscaler=self, model=None, scale=1),
+            UpscalerData(name="SeedVR2 7B Sharp", path=None, upscaler=self, model=None, scale=1),
+        ]
+        self.model = None
+        self.model_loaded = None
+
+    def load_model(self, path: str):
+        model_name = MODELS_MAP.get(path, None)
+        if (self.model is None) or (self.model_loaded != model_name):
+            log.debug(f'Upscaler load: name="{self.name}" model="{model_name}"')
+            from modules.seedvr.src.core.model_manager import configure_runner
+            self.model = configure_runner(
+                model_name=model_name,
+                cache_dir=opts.hfcache_dir,
+                device=devices.device,
+                dtype=devices.dtype,
+            )
+
+    def do_upscale(self, img: Image.Image, selected_file):
+        devices.torch_gc()
+        self.load_model(selected_file)
+        if self.model is None:
+            return img
+
+        from modules.seedvr.src.core.generation import generation_loop
+
+        width = int(self.scale * img.width) // 8 * 8
+        image_tensor = np.array(img)
+        image_tensor = torch.from_numpy(image_tensor).to(device=devices.device, dtype=devices.dtype).unsqueeze(0) / 255.0
+
+        t0 = time.time()
+        result_tensor = generation_loop(
+            runner=self.model,
+            images=image_tensor,
+            cfg_scale=1.0,
+            seed=42,
+            res_w=width,
+            batch_size=1,
+            temporal_overlap=0,
+            device=devices.device,
+        )
+        t1 = time.time()
+        log.info(f'Upscaler: type="{self.name}" model="{selected_file}" scale={self.scale} time={t1 - t0:.2f}')
+        img = to_pil(result_tensor.squeeze().permute((2, 0, 1)))
+        devices.torch_gc()
+
+        if opts.upscaler_unload:
+            self.model = None
+            log.debug(f'Upscaler unload: type="{self.name}" model="{selected_file}"')
+            devices.torch_gc(force=True)
+        return img
diff --git a/modules/seedvr/__init__.py b/modules/seedvr/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/modules/seedvr/config_3b.yaml b/modules/seedvr/config_3b.yaml
new file mode 100644
index 000000000..d2ffda469
--- /dev/null
+++ b/modules/seedvr/config_3b.yaml
@@ -0,0 +1,97 @@
+__object__:
+  path: projects.video_diffusion_sr.train
+  name: VideoDiffusionTrainer
+
+dit:
+  model:
+    __object__:
+      path:
+        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit_v2.nadit"
+        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit_v2.nadit"
+        - "src.models.dit_v2.nadit"
+      name: "NaDiT"
+      args: "as_params"
+    vid_in_channels: 33
+    vid_out_channels: 16
+    vid_dim: 2560
+    vid_out_norm: fusedrms
+    txt_in_dim: 5120
+    txt_in_norm: fusedln
+    txt_dim: ${.vid_dim}
+    emb_dim: ${eval:'6 * ${.vid_dim}'}
+    heads: 20
+    head_dim: 128 # llm-like
+    expand_ratio: 4
+    norm: fusedrms
+    norm_eps: 1.0e-05
+    ada: single
+    qk_bias: False
+    qk_norm: fusedrms
+    patch_size: [1, 2, 2]
+    num_layers: 32 # llm-like
+    mm_layers: 10
+    mlp_type: swiglu
+    msa_type: None
+    block_type: ${eval:'${.num_layers} * ["mmdit_sr"]'} # space-full
+    window: ${eval:'${.num_layers} * [(4,3,3)]'} # space-full
+    window_method: ${eval:'${.num_layers} // 2 * ["720pwin_by_size_bysize","720pswin_by_size_bysize"]'} # space-full
+    rope_type: mmrope3d
+    rope_dim: 128
+  compile: False
+  gradient_checkpoint: True
+  fsdp:
+    sharding_strategy: _HYBRID_SHARD_ZERO2
+
+ema:
+  decay: 0.9998
+
+vae:
+  model:
+    __object__:
+      path:
+        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "src.models.video_vae_v3.modules.attn_video_vae"
+      name: "VideoAutoencoderKLWrapper"
+      args: "as_params"
+    freeze_encoder: False
+    gradient_checkpoint: True # Disabled to prevent VRAM leaks in inference
+  slicing:
+    split_size: 4
+    memory_device: same
+  memory_limit:
+    conv_max_mem: 0.5
+    norm_max_mem: 0.5
+  checkpoint: ema_vae_fp16.safetensors
+  scaling_factor: 0.9152
+  compile: False
+  grouping: False
+  dtype: float16
+
+diffusion:
+  schedule:
+    type: lerp
+    T: 1000.0
+  sampler:
+    type: euler
+    prediction_type: v_lerp
+  timesteps:
+    training:
+      type: logitnormal
+      loc: 0.0
+      scale: 1.0
+    sampling:
+      type: uniform_trailing
+      steps: 50
+    transform: True
+  loss:
+    type: v_lerp
+  cfg:
+    scale: 7.5
+    rescale: 0
+
+condition:
+  i2v: 0.0
+  v2v: 0.0
+  sr: 1.0
+  noise_scale: 0.25
diff --git a/modules/seedvr/config_7b.yaml b/modules/seedvr/config_7b.yaml
new file mode 100644
index 000000000..3f80813d9
--- /dev/null
+++ b/modules/seedvr/config_7b.yaml
@@ -0,0 +1,94 @@
+__object__:
+  path: projects.video_diffusion_sr.train
+  name: VideoDiffusionTrainer
+
+dit:
+  model:
+    __object__:
+      path:
+        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit.nadit"
+        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit.nadit"
+        - "src.models.dit.nadit"
+      name: "NaDiT"
+      args: "as_params"
+    vid_in_channels: 33
+    vid_out_channels: 16
+    vid_dim: 3072
+    txt_in_dim: 5120
+    txt_dim: ${.vid_dim}
+    emb_dim: ${eval:'6 * ${.vid_dim}'}
+    heads: 24
+    head_dim: 128 # llm-like
+    expand_ratio: 4
+    norm: fusedrms
+    norm_eps: 1e-5
+    ada: single
+    qk_bias: False
+    qk_rope: True
+    qk_norm: fusedrms
+    patch_size: [1, 2, 2]
+    num_layers: 36 # llm-like
+    shared_mlp: False
+    shared_qkv: False
+    mlp_type: normal
+    block_type: ${eval:'${.num_layers} * ["mmdit_sr"]'} # space-full
+    window: ${eval:'${.num_layers} * [(4,3,3)]'} # space-full
+    window_method: ${eval:'${.num_layers} // 2 * ["720pwin_by_size_bysize","720pswin_by_size_bysize"]'} # space-full
+  compile: False
+  gradient_checkpoint: True
+  fsdp:
+    sharding_strategy: _HYBRID_SHARD_ZERO2
+
+ema:
+  decay: 0.9998
+
+vae:
+  model:
+    __object__:
+      path:
+        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "src.models.video_vae_v3.modules.attn_video_vae"
+      name: "VideoAutoencoderKLWrapper"
+      args: "as_params"
+    freeze_encoder: False
+    # gradient_checkpoint: True
+  slicing:
+    split_size: 4
+    memory_device: same
+  memory_limit:
+    conv_max_mem: 0.5
+    norm_max_mem: 0.5
+  checkpoint: ema_vae_fp16.safetensors
+  scaling_factor: 0.9152
+  compile: False
+  grouping: False
+  dtype: float16
+
+diffusion:
+  schedule:
+    type: lerp
+    T: 1000.0
+  sampler:
+    type: euler
+    prediction_type: v_lerp
+  timesteps:
+    training:
+      type: logitnormal
+      loc: 0.0
+      scale: 1.0
+    sampling:
+      type: uniform_trailing
+      steps: 50
+    transform: True
+  loss:
+    type: v_lerp
+  cfg:
+    scale: 7.5
+    rescale: 0
+
+condition:
+  i2v: 0.0
+  v2v: 0.0
+  sr: 1.0
+  noise_scale: 0.25
diff --git a/modules/seedvr/src/__init__.py b/modules/seedvr/src/__init__.py
new file mode 100644
index 000000000..ebff00ffb
--- /dev/null
+++ b/modules/seedvr/src/__init__.py
@@ -0,0 +1,37 @@
+"""
+SeedVR2 Video Upscaler - Modular Architecture
+Refactored from monolithic seedvr2.py for better maintainability
+
+Author: Refactored codebase
+Version: 2.0.0 - Modular
+
+Available Modules:
+- utils: Download and path utilities
+- optimization: Memory, performance, and compatibility optimizations  
+- core: Model management and generation pipeline (NEW)
+- processing: Video and tensor processing (coming next)
+- interfaces: ComfyUI integration
+"""
+'''
+# Track which modules are available for progressive migration
+MODULES_AVAILABLE = {
+    'downloads': True,          # ✅ Module 1 - Downloads and model management
+    'memory_manager': True,     # ✅ Module 2 - Memory optimization
+    'performance': True,        # ✅ Module 3 - Performance optimizations  
+    'compatibility': True,      # ✅ Module 4 - FP8/FP16 compatibility
+    'model_manager': True,      # ✅ Module 5 - Model configuration and loading
+    'generation': True,         # ✅ Module 6 - Generation loop and inference
+    'video_transforms': True,   # ✅ Module 7 - Video processing and transforms
+    'comfyui_node': True,       # ✅ Module 8 - ComfyUI node interface (COMPLETE!)
+    'infer': True,              # ✅ Module 9 - Infer
+}
+'''
+# Core imports (always available)
+import os
+import sys
+
+# Add current directory to path for fallback imports
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(current_dir)
+if parent_dir not in sys.path:
+    sys.path.insert(0, parent_dir)
diff --git a/modules/seedvr/src/common/__init__.py b/modules/seedvr/src/common/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/modules/seedvr/src/common/cache.py b/modules/seedvr/src/common/cache.py
new file mode 100644
index 000000000..3566852ba
--- /dev/null
+++ b/modules/seedvr/src/common/cache.py
@@ -0,0 +1,33 @@
+from typing import Callable
+
+
+class Cache:
+    """Caching reusable args for faster inference"""
+
+    def __init__(self, disable=False, prefix="", cache=None):
+        self.cache = cache if cache is not None else {}
+        self.disable = disable
+        self.prefix = prefix
+
+    def __call__(self, key: str, fn: Callable):
+        if self.disable:
+            return fn()
+
+        key = self.prefix + key
+        try:
+            result = self.cache[key]
+        except KeyError:
+            result = fn()
+            self.cache[key] = result
+        return result
+
+    def namespace(self, namespace: str):
+        return Cache(
+            disable=self.disable,
+            prefix=self.prefix + namespace + ".",
+            cache=self.cache,
+        )
+
+    def get(self, key: str):
+        key = self.prefix + key
+        return self.cache[key]
diff --git a/modules/seedvr/src/common/config.py b/modules/seedvr/src/common/config.py
new file mode 100644
index 000000000..3667b4d2b
--- /dev/null
+++ b/modules/seedvr/src/common/config.py
@@ -0,0 +1,128 @@
+import importlib
+from typing import Any, Callable, List, Union
+from omegaconf import DictConfig, ListConfig, OmegaConf
+
+try:
+    OmegaConf.register_new_resolver("eval", eval)
+except Exception as e:
+    if "already registered" not in str(e):
+        raise
+
+
+
+def load_config(path: str, argv: List[str] = None) -> Union[DictConfig, ListConfig]:
+    """
+    Load a configuration. Will resolve inheritance.
+    """
+
+    config = OmegaConf.load(path)
+    if argv is not None:
+        config_argv = OmegaConf.from_dotlist(argv)
+        config = OmegaConf.merge(config, config_argv)
+    config = resolve_recursive(config, resolve_inheritance)
+    return config
+
+
+def resolve_recursive(
+    config: Any,
+    resolver: Callable[[Union[DictConfig, ListConfig]], Union[DictConfig, ListConfig]],
+) -> Any:
+    config = resolver(config)
+    if isinstance(config, DictConfig):
+        for k in config.keys():
+            v = config.get(k)
+            if isinstance(v, (DictConfig, ListConfig)):
+                config[k] = resolve_recursive(v, resolver)
+    if isinstance(config, ListConfig):
+        for i in range(len(config)):
+            v = config.get(i)
+            if isinstance(v, (DictConfig, ListConfig)):
+                config[i] = resolve_recursive(v, resolver)
+    return config
+
+
+def resolve_inheritance(config: Union[DictConfig, ListConfig]) -> Any:
+    """
+    Recursively resolve inheritance if the config contains:
+    __inherit__: path/to/parent.yaml or a ListConfig of such paths.
+    """
+    if isinstance(config, DictConfig):
+        inherit = config.pop("__inherit__", None)
+
+        if inherit:
+            inherit_list = inherit if isinstance(inherit, ListConfig) else [inherit]
+
+            parent_config = None
+            for parent_path in inherit_list:
+                assert isinstance(parent_path, str)
+                parent_config = (
+                    load_config(parent_path)
+                    if parent_config is None
+                    else OmegaConf.merge(parent_config, load_config(parent_path))
+                )
+
+            if len(config.keys()) > 0:
+                config = OmegaConf.merge(parent_config, config)
+            else:
+                config = parent_config
+    return config
+
+
+def import_item(path: Union[str, List[str]], name: str) -> Any:
+    """
+    Import a python item with fallback support.
+    
+    Args:
+        path: Single path string or list of paths to try (fallback order)
+        name: Class/function name to import
+        
+    Returns:
+        Imported object
+        
+    Example: 
+        import_item("path.to.file", "MyClass") -> MyClass
+        import_item(["path1.to.file", "path2.to.file"], "MyClass") -> MyClass (first working path)
+    """
+    if isinstance(path, str):
+        # Single path - original behavior
+        return getattr(importlib.import_module(path), name)
+
+    elif isinstance(path, (list, ListConfig)):
+        # Multiple paths - try each until one works
+        last_error = None
+        for single_path in path:
+            try:
+                return getattr(importlib.import_module(single_path), name)
+            except ImportError as e:
+                last_error = e
+                continue
+
+        # If we get here, none of the paths worked
+        raise ImportError(f"Could not import '{name}' from any of the paths: {path}. Last error: {last_error}")
+
+    else:
+        raise ValueError(f"Path must be string or list of strings, got: {type(path)}")
+
+
+def create_object(config: DictConfig) -> Any:
+    """
+    Create an object from config.
+    The config is expected to contains the following:
+    __object__:
+      path: path.to.module
+      name: MyClass
+      args: as_config | as_params (default to as_config)
+    """
+
+    item = import_item(
+        path=config.__object__.path,
+        name=config.__object__.name,
+    )
+    args = config.__object__.get("args", "as_config")
+    if args == "as_config":
+        return item(config)
+    if args == "as_params":
+        config = OmegaConf.to_object(config)
+        config.pop("__object__")
+        return item(**config)
+    raise NotImplementedError(f"Unknown args type: {args}")
diff --git a/modules/seedvr/src/common/decorators.py b/modules/seedvr/src/common/decorators.py
new file mode 100644
index 000000000..6eacf8bd5
--- /dev/null
+++ b/modules/seedvr/src/common/decorators.py
@@ -0,0 +1,126 @@
+import functools
+import threading
+from typing import Callable
+import torch
+
+from .distributed import barrier_if_distributed, get_global_rank, get_local_rank
+from .logger import get_logger
+
+logger = get_logger(__name__)
+
+
+def log_on_entry(func: Callable) -> Callable:
+    """
+    Functions with this decorator will log the function name at entry.
+    When using multiple decorators, this must be applied innermost to properly capture the name.
+    """
+
+    def log_on_entry_wrapper(*args, **kwargs):
+        logger.info(f"Entering {func.__name__}")
+        return func(*args, **kwargs)
+
+    return log_on_entry_wrapper
+
+
+def barrier_on_entry(func: Callable) -> Callable:
+    """
+    Functions with this decorator will start executing when all ranks are ready to enter.
+    """
+
+    def barrier_on_entry_wrapper(*args, **kwargs):
+        barrier_if_distributed()
+        return func(*args, **kwargs)
+
+    return barrier_on_entry_wrapper
+
+
+def _conditional_execute_wrapper_factory(execute: bool, func: Callable) -> Callable:
+    """
+    Helper function for local_rank_zero_only and global_rank_zero_only.
+    """
+
+    def conditional_execute_wrapper(*args, **kwargs):
+        # Only execute if needed.
+        result = func(*args, **kwargs) if execute else None
+        # All GPUs must wait.
+        barrier_if_distributed()
+        # Return results.
+        return result
+
+    return conditional_execute_wrapper
+
+
+def _asserted_wrapper_factory(condition: bool, func: Callable, err_msg: str = "") -> Callable:
+    """
+    Helper function for some functions with special constraints,
+    especially functions called by other global_rank_zero_only / local_rank_zero_only ones,
+    in case they are wrongly invoked in other scenarios.
+    """
+
+    def asserted_execute_wrapper(*args, **kwargs):
+        assert condition, err_msg
+        result = func(*args, **kwargs)
+        return result
+
+    return asserted_execute_wrapper
+
+
+def local_rank_zero_only(func: Callable) -> Callable:
+    """
+    Functions with this decorator will only execute on local rank zero.
+    """
+    return _conditional_execute_wrapper_factory(get_local_rank() == 0, func)
+
+
+def global_rank_zero_only(func: Callable) -> Callable:
+    """
+    Functions with this decorator will only execute on global rank zero.
+    """
+    return _conditional_execute_wrapper_factory(get_global_rank() == 0, func)
+
+
+def assert_only_global_rank_zero(func: Callable) -> Callable:
+    """
+    Functions with this decorator are only accessible to processes with global rank zero.
+    """
+    return _asserted_wrapper_factory(
+        get_global_rank() == 0, func, err_msg="Not accessible to processes with global_rank != 0"
+    )
+
+
+def assert_only_local_rank_zero(func: Callable) -> Callable:
+    """
+    Functions with this decorator are only accessible to processes with local rank zero.
+    """
+    return _asserted_wrapper_factory(
+        get_local_rank() == 0, func, err_msg="Not accessible to processes with local_rank != 0"
+    )
+
+
+def new_thread(func: Callable) -> Callable:
+    """
+    Functions with this decorator will run in a new thread.
+    The function will return the thread, which can be joined to wait for completion.
+    """
+
+    def new_thread_wrapper(*args, **kwargs):
+        thread = threading.Thread(target=func, args=args, kwargs=kwargs)
+        thread.start()
+        return thread
+
+    return new_thread_wrapper
+
+
+def log_runtime(func: Callable) -> Callable:
+    """
+    Functions with this decorator will logging the runtime.
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args, **kwargs):
+        torch.distributed.barrier()
+        result = func(*args, **kwargs)
+        torch.distributed.barrier()
+        return result
+
+    return wrapped
diff --git a/modules/seedvr/src/common/diffusion/__init__.py b/modules/seedvr/src/common/diffusion/__init__.py
new file mode 100644
index 000000000..034e36ef7
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/__init__.py
@@ -0,0 +1,56 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Diffusion package.
+"""
+
+from .config import (
+    create_sampler_from_config,
+    create_sampling_timesteps_from_config,
+    create_schedule_from_config,
+)
+from .samplers.base import Sampler
+from .samplers.euler import EulerSampler
+from .schedules.base import Schedule
+from .schedules.lerp import LinearInterpolationSchedule
+from .timesteps.base import SamplingTimesteps, Timesteps
+from .timesteps.sampling.trailing import UniformTrailingSamplingTimesteps
+from .types import PredictionType, SamplingDirection
+from .utils import classifier_free_guidance, classifier_free_guidance_dispatcher, expand_dims
+
+__all__ = [
+    # Configs
+    "create_sampler_from_config",
+    "create_sampling_timesteps_from_config",
+    "create_schedule_from_config",
+    # Schedules
+    "Schedule",
+    "DiscreteVariancePreservingSchedule",
+    "LinearInterpolationSchedule",
+    # Samplers
+    "Sampler",
+    "EulerSampler",
+    # Timesteps
+    "Timesteps",
+    "SamplingTimesteps",
+    # Types
+    "PredictionType",
+    "SamplingDirection",
+    "UniformTrailingSamplingTimesteps",
+    # Utils
+    "classifier_free_guidance",
+    "classifier_free_guidance_dispatcher",
+    "expand_dims",
+]
diff --git a/modules/seedvr/src/common/diffusion/config.py b/modules/seedvr/src/common/diffusion/config.py
new file mode 100644
index 000000000..0a7183584
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/config.py
@@ -0,0 +1,71 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Utility functions for creating schedules and samplers from config.
+"""
+
+import torch
+from omegaconf import DictConfig
+
+from .samplers.base import Sampler
+from .samplers.euler import EulerSampler
+from .schedules.base import Schedule
+from .schedules.lerp import LinearInterpolationSchedule
+from .timesteps.base import SamplingTimesteps
+from .timesteps.sampling.trailing import UniformTrailingSamplingTimesteps
+
+
+def create_schedule_from_config(
+    config: DictConfig,
+) -> Schedule:
+    """
+    Create a schedule from configuration.
+    """
+    if config.type == "lerp":
+        return LinearInterpolationSchedule(T=config.get("T", 1.0))
+
+    raise NotImplementedError
+
+
+def create_sampler_from_config(
+    config: DictConfig,
+    schedule: Schedule,
+    timesteps: SamplingTimesteps,
+) -> Sampler:
+    """
+    Create a sampler from configuration.
+    """
+    if config.type == "euler":
+        return EulerSampler(
+            schedule=schedule,
+            timesteps=timesteps,
+            prediction_type=config.prediction_type,
+        )
+    raise NotImplementedError
+
+
+def create_sampling_timesteps_from_config(
+    config: DictConfig,
+    schedule: Schedule,
+    device: torch.device,
+) -> SamplingTimesteps:
+    if config.type == "uniform_trailing":
+        return UniformTrailingSamplingTimesteps(
+            T=schedule.T,
+            steps=config.steps,
+            shift=config.get("shift", 1.0),
+            device=device,
+        )
+    raise NotImplementedError
diff --git a/modules/seedvr/src/common/diffusion/samplers/base.py b/modules/seedvr/src/common/diffusion/samplers/base.py
new file mode 100644
index 000000000..8e65f1989
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/samplers/base.py
@@ -0,0 +1,108 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Sampler base class.
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Callable
+import torch
+from tqdm import tqdm
+
+from ..schedules.base import Schedule
+from ..timesteps.base import SamplingTimesteps
+from ..types import PredictionType, SamplingDirection
+from ..utils import assert_schedule_timesteps_compatible
+
+
+@dataclass
+class SamplerModelArgs:
+    x_t: torch.Tensor
+    t: torch.Tensor
+    i: int
+
+
+class Sampler(ABC):
+    """
+    Samplers are ODE/SDE solvers.
+    """
+
+    def __init__(
+        self,
+        schedule: Schedule,
+        timesteps: SamplingTimesteps,
+        prediction_type: PredictionType,
+        return_endpoint: bool = True,
+    ):
+        assert_schedule_timesteps_compatible(
+            schedule=schedule,
+            timesteps=timesteps,
+        )
+        self.schedule = schedule
+        self.timesteps = timesteps
+        self.prediction_type = prediction_type
+        self.return_endpoint = return_endpoint
+
+    @abstractmethod
+    def sample(
+        self,
+        x: torch.Tensor,
+        f: Callable[[SamplerModelArgs], torch.Tensor],
+    ) -> torch.Tensor:
+        """
+        Generate a new sample given the the intial sample x and score function f.
+        """
+
+    def get_next_timestep(
+        self,
+        t: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Get the next sample timestep.
+        Support multiple different timesteps t in a batch.
+        If no more steps, return out of bound value -1 or T+1.
+        """
+        T = self.timesteps.T
+        steps = len(self.timesteps)
+        curr_idx = self.timesteps.index(t)
+        next_idx = curr_idx + 1
+        bound = -1 if self.timesteps.direction == SamplingDirection.backward else T + 1
+
+        s = self.timesteps[next_idx.clamp_max(steps - 1)]
+        s = s.where(next_idx < steps, bound)
+        return s
+
+    def get_endpoint(
+        self,
+        pred: torch.Tensor,
+        x_t: torch.Tensor,
+        t: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Get to the endpoint of the probability flow.
+        """
+        x_0, x_T = self.schedule.convert_from_pred(pred, self.prediction_type, x_t, t)
+        return x_0 if self.timesteps.direction == SamplingDirection.backward else x_T
+
+    def get_progress_bar(self):
+        """
+        Get progress bar for sampling.
+        """
+        return tqdm(
+            iterable=range(len(self.timesteps) - (0 if self.return_endpoint else 1)),
+            dynamic_ncols=True,
+            desc=self.__class__.__name__,
+        )
diff --git a/modules/seedvr/src/common/diffusion/samplers/euler.py b/modules/seedvr/src/common/diffusion/samplers/euler.py
new file mode 100644
index 000000000..185669acb
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/samplers/euler.py
@@ -0,0 +1,107 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+
+"""
+Euler ODE solver.
+"""
+
+from typing import Callable
+import torch
+from einops import rearrange
+from torch.nn import functional as F
+
+#from ....models.dit_v2 import na
+
+from ..types import PredictionType
+from ..utils import expand_dims
+from .base import Sampler, SamplerModelArgs
+
+
+class EulerSampler(Sampler):
+    """
+    The Euler method is the simplest ODE solver.
+    <https://en.wikipedia.org/wiki/Euler_method>
+    """
+
+    def sample(
+        self,
+        x: torch.Tensor,
+        f: Callable[[SamplerModelArgs], torch.Tensor],
+    ) -> torch.Tensor:
+        timesteps = self.timesteps.timesteps
+        #progress = self.get_progress_bar()
+        i = 0
+
+        # Optimisations VRAM
+        original_dtype = x.dtype
+        device = x.device
+
+        for t, s in zip(timesteps[:-1], timesteps[1:]):
+            # Appel du modèle avec monitoring
+            pred = f(SamplerModelArgs(x, t, i))
+
+            # Étape suivante
+            x = self.step_to(pred, x, t, s)
+
+            # Nettoyer les tenseurs temporaires
+            del pred
+
+            i += 1
+            #progress.update()
+
+        if self.return_endpoint:
+            t = timesteps[-1]
+            pred = f(SamplerModelArgs(x, t, i))
+            x = self.get_endpoint(pred, x, t)
+            del pred
+            #progress.update()
+
+        # Restaurer le dtype original si nécessaire
+        if original_dtype != torch.float16:
+            x = x.to(original_dtype)
+
+        return x
+
+    def step(
+        self,
+        pred: torch.Tensor,
+        x_t: torch.Tensor,
+        t: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Step to the next timestep.
+        """
+        return self.step_to(pred, x_t, t, self.get_next_timestep(t))
+
+    def step_to(
+        self,
+        pred: torch.Tensor,
+        x_t: torch.Tensor,
+        t: torch.Tensor,
+        s: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Steps from x_t at timestep t to x_s at timestep s. Returns x_s.
+        """
+        t = expand_dims(t, x_t.ndim)
+        s = expand_dims(s, x_t.ndim)
+        T = self.schedule.T
+        # Step from x_t to x_s.
+        pred_x_0, pred_x_T = self.schedule.convert_from_pred(pred, self.prediction_type, x_t, t)
+        pred_x_s = self.schedule.forward(pred_x_0, pred_x_T, s.clamp(0, T))
+        # Clamp x_s to x_0 and x_T if s is out of bound.
+        pred_x_s = pred_x_s.where(s >= 0, pred_x_0)
+        pred_x_s = pred_x_s.where(s <= T, pred_x_T)
+        return pred_x_s
diff --git a/modules/seedvr/src/common/diffusion/schedules/base.py b/modules/seedvr/src/common/diffusion/schedules/base.py
new file mode 100644
index 000000000..bcf6c6b64
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/schedules/base.py
@@ -0,0 +1,131 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Schedule base class.
+"""
+
+from abc import ABC, abstractmethod, abstractproperty
+from typing import Tuple, Union
+import torch
+
+from ..types import PredictionType
+from ..utils import expand_dims
+
+
+class Schedule(ABC):
+    """
+    Diffusion schedules are uniquely defined by T, A, B:
+
+        x_t = A(t) * x_0 + B(t) * x_T, where t in [0, T]
+
+    Schedules can be continuous or discrete.
+    """
+
+    @abstractproperty
+    def T(self) -> Union[int, float]:
+        """
+        Maximum timestep inclusive.
+        Schedule is continuous if float, discrete if int.
+        """
+
+    @abstractmethod
+    def A(self, t: torch.Tensor) -> torch.Tensor:
+        """
+        Interpolation coefficient A.
+        Returns tensor with the same shape as t.
+        """
+
+    @abstractmethod
+    def B(self, t: torch.Tensor) -> torch.Tensor:
+        """
+        Interpolation coefficient B.
+        Returns tensor with the same shape as t.
+        """
+
+    # ----------------------------------------------------
+
+    def snr(self, t: torch.Tensor) -> torch.Tensor:
+        """
+        Signal to noise ratio.
+        Returns tensor with the same shape as t.
+        """
+        return (self.A(t) ** 2) / (self.B(t) ** 2)
+
+    def isnr(self, snr: torch.Tensor) -> torch.Tensor:
+        """
+        Inverse signal to noise ratio.
+        Returns tensor with the same shape as snr.
+        Subclass may implement.
+        """
+        raise NotImplementedError
+
+    # ----------------------------------------------------
+
+    def is_continuous(self) -> bool:
+        """
+        Whether the schedule is continuous.
+        """
+        return isinstance(self.T, float)
+
+    def forward(self, x_0: torch.Tensor, x_T: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
+        """
+        Diffusion forward function.
+        """
+        t = expand_dims(t, x_0.ndim)
+        return self.A(t) * x_0 + self.B(t) * x_T
+
+    def convert_from_pred(
+        self, pred: torch.Tensor, pred_type: PredictionType, x_t: torch.Tensor, t: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Convert from prediction. Return predicted x_0 and x_T.
+        """
+        t = expand_dims(t, x_t.ndim)
+        A_t = self.A(t)
+        B_t = self.B(t)
+
+        if pred_type == PredictionType.x_T:
+            pred_x_T = pred
+            pred_x_0 = (x_t - B_t * pred_x_T) / A_t
+        elif pred_type == PredictionType.x_0:
+            pred_x_0 = pred
+            pred_x_T = (x_t - A_t * pred_x_0) / B_t
+        elif pred_type == PredictionType.v_cos:
+            pred_x_0 = A_t * x_t - B_t * pred
+            pred_x_T = A_t * pred + B_t * x_t
+        elif pred_type == PredictionType.v_lerp:
+            pred_x_0 = (x_t - B_t * pred) / (A_t + B_t)
+            pred_x_T = (x_t + A_t * pred) / (A_t + B_t)
+        else:
+            raise NotImplementedError
+
+        return pred_x_0, pred_x_T
+
+    def convert_to_pred(
+        self, x_0: torch.Tensor, x_T: torch.Tensor, t: torch.Tensor, pred_type: PredictionType
+    ) -> torch.FloatTensor:
+        """
+        Convert to prediction target given x_0 and x_T.
+        """
+        if pred_type == PredictionType.x_T:
+            return x_T
+        if pred_type == PredictionType.x_0:
+            return x_0
+        if pred_type == PredictionType.v_cos:
+            t = expand_dims(t, x_0.ndim)
+            return self.A(t) * x_T - self.B(t) * x_0
+        if pred_type == PredictionType.v_lerp:
+            return x_T - x_0
+        raise NotImplementedError
diff --git a/modules/seedvr/src/common/diffusion/schedules/lerp.py b/modules/seedvr/src/common/diffusion/schedules/lerp.py
new file mode 100644
index 000000000..56b42bc17
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/schedules/lerp.py
@@ -0,0 +1,55 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Linear interpolation schedule (lerp).
+"""
+
+from typing import Union
+import torch
+
+from .base import Schedule
+
+
+class LinearInterpolationSchedule(Schedule):
+    """
+    Linear interpolation schedule (lerp) is proposed by flow matching and rectified flow.
+    It leads to straighter probability flow theoretically. It is also used by Stable Diffusion 3.
+    <https://arxiv.org/abs/2209.03003>
+    <https://arxiv.org/abs/2210.02747>
+
+        x_t = (1 - t) * x_0 + t * x_T
+
+    Can be either continuous or discrete.
+    """
+
+    def __init__(self, T: Union[int, float] = 1.0):
+        self._T = T
+
+    @property
+    def T(self) -> Union[int, float]:
+        return self._T
+
+    def A(self, t: torch.Tensor) -> torch.Tensor:
+        return 1 - (t / self.T)
+
+    def B(self, t: torch.Tensor) -> torch.Tensor:
+        return t / self.T
+
+    # ----------------------------------------------------
+
+    def isnr(self, snr: torch.Tensor) -> torch.Tensor:
+        t = self.T / (1 + snr**0.5)
+        t = t if self.is_continuous() else t.round().int()
+        return t
diff --git a/modules/seedvr/src/common/diffusion/timesteps/base.py b/modules/seedvr/src/common/diffusion/timesteps/base.py
new file mode 100644
index 000000000..d1a598103
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/timesteps/base.py
@@ -0,0 +1,72 @@
+from abc import ABC, abstractmethod
+from typing import Sequence, Union
+import torch
+
+from ..types import SamplingDirection
+
+
+class Timesteps(ABC):
+    """
+    Timesteps base class.
+    """
+
+    def __init__(self, T: Union[int, float]):
+        assert T > 0
+        self._T = T
+
+    @property
+    def T(self) -> Union[int, float]:
+        """
+        Maximum timestep inclusive.
+        int if discrete, float if continuous.
+        """
+        return self._T
+
+    def is_continuous(self) -> bool:
+        """
+        Whether the schedule is continuous.
+        """
+        return isinstance(self.T, float)
+
+
+class SamplingTimesteps(Timesteps):
+    """
+    Sampling timesteps.
+    It defines the discretization of sampling steps.
+    """
+
+    def __init__(
+        self,
+        T: Union[int, float],
+        timesteps: torch.Tensor,
+        direction: SamplingDirection,
+    ):
+        assert timesteps.ndim == 1
+        super().__init__(T)
+        self.timesteps = timesteps
+        self.direction = direction
+
+    def __len__(self) -> int:
+        """
+        Number of sampling steps.
+        """
+        return len(self.timesteps)
+
+    def __getitem__(self, idx: Union[int, torch.IntTensor]) -> torch.Tensor:
+        """
+        The timestep at the sampling step.
+        Returns a scalar tensor if idx is int,
+        or tensor of the same size if idx is a tensor.
+        """
+        return self.timesteps[idx]
+
+    def index(self, t: torch.Tensor) -> torch.Tensor:
+        """
+        Find index by t.
+        Return index of the same shape as t.
+        Index is -1 if t not found in timesteps.
+        """
+        i, j = t.reshape(-1, 1).eq(self.timesteps).nonzero(as_tuple=True)
+        idx = torch.full_like(t, fill_value=-1, dtype=torch.int)
+        idx.view(-1)[i] = j.int()
+        return idx
diff --git a/modules/seedvr/src/common/diffusion/timesteps/sampling/trailing.py b/modules/seedvr/src/common/diffusion/timesteps/sampling/trailing.py
new file mode 100644
index 000000000..248d986ae
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/timesteps/sampling/trailing.py
@@ -0,0 +1,49 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import torch
+
+from ...types import SamplingDirection
+from ..base import SamplingTimesteps
+
+
+class UniformTrailingSamplingTimesteps(SamplingTimesteps):
+    """
+    Uniform trailing sampling timesteps.
+    Defined in (https://arxiv.org/abs/2305.08891)
+
+    Shift is proposed in SD3 for RF schedule.
+    Defined in (https://arxiv.org/pdf/2403.03206) eq.23
+    """
+
+    def __init__(
+        self,
+        T: int,
+        steps: int,
+        shift: float = 1.0,
+        device: torch.device = "cpu",
+    ):
+        # Create trailing timesteps.
+        timesteps = torch.arange(1.0, 0.0, -1.0 / steps, device=device)
+
+        # Shift timesteps.
+        timesteps = shift * timesteps / (1 + (shift - 1) * timesteps)
+
+        # Scale to T range.
+        if isinstance(T, float):
+            timesteps = timesteps * T
+        else:
+            timesteps = timesteps.mul(T + 1).sub(1).round().int()
+
+        super().__init__(T=T, timesteps=timesteps, direction=SamplingDirection.backward)
diff --git a/modules/seedvr/src/common/diffusion/types.py b/modules/seedvr/src/common/diffusion/types.py
new file mode 100644
index 000000000..076295f2b
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/types.py
@@ -0,0 +1,59 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Type definitions.
+"""
+
+from enum import Enum
+
+
+class PredictionType(str, Enum):
+    """
+    x_0:
+        Predict data sample.
+    x_T:
+        Predict noise sample.
+        Proposed by DDPM (https://arxiv.org/abs/2006.11239)
+        Proved problematic by zsnr paper (https://arxiv.org/abs/2305.08891)
+    v_cos:
+        Predict velocity dx/dt based on the cosine schedule (A_t * x_T - B_t * x_0).
+        Proposed by progressive distillation (https://arxiv.org/abs/2202.00512)
+    v_lerp:
+        Predict velocity dx/dt based on the lerp schedule (x_T - x_0).
+        Proposed by rectified flow (https://arxiv.org/abs/2209.03003)
+    """
+
+    x_0 = "x_0"
+    x_T = "x_T"
+    v_cos = "v_cos"
+    v_lerp = "v_lerp"
+
+
+class SamplingDirection(str, Enum):
+    """
+    backward: Sample from x_T to x_0 for data generation.
+    forward:  Sample from x_0 to x_T for noise inversion.
+    """
+
+    backward = "backward"
+    forward = "forward"
+
+    @staticmethod
+    def reverse(direction):
+        if direction == SamplingDirection.backward:
+            return SamplingDirection.forward
+        if direction == SamplingDirection.forward:
+            return SamplingDirection.backward
+        raise NotImplementedError
diff --git a/modules/seedvr/src/common/diffusion/utils.py b/modules/seedvr/src/common/diffusion/utils.py
new file mode 100644
index 000000000..69d4aec34
--- /dev/null
+++ b/modules/seedvr/src/common/diffusion/utils.py
@@ -0,0 +1,84 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Utility functions.
+"""
+
+from typing import Callable
+import torch
+
+
+def expand_dims(tensor: torch.Tensor, ndim: int):
+    """
+    Expand tensor to target ndim. New dims are added to the right.
+    For example, if the tensor shape was (8,), target ndim is 4, return (8, 1, 1, 1).
+    """
+    shape = tensor.shape + (1,) * (ndim - tensor.ndim)
+    return tensor.reshape(shape)
+
+
+def assert_schedule_timesteps_compatible(schedule, timesteps):
+    """
+    Check if schedule and timesteps are compatible.
+    """
+    if schedule.T != timesteps.T:
+        raise ValueError("Schedule and timesteps must have the same T.")
+    if schedule.is_continuous() != timesteps.is_continuous():
+        raise ValueError("Schedule and timesteps must have the same continuity.")
+
+
+def classifier_free_guidance(
+    pos: torch.Tensor,
+    neg: torch.Tensor,
+    scale: float,
+    rescale: float = 0.0,
+):
+    """
+    Apply classifier-free guidance.
+    """
+    # Classifier-free guidance (https://arxiv.org/abs/2207.12598)
+    cfg = neg + scale * (pos - neg)
+
+    # Classifier-free guidance rescale (https://arxiv.org/pdf/2305.08891.pdf)
+    if rescale != 0.0:
+        pos_std = pos.std(dim=list(range(1, pos.ndim)), keepdim=True)
+        cfg_std = cfg.std(dim=list(range(1, cfg.ndim)), keepdim=True)
+        factor = pos_std / cfg_std
+        factor = rescale * factor + (1 - rescale)
+        cfg *= factor
+
+    return cfg
+
+
+def classifier_free_guidance_dispatcher(
+    pos: Callable,
+    neg: Callable,
+    scale: float,
+    rescale: float = 0.0,
+):
+    """
+    Optionally execute models depending on classifer-free guidance scale.
+    """
+    # If scale is 1, no need to execute neg model.
+    if scale == 1.0:
+        return pos()
+
+    # Otherwise, execute both pos nad neg models and apply cfg.
+    return classifier_free_guidance(
+        pos=pos(),
+        neg=neg(),
+        scale=scale,
+        rescale=rescale,
+    )
diff --git a/modules/seedvr/src/common/distributed/__init__.py b/modules/seedvr/src/common/distributed/__init__.py
new file mode 100644
index 000000000..9f53a78eb
--- /dev/null
+++ b/modules/seedvr/src/common/distributed/__init__.py
@@ -0,0 +1,35 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Distributed package.
+"""
+
+from .basic import (
+    barrier_if_distributed,
+    convert_to_ddp,
+    get_device,
+    get_global_rank,
+    get_local_rank,
+    get_world_size,
+)
+
+__all__ = [
+    "barrier_if_distributed",
+    "convert_to_ddp",
+    "get_device",
+    "get_global_rank",
+    "get_local_rank",
+    "get_world_size",
+]
diff --git a/modules/seedvr/src/common/distributed/advanced.py b/modules/seedvr/src/common/distributed/advanced.py
new file mode 100644
index 000000000..d2479bebf
--- /dev/null
+++ b/modules/seedvr/src/common/distributed/advanced.py
@@ -0,0 +1,208 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Advanced distributed functions for sequence parallel.
+"""
+
+from typing import Optional, List
+import torch
+import torch.distributed as dist
+from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
+from torch.distributed.fsdp import ShardingStrategy
+
+from .basic import get_global_rank, get_world_size
+
+
+_DATA_PARALLEL_GROUP = None
+_SEQUENCE_PARALLEL_GROUP = None
+_SEQUENCE_PARALLEL_CPU_GROUP = None
+_MODEL_SHARD_CPU_INTER_GROUP = None
+_MODEL_SHARD_CPU_INTRA_GROUP = None
+_MODEL_SHARD_INTER_GROUP = None
+_MODEL_SHARD_INTRA_GROUP = None
+_SEQUENCE_PARALLEL_GLOBAL_RANKS = None
+
+
+def get_data_parallel_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get data parallel process group.
+    """
+    return _DATA_PARALLEL_GROUP
+
+
+def get_sequence_parallel_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get sequence parallel process group.
+    """
+    return _SEQUENCE_PARALLEL_GROUP
+
+
+def get_sequence_parallel_cpu_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get sequence parallel CPU process group.
+    """
+    return _SEQUENCE_PARALLEL_CPU_GROUP
+
+
+def get_data_parallel_rank() -> int:
+    """
+    Get data parallel rank.
+    """
+    group = get_data_parallel_group()
+    return dist.get_rank(group) if group else get_global_rank()
+
+
+def get_data_parallel_world_size() -> int:
+    """
+    Get data parallel world size.
+    """
+    group = get_data_parallel_group()
+    return dist.get_world_size(group) if group else get_world_size()
+
+
+def get_sequence_parallel_rank() -> int:
+    """
+    Get sequence parallel rank.
+    """
+    group = get_sequence_parallel_group()
+    return dist.get_rank(group) if group else 0
+
+
+def get_sequence_parallel_world_size() -> int:
+    """
+    Get sequence parallel world size.
+    """
+    group = get_sequence_parallel_group()
+    return dist.get_world_size(group) if group else 1
+
+
+def get_model_shard_cpu_intra_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get the CPU intra process group of model sharding.
+    """
+    return _MODEL_SHARD_CPU_INTRA_GROUP
+
+
+def get_model_shard_cpu_inter_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get the CPU inter process group of model sharding.
+    """
+    return _MODEL_SHARD_CPU_INTER_GROUP
+
+
+def get_model_shard_intra_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get the GPU intra process group of model sharding.
+    """
+    return _MODEL_SHARD_INTRA_GROUP
+
+
+def get_model_shard_inter_group() -> Optional[dist.ProcessGroup]:
+    """
+    Get the GPU inter process group of model sharding.
+    """
+    return _MODEL_SHARD_INTER_GROUP
+
+
+def init_sequence_parallel(sequence_parallel_size: int):
+    """
+    Initialize sequence parallel.
+    """
+    global _DATA_PARALLEL_GROUP
+    global _SEQUENCE_PARALLEL_GROUP
+    global _SEQUENCE_PARALLEL_CPU_GROUP
+    global _SEQUENCE_PARALLEL_GLOBAL_RANKS
+    assert dist.is_initialized()
+    world_size = dist.get_world_size()
+    rank = dist.get_rank()
+    data_parallel_size = world_size // sequence_parallel_size
+    for i in range(data_parallel_size):
+        start_rank = i * sequence_parallel_size
+        end_rank = (i + 1) * sequence_parallel_size
+        ranks = range(start_rank, end_rank)
+        group = dist.new_group(ranks)
+        cpu_group = dist.new_group(ranks, backend="gloo")
+        if rank in ranks:
+            _SEQUENCE_PARALLEL_GROUP = group
+            _SEQUENCE_PARALLEL_CPU_GROUP = cpu_group
+            _SEQUENCE_PARALLEL_GLOBAL_RANKS = list(ranks)
+
+
+def init_model_shard_group(
+    *,
+    sharding_strategy: ShardingStrategy,
+    device_mesh: Optional[DeviceMesh] = None,
+):
+    """
+    Initialize process group of model sharding.
+    """
+    global _MODEL_SHARD_INTER_GROUP
+    global _MODEL_SHARD_INTRA_GROUP
+    global _MODEL_SHARD_CPU_INTER_GROUP
+    global _MODEL_SHARD_CPU_INTRA_GROUP
+    assert dist.is_initialized()
+    world_size = dist.get_world_size()
+    if device_mesh is not None:
+        num_shards_per_group = device_mesh.shape[1]
+    elif sharding_strategy == ShardingStrategy.NO_SHARD:
+        num_shards_per_group = 1
+    elif sharding_strategy in [
+        ShardingStrategy.HYBRID_SHARD,
+        ShardingStrategy._HYBRID_SHARD_ZERO2,
+    ]:
+        num_shards_per_group = torch.cuda.device_count()
+    else:
+        num_shards_per_group = world_size
+    num_groups = world_size // num_shards_per_group
+    device_mesh = (num_groups, num_shards_per_group)
+
+    gpu_mesh_2d = init_device_mesh("cuda", device_mesh, mesh_dim_names=("inter", "intra"))
+    cpu_mesh_2d = init_device_mesh("cpu", device_mesh, mesh_dim_names=("inter", "intra"))
+
+    _MODEL_SHARD_INTER_GROUP = gpu_mesh_2d.get_group("inter")
+    _MODEL_SHARD_INTRA_GROUP = gpu_mesh_2d.get_group("intra")
+    _MODEL_SHARD_CPU_INTER_GROUP = cpu_mesh_2d.get_group("inter")
+    _MODEL_SHARD_CPU_INTRA_GROUP = cpu_mesh_2d.get_group("intra")
+
+def get_sequence_parallel_global_ranks() -> List[int]:
+    """
+    Get all global ranks of the sequence parallel process group
+    that the caller rank belongs to.
+    """
+    if _SEQUENCE_PARALLEL_GLOBAL_RANKS is None:
+        return [dist.get_rank()]
+    return _SEQUENCE_PARALLEL_GLOBAL_RANKS
+
+
+def get_next_sequence_parallel_rank() -> int:
+    """
+    Get the next global rank of the sequence parallel process group
+    that the caller rank belongs to.
+    """
+    sp_global_ranks = get_sequence_parallel_global_ranks()
+    sp_rank = get_sequence_parallel_rank()
+    sp_size = get_sequence_parallel_world_size()
+    return sp_global_ranks[(sp_rank + 1) % sp_size]
+
+
+def get_prev_sequence_parallel_rank() -> int:
+    """
+    Get the previous global rank of the sequence parallel process group
+    that the caller rank belongs to.
+    """
+    sp_global_ranks = get_sequence_parallel_global_ranks()
+    sp_rank = get_sequence_parallel_rank()
+    sp_size = get_sequence_parallel_world_size()
+    return sp_global_ranks[(sp_rank + sp_size - 1) % sp_size]
diff --git a/modules/seedvr/src/common/distributed/basic.py b/modules/seedvr/src/common/distributed/basic.py
new file mode 100644
index 000000000..d880009c9
--- /dev/null
+++ b/modules/seedvr/src/common/distributed/basic.py
@@ -0,0 +1,67 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Distributed basic functions.
+"""
+
+import os
+import torch
+
+
+def get_global_rank() -> int:
+    """
+    Get the global rank, the global index of the GPU.
+    """
+    return int(os.environ.get("RANK", "0"))
+
+
+def get_local_rank() -> int:
+    """
+    Get the local rank, the local index of the GPU.
+    """
+    return int(os.environ.get("LOCAL_RANK", "0"))
+
+
+def get_world_size() -> int:
+    """
+    Get the world size, the total amount of GPUs.
+    """
+    return int(os.environ.get("WORLD_SIZE", "1"))
+
+
+def get_device() -> torch.device:
+    """
+    Get current rank device.
+    """
+    return torch.device("cuda", get_local_rank())
+
+
+def barrier_if_distributed(*args, **kwargs):
+    """
+    Synchronizes all processes if under distributed context.
+    """
+    import torch.distributed as dist
+    if dist.is_initialized():
+        return dist.barrier(*args, **kwargs)
+
+
+def convert_to_ddp(module: torch.nn.Module, **kwargs):
+    from torch.nn.parallel import DistributedDataParallel
+    return DistributedDataParallel(
+        module=module,
+        device_ids=[get_local_rank()],
+        output_device=get_local_rank(),
+        **kwargs,
+    )
diff --git a/modules/seedvr/src/common/distributed/meta_init_utils.py b/modules/seedvr/src/common/distributed/meta_init_utils.py
new file mode 100644
index 000000000..794cd0b81
--- /dev/null
+++ b/modules/seedvr/src/common/distributed/meta_init_utils.py
@@ -0,0 +1,41 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import torch
+from rotary_embedding_torch import RotaryEmbedding
+from torch import nn
+from torch.distributed.fsdp._common_utils import _is_fsdp_flattened
+
+__all__ = ["meta_non_persistent_buffer_init_fn"]
+
+
+def meta_non_persistent_buffer_init_fn(module: nn.Module) -> nn.Module:
+    """
+    Used for materializing `non-persistent tensor buffers` while model resuming.
+
+    Since non-persistent tensor buffers are not saved in state_dict,
+    when initializing model with meta device, user should materialize those buffers manually.
+
+    Currently, only `rope.dummy` is this special case.
+    """
+    with torch.no_grad():
+        for submodule in module.modules():
+            if not isinstance(submodule, RotaryEmbedding):
+                continue
+            for buffer_name, buffer in submodule.named_buffers(recurse=False):
+                if buffer.is_meta and "dummy" in buffer_name:
+                    materialized_buffer = torch.zeros_like(buffer, device="cpu")
+                    setattr(submodule, buffer_name, materialized_buffer)
+    assert not any(b.is_meta for n, b in module.named_buffers())
+    return module
diff --git a/modules/seedvr/src/common/distributed/ops.py b/modules/seedvr/src/common/distributed/ops.py
new file mode 100644
index 000000000..7c9256706
--- /dev/null
+++ b/modules/seedvr/src/common/distributed/ops.py
@@ -0,0 +1,494 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Distributed ops for supporting sequence parallel.
+"""
+
+from collections import defaultdict
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import torch
+import torch.distributed as dist
+from torch import Tensor
+
+from ..cache import Cache
+from .advanced import (
+    get_sequence_parallel_group,
+    get_sequence_parallel_rank,
+    get_sequence_parallel_world_size,
+)
+
+from .basic import get_device
+
+_SEQ_DATA_BUF = defaultdict(lambda: [None, None, None])
+_SEQ_DATA_META_SHAPES = defaultdict()
+_SEQ_DATA_META_DTYPES = defaultdict()
+_SEQ_DATA_ASYNC_COMMS = defaultdict(list)
+_SYNC_BUFFER = defaultdict(dict)
+
+
+def single_all_to_all(
+    local_input: Tensor,
+    scatter_dim: int,
+    gather_dim: int,
+    group: dist.ProcessGroup,
+    async_op: bool = False,
+):
+    """
+    A function to do all-to-all on a tensor
+    """
+    seq_world_size = dist.get_world_size(group)
+    prev_scatter_dim = scatter_dim
+    if scatter_dim != 0:
+        local_input = local_input.transpose(0, scatter_dim)
+        if gather_dim == 0:
+            gather_dim = scatter_dim
+        scatter_dim = 0
+
+    inp_shape = list(local_input.shape)
+    inp_shape[scatter_dim] = inp_shape[scatter_dim] // seq_world_size
+    input_t = local_input.reshape(
+        [seq_world_size, inp_shape[scatter_dim]] + inp_shape[scatter_dim + 1 :]
+    ).contiguous()
+    output = torch.empty_like(input_t)
+    comm = dist.all_to_all_single(output, input_t, group=group, async_op=async_op)
+    if async_op:
+        # let user's code transpose & reshape
+        return output, comm, prev_scatter_dim
+
+    # first dim is seq_world_size, so we can split it directly
+    output = torch.cat(output.split(1), dim=gather_dim + 1).squeeze(0)
+    if prev_scatter_dim:
+        output = output.transpose(0, prev_scatter_dim).contiguous()
+    return output
+
+
+def _all_to_all(
+    local_input: Tensor,
+    scatter_dim: int,
+    gather_dim: int,
+    group: dist.ProcessGroup,
+):
+    seq_world_size = dist.get_world_size(group)
+    input_list = [
+        t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)
+    ]
+    output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)]
+    dist.all_to_all(output_list, input_list, group=group)
+    return torch.cat(output_list, dim=gather_dim).contiguous()
+
+
+class SeqAllToAll(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx: Any,
+        group: dist.ProcessGroup,
+        local_input: Tensor,
+        scatter_dim: int,
+        gather_dim: int,
+        async_op: bool,
+    ) -> Tensor:
+        ctx.group = group
+        ctx.scatter_dim = scatter_dim
+        ctx.gather_dim = gather_dim
+        ctx.async_op = async_op
+        if async_op:
+            output, comm, prev_scatter_dim = single_all_to_all(
+                local_input, scatter_dim, gather_dim, group, async_op=async_op
+            )
+            ctx.prev_scatter_dim = prev_scatter_dim
+            return output, comm
+
+        return _all_to_all(local_input, scatter_dim, gather_dim, group)
+
+    @staticmethod
+    def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor, None, None]:
+        if ctx.async_op:
+            input_t = torch.cat(grad_output[0].split(1), dim=ctx.gather_dim + 1).squeeze(0)
+            if ctx.prev_scatter_dim:
+                input_t = input_t.transpose(0, ctx.prev_scatter_dim)
+        else:
+            input_t = grad_output[0]
+        return (
+            None,
+            _all_to_all(input_t, ctx.gather_dim, ctx.scatter_dim, ctx.group),
+            None,
+            None,
+            None,
+        )
+
+
+class Slice(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx: Any, group: dist.ProcessGroup, local_input: Tensor, dim: int) -> Tensor:
+        ctx.group = group
+        ctx.rank = dist.get_rank(group)
+        seq_world_size = dist.get_world_size(group)
+        ctx.seq_world_size = seq_world_size
+        ctx.dim = dim
+        dim_size = local_input.shape[dim]
+        return local_input.split(dim_size // seq_world_size, dim=dim)[ctx.rank].contiguous()
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: Tensor) -> Tuple[None, Tensor, None]:
+        dim_size = list(grad_output.size())
+        split_size = dim_size[0]
+        dim_size[0] = dim_size[0] * ctx.seq_world_size
+        output = torch.empty(dim_size, dtype=grad_output.dtype, device=torch.cuda.current_device())
+        dist._all_gather_base(output, grad_output, group=ctx.group)
+        return (None, torch.cat(output.split(split_size), dim=ctx.dim), None)
+
+
+class Gather(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx: Any,
+        group: dist.ProcessGroup,
+        local_input: Tensor,
+        dim: int,
+        grad_scale: Optional[bool] = False,
+    ) -> Tensor:
+        ctx.group = group
+        ctx.rank = dist.get_rank(group)
+        ctx.dim = dim
+        ctx.grad_scale = grad_scale
+        seq_world_size = dist.get_world_size(group)
+        ctx.seq_world_size = seq_world_size
+        dim_size = list(local_input.size())
+        split_size = dim_size[0]
+        ctx.part_size = dim_size[dim]
+        dim_size[0] = dim_size[0] * seq_world_size
+        output = torch.empty(dim_size, dtype=local_input.dtype, device=torch.cuda.current_device())
+        dist._all_gather_base(output, local_input.contiguous(), group=ctx.group)
+        return torch.cat(output.split(split_size), dim=dim)
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: Tensor) -> Tuple[None, Tensor]:
+        if ctx.grad_scale:
+            grad_output = grad_output * ctx.seq_world_size
+        return (
+            None,
+            grad_output.split(ctx.part_size, dim=ctx.dim)[ctx.rank].contiguous(),
+            None,
+            None,
+        )
+
+
+def gather_seq_scatter_heads_qkv(
+    qkv_tensor: Tensor,
+    *,
+    seq_dim: int,
+    qkv_shape: Optional[Tensor] = None,
+    cache: Cache = Cache(disable=True),
+    restore_shape: bool = True,
+):
+    """
+    A func to sync splited qkv tensor
+    qkv_tensor: the tensor we want to do alltoall with. The last dim must
+        be the projection_idx, which we will split into 3 part. After
+        spliting, the gather idx will be projecttion_idx + 1
+    seq_dim: gather_dim for all2all comm
+    restore_shape: if True, output will has the same shape length as input
+    """
+    group = get_sequence_parallel_group()
+    if not group:
+        return qkv_tensor
+    world = get_sequence_parallel_world_size()
+    orig_shape = qkv_tensor.shape
+    scatter_dim = qkv_tensor.dim()
+    bef_all2all_shape = list(orig_shape)
+    qkv_proj_dim = bef_all2all_shape[-1]
+    bef_all2all_shape = bef_all2all_shape[:-1] + [3, qkv_proj_dim // 3]
+    qkv_tensor = qkv_tensor.view(bef_all2all_shape)
+    qkv_tensor = SeqAllToAll.apply(group, qkv_tensor, scatter_dim, seq_dim, False)
+    if restore_shape:
+        out_shape = list(orig_shape)
+        out_shape[seq_dim] *= world
+        out_shape[-1] = qkv_proj_dim // world
+        qkv_tensor = qkv_tensor.view(out_shape)
+
+    # remove padding
+    if qkv_shape is not None:
+        unpad_dim_size = cache(
+            "unpad_dim_size", lambda: torch.sum(torch.prod(qkv_shape, dim=-1)).item()
+        )
+        if unpad_dim_size % world != 0:
+            padding_size = qkv_tensor.size(seq_dim) - unpad_dim_size
+            qkv_tensor = _unpad_tensor(qkv_tensor, seq_dim, padding_size)
+    return qkv_tensor
+
+
+def slice_inputs(x: Tensor, dim: int, padding: bool = True):
+    """
+    A func to slice the input sequence in sequence parallel
+    """
+    group = get_sequence_parallel_group()
+    if group is None:
+        return x
+    sp_rank = get_sequence_parallel_rank()
+    sp_world = get_sequence_parallel_world_size()
+    dim_size = x.shape[dim]
+    unit = (dim_size + sp_world - 1) // sp_world
+    if padding and dim_size % sp_world:
+        padding_size = sp_world - (dim_size % sp_world)
+        x = _pad_tensor(x, dim, padding_size)
+    slc = [slice(None)] * len(x.shape)
+    slc[dim] = slice(unit * sp_rank, unit * (sp_rank + 1))
+    return x[slc]
+
+
+def remove_seqeunce_parallel_padding(x: Tensor, dim: int, unpad_dim_size: int):
+    """
+    A func to remove the padding part of the tensor based on its original shape
+    """
+    group = get_sequence_parallel_group()
+    if group is None:
+        return x
+    sp_world = get_sequence_parallel_world_size()
+    if unpad_dim_size % sp_world == 0:
+        return x
+    padding_size = sp_world - (unpad_dim_size % sp_world)
+    assert (padding_size + unpad_dim_size) % sp_world == 0
+    return _unpad_tensor(x, dim=dim, padding_size=padding_size)
+
+
+def gather_heads_scatter_seq(x: Tensor, head_dim: int, seq_dim: int) -> Tensor:
+    """
+    A func to sync attention result with alltoall in sequence parallel
+    """
+    group = get_sequence_parallel_group()
+    if not group:
+        return x
+    dim_size = x.size(seq_dim)
+    sp_world = get_sequence_parallel_world_size()
+    if dim_size % sp_world != 0:
+        padding_size = sp_world - (dim_size % sp_world)
+        x = _pad_tensor(x, seq_dim, padding_size)
+    return SeqAllToAll.apply(group, x, seq_dim, head_dim, False)
+
+
+def gather_seq_scatter_heads(x: Tensor, seq_dim: int, head_dim: int) -> Tensor:
+    """
+    A func to sync embedding input with alltoall in sequence parallel
+    """
+    group = get_sequence_parallel_group()
+    if not group:
+        return x
+    return SeqAllToAll.apply(group, x, head_dim, seq_dim, False)
+
+
+def scatter_heads(x: Tensor, dim: int) -> Tensor:
+    """
+    A func to split heads before attention in sequence parallel
+    """
+    group = get_sequence_parallel_group()
+    if not group:
+        return x
+    return Slice.apply(group, x, dim)
+
+
+def gather_heads(x: Tensor, dim: int, grad_scale: Optional[bool] = False) -> Tensor:
+    """
+    A func to gather heads for the attention result in sequence parallel
+    """
+    group = get_sequence_parallel_group()
+    if not group:
+        return x
+    return Gather.apply(group, x, dim, grad_scale)
+
+
+def gather_outputs(
+    x: Tensor,
+    *,
+    gather_dim: int,
+    padding_dim: Optional[int] = None,
+    unpad_shape: Optional[Tensor] = None,
+    cache: Cache = Cache(disable=True),
+    scale_grad=True,
+):
+    """
+    A func to gather the outputs for the model result in sequence parallel
+    """
+    group = get_sequence_parallel_group()
+    if not group:
+        return x
+    x = Gather.apply(group, x, gather_dim, scale_grad)
+    if padding_dim is not None:
+        unpad_dim_size = cache(
+            "unpad_dim_size", lambda: torch.sum(torch.prod(unpad_shape, dim=1)).item()
+        )
+        x = remove_seqeunce_parallel_padding(x, padding_dim, unpad_dim_size)
+    return x
+
+
+def _pad_tensor(x: Tensor, dim: int, padding_size: int):
+    shape = list(x.shape)
+    shape[dim] = padding_size
+    pad = torch.zeros(shape, dtype=x.dtype, device=x.device)
+    return torch.cat([x, pad], dim=dim)
+
+
+def _unpad_tensor(x: Tensor, dim: int, padding_size):
+    slc = [slice(None)] * len(x.shape)
+    slc[dim] = slice(0, -padding_size)
+    return x[slc]
+
+
+def _broadcast_data(data, shape, dtype, src, group, async_op):
+    comms = []
+    if isinstance(data, (list, tuple)):
+        for i, sub_shape in enumerate(shape):
+            comms += _broadcast_data(data[i], sub_shape, dtype[i], src, group, async_op)
+    elif isinstance(data, dict):
+        for key, sub_data in data.items():
+            comms += _broadcast_data(sub_data, shape[key], dtype[key], src, group, async_op)
+    elif isinstance(data, Tensor):
+        comms.append(dist.broadcast(data, src=src, group=group, async_op=async_op))
+    return comms
+
+
+def _traverse(data: Any, op: Callable) -> Union[None, List, Dict, Any]:
+    if isinstance(data, (list, tuple)):
+        return [_traverse(sub_data, op) for sub_data in data]
+    elif isinstance(data, dict):
+        return {key: _traverse(sub_data, op) for key, sub_data in data.items()}
+    elif isinstance(data, Tensor):
+        return op(data)
+    else:
+        return None
+
+
+def _get_shapes(data):
+    return _traverse(data, op=lambda x: x.shape)
+
+
+def _get_dtypes(data):
+    return _traverse(data, op=lambda x: x.dtype)
+
+
+def _construct_broadcast_buffer(shapes, dtypes, device):
+    if isinstance(shapes, torch.Size):
+        return torch.empty(shapes, dtype=dtypes, device=device)
+
+    if isinstance(shapes, (list, tuple)):
+        buffer = []
+        for i, sub_shape in enumerate(shapes):
+            buffer.append(_construct_broadcast_buffer(sub_shape, dtypes[i], device))
+    elif isinstance(shapes, dict):
+        buffer = {}
+        for key, sub_shape in shapes.items():
+            buffer[key] = _construct_broadcast_buffer(sub_shape, dtypes[key], device)
+    else:
+        return None
+    return buffer
+
+
+class SPDistForward:
+    """A forward tool to sync different result across sp group
+
+    Args:
+        module: a function or module to process users input
+        sp_step: current training step to judge which rank to broadcast its result to all
+        name: a distinct str to save meta and async comm
+        comm_shape: if different ranks have different shape, mark this arg to True
+        device: the device for current rank, can be empty
+    """
+
+    def __init__(
+        self,
+        name: str,
+        comm_shape: bool,
+        device: torch.device = None,
+    ):
+        self.name = name
+        self.comm_shape = comm_shape
+        if device:
+            self.device = device
+        else:
+            self.device = get_device()
+
+    def __call__(self, inputs) -> Any:
+        group = get_sequence_parallel_group()
+        if not group:
+            yield inputs
+        else:
+            device = self.device
+            sp_world = get_sequence_parallel_world_size()
+            sp_rank = get_sequence_parallel_rank()
+            for local_step in range(sp_world):
+                src_rank = dist.get_global_rank(group, local_step)
+                is_src = sp_rank == local_step
+                local_shapes = []
+                local_dtypes = []
+                if local_step == 0:
+                    local_result = inputs
+                    _SEQ_DATA_BUF[self.name][-1] = local_result
+                    local_shapes = _get_shapes(local_result)
+                    local_dtypes = _get_dtypes(local_result)
+                    if self.comm_shape:
+                        group_shapes_lists = [None] * sp_world
+                        dist.all_gather_object(group_shapes_lists, local_shapes, group=group)
+                        _SEQ_DATA_META_SHAPES[self.name] = group_shapes_lists
+                    else:
+                        _SEQ_DATA_META_SHAPES[self.name] = [local_shapes] * sp_world
+                    _SEQ_DATA_META_DTYPES[self.name] = local_dtypes
+                shapes = _SEQ_DATA_META_SHAPES[self.name][local_step]
+                dtypes = _SEQ_DATA_META_DTYPES[self.name]
+                buf_id = local_step % 2
+                if local_step == 0:
+                    sync_data = (
+                        local_result
+                        if is_src
+                        else _construct_broadcast_buffer(shapes, dtypes, device)
+                    )
+                    _broadcast_data(sync_data, shapes, dtypes, src_rank, group, False)
+                    _SEQ_DATA_BUF[self.name][buf_id] = sync_data
+
+                # wait for async comm ops
+                if _SEQ_DATA_ASYNC_COMMS[self.name]:
+                    for comm in _SEQ_DATA_ASYNC_COMMS[self.name]:
+                        comm.wait()
+                # before return the sync result, do async broadcast for next batch
+                if local_step < sp_world - 1:
+                    next_buf_id = 1 - buf_id
+                    shapes = _SEQ_DATA_META_SHAPES[self.name][local_step + 1]
+                    src_rank = dist.get_global_rank(group, local_step + 1)
+                    is_src = sp_rank == local_step + 1
+                    next_sync_data = (
+                        _SEQ_DATA_BUF[self.name][-1]
+                        if is_src
+                        else _construct_broadcast_buffer(shapes, dtypes, device)
+                    )
+                    _SEQ_DATA_ASYNC_COMMS[self.name] = _broadcast_data(
+                        next_sync_data, shapes, dtypes, src_rank, group, True
+                    )
+                    _SEQ_DATA_BUF[self.name][next_buf_id] = next_sync_data
+                yield _SEQ_DATA_BUF[self.name][buf_id]
+
+
+sync_inputs = SPDistForward(name="bef_fwd", comm_shape=True)
+
+
+def sync_data(data, sp_idx, name="tmp"):
+    group = get_sequence_parallel_group()
+    if group is None:
+        return data
+    # if sp_idx in _SYNC_BUFFER[name]:
+    #     return _SYNC_BUFFER[name][sp_idx]
+    sp_rank = get_sequence_parallel_rank()
+    src_rank = dist.get_global_rank(group, sp_idx)
+    objects = [data] if sp_rank == sp_idx else [None]
+    dist.broadcast_object_list(objects, src=src_rank, group=group)
+    # _SYNC_BUFFER[name] = {sp_idx: objects[0]}
+    return objects[0]
diff --git a/modules/seedvr/src/common/half_precision_fixes.py b/modules/seedvr/src/common/half_precision_fixes.py
new file mode 100644
index 000000000..285f336c7
--- /dev/null
+++ b/modules/seedvr/src/common/half_precision_fixes.py
@@ -0,0 +1,61 @@
+import torch.nn.functional as F
+
+
+def safe_pad_operation(x, padding, mode='constant', value=0.0):
+    """Safe padding operation that handles Half precision only for problematic modes"""
+    # Modes qui nécessitent le fix Half precision
+    problematic_modes = ['replicate', 'reflect', 'circular']
+
+    if mode in problematic_modes:
+        try:
+            return F.pad(x, padding, mode=mode, value=value)
+        except RuntimeError as e:
+            if "not implemented for 'Half'" in str(e):
+                original_dtype = x.dtype
+                return F.pad(x.float(), padding, mode=mode, value=value).to(original_dtype)
+            else:
+                raise e
+    else:
+        # Pour 'constant' et autres modes compatibles, pas de fix nécessaire
+        return F.pad(x, padding, mode=mode, value=value)
+
+
+def safe_interpolate_operation(x, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None):
+    """Safe interpolate operation that handles Half precision for problematic modes"""
+    # Modes qui peuvent causer des problèmes avec Half precision
+    problematic_modes = ['bilinear', 'bicubic', 'trilinear']
+
+    if mode in problematic_modes:
+        try:
+            return F.interpolate(
+                x,
+                size=size,
+                scale_factor=scale_factor,
+                mode=mode,
+                align_corners=align_corners,
+                recompute_scale_factor=recompute_scale_factor
+            )
+        except RuntimeError as e:
+            if ("not implemented for 'Half'" in str(e) or
+                "compute_indices_weights" in str(e)):
+                original_dtype = x.dtype
+                return F.interpolate(
+                    x.float(),
+                    size=size,
+                    scale_factor=scale_factor,
+                    mode=mode,
+                    align_corners=align_corners,
+                    recompute_scale_factor=recompute_scale_factor
+                ).to(original_dtype)
+            else:
+                raise e
+    else:
+        # Pour 'nearest' et autres modes compatibles, pas de fix nécessaire
+        return F.interpolate(
+            x,
+            size=size,
+            scale_factor=scale_factor,
+            mode=mode,
+            align_corners=align_corners,
+            recompute_scale_factor=recompute_scale_factor
+        )
diff --git a/modules/seedvr/src/common/logger.py b/modules/seedvr/src/common/logger.py
new file mode 100644
index 000000000..ada368e79
--- /dev/null
+++ b/modules/seedvr/src/common/logger.py
@@ -0,0 +1,44 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Logging utility functions.
+"""
+
+import logging
+import sys
+from typing import Optional
+
+from .distributed import get_global_rank, get_local_rank, get_world_size
+
+_default_handler = logging.StreamHandler(sys.stdout)
+_default_handler.setFormatter(
+    logging.Formatter(
+        "%(asctime)s "
+        + (f"[Rank:{get_global_rank()}]" if get_world_size() > 1 else "")
+        + (f"[LocalRank:{get_local_rank()}]" if get_world_size() > 1 else "")
+        + "[%(threadName).12s][%(name)s][%(levelname).5s] "
+        + "%(message)s"
+    )
+)
+
+
+def get_logger(name: Optional[str] = None) -> logging.Logger:
+    """
+    Get a logger.
+    """
+    logger = logging.getLogger(name)
+    logger.addHandler(_default_handler)
+    logger.setLevel(logging.INFO)
+    return logger
diff --git a/modules/seedvr/src/common/partition.py b/modules/seedvr/src/common/partition.py
new file mode 100644
index 000000000..648c87fe2
--- /dev/null
+++ b/modules/seedvr/src/common/partition.py
@@ -0,0 +1,59 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+"""
+Partition utility functions.
+"""
+
+from typing import Any, List
+
+
+def partition_by_size(data: List[Any], size: int) -> List[List[Any]]:
+    """
+    Partition a list by size.
+    When indivisible, the last group contains fewer items than the target size.
+
+    Examples:
+        - data: [1,2,3,4,5]
+        - size: 2
+        - return: [[1,2], [3,4], [5]]
+    """
+    assert size > 0
+    return [data[i : (i + size)] for i in range(0, len(data), size)]
+
+
+def partition_by_groups(data: List[Any], groups: int) -> List[List[Any]]:
+    """
+    Partition a list by groups.
+    When indivisible, some groups may have more items than others.
+
+    Examples:
+        - data: [1,2,3,4,5]
+        - groups: 2
+        - return: [[1,3,5], [2,4]]
+    """
+    assert groups > 0
+    return [data[i::groups] for i in range(groups)]
+
+
+def shift_list(data: List[Any], n: int) -> List[Any]:
+    """
+    Rotate a list by n elements.
+
+    Examples:
+        - data: [1,2,3,4,5]
+        - n: 3
+        - return: [4,5,1,2,3]
+    """
+    return data[(n % len(data)) :] + data[: (n % len(data))]
diff --git a/modules/seedvr/src/common/seed.py b/modules/seedvr/src/common/seed.py
new file mode 100644
index 000000000..f00e33db6
--- /dev/null
+++ b/modules/seedvr/src/common/seed.py
@@ -0,0 +1,30 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import random
+from typing import Optional
+import numpy as np
+import torch
+
+from .distributed import get_global_rank
+
+
+def set_seed(seed: Optional[int], same_across_ranks: bool = False):
+    """Function that sets the seed for pseudo-random number generators."""
+    if seed is not None:
+        seed += get_global_rank() if not same_across_ranks else 0
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+
diff --git a/modules/seedvr/src/core/__init__.py b/modules/seedvr/src/core/__init__.py
new file mode 100644
index 000000000..271ba39b8
--- /dev/null
+++ b/modules/seedvr/src/core/__init__.py
@@ -0,0 +1,47 @@
+"""
+Core Module for SeedVR2
+
+Contains the main business logic and model management functionality:
+- Model configuration and loading
+- Architecture detection and memory estimation
+- Runner creation and management
+- Generation pipeline and logic
+"""
+'''
+from .model_manager import (
+    configure_runner,
+    load_quantized_state_dict,
+    configure_dit_model_inference,
+    configure_vae_model_inference,
+)
+
+from .generation import (
+    generation_step,
+    generation_loop,
+    cut_videos,
+    prepare_video_transforms,
+    load_text_embeddings,
+    calculate_optimal_batch_params
+)
+
+from .infer import VideoDiffusionInfer
+
+__all__ = [
+    # Model management
+    'configure_runner',
+    'load_quantized_state_dict', 
+    'configure_dit_model_inference',
+    'configure_vae_model_inference',
+    
+    # Generation logic
+    'generation_step',
+    'generation_loop', 
+    'cut_videos',
+    'prepare_video_transforms',
+    'load_text_embeddings',
+    'calculate_optimal_batch_params',
+
+    # Infer
+    'VideoDiffusionInfer'
+] 
+'''
diff --git a/modules/seedvr/src/core/generation.py b/modules/seedvr/src/core/generation.py
new file mode 100644
index 000000000..daac4020f
--- /dev/null
+++ b/modules/seedvr/src/core/generation.py
@@ -0,0 +1,365 @@
+import torch
+from torchvision.transforms import Compose, Lambda, Normalize
+
+from src.optimization.performance import optimized_video_rearrange, optimized_single_video_rearrange, optimized_sample_to_image_format
+from src.common.seed import set_seed
+from src.data.image.transforms.divisible_crop import DivisibleCrop
+from src.data.image.transforms.na_resize import NaResize
+from src.utils.color_fix import wavelet_reconstruction
+
+
+
+def generation_step(runner, text_embeds_dict, cond_latents, temporal_overlap, device):
+    """
+    Execute a single generation step with adaptive dtype handling
+    
+    Args:
+        runner: VideoDiffusionInfer instance
+        text_embeds_dict (dict): Text embeddings for positive and negative prompts
+        cond_latents (list): Conditional latents for generation
+        temporal_overlap (int): Number of frames for temporal overlap
+        
+    Returns:
+        tuple: (samples, last_latents) for potential temporal continuation
+        
+    Features:
+        - Adaptive dtype detection (FP8/FP16/BFloat16)
+        - Optimal autocast configuration for each model type
+        - Memory-efficient noise generation and reuse
+        - Automatic device placement with dtype preservation
+        - Advanced inference optimization
+    """
+    # Adaptive dtype detection for optimal performance
+    model_dtype = next(runner.dit.parameters()).dtype
+
+    # Configure dtypes according to model architecture
+    if model_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+        # FP8 native: use BFloat16 for intermediate calculations (optimal compatibility)
+        dtype = torch.bfloat16
+    elif model_dtype == torch.float16:
+        dtype = torch.float16
+    else:
+        dtype = torch.bfloat16
+
+    def _move_to_cuda(x):
+        """Move tensors to CUDA with adaptive optimal dtype"""
+        return [i.to(device, dtype=dtype) for i in x]
+
+    # Memory optimization: Generate noise once and reuse to save VRAM
+    with torch.cuda.device(device):
+        base_noise = torch.randn_like(cond_latents[0], dtype=dtype)
+        noises = [base_noise]
+        aug_noises = [base_noise * 0.1 + torch.randn_like(base_noise) * 0.05]
+
+    # Move tensors with adaptive dtype (optimized for FP8/FP16/BFloat16)
+    noises, aug_noises, cond_latents = _move_to_cuda(noises), _move_to_cuda(aug_noises), _move_to_cuda(cond_latents)
+
+    cond_noise_scale = 0.0
+
+    def _add_noise(x, aug_noise):
+        # Use adaptive optimal dtype
+        t = (
+            torch.tensor([1000.0], device=device, dtype=dtype)
+            * cond_noise_scale
+        )
+        shape = torch.tensor(x.shape[1:], device=device)[None]
+        t = runner.timestep_transform(t, shape)
+        x = runner.schedule.forward(x, aug_noise, t)
+        return x
+
+    # Generate conditions with memory optimization
+    runner.dit.to(device=device)
+    condition = runner.get_condition(
+        noises[0],
+        task="sr",
+        latent_blur=_add_noise(cond_latents[0], aug_noises[0]),
+    )
+    conditions = [condition]
+
+    # Use adaptive autocast for optimal performance
+    with torch.no_grad():
+        video_tensors = runner.inference(
+            noises=noises,
+            conditions=conditions,
+            temporal_overlap=temporal_overlap,
+            **text_embeds_dict,
+        )
+
+    # Process samples with advanced optimization
+    samples = optimized_video_rearrange(video_tensors)
+    noises = noises[0].to("cpu")
+    aug_noises = aug_noises[0].to("cpu")
+    cond_latents = cond_latents[0].to("cpu")
+    conditions = conditions[0].to("cpu")
+    condition = condition.to("cpu")
+
+    return samples #, last_latents
+
+
+def cut_videos(videos):
+    t = videos.size(1)
+
+    if t % 4 == 1:
+        return videos
+
+    padding_needed = (4 - (t % 4)) % 4 + 1
+    last_frame = videos[:, -1:].expand(-1, padding_needed, -1, -1).contiguous()
+    result = torch.cat([videos, last_frame], dim=1)
+    return result
+
+
+def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_size=90, temporal_overlap=0, progress_callback=None, device:str='cpu'):
+    """
+    Main generation loop with context-aware temporal processing
+    
+    Args:
+        runner: VideoDiffusionInfer instance
+        images (torch.Tensor): Input images for upscaling
+        cfg_scale (float): Classifier-free guidance scale
+        seed (int): Random seed for reproducibility
+        res_w (int): Target resolution width
+        batch_size (int): Batch size for processing
+        temporal_overlap (int): Frames for temporal continuity
+        progress_callback (callable): Optional callback for progress reporting
+        
+    Returns:
+        torch.Tensor: Generated video frames
+        
+    Features:
+        - Context-aware generation with temporal overlap
+        - Adaptive dtype pipeline (FP8/FP16/BFloat16)
+        - Memory-optimized batch processing
+        - Advanced video transformation pipeline
+        - Intelligent VRAM management throughout process
+        - Real-time progress reporting
+    """
+
+    model_dtype = None
+    model_dtype = next(runner.dit.parameters()).dtype
+    compute_dtype = model_dtype
+
+    # Configure classifier-free guidance
+    runner.config.diffusion.cfg.scale = cfg_scale
+    runner.config.diffusion.cfg.rescale = 0.0
+    # Configure sampling steps
+    runner.config.diffusion.timesteps.sampling.steps = 1
+    runner.configure_diffusion()
+
+    # Set random seed
+    set_seed(seed)
+
+    # Advanced video transformation pipeline
+    video_transform = Compose([
+        NaResize(
+            resolution=(res_w),
+            mode="side",
+            downsample_only=False,
+        ),
+        Lambda(lambda x: torch.clamp(x, 0.0, 1.0)),
+        DivisibleCrop((16, 16)),
+        Normalize(0.5, 0.5),
+        Lambda(lambda x: x.permute(1, 0, 2, 3)),  # t c h w -> c t h w (faster than Rearrange)
+    ])
+
+    # Initialize generation state
+    batch_samples = []
+
+    # Load text embeddings with adaptive dtype
+    text_embeds = {"texts_pos": [runner.text_pos_embeds], "texts_neg": [runner.text_neg_embeds]}
+
+    # Calculate processing parameters
+    step = batch_size - temporal_overlap
+    if step <= 0:
+        step = batch_size
+        temporal_overlap = 0
+
+    # Calculate total batches for progress reporting
+    total_batches = len(range(0, len(images), step))
+
+    # Main processing loop with context awareness
+    for batch_count, batch_idx in enumerate(range(0, len(images), step)):
+        # Calculate batch indices with overlap
+        if batch_idx == 0:
+            # First batch: no overlap
+            start_idx = 0
+            end_idx = min(batch_size, len(images))
+            effective_batch_size = end_idx - start_idx
+        else:
+            # Subsequent batches: temporal overlap
+            start_idx = batch_idx
+            end_idx = min(start_idx + batch_size, len(images))
+            effective_batch_size = end_idx - start_idx
+            if effective_batch_size <= temporal_overlap:
+                break  # Not enough new frames, stop
+
+        current_frames = end_idx - start_idx
+
+        # Process current batch
+        video = images[start_idx:end_idx]
+        # Use adaptive computation dtype
+        video = video.permute(0, 3, 1, 2).to(device, dtype=compute_dtype)
+
+        # Apply video transformations with memory optimization
+        transformed_video = video_transform(video)
+        del video
+        #video = video.to("cpu")
+        #del video
+        ori_lengths = [transformed_video.size(1)]
+
+        # Handle correct format: frames % 4 == 1
+        t = transformed_video.size(1)
+
+        if len(images) >= 5 and t % 4 != 1:
+            transformed_video = cut_videos(transformed_video)
+
+        # Context-aware temporal strategy
+        # First batch: standard complete diffusion
+        cond_latents = runner.vae_encode([transformed_video])
+
+        # Normal generation
+        samples = generation_step(runner, text_embeds, cond_latents=cond_latents, temporal_overlap=temporal_overlap, device=device)
+        #del cond_latents
+        del cond_latents
+
+        # Post-process samples
+        sample = samples[0]
+        del samples
+        #del samples
+        if ori_lengths[0] < sample.shape[0]:
+            sample = sample[:ori_lengths[0]]
+
+        # Apply color correction if available
+        transformed_video = transformed_video.to(device)
+
+        input_video = [optimized_single_video_rearrange(transformed_video)]
+        del transformed_video
+        sample = wavelet_reconstruction(sample, input_video[0][:sample.size(0)])
+        del input_video
+
+        # Convert to final image format
+        sample = optimized_sample_to_image_format(sample)
+        sample = sample.clip(-1, 1).mul_(0.5).add_(0.5)
+        sample_cpu = sample.to(torch.float16).to("cpu")
+        del sample
+        batch_samples.append(sample_cpu)
+        #del sample
+
+        # Aggressive cleanup after each batch
+        # Progress callback - batch start
+        if progress_callback:
+            progress_callback(batch_count+1, total_batches, current_frames, "Processing batch...")
+
+    runner.vae.to(device="cpu")
+    runner.dit.to(device="cpu")
+    # OPTIMISATION ULTIME : Pré-allocation et copie directe (évite les torch.cat multiples)
+
+    # 1. Calculer la taille totale finale
+    total_frames = sum(batch.shape[0] for batch in batch_samples)
+    if len(batch_samples) > 0:
+        sample_shape = batch_samples[0].shape
+        H, W, C = sample_shape[1], sample_shape[2], sample_shape[3]
+
+        # 2. Pré-allouer le tensor final directement sur CPU (évite concatenations)
+        final_video_images = torch.empty((total_frames, H, W, C), dtype=torch.float16)
+
+        # 3. Copier par blocs directement dans le tensor final
+        block_size = 500
+        current_idx = 0
+
+        for block_start in range(0, len(batch_samples), block_size):
+            block_end = min(block_start + block_size, len(batch_samples))
+
+            # Charger le bloc en VRAM
+            current_block = []
+            for i in range(block_start, block_end):
+                current_block.append(batch_samples[i].to(device))
+
+            # Concatener en VRAM (rapide)
+            block_result = torch.cat(current_block, dim=0)
+
+            # Convertir en Float16 sur GPU
+            #if block_result.dtype != torch.float16:
+            #    block_result = block_result.to(torch.float16)
+
+            # Copier directement dans le tensor final (pas de concatenation!)
+            block_frames = block_result.shape[0]
+            final_video_images[current_idx:current_idx + block_frames] = block_result.to("cpu")
+            current_idx += block_frames
+
+            # Nettoyage immédiat VRAM
+            del current_block, block_result
+    else:
+        print("SeedVR2: No batch_samples to process")
+        final_video_images = torch.empty((0, 0, 0, 0), dtype=torch.float16)
+
+    # Cleanup batch_samples
+    #del batch_samples
+    return final_video_images
+
+
+def prepare_video_transforms(res_w):
+    """
+    Prepare optimized video transformation pipeline
+    
+    Args:
+        res_w (int): Target resolution width
+        
+    Returns:
+        Compose: Configured transformation pipeline
+        
+    Features:
+        - Resolution-aware upscaling (no downsampling)
+        - Proper normalization for model compatibility
+        - Memory-efficient tensor operations
+    """
+    return Compose([
+        NaResize(
+            resolution=(res_w),
+            mode="side",
+            downsample_only=False,  # Model trained for high resolution
+        ),
+        Lambda(lambda x: torch.clamp(x, 0.0, 1.0)),
+        DivisibleCrop((16, 16)),
+        Normalize(0.5, 0.5),
+        Lambda(lambda x: x.permute(1, 0, 2, 3)),  # t c h w -> c t h w
+    ])
+
+
+def calculate_optimal_batch_params(total_frames, batch_size, temporal_overlap):
+    """
+    Calculate optimal batch processing parameters
+    
+    Args:
+        total_frames (int): Total number of frames
+        batch_size (int): Desired batch size
+        temporal_overlap (int): Temporal overlap frames
+        
+    Returns:
+        dict: Optimized parameters and recommendations
+        
+    Features:
+        - 4n+1 constraint optimization
+        - Padding waste calculation
+        - Performance recommendations
+    """
+    step = batch_size - temporal_overlap
+    if step <= 0:
+        step = batch_size
+        temporal_overlap = 0
+
+    # Find optimal batch sizes (4n+1 constraint)
+    optimal_batches = [x for x in [i for i in range(1, 200) if i % 4 == 1] if x <= total_frames]
+    best_batch = max(optimal_batches) if optimal_batches else 1
+
+    # Calculate potential padding waste
+    padding_waste = 0
+    if batch_size not in optimal_batches:
+        padding_waste = sum(((i // 4) + 1) * 4 + 1 - i for i in range(batch_size, total_frames, batch_size))
+
+    return {
+        'step': step,
+        'temporal_overlap': temporal_overlap,
+        'best_batch': best_batch,
+        'padding_waste': padding_waste,
+        'is_optimal': batch_size in optimal_batches
+    }
diff --git a/modules/seedvr/src/core/infer.py b/modules/seedvr/src/core/infer.py
new file mode 100644
index 000000000..fd8a595a2
--- /dev/null
+++ b/modules/seedvr/src/core/infer.py
@@ -0,0 +1,357 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import List, Optional, Tuple, Union
+import torch
+from einops import rearrange
+from omegaconf import DictConfig, ListConfig
+from torch import Tensor
+from src.common.diffusion import (
+    classifier_free_guidance_dispatcher,
+    create_sampler_from_config,
+    create_sampling_timesteps_from_config,
+    create_schedule_from_config,
+)
+from src.common.distributed import (
+    get_device,
+)
+
+# from common.fs import download
+
+from src.models.dit_v2 import na
+
+
+def optimized_channels_to_last(tensor):
+    """🚀 Optimized replacement for rearrange(tensor, 'b c ... -> b ... c')
+    Moves channels from position 1 to last position using PyTorch native operations.
+    """
+    if tensor.ndim == 3:  # [batch, channels, spatial]
+        return tensor.permute(0, 2, 1)
+    elif tensor.ndim == 4:  # [batch, channels, height, width]
+        return tensor.permute(0, 2, 3, 1)
+    elif tensor.ndim == 5:  # [batch, channels, depth, height, width]
+        return tensor.permute(0, 2, 3, 4, 1)
+    else:
+        # Fallback for other dimensions - move channel (dim=1) to last
+        dims = list(range(tensor.ndim))
+        dims = [dims[0]] + dims[2:] + [dims[1]]  # [0, 2, 3, ..., 1]
+        return tensor.permute(*dims)
+
+def optimized_channels_to_second(tensor):
+    """🚀 Optimized replacement for rearrange(tensor, 'b ... c -> b c ...')
+    Moves channels from last position to position 1 using PyTorch native operations.
+    """
+    if tensor.ndim == 3:  # [batch, spatial, channels]
+        return tensor.permute(0, 2, 1)
+    elif tensor.ndim == 4:  # [batch, height, width, channels]
+        return tensor.permute(0, 3, 1, 2)
+    elif tensor.ndim == 5:  # [batch, depth, height, width, channels]
+        return tensor.permute(0, 4, 1, 2, 3)
+    else:
+        # Fallback for other dimensions - move last dim to position 1
+        dims = list(range(tensor.ndim))
+        dims = [dims[0], dims[-1]] + dims[1:-1]  # [0, -1, 1, 2, ..., -2]
+        return tensor.permute(*dims)
+
+
+class VideoDiffusionInfer():
+    def __init__(self, config: DictConfig, device: str, dtype: torch.dtype):
+        self.config = config
+        self.device = device
+        self.dtype = dtype
+        self.vae = None
+        self.dit = None
+        self.sampler = None
+        self.schedule = None
+    def get_condition(self, latent: Tensor, latent_blur: Tensor, task: str) -> Tensor:
+        t, h, w, c = latent.shape
+        cond = torch.zeros([t, h, w, c + 1], device=latent.device, dtype=latent.dtype)
+        if task == "t2v" or t == 1:
+            # t2i or t2v generation.
+            if task == "sr":
+                cond[:, ..., :-1] = latent_blur[:]
+                cond[:, ..., -1:] = 1.0
+            return cond
+        if task == "i2v":
+            # i2v generation.
+            cond[:1, ..., :-1] = latent[:1]
+            cond[:1, ..., -1:] = 1.0
+            return cond
+        if task == "v2v":
+            # v2v frame extension.
+            cond[:2, ..., :-1] = latent[:2]
+            cond[:2, ..., -1:] = 1.0
+            return cond
+        if task == "sr":
+            # sr generation.
+            cond[:, ..., :-1] = latent_blur[:]
+            cond[:, ..., -1:] = 1.0
+            return cond
+        raise NotImplementedError
+
+    def configure_diffusion(self):
+        self.schedule = create_schedule_from_config(
+            config=self.config.diffusion.schedule,
+        )
+        self.sampling_timesteps = create_sampling_timesteps_from_config( # pylint: disable=attribute-defined-outside-init
+            config=self.config.diffusion.timesteps.sampling,
+            schedule=self.schedule,
+            device=self.device,
+        )
+        self.sampler = create_sampler_from_config(
+            config=self.config.diffusion.sampler,
+            schedule=self.schedule,
+            timesteps=self.sampling_timesteps,
+        )
+
+    # -------------------------------- Helper ------------------------------- #
+
+    @torch.no_grad()
+    def vae_encode(self, samples: List[Tensor]) -> List[Tensor]:
+        self.dit.to(device="cpu")
+        self.vae.to(device=self.device)
+
+        use_sample = self.config.vae.get("use_sample", True)
+        latents = []
+        if len(samples) > 0:
+            device = get_device()
+            dtype = self.vae.dtype
+            scale = self.config.vae.scaling_factor
+            shift = self.config.vae.get("shifting_factor", 0.0)
+
+            if isinstance(scale, ListConfig):
+                scale = torch.tensor(scale, device=device, dtype=dtype)
+            if isinstance(shift, ListConfig):
+                shift = torch.tensor(shift, device=device, dtype=dtype)
+
+            # Group samples of the same shape to batches if enabled.
+            if self.config.vae.grouping:
+                batches, indices = na.pack(samples)
+            else:
+                batches = [sample.unsqueeze(0) for sample in samples]
+
+            # Vae process by each group.
+            for sample in batches:
+                sample = sample.to(device, dtype)
+                if hasattr(self.vae, "preprocess"):
+                    sample = self.vae.preprocess(sample)
+                if use_sample:
+                    latent = self.vae.encode(sample).latent
+                else:
+                    # Deterministic vae encode, only used for i2v inference (optionally)
+                    latent = self.vae.encode(sample).posterior.mode().squeeze(2)
+                latent = latent.unsqueeze(2) if latent.ndim == 4 else latent
+                latent = rearrange(latent, "b c ... -> b ... c")
+                #latent = optimized_channels_to_last(latent)
+                latent = (latent - shift) * scale
+                latents.append(latent)
+
+            # Ungroup back to individual latent with the original order.
+            if self.config.vae.grouping:
+                latents = na.unpack(latents, indices)
+            else:
+                latents = [latent.squeeze(0) for latent in latents]
+
+        self.vae.to(device="cpu")
+        return latents
+
+
+    @torch.no_grad()
+    def vae_decode(self, latents: List[Tensor], target_dtype: torch.dtype = None) -> List[Tensor]:
+        """🚀 VAE decode optimisé - décodage direct sans chunking, compatible avec autocast externe"""
+        self.dit.to(device="cpu")
+        self.vae.to(device=self.device)
+        samples = []
+        if len(latents) > 0:
+            device = get_device()
+            dtype = self.vae.dtype
+            scale = self.config.vae.scaling_factor
+            shift = self.config.vae.get("shifting_factor", 0.0)
+
+            if isinstance(scale, ListConfig):
+                scale = torch.tensor(scale, device=device, dtype=dtype)
+            if isinstance(shift, ListConfig):
+                shift = torch.tensor(shift, device=device, dtype=dtype)
+
+
+            # 🚀 OPTIMISATION 1: Group latents intelligemment pour batch processing
+            if self.config.vae.grouping:
+                latents, indices = na.pack(latents)
+            else:
+                latents = [latent.unsqueeze(0) for latent in latents]
+
+            # 🚀 OPTIMISATION 2: Traitement batch optimisé avec dtype adaptatif
+            for _i, latent in enumerate(latents):
+                # Préparation optimisée du latent
+                # Utiliser target_dtype si fourni (évite double autocast)
+                effective_dtype = target_dtype if target_dtype is not None else dtype
+                latent = latent.to(device, effective_dtype, non_blocking=True)
+                latent = latent / scale + shift
+                latent = rearrange(latent, "b ... c -> b c ...")
+                #latent = optimized_channels_to_second(latent)
+                latent = latent.squeeze(2)
+
+                # 🚀 OPTIMISATION 3: Décodage direct SANS autocast (utilise l'autocast externe)
+                sample = self.vae.decode(latent).sample
+                #sample = self.vae.decode(latent).sample
+                #sample = self.vae.decode(latent).sample
+
+                # 🚀 OPTIMISATION 4: Post-processing conditionnel
+                if hasattr(self.vae, "postprocess"):
+                    sample = self.vae.postprocess(sample)
+
+                samples.append(sample)
+
+            # Ungroup back to individual sample with the original order.
+            if self.config.vae.grouping:
+                samples = na.unpack(samples, indices)
+            else:
+                samples = [sample.squeeze(0) for sample in samples]
+        self.vae.to(device="cpu")
+        return samples
+
+    def timestep_transform(self, timesteps: Tensor, latents_shapes: Tensor):
+        # Skip if not needed.
+        if not self.config.diffusion.timesteps.get("transform", False):
+            return timesteps
+
+        # Compute resolution.
+        vt = self.config.vae.model.get("temporal_downsample_factor", 4)
+        vs = self.config.vae.model.get("spatial_downsample_factor", 8)
+        frames = (latents_shapes[:, 0] - 1) * vt + 1
+        heights = latents_shapes[:, 1] * vs
+        widths = latents_shapes[:, 2] * vs
+
+        # Compute shift factor.
+        def get_lin_function(x1, y1, x2, y2):
+            m = (y2 - y1) / (x2 - x1)
+            b = y1 - m * x1
+            return lambda x: m * x + b
+
+        img_shift_fn = get_lin_function(x1=256 * 256, y1=1.0, x2=1024 * 1024, y2=3.2)
+        vid_shift_fn = get_lin_function(x1=256 * 256 * 37, y1=1.0, x2=1280 * 720 * 145, y2=5.0)
+        shift = torch.where(
+            frames > 1,
+            vid_shift_fn(heights * widths * frames),
+            img_shift_fn(heights * widths),
+        )
+
+        # Shift timesteps.
+        timesteps = timesteps / self.schedule.T
+        timesteps = shift * timesteps / (1 + (shift - 1) * timesteps)
+        timesteps = timesteps * self.schedule.T
+        return timesteps
+
+    @torch.no_grad()
+    def inference(
+        self,
+        noises: List[Tensor],
+        conditions: List[Tensor],
+        texts_pos: Union[List[str], List[Tensor], List[Tuple[Tensor]]],
+        texts_neg: Union[List[str], List[Tensor], List[Tuple[Tensor]]],
+        cfg_scale: Optional[float] = None,
+        temporal_overlap: int = 0, # pylint: disable=unused-argument
+    ) -> List[Tensor]:
+        assert len(noises) == len(conditions) == len(texts_pos) == len(texts_neg)
+        batch_size = len(noises)
+
+        # Return if empty.
+        if batch_size == 0:
+            return []
+
+        # Set cfg scale
+        if cfg_scale is None:
+            cfg_scale = self.config.diffusion.cfg.scale
+
+        # 🚀 OPTIMISATION: Détecter le dtype du modèle pour performance optimale
+        model_dtype = next(self.dit.parameters()).dtype
+        # Adapter les dtypes selon le modèle
+        if model_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+            target_dtype = torch.float16
+        elif model_dtype == torch.float16:
+            target_dtype = torch.float16
+        else:
+            target_dtype = torch.bfloat16
+        # Text embeddings.
+        assert type(texts_pos[0]) is type(texts_neg[0])
+        if isinstance(texts_pos[0], str):
+            text_pos_embeds, text_pos_shapes = self.text_encode(texts_pos) # pylint: disable=no-member
+            text_neg_embeds, text_neg_shapes = self.text_encode(texts_neg) # pylint: disable=no-member
+        elif isinstance(texts_pos[0], tuple):
+            text_pos_embeds, text_pos_shapes = [], []
+            text_neg_embeds, text_neg_shapes = [], []
+            for pos in zip(*texts_pos):
+                emb, shape = na.flatten(pos)
+                text_pos_embeds.append(emb)
+                text_pos_shapes.append(shape)
+            for neg in zip(*texts_neg):
+                emb, shape = na.flatten(neg)
+                text_neg_embeds.append(emb)
+                text_neg_shapes.append(shape)
+        else:
+            text_pos_embeds, text_pos_shapes = na.flatten(texts_pos)
+            text_neg_embeds, text_neg_shapes = na.flatten(texts_neg)
+
+        # Adapter les embeddings texte au dtype cible (compatible avec FP8)
+        if isinstance(text_pos_embeds, torch.Tensor):
+            text_pos_embeds = text_pos_embeds.to(target_dtype)
+        if isinstance(text_neg_embeds, torch.Tensor):
+            text_neg_embeds = text_neg_embeds.to(target_dtype)
+
+        # Flatten.
+        latents, latents_shapes = na.flatten(noises)
+        latents_cond, _ = na.flatten(conditions)
+
+        # Adapter les latents au dtype cible (compatible avec FP8)
+        latents = latents.to(target_dtype) if latents.dtype != target_dtype else latents
+        latents_cond = latents_cond.to(target_dtype) if latents_cond.dtype != target_dtype else latents_cond
+
+        latents = self.sampler.sample(
+            x=latents,
+            f=lambda args: classifier_free_guidance_dispatcher(
+                pos=lambda: self.dit(
+                    vid=torch.cat([args.x_t, latents_cond], dim=-1),
+                    txt=text_pos_embeds,
+                    vid_shape=latents_shapes,
+                    txt_shape=text_pos_shapes,
+                    timestep=args.t.repeat(batch_size),
+                ).vid_sample,
+                neg=lambda: self.dit(
+                    vid=torch.cat([args.x_t, latents_cond], dim=-1),
+                    txt=text_neg_embeds,
+                    vid_shape=latents_shapes,
+                    txt_shape=text_neg_shapes,
+                    timestep=args.t.repeat(batch_size),
+                ).vid_sample,
+                scale=(
+                    cfg_scale
+                    if (args.i + 1) / len(self.sampler.timesteps)
+                    <= self.config.diffusion.cfg.get("partial", 1)
+                    else 1.0
+                ),
+                rescale=self.config.diffusion.cfg.rescale,
+            ),
+        )
+
+        latents = na.unflatten(latents, latents_shapes)
+
+        # 🎯 Pré-calcul des dtypes (une seule fois)
+        vae_dtype = self.vae.dtype
+        decode_dtype = torch.float16 if (vae_dtype == torch.float16 or target_dtype == torch.float16) else vae_dtype
+        samples = self.vae_decode(latents, target_dtype=decode_dtype)
+
+        if samples and len(samples) > 0 and samples[0].dtype != torch.float16:
+            samples = [sample.to(torch.float16, non_blocking=True) for sample in samples]
+
+        return samples
diff --git a/modules/seedvr/src/core/model_manager.py b/modules/seedvr/src/core/model_manager.py
new file mode 100644
index 000000000..043e5ac61
--- /dev/null
+++ b/modules/seedvr/src/core/model_manager.py
@@ -0,0 +1,61 @@
+import os
+import torch
+from omegaconf import OmegaConf
+from safetensors.torch import load_file as load_safetensors_file
+from huggingface_hub import hf_hub_download
+
+from src.optimization.memory_manager import preinitialize_rope_cache
+from src.common.config import load_config, create_object
+from src.core.infer import VideoDiffusionInfer
+
+
+def configure_runner(model_name, cache_dir, device:str='cpu', dtype:torch.dtype=None):
+    repo_id = "vladmandic/SeedVR2"
+    script_directory = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    config_path = os.path.join(script_directory, './config_7b.yaml') if "7b" in model_name else os.path.join(script_directory, './config_3b.yaml')
+    config = load_config(config_path)
+
+    vae_config_path = os.path.join(script_directory, 'src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml')
+    vae_config = OmegaConf.load(vae_config_path)
+    vae_config.spatial_downsample_factor = vae_config.get('spatial_downsample_factor', 8)
+    vae_config.temporal_downsample_factor = vae_config.get('temporal_downsample_factor', 4)
+    config.vae.model = OmegaConf.merge(config.vae.model, vae_config)
+
+    runner = VideoDiffusionInfer(config, device=device, dtype=dtype)
+    OmegaConf.set_readonly(runner.config, False)
+
+    # load dit
+    with torch.device("meta"):
+        runner.dit = create_object(config.dit.model)
+        runner.dit.eval().to(dtype)
+        runner.dit.to_empty(device="cpu")
+
+    model_file = hf_hub_download(repo_id=repo_id, filename=model_name, cache_dir=cache_dir)
+    state_dict = load_safetensors_file(model_file)
+    runner.dit.load_state_dict(state_dict, assign=True)
+    del state_dict
+    runner.dit = runner.dit.to(device=device, dtype=dtype)
+
+    # load vae
+    vae_file = hf_hub_download(repo_id=repo_id, filename=config.vae.checkpoint, cache_dir=cache_dir)
+    with torch.device("meta"):
+        runner.vae = create_object(config.vae.model)
+        runner.vae.requires_grad_(False).eval()
+        runner.vae.to_empty(device="cpu")
+
+    state_dict = load_safetensors_file(vae_file)
+    runner.vae.load_state_dict(state_dict)
+    del state_dict
+    runner.vae = runner.vae.to(device=device, dtype=dtype)
+    runner.config.vae.dtype = str(dtype)
+    runner.vae.set_causal_slicing(**config.vae.slicing)
+    runner.vae.set_memory_limit(**runner.config.vae.memory_limit)
+
+    # load embeds
+    pos_embeds_file = hf_hub_download(repo_id=repo_id, filename='pos_emb.pt', cache_dir=cache_dir)
+    neg_embeds_file = hf_hub_download(repo_id=repo_id, filename='neg_emb.pt', cache_dir=cache_dir)
+    runner.text_pos_embeds = torch.load(pos_embeds_file).to(device=device, dtype=dtype)
+    runner.text_neg_embeds = torch.load(neg_embeds_file).to(device=device, dtype=dtype)
+
+    preinitialize_rope_cache(runner)
+    return runner
diff --git a/modules/seedvr/src/data/image/transforms/area_resize.py b/modules/seedvr/src/data/image/transforms/area_resize.py
new file mode 100644
index 000000000..dd3ca9d4b
--- /dev/null
+++ b/modules/seedvr/src/data/image/transforms/area_resize.py
@@ -0,0 +1,131 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import math
+import random
+from typing import Union
+import torch
+from PIL import Image
+from torchvision.transforms import functional as TVF
+from torchvision.transforms.functional import InterpolationMode
+
+
+class AreaResize:
+    def __init__(
+        self,
+        max_area: float,
+        downsample_only: bool = False,
+        interpolation: InterpolationMode = InterpolationMode.BICUBIC,
+    ):
+        self.max_area = max_area
+        self.downsample_only = downsample_only
+        self.interpolation = interpolation
+
+    def __call__(self, image: Union[torch.Tensor, Image.Image]):
+
+        if isinstance(image, torch.Tensor):
+            height, width = image.shape[-2:]
+        elif isinstance(image, Image.Image):
+            width, height = image.size
+        else:
+            raise NotImplementedError
+
+        scale = math.sqrt(self.max_area / (height * width))
+
+        # keep original height and width for small pictures.
+        scale = 1 if scale >= 1 and self.downsample_only else scale
+
+        resized_height, resized_width = round(height * scale), round(width * scale)
+
+        return TVF.resize(
+            image,
+            size=(resized_height, resized_width),
+            interpolation=self.interpolation,
+        )
+
+
+class AreaRandomCrop:
+    def __init__(
+        self,
+        max_area: float,
+    ):
+        self.max_area = max_area
+
+    def get_params(self, input_size, output_size):
+        """Get parameters for ``crop`` for a random crop.
+
+        Args:
+            img (PIL Image): Image to be cropped.
+            output_size (tuple): Expected output size of the crop.
+
+        Returns:
+            tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
+        """
+        # w, h = _get_image_size(img)
+        h, w = input_size
+        th, tw = output_size
+        if w <= tw and h <= th:
+            return 0, 0, h, w
+
+        i = random.randint(0, h - th)
+        j = random.randint(0, w - tw)
+        return i, j, th, tw
+
+    def __call__(self, image: Union[torch.Tensor, Image.Image]):
+        if isinstance(image, torch.Tensor):
+            height, width = image.shape[-2:]
+        elif isinstance(image, Image.Image):
+            width, height = image.size
+        else:
+            raise NotImplementedError
+
+        resized_height = math.sqrt(self.max_area / (width / height))
+        resized_width = (width / height) * resized_height
+
+        resized_height, resized_width = round(resized_height), round(resized_width)
+        i, j, h, w = self.get_params((height, width), (resized_height, resized_width))
+        image = TVF.crop(image, i, j, h, w)
+        return image
+
+class ScaleResize:
+    def __init__(
+        self,
+        scale: float,
+    ):
+        self.scale = scale
+
+    def __call__(self, image: Union[torch.Tensor, Image.Image]):
+        if isinstance(image, torch.Tensor):
+            height, width = image.shape[-2:]
+            interpolation_mode = InterpolationMode.BILINEAR
+            antialias = True if image.ndim == 4 else "warn"
+        elif isinstance(image, Image.Image):
+            width, height = image.size
+            interpolation_mode = InterpolationMode.LANCZOS
+            antialias = "warn"
+        else:
+            raise NotImplementedError
+
+        scale = self.scale
+
+        # keep original height and width for small pictures
+
+        resized_height, resized_width = round(height * scale), round(width * scale)
+        image = TVF.resize(
+            image,
+            size=(resized_height, resized_width),
+            interpolation=interpolation_mode,
+            antialias=antialias,
+        )
+        return image
diff --git a/modules/seedvr/src/data/image/transforms/divisible_crop.py b/modules/seedvr/src/data/image/transforms/divisible_crop.py
new file mode 100644
index 000000000..d1815b03e
--- /dev/null
+++ b/modules/seedvr/src/data/image/transforms/divisible_crop.py
@@ -0,0 +1,40 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Union
+import torch
+from PIL import Image
+from torchvision.transforms import functional as TVF
+
+
+class DivisibleCrop:
+    def __init__(self, factor):
+        if not isinstance(factor, tuple):
+            factor = (factor, factor)
+
+        self.height_factor, self.width_factor = factor[0], factor[1]
+
+    def __call__(self, image: Union[torch.Tensor, Image.Image]):
+        if isinstance(image, torch.Tensor):
+            height, width = image.shape[-2:]
+        elif isinstance(image, Image.Image):
+            width, height = image.size
+        else:
+            raise NotImplementedError
+
+        cropped_height = height - (height % self.height_factor)
+        cropped_width = width - (width % self.width_factor)
+
+        image = TVF.center_crop(img=image, output_size=(cropped_height, cropped_width))
+        return image
diff --git a/modules/seedvr/src/data/image/transforms/na_resize.py b/modules/seedvr/src/data/image/transforms/na_resize.py
new file mode 100644
index 000000000..d230e25e3
--- /dev/null
+++ b/modules/seedvr/src/data/image/transforms/na_resize.py
@@ -0,0 +1,50 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Literal
+from torchvision.transforms import CenterCrop, Compose, InterpolationMode, Resize
+
+from .area_resize import AreaResize
+from .side_resize import SideResize
+
+
+def NaResize(
+    resolution: int,
+    mode: Literal["area", "side"],
+    downsample_only: bool,
+    interpolation: InterpolationMode = InterpolationMode.BICUBIC,
+):
+    if mode == "area":
+        return AreaResize(
+            max_area=resolution**2,
+            downsample_only=downsample_only,
+            interpolation=interpolation,
+        )
+    if mode == "side":
+        return SideResize(
+            size=resolution,
+            downsample_only=downsample_only,
+            interpolation=interpolation,
+        )
+    if mode == "square":
+        return Compose(
+            [
+                Resize(
+                    size=resolution,
+                    interpolation=interpolation,
+                ),
+                CenterCrop(resolution),
+            ]
+        )
+    raise ValueError(f"Unknown resize mode: {mode}")
diff --git a/modules/seedvr/src/data/image/transforms/side_resize.py b/modules/seedvr/src/data/image/transforms/side_resize.py
new file mode 100644
index 000000000..6e07402b2
--- /dev/null
+++ b/modules/seedvr/src/data/image/transforms/side_resize.py
@@ -0,0 +1,54 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Union
+import torch
+from PIL import Image
+from torchvision.transforms import InterpolationMode
+from torchvision.transforms import functional as TVF
+
+
+class SideResize:
+    def __init__(
+        self,
+        size: int,
+        downsample_only: bool = False,
+        interpolation: InterpolationMode = InterpolationMode.BICUBIC,
+    ):
+        self.size = size
+        self.downsample_only = downsample_only
+        self.interpolation = interpolation
+
+    def __call__(self, image: Union[torch.Tensor, Image.Image]):
+        """
+        Args:
+            image (PIL Image or Tensor): Image to be scaled.
+
+        Returns:
+            PIL Image or Tensor: Rescaled image.
+        """
+        if isinstance(image, torch.Tensor):
+            height, width = image.shape[-2:]
+        elif isinstance(image, Image.Image):
+            width, height = image.size
+        else:
+            raise NotImplementedError
+
+        if self.downsample_only and min(width, height) < self.size:
+            # keep original height and width for small pictures.
+            size = min(width, height)
+        else:
+            size = self.size
+
+        return TVF.resize(image, size, self.interpolation)
diff --git a/modules/seedvr/src/models/dit/attention.py b/modules/seedvr/src/models/dit/attention.py
new file mode 100644
index 000000000..b0dda518a
--- /dev/null
+++ b/modules/seedvr/src/models/dit/attention.py
@@ -0,0 +1,94 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import torch
+import torch.nn.functional as F
+
+#from flash_attn import flash_attn_varlen_func
+
+from torch import nn
+
+
+def pytorch_varlen_attention(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p=0.0, softmax_scale=None, causal=False, deterministic=False):
+    """
+    A PyTorch-based implementation of variable-length attention to replace flash_attn_varlen_func.
+    It processes each sequence in the batch individually.
+    """
+    # Create an empty tensor to store the output.
+    output = torch.empty_like(q)
+
+    # Iterate over each sequence in the batch. The batch size is the number of sequences.
+    for i in range(len(cu_seqlens_q) - 1):
+        # Determine the start and end indices for the current sequence.
+        start_q, end_q = cu_seqlens_q[i], cu_seqlens_q[i+1]
+        start_k, end_k = cu_seqlens_k[i], cu_seqlens_k[i+1]
+
+        # Slice the q, k, and v tensors to get the data for the current sequence.
+        # The shape is (seq_len, heads, head_dim).
+        q_i = q[start_q:end_q]
+        k_i = k[start_k:end_k]
+        v_i = v[start_k:end_k]
+
+        # Reshape for torch's scaled_dot_product_attention which expects (batch, heads, seq, dim).
+        # Here, we treat each sequence as a batch of 1.
+        q_i = q_i.permute(1, 0, 2).unsqueeze(0) # (1, heads, seq_len_q, head_dim)
+        k_i = k_i.permute(1, 0, 2).unsqueeze(0) # (1, heads, seq_len_k, head_dim)
+        v_i = v_i.permute(1, 0, 2).unsqueeze(0) # (1, heads, seq_len_k, head_dim)
+
+        # Use PyTorch's built-in scaled dot-product attention.
+        output_i = F.scaled_dot_product_attention(
+            q_i, k_i, v_i,
+            dropout_p=dropout_p if not deterministic else 0.0,
+            is_causal=causal
+        )
+
+        # Reshape the output back to the original format (seq_len, heads, head_dim)
+        output_i = output_i.squeeze(0).permute(1, 0, 2)
+
+        # Place the result for the current sequence into the main output tensor.
+        output[start_q:end_q] = output_i
+
+    return output
+
+
+class TorchAttention(nn.Module):
+    def tflops(self, args, kwargs, output) -> float:
+        assert len(args) == 0 or len(args) > 2, "query, key should both provided by args / kwargs"
+        q = kwargs.get("query") or args[0]
+        k = kwargs.get("key") or args[1]
+        b, h, sq, d = q.shape
+        b, h, sk, d = k.shape
+        return b * h * (4 * d * (sq / 1e6) * (sk / 1e6))
+
+    def forward(self, *args, **kwargs):
+        #return pytorch_varlen_attention(*args, **kwargs)
+        return F.scaled_dot_product_attention(*args, **kwargs)
+
+
+class FlashAttentionVarlen(nn.Module):
+    def tflops(self, args, kwargs, output) -> float:
+        cu_seqlens_q = kwargs["cu_seqlens_q"]
+        cu_seqlens_k = kwargs["cu_seqlens_k"]
+        _, h, d = output.shape
+        seqlens_q = (cu_seqlens_q[1:] - cu_seqlens_q[:-1]) / 1e6
+        seqlens_k = (cu_seqlens_k[1:] - cu_seqlens_k[:-1]) / 1e6
+        return h * (4 * d * (seqlens_q * seqlens_k).sum())
+
+    def forward(self, *args, **kwargs):
+        kwargs["deterministic"] = torch.are_deterministic_algorithms_enabled()
+        try:
+            from flash_attn import flash_attn_varlen_func
+            return flash_attn_varlen_func(*args, **kwargs)
+        except ImportError:
+            return pytorch_varlen_attention(*args, **kwargs)
diff --git a/modules/seedvr/src/models/dit/blocks/__init__.py b/modules/seedvr/src/models/dit/blocks/__init__.py
new file mode 100644
index 000000000..3195b400a
--- /dev/null
+++ b/modules/seedvr/src/models/dit/blocks/__init__.py
@@ -0,0 +1,25 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from .mmdit_window_block import MMWindowTransformerBlock
+
+dit_blocks = {
+    "mmdit_window": MMWindowTransformerBlock,
+}
+
+
+def get_block(block_type: str):
+    if block_type in dit_blocks:
+        return dit_blocks[block_type]
+    raise NotImplementedError(f"{block_type} is not supported")
diff --git a/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py b/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py
new file mode 100644
index 000000000..ee9ae9ae8
--- /dev/null
+++ b/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py
@@ -0,0 +1,233 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Tuple, Union
+import torch
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.modules.utils import _triple
+from ....common.half_precision_fixes import safe_pad_operation
+from ....common.distributed.ops import (
+    gather_heads,
+    gather_heads_scatter_seq,
+    gather_seq_scatter_heads_qkv,
+    scatter_heads,
+)
+
+from ..attention import TorchAttention
+from ..mlp import get_mlp
+from ..mm import MMArg, MMModule
+from ..modulation import ada_layer_type
+from ..normalization import norm_layer_type
+from ..rope import RotaryEmbedding3d
+
+
+class MMWindowAttention(nn.Module):
+    def __init__(
+        self,
+        vid_dim: int,
+        txt_dim: int,
+        heads: int,
+        head_dim: int,
+        qk_bias: bool,
+        qk_rope: bool,
+        qk_norm: norm_layer_type,
+        qk_norm_eps: float,
+        window: Union[int, Tuple[int, int, int]],
+        window_method: str,
+        shared_qkv: bool,
+    ):
+        super().__init__()
+        dim = MMArg(vid_dim, txt_dim)
+        inner_dim = heads * head_dim
+        qkv_dim = inner_dim * 3
+
+        self.window = _triple(window)
+        self.window_method = window_method
+        assert all(map(lambda v: isinstance(v, int) and v >= 0, self.window))
+
+        self.head_dim = head_dim
+        self.proj_qkv = MMModule(nn.Linear, dim, qkv_dim, bias=qk_bias, shared_weights=shared_qkv)
+        self.proj_out = MMModule(nn.Linear, inner_dim, dim, shared_weights=shared_qkv)
+        self.norm_q = MMModule(qk_norm, dim=head_dim, eps=qk_norm_eps, elementwise_affine=True)
+        self.norm_k = MMModule(qk_norm, dim=head_dim, eps=qk_norm_eps, elementwise_affine=True)
+        self.rope = RotaryEmbedding3d(dim=head_dim // 2) if qk_rope else None
+        self.attn = TorchAttention()
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # b T H W c
+        txt: torch.FloatTensor,  # b L c
+        txt_mask: torch.BoolTensor,  # b L
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        # Project q, k, v.
+        vid_qkv, txt_qkv = self.proj_qkv(vid, txt)
+        vid_qkv = gather_seq_scatter_heads_qkv(vid_qkv, seq_dim=2)
+        _, T, H, W, _ = vid_qkv.shape
+        _, L, _ = txt.shape
+
+        if self.window_method == "win":
+            nt, nh, nw = self.window
+            tt, hh, ww = T // nt, H // nh, W // nw
+        elif self.window_method == "win_by_size":
+            tt, hh, ww = self.window
+            tt, hh, ww = (
+                tt if tt > 0 else T,
+                hh if hh > 0 else H,
+                ww if ww > 0 else W,
+            )
+            nt, nh, nw = T // tt, H // hh, W // ww
+        else:
+            raise NotImplementedError
+
+        vid_qkv = rearrange(vid_qkv, "b T H W (o h d) -> o b h (T H W) d", o=3, d=self.head_dim)
+        txt_qkv = rearrange(txt_qkv, "b L (o h d) -> o b h L d", o=3, d=self.head_dim)
+        txt_qkv = scatter_heads(txt_qkv, dim=2)
+
+        vid_q, vid_k, vid_v = vid_qkv.unbind()
+        txt_q, txt_k, txt_v = txt_qkv.unbind()
+
+        vid_q, txt_q = self.norm_q(vid_q, txt_q)
+        vid_k, txt_k = self.norm_k(vid_k, txt_k)
+
+        if self.rope:
+            vid_q, vid_k = self.rope(vid_q, vid_k, (T, H, W))
+
+        def vid_window(v):
+            return rearrange(
+                v,
+                "b h (nt tt nh hh nw ww) d -> b h (nt nh nw) (tt hh ww) d",
+                hh=hh,
+                ww=ww,
+                tt=tt,
+                nh=nh,
+                nw=nw,
+                nt=nt,
+            )
+
+        def txt_window(t):
+            return rearrange(t, "b h L d -> b h 1 L d").expand(-1, -1, nt * nh * nw, -1, -1)
+
+        # Process video attention.
+        vid_msk = safe_pad_operation(txt_mask, (tt * hh * ww, 0), value=True)
+        vid_msk = rearrange(vid_msk, "b l -> b 1 1 1 l").expand(-1, 1, 1, tt * hh * ww, -1)
+        vid_out = self.attn(
+            vid_window(vid_q),
+            torch.cat([vid_window(vid_k), txt_window(txt_k)], dim=-2),
+            torch.cat([vid_window(vid_v), txt_window(txt_v)], dim=-2),
+            vid_msk,
+        )
+        vid_out = rearrange(
+            vid_out,
+            "b h (nt nh nw) (tt hh ww) d -> b (nt tt) (nh hh) (nw ww) (h d)",
+            hh=hh,
+            ww=ww,
+            tt=tt,
+            nh=nh,
+            nw=nw,
+        )
+        vid_out = gather_heads_scatter_seq(vid_out, head_dim=4, seq_dim=2)
+
+        # Process text attention.
+        txt_msk = safe_pad_operation(txt_mask, (T * H * W, 0), value=True)
+        txt_msk = rearrange(txt_msk, "b l -> b 1 1 l").expand(-1, 1, L, -1)
+        txt_out = self.attn(
+            txt_q,
+            torch.cat([vid_k, txt_k], dim=-2),
+            torch.cat([vid_v, txt_v], dim=-2),
+            txt_msk,
+        )
+        txt_out = rearrange(txt_out, "b h L d -> b L (h d)")
+        txt_out = gather_heads(txt_out, dim=2)
+
+        # Project output.
+        vid_out, txt_out = self.proj_out(vid_out, txt_out)
+        return vid_out, txt_out
+
+
+class MMWindowTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        vid_dim: int,
+        txt_dim: int,
+        emb_dim: int,
+        heads: int,
+        head_dim: int,
+        expand_ratio: int,
+        norm: norm_layer_type,
+        norm_eps: float,
+        ada: ada_layer_type,
+        qk_bias: bool,
+        qk_rope: bool,
+        qk_norm: norm_layer_type,
+        window: Union[int, Tuple[int, int, int]],
+        window_method: str,
+        shared_qkv: bool,
+        shared_mlp: bool,
+        mlp_type: str,
+        **kwargs,
+    ):
+        super().__init__()
+        dim = MMArg(vid_dim, txt_dim)
+        self.attn_norm = MMModule(norm, dim=dim, eps=norm_eps, elementwise_affine=False)
+        self.attn = MMWindowAttention(
+            vid_dim=vid_dim,
+            txt_dim=txt_dim,
+            heads=heads,
+            head_dim=head_dim,
+            qk_bias=qk_bias,
+            qk_rope=qk_rope,
+            qk_norm=qk_norm,
+            qk_norm_eps=norm_eps,
+            window=window,
+            window_method=window_method,
+            shared_qkv=shared_qkv,
+        )
+        self.mlp_norm = MMModule(norm, dim=dim, eps=norm_eps, elementwise_affine=False)
+        self.mlp = MMModule(
+            get_mlp(mlp_type),
+            dim=dim,
+            expand_ratio=expand_ratio,
+            shared_weights=shared_mlp,
+        )
+        self.ada = MMModule(ada, dim=dim, emb_dim=emb_dim, layers=["attn", "mlp"])
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,
+        txt: torch.FloatTensor,
+        txt_mask: torch.BoolTensor,
+        emb: torch.FloatTensor,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        vid_attn, txt_attn = self.attn_norm(vid, txt)
+        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, emb=emb, layer="attn", mode="in")
+        vid_attn, txt_attn = self.attn(vid_attn, txt_attn, txt_mask=txt_mask)
+        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, emb=emb, layer="attn", mode="out")
+        vid_attn, txt_attn = (vid_attn + vid), (txt_attn + txt)
+
+        vid_mlp, txt_mlp = self.mlp_norm(vid_attn, txt_attn)
+        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, emb=emb, layer="mlp", mode="in")
+        vid_mlp, txt_mlp = self.mlp(vid_mlp, txt_mlp)
+        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, emb=emb, layer="mlp", mode="out")
+        vid_mlp, txt_mlp = (vid_mlp + vid_attn), (txt_mlp + txt_attn)
+
+        return vid_mlp, txt_mlp
diff --git a/modules/seedvr/src/models/dit/embedding.py b/modules/seedvr/src/models/dit/embedding.py
new file mode 100644
index 000000000..e972244f5
--- /dev/null
+++ b/modules/seedvr/src/models/dit/embedding.py
@@ -0,0 +1,62 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Optional, Union
+import torch
+from diffusers.models.embeddings import get_timestep_embedding
+from torch import nn
+
+
+def emb_add(emb1: torch.Tensor, emb2: Optional[torch.Tensor]):
+    return emb1 if emb2 is None else emb1 + emb2
+
+
+class TimeEmbedding(nn.Module):
+    def __init__(
+        self,
+        sinusoidal_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+    ):
+        super().__init__()
+        self.sinusoidal_dim = sinusoidal_dim
+        self.proj_in = nn.Linear(sinusoidal_dim, hidden_dim)
+        self.proj_hid = nn.Linear(hidden_dim, hidden_dim)
+        self.proj_out = nn.Linear(hidden_dim, output_dim)
+        self.act = nn.SiLU()
+
+    def forward(
+        self,
+        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> torch.FloatTensor:
+        if not torch.is_tensor(timestep):
+            timestep = torch.tensor([timestep], device=device, dtype=dtype)
+        if timestep.ndim == 0:
+            timestep = timestep[None]
+
+        emb = get_timestep_embedding(
+            timesteps=timestep,
+            embedding_dim=self.sinusoidal_dim,
+            flip_sin_to_cos=False,
+            downscale_freq_shift=0,
+        )
+        emb = emb.to(dtype)
+        emb = self.proj_in(emb)
+        emb = self.act(emb)
+        emb = self.proj_hid(emb)
+        emb = self.act(emb)
+        emb = self.proj_out(emb)
+        return emb
diff --git a/modules/seedvr/src/models/dit/mlp.py b/modules/seedvr/src/models/dit/mlp.py
new file mode 100644
index 000000000..2d05cb021
--- /dev/null
+++ b/modules/seedvr/src/models/dit/mlp.py
@@ -0,0 +1,62 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Optional
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+def get_mlp(mlp_type: Optional[str] = "normal"):
+    if mlp_type == "normal":
+        return MLP
+    elif mlp_type == "swiglu":
+        return SwiGLUMLP
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        expand_ratio: int,
+    ):
+        super().__init__()
+        self.proj_in = nn.Linear(dim, dim * expand_ratio)
+        self.act = nn.GELU("tanh")
+        self.proj_out = nn.Linear(dim * expand_ratio, dim)
+
+    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        x = self.proj_in(x)
+        x = self.act(x)
+        x = self.proj_out(x)
+        return x
+
+
+class SwiGLUMLP(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        expand_ratio: int,
+        multiple_of: int = 256,
+    ):
+        super().__init__()
+        hidden_dim = int(2 * dim * expand_ratio / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        self.proj_in_gate = nn.Linear(dim, hidden_dim, bias=False)
+        self.proj_out = nn.Linear(hidden_dim, dim, bias=False)
+        self.proj_in = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        x = self.proj_out(F.silu(self.proj_in_gate(x)) * self.proj_in(x))
+        return x
diff --git a/modules/seedvr/src/models/dit/mm.py b/modules/seedvr/src/models/dit/mm.py
new file mode 100644
index 000000000..49be1f591
--- /dev/null
+++ b/modules/seedvr/src/models/dit/mm.py
@@ -0,0 +1,67 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Tuple
+import torch
+from torch import nn
+
+
+@dataclass
+class MMArg:
+    vid: Any
+    txt: Any
+
+
+def get_args(key: str, args: List[Any]) -> List[Any]:
+    return [getattr(v, key) if isinstance(v, MMArg) else v for v in args]
+
+
+def get_kwargs(key: str, kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    return {k: getattr(v, key) if isinstance(v, MMArg) else v for k, v in kwargs.items()}
+
+
+class MMModule(nn.Module):
+    def __init__(
+        self,
+        module: Callable[..., nn.Module],
+        *args,
+        shared_weights: bool = False,
+        **kwargs,
+    ):
+        super().__init__()
+        self.shared_weights = shared_weights
+        if self.shared_weights:
+            assert get_args("vid", args) == get_args("txt", args)
+            assert get_kwargs("vid", kwargs) == get_kwargs("txt", kwargs)
+            self.all = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
+        else:
+            self.vid = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
+            self.txt = module(*get_args("txt", args), **get_kwargs("txt", kwargs))
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,
+        txt: torch.FloatTensor,
+        *args,
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        vid_module = self.vid if not self.shared_weights else self.all
+        txt_module = self.txt if not self.shared_weights else self.all
+        vid = vid_module(vid, *get_args("vid", args), **get_kwargs("vid", kwargs))
+        txt = txt_module(txt, *get_args("txt", args), **get_kwargs("txt", kwargs))
+        return vid, txt
diff --git a/modules/seedvr/src/models/dit/modulation.py b/modules/seedvr/src/models/dit/modulation.py
new file mode 100644
index 000000000..7a4975646
--- /dev/null
+++ b/modules/seedvr/src/models/dit/modulation.py
@@ -0,0 +1,97 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Callable, List, Optional
+import torch
+from einops import rearrange
+from torch import nn
+
+from ...common.cache import Cache
+from ...common.distributed.ops import slice_inputs
+
+# (dim: int, emb_dim: int)
+ada_layer_type = Callable[[int, int], nn.Module]
+
+
+def get_ada_layer(ada_layer: str) -> ada_layer_type:
+    if ada_layer == "single":
+        return AdaSingle
+    raise NotImplementedError(f"{ada_layer} is not supported")
+
+
+def expand_dims(x: torch.Tensor, dim: int, ndim: int):
+    """
+    Expand tensor "x" to "ndim" by adding empty dims at "dim".
+    Example: x is (b d), target ndim is 5, add dim at 1, return (b 1 1 1 d).
+    """
+    shape = x.shape
+    shape = shape[:dim] + (1,) * (ndim - len(shape)) + shape[dim:]
+    return x.reshape(shape)
+
+
+class AdaSingle(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        emb_dim: int,
+        layers: List[str],
+    ):
+        assert emb_dim == 6 * dim, "AdaSingle requires emb_dim == 6 * dim"
+        super().__init__()
+        self.dim = dim
+        self.emb_dim = emb_dim
+        self.layers = layers
+        for l in layers:
+            self.register_parameter(f"{l}_shift", nn.Parameter(torch.randn(dim) / dim**0.5))
+            self.register_parameter(f"{l}_scale", nn.Parameter(torch.randn(dim) / dim**0.5 + 1))
+            self.register_parameter(f"{l}_gate", nn.Parameter(torch.randn(dim) / dim**0.5))
+
+    def forward(
+        self,
+        hid: torch.FloatTensor,  # b ... c
+        emb: torch.FloatTensor,  # b d
+        layer: str,
+        mode: str,
+        cache: Cache = Cache(disable=True),
+        branch_tag: str = "",
+        hid_len: Optional[torch.LongTensor] = None,  # b
+    ) -> torch.FloatTensor:
+        idx = self.layers.index(layer)
+        emb = rearrange(emb, "b (d l g) -> b d l g", l=len(self.layers), g=3)[..., idx, :]
+        emb = expand_dims(emb, 1, hid.ndim + 1)
+
+        if hid_len is not None:
+            emb = cache(
+                f"emb_repeat_{idx}_{branch_tag}",
+                lambda: slice_inputs(
+                    torch.cat([e.repeat(l, *([1] * e.ndim)) for e, l in zip(emb, hid_len)]),
+                    dim=0,
+                ),
+            )
+
+        shiftA, scaleA, gateA = emb.unbind(-1)
+        shiftB, scaleB, gateB = (
+            getattr(self, f"{layer}_shift"),
+            getattr(self, f"{layer}_scale"),
+            getattr(self, f"{layer}_gate"),
+        )
+
+        if mode == "in":
+            return hid.mul_(scaleA + scaleB).add_(shiftA + shiftB)
+        if mode == "out":
+            return hid.mul_(gateA + gateB)
+        raise NotImplementedError
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, emb_dim={self.emb_dim}, layers={self.layers}"
diff --git a/modules/seedvr/src/models/dit/na.py b/modules/seedvr/src/models/dit/na.py
new file mode 100644
index 000000000..0dbd546c4
--- /dev/null
+++ b/modules/seedvr/src/models/dit/na.py
@@ -0,0 +1,241 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from itertools import chain
+from typing import Callable, Dict, List, Tuple
+import einops
+import torch
+
+
+def flatten(
+    hid: List[torch.FloatTensor],  # List of (*** c)
+) -> Tuple[
+    torch.FloatTensor,  # (L c)
+    torch.LongTensor,  # (b n)
+]:
+    assert len(hid) > 0
+    shape = torch.stack([torch.tensor(x.shape[:-1], device=hid[0].device) for x in hid])
+    hid = torch.cat([x.flatten(0, -2) for x in hid])
+    return hid, shape
+
+
+def unflatten(
+    hid: torch.FloatTensor,  # (L c) or (L ... c)
+    hid_shape: torch.LongTensor,  # (b n)
+) -> List[torch.Tensor]:  # List of (*** c) or (*** ... c)
+    hid_len = hid_shape.prod(-1)
+    hid = hid.split(hid_len.tolist())
+    hid = [x.unflatten(0, s.tolist()) for x, s in zip(hid, hid_shape)]
+    return hid
+
+
+def concat(
+    vid: torch.FloatTensor,  # (VL ... c)
+    txt: torch.FloatTensor,  # (TL ... c)
+    vid_len: torch.LongTensor,  # (b)
+    txt_len: torch.LongTensor,  # (b)
+) -> torch.FloatTensor:  # (L ... c)
+    vid = torch.split(vid, vid_len.tolist())
+    txt = torch.split(txt, txt_len.tolist())
+    return torch.cat(list(chain(*zip(vid, txt))))
+
+
+def concat_idx(
+    vid_len: torch.LongTensor,  # (b)
+    txt_len: torch.LongTensor,  # (b)
+) -> Tuple[
+    Callable,
+    Callable,
+]:
+    device = vid_len.device
+    vid_idx = torch.arange(vid_len.sum(), device=device)
+    txt_idx = torch.arange(len(vid_idx), len(vid_idx) + txt_len.sum(), device=device)
+    tgt_idx = concat(vid_idx, txt_idx, vid_len, txt_len)
+    src_idx = torch.argsort(tgt_idx)
+    return (
+        lambda vid, txt: torch.index_select(torch.cat([vid, txt]), 0, tgt_idx),
+        lambda all: torch.index_select(all, 0, src_idx).split([len(vid_idx), len(txt_idx)]),
+    )
+
+
+def unconcat(
+    all: torch.FloatTensor,  # (L ... c)
+    vid_len: torch.LongTensor,  # (b)
+    txt_len: torch.LongTensor,  # (b)
+) -> Tuple[
+    torch.FloatTensor,  # (VL ... c)
+    torch.FloatTensor,  # (TL ... c)
+]:
+    interleave_len = list(chain(*zip(vid_len.tolist(), txt_len.tolist())))
+    all = all.split(interleave_len)
+    vid = torch.cat(all[0::2])
+    txt = torch.cat(all[1::2])
+    return vid, txt
+
+
+def repeat_concat(
+    vid: torch.FloatTensor,  # (VL ... c)
+    txt: torch.FloatTensor,  # (TL ... c)
+    vid_len: torch.LongTensor,  # (n*b)
+    txt_len: torch.LongTensor,  # (b)
+    txt_repeat: List,  # (n)
+) -> torch.FloatTensor:  # (L ... c)
+    vid = torch.split(vid, vid_len.tolist())
+    txt = torch.split(txt, txt_len.tolist())
+    txt = [[x] * n for x, n in zip(txt, txt_repeat)]
+    txt = list(chain(*txt))
+    return torch.cat(list(chain(*zip(vid, txt))))
+
+
+def repeat_concat_idx(
+    vid_len: torch.LongTensor,  # (n*b)
+    txt_len: torch.LongTensor,  # (b)
+    txt_repeat: torch.LongTensor,  # (n)
+) -> Tuple[
+    Callable,
+    Callable,
+]:
+    device = vid_len.device
+    vid_idx = torch.arange(vid_len.sum(), device=device)
+    txt_idx = torch.arange(len(vid_idx), len(vid_idx) + txt_len.sum(), device=device)
+    txt_repeat_list = txt_repeat.tolist()
+    tgt_idx = repeat_concat(vid_idx, txt_idx, vid_len, txt_len, txt_repeat)
+    src_idx = torch.argsort(tgt_idx)
+    txt_idx_len = len(tgt_idx) - len(vid_idx)
+    repeat_txt_len = (txt_len * txt_repeat).tolist()
+
+    def unconcat_coalesce(all):
+        """
+        Un-concat vid & txt, and coalesce the repeated txt.
+        e.g. vid [0 1 2 3 4 5 6 7 8] -> 3 splits -> [0 1 2] [3 4 5] [6 7 8]
+             txt [9 10]
+             repeat_concat ==> [0 1 2 9 10 3 4 5 9 10 6 7 8 9 10]
+             1. argsort re-index ==> [0 1 2 3 4 5 6 7 8 9 9 9 10 10 10]
+                           split ==> vid_out [0 1 2 3 4 5 6 7 8] txt_out [9 9 9 10 10 10]
+             2. reshape & mean for each sample to coalesce the repeated txt.
+        """
+        vid_out, txt_out = all[src_idx].split([len(vid_idx), txt_idx_len])
+        txt_out_coalesced = []
+        for txt, repeat_time in zip(txt_out.split(repeat_txt_len), txt_repeat_list):
+            txt = txt.reshape(-1, repeat_time, *txt.shape[1:]).mean(1)
+            txt_out_coalesced.append(txt)
+        return vid_out, torch.cat(txt_out_coalesced)
+
+    # Note: Backward of torch.index_select is non-deterministic when existing repeated index,
+    # the difference may cumulative like torch.repeat_interleave, so we use vanilla index here.
+    return (
+        lambda vid, txt: torch.cat([vid, txt])[tgt_idx],
+        lambda all: unconcat_coalesce(all),
+    )
+
+
+def rearrange(
+    hid: torch.FloatTensor,  # (L c)
+    hid_shape: torch.LongTensor,  # (b n)
+    pattern: str,
+    **kwargs: Dict[str, int],
+) -> Tuple[
+    torch.FloatTensor,
+    torch.LongTensor,
+]:
+    return flatten([einops.rearrange(h, pattern, **kwargs) for h in unflatten(hid, hid_shape)])
+
+
+def rearrange_idx(
+    hid_shape: torch.LongTensor,  # (b n)
+    pattern: str,
+    **kwargs: Dict[str, int],
+) -> Tuple[Callable, Callable, torch.LongTensor]:
+    hid_idx = torch.arange(hid_shape.prod(-1).sum(), device=hid_shape.device).unsqueeze(-1)
+    tgt_idx, tgt_shape = rearrange(hid_idx, hid_shape, pattern, **kwargs)
+    tgt_idx = tgt_idx.squeeze(-1)
+    src_idx = torch.argsort(tgt_idx)
+    return (
+        lambda hid: torch.index_select(hid, 0, tgt_idx),
+        lambda hid: torch.index_select(hid, 0, src_idx),
+        tgt_shape,
+    )
+
+
+def repeat(
+    hid: torch.FloatTensor,  # (L c)
+    hid_shape: torch.LongTensor,  # (b n)
+    pattern: str,
+    **kwargs: Dict[str, torch.LongTensor],  # (b)
+) -> Tuple[
+    torch.FloatTensor,
+    torch.LongTensor,
+]:
+    hid = unflatten(hid, hid_shape)
+    kwargs = [{k: v[i].item() for k, v in kwargs.items()} for i in range(len(hid))]
+    return flatten([einops.repeat(h, pattern, **a) for h, a in zip(hid, kwargs)])
+
+
+def pack(
+    samples: List[torch.Tensor],  # List of (h w c).
+) -> Tuple[
+    List[torch.Tensor],  # groups [(b1 h1 w1 c1), (b2 h2 w2 c2)]
+    List[List[int]],  # reversal indices.
+]:
+    batches = {}
+    indices = {}
+    for i, sample in enumerate(samples):
+        shape = sample.shape
+        batches[shape] = batches.get(shape, [])
+        indices[shape] = indices.get(shape, [])
+        batches[shape].append(sample)
+        indices[shape].append(i)
+
+    batches = list(map(torch.stack, batches.values()))
+    indices = list(indices.values())
+    return batches, indices
+
+
+def unpack(
+    batches: List[torch.Tensor],
+    indices: List[List[int]],
+) -> List[torch.Tensor]:
+    samples = [None] * (max(chain(*indices)) + 1)
+    for batch, index in zip(batches, indices):
+        for sample, i in zip(batch.unbind(), index):
+            samples[i] = sample
+    return samples
+
+
+def window(
+    hid: torch.FloatTensor,  # (L c)
+    hid_shape: torch.LongTensor,  # (b n)
+    window_fn: Callable[[torch.Tensor], List[torch.Tensor]],
+):
+    hid = unflatten(hid, hid_shape)
+    hid = list(map(window_fn, hid))
+    hid_windows = torch.tensor(list(map(len, hid)), device=hid_shape.device)
+    hid, hid_shape = flatten(list(chain(*hid)))
+    return hid, hid_shape, hid_windows
+
+
+def window_idx(
+    hid_shape: torch.LongTensor,  # (b n)
+    window_fn: Callable[[torch.Tensor], List[torch.Tensor]],
+):
+    hid_idx = torch.arange(hid_shape.prod(-1).sum(), device=hid_shape.device).unsqueeze(-1)
+    tgt_idx, tgt_shape, tgt_windows = window(hid_idx, hid_shape, window_fn)
+    tgt_idx = tgt_idx.squeeze(-1)
+    src_idx = torch.argsort(tgt_idx)
+    return (
+        lambda hid: torch.index_select(hid, 0, tgt_idx),
+        lambda hid: torch.index_select(hid, 0, src_idx),
+        tgt_shape,
+        tgt_windows,
+    )
diff --git a/modules/seedvr/src/models/dit/nablocks/__init__.py b/modules/seedvr/src/models/dit/nablocks/__init__.py
new file mode 100644
index 000000000..afa206db1
--- /dev/null
+++ b/modules/seedvr/src/models/dit/nablocks/__init__.py
@@ -0,0 +1,25 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from .mmsr_block import NaMMSRTransformerBlock
+
+nadit_blocks = {
+    "mmdit_sr": NaMMSRTransformerBlock,
+}
+
+
+def get_nablock(block_type: str):
+    if block_type in nadit_blocks:
+        return nadit_blocks[block_type]
+    raise NotImplementedError(f"{block_type} is not supported")
diff --git a/modules/seedvr/src/models/dit/nablocks/mmsr_block.py b/modules/seedvr/src/models/dit/nablocks/mmsr_block.py
new file mode 100644
index 000000000..48e548262
--- /dev/null
+++ b/modules/seedvr/src/models/dit/nablocks/mmsr_block.py
@@ -0,0 +1,248 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Tuple, Union
+import torch
+from einops import rearrange
+from torch.nn import functional as F
+
+# from ..cache import Cache
+from ....common.cache import Cache
+from ....common.distributed.ops import gather_heads_scatter_seq, gather_seq_scatter_heads_qkv
+
+from .. import na
+from ..attention import FlashAttentionVarlen
+from ..blocks.mmdit_window_block import MMWindowAttention, MMWindowTransformerBlock
+from ..mm import MMArg
+from ..modulation import ada_layer_type
+from ..normalization import norm_layer_type
+from ..rope import NaRotaryEmbedding3d
+from ..window import get_window_op
+from ....common.half_precision_fixes import safe_pad_operation
+
+class NaSwinAttention(MMWindowAttention):
+    def __init__(
+        self,
+        vid_dim: int,
+        txt_dim: int,
+        heads: int,
+        head_dim: int,
+        qk_bias: bool,
+        qk_rope: bool,
+        qk_norm: norm_layer_type,
+        qk_norm_eps: float,
+        window: Union[int, Tuple[int, int, int]],
+        window_method: str,
+        shared_qkv: bool,
+        **kwargs,
+    ):
+        super().__init__(
+            vid_dim=vid_dim,
+            txt_dim=txt_dim,
+            heads=heads,
+            head_dim=head_dim,
+            qk_bias=qk_bias,
+            qk_rope=qk_rope,
+            qk_norm=qk_norm,
+            qk_norm_eps=qk_norm_eps,
+            window=window,
+            window_method=window_method,
+            shared_qkv=shared_qkv,
+        )
+        self.rope = NaRotaryEmbedding3d(dim=head_dim // 2) if qk_rope else None
+        self.attn = FlashAttentionVarlen()
+        self.window_op = get_window_op(window_method)
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+
+        vid_qkv, txt_qkv = self.proj_qkv(vid, txt)
+        vid_qkv = gather_seq_scatter_heads_qkv(
+            vid_qkv,
+            seq_dim=0,
+            qkv_shape=vid_shape,
+            cache=cache.namespace("vid"),
+        )
+        txt_qkv = gather_seq_scatter_heads_qkv(
+            txt_qkv,
+            seq_dim=0,
+            qkv_shape=txt_shape,
+            cache=cache.namespace("txt"),
+        )
+
+        # re-org the input seq for window attn
+        cache_win = cache.namespace(f"{self.window_method}_{self.window}_sd3")
+
+        def make_window(x: torch.Tensor):
+            t, h, w, _ = x.shape
+            window_slices = self.window_op((t, h, w), self.window)
+            return [x[st, sh, sw] for (st, sh, sw) in window_slices]
+
+        window_partition, window_reverse, window_shape, window_count = cache_win(
+            "win_transform",
+            lambda: na.window_idx(vid_shape, make_window),
+        )
+        vid_qkv_win = window_partition(vid_qkv)
+
+        vid_qkv_win = rearrange(vid_qkv_win, "l (o h d) -> l o h d", o=3, d=self.head_dim)
+        txt_qkv = rearrange(txt_qkv, "l (o h d) -> l o h d", o=3, d=self.head_dim)
+
+        vid_q, vid_k, vid_v = vid_qkv_win.unbind(1)
+        txt_q, txt_k, txt_v = txt_qkv.unbind(1)
+
+        vid_q, txt_q = self.norm_q(vid_q, txt_q)
+        vid_k, txt_k = self.norm_k(vid_k, txt_k)
+
+        txt_len = cache("txt_len", lambda: txt_shape.prod(-1))
+
+        vid_len_win = cache_win("vid_len", lambda: window_shape.prod(-1))
+        txt_len_win = cache_win("txt_len", lambda: txt_len.repeat_interleave(window_count))
+        all_len_win = cache_win("all_len", lambda: vid_len_win + txt_len_win)
+        concat_win, unconcat_win = cache_win(
+            "mm_pnp", lambda: na.repeat_concat_idx(vid_len_win, txt_len, window_count)
+        )
+
+        # window rope
+        if self.rope:
+            vid_q, vid_k = self.rope(vid_q, vid_k, window_shape, cache_win)
+
+        out = self.attn(
+            q=concat_win(vid_q, txt_q).bfloat16(),
+            k=concat_win(vid_k, txt_k).bfloat16(),
+            v=concat_win(vid_v, txt_v).bfloat16(),
+            cu_seqlens_q=cache_win(
+                "vid_seqlens_q", lambda: safe_pad_operation(all_len_win.cumsum(0), (1, 0)).int()
+            ),
+            cu_seqlens_k=cache_win(
+                "vid_seqlens_k", lambda: safe_pad_operation(all_len_win.cumsum(0), (1, 0)).int()
+            ),
+            max_seqlen_q=cache_win("vid_max_seqlen_q", lambda: all_len_win.max().item()),
+            max_seqlen_k=cache_win("vid_max_seqlen_k", lambda: all_len_win.max().item()),
+        ).type_as(vid_q)
+
+        # text pooling
+        vid_out, txt_out = unconcat_win(out)
+
+        vid_out = rearrange(vid_out, "l h d -> l (h d)")
+        txt_out = rearrange(txt_out, "l h d -> l (h d)")
+        vid_out = window_reverse(vid_out)
+
+        vid_out = gather_heads_scatter_seq(vid_out, head_dim=1, seq_dim=0)
+        txt_out = gather_heads_scatter_seq(txt_out, head_dim=1, seq_dim=0)
+
+        vid_out, txt_out = self.proj_out(vid_out, txt_out)
+
+        return vid_out, txt_out
+
+
+class NaMMSRTransformerBlock(MMWindowTransformerBlock):
+    def __init__(
+        self,
+        *,
+        vid_dim: int,
+        txt_dim: int,
+        emb_dim: int,
+        heads: int,
+        head_dim: int,
+        expand_ratio: int,
+        norm: norm_layer_type,
+        norm_eps: float,
+        ada: ada_layer_type,
+        qk_bias: bool,
+        qk_rope: bool,
+        qk_norm: norm_layer_type,
+        shared_qkv: bool,
+        shared_mlp: bool,
+        mlp_type: str,
+        **kwargs,
+    ):
+        super().__init__(
+            vid_dim=vid_dim,
+            txt_dim=txt_dim,
+            emb_dim=emb_dim,
+            heads=heads,
+            head_dim=head_dim,
+            expand_ratio=expand_ratio,
+            norm=norm,
+            norm_eps=norm_eps,
+            ada=ada,
+            qk_bias=qk_bias,
+            qk_rope=qk_rope,
+            qk_norm=qk_norm,
+            shared_qkv=shared_qkv,
+            shared_mlp=shared_mlp,
+            mlp_type=mlp_type,
+            **kwargs,
+        )
+
+        self.attn = NaSwinAttention(
+            vid_dim=vid_dim,
+            txt_dim=txt_dim,
+            heads=heads,
+            head_dim=head_dim,
+            qk_bias=qk_bias,
+            qk_rope=qk_rope,
+            qk_norm=qk_norm,
+            qk_norm_eps=norm_eps,
+            shared_qkv=shared_qkv,
+            **kwargs,
+        )
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        emb: torch.FloatTensor,
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+        torch.LongTensor,
+        torch.LongTensor,
+    ]:
+        hid_len = MMArg(
+            cache("vid_len", lambda: vid_shape.prod(-1)),
+            cache("txt_len", lambda: txt_shape.prod(-1)),
+        )
+        ada_kwargs = {
+            "emb": emb,
+            "hid_len": hid_len,
+            "cache": cache,
+            "branch_tag": MMArg("vid", "txt"),
+        }
+
+        vid_attn, txt_attn = self.attn_norm(vid, txt)
+        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, layer="attn", mode="in", **ada_kwargs)
+        vid_attn, txt_attn = self.attn(vid_attn, txt_attn, vid_shape, txt_shape, cache)
+        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, layer="attn", mode="out", **ada_kwargs)
+        vid_attn, txt_attn = (vid_attn + vid), (txt_attn + txt)
+
+        vid_mlp, txt_mlp = self.mlp_norm(vid_attn, txt_attn)
+        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, layer="mlp", mode="in", **ada_kwargs)
+        vid_mlp, txt_mlp = self.mlp(vid_mlp, txt_mlp)
+        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, layer="mlp", mode="out", **ada_kwargs)
+        vid_mlp, txt_mlp = (vid_mlp + vid_attn), (txt_mlp + txt_attn)
+
+        return vid_mlp, txt_mlp, vid_shape, txt_shape
diff --git a/modules/seedvr/src/models/dit/nadit.py b/modules/seedvr/src/models/dit/nadit.py
new file mode 100644
index 000000000..a67695206
--- /dev/null
+++ b/modules/seedvr/src/models/dit/nadit.py
@@ -0,0 +1,350 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union, Callable
+import torch
+from torch import nn
+
+from ...common.cache import Cache
+from ...common.distributed.ops import slice_inputs
+
+from . import na
+from .embedding import TimeEmbedding
+from .modulation import get_ada_layer
+from .nablocks import get_nablock
+from .normalization import get_norm_layer
+from .patch import NaPatchIn, NaPatchOut
+
+# Fake func, no checkpointing is required for inference
+def gradient_checkpointing(module: Union[Callable, nn.Module], *args, enabled: bool, **kwargs):
+    return module(*args, **kwargs)
+
+@dataclass
+class NaDiTOutput:
+    vid_sample: torch.Tensor
+
+
+class NaDiT(nn.Module):
+    """
+    Native Resolution Diffusion Transformer (NaDiT)
+    """
+
+    gradient_checkpointing = False
+
+    def __init__(
+        self,
+        vid_in_channels: int,
+        vid_out_channels: int,
+        vid_dim: int,
+        txt_in_dim: Optional[int],
+        txt_dim: Optional[int],
+        emb_dim: int,
+        heads: int,
+        head_dim: int,
+        expand_ratio: int,
+        norm: Optional[str],
+        norm_eps: float,
+        ada: str,
+        qk_bias: bool,
+        qk_rope: bool,
+        qk_norm: Optional[str],
+        patch_size: Union[int, Tuple[int, int, int]],
+        num_layers: int,
+        block_type: Union[str, Tuple[str]],
+        shared_qkv: bool = False,
+        shared_mlp: bool = False,
+        mlp_type: str = "normal",
+        window: Optional[Tuple] = None,
+        window_method: Optional[Tuple[str]] = None,
+        temporal_window_size: int = None,
+        temporal_shifted: bool = False,
+        **kwargs,
+    ):
+        ada = get_ada_layer(ada)
+        norm = get_norm_layer(norm)
+        qk_norm = get_norm_layer(qk_norm)
+        if isinstance(block_type, str):
+            block_type = [block_type] * num_layers
+        elif len(block_type) != num_layers:
+            raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
+        super().__init__()
+        self.vid_in = NaPatchIn(
+            in_channels=vid_in_channels,
+            patch_size=patch_size,
+            dim=vid_dim,
+        )
+        self.txt_in = (
+            nn.Linear(txt_in_dim, txt_dim)
+            if txt_in_dim and txt_in_dim != txt_dim
+            else nn.Identity()
+        )
+        self.emb_in = TimeEmbedding(
+            sinusoidal_dim=256,
+            hidden_dim=max(vid_dim, txt_dim),
+            output_dim=emb_dim,
+        )
+
+        if window is None or isinstance(window[0], int):
+            window = [window] * num_layers
+        if window_method is None or isinstance(window_method, str):
+            window_method = [window_method] * num_layers
+        if temporal_window_size is None or isinstance(temporal_window_size, int):
+            temporal_window_size = [temporal_window_size] * num_layers
+        if temporal_shifted is None or isinstance(temporal_shifted, bool):
+            temporal_shifted = [temporal_shifted] * num_layers
+
+        self.blocks = nn.ModuleList(
+            [
+                get_nablock(block_type[i])(
+                    vid_dim=vid_dim,
+                    txt_dim=txt_dim,
+                    emb_dim=emb_dim,
+                    heads=heads,
+                    head_dim=head_dim,
+                    expand_ratio=expand_ratio,
+                    norm=norm,
+                    norm_eps=norm_eps,
+                    ada=ada,
+                    qk_bias=qk_bias,
+                    qk_rope=qk_rope,
+                    qk_norm=qk_norm,
+                    shared_qkv=shared_qkv,
+                    shared_mlp=shared_mlp,
+                    mlp_type=mlp_type,
+                    window=window[i],
+                    window_method=window_method[i],
+                    temporal_window_size=temporal_window_size[i],
+                    temporal_shifted=temporal_shifted[i],
+                    **kwargs,
+                )
+                for i in range(num_layers)
+            ]
+        )
+        self.vid_out = NaPatchOut(
+            out_channels=vid_out_channels,
+            patch_size=patch_size,
+            dim=vid_dim,
+        )
+
+        self.need_txt_repeat = block_type[0] in [
+            "mmdit_stwin",
+            "mmdit_stwin_spatial",
+            "mmdit_stwin_3d_spatial",
+        ]
+
+    def set_gradient_checkpointing(self, enable: bool):
+        self.gradient_checkpointing = enable
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
+        disable_cache: bool = True,  # for test
+    ):
+        # Text input.
+        if txt_shape.size(-1) == 1 and self.need_txt_repeat:
+            txt, txt_shape = na.repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
+        # slice vid after patching in when using sequence parallelism
+        txt = slice_inputs(txt, dim=0)
+        txt = self.txt_in(txt)
+
+        # Video input.
+        # Sequence parallel slicing is done inside patching class.
+        vid, vid_shape = self.vid_in(vid, vid_shape)
+
+        # Embedding input.
+        emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
+
+        # Body
+        cache = Cache(disable=disable_cache)
+        for i, block in enumerate(self.blocks):
+            vid, txt, vid_shape, txt_shape = gradient_checkpointing(
+                enabled=(self.gradient_checkpointing and self.training),
+                module=block,
+                vid=vid,
+                txt=txt,
+                vid_shape=vid_shape,
+                txt_shape=txt_shape,
+                emb=emb,
+                cache=cache,
+            )
+
+        vid, vid_shape = self.vid_out(vid, vid_shape, cache)
+        return NaDiTOutput(vid_sample=vid)
+
+
+class NaDiTUpscaler(nn.Module):
+    """
+    Native Resolution Diffusion Transformer (NaDiT)
+    """
+
+    gradient_checkpointing = False
+
+    def __init__(
+        self,
+        vid_in_channels: int,
+        vid_out_channels: int,
+        vid_dim: int,
+        txt_in_dim: Optional[int],
+        txt_dim: Optional[int],
+        emb_dim: int,
+        heads: int,
+        head_dim: int,
+        expand_ratio: int,
+        norm: Optional[str],
+        norm_eps: float,
+        ada: str,
+        qk_bias: bool,
+        qk_rope: bool,
+        qk_norm: Optional[str],
+        patch_size: Union[int, Tuple[int, int, int]],
+        num_layers: int,
+        block_type: Union[str, Tuple[str]],
+        shared_qkv: bool = False,
+        shared_mlp: bool = False,
+        mlp_type: str = "normal",
+        window: Optional[Tuple] = None,
+        window_method: Optional[Tuple[str]] = None,
+        temporal_window_size: int = None,
+        temporal_shifted: bool = False,
+        **kwargs,
+    ):
+        ada = get_ada_layer(ada)
+        norm = get_norm_layer(norm)
+        qk_norm = get_norm_layer(qk_norm)
+        if isinstance(block_type, str):
+            block_type = [block_type] * num_layers
+        elif len(block_type) != num_layers:
+            raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
+        super().__init__()
+        self.vid_in = NaPatchIn(
+            in_channels=vid_in_channels,
+            patch_size=patch_size,
+            dim=vid_dim,
+        )
+        self.txt_in = (
+            nn.Linear(txt_in_dim, txt_dim)
+            if txt_in_dim and txt_in_dim != txt_dim
+            else nn.Identity()
+        )
+        self.emb_in = TimeEmbedding(
+            sinusoidal_dim=256,
+            hidden_dim=max(vid_dim, txt_dim),
+            output_dim=emb_dim,
+        )
+
+        self.emb_scale = TimeEmbedding(
+            sinusoidal_dim=256,
+            hidden_dim=max(vid_dim, txt_dim),
+            output_dim=emb_dim,
+        )
+
+        if window is None or isinstance(window[0], int):
+            window = [window] * num_layers
+        if window_method is None or isinstance(window_method, str):
+            window_method = [window_method] * num_layers
+        if temporal_window_size is None or isinstance(temporal_window_size, int):
+            temporal_window_size = [temporal_window_size] * num_layers
+        if temporal_shifted is None or isinstance(temporal_shifted, bool):
+            temporal_shifted = [temporal_shifted] * num_layers
+
+        self.blocks = nn.ModuleList(
+            [
+                get_nablock(block_type[i])(
+                    vid_dim=vid_dim,
+                    txt_dim=txt_dim,
+                    emb_dim=emb_dim,
+                    heads=heads,
+                    head_dim=head_dim,
+                    expand_ratio=expand_ratio,
+                    norm=norm,
+                    norm_eps=norm_eps,
+                    ada=ada,
+                    qk_bias=qk_bias,
+                    qk_rope=qk_rope,
+                    qk_norm=qk_norm,
+                    shared_qkv=shared_qkv,
+                    shared_mlp=shared_mlp,
+                    mlp_type=mlp_type,
+                    window=window[i],
+                    window_method=window_method[i],
+                    temporal_window_size=temporal_window_size[i],
+                    temporal_shifted=temporal_shifted[i],
+                    **kwargs,
+                )
+                for i in range(num_layers)
+            ]
+        )
+        self.vid_out = NaPatchOut(
+            out_channels=vid_out_channels,
+            patch_size=patch_size,
+            dim=vid_dim,
+        )
+
+        self.need_txt_repeat = block_type[0] in [
+            "mmdit_stwin",
+            "mmdit_stwin_spatial",
+            "mmdit_stwin_3d_spatial",
+        ]
+
+    def set_gradient_checkpointing(self, enable: bool):
+        self.gradient_checkpointing = enable
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
+        downscale: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
+        disable_cache: bool = False,  # for test
+    ):
+
+        # Text input.
+        if txt_shape.size(-1) == 1 and self.need_txt_repeat:
+            txt, txt_shape = na.repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
+        # slice vid after patching in when using sequence parallelism
+        txt = slice_inputs(txt, dim=0)
+        txt = self.txt_in(txt)
+
+        # Video input.
+        # Sequence parallel slicing is done inside patching class.
+        vid, vid_shape = self.vid_in(vid, vid_shape)
+
+        # Embedding input.
+        emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
+        emb_scale = self.emb_scale(downscale, device=vid.device, dtype=vid.dtype)
+        emb = emb + emb_scale
+
+        # Body
+        cache = Cache(disable=disable_cache)
+        for i, block in enumerate(self.blocks):
+            vid, txt, vid_shape, txt_shape = gradient_checkpointing(
+                enabled=(self.gradient_checkpointing and self.training),
+                module=block,
+                vid=vid,
+                txt=txt,
+                vid_shape=vid_shape,
+                txt_shape=txt_shape,
+                emb=emb,
+                cache=cache,
+            )
+
+        vid, vid_shape = self.vid_out(vid, vid_shape, cache)
+        return NaDiTOutput(vid_sample=vid)
diff --git a/modules/seedvr/src/models/dit/normalization.py b/modules/seedvr/src/models/dit/normalization.py
new file mode 100644
index 000000000..faa2d0d88
--- /dev/null
+++ b/modules/seedvr/src/models/dit/normalization.py
@@ -0,0 +1,131 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Callable, Optional
+from diffusers.models.normalization import RMSNorm
+from torch import nn
+import torch
+import torch.nn.functional as F
+import numbers
+from torch.nn.parameter import Parameter
+from torch.nn import init
+
+# (dim: int, eps: float, elementwise_affine: bool)
+norm_layer_type = Callable[[int, float, bool], nn.Module]
+
+
+class CustomLayerNorm(nn.Module):
+    """
+    Custom LayerNorm implementation to replace Apex FusedLayerNorm
+    """
+    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
+        super(CustomLayerNorm, self).__init__()
+
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = torch.Size(normalized_shape)
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+
+        if self.elementwise_affine:
+            self.weight = Parameter(torch.Tensor(*normalized_shape))
+            self.bias = Parameter(torch.Tensor(*normalized_shape))
+        else:
+            self.register_parameter('weight', None)
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.elementwise_affine:
+            init.ones_(self.weight)
+            init.zeros_(self.bias)
+
+    def forward(self, input):
+        return F.layer_norm(
+            input, self.normalized_shape, self.weight, self.bias, self.eps)
+
+
+class CustomRMSNorm(nn.Module):
+    """
+    Custom RMSNorm implementation to replace Apex FusedRMSNorm
+    """
+    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
+        super(CustomRMSNorm, self).__init__()
+
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = torch.Size(normalized_shape)
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+
+        if self.elementwise_affine:
+            self.weight = Parameter(torch.ones(*normalized_shape))
+        else:
+            self.register_parameter('weight', None)
+
+    def forward(self, input):
+        # RMS normalization: x / sqrt(mean(x^2) + eps) * weight
+        dims = tuple(range(-len(self.normalized_shape), 0))
+
+        # Calculate RMS: sqrt(mean(x^2))
+        variance = input.pow(2).mean(dim=dims, keepdim=True)
+        rms = torch.sqrt(variance + self.eps)
+
+        # Normalize
+        normalized = input / rms
+
+        if self.elementwise_affine:
+            return normalized * self.weight
+        return normalized
+
+
+def get_norm_layer(norm_type: Optional[str]) -> norm_layer_type:
+
+    def _norm_layer(dim: int, eps: float, elementwise_affine: bool):
+        if norm_type is None:
+            return nn.Identity()
+
+        if norm_type == "layer":
+            return nn.LayerNorm(
+                normalized_shape=dim,
+                eps=eps,
+                elementwise_affine=elementwise_affine,
+            )
+
+        if norm_type == "rms":
+            return RMSNorm(
+                dim=dim,
+                eps=eps,
+                elementwise_affine=elementwise_affine,
+            )
+
+        if norm_type == "fusedln":
+            # Use custom LayerNorm instead of Apex FusedLayerNorm
+            return CustomLayerNorm(
+                normalized_shape=dim,
+                elementwise_affine=elementwise_affine,
+                eps=eps,
+            )
+
+        if norm_type == "fusedrms":
+            # Use custom RMSNorm instead of Apex FusedRMSNorm
+            return CustomRMSNorm(
+                normalized_shape=dim,
+                elementwise_affine=elementwise_affine,
+                eps=eps,
+            )
+
+        raise NotImplementedError(f"{norm_type} is not supported")
+
+    return _norm_layer
diff --git a/modules/seedvr/src/models/dit/patch.py b/modules/seedvr/src/models/dit/patch.py
new file mode 100644
index 000000000..570e3ae40
--- /dev/null
+++ b/modules/seedvr/src/models/dit/patch.py
@@ -0,0 +1,112 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Tuple, Union
+import torch
+from einops import rearrange
+from torch import nn
+from torch.nn.modules.utils import _triple
+
+from ...common.cache import Cache
+from ...common.distributed.ops import gather_outputs, slice_inputs
+
+from . import na
+
+
+class PatchIn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        patch_size: Union[int, Tuple[int, int, int]],
+        dim: int,
+    ):
+        super().__init__()
+        t, h, w = _triple(patch_size)
+        self.patch_size = t, h, w
+        self.proj = nn.Linear(in_channels * t * h * w, dim)
+
+    def forward(
+        self,
+        vid: torch.Tensor,
+    ) -> torch.Tensor:
+        t, h, w = self.patch_size
+        vid = rearrange(vid, "b c (T t) (H h) (W w) -> b T H W (t h w c)", t=t, h=h, w=w)
+        vid = self.proj(vid)
+        return vid
+
+
+class PatchOut(nn.Module):
+    def __init__(
+        self,
+        out_channels: int,
+        patch_size: Union[int, Tuple[int, int, int]],
+        dim: int,
+    ):
+        super().__init__()
+        t, h, w = _triple(patch_size)
+        self.patch_size = t, h, w
+        self.proj = nn.Linear(dim, out_channels * t * h * w)
+
+    def forward(
+        self,
+        vid: torch.Tensor,
+    ) -> torch.Tensor:
+        t, h, w = self.patch_size
+        vid = self.proj(vid)
+        vid = rearrange(vid, "b T H W (t h w c) -> b c (T t) (H h) (W w)", t=t, h=h, w=w)
+        return vid
+
+
+class NaPatchIn(PatchIn):
+    def forward(
+        self,
+        vid: torch.Tensor,  # l c
+        vid_shape: torch.LongTensor,
+    ) -> torch.Tensor:
+        t, h, w = self.patch_size
+        if not (t == h == w == 1):
+            vid, vid_shape = na.rearrange(
+                vid, vid_shape, "(T t) (H h) (W w) c -> T H W (t h w c)", t=t, h=h, w=w
+            )
+        # slice vid after patching in when using sequence parallelism
+        vid = slice_inputs(vid, dim=0)
+        vid = self.proj(vid)
+        return vid, vid_shape
+
+
+class NaPatchOut(PatchOut):
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,
+        cache: Cache = Cache(disable=True),
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.LongTensor,
+    ]:
+        t, h, w = self.patch_size
+        vid = self.proj(vid)
+        # gather vid before patching out when enabling sequence parallelism
+        vid = gather_outputs(
+            vid,
+            gather_dim=0,
+            padding_dim=0,
+            unpad_shape=vid_shape,
+            cache=cache.namespace("vid"),
+        )
+        if not (t == h == w == 1):
+            vid, vid_shape = na.rearrange(
+                vid, vid_shape, "T H W (t h w c) -> (T t) (H h) (W w) c", t=t, h=h, w=w
+            )
+        return vid, vid_shape
diff --git a/modules/seedvr/src/models/dit/rope.py b/modules/seedvr/src/models/dit/rope.py
new file mode 100644
index 000000000..3aded8fed
--- /dev/null
+++ b/modules/seedvr/src/models/dit/rope.py
@@ -0,0 +1,102 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from functools import lru_cache
+from typing import Tuple
+import torch
+from einops import rearrange
+from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
+from torch import nn
+
+from ...common.cache import Cache
+
+
+class RotaryEmbeddingBase(nn.Module):
+    def __init__(self, dim: int, rope_dim: int):
+        super().__init__()
+        self.rope = RotaryEmbedding(
+            dim=dim // rope_dim,
+            freqs_for="pixel",
+            max_freq=256,
+        )
+        # 1. Set model.requires_grad_(True) after model creation will make
+        #    the `requires_grad=False` for rope freqs no longer hold.
+        # 2. Even if we don't set requires_grad_(True) explicitly,
+        #    FSDP is not memory efficient when handling fsdp_wrap
+        #    with mixed requires_grad=True/False.
+        # With above consideration, it is easier just remove the freqs
+        # out of nn.Parameters when `learned_freq=False`
+        freqs = self.rope.freqs
+        del self.rope.freqs
+        self.rope.register_buffer("freqs", freqs.data)
+
+    @lru_cache(maxsize=128)
+    def get_axial_freqs(self, *dims):
+        return self.rope.get_axial_freqs(*dims)
+
+
+class RotaryEmbedding3d(RotaryEmbeddingBase):
+    def __init__(self, dim: int):
+        super().__init__(dim, rope_dim=3)
+
+    def forward(
+        self,
+        q: torch.FloatTensor,  # b h l d
+        k: torch.FloatTensor,  # b h l d
+        size: Tuple[int, int, int],
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        T, H, W = size
+        freqs = self.get_axial_freqs(T, H, W)
+        q = rearrange(q, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
+        k = rearrange(k, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
+        q = apply_rotary_emb(freqs, q)
+        k = apply_rotary_emb(freqs, k)
+        q = rearrange(q, "b h T H W d -> b h (T H W) d")
+        k = rearrange(k, "b h T H W d -> b h (T H W) d")
+        return q, k
+
+
+class NaRotaryEmbedding3d(RotaryEmbedding3d):
+    def forward(
+        self,
+        q: torch.FloatTensor,  # L h d
+        k: torch.FloatTensor,  # L h d
+        shape: torch.LongTensor,
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        freqs = cache("rope_freqs_3d", lambda: self.get_freqs(shape))
+        freqs = freqs.to(device=q.device, dtype=q.dtype)
+        q = rearrange(q, "L h d -> h L d")
+        k = rearrange(k, "L h d -> h L d")
+        q = apply_rotary_emb(freqs, q.float()).to(q.dtype)
+        k = apply_rotary_emb(freqs, k.float()).to(k.dtype)
+        q = rearrange(q, "h L d -> L h d")
+        k = rearrange(k, "h L d -> L h d")
+        return q, k
+
+    def get_freqs(
+        self,
+        shape: torch.LongTensor,
+    ) -> torch.Tensor:
+        freq_list = []
+        for f, h, w in shape.tolist():
+            freqs = self.get_axial_freqs(f, h, w)
+            freq_list.append(freqs.view(-1, freqs.size(-1)))
+        return torch.cat(freq_list, dim=0)
diff --git a/modules/seedvr/src/models/dit/window.py b/modules/seedvr/src/models/dit/window.py
new file mode 100644
index 000000000..f77ea57d7
--- /dev/null
+++ b/modules/seedvr/src/models/dit/window.py
@@ -0,0 +1,83 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from math import ceil
+from typing import Tuple
+import math
+
+def get_window_op(name: str):
+    if name == "720pwin_by_size_bysize":
+        return make_720Pwindows_bysize
+    if name == "720pswin_by_size_bysize":
+        return make_shifted_720Pwindows_bysize
+    raise ValueError(f"Unknown windowing method: {name}")
+
+
+# -------------------------------- Windowing -------------------------------- #
+def make_720Pwindows_bysize(size: Tuple[int, int, int], num_windows: Tuple[int, int, int]):
+    t, h, w = size
+    resized_nt, resized_nh, resized_nw = num_windows
+    #cal windows under 720p
+    scale = math.sqrt((45 * 80) / (h * w))
+    resized_h, resized_w = round(h * scale), round(w * scale)
+    wh, ww = ceil(resized_h / resized_nh), ceil(resized_w / resized_nw)  # window size.
+    wt = ceil(min(t, 30) / resized_nt)  # window size.
+    nt, nh, nw = ceil(t / wt), ceil(h / wh), ceil(w / ww)  # window size.
+    return [
+        (
+            slice(it * wt, min((it + 1) * wt, t)),
+            slice(ih * wh, min((ih + 1) * wh, h)),
+            slice(iw * ww, min((iw + 1) * ww, w)),
+        )
+        for iw in range(nw)
+        if min((iw + 1) * ww, w) > iw * ww
+        for ih in range(nh)
+        if min((ih + 1) * wh, h) > ih * wh
+        for it in range(nt)
+        if min((it + 1) * wt, t) > it * wt
+    ]
+
+def make_shifted_720Pwindows_bysize(size: Tuple[int, int, int], num_windows: Tuple[int, int, int]):
+    t, h, w = size
+    resized_nt, resized_nh, resized_nw = num_windows
+    #cal windows under 720p
+    scale = math.sqrt((45 * 80) / (h * w))
+    resized_h, resized_w = round(h * scale), round(w * scale)
+    wh, ww = ceil(resized_h / resized_nh), ceil(resized_w / resized_nw)  # window size.
+    wt = ceil(min(t, 30) / resized_nt)  # window size.
+
+    st, sh, sw = (  # shift size.
+        0.5 if wt < t else 0,
+        0.5 if wh < h else 0,
+        0.5 if ww < w else 0,
+    )
+    nt, nh, nw = ceil((t - st) / wt), ceil((h - sh) / wh), ceil((w - sw) / ww)  # window size.
+    nt, nh, nw = (  # number of window.
+        nt + 1 if st > 0 else 1,
+        nh + 1 if sh > 0 else 1,
+        nw + 1 if sw > 0 else 1,
+    )
+    return [
+        (
+            slice(max(int((it - st) * wt), 0), min(int((it - st + 1) * wt), t)),
+            slice(max(int((ih - sh) * wh), 0), min(int((ih - sh + 1) * wh), h)),
+            slice(max(int((iw - sw) * ww), 0), min(int((iw - sw + 1) * ww), w)),
+        )
+        for iw in range(nw)
+        if min(int((iw - sw + 1) * ww), w) > max(int((iw - sw) * ww), 0)
+        for ih in range(nh)
+        if min(int((ih - sh + 1) * wh), h) > max(int((ih - sh) * wh), 0)
+        for it in range(nt)
+        if min(int((it - st + 1) * wt), t) > max(int((it - st) * wt), 0)
+    ]
diff --git a/modules/seedvr/src/models/dit_v2/attention.py b/modules/seedvr/src/models/dit_v2/attention.py
new file mode 100644
index 000000000..5a93ba30a
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/attention.py
@@ -0,0 +1,93 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import torch
+import torch.nn.functional as F
+
+#from flash_attn import flash_attn_varlen_func
+
+from torch import nn
+
+
+def pytorch_varlen_attention(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p=0.0, softmax_scale=None, causal=False, deterministic=False):
+    """
+    A PyTorch-based implementation of variable-length attention to replace flash_attn_varlen_func.
+    It processes each sequence in the batch individually.
+    """
+    # Create an empty tensor to store the output.
+    output = torch.empty_like(q)
+
+    # Iterate over each sequence in the batch. The batch size is the number of sequences.
+    for i in range(len(cu_seqlens_q) - 1):
+        # Determine the start and end indices for the current sequence.
+        start_q, end_q = cu_seqlens_q[i], cu_seqlens_q[i+1]
+        start_k, end_k = cu_seqlens_k[i], cu_seqlens_k[i+1]
+
+        # Slice the q, k, and v tensors to get the data for the current sequence.
+        # The shape is (seq_len, heads, head_dim).
+        q_i = q[start_q:end_q]
+        k_i = k[start_k:end_k]
+        v_i = v[start_k:end_k]
+
+        # Reshape for torch's scaled_dot_product_attention which expects (batch, heads, seq, dim).
+        # Here, we treat each sequence as a batch of 1.
+        q_i = q_i.permute(1, 0, 2).unsqueeze(0) # (1, heads, seq_len_q, head_dim)
+        k_i = k_i.permute(1, 0, 2).unsqueeze(0) # (1, heads, seq_len_k, head_dim)
+        v_i = v_i.permute(1, 0, 2).unsqueeze(0) # (1, heads, seq_len_k, head_dim)
+
+        # Use PyTorch's built-in scaled dot-product attention.
+        output_i = F.scaled_dot_product_attention(
+            q_i, k_i, v_i,
+            dropout_p=dropout_p if not deterministic else 0.0,
+            is_causal=causal
+        )
+
+        # Reshape the output back to the original format (seq_len, heads, head_dim)
+        output_i = output_i.squeeze(0).permute(1, 0, 2)
+
+        # Place the result for the current sequence into the main output tensor.
+        output[start_q:end_q] = output_i
+
+    return output
+
+class TorchAttention(nn.Module):
+    def tflops(self, args, kwargs, output) -> float:
+        assert len(args) == 0 or len(args) > 2, "query, key should both provided by args / kwargs"
+        q = kwargs.get("query") or args[0]
+        k = kwargs.get("key") or args[1]
+        b, h, sq, d = q.shape
+        b, h, sk, d = k.shape
+        return b * h * (4 * d * (sq / 1e6) * (sk / 1e6))
+
+    def forward(self, *args, **kwargs):
+        return F.scaled_dot_product_attention(*args, **kwargs)
+
+
+class FlashAttentionVarlen(nn.Module):
+    def tflops(self, args, kwargs, output) -> float:
+        cu_seqlens_q = kwargs["cu_seqlens_q"]
+        cu_seqlens_k = kwargs["cu_seqlens_k"]
+        _, h, d = output.shape
+        seqlens_q = (cu_seqlens_q[1:] - cu_seqlens_q[:-1]) / 1e6
+        seqlens_k = (cu_seqlens_k[1:] - cu_seqlens_k[:-1]) / 1e6
+        return h * (4 * d * (seqlens_q * seqlens_k).sum())
+
+    def forward(self, *args, **kwargs):
+        kwargs["deterministic"] = torch.are_deterministic_algorithms_enabled()
+        try:
+            from flash_attn import flash_attn_varlen_func
+            return flash_attn_varlen_func(*args, **kwargs)
+        except ImportError:
+            return pytorch_varlen_attention(*args, **kwargs)
+
diff --git a/modules/seedvr/src/models/dit_v2/embedding.py b/modules/seedvr/src/models/dit_v2/embedding.py
new file mode 100644
index 000000000..e972244f5
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/embedding.py
@@ -0,0 +1,62 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Optional, Union
+import torch
+from diffusers.models.embeddings import get_timestep_embedding
+from torch import nn
+
+
+def emb_add(emb1: torch.Tensor, emb2: Optional[torch.Tensor]):
+    return emb1 if emb2 is None else emb1 + emb2
+
+
+class TimeEmbedding(nn.Module):
+    def __init__(
+        self,
+        sinusoidal_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+    ):
+        super().__init__()
+        self.sinusoidal_dim = sinusoidal_dim
+        self.proj_in = nn.Linear(sinusoidal_dim, hidden_dim)
+        self.proj_hid = nn.Linear(hidden_dim, hidden_dim)
+        self.proj_out = nn.Linear(hidden_dim, output_dim)
+        self.act = nn.SiLU()
+
+    def forward(
+        self,
+        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> torch.FloatTensor:
+        if not torch.is_tensor(timestep):
+            timestep = torch.tensor([timestep], device=device, dtype=dtype)
+        if timestep.ndim == 0:
+            timestep = timestep[None]
+
+        emb = get_timestep_embedding(
+            timesteps=timestep,
+            embedding_dim=self.sinusoidal_dim,
+            flip_sin_to_cos=False,
+            downscale_freq_shift=0,
+        )
+        emb = emb.to(dtype)
+        emb = self.proj_in(emb)
+        emb = self.act(emb)
+        emb = self.proj_hid(emb)
+        emb = self.act(emb)
+        emb = self.proj_out(emb)
+        return emb
diff --git a/modules/seedvr/src/models/dit_v2/mlp.py b/modules/seedvr/src/models/dit_v2/mlp.py
new file mode 100644
index 000000000..2d05cb021
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/mlp.py
@@ -0,0 +1,62 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Optional
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+def get_mlp(mlp_type: Optional[str] = "normal"):
+    if mlp_type == "normal":
+        return MLP
+    elif mlp_type == "swiglu":
+        return SwiGLUMLP
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        expand_ratio: int,
+    ):
+        super().__init__()
+        self.proj_in = nn.Linear(dim, dim * expand_ratio)
+        self.act = nn.GELU("tanh")
+        self.proj_out = nn.Linear(dim * expand_ratio, dim)
+
+    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        x = self.proj_in(x)
+        x = self.act(x)
+        x = self.proj_out(x)
+        return x
+
+
+class SwiGLUMLP(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        expand_ratio: int,
+        multiple_of: int = 256,
+    ):
+        super().__init__()
+        hidden_dim = int(2 * dim * expand_ratio / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        self.proj_in_gate = nn.Linear(dim, hidden_dim, bias=False)
+        self.proj_out = nn.Linear(hidden_dim, dim, bias=False)
+        self.proj_in = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        x = self.proj_out(F.silu(self.proj_in_gate(x)) * self.proj_in(x))
+        return x
diff --git a/modules/seedvr/src/models/dit_v2/mm.py b/modules/seedvr/src/models/dit_v2/mm.py
new file mode 100644
index 000000000..344f89a8f
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/mm.py
@@ -0,0 +1,74 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Tuple
+import torch
+from torch import nn
+
+
+@dataclass
+class MMArg:
+    vid: Any
+    txt: Any
+
+
+def get_args(key: str, args: List[Any]) -> List[Any]:
+    return [getattr(v, key) if isinstance(v, MMArg) else v for v in args]
+
+
+def get_kwargs(key: str, kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    return {k: getattr(v, key) if isinstance(v, MMArg) else v for k, v in kwargs.items()}
+
+
+class MMModule(nn.Module):
+    def __init__(
+        self,
+        module: Callable[..., nn.Module],
+        *args,
+        shared_weights: bool = False,
+        vid_only: bool = False,
+        **kwargs,
+    ):
+        super().__init__()
+        self.shared_weights = shared_weights
+        self.vid_only = vid_only
+        if self.shared_weights:
+            assert get_args("vid", args) == get_args("txt", args)
+            assert get_kwargs("vid", kwargs) == get_kwargs("txt", kwargs)
+            self.all = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
+        else:
+            self.vid = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
+            self.txt = (
+                module(*get_args("txt", args), **get_kwargs("txt", kwargs))
+                if not vid_only
+                else None
+            )
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,
+        txt: torch.FloatTensor,
+        *args,
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        vid_module = self.vid if not self.shared_weights else self.all
+        vid = vid_module(vid, *get_args("vid", args), **get_kwargs("vid", kwargs))
+        if not self.vid_only:
+            txt_module = self.txt if not self.shared_weights else self.all
+            txt = txt_module(txt, *get_args("txt", args), **get_kwargs("txt", kwargs))
+        return vid, txt
diff --git a/modules/seedvr/src/models/dit_v2/modulation.py b/modules/seedvr/src/models/dit_v2/modulation.py
new file mode 100644
index 000000000..a10c49f3e
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/modulation.py
@@ -0,0 +1,118 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Callable, List, Optional
+import torch
+from einops import rearrange
+from torch import nn
+
+from ...common.cache import Cache
+from ...common.distributed.ops import slice_inputs
+
+# (dim: int, emb_dim: int)
+ada_layer_type = Callable[[int, int], nn.Module]
+
+
+def get_ada_layer(ada_layer: str) -> ada_layer_type:
+    if ada_layer == "single":
+        return AdaSingle
+    raise NotImplementedError(f"{ada_layer} is not supported")
+
+
+def expand_dims(x: torch.Tensor, dim: int, ndim: int):
+    """
+    Expand tensor "x" to "ndim" by adding empty dims at "dim".
+    Example: x is (b d), target ndim is 5, add dim at 1, return (b 1 1 1 d).
+    """
+    shape = x.shape
+    shape = shape[:dim] + (1,) * (ndim - len(shape)) + shape[dim:]
+    return x.reshape(shape)
+
+
+class AdaSingle(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        emb_dim: int,
+        layers: List[str],
+        modes: List[str] = ["in", "out"],
+    ):
+        assert emb_dim == 6 * dim, "AdaSingle requires emb_dim == 6 * dim"
+        super().__init__()
+        self.dim = dim
+        self.emb_dim = emb_dim
+        self.layers = layers
+        for l in layers:
+            if "in" in modes:
+                self.register_parameter(f"{l}_shift", nn.Parameter(torch.randn(dim) / dim**0.5))
+                self.register_parameter(
+                    f"{l}_scale", nn.Parameter(torch.randn(dim) / dim**0.5 + 1)
+                )
+            if "out" in modes:
+                self.register_parameter(f"{l}_gate", nn.Parameter(torch.randn(dim) / dim**0.5))
+
+    def forward(
+        self,
+        hid: torch.FloatTensor,  # b ... c
+        emb: torch.FloatTensor,  # b d
+        layer: str,
+        mode: str,
+        cache: Cache = Cache(disable=True),
+        branch_tag: str = "",
+        hid_len: Optional[torch.LongTensor] = None,  # b
+    ) -> torch.FloatTensor:
+        idx = self.layers.index(layer)
+        emb = rearrange(emb, "b (d l g) -> b d l g", l=len(self.layers), g=3)[..., idx, :]
+        emb = expand_dims(emb, 1, hid.ndim + 1)
+
+        if hid_len is not None:
+            emb = cache(
+                f"emb_repeat_{idx}_{branch_tag}",
+                lambda: slice_inputs(
+                    torch.cat([e.repeat(l, *([1] * e.ndim)) for e, l in zip(emb, hid_len)]),
+                    dim=0,
+                ),
+            )
+
+        shiftA, scaleA, gateA = emb.unbind(-1)
+        shiftB, scaleB, gateB = (
+            getattr(self, f"{layer}_shift", None),
+            getattr(self, f"{layer}_scale", None),
+            getattr(self, f"{layer}_gate", None),
+        )
+
+        # 🚀 FP8 COMPATIBILITY: Convert parameters to match embedding dtype
+        # This prevents "Promotion for Float8 Types is not supported" errors
+        target_dtype = shiftA.dtype
+
+        if mode == "in":
+            # Convert parameters to match embedding dtype for FP8 compatibility
+            if scaleB is not None and scaleB.dtype != target_dtype:
+                scaleB = scaleB.to(target_dtype)
+            if shiftB is not None and shiftB.dtype != target_dtype:
+                shiftB = shiftB.to(target_dtype)
+
+            return hid.mul_(scaleA + scaleB).add_(shiftA + shiftB)
+
+        if mode == "out":
+            # Convert gate parameter to match embedding dtype for FP8 compatibility
+            if gateB is not None and gateB.dtype != target_dtype:
+                gateB = gateB.to(target_dtype)
+
+            return hid.mul_(gateA + gateB)
+
+        raise NotImplementedError
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, emb_dim={self.emb_dim}, layers={self.layers}"
diff --git a/modules/seedvr/src/models/dit_v2/na.py b/modules/seedvr/src/models/dit_v2/na.py
new file mode 100644
index 000000000..0dbd546c4
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/na.py
@@ -0,0 +1,241 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from itertools import chain
+from typing import Callable, Dict, List, Tuple
+import einops
+import torch
+
+
+def flatten(
+    hid: List[torch.FloatTensor],  # List of (*** c)
+) -> Tuple[
+    torch.FloatTensor,  # (L c)
+    torch.LongTensor,  # (b n)
+]:
+    assert len(hid) > 0
+    shape = torch.stack([torch.tensor(x.shape[:-1], device=hid[0].device) for x in hid])
+    hid = torch.cat([x.flatten(0, -2) for x in hid])
+    return hid, shape
+
+
+def unflatten(
+    hid: torch.FloatTensor,  # (L c) or (L ... c)
+    hid_shape: torch.LongTensor,  # (b n)
+) -> List[torch.Tensor]:  # List of (*** c) or (*** ... c)
+    hid_len = hid_shape.prod(-1)
+    hid = hid.split(hid_len.tolist())
+    hid = [x.unflatten(0, s.tolist()) for x, s in zip(hid, hid_shape)]
+    return hid
+
+
+def concat(
+    vid: torch.FloatTensor,  # (VL ... c)
+    txt: torch.FloatTensor,  # (TL ... c)
+    vid_len: torch.LongTensor,  # (b)
+    txt_len: torch.LongTensor,  # (b)
+) -> torch.FloatTensor:  # (L ... c)
+    vid = torch.split(vid, vid_len.tolist())
+    txt = torch.split(txt, txt_len.tolist())
+    return torch.cat(list(chain(*zip(vid, txt))))
+
+
+def concat_idx(
+    vid_len: torch.LongTensor,  # (b)
+    txt_len: torch.LongTensor,  # (b)
+) -> Tuple[
+    Callable,
+    Callable,
+]:
+    device = vid_len.device
+    vid_idx = torch.arange(vid_len.sum(), device=device)
+    txt_idx = torch.arange(len(vid_idx), len(vid_idx) + txt_len.sum(), device=device)
+    tgt_idx = concat(vid_idx, txt_idx, vid_len, txt_len)
+    src_idx = torch.argsort(tgt_idx)
+    return (
+        lambda vid, txt: torch.index_select(torch.cat([vid, txt]), 0, tgt_idx),
+        lambda all: torch.index_select(all, 0, src_idx).split([len(vid_idx), len(txt_idx)]),
+    )
+
+
+def unconcat(
+    all: torch.FloatTensor,  # (L ... c)
+    vid_len: torch.LongTensor,  # (b)
+    txt_len: torch.LongTensor,  # (b)
+) -> Tuple[
+    torch.FloatTensor,  # (VL ... c)
+    torch.FloatTensor,  # (TL ... c)
+]:
+    interleave_len = list(chain(*zip(vid_len.tolist(), txt_len.tolist())))
+    all = all.split(interleave_len)
+    vid = torch.cat(all[0::2])
+    txt = torch.cat(all[1::2])
+    return vid, txt
+
+
+def repeat_concat(
+    vid: torch.FloatTensor,  # (VL ... c)
+    txt: torch.FloatTensor,  # (TL ... c)
+    vid_len: torch.LongTensor,  # (n*b)
+    txt_len: torch.LongTensor,  # (b)
+    txt_repeat: List,  # (n)
+) -> torch.FloatTensor:  # (L ... c)
+    vid = torch.split(vid, vid_len.tolist())
+    txt = torch.split(txt, txt_len.tolist())
+    txt = [[x] * n for x, n in zip(txt, txt_repeat)]
+    txt = list(chain(*txt))
+    return torch.cat(list(chain(*zip(vid, txt))))
+
+
+def repeat_concat_idx(
+    vid_len: torch.LongTensor,  # (n*b)
+    txt_len: torch.LongTensor,  # (b)
+    txt_repeat: torch.LongTensor,  # (n)
+) -> Tuple[
+    Callable,
+    Callable,
+]:
+    device = vid_len.device
+    vid_idx = torch.arange(vid_len.sum(), device=device)
+    txt_idx = torch.arange(len(vid_idx), len(vid_idx) + txt_len.sum(), device=device)
+    txt_repeat_list = txt_repeat.tolist()
+    tgt_idx = repeat_concat(vid_idx, txt_idx, vid_len, txt_len, txt_repeat)
+    src_idx = torch.argsort(tgt_idx)
+    txt_idx_len = len(tgt_idx) - len(vid_idx)
+    repeat_txt_len = (txt_len * txt_repeat).tolist()
+
+    def unconcat_coalesce(all):
+        """
+        Un-concat vid & txt, and coalesce the repeated txt.
+        e.g. vid [0 1 2 3 4 5 6 7 8] -> 3 splits -> [0 1 2] [3 4 5] [6 7 8]
+             txt [9 10]
+             repeat_concat ==> [0 1 2 9 10 3 4 5 9 10 6 7 8 9 10]
+             1. argsort re-index ==> [0 1 2 3 4 5 6 7 8 9 9 9 10 10 10]
+                           split ==> vid_out [0 1 2 3 4 5 6 7 8] txt_out [9 9 9 10 10 10]
+             2. reshape & mean for each sample to coalesce the repeated txt.
+        """
+        vid_out, txt_out = all[src_idx].split([len(vid_idx), txt_idx_len])
+        txt_out_coalesced = []
+        for txt, repeat_time in zip(txt_out.split(repeat_txt_len), txt_repeat_list):
+            txt = txt.reshape(-1, repeat_time, *txt.shape[1:]).mean(1)
+            txt_out_coalesced.append(txt)
+        return vid_out, torch.cat(txt_out_coalesced)
+
+    # Note: Backward of torch.index_select is non-deterministic when existing repeated index,
+    # the difference may cumulative like torch.repeat_interleave, so we use vanilla index here.
+    return (
+        lambda vid, txt: torch.cat([vid, txt])[tgt_idx],
+        lambda all: unconcat_coalesce(all),
+    )
+
+
+def rearrange(
+    hid: torch.FloatTensor,  # (L c)
+    hid_shape: torch.LongTensor,  # (b n)
+    pattern: str,
+    **kwargs: Dict[str, int],
+) -> Tuple[
+    torch.FloatTensor,
+    torch.LongTensor,
+]:
+    return flatten([einops.rearrange(h, pattern, **kwargs) for h in unflatten(hid, hid_shape)])
+
+
+def rearrange_idx(
+    hid_shape: torch.LongTensor,  # (b n)
+    pattern: str,
+    **kwargs: Dict[str, int],
+) -> Tuple[Callable, Callable, torch.LongTensor]:
+    hid_idx = torch.arange(hid_shape.prod(-1).sum(), device=hid_shape.device).unsqueeze(-1)
+    tgt_idx, tgt_shape = rearrange(hid_idx, hid_shape, pattern, **kwargs)
+    tgt_idx = tgt_idx.squeeze(-1)
+    src_idx = torch.argsort(tgt_idx)
+    return (
+        lambda hid: torch.index_select(hid, 0, tgt_idx),
+        lambda hid: torch.index_select(hid, 0, src_idx),
+        tgt_shape,
+    )
+
+
+def repeat(
+    hid: torch.FloatTensor,  # (L c)
+    hid_shape: torch.LongTensor,  # (b n)
+    pattern: str,
+    **kwargs: Dict[str, torch.LongTensor],  # (b)
+) -> Tuple[
+    torch.FloatTensor,
+    torch.LongTensor,
+]:
+    hid = unflatten(hid, hid_shape)
+    kwargs = [{k: v[i].item() for k, v in kwargs.items()} for i in range(len(hid))]
+    return flatten([einops.repeat(h, pattern, **a) for h, a in zip(hid, kwargs)])
+
+
+def pack(
+    samples: List[torch.Tensor],  # List of (h w c).
+) -> Tuple[
+    List[torch.Tensor],  # groups [(b1 h1 w1 c1), (b2 h2 w2 c2)]
+    List[List[int]],  # reversal indices.
+]:
+    batches = {}
+    indices = {}
+    for i, sample in enumerate(samples):
+        shape = sample.shape
+        batches[shape] = batches.get(shape, [])
+        indices[shape] = indices.get(shape, [])
+        batches[shape].append(sample)
+        indices[shape].append(i)
+
+    batches = list(map(torch.stack, batches.values()))
+    indices = list(indices.values())
+    return batches, indices
+
+
+def unpack(
+    batches: List[torch.Tensor],
+    indices: List[List[int]],
+) -> List[torch.Tensor]:
+    samples = [None] * (max(chain(*indices)) + 1)
+    for batch, index in zip(batches, indices):
+        for sample, i in zip(batch.unbind(), index):
+            samples[i] = sample
+    return samples
+
+
+def window(
+    hid: torch.FloatTensor,  # (L c)
+    hid_shape: torch.LongTensor,  # (b n)
+    window_fn: Callable[[torch.Tensor], List[torch.Tensor]],
+):
+    hid = unflatten(hid, hid_shape)
+    hid = list(map(window_fn, hid))
+    hid_windows = torch.tensor(list(map(len, hid)), device=hid_shape.device)
+    hid, hid_shape = flatten(list(chain(*hid)))
+    return hid, hid_shape, hid_windows
+
+
+def window_idx(
+    hid_shape: torch.LongTensor,  # (b n)
+    window_fn: Callable[[torch.Tensor], List[torch.Tensor]],
+):
+    hid_idx = torch.arange(hid_shape.prod(-1).sum(), device=hid_shape.device).unsqueeze(-1)
+    tgt_idx, tgt_shape, tgt_windows = window(hid_idx, hid_shape, window_fn)
+    tgt_idx = tgt_idx.squeeze(-1)
+    src_idx = torch.argsort(tgt_idx)
+    return (
+        lambda hid: torch.index_select(hid, 0, tgt_idx),
+        lambda hid: torch.index_select(hid, 0, src_idx),
+        tgt_shape,
+        tgt_windows,
+    )
diff --git a/modules/seedvr/src/models/dit_v2/nablocks/__init__.py b/modules/seedvr/src/models/dit_v2/nablocks/__init__.py
new file mode 100644
index 000000000..c1a9da26e
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/nablocks/__init__.py
@@ -0,0 +1,26 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from .mmsr_block import NaMMSRTransformerBlock
+
+
+nadit_blocks = {
+    "mmdit_sr": NaMMSRTransformerBlock,
+}
+
+
+def get_nablock(block_type: str):
+    if block_type in nadit_blocks:
+        return nadit_blocks[block_type]
+    raise NotImplementedError(f"{block_type} is not supported")
diff --git a/modules/seedvr/src/models/dit_v2/nablocks/attention/__init__.py b/modules/seedvr/src/models/dit_v2/nablocks/attention/__init__.py
new file mode 100644
index 000000000..a75610252
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/nablocks/attention/__init__.py
@@ -0,0 +1,25 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from .mmattn import NaMMAttention
+
+attns = {
+    "mm_full": NaMMAttention,
+}
+
+
+def get_attn(attn_type: str):
+    if attn_type in attns:
+        return attns[attn_type]
+    raise NotImplementedError(f"{attn_type} is not supported")
diff --git a/modules/seedvr/src/models/dit_v2/nablocks/attention/mmattn.py b/modules/seedvr/src/models/dit_v2/nablocks/attention/mmattn.py
new file mode 100644
index 000000000..70f3bb90d
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/nablocks/attention/mmattn.py
@@ -0,0 +1,267 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Optional, Tuple, Union
+import torch
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.modules.utils import _triple
+
+from .....common.cache import Cache
+from .....common.distributed.ops import gather_heads_scatter_seq, gather_seq_scatter_heads_qkv
+from .....common.half_precision_fixes import safe_pad_operation
+
+from ... import na
+from ...attention import FlashAttentionVarlen
+from ...mm import MMArg, MMModule
+from ...normalization import norm_layer_type
+from ...rope import get_na_rope
+from ...window import get_window_op
+from itertools import chain
+
+
+class NaMMAttention(nn.Module):
+    def __init__(
+        self,
+        vid_dim: int,
+        txt_dim: int,
+        heads: int,
+        head_dim: int,
+        qk_bias: bool,
+        qk_norm: norm_layer_type,
+        qk_norm_eps: float,
+        rope_type: Optional[str],
+        rope_dim: int,
+        shared_weights: bool,
+        **kwargs,
+    ):
+        super().__init__()
+        dim = MMArg(vid_dim, txt_dim)
+        inner_dim = heads * head_dim
+        qkv_dim = inner_dim * 3
+        self.head_dim = head_dim
+        self.proj_qkv = MMModule(
+            nn.Linear, dim, qkv_dim, bias=qk_bias, shared_weights=shared_weights
+        )
+        self.proj_out = MMModule(nn.Linear, inner_dim, dim, shared_weights=shared_weights)
+        self.norm_q = MMModule(
+            qk_norm,
+            dim=head_dim,
+            eps=qk_norm_eps,
+            elementwise_affine=True,
+            shared_weights=shared_weights,
+        )
+        self.norm_k = MMModule(
+            qk_norm,
+            dim=head_dim,
+            eps=qk_norm_eps,
+            elementwise_affine=True,
+            shared_weights=shared_weights,
+        )
+
+        self.rope = get_na_rope(rope_type=rope_type, dim=rope_dim)
+        self.attn = FlashAttentionVarlen()
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        vid_qkv, txt_qkv = self.proj_qkv(vid, txt)
+        vid_qkv = gather_seq_scatter_heads_qkv(
+            vid_qkv,
+            seq_dim=0,
+            qkv_shape=vid_shape,
+            cache=cache.namespace("vid"),
+        )
+        txt_qkv = gather_seq_scatter_heads_qkv(
+            txt_qkv,
+            seq_dim=0,
+            qkv_shape=txt_shape,
+            cache=cache.namespace("txt"),
+        )
+        vid_qkv = rearrange(vid_qkv, "l (o h d) -> l o h d", o=3, d=self.head_dim)
+        txt_qkv = rearrange(txt_qkv, "l (o h d) -> l o h d", o=3, d=self.head_dim)
+
+        vid_q, vid_k, vid_v = vid_qkv.unbind(1)
+        txt_q, txt_k, txt_v = txt_qkv.unbind(1)
+
+        vid_q, txt_q = self.norm_q(vid_q, txt_q)
+        vid_k, txt_k = self.norm_k(vid_k, txt_k)
+
+        if self.rope:
+            if self.rope.mm:
+                vid_q, vid_k, txt_q, txt_k = self.rope(
+                    vid_q, vid_k, vid_shape, txt_q, txt_k, txt_shape, cache
+                )
+            else:
+                vid_q, vid_k = self.rope(vid_q, vid_k, vid_shape, cache)
+
+        vid_len = cache("vid_len", lambda: vid_shape.prod(-1))
+        txt_len = cache("txt_len", lambda: txt_shape.prod(-1))
+        all_len = cache("all_len", lambda: vid_len + txt_len)
+
+        concat, unconcat = cache("mm_pnp", lambda: na.concat_idx(vid_len, txt_len))
+
+        attn = self.attn(
+            q=concat(vid_q, txt_q).bfloat16(),
+            k=concat(vid_k, txt_k).bfloat16(),
+            v=concat(vid_v, txt_v).bfloat16(),
+            cu_seqlens_q=cache("mm_seqlens", lambda: safe_pad_operation(all_len.cumsum(0), (1, 0)).int()),
+            cu_seqlens_k=cache("mm_seqlens", lambda: safe_pad_operation(all_len.cumsum(0), (1, 0)).int()),
+            max_seqlen_q=cache("mm_maxlen", lambda: all_len.max().item()),
+            max_seqlen_k=cache("mm_maxlen", lambda: all_len.max().item()),
+        ).type_as(vid_q)
+
+        attn = rearrange(attn, "l h d -> l (h d)")
+        vid_out, txt_out = unconcat(attn)
+        vid_out = gather_heads_scatter_seq(vid_out, head_dim=1, seq_dim=0)
+        txt_out = gather_heads_scatter_seq(txt_out, head_dim=1, seq_dim=0)
+
+        vid_out, txt_out = self.proj_out(vid_out, txt_out)
+        return vid_out, txt_out
+
+
+class NaSwinAttention(NaMMAttention):
+    def __init__(
+        self,
+        *args,
+        window: Union[int, Tuple[int, int, int]],
+        window_method: str,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.window = _triple(window)
+        self.window_method = window_method
+        assert all(map(lambda v: isinstance(v, int) and v >= 0, self.window))
+
+        self.window_op = get_window_op(window_method)
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+
+        vid_qkv, txt_qkv = self.proj_qkv(vid, txt)
+        vid_qkv = gather_seq_scatter_heads_qkv(
+            vid_qkv,
+            seq_dim=0,
+            qkv_shape=vid_shape,
+            cache=cache.namespace("vid"),
+        )
+        txt_qkv = gather_seq_scatter_heads_qkv(
+            txt_qkv,
+            seq_dim=0,
+            qkv_shape=txt_shape,
+            cache=cache.namespace("txt"),
+        )
+
+        # re-org the input seq for window attn
+        cache_win = cache.namespace(f"{self.window_method}_{self.window}_sd3")
+
+        def make_window(x: torch.Tensor):
+            t, h, w, _ = x.shape
+            window_slices = self.window_op((t, h, w), self.window)
+            return [x[st, sh, sw] for (st, sh, sw) in window_slices]
+
+        window_partition, window_reverse, window_shape, window_count = cache_win(
+            "win_transform",
+            lambda: na.window_idx(vid_shape, make_window),
+        )
+        vid_qkv_win = window_partition(vid_qkv)
+
+        vid_qkv_win = rearrange(vid_qkv_win, "l (o h d) -> l o h d", o=3, d=self.head_dim)
+        txt_qkv = rearrange(txt_qkv, "l (o h d) -> l o h d", o=3, d=self.head_dim)
+
+        vid_q, vid_k, vid_v = vid_qkv_win.unbind(1)
+        txt_q, txt_k, txt_v = txt_qkv.unbind(1)
+
+        vid_q, txt_q = self.norm_q(vid_q, txt_q)
+        vid_k, txt_k = self.norm_k(vid_k, txt_k)
+
+        txt_len = cache("txt_len", lambda: txt_shape.prod(-1))
+
+        vid_len_win = cache_win("vid_len", lambda: window_shape.prod(-1))
+        txt_len_win = cache_win("txt_len", lambda: txt_len.repeat_interleave(window_count))
+        all_len_win = cache_win("all_len", lambda: vid_len_win + txt_len_win)
+        concat_win, unconcat_win = cache_win(
+            "mm_pnp", lambda: na.repeat_concat_idx(vid_len_win, txt_len, window_count)
+        )
+
+        # window rope
+        if self.rope:
+            if self.rope.mm:
+                # repeat text q and k for window mmrope
+                _, num_h, _ = txt_q.shape
+                txt_q_repeat = rearrange(txt_q, "l h d -> l (h d)")
+                txt_q_repeat = na.unflatten(txt_q_repeat, txt_shape)
+                txt_q_repeat = [[x] * n for x, n in zip(txt_q_repeat, window_count)]
+                txt_q_repeat = list(chain(*txt_q_repeat))
+                txt_q_repeat, txt_shape_repeat = na.flatten(txt_q_repeat)
+                txt_q_repeat = rearrange(txt_q_repeat, "l (h d) -> l h d", h=num_h)
+
+                txt_k_repeat = rearrange(txt_k, "l h d -> l (h d)")
+                txt_k_repeat = na.unflatten(txt_k_repeat, txt_shape)
+                txt_k_repeat = [[x] * n for x, n in zip(txt_k_repeat, window_count)]
+                txt_k_repeat = list(chain(*txt_k_repeat))
+                txt_k_repeat, _ = na.flatten(txt_k_repeat)
+                txt_k_repeat = rearrange(txt_k_repeat, "l (h d) -> l h d", h=num_h)
+
+                vid_q, vid_k, txt_q, txt_k = self.rope(
+                    vid_q, vid_k, window_shape, txt_q_repeat, txt_k_repeat, txt_shape_repeat, cache_win
+                )
+            else:
+                vid_q, vid_k = self.rope(vid_q, vid_k, window_shape, cache_win)
+
+        out = self.attn(
+            q=concat_win(vid_q, txt_q).bfloat16(),
+            k=concat_win(vid_k, txt_k).bfloat16(),
+            v=concat_win(vid_v, txt_v).bfloat16(),
+            cu_seqlens_q=cache_win(
+                "vid_seqlens_q", lambda: safe_pad_operation(all_len_win.cumsum(0), (1, 0)).int()
+            ),
+            cu_seqlens_k=cache_win(
+                "vid_seqlens_k", lambda: safe_pad_operation(all_len_win.cumsum(0), (1, 0)).int()
+            ),
+            max_seqlen_q=cache_win("vid_max_seqlen_q", lambda: all_len_win.max().item()),
+            max_seqlen_k=cache_win("vid_max_seqlen_k", lambda: all_len_win.max().item()),
+        ).type_as(vid_q)
+
+        # text pooling
+        vid_out, txt_out = unconcat_win(out)
+
+        vid_out = rearrange(vid_out, "l h d -> l (h d)")
+        txt_out = rearrange(txt_out, "l h d -> l (h d)")
+        vid_out = window_reverse(vid_out)
+
+        vid_out = gather_heads_scatter_seq(vid_out, head_dim=1, seq_dim=0)
+        txt_out = gather_heads_scatter_seq(txt_out, head_dim=1, seq_dim=0)
+
+        vid_out, txt_out = self.proj_out(vid_out, txt_out)
+
+        return vid_out, txt_out
diff --git a/modules/seedvr/src/models/dit_v2/nablocks/mmsr_block.py b/modules/seedvr/src/models/dit_v2/nablocks/mmsr_block.py
new file mode 100644
index 000000000..52cb67e1f
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/nablocks/mmsr_block.py
@@ -0,0 +1,126 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Tuple
+import torch
+import torch.nn as nn
+
+# from ..cache import Cache
+from ....common.cache import Cache
+
+from .attention.mmattn import NaSwinAttention
+from ..mm import MMArg
+from ..modulation import ada_layer_type
+from ..normalization import norm_layer_type
+from ..mm import MMModule
+from ..mlp import get_mlp
+
+
+class NaMMSRTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        vid_dim: int,
+        txt_dim: int,
+        emb_dim: int,
+        heads: int,
+        head_dim: int,
+        expand_ratio: int,
+        norm: norm_layer_type,
+        norm_eps: float,
+        ada: ada_layer_type,
+        qk_bias: bool,
+        qk_norm: norm_layer_type,
+        mlp_type: str,
+        shared_weights: bool,
+        rope_type: str,
+        rope_dim: int,
+        is_last_layer: bool,
+        **kwargs,
+    ):
+        super().__init__()
+        dim = MMArg(vid_dim, txt_dim)
+        self.attn_norm = MMModule(norm, dim=dim, eps=norm_eps, elementwise_affine=False, shared_weights=shared_weights,)
+
+        self.attn = NaSwinAttention(
+            vid_dim=vid_dim,
+            txt_dim=txt_dim,
+            heads=heads,
+            head_dim=head_dim,
+            qk_bias=qk_bias,
+            qk_norm=qk_norm,
+            qk_norm_eps=norm_eps,
+            rope_type=rope_type,
+            rope_dim=rope_dim,
+            shared_weights=shared_weights,
+            window=kwargs.pop("window", None),
+            window_method=kwargs.pop("window_method", None),
+        )
+
+        self.mlp_norm = MMModule(norm, dim=dim, eps=norm_eps, elementwise_affine=False, shared_weights=shared_weights, vid_only=is_last_layer)
+        self.mlp = MMModule(
+            get_mlp(mlp_type),
+            dim=dim,
+            expand_ratio=expand_ratio,
+            shared_weights=shared_weights,
+            vid_only=is_last_layer
+        )
+        self.ada = MMModule(ada, dim=dim, emb_dim=emb_dim, layers=["attn", "mlp"], shared_weights=shared_weights, vid_only=is_last_layer)
+        self.is_last_layer = is_last_layer
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: torch.LongTensor,  # b 1
+        emb: torch.FloatTensor,
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+        torch.LongTensor,
+        torch.LongTensor,
+    ]:
+        hid_len = MMArg(
+            cache("vid_len", lambda: vid_shape.prod(-1)),
+            cache("txt_len", lambda: txt_shape.prod(-1)),
+        )
+        ada_kwargs = {
+            "emb": emb,
+            "hid_len": hid_len,
+            "cache": cache,
+            "branch_tag": MMArg("vid", "txt"),
+        }
+
+        vid_attn, txt_attn = self.attn_norm(vid, txt)
+
+        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, layer="attn", mode="in", **ada_kwargs)
+        vid_attn, txt_attn = self.attn(vid_attn, txt_attn, vid_shape, txt_shape, cache)
+        vid_attn, txt_attn = self.ada(vid_attn, txt_attn, layer="attn", mode="out", **ada_kwargs)
+        vid_attn, txt_attn = (vid_attn + vid), (txt_attn + txt)
+
+        vid_mlp, txt_mlp = self.mlp_norm(vid_attn, txt_attn)
+        # ADD BY NUMZ
+        if vid_mlp.dtype != vid_attn.dtype:
+            vid_mlp = vid_mlp.to(vid_attn.dtype)
+        if txt_mlp.dtype != txt_attn.dtype:
+            txt_mlp = txt_mlp.to(txt_attn.dtype)
+        # END BY NUMZ
+        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, layer="mlp", mode="in", **ada_kwargs)
+        vid_mlp, txt_mlp = self.mlp(vid_mlp, txt_mlp)
+        vid_mlp, txt_mlp = self.ada(vid_mlp, txt_mlp, layer="mlp", mode="out", **ada_kwargs)
+        vid_mlp, txt_mlp = (vid_mlp + vid_attn), (txt_mlp + txt_attn)
+
+        return vid_mlp, txt_mlp, vid_shape, txt_shape
diff --git a/modules/seedvr/src/models/dit_v2/nadit.py b/modules/seedvr/src/models/dit_v2/nadit.py
new file mode 100644
index 000000000..1f37ef8c3
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/nadit.py
@@ -0,0 +1,246 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union, Callable
+import torch
+from torch import nn
+
+from ...common.cache import Cache
+from ...common.distributed.ops import slice_inputs
+
+from . import na
+from .embedding import TimeEmbedding
+from .modulation import get_ada_layer
+from .nablocks import get_nablock
+from .normalization import get_norm_layer
+from .patch import get_na_patch_layers
+
+# Fake func, no checkpointing is required for inference
+def gradient_checkpointing(module: Union[Callable, nn.Module], *args, enabled: bool, **kwargs):
+    return module(*args, **kwargs)
+
+@dataclass
+class NaDiTOutput:
+    vid_sample: torch.Tensor
+
+
+class NaDiT(nn.Module):
+    """
+    Native Resolution Diffusion Transformer (NaDiT)
+    """
+
+    gradient_checkpointing = False
+
+    def __init__(
+        self,
+        vid_in_channels: int,
+        vid_out_channels: int,
+        vid_dim: int,
+        txt_in_dim: Union[int, List[int]],
+        txt_dim: Optional[int],
+        emb_dim: int,
+        heads: int,
+        head_dim: int,
+        expand_ratio: int,
+        norm: Optional[str],
+        norm_eps: float,
+        ada: str,
+        qk_bias: bool,
+        qk_norm: Optional[str],
+        patch_size: Union[int, Tuple[int, int, int]],
+        num_layers: int,
+        block_type: Union[str, Tuple[str]],
+        mm_layers: Union[int, Tuple[bool]],
+        mlp_type: str = "normal",
+        patch_type: str = "v1",
+        rope_type: Optional[str] = "rope3d",
+        rope_dim: Optional[int] = None,
+        window: Optional[Tuple] = None,
+        window_method: Optional[Tuple[str]] = None,
+        msa_type: Optional[Tuple[str]] = None,
+        mca_type: Optional[Tuple[str]] = None,
+        txt_in_norm: Optional[str] = None,
+        txt_in_norm_scale_factor: int = 0.01,
+        txt_proj_type: Optional[str] = "linear",
+        vid_out_norm: Optional[str] = None,
+        **kwargs,
+    ):
+        ada = get_ada_layer(ada)
+        norm = get_norm_layer(norm)
+        qk_norm = get_norm_layer(qk_norm)
+        rope_dim = rope_dim if rope_dim is not None else head_dim // 2
+        if isinstance(block_type, str):
+            block_type = [block_type] * num_layers
+        elif len(block_type) != num_layers:
+            raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
+        super().__init__()
+        NaPatchIn, NaPatchOut = get_na_patch_layers(patch_type)
+        self.vid_in = NaPatchIn(
+            in_channels=vid_in_channels,
+            patch_size=patch_size,
+            dim=vid_dim,
+        )
+        if not isinstance(txt_in_dim, int):
+            self.txt_in = nn.ModuleList([])
+            for in_dim in txt_in_dim:
+                txt_norm_layer = get_norm_layer(txt_in_norm)(txt_dim, norm_eps, True)
+                if txt_proj_type == "linear":
+                    txt_proj_layer = nn.Linear(in_dim, txt_dim)
+                else:
+                    txt_proj_layer = nn.Sequential(
+                        nn.Linear(in_dim, in_dim), nn.GELU("tanh"), nn.Linear(in_dim, txt_dim)
+                    )
+                torch.nn.init.constant_(txt_norm_layer.weight, txt_in_norm_scale_factor)
+                self.txt_in.append(
+                    nn.Sequential(
+                        txt_proj_layer,
+                        txt_norm_layer,
+                    )
+                )
+        else:
+            self.txt_in = (
+                nn.Linear(txt_in_dim, txt_dim)
+                if txt_in_dim and txt_in_dim != txt_dim
+                else nn.Identity()
+            )
+        self.emb_in = TimeEmbedding(
+            sinusoidal_dim=256,
+            hidden_dim=max(vid_dim, txt_dim),
+            output_dim=emb_dim,
+        )
+
+        if window is None or isinstance(window[0], int):
+            window = [window] * num_layers
+        if window_method is None or isinstance(window_method, str):
+            window_method = [window_method] * num_layers
+
+        if msa_type is None or isinstance(msa_type, str):
+            msa_type = [msa_type] * num_layers
+        if mca_type is None or isinstance(mca_type, str):
+            mca_type = [mca_type] * num_layers
+
+        self.blocks = nn.ModuleList(
+            [
+                get_nablock(block_type[i])(
+                    vid_dim=vid_dim,
+                    txt_dim=txt_dim,
+                    emb_dim=emb_dim,
+                    heads=heads,
+                    head_dim=head_dim,
+                    expand_ratio=expand_ratio,
+                    norm=norm,
+                    norm_eps=norm_eps,
+                    ada=ada,
+                    qk_bias=qk_bias,
+                    qk_norm=qk_norm,
+                    shared_weights=not (
+                        (i < mm_layers) if isinstance(mm_layers, int) else mm_layers[i]
+                    ),
+                    mlp_type=mlp_type,
+                    window=window[i],
+                    window_method=window_method[i],
+                    msa_type=msa_type[i],
+                    mca_type=mca_type[i],
+                    rope_type=rope_type,
+                    rope_dim=rope_dim,
+                    is_last_layer=(i == num_layers - 1),
+                    **kwargs,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.vid_out_norm = None
+        if vid_out_norm is not None:
+            self.vid_out_norm = get_norm_layer(vid_out_norm)(
+                dim=vid_dim,
+                eps=norm_eps,
+                elementwise_affine=True,
+            )
+            self.vid_out_ada = ada(
+                dim=vid_dim,
+                emb_dim=emb_dim,
+                layers=["out"],
+                modes=["in"],
+            )
+
+        self.vid_out = NaPatchOut(
+            out_channels=vid_out_channels,
+            patch_size=patch_size,
+            dim=vid_dim,
+        )
+
+    def set_gradient_checkpointing(self, enable: bool):
+        self.gradient_checkpointing = enable
+
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        txt: Union[torch.FloatTensor, List[torch.FloatTensor]],  # l c
+        vid_shape: torch.LongTensor,  # b 3
+        txt_shape: Union[torch.LongTensor, List[torch.LongTensor]],  # b 1
+        timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],  # b
+        disable_cache: bool = False,  # for test
+    ):
+        cache = Cache(disable=disable_cache)
+
+        # slice vid after patching in when using sequence parallelism
+        if isinstance(txt, list):
+            assert isinstance(self.txt_in, nn.ModuleList)
+            txt = [
+                na.unflatten(fc(i), s) for fc, i, s in zip(self.txt_in, txt, txt_shape)
+            ]  # B L D
+            txt, txt_shape = na.flatten([torch.cat(t, dim=0) for t in zip(*txt)])
+            txt = slice_inputs(txt, dim=0)
+        else:
+            txt = slice_inputs(txt, dim=0)
+            txt = self.txt_in(txt)
+
+        # Video input.
+        # Sequence parallel slicing is done inside patching class.
+        vid, vid_shape = self.vid_in(vid, vid_shape, cache)
+
+        # Embedding input.
+        emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
+
+        # Body
+        for i, block in enumerate(self.blocks):
+            vid, txt, vid_shape, txt_shape = gradient_checkpointing(
+                enabled=(self.gradient_checkpointing and self.training),
+                module=block,
+                vid=vid,
+                txt=txt,
+                vid_shape=vid_shape,
+                txt_shape=txt_shape,
+                emb=emb,
+                cache=cache,
+            )
+
+        # Video output norm.
+        if self.vid_out_norm:
+            vid = self.vid_out_norm(vid)
+            vid = self.vid_out_ada(
+                vid,
+                emb=emb,
+                layer="out",
+                mode="in",
+                hid_len=cache("vid_len", lambda: vid_shape.prod(-1)),
+                cache=cache,
+                branch_tag="vid",
+            )
+
+        # Video output.
+        vid, vid_shape = self.vid_out(vid, vid_shape, cache)
+        return NaDiTOutput(vid_sample=vid)
diff --git a/modules/seedvr/src/models/dit_v2/normalization.py b/modules/seedvr/src/models/dit_v2/normalization.py
new file mode 100644
index 000000000..41ba35a39
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/normalization.py
@@ -0,0 +1,147 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Callable, Optional
+from diffusers.models.normalization import RMSNorm
+from torch import nn
+import torch
+import torch.nn.functional as F
+import numbers
+from torch.nn.parameter import Parameter
+from torch.nn import init
+
+# (dim: int, eps: float, elementwise_affine: bool)
+norm_layer_type = Callable[[int, float, bool], nn.Module]
+
+
+class CustomLayerNorm(nn.Module):
+    """
+    Custom LayerNorm implementation to replace Apex FusedLayerNorm
+    """
+    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
+        super(CustomLayerNorm, self).__init__()
+
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = torch.Size(normalized_shape)
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+
+        if self.elementwise_affine:
+            self.weight = Parameter(torch.Tensor(*normalized_shape))
+            self.bias = Parameter(torch.Tensor(*normalized_shape))
+        else:
+            self.register_parameter('weight', None)
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.elementwise_affine:
+            init.ones_(self.weight)
+            init.zeros_(self.bias)
+
+    def forward(self, input):
+        # 🚀 FP8 COMPATIBILITY: Convert parameters to match input dtype
+        # This prevents "Promotion for Float8 Types is not supported" errors
+        weight = self.weight
+        bias = self.bias
+
+        if self.elementwise_affine and weight is not None:
+            if weight.dtype != input.dtype:
+                weight = weight.to(input.dtype)
+            if bias is not None and bias.dtype != input.dtype:
+                bias = bias.to(input.dtype)
+
+        return F.layer_norm(
+            input, self.normalized_shape, weight, bias, self.eps)
+
+
+class CustomRMSNorm(nn.Module):
+    """
+    Custom RMSNorm implementation to replace Apex FusedRMSNorm
+    """
+    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
+        super(CustomRMSNorm, self).__init__()
+
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = torch.Size(normalized_shape)
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+
+        if self.elementwise_affine:
+            self.weight = Parameter(torch.ones(*normalized_shape))
+        else:
+            self.register_parameter('weight', None)
+
+    def forward(self, input):
+        # RMS normalization: x / sqrt(mean(x^2) + eps) * weight
+        dims = tuple(range(-len(self.normalized_shape), 0))
+
+        # Calculate RMS: sqrt(mean(x^2))
+        variance = input.pow(2).mean(dim=dims, keepdim=True)
+        rms = torch.sqrt(variance + self.eps)
+
+        # Normalize
+        normalized = input / rms
+
+        if self.elementwise_affine:
+            # 🚀 FP8 COMPATIBILITY: Convert weight to match normalized dtype
+            # This prevents "Promotion for Float8 Types is not supported" errors
+            weight = self.weight
+            if weight.dtype != normalized.dtype:
+                weight = weight.to(normalized.dtype)
+            return normalized * weight
+        return normalized
+
+
+def get_norm_layer(norm_type: Optional[str]) -> norm_layer_type:
+
+    def _norm_layer(dim: int, eps: float, elementwise_affine: bool):
+        if norm_type is None:
+            return nn.Identity()
+
+        if norm_type == "layer":
+            return nn.LayerNorm(
+                normalized_shape=dim,
+                eps=eps,
+                elementwise_affine=elementwise_affine,
+            )
+
+        if norm_type == "rms":
+            return RMSNorm(
+                dim=dim,
+                eps=eps,
+                elementwise_affine=elementwise_affine,
+            )
+
+        if norm_type == "fusedln":
+            # Use custom LayerNorm instead of Apex FusedLayerNorm
+            return CustomLayerNorm(
+                normalized_shape=dim,
+                elementwise_affine=elementwise_affine,
+                eps=eps,
+            )
+
+        if norm_type == "fusedrms":
+            # Use custom RMSNorm instead of Apex FusedRMSNorm
+            return CustomRMSNorm(
+                normalized_shape=dim,
+                elementwise_affine=elementwise_affine,
+                eps=eps,
+            )
+
+        raise NotImplementedError(f"{norm_type} is not supported")
+
+    return _norm_layer
diff --git a/modules/seedvr/src/models/dit_v2/patch/__init__.py b/modules/seedvr/src/models/dit_v2/patch/__init__.py
new file mode 100644
index 000000000..4e3c97831
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/patch/__init__.py
@@ -0,0 +1,19 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+def get_na_patch_layers(patch_type="v1"):
+    assert patch_type in ["v1"]
+    if patch_type == "v1":
+        from .patch_v1 import NaPatchIn, NaPatchOut
+    return NaPatchIn, NaPatchOut
diff --git a/modules/seedvr/src/models/dit_v2/patch/patch_v1.py b/modules/seedvr/src/models/dit_v2/patch/patch_v1.py
new file mode 100644
index 000000000..14b5a62d5
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/patch/patch_v1.py
@@ -0,0 +1,127 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Tuple, Union
+import torch
+from einops import rearrange
+from torch import nn
+from torch.nn.modules.utils import _triple
+
+from ....common.cache import Cache
+from ....common.distributed.ops import gather_outputs, slice_inputs
+
+from .. import na
+
+
+class PatchIn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        patch_size: Union[int, Tuple[int, int, int]],
+        dim: int,
+    ):
+        super().__init__()
+        t, h, w = _triple(patch_size)
+        self.patch_size = t, h, w
+        self.proj = nn.Linear(in_channels * t * h * w, dim)
+
+    def forward(
+        self,
+        vid: torch.Tensor,
+    ) -> torch.Tensor:
+        t, h, w = self.patch_size
+        if t > 1:
+            assert vid.size(2) % t == 1
+            vid = torch.cat([vid[:, :, :1]] * (t - 1) + [vid], dim=2)
+        vid = rearrange(vid, "b c (T t) (H h) (W w) -> b T H W (t h w c)", t=t, h=h, w=w)
+        vid = self.proj(vid)
+        return vid
+
+
+class PatchOut(nn.Module):
+    def __init__(
+        self,
+        out_channels: int,
+        patch_size: Union[int, Tuple[int, int, int]],
+        dim: int,
+    ):
+        super().__init__()
+        t, h, w = _triple(patch_size)
+        self.patch_size = t, h, w
+        self.proj = nn.Linear(dim, out_channels * t * h * w)
+
+    def forward(
+        self,
+        vid: torch.Tensor,
+    ) -> torch.Tensor:
+        t, h, w = self.patch_size
+        vid = self.proj(vid)
+        vid = rearrange(vid, "b T H W (t h w c) -> b c (T t) (H h) (W w)", t=t, h=h, w=w)
+        if t > 1:
+            vid = vid[:, :, (t - 1) :]
+        return vid
+
+
+class NaPatchIn(PatchIn):
+    def forward(
+        self,
+        vid: torch.Tensor,  # l c
+        vid_shape: torch.LongTensor,
+        cache: Cache = Cache(disable=True),  # for test
+    ) -> torch.Tensor:
+        cache = cache.namespace("patch")
+        vid_shape_before_patchify = cache("vid_shape_before_patchify", lambda: vid_shape)
+        t, h, w = self.patch_size
+        if not (t == h == w == 1):
+            vid = na.unflatten(vid, vid_shape)
+            for i in range(len(vid)):
+                if t > 1 and vid_shape_before_patchify[i, 0] % t != 0:
+                    vid[i] = torch.cat([vid[i][:1]] * (t - vid[i].size(0) % t) + [vid[i]], dim=0)
+                vid[i] = rearrange(vid[i], "(T t) (H h) (W w) c -> T H W (t h w c)", t=t, h=h, w=w)
+            vid, vid_shape = na.flatten(vid)
+
+        # slice vid after patching in when using sequence parallelism
+        vid = slice_inputs(vid, dim=0)
+        vid = self.proj(vid)
+        return vid, vid_shape
+
+
+class NaPatchOut(PatchOut):
+    def forward(
+        self,
+        vid: torch.FloatTensor,  # l c
+        vid_shape: torch.LongTensor,
+        cache: Cache = Cache(disable=True),  # for test
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.LongTensor,
+    ]:
+        cache = cache.namespace("patch")
+        vid_shape_before_patchify = cache.get("vid_shape_before_patchify")
+
+        t, h, w = self.patch_size
+        vid = self.proj(vid)
+        # gather vid before patching out when enabling sequence parallelism
+        vid = gather_outputs(
+            vid, gather_dim=0, padding_dim=0, unpad_shape=vid_shape, cache=cache.namespace("vid")
+        )
+        if not (t == h == w == 1):
+            vid = na.unflatten(vid, vid_shape)
+            for i in range(len(vid)):
+                vid[i] = rearrange(vid[i], "T H W (t h w c) -> (T t) (H h) (W w) c", t=t, h=h, w=w)
+                if t > 1 and vid_shape_before_patchify[i, 0] % t != 0:
+                    vid[i] = vid[i][(t - vid_shape_before_patchify[i, 0] % t) :]
+            vid, vid_shape = na.flatten(vid)
+
+        return vid, vid_shape
diff --git a/modules/seedvr/src/models/dit_v2/rope.py b/modules/seedvr/src/models/dit_v2/rope.py
new file mode 100644
index 000000000..dde5b4756
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/rope.py
@@ -0,0 +1,155 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from functools import lru_cache
+from typing import Optional, Tuple
+import torch
+from einops import rearrange
+from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
+from torch import nn
+
+from src.common.cache import Cache
+
+
+class RotaryEmbeddingBase(nn.Module):
+    def __init__(self, dim: int, rope_dim: int):
+        super().__init__()
+        self.rope = RotaryEmbedding(
+            dim=dim // rope_dim,
+            freqs_for="pixel",
+            max_freq=256,
+        )
+        # 1. Set model.requires_grad_(True) after model creation will make
+        #    the `requires_grad=False` for rope freqs no longer hold.
+        # 2. Even if we don't set requires_grad_(True) explicitly,
+        #    FSDP is not memory efficient when handling fsdp_wrap
+        #    with mixed requires_grad=True/False.
+        # With above consideration, it is easier just remove the freqs
+        # out of nn.Parameters when `learned_freq=False`
+        freqs = self.rope.freqs
+        del self.rope.freqs
+        self.rope.register_buffer("freqs", freqs.data)
+
+    @lru_cache(maxsize=128)
+    def get_axial_freqs(self, *dims):
+        return self.rope.get_axial_freqs(*dims)
+
+
+class RotaryEmbedding3d(RotaryEmbeddingBase):
+    def __init__(self, dim: int):
+        super().__init__(dim, rope_dim=3)
+        self.mm = False
+
+    def forward(
+        self,
+        q: torch.FloatTensor,  # b h l d
+        k: torch.FloatTensor,  # b h l d
+        size: Tuple[int, int, int],
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        T, H, W = size
+        freqs = self.get_axial_freqs(T, H, W)
+        q = rearrange(q, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
+        k = rearrange(k, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
+        q = apply_rotary_emb(freqs, q.float()).to(q.dtype)
+        k = apply_rotary_emb(freqs, k.float()).to(k.dtype)
+        q = rearrange(q, "b h T H W d -> b h (T H W) d")
+        k = rearrange(k, "b h T H W d -> b h (T H W) d")
+        return q, k
+
+
+class MMRotaryEmbeddingBase(RotaryEmbeddingBase):
+    def __init__(self, dim: int, rope_dim: int):
+        super().__init__(dim, rope_dim)
+        self.rope = RotaryEmbedding(
+            dim=dim // rope_dim,
+            freqs_for="lang",
+            theta=10000,
+        )
+        freqs = self.rope.freqs
+        del self.rope.freqs
+        self.rope.register_buffer("freqs", freqs.data)
+        self.mm = True
+
+
+class NaMMRotaryEmbedding3d(MMRotaryEmbeddingBase):
+    def __init__(self, dim: int):
+        super().__init__(dim, rope_dim=3)
+
+    def forward(
+        self,
+        vid_q: torch.FloatTensor,  # L h d
+        vid_k: torch.FloatTensor,  # L h d
+        vid_shape: torch.LongTensor,  # B 3
+        txt_q: torch.FloatTensor,  # L h d
+        txt_k: torch.FloatTensor,  # L h d
+        txt_shape: torch.LongTensor,  # B 1
+        cache: Cache,
+    ) -> Tuple[
+        torch.FloatTensor,
+        torch.FloatTensor,
+        torch.FloatTensor,
+        torch.FloatTensor,
+    ]:
+        vid_freqs, txt_freqs = cache(
+            "mmrope_freqs_3d",
+            lambda: self.get_freqs(vid_shape, txt_shape),
+        )
+        target_device = vid_q.device
+        if vid_freqs.device != target_device:
+            vid_freqs = vid_freqs.to(target_device)
+        if txt_freqs.device != target_device:
+            txt_freqs = txt_freqs.to(target_device)
+        vid_q = rearrange(vid_q, "L h d -> h L d")
+        vid_k = rearrange(vid_k, "L h d -> h L d")
+        vid_q = apply_rotary_emb(vid_freqs, vid_q.float()).to(vid_q.dtype)
+        vid_k = apply_rotary_emb(vid_freqs, vid_k.float()).to(vid_k.dtype)
+        vid_q = rearrange(vid_q, "h L d -> L h d")
+        vid_k = rearrange(vid_k, "h L d -> L h d")
+
+        txt_q = rearrange(txt_q, "L h d -> h L d")
+        txt_k = rearrange(txt_k, "L h d -> h L d")
+        txt_q = apply_rotary_emb(txt_freqs, txt_q.float()).to(txt_q.dtype)
+        txt_k = apply_rotary_emb(txt_freqs, txt_k.float()).to(txt_k.dtype)
+        txt_q = rearrange(txt_q, "h L d -> L h d")
+        txt_k = rearrange(txt_k, "h L d -> L h d")
+        return vid_q, vid_k, txt_q, txt_k
+
+    def get_freqs(
+        self,
+        vid_shape: torch.LongTensor,
+        txt_shape: torch.LongTensor,
+    ) -> Tuple[
+        torch.Tensor,
+        torch.Tensor,
+    ]:
+        vid_freqs = self.get_axial_freqs(1024, 128, 128)
+        txt_freqs = self.get_axial_freqs(1024)
+        vid_freq_list, txt_freq_list = [], []
+        for (f, h, w), l in zip(vid_shape.tolist(), txt_shape[:, 0].tolist()):
+            vid_freq = vid_freqs[l : l + f, :h, :w].reshape(-1, vid_freqs.size(-1))
+            txt_freq = txt_freqs[:l].repeat(1, 3).reshape(-1, vid_freqs.size(-1))
+            vid_freq_list.append(vid_freq)
+            txt_freq_list.append(txt_freq)
+        return torch.cat(vid_freq_list, dim=0), torch.cat(txt_freq_list, dim=0)
+
+
+def get_na_rope(rope_type: Optional[str], dim: int):
+    if rope_type is None:
+        return None
+    if rope_type == "mmrope3d":
+        return NaMMRotaryEmbedding3d(dim=dim)
+    raise NotImplementedError(f"{rope_type} is not supported.")
diff --git a/modules/seedvr/src/models/dit_v2/window.py b/modules/seedvr/src/models/dit_v2/window.py
new file mode 100644
index 000000000..f77ea57d7
--- /dev/null
+++ b/modules/seedvr/src/models/dit_v2/window.py
@@ -0,0 +1,83 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from math import ceil
+from typing import Tuple
+import math
+
+def get_window_op(name: str):
+    if name == "720pwin_by_size_bysize":
+        return make_720Pwindows_bysize
+    if name == "720pswin_by_size_bysize":
+        return make_shifted_720Pwindows_bysize
+    raise ValueError(f"Unknown windowing method: {name}")
+
+
+# -------------------------------- Windowing -------------------------------- #
+def make_720Pwindows_bysize(size: Tuple[int, int, int], num_windows: Tuple[int, int, int]):
+    t, h, w = size
+    resized_nt, resized_nh, resized_nw = num_windows
+    #cal windows under 720p
+    scale = math.sqrt((45 * 80) / (h * w))
+    resized_h, resized_w = round(h * scale), round(w * scale)
+    wh, ww = ceil(resized_h / resized_nh), ceil(resized_w / resized_nw)  # window size.
+    wt = ceil(min(t, 30) / resized_nt)  # window size.
+    nt, nh, nw = ceil(t / wt), ceil(h / wh), ceil(w / ww)  # window size.
+    return [
+        (
+            slice(it * wt, min((it + 1) * wt, t)),
+            slice(ih * wh, min((ih + 1) * wh, h)),
+            slice(iw * ww, min((iw + 1) * ww, w)),
+        )
+        for iw in range(nw)
+        if min((iw + 1) * ww, w) > iw * ww
+        for ih in range(nh)
+        if min((ih + 1) * wh, h) > ih * wh
+        for it in range(nt)
+        if min((it + 1) * wt, t) > it * wt
+    ]
+
+def make_shifted_720Pwindows_bysize(size: Tuple[int, int, int], num_windows: Tuple[int, int, int]):
+    t, h, w = size
+    resized_nt, resized_nh, resized_nw = num_windows
+    #cal windows under 720p
+    scale = math.sqrt((45 * 80) / (h * w))
+    resized_h, resized_w = round(h * scale), round(w * scale)
+    wh, ww = ceil(resized_h / resized_nh), ceil(resized_w / resized_nw)  # window size.
+    wt = ceil(min(t, 30) / resized_nt)  # window size.
+
+    st, sh, sw = (  # shift size.
+        0.5 if wt < t else 0,
+        0.5 if wh < h else 0,
+        0.5 if ww < w else 0,
+    )
+    nt, nh, nw = ceil((t - st) / wt), ceil((h - sh) / wh), ceil((w - sw) / ww)  # window size.
+    nt, nh, nw = (  # number of window.
+        nt + 1 if st > 0 else 1,
+        nh + 1 if sh > 0 else 1,
+        nw + 1 if sw > 0 else 1,
+    )
+    return [
+        (
+            slice(max(int((it - st) * wt), 0), min(int((it - st + 1) * wt), t)),
+            slice(max(int((ih - sh) * wh), 0), min(int((ih - sh + 1) * wh), h)),
+            slice(max(int((iw - sw) * ww), 0), min(int((iw - sw + 1) * ww), w)),
+        )
+        for iw in range(nw)
+        if min(int((iw - sw + 1) * ww), w) > max(int((iw - sw) * ww), 0)
+        for ih in range(nh)
+        if min(int((ih - sh + 1) * wh), h) > max(int((ih - sh) * wh), 0)
+        for it in range(nt)
+        if min(int((it - st + 1) * wt), t) > max(int((it - st) * wt), 0)
+    ]
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py b/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
new file mode 100644
index 000000000..1f282dfa1
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
@@ -0,0 +1,1345 @@
+# Copyright (c) 2023 HuggingFace Team
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
+# SPDX-License-Identifier: Apache License, Version 2.0 (the "License")
+#
+# This file has been modified by ByteDance Ltd. and/or its affiliates. on 1st June 2025
+#
+# Original file was released under Apache License, Version 2.0 (the "License"), with the full license text
+# available at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# This modified file is released under the same license.
+
+
+from contextlib import nullcontext
+from typing import Literal, Optional, Tuple, Union
+import diffusers
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models.attention_processor import Attention, SpatialNorm
+from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
+from diffusers.models.downsampling import Downsample2D
+from diffusers.models.lora import LoRACompatibleConv
+from diffusers.models.modeling_outputs import AutoencoderKLOutput
+from diffusers.models.resnet import ResnetBlock2D
+from diffusers.models.unets.unet_2d_blocks import DownEncoderBlock2D, UpDecoderBlock2D
+from diffusers.models.upsampling import Upsample2D
+from diffusers.utils import is_torch_version
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from einops import rearrange
+from ....common.half_precision_fixes import safe_pad_operation, safe_interpolate_operation
+
+from ....common.distributed.advanced import get_sequence_parallel_world_size
+from ....common.logger import get_logger
+from .causal_inflation_lib import (
+    InflatedCausalConv3d,
+    causal_norm_wrapper,
+    init_causal_conv3d,
+    remove_head,
+)
+from .context_parallel_lib import (
+    causal_conv_gather_outputs,
+    causal_conv_slice_inputs,
+)
+from .global_config import set_norm_limit
+from .types import (
+    CausalAutoencoderOutput,
+    CausalDecoderOutput,
+    CausalEncoderOutput,
+    MemoryState,
+    _inflation_mode_t,
+    _memory_device_t,
+    _receptive_field_t,
+)
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class Upsample3D(Upsample2D):
+    """A 3D upsampling layer with an optional convolution."""
+
+    def __init__(
+        self,
+        *args,
+        inflation_mode: _inflation_mode_t = "tail",
+        temporal_up: bool = False,
+        spatial_up: bool = True,
+        slicing: bool = False,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        conv = self.conv if self.name == "conv" else self.Conv2d_0
+
+        assert type(conv) is not nn.ConvTranspose2d
+        # Note: lora_layer is not passed into constructor in the original implementation.
+        # So we make a simplification.
+        conv = init_causal_conv3d(
+            self.channels,
+            self.out_channels,
+            3,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        self.temporal_up = temporal_up
+        self.spatial_up = spatial_up
+        self.temporal_ratio = 2 if temporal_up else 1
+        self.spatial_ratio = 2 if spatial_up else 1
+        self.slicing = slicing
+
+        assert not self.interpolate
+        # [Override] MAGViT v2 implementation
+        if not self.interpolate:
+            upscale_ratio = (self.spatial_ratio**2) * self.temporal_ratio
+            self.upscale_conv = nn.Conv3d(
+                self.channels, self.channels * upscale_ratio, kernel_size=1, padding=0
+            )
+            identity = (
+                torch.eye(self.channels)
+                .repeat(upscale_ratio, 1)
+                .reshape_as(self.upscale_conv.weight)
+            )
+            self.upscale_conv.weight.data.copy_(identity)
+            nn.init.zeros_(self.upscale_conv.bias)
+
+        if self.name == "conv":
+            self.conv = conv
+        else:
+            self.Conv2d_0 = conv
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        output_size: Optional[int] = None,
+        memory_state: MemoryState = MemoryState.DISABLED,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        assert hidden_states.shape[1] == self.channels
+
+        if hasattr(self, "norm") and self.norm is not None:
+            # [Overridden] change to causal norm.
+            hidden_states = causal_norm_wrapper(self.norm, hidden_states)
+
+        if self.use_conv_transpose:
+            return self.conv(hidden_states)
+
+        if self.slicing:
+            split_size = hidden_states.size(2) // 2
+            hidden_states = list(
+                hidden_states.split([split_size, hidden_states.size(2) - split_size], dim=2)
+            )
+        else:
+            hidden_states = [hidden_states]
+        # ADD BY NUMZ
+        for i in range(len(hidden_states)):
+            hidden_states[i] = self.upscale_conv(hidden_states[i])
+            hidden_states[i] = rearrange(
+                hidden_states[i],
+                "b (x y z c) f h w -> b c (f z) (h x) (w y)",
+                x=self.spatial_ratio,
+                y=self.spatial_ratio,
+                z=self.temporal_ratio,
+            )
+
+        # [Overridden] For causal temporal conv
+        if self.temporal_up and memory_state != MemoryState.ACTIVE:
+            hidden_states[0] = remove_head(hidden_states[0])
+
+        if not self.slicing:
+            hidden_states = hidden_states[0]
+        # ADD BY NUMZ
+        if self.use_conv:
+            if self.name == "conv":
+                hidden_states = self.conv(hidden_states, memory_state=memory_state)
+            else:
+                hidden_states = self.Conv2d_0(hidden_states, memory_state=memory_state)
+
+        if not self.slicing:
+            return hidden_states
+        else:
+            return torch.cat(hidden_states, dim=2)
+
+
+class Downsample3D(Downsample2D):
+    """A 3D downsampling layer with an optional convolution."""
+
+    def __init__(
+        self,
+        *args,
+        inflation_mode: _inflation_mode_t = "tail",
+        spatial_down: bool = False,
+        temporal_down: bool = False,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        conv = self.conv
+        self.temporal_down = temporal_down
+        self.spatial_down = spatial_down
+
+        self.temporal_ratio = 2 if temporal_down else 1
+        self.spatial_ratio = 2 if spatial_down else 1
+
+        self.temporal_kernel = 3 if temporal_down else 1
+        self.spatial_kernel = 3 if spatial_down else 1
+
+        if type(conv) in [nn.Conv2d, LoRACompatibleConv]:
+            # Note: lora_layer is not passed into constructor in the original implementation.
+            # So we make a simplification.
+            conv = init_causal_conv3d(
+                self.channels,
+                self.out_channels,
+                kernel_size=(self.temporal_kernel, self.spatial_kernel, self.spatial_kernel),
+                stride=(self.temporal_ratio, self.spatial_ratio, self.spatial_ratio),
+                padding=(
+                    1 if self.temporal_down else 0,
+                    self.padding if self.spatial_down else 0,
+                    self.padding if self.spatial_down else 0,
+                ),
+                inflation_mode=inflation_mode,
+            )
+        elif type(conv) is nn.AvgPool2d:
+            assert self.channels == self.out_channels
+            conv = nn.AvgPool3d(
+                kernel_size=(self.temporal_ratio, self.spatial_ratio, self.spatial_ratio),
+                stride=(self.temporal_ratio, self.spatial_ratio, self.spatial_ratio),
+            )
+        else:
+            raise NotImplementedError
+
+        if self.name == "conv":
+            self.Conv2d_0 = conv
+            self.conv = conv
+        else:
+            self.conv = conv
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        memory_state: MemoryState = MemoryState.DISABLED,
+        **kwargs,
+    ) -> torch.FloatTensor:
+
+        assert hidden_states.shape[1] == self.channels
+
+        if hasattr(self, "norm") and self.norm is not None:
+            # [Overridden] change to causal norm.
+            hidden_states = causal_norm_wrapper(self.norm, hidden_states)
+
+        if self.use_conv and self.padding == 0 and self.spatial_down:
+            pad = (0, 1, 0, 1)
+            hidden_states = safe_pad_operation(hidden_states, pad, mode="constant", value=0)
+
+        assert hidden_states.shape[1] == self.channels
+
+        hidden_states = self.conv(hidden_states, memory_state=memory_state)
+
+        return hidden_states
+
+
+class ResnetBlock3D(ResnetBlock2D):
+    def __init__(
+        self,
+        *args,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        slicing: bool = False,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.conv1 = init_causal_conv3d(
+            self.in_channels,
+            self.out_channels,
+            kernel_size=(1, 3, 3) if time_receptive_field == "half" else (3, 3, 3),
+            stride=1,
+            padding=(0, 1, 1) if time_receptive_field == "half" else (1, 1, 1),
+            inflation_mode=inflation_mode,
+        )
+
+        self.conv2 = init_causal_conv3d(
+            self.out_channels,
+            self.conv2.out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        if self.up:
+            assert type(self.upsample) is Upsample2D
+            self.upsample = Upsample3D(
+                self.in_channels,
+                use_conv=False,
+                inflation_mode=inflation_mode,
+                slicing=slicing,
+            )
+        elif self.down:
+            assert type(self.downsample) is Downsample2D
+            self.downsample = Downsample3D(
+                self.in_channels,
+                use_conv=False,
+                padding=1,
+                name="op",
+                inflation_mode=inflation_mode,
+            )
+
+        if self.use_in_shortcut:
+            self.conv_shortcut = init_causal_conv3d(
+                self.in_channels,
+                self.conv_shortcut.out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=(self.conv_shortcut.bias is not None),
+                inflation_mode=inflation_mode,
+            )
+
+    def forward(
+        self, input_tensor, temb, memory_state: MemoryState = MemoryState.DISABLED, **kwargs
+    ):
+        hidden_states = input_tensor
+
+        hidden_states = causal_norm_wrapper(self.norm1, hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+
+        if self.upsample is not None:
+            # upsample_nearest_nhwc fails with large batch sizes.
+            # see https://github.com/huggingface/diffusers/issues/984
+            if hidden_states.shape[0] >= 64:
+                input_tensor = input_tensor.contiguous()
+                hidden_states = hidden_states.contiguous()
+            input_tensor = self.upsample(input_tensor, memory_state=memory_state)
+            hidden_states = self.upsample(hidden_states, memory_state=memory_state)
+        elif self.downsample is not None:
+            input_tensor = self.downsample(input_tensor, memory_state=memory_state)
+            hidden_states = self.downsample(hidden_states, memory_state=memory_state)
+
+        hidden_states = self.conv1(hidden_states, memory_state=memory_state)
+
+        if self.time_emb_proj is not None:
+            if not self.skip_time_act:
+                temb = self.nonlinearity(temb)
+            temb = self.time_emb_proj(temb)[:, :, None, None]
+
+        if temb is not None and self.time_embedding_norm == "default":
+            hidden_states = hidden_states + temb
+
+        hidden_states = causal_norm_wrapper(self.norm2, hidden_states)
+
+        if temb is not None and self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+            hidden_states = hidden_states * (1 + scale) + shift
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states, memory_state=memory_state)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor, memory_state=memory_state)
+
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+        return output_tensor
+
+
+class DownEncoderBlock3D(DownEncoderBlock2D):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor: float = 1.0,
+        add_downsample: bool = True,
+        downsample_padding: int = 1,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        temporal_down: bool = True,
+        spatial_down: bool = True,
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            dropout=dropout,
+            num_layers=num_layers,
+            resnet_eps=resnet_eps,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_pre_norm=resnet_pre_norm,
+            output_scale_factor=output_scale_factor,
+            add_downsample=add_downsample,
+            downsample_padding=downsample_padding,
+        )
+        resnets = []
+        temporal_modules = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                # [Override] Replace module.
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=None,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                )
+            )
+            temporal_modules.append(nn.Identity())
+
+        self.resnets = nn.ModuleList(resnets)
+        self.temporal_modules = nn.ModuleList(temporal_modules)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    # [Override] Replace module.
+                    Downsample3D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        padding=downsample_padding,
+                        name="op",
+                        temporal_down=temporal_down,
+                        spatial_down=spatial_down,
+                        inflation_mode=inflation_mode,
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        memory_state: MemoryState = MemoryState.DISABLED,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        for resnet, temporal in zip(self.resnets, self.temporal_modules):
+            hidden_states = resnet(hidden_states, temb=None, memory_state=memory_state)
+            hidden_states = temporal(hidden_states)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states, memory_state=memory_state)
+
+        return hidden_states
+
+
+class UpDecoderBlock3D(UpDecoderBlock2D):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",  # default, spatial
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor: float = 1.0,
+        add_upsample: bool = True,
+        temb_channels: Optional[int] = None,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        temporal_up: bool = True,
+        spatial_up: bool = True,
+        slicing: bool = False,
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            dropout=dropout,
+            num_layers=num_layers,
+            resnet_eps=resnet_eps,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_pre_norm=resnet_pre_norm,
+            output_scale_factor=output_scale_factor,
+            add_upsample=add_upsample,
+            temb_channels=temb_channels,
+        )
+        resnets = []
+        temporal_modules = []
+
+        for i in range(num_layers):
+            input_channels = in_channels if i == 0 else out_channels
+
+            resnets.append(
+                # [Override] Replace module.
+                ResnetBlock3D(
+                    in_channels=input_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                    slicing=slicing,
+                )
+            )
+
+            temporal_modules.append(nn.Identity())
+
+        self.resnets = nn.ModuleList(resnets)
+        self.temporal_modules = nn.ModuleList(temporal_modules)
+
+        if add_upsample:
+            # [Override] Replace module & use learnable upsample
+            self.upsamplers = nn.ModuleList(
+                [
+                    Upsample3D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        temporal_up=temporal_up,
+                        spatial_up=spatial_up,
+                        interpolate=False,
+                        inflation_mode=inflation_mode,
+                        slicing=slicing,
+                    )
+                ]
+            )
+        else:
+            self.upsamplers = None
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        memory_state: MemoryState = MemoryState.DISABLED,
+    ) -> torch.FloatTensor:
+        for resnet, temporal in zip(self.resnets, self.temporal_modules):
+            hidden_states = resnet(hidden_states, temb=None, memory_state=memory_state)
+            hidden_states = temporal(hidden_states)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, memory_state=memory_state)
+
+        return hidden_states
+
+
+class UNetMidBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",  # default, spatial
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        add_attention: bool = True,
+        attention_head_dim: int = 1,
+        output_scale_factor: float = 1.0,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+    ):
+        super().__init__()
+        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+        self.add_attention = add_attention
+
+        # there is always at least one resnet
+        resnets = [
+            # [Override] Replace module.
+            ResnetBlock3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+                inflation_mode=inflation_mode,
+                time_receptive_field=time_receptive_field,
+            )
+        ]
+        attentions = []
+
+        if attention_head_dim is None:
+            logger.warn(
+                f"It is not recommend to pass `attention_head_dim=None`. "
+                f"Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
+            )
+            attention_head_dim = in_channels
+
+        for _ in range(num_layers):
+            if self.add_attention:
+                attentions.append(
+                    Attention(
+                        in_channels,
+                        heads=in_channels // attention_head_dim,
+                        dim_head=attention_head_dim,
+                        rescale_output_factor=output_scale_factor,
+                        eps=resnet_eps,
+                        norm_num_groups=(
+                            resnet_groups if resnet_time_scale_shift == "default" else None
+                        ),
+                        spatial_norm_dim=(
+                            temb_channels if resnet_time_scale_shift == "spatial" else None
+                        ),
+                        residual_connection=True,
+                        bias=True,
+                        upcast_softmax=True,
+                        _from_deprecated_attn_block=True,
+                    )
+                )
+            else:
+                attentions.append(None)
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(self, hidden_states, temb=None, memory_state: MemoryState = MemoryState.DISABLED):
+        video_length, frame_height, frame_width = hidden_states.size()[-3:]
+        hidden_states = self.resnets[0](hidden_states, temb, memory_state=memory_state)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+                hidden_states = attn(hidden_states, temb=temb)
+                hidden_states = rearrange(
+                    hidden_states, "(b f) c h w -> b c f h w", f=video_length
+                )
+            hidden_states = resnet(hidden_states, temb, memory_state=memory_state)
+
+        return hidden_states
+
+
+class Encoder3D(nn.Module):
+    r"""
+    [Override] override most logics to support extra condition input and causal conv
+
+    The `Encoder` layer of a variational autoencoder that encodes
+    its input into a latent representation.
+
+    Args:
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
+            The types of down blocks to use.
+            See `~diffusers.models.unet_2d_blocks.get_down_block`
+            for available options.
+        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
+            The number of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2):
+            The number of layers per block.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+        act_fn (`str`, *optional*, defaults to `"silu"`):
+            The activation function to use.
+            See `~diffusers.models.activations.get_activation` for available options.
+        double_z (`bool`, *optional*, defaults to `True`):
+            Whether to double the number of output channels for the last block.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        down_block_types: Tuple[str, ...] = ("DownEncoderBlock3D",),
+        block_out_channels: Tuple[int, ...] = (64,),
+        layers_per_block: int = 2,
+        norm_num_groups: int = 32,
+        act_fn: str = "silu",
+        double_z: bool = True,
+        mid_block_add_attention=True,
+        # [Override] add extra_cond_dim, temporal down num
+        temporal_down_num: int = 2,
+        extra_cond_dim: int = None,
+        gradient_checkpoint: bool = False,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+        self.temporal_down_num = temporal_down_num
+
+        self.conv_in = init_causal_conv3d(
+            in_channels,
+            block_out_channels[0],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        self.mid_block = None
+        self.down_blocks = nn.ModuleList([])
+        self.extra_cond_dim = extra_cond_dim
+
+        self.conv_extra_cond = nn.ModuleList([])
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            # [Override] to support temporal down block design
+            is_temporal_down_block = i >= len(block_out_channels) - self.temporal_down_num - 1
+            # Note: take the last ones
+
+            assert down_block_type == "DownEncoderBlock3D"
+
+            down_block = DownEncoderBlock3D(
+                num_layers=self.layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                add_downsample=not is_final_block,
+                resnet_eps=1e-6,
+                downsample_padding=0,
+                # Note: Don't know why set it as 0
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                temporal_down=is_temporal_down_block,
+                spatial_down=True,
+                inflation_mode=inflation_mode,
+                time_receptive_field=time_receptive_field,
+            )
+            self.down_blocks.append(down_block)
+
+            def zero_module(module):
+                # Zero out the parameters of a module and return it.
+                for p in module.parameters():
+                    p.detach().zero_()
+                return module
+
+            self.conv_extra_cond.append(
+                zero_module(
+                    nn.Conv3d(extra_cond_dim, output_channel, kernel_size=1, stride=1, padding=0)
+                )
+                if self.extra_cond_dim is not None and self.extra_cond_dim > 0
+                else None
+            )
+
+        # mid
+        self.mid_block = UNetMidBlock3D(
+            in_channels=block_out_channels[-1],
+            resnet_eps=1e-6,
+            resnet_act_fn=act_fn,
+            output_scale_factor=1,
+            resnet_time_scale_shift="default",
+            attention_head_dim=block_out_channels[-1],
+            resnet_groups=norm_num_groups,
+            temb_channels=None,
+            add_attention=mid_block_add_attention,
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(
+            num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6
+        )
+        self.conv_act = nn.SiLU()
+
+        conv_out_channels = 2 * out_channels if double_z else out_channels
+        self.conv_out = init_causal_conv3d(
+            block_out_channels[-1], conv_out_channels, 3, padding=1, inflation_mode=inflation_mode
+        )
+
+        self.gradient_checkpointing = gradient_checkpoint
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        extra_cond=None,
+        memory_state: MemoryState = MemoryState.DISABLED,
+    ) -> torch.FloatTensor:
+        r"""The forward method of the `Encoder` class."""
+        sample = self.conv_in(sample, memory_state=memory_state)
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # down
+            # [Override] add extra block and extra cond
+            for down_block, extra_block in zip(self.down_blocks, self.conv_extra_cond):
+                sample = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(down_block), sample, memory_state, use_reentrant=False
+                )
+                if extra_block is not None:
+                    sample = sample + safe_interpolate_operation(extra_block(extra_cond), size=sample.shape[2:])
+
+            # middle
+            sample = self.mid_block(sample, memory_state=memory_state)
+
+            # sample = torch.utils.checkpoint.checkpoint(
+            #     create_custom_forward(self.mid_block), sample, use_reentrant=False
+            # )
+
+        else:
+            # down
+            # [Override] add extra block and extra cond
+            for down_block, extra_block in zip(self.down_blocks, self.conv_extra_cond):
+                sample = down_block(sample, memory_state=memory_state)
+                if extra_block is not None:
+                    sample = sample + safe_interpolate_operation(extra_block(extra_cond), size=sample.shape[2:])
+
+            # middle
+            sample = self.mid_block(sample, memory_state=memory_state)
+
+        # post-process
+        sample = causal_norm_wrapper(self.conv_norm_out, sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample, memory_state=memory_state)
+
+        return sample
+
+
+class Decoder3D(nn.Module):
+    r"""
+    The `Decoder` layer of a variational autoencoder that
+    decodes its latent representation into an output sample.
+
+    Args:
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
+            The types of up blocks to use.
+            See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
+        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
+            The number of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2):
+            The number of layers per block.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+        act_fn (`str`, *optional*, defaults to `"silu"`):
+            The activation function to use.
+            See `~diffusers.models.activations.get_activation` for available options.
+        norm_type (`str`, *optional*, defaults to `"group"`):
+            The normalization type to use. Can be either `"group"` or `"spatial"`.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        up_block_types: Tuple[str, ...] = ("UpDecoderBlock3D",),
+        block_out_channels: Tuple[int, ...] = (64,),
+        layers_per_block: int = 2,
+        norm_num_groups: int = 32,
+        act_fn: str = "silu",
+        norm_type: str = "group",  # group, spatial
+        mid_block_add_attention=True,
+        # [Override] add temporal up block
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        temporal_up_num: int = 2,
+        slicing_up_num: int = 0,
+        gradient_checkpoint: bool = False,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+        self.temporal_up_num = temporal_up_num
+
+        self.conv_in = init_causal_conv3d(
+            in_channels,
+            block_out_channels[-1],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        temb_channels = in_channels if norm_type == "spatial" else None
+
+        # mid
+        self.mid_block = UNetMidBlock3D(
+            in_channels=block_out_channels[-1],
+            resnet_eps=1e-6,
+            resnet_act_fn=act_fn,
+            output_scale_factor=1,
+            resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
+            attention_head_dim=block_out_channels[-1],
+            resnet_groups=norm_num_groups,
+            temb_channels=temb_channels,
+            add_attention=mid_block_add_attention,
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+
+            is_final_block = i == len(block_out_channels) - 1
+            is_temporal_up_block = i < self.temporal_up_num
+            is_slicing_up_block = i >= len(block_out_channels) - slicing_up_num
+            # Note: Keep symmetric
+
+            assert up_block_type == "UpDecoderBlock3D"
+            up_block = UpDecoderBlock3D(
+                num_layers=self.layers_per_block + 1,
+                in_channels=prev_output_channel,
+                out_channels=output_channel,
+                add_upsample=not is_final_block,
+                resnet_eps=1e-6,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=norm_type,
+                temb_channels=temb_channels,
+                temporal_up=is_temporal_up_block,
+                slicing=is_slicing_up_block,
+                inflation_mode=inflation_mode,
+                time_receptive_field=time_receptive_field,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if norm_type == "spatial":
+            self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
+        else:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6
+            )
+        self.conv_act = nn.SiLU()
+        self.conv_out = init_causal_conv3d(
+            block_out_channels[0], out_channels, 3, padding=1, inflation_mode=inflation_mode
+        )
+
+        self.gradient_checkpointing = gradient_checkpoint
+
+    # Note: Just copy from Decoder.
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        latent_embeds: Optional[torch.FloatTensor] = None,
+        memory_state: MemoryState = MemoryState.DISABLED,
+    ) -> torch.FloatTensor:
+        r"""The forward method of the `Decoder` class."""
+
+        sample = self.conv_in(sample, memory_state=memory_state)
+
+        #upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
+        upscale_dtype = sample.dtype
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            if is_torch_version(">=", "1.11.0"):
+                sample = self.mid_block(sample, latent_embeds, memory_state=memory_state)
+                sample = sample.to(upscale_dtype)
+
+                # up
+                for up_block in self.up_blocks:
+                    sample = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(up_block),
+                        sample,
+                        latent_embeds,
+                        memory_state,
+                        use_reentrant=False,
+                    )
+            else:
+                # middle
+                sample = self.mid_block(sample, latent_embeds, memory_state=memory_state)
+                sample = sample.to(upscale_dtype)
+
+                # up
+                for up_block in self.up_blocks:
+                    sample = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(up_block), sample, latent_embeds, memory_state
+                    )
+        else:
+            # middle
+            sample = self.mid_block(sample, latent_embeds, memory_state=memory_state)
+            sample = sample.to(upscale_dtype)
+
+            # up
+            for up_block in self.up_blocks:
+                sample = up_block(sample, latent_embeds, memory_state=memory_state)
+
+        # post-process
+        sample = causal_norm_wrapper(self.conv_norm_out, sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample, memory_state=memory_state)
+
+        return sample
+
+
+class AutoencoderKL(diffusers.AutoencoderKL):
+    """
+    We simply inherit the model code from diffusers
+    """
+
+    def __init__(self, attention: bool = True, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # A hacky way to remove attention.
+        if not attention:
+            self.encoder.mid_block.attentions = torch.nn.ModuleList([None])
+            self.decoder.mid_block.attentions = torch.nn.ModuleList([None])
+
+    def load_state_dict(self, state_dict, strict=True):
+        # Newer version of diffusers changed the model keys,
+        # causing incompatibility with old checkpoints.
+        # They provided a method for conversion. We call conversion before loading state_dict.
+        convert_deprecated_attention_blocks = getattr(
+            self, "_convert_deprecated_attention_blocks", None
+        )
+        if callable(convert_deprecated_attention_blocks):
+            convert_deprecated_attention_blocks(state_dict)
+        return super().load_state_dict(state_dict, strict)
+
+
+class VideoAutoencoderKL(diffusers.AutoencoderKL):
+    """
+    We simply inherit the model code from diffusers
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        down_block_types: Tuple[str] = ("DownEncoderBlock3D",),
+        up_block_types: Tuple[str] = ("UpDecoderBlock3D",),
+        block_out_channels: Tuple[int] = (64,),
+        layers_per_block: int = 1,
+        act_fn: str = "silu",
+        latent_channels: int = 4,
+        norm_num_groups: int = 32,
+        sample_size: int = 32,
+        scaling_factor: float = 0.18215,
+        force_upcast: float = True,
+        attention: bool = True,
+        temporal_scale_num: int = 2,
+        slicing_up_num: int = 0,
+        gradient_checkpoint: bool = False,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "full",
+        slicing_sample_min_size: int = 32,
+        use_quant_conv: bool = True,
+        use_post_quant_conv: bool = True,
+        *args,
+        **kwargs,
+    ):
+        extra_cond_dim = kwargs.pop("extra_cond_dim") if "extra_cond_dim" in kwargs else None
+        self.slicing_sample_min_size = slicing_sample_min_size
+        self.slicing_latent_min_size = slicing_sample_min_size // (2**temporal_scale_num)
+
+        super().__init__(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            # [Override] make sure it can be normally initialized
+            down_block_types=tuple(
+                [down_block_type.replace("3D", "2D") for down_block_type in down_block_types]
+            ),
+            up_block_types=tuple(
+                [up_block_type.replace("3D", "2D") for up_block_type in up_block_types]
+            ),
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            act_fn=act_fn,
+            latent_channels=latent_channels,
+            norm_num_groups=norm_num_groups,
+            sample_size=sample_size,
+            scaling_factor=scaling_factor,
+            force_upcast=force_upcast,
+            *args,
+            **kwargs,
+        )
+
+        # pass init params to Encoder
+        self.encoder = Encoder3D(
+            in_channels=in_channels,
+            out_channels=latent_channels,
+            down_block_types=down_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            act_fn=act_fn,
+            norm_num_groups=norm_num_groups,
+            double_z=True,
+            extra_cond_dim=extra_cond_dim,
+            # [Override] add temporal_down_num parameter
+            temporal_down_num=temporal_scale_num,
+            gradient_checkpoint=gradient_checkpoint,
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        # pass init params to Decoder
+        self.decoder = Decoder3D(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            up_block_types=up_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            norm_num_groups=norm_num_groups,
+            act_fn=act_fn,
+            # [Override] add temporal_up_num parameter
+            temporal_up_num=temporal_scale_num,
+            slicing_up_num=slicing_up_num,
+            gradient_checkpoint=gradient_checkpoint,
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        self.quant_conv = (
+            init_causal_conv3d(
+                in_channels=2 * latent_channels,
+                out_channels=2 * latent_channels,
+                kernel_size=1,
+                inflation_mode=inflation_mode,
+            )
+            if use_quant_conv
+            else None
+        )
+        self.post_quant_conv = (
+            init_causal_conv3d(
+                in_channels=latent_channels,
+                out_channels=latent_channels,
+                kernel_size=1,
+                inflation_mode=inflation_mode,
+            )
+            if use_post_quant_conv
+            else None
+        )
+
+        # A hacky way to remove attention.
+        if not attention:
+            self.encoder.mid_block.attentions = torch.nn.ModuleList([None])
+            self.decoder.mid_block.attentions = torch.nn.ModuleList([None])
+
+    @apply_forward_hook
+    def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
+        h = self.slicing_encode(x)
+        posterior = DiagonalGaussianDistribution(h)
+
+        if not return_dict:
+            return (posterior,)
+
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    @apply_forward_hook
+    def decode(
+        self, z: torch.Tensor, return_dict: bool = True
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        decoded = self.slicing_decode(z)
+
+        if not return_dict:
+            return (decoded,)
+
+        return DecoderOutput(sample=decoded)
+
+    def _encode(
+        self, x: torch.Tensor, memory_state: MemoryState = MemoryState.DISABLED
+    ) -> torch.Tensor:
+        _x = x.to(self.device)
+        _x = causal_conv_slice_inputs(_x, self.slicing_sample_min_size, memory_state=memory_state)
+        h = self.encoder(_x, memory_state=memory_state)
+        if self.quant_conv is not None:
+            output = self.quant_conv(h, memory_state=memory_state)
+        else:
+            output = h
+        output = causal_conv_gather_outputs(output)
+        return output.to(x.device)
+
+    def _decode(
+        self, z: torch.Tensor, memory_state: MemoryState = MemoryState.DISABLED
+    ) -> torch.Tensor:
+        _z = z.to(self.device)
+        _z = causal_conv_slice_inputs(_z, self.slicing_latent_min_size, memory_state=memory_state)
+        if self.post_quant_conv is not None:
+            _z = self.post_quant_conv(_z, memory_state=memory_state)
+        output = self.decoder(_z, memory_state=memory_state)
+        output = causal_conv_gather_outputs(output)
+        return output.to(z.device)
+
+    def slicing_encode(self, x: torch.Tensor) -> torch.Tensor:
+        sp_size = get_sequence_parallel_world_size()
+        if self.use_slicing and (x.shape[2] - 1) > self.slicing_sample_min_size * sp_size:
+            x_slices = x[:, :, 1:].split(split_size=self.slicing_sample_min_size * sp_size, dim=2)
+            encoded_slices = [
+                self._encode(
+                    torch.cat((x[:, :, :1], x_slices[0]), dim=2),
+                    memory_state=MemoryState.INITIALIZING,
+                )
+            ]
+            for x_idx in range(1, len(x_slices)):
+                encoded_slices.append(
+                    self._encode(x_slices[x_idx], memory_state=MemoryState.ACTIVE)
+                )
+            return torch.cat(encoded_slices, dim=2)
+        else:
+            return self._encode(x)
+
+    def slicing_decode(self, z: torch.Tensor) -> torch.Tensor:
+        sp_size = get_sequence_parallel_world_size()
+        if self.use_slicing and (z.shape[2] - 1) > self.slicing_latent_min_size * sp_size:
+            z_slices = z[:, :, 1:].split(split_size=self.slicing_latent_min_size * sp_size, dim=2)
+            decoded_slices = [
+                self._decode(
+                    torch.cat((z[:, :, :1], z_slices[0]), dim=2),
+                    memory_state=MemoryState.INITIALIZING,
+                )
+            ]
+            for z_idx in range(1, len(z_slices)):
+                decoded_slices.append(
+                    self._decode(z_slices[z_idx], memory_state=MemoryState.ACTIVE)
+                )
+            return torch.cat(decoded_slices, dim=2)
+        else:
+            return self._decode(z)
+
+    def tiled_encode(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
+        raise NotImplementedError
+
+    def tiled_decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
+        raise NotImplementedError
+
+    def forward(
+        self, x: torch.FloatTensor, mode: Literal["encode", "decode", "all"] = "all", **kwargs
+    ):
+        # x: [b c t h w]
+        if mode == "encode":
+            h = self.encode(x)
+            return h.latent_dist
+        elif mode == "decode":
+            h = self.decode(x)
+            return h.sample
+        else:
+            h = self.encode(x)
+            h = self.decode(h.latent_dist.mode())
+            return h.sample
+
+    def load_state_dict(self, state_dict, strict=False):
+        # Newer version of diffusers changed the model keys,
+        # causing incompatibility with old checkpoints.
+        # They provided a method for conversion.
+        # We call conversion before loading state_dict.
+        convert_deprecated_attention_blocks = getattr(
+            self, "_convert_deprecated_attention_blocks", None
+        )
+        if callable(convert_deprecated_attention_blocks):
+            convert_deprecated_attention_blocks(state_dict)
+        return super().load_state_dict(state_dict, strict)
+
+
+class VideoAutoencoderKLWrapper(VideoAutoencoderKL):
+    def __init__(
+        self,
+        *args,
+        spatial_downsample_factor: int,
+        temporal_downsample_factor: int,
+        freeze_encoder: bool,
+        **kwargs,
+    ):
+        self.spatial_downsample_factor = spatial_downsample_factor
+        self.temporal_downsample_factor = temporal_downsample_factor
+        self.freeze_encoder = freeze_encoder
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x: torch.FloatTensor) -> CausalAutoencoderOutput:
+        with torch.no_grad() if self.freeze_encoder else nullcontext():
+            z, p = self.encode(x)
+        x = self.decode(z).sample
+        return CausalAutoencoderOutput(x, z, p)
+
+    def encode(self, x: torch.FloatTensor) -> CausalEncoderOutput:
+        if x.ndim == 4:
+            x = x.unsqueeze(2)
+        p = super().encode(x).latent_dist
+        z = p.sample().squeeze(2)
+        return CausalEncoderOutput(z, p)
+
+    def decode(self, z: torch.FloatTensor) -> CausalDecoderOutput:
+        if z.ndim == 4:
+            z = z.unsqueeze(2)
+        x = super().decode(z).sample.squeeze(2)
+        return CausalDecoderOutput(x)
+
+    def preprocess(self, x: torch.Tensor):
+        # x should in [B, C, T, H, W], [B, C, H, W]
+        assert x.ndim == 4 or x.size(2) % 4 == 1
+        return x
+
+    def postprocess(self, x: torch.Tensor):
+        # x should in [B, C, T, H, W], [B, C, H, W]
+        return x
+
+    def set_causal_slicing(
+        self,
+        *,
+        split_size: Optional[int],
+        memory_device: _memory_device_t,
+    ):
+        assert (
+            split_size is None or memory_device is not None
+        ), "if split_size is set, memory_device must not be None."
+        if split_size is not None:
+            self.enable_slicing()
+            self.slicing_sample_min_size = split_size
+            self.slicing_latent_min_size = split_size // self.temporal_downsample_factor
+        else:
+            self.disable_slicing()
+        for module in self.modules():
+            if isinstance(module, InflatedCausalConv3d):
+                module.set_memory_device(memory_device)
+
+    def set_memory_limit(self, conv_max_mem: Optional[float], norm_max_mem: Optional[float]):
+        set_norm_limit(norm_max_mem)
+        for m in self.modules():
+            if isinstance(m, InflatedCausalConv3d):
+                m.set_memory_limit(conv_max_mem if conv_max_mem is not None else float("inf"))
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py b/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py
new file mode 100644
index 000000000..26f14739e
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py
@@ -0,0 +1,437 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+import math
+from contextlib import contextmanager
+from typing import List, Optional, Union
+import torch
+import torch.nn.functional as F
+from diffusers.models.normalization import RMSNorm
+from einops import rearrange
+from torch import Tensor, nn
+from torch.nn import Conv3d
+
+from .context_parallel_lib import cache_send_recv, get_cache_size
+from .global_config import get_norm_limit
+from .types import MemoryState, _inflation_mode_t, _memory_device_t
+from ....common.half_precision_fixes import safe_pad_operation
+
+# Single GPU inference - no distributed processing needed
+
+# Mock distributed functions for single GPU inference
+def get_sequence_parallel_group():
+    return None
+
+def get_sequence_parallel_rank():
+    return 0
+
+def get_sequence_parallel_world_size():
+    return 1
+
+def get_next_sequence_parallel_rank():
+    return 0
+
+def get_prev_sequence_parallel_rank():
+    return 0
+
+
+@contextmanager
+def ignore_padding(model):
+    orig_padding = model.padding
+    model.padding = (0, 0, 0)
+    try:
+        yield
+    finally:
+        model.padding = orig_padding
+
+
+class InflatedCausalConv3d(Conv3d):
+    def __init__(
+        self,
+        *args,
+        inflation_mode: _inflation_mode_t,
+        memory_device: _memory_device_t = "same",
+        **kwargs,
+    ):
+        self.inflation_mode = inflation_mode
+        self.memory = None
+        super().__init__(*args, **kwargs)
+        self.temporal_padding = self.padding[0]
+        self.memory_device = memory_device
+        self.padding = (0, *self.padding[1:])  # Remove temporal pad to keep causal.
+        self.memory_limit = float("inf")
+
+    def set_memory_limit(self, value: float):
+        self.memory_limit = value
+
+    def set_memory_device(self, memory_device: _memory_device_t):
+        self.memory_device = memory_device
+
+    def memory_limit_conv(
+        self,
+        x,
+        *,
+        split_dim=3,
+        padding=(0, 0, 0, 0, 0, 0),
+        prev_cache=None,
+    ):
+        # Compatible with no limit.
+        if math.isinf(self.memory_limit):
+            if prev_cache is not None:
+                x = torch.cat([prev_cache, x], dim=split_dim - 1)
+            return super().forward(x)
+
+        # Compute tensor shape after concat & padding.
+        shape = torch.tensor(x.size())
+        if prev_cache is not None:
+            shape[split_dim - 1] += prev_cache.size(split_dim - 1)
+        shape[-3:] += torch.tensor(padding).view(3, 2).sum(-1).flip(0)
+        memory_occupy = shape.prod() * x.element_size() / 1024**3  # GiB
+        if memory_occupy < self.memory_limit or split_dim == x.ndim:
+            if prev_cache is not None:
+                x = torch.cat([prev_cache, x], dim=split_dim - 1)
+            x = safe_pad_operation(x, padding, mode='constant', value=0.0)
+            with ignore_padding(self):
+                return super().forward(x)
+
+        # Exceed memory limit, splitting tensor
+
+        # Split input (& prev_cache).
+        num_splits = math.ceil(memory_occupy / self.memory_limit)
+        size_per_split = x.size(split_dim) // num_splits
+        split_sizes = [size_per_split] * (num_splits - 1)
+        split_sizes += [x.size(split_dim) - sum(split_sizes)]
+
+        x = list(x.split(split_sizes, dim=split_dim))
+        if prev_cache is not None:
+            prev_cache = list(prev_cache.split(split_sizes, dim=split_dim))
+        # Loop Fwd.
+        cache = None
+        for idx in range(len(x)):
+            # Concat prev cache from last dim
+            if prev_cache is not None:
+                x[idx] = torch.cat([prev_cache[idx], x[idx]], dim=split_dim - 1)
+
+            # Get padding pattern.
+            lpad_dim = (x[idx].ndim - split_dim - 1) * 2
+            rpad_dim = lpad_dim + 1
+            padding = list(padding)
+            padding[lpad_dim] = self.padding[split_dim - 2] if idx == 0 else 0
+            padding[rpad_dim] = self.padding[split_dim - 2] if idx == len(x) - 1 else 0
+            pad_len = padding[lpad_dim] + padding[rpad_dim]
+            padding = tuple(padding)
+
+            # Prepare cache for next slice (this dim).
+            next_cache = None
+            cache_len = cache.size(split_dim) if cache is not None else 0
+            next_catch_size = get_cache_size(
+                conv_module=self,
+                input_len=x[idx].size(split_dim) + cache_len,
+                pad_len=pad_len,
+                dim=split_dim - 2,
+            )
+            if next_catch_size != 0:
+                assert next_catch_size <= x[idx].size(split_dim)
+                next_cache = (
+                    x[idx].transpose(0, split_dim)[-next_catch_size:].transpose(0, split_dim)
+                )
+
+            # Recursive.
+            x[idx] = self.memory_limit_conv(
+                x[idx],
+                split_dim=split_dim + 1,
+                padding=padding,
+                prev_cache=cache,
+            )
+
+            # Update cache.
+            cache = next_cache
+
+        output = torch.cat(x, split_dim)
+        return output
+
+    def forward(
+        self,
+        input: Union[Tensor, List[Tensor]],
+        memory_state: MemoryState = MemoryState.UNSET,
+    ) -> Tensor:
+        assert memory_state != MemoryState.UNSET
+        if memory_state != MemoryState.ACTIVE:
+            self.memory = None
+        if (
+            math.isinf(self.memory_limit)
+            and torch.is_tensor(input)
+            and get_sequence_parallel_group() is None
+        ):
+            return self.basic_forward(input, memory_state)
+        return self.slicing_forward(input, memory_state)
+
+    def basic_forward(self, input: Tensor, memory_state: MemoryState = MemoryState.UNSET):
+        mem_size = self.stride[0] - self.kernel_size[0]
+        if (self.memory is not None) and (memory_state == MemoryState.ACTIVE):
+            input = extend_head(input, memory=self.memory, times=-1)
+        else:
+            input = extend_head(input, times=self.temporal_padding * 2)
+        memory = (
+            input[:, :, mem_size:].detach()
+            if (mem_size != 0 and memory_state != MemoryState.DISABLED)
+            else None
+        )
+        if (
+            memory_state != MemoryState.DISABLED
+            and not self.training
+            and (self.memory_device is not None)
+        ):
+            self.memory = memory
+            if self.memory_device == "cpu" and self.memory is not None:
+                self.memory = self.memory.to("cpu")
+        return super().forward(input)
+
+    def slicing_forward(
+        self,
+        input: Union[Tensor, List[Tensor]],
+        memory_state: MemoryState = MemoryState.UNSET,
+    ) -> Tensor:
+        squeeze_out = False
+        if torch.is_tensor(input):
+            input = [input]
+            squeeze_out = True
+
+        cache_size = self.kernel_size[0] - self.stride[0]
+        cache = cache_send_recv(
+            input, cache_size=cache_size, memory=self.memory, times=self.temporal_padding * 2
+        )
+
+        # Single GPU inference - simplified memory management
+        if (
+            memory_state in [MemoryState.INITIALIZING, MemoryState.ACTIVE]  # use_slicing
+            and not self.training
+            and (self.memory_device is not None)
+            and cache_size != 0
+        ):
+            if cache_size > input[-1].size(2) and cache is not None and len(input) == 1:
+                input[0] = torch.cat([cache, input[0]], dim=2)
+                cache = None
+            if cache_size <= input[-1].size(2):
+                self.memory = input[-1][:, :, -cache_size:].detach().contiguous()
+                if self.memory_device == "cpu" and self.memory is not None:
+                    self.memory = self.memory.to("cpu")
+
+        padding = tuple(x for x in reversed(self.padding) for _ in range(2))
+        for i in range(len(input)):
+            # Prepare cache for next input slice.
+            next_cache = None
+            cache_size = 0
+            if i < len(input) - 1:
+                cache_len = cache.size(2) if cache is not None else 0
+                cache_size = get_cache_size(self, input[i].size(2) + cache_len, pad_len=0)
+            if cache_size != 0:
+                if cache_size > input[i].size(2) and cache is not None:
+                    input[i] = torch.cat([cache, input[i]], dim=2)
+                    cache = None
+                assert cache_size <= input[i].size(2), f"{cache_size} > {input[i].size(2)}"
+                next_cache = input[i][:, :, -cache_size:]
+
+            # Conv forward for this input slice.
+            input[i] = self.memory_limit_conv(
+                input[i],
+                padding=padding,
+                prev_cache=cache,
+            )
+
+            # Update cache.
+            cache = next_cache
+
+        return input[0] if squeeze_out else input
+
+    def tflops(self, args, kwargs, output) -> float:
+        if torch.is_tensor(output):
+            output_numel = output.numel()
+        elif isinstance(output, list):
+            output_numel = sum(o.numel() for o in output)
+        else:
+            raise NotImplementedError
+        return (2 * math.prod(self.kernel_size) * self.in_channels * (output_numel / 1e6)) / 1e6
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        if self.inflation_mode != "none":
+            state_dict = modify_state_dict(
+                self,
+                state_dict,
+                prefix,
+                inflate_weight_fn=inflate_weight,
+                inflate_bias_fn=inflate_bias,
+            )
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            (strict and self.inflation_mode == "none"),
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+def init_causal_conv3d(
+    *args,
+    inflation_mode: _inflation_mode_t,
+    **kwargs,
+):
+    """
+    Initialize a Causal-3D convolution layer.
+    Parameters:
+        inflation_mode: Listed as below. It's compatible with all the 3D-VAE checkpoints we have.
+            - none: No inflation will be conducted.
+                    The loading logic of state dict will fall back to default.
+            - tail / replicate: Refer to the definition of `InflatedCausalConv3d`.
+    """
+    return InflatedCausalConv3d(*args, inflation_mode=inflation_mode, **kwargs)
+
+
+def causal_norm_wrapper(norm_layer: nn.Module, x: torch.Tensor) -> torch.Tensor:
+    input_dtype = x.dtype
+    if isinstance(norm_layer, (nn.LayerNorm, RMSNorm)):
+        if x.ndim == 4:
+            x = rearrange(x, "b c h w -> b h w c")
+            x = norm_layer(x)
+            x = rearrange(x, "b h w c -> b c h w")
+            return x.to(input_dtype)
+        if x.ndim == 5:
+            x = rearrange(x, "b c t h w -> b t h w c")
+            x = norm_layer(x)
+            x = rearrange(x, "b t h w c -> b c t h w")
+            return x.to(input_dtype)
+    if isinstance(norm_layer, (nn.GroupNorm, nn.BatchNorm2d, nn.SyncBatchNorm)):
+        if x.ndim <= 4:
+            return norm_layer(x).to(input_dtype)
+        if x.ndim == 5:
+            t = x.size(2)
+            x = rearrange(x, "b c t h w -> (b t) c h w")
+            memory_occupy = x.numel() * x.element_size() / 1024**3
+            if isinstance(norm_layer, nn.GroupNorm) and memory_occupy > get_norm_limit():
+                num_chunks = min(4 if x.element_size() == 2 else 2, norm_layer.num_groups)
+                assert norm_layer.num_groups % num_chunks == 0
+                num_groups_per_chunk = norm_layer.num_groups // num_chunks
+
+                x = list(x.chunk(num_chunks, dim=1))
+                weights = norm_layer.weight.chunk(num_chunks, dim=0)
+                biases = norm_layer.bias.chunk(num_chunks, dim=0)
+                for i, (w, b) in enumerate(zip(weights, biases)):
+                    x[i] = F.group_norm(x[i], num_groups_per_chunk, w, b, norm_layer.eps)
+                    x[i] = x[i].to(input_dtype)
+                # ADD BY NUMZ
+                # ADD BY NUMZ
+                x = torch.cat(x, dim=1)
+            else:
+                x = norm_layer(x)
+            x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
+            return x.to(input_dtype)
+    raise NotImplementedError
+
+
+def remove_head(tensor: Tensor, times: int = 1) -> Tensor:
+    """
+    Remove duplicated first frame features in the up-sampling process.
+    """
+    # Single GPU inference - always process
+    if times == 0:
+        return tensor
+    return torch.cat(tensors=(tensor[:, :, :1], tensor[:, :, times + 1 :]), dim=2)
+
+
+def extend_head(tensor: Tensor, times: int = 2, memory: Optional[Tensor] = None) -> Tensor:
+    """
+    When memory is None:
+        - Duplicate first frame features in the down-sampling process.
+    When memory is not None:
+        - Concatenate memory features with the input features to keep temporal consistency.
+    """
+    if memory is not None:
+        return torch.cat((memory.to(tensor), tensor), dim=2)
+    assert times >= 0, "Invalid input for function 'extend_head'!"
+    if times == 0:
+        return tensor
+    else:
+        tile_repeat = [1] * tensor.ndim
+        tile_repeat[2] = times
+        return torch.cat(tensors=(torch.tile(tensor[:, :, :1], tile_repeat), tensor), dim=2)
+
+
+def inflate_weight(weight_2d: torch.Tensor, weight_3d: torch.Tensor, inflation_mode: str):
+    """
+    Inflate a 2D convolution weight matrix to a 3D one.
+    Parameters:
+        weight_2d:      The weight matrix of 2D conv to be inflated.
+        weight_3d:      The weight matrix of 3D conv to be initialized.
+        inflation_mode: the mode of inflation
+    """
+    assert inflation_mode in ["tail", "replicate"]
+    assert weight_3d.shape[:2] == weight_2d.shape[:2]
+    with torch.no_grad():
+        if inflation_mode == "replicate":
+            depth = weight_3d.size(2)
+            weight_3d.copy_(weight_2d.unsqueeze(2).repeat(1, 1, depth, 1, 1) / depth)
+        else:
+            weight_3d.fill_(0.0)
+            weight_3d[:, :, -1].copy_(weight_2d)
+    return weight_3d
+
+
+def inflate_bias(bias_2d: torch.Tensor, bias_3d: torch.Tensor, inflation_mode: str):
+    """
+    Inflate a 2D convolution bias tensor to a 3D one
+    Parameters:
+        bias_2d:        The bias tensor of 2D conv to be inflated.
+        bias_3d:        The bias tensor of 3D conv to be initialized.
+        inflation_mode: Placeholder to align `inflate_weight`.
+    """
+    assert bias_3d.shape == bias_2d.shape
+    with torch.no_grad():
+        bias_3d.copy_(bias_2d)
+    return bias_3d
+
+
+def modify_state_dict(layer, state_dict, prefix, inflate_weight_fn, inflate_bias_fn):
+    """
+    the main function to inflated 2D parameters to 3D.
+    """
+    weight_name = prefix + "weight"
+    bias_name = prefix + "bias"
+    if weight_name in state_dict:
+        weight_2d = state_dict[weight_name]
+        if weight_2d.dim() == 4:
+            # Assuming the 2D weights are 4D tensors (out_channels, in_channels, h, w)
+            weight_3d = inflate_weight_fn(
+                weight_2d=weight_2d,
+                weight_3d=layer.weight,
+                inflation_mode=layer.inflation_mode,
+            )
+            state_dict[weight_name] = weight_3d
+        else:
+            return state_dict
+            # It's a 3d state dict, should not do inflation on both bias and weight.
+    if bias_name in state_dict:
+        bias_2d = state_dict[bias_name]
+        if bias_2d.dim() == 1:
+            # Assuming the 2D biases are 1D tensors (out_channels,)
+            bias_3d = inflate_bias_fn(
+                bias_2d=bias_2d,
+                bias_3d=layer.bias,
+                inflation_mode=layer.inflation_mode,
+            )
+            state_dict[bias_name] = bias_3d
+    return state_dict
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py b/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py
new file mode 100644
index 000000000..ffc16938a
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py
@@ -0,0 +1,66 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import List
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+from .types import MemoryState
+
+# Single GPU inference - no distributed processing needed
+
+
+def causal_conv_slice_inputs(x, split_size, memory_state):
+    # Single GPU inference - no slicing needed, return full tensor
+    return x
+
+
+def causal_conv_gather_outputs(x):
+    # Single GPU inference - no gathering needed, return tensor as is
+    return x
+
+
+def get_output_len(conv_module, input_len, pad_len, dim=0):
+    dilated_kernerl_size = conv_module.dilation[dim] * (conv_module.kernel_size[dim] - 1) + 1
+    output_len = (input_len + pad_len - dilated_kernerl_size) // conv_module.stride[dim] + 1
+    return output_len
+
+
+def get_cache_size(conv_module, input_len, pad_len, dim=0):
+    dilated_kernerl_size = conv_module.dilation[dim] * (conv_module.kernel_size[dim] - 1) + 1
+    output_len = (input_len + pad_len - dilated_kernerl_size) // conv_module.stride[dim] + 1
+    remain_len = (
+        input_len + pad_len - ((output_len - 1) * conv_module.stride[dim] + dilated_kernerl_size)
+    )
+    overlap_len = dilated_kernerl_size - conv_module.stride[dim]
+    cache_len = overlap_len + remain_len  # >= 0
+
+    assert output_len > 0
+    return cache_len
+
+
+def cache_send_recv(tensor: List[Tensor], cache_size, times, memory=None):
+    # Single GPU inference - simplified cache handling
+    recv_buffer = None
+
+    # Handle memory buffer for single GPU case
+    if memory is not None:
+        recv_buffer = memory.to(tensor[0])
+    elif times > 0:
+        tile_repeat = [1] * tensor[0].ndim
+        tile_repeat[2] = times
+        recv_buffer = torch.tile(tensor[0][:, :, :1], tile_repeat)
+
+    return recv_buffer
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/global_config.py b/modules/seedvr/src/models/video_vae_v3/modules/global_config.py
new file mode 100644
index 000000000..863117570
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/global_config.py
@@ -0,0 +1,28 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from typing import Optional
+
+_NORM_LIMIT = float("inf")
+
+
+def get_norm_limit():
+    return _NORM_LIMIT
+
+
+def set_norm_limit(value: Optional[float] = None):
+    global _NORM_LIMIT
+    if value is None:
+        value = float("inf")
+    _NORM_LIMIT = value
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py b/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py
new file mode 100644
index 000000000..4b8e6dfb6
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py
@@ -0,0 +1,106 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from functools import partial
+from typing import Literal, Optional
+from torch import Tensor
+from torch.nn import Conv3d
+
+from .inflated_lib import (
+    MemoryState,
+    extend_head,
+    inflate_bias,
+    inflate_weight,
+    modify_state_dict,
+)
+
+_inflation_mode_t = Literal["none", "tail", "replicate"]
+_memory_device_t = Optional[Literal["cpu", "same"]]
+
+
+class InflatedCausalConv3d(Conv3d):
+    def __init__(
+        self,
+        *args,
+        inflation_mode: _inflation_mode_t,
+        memory_device: _memory_device_t = "same",
+        **kwargs,
+    ):
+        self.inflation_mode = inflation_mode
+        self.memory = None
+        super().__init__(*args, **kwargs)
+        self.temporal_padding = self.padding[0]
+        self.memory_device = memory_device
+        self.padding = (0, *self.padding[1:])  # Remove temporal pad to keep causal.
+
+    def set_memory_device(self, memory_device: _memory_device_t):
+        self.memory_device = memory_device
+
+    def forward(self, input: Tensor, memory_state: MemoryState = MemoryState.DISABLED) -> Tensor:
+        mem_size = self.stride[0] - self.kernel_size[0]
+        if (self.memory is not None) and (memory_state == MemoryState.ACTIVE):
+            input = extend_head(input, memory=self.memory)
+        else:
+            input = extend_head(input, times=self.temporal_padding * 2)
+        memory = (
+            input[:, :, mem_size:].detach()
+            if (mem_size != 0 and memory_state != MemoryState.DISABLED)
+            else None
+        )
+        if (
+            memory_state != MemoryState.DISABLED
+            and not self.training
+            and (self.memory_device is not None)
+        ):
+            self.memory = memory
+            if self.memory_device == "cpu" and self.memory is not None:
+                self.memory = self.memory.to("cpu")
+        return super().forward(input)
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        if self.inflation_mode != "none":
+            state_dict = modify_state_dict(
+                self,
+                state_dict,
+                prefix,
+                inflate_weight_fn=partial(inflate_weight, position="tail"),
+                inflate_bias_fn=partial(inflate_bias, position="tail"),
+            )
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            (strict and self.inflation_mode == "none"),
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+def init_causal_conv3d(
+    *args,
+    inflation_mode: _inflation_mode_t,
+    **kwargs,
+):
+    """
+    Initialize a Causal-3D convolution layer.
+    Parameters:
+        inflation_mode: Listed as below. It's compatible with all the 3D-VAE checkpoints we have.
+            - none: No inflation will be conducted.
+                    The loading logic of state dict will fall back to default.
+            - tail / replicate: Refer to the definition of `InflatedCausalConv3d`.
+    """
+    return InflatedCausalConv3d(*args, inflation_mode=inflation_mode, **kwargs)
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py b/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py
new file mode 100644
index 000000000..486c63ca1
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py
@@ -0,0 +1,156 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from enum import Enum
+from typing import Optional
+import numpy as np
+import torch
+from diffusers.models.normalization import RMSNorm
+from einops import rearrange
+from torch import Tensor, nn
+
+from ....common.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class MemoryState(Enum):
+    """
+    State[Disabled]:        No memory bank will be enabled.
+    State[Initializing]:    The model is handling the first clip,
+                            need to reset / initialize the memory bank.
+    State[Active]:          There has been some data in the memory bank.
+    """
+
+    DISABLED = 0
+    INITIALIZING = 1
+    ACTIVE = 2
+
+
+def causal_norm_wrapper(norm_layer: nn.Module, x: torch.Tensor) -> torch.Tensor:
+    if isinstance(norm_layer, (nn.LayerNorm, RMSNorm)):
+        if x.ndim == 4:
+            x = rearrange(x, "b c h w -> b h w c")
+            x = norm_layer(x)
+            x = rearrange(x, "b h w c -> b c h w")
+            return x
+        if x.ndim == 5:
+            x = rearrange(x, "b c t h w -> b t h w c")
+            x = norm_layer(x)
+            x = rearrange(x, "b t h w c -> b c t h w")
+            return x
+    if isinstance(norm_layer, (nn.GroupNorm, nn.BatchNorm2d, nn.SyncBatchNorm)):
+        if x.ndim <= 4:
+            return norm_layer(x)
+        if x.ndim == 5:
+            t = x.size(2)
+            x = rearrange(x, "b c t h w -> (b t) c h w")
+            x = norm_layer(x)
+            x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
+            return x
+    raise NotImplementedError
+
+
+def remove_head(tensor: Tensor, times: int = 1) -> Tensor:
+    """
+    Remove duplicated first frame features in the up-sampling process.
+    """
+    if times == 0:
+        return tensor
+    return torch.cat(tensors=(tensor[:, :, :1], tensor[:, :, times + 1 :]), dim=2)
+
+
+def extend_head(
+    tensor: Tensor, times: Optional[int] = 2, memory: Optional[Tensor] = None
+) -> Tensor:
+    """
+    When memory is None:
+        - Duplicate first frame features in the down-sampling process.
+    When memory is not None:
+        - Concatenate memory features with the input features to keep temporal consistency.
+    """
+    if times == 0:
+        return tensor
+    if memory is not None:
+        return torch.cat((memory.to(tensor), tensor), dim=2)
+    else:
+        tile_repeat = np.ones(tensor.ndim).astype(int)
+        tile_repeat[2] = times
+        return torch.cat(tensors=(torch.tile(tensor[:, :, :1], list(tile_repeat)), tensor), dim=2)
+
+
+def inflate_weight(weight_2d: torch.Tensor, weight_3d: torch.Tensor, inflation_mode: str):
+    """
+    Inflate a 2D convolution weight matrix to a 3D one.
+    Parameters:
+        weight_2d:      The weight matrix of 2D conv to be inflated.
+        weight_3d:      The weight matrix of 3D conv to be initialized.
+        inflation_mode: the mode of inflation
+    """
+    assert inflation_mode in ["constant", "replicate"]
+    assert weight_3d.shape[:2] == weight_2d.shape[:2]
+    with torch.no_grad():
+        if inflation_mode == "replicate":
+            depth = weight_3d.size(2)
+            weight_3d.copy_(weight_2d.unsqueeze(2).repeat(1, 1, depth, 1, 1) / depth)
+        else:
+            weight_3d.fill_(0.0)
+            weight_3d[:, :, -1].copy_(weight_2d)
+    return weight_3d
+
+
+def inflate_bias(bias_2d: torch.Tensor, bias_3d: torch.Tensor, inflation_mode: str):
+    """
+    Inflate a 2D convolution bias tensor to a 3D one
+    Parameters:
+        bias_2d:        The bias tensor of 2D conv to be inflated.
+        bias_3d:        The bias tensor of 3D conv to be initialized.
+        inflation_mode: Placeholder to align `inflate_weight`.
+    """
+    assert bias_3d.shape == bias_2d.shape
+    with torch.no_grad():
+        bias_3d.copy_(bias_2d)
+    return bias_3d
+
+
+def modify_state_dict(layer, state_dict, prefix, inflate_weight_fn, inflate_bias_fn):
+    """
+    the main function to inflated 2D parameters to 3D.
+    """
+    weight_name = prefix + "weight"
+    bias_name = prefix + "bias"
+    if weight_name in state_dict:
+        weight_2d = state_dict[weight_name]
+        if weight_2d.dim() == 4:
+            # Assuming the 2D weights are 4D tensors (out_channels, in_channels, h, w)
+            weight_3d = inflate_weight_fn(
+                weight_2d=weight_2d,
+                weight_3d=layer.weight,
+                inflation_mode=layer.inflation_mode,
+            )
+            state_dict[weight_name] = weight_3d
+        else:
+            return state_dict
+            # It's a 3d state dict, should not do inflation on both bias and weight.
+    if bias_name in state_dict:
+        bias_2d = state_dict[bias_name]
+        if bias_2d.dim() == 1:
+            # Assuming the 2D biases are 1D tensors (out_channels,)
+            bias_3d = inflate_bias_fn(
+                bias_2d=bias_2d,
+                bias_3d=layer.bias,
+                inflation_mode=layer.inflation_mode,
+            )
+            state_dict[bias_name] = bias_3d
+    return state_dict
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/types.py b/modules/seedvr/src/models/video_vae_v3/modules/types.py
new file mode 100644
index 000000000..5a030d2d2
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/types.py
@@ -0,0 +1,76 @@
+# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# //
+# // Licensed under the Apache License, Version 2.0 (the "License");
+# // you may not use this file except in compliance with the License.
+# // You may obtain a copy of the License at
+# //
+# //     http://www.apache.org/licenses/LICENSE-2.0
+# //
+# // Unless required by applicable law or agreed to in writing, software
+# // distributed under the License is distributed on an "AS IS" BASIS,
+# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# // See the License for the specific language governing permissions and
+# // limitations under the License.
+
+from enum import Enum
+from typing import Dict, Literal, NamedTuple, Optional
+import torch
+
+_receptive_field_t = Literal["half", "full"]
+_inflation_mode_t = Literal["none", "tail", "replicate"]
+_memory_device_t = Optional[Literal["cpu", "same"]]
+_gradient_checkpointing_t = Optional[Literal["half", "full"]]
+_selective_checkpointing_t = Optional[Literal["coarse", "fine"]]
+
+class DiagonalGaussianDistribution:
+    def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
+        self.mean = mean
+        self.logvar = torch.clamp(logvar, -30.0, 20.0)
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+
+    def mode(self) -> torch.Tensor:
+        return self.mean
+
+    def sample(self) -> torch.FloatTensor:
+        return self.mean + self.std * torch.randn_like(self.mean)
+
+    def kl(self) -> torch.Tensor:
+        return 0.5 * torch.sum(
+            self.mean**2 + self.var - 1.0 - self.logvar,
+            dim=list(range(1, self.mean.ndim)),
+        )
+
+class MemoryState(Enum):
+    """
+    State[Disabled]:        No memory bank will be enabled.
+    State[Initializing]:    The model is handling the first clip, need to reset the memory bank.
+    State[Active]:          There has been some data in the memory bank.
+    State[Unset]:           Error state, indicating users didn't pass correct memory state in.
+    """
+
+    DISABLED = 0
+    INITIALIZING = 1
+    ACTIVE = 2
+    UNSET = 3
+
+
+class QuantizerOutput(NamedTuple):
+    latent: torch.Tensor
+    extra_loss: torch.Tensor
+    statistics: Dict[str, torch.Tensor]
+
+
+class CausalAutoencoderOutput(NamedTuple):
+    sample: torch.Tensor
+    latent: torch.Tensor
+    posterior: Optional[DiagonalGaussianDistribution]
+
+
+class CausalEncoderOutput(NamedTuple):
+    latent: torch.Tensor
+    posterior: Optional[DiagonalGaussianDistribution]
+
+
+class CausalDecoderOutput(NamedTuple):
+    sample: torch.Tensor
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py b/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py
new file mode 100644
index 000000000..98147e82f
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py
@@ -0,0 +1,956 @@
+# Copyright (c) 2023 HuggingFace Team
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
+# SPDX-License-Identifier: Apache License, Version 2.0 (the "License")
+#
+# This file has been modified by ByteDance Ltd. and/or its affiliates. on 1st June 2025
+#
+# Original file was released under Apache License, Version 2.0 (the "License"), with the full license text
+# available at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# This modified file is released under the same license.
+
+from contextlib import nullcontext
+from typing import Optional, Tuple, Literal, Callable, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
+from einops import rearrange
+from ....common.half_precision_fixes import safe_pad_operation
+
+from ....common.distributed.advanced import get_sequence_parallel_world_size
+from ....common.logger import get_logger
+from .causal_inflation_lib import (
+    InflatedCausalConv3d,
+    causal_norm_wrapper,
+    init_causal_conv3d,
+    remove_head,
+)
+from .context_parallel_lib import (
+    causal_conv_gather_outputs,
+    causal_conv_slice_inputs,
+)
+from .global_config import set_norm_limit
+from .types import (
+    CausalAutoencoderOutput,
+    CausalDecoderOutput,
+    CausalEncoderOutput,
+    MemoryState,
+    _inflation_mode_t,
+    _memory_device_t,
+    _receptive_field_t,
+    _selective_checkpointing_t,
+)
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+# Fake func, no checkpointing is required for inference
+def gradient_checkpointing(module: Union[Callable, nn.Module], *args, enabled: bool, **kwargs):
+    return module(*args, **kwargs)
+
+class ResnetBlock2D(nn.Module):
+    r"""
+    A Resnet block.
+
+    Parameters:
+        in_channels (`int`): The number of channels in the input.
+        out_channels (`int`, *optional*, default to be `None`):
+            The number of output channels for the first conv2d layer.
+            If None, same as `in_channels`.
+        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
+    """
+
+    def __init__(
+        self, *, in_channels: int, out_channels: Optional[int] = None, dropout: float = 0.0
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.nonlinearity = nn.SiLU()
+
+        self.norm1 = torch.nn.GroupNorm(
+            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+        )
+
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.norm2 = torch.nn.GroupNorm(
+            num_groups=32, num_channels=out_channels, eps=1e-6, affine=True
+        )
+
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.use_in_shortcut = self.in_channels != out_channels
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = nn.Conv2d(
+                in_channels, out_channels, kernel_size=1, stride=1, padding=0
+            )
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden = input_tensor
+
+        hidden = self.norm1(hidden)
+        hidden = self.nonlinearity(hidden)
+        hidden = self.conv1(hidden)
+
+        hidden = self.norm2(hidden)
+        hidden = self.nonlinearity(hidden)
+        hidden = self.dropout(hidden)
+        hidden = self.conv2(hidden)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = input_tensor + hidden
+
+        return output_tensor
+
+class Upsample3D(nn.Module):
+    """A 3D upsampling layer."""
+
+    def __init__(
+        self,
+        channels: int,
+        inflation_mode: _inflation_mode_t = "tail",
+        temporal_up: bool = False,
+        spatial_up: bool = True,
+        slicing: bool = False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.conv = init_causal_conv3d(
+            self.channels, self.channels, kernel_size=3, padding=1, inflation_mode=inflation_mode
+        )
+
+        self.temporal_up = temporal_up
+        self.spatial_up = spatial_up
+        self.temporal_ratio = 2 if temporal_up else 1
+        self.spatial_ratio = 2 if spatial_up else 1
+        self.slicing = slicing
+
+        upscale_ratio = (self.spatial_ratio**2) * self.temporal_ratio
+        self.upscale_conv = nn.Conv3d(
+            self.channels, self.channels * upscale_ratio, kernel_size=1, padding=0
+        )
+        identity = (
+            torch.eye(self.channels).repeat(upscale_ratio, 1).reshape_as(self.upscale_conv.weight)
+        )
+
+        self.upscale_conv.weight.data.copy_(identity)
+        nn.init.zeros_(self.upscale_conv.bias)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        memory_state: MemoryState,
+    ) -> torch.FloatTensor:
+        return gradient_checkpointing(
+            self.custom_forward,
+            hidden_states,
+            memory_state,
+            enabled=self.training and self.gradient_checkpointing,
+        )
+
+    def custom_forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        memory_state: MemoryState,
+    ) -> torch.FloatTensor:
+        assert hidden_states.shape[1] == self.channels
+
+        if self.slicing:
+            split_size = hidden_states.size(2) // 2
+            hidden_states = list(
+                hidden_states.split([split_size, hidden_states.size(2) - split_size], dim=2)
+            )
+        else:
+            hidden_states = [hidden_states]
+
+        for i in range(len(hidden_states)):
+            hidden_states[i] = self.upscale_conv(hidden_states[i])
+            hidden_states[i] = rearrange(
+                hidden_states[i],
+                "b (x y z c) f h w -> b c (f z) (h x) (w y)",
+                x=self.spatial_ratio,
+                y=self.spatial_ratio,
+                z=self.temporal_ratio,
+            )
+
+        # [Overridden] For causal temporal conv
+        if self.temporal_up and memory_state != MemoryState.ACTIVE:
+            hidden_states[0] = remove_head(hidden_states[0])
+
+        if self.slicing:
+            hidden_states = self.conv(hidden_states, memory_state=memory_state)
+            return torch.cat(hidden_states, dim=2)
+        else:
+            return self.conv(hidden_states[0], memory_state=memory_state)
+
+
+class Downsample3D(nn.Module):
+    """A 3D downsampling layer."""
+
+    def __init__(
+        self,
+        channels: int,
+        inflation_mode: _inflation_mode_t = "tail",
+        temporal_down: bool = False,
+        spatial_down: bool = True,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.temporal_down = temporal_down
+        self.spatial_down = spatial_down
+
+        self.temporal_ratio = 2 if temporal_down else 1
+        self.spatial_ratio = 2 if spatial_down else 1
+
+        self.temporal_kernel = 3 if temporal_down else 1
+        self.spatial_kernel = 3 if spatial_down else 1
+
+        self.conv = init_causal_conv3d(
+            self.channels,
+            self.channels,
+            kernel_size=(self.temporal_kernel, self.spatial_kernel, self.spatial_kernel),
+            stride=(self.temporal_ratio, self.spatial_ratio, self.spatial_ratio),
+            padding=((1 if self.temporal_down else 0), 0, 0),
+            inflation_mode=inflation_mode,
+        )
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        memory_state: MemoryState,
+    ) -> torch.FloatTensor:
+        return gradient_checkpointing(
+            self.custom_forward,
+            hidden_states,
+            memory_state,
+            enabled=self.training and self.gradient_checkpointing,
+        )
+
+    def custom_forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        memory_state: MemoryState,
+    ) -> torch.FloatTensor:
+
+        assert hidden_states.shape[1] == self.channels
+
+        if self.spatial_down:
+            hidden_states = safe_pad_operation(hidden_states, (0, 1, 0, 1), mode="constant", value=0)
+
+        hidden_states = self.conv(hidden_states, memory_state=memory_state)
+        return hidden_states
+
+
+class ResnetBlock3D(ResnetBlock2D):
+    def __init__(
+        self,
+        *args,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.conv1 = init_causal_conv3d(
+            self.in_channels,
+            self.out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        self.conv2 = init_causal_conv3d(
+            self.out_channels,
+            self.out_channels,
+            kernel_size=(1, 3, 3) if time_receptive_field == "half" else (3, 3, 3),
+            stride=1,
+            padding=(0, 1, 1) if time_receptive_field == "half" else (1, 1, 1),
+            inflation_mode=inflation_mode,
+        )
+
+        if self.use_in_shortcut:
+            self.conv_shortcut = init_causal_conv3d(
+                self.in_channels,
+                self.out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=(self.conv_shortcut.bias is not None),
+                inflation_mode=inflation_mode,
+            )
+        self.gradient_checkpointing = False
+
+    def forward(self, input_tensor: torch.Tensor, memory_state: MemoryState = MemoryState.UNSET):
+        return gradient_checkpointing(
+            self.custom_forward,
+            input_tensor,
+            memory_state,
+            enabled=self.training and self.gradient_checkpointing,
+        )
+
+    def custom_forward(
+        self, input_tensor: torch.Tensor, memory_state: MemoryState = MemoryState.UNSET
+    ):
+        assert memory_state != MemoryState.UNSET
+        hidden_states = input_tensor
+
+        hidden_states = causal_norm_wrapper(self.norm1, hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states, memory_state=memory_state)
+
+        hidden_states = causal_norm_wrapper(self.norm2, hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states, memory_state=memory_state)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor, memory_state=memory_state)
+
+        output_tensor = input_tensor + hidden_states
+
+        return output_tensor
+
+
+class DownEncoderBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        add_downsample: bool = True,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        temporal_down: bool = True,
+        spatial_down: bool = True,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    dropout=dropout,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        self.downsamplers = None
+        if add_downsample:
+            # Todo: Refactor this line before V5 Image VAE Training.
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        channels=out_channels,
+                        inflation_mode=inflation_mode,
+                        temporal_down=temporal_down,
+                        spatial_down=spatial_down,
+                    )
+                ]
+            )
+
+    def forward(
+        self, hidden_states: torch.FloatTensor, memory_state: MemoryState
+    ) -> torch.FloatTensor:
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, memory_state=memory_state)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states, memory_state=memory_state)
+
+        return hidden_states
+
+
+class UpDecoderBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        add_upsample: bool = True,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        temporal_up: bool = True,
+        spatial_up: bool = True,
+        slicing: bool = False,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            input_channels = in_channels if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=input_channels,
+                    out_channels=out_channels,
+                    dropout=dropout,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        self.upsamplers = None
+        # Todo: Refactor this line before V5 Image VAE Training.
+        if add_upsample:
+            self.upsamplers = nn.ModuleList(
+                [
+                    Upsample3D(
+                        channels=out_channels,
+                        inflation_mode=inflation_mode,
+                        temporal_up=temporal_up,
+                        spatial_up=spatial_up,
+                        slicing=slicing,
+                    )
+                ]
+            )
+
+    def forward(
+        self, hidden_states: torch.FloatTensor, memory_state: MemoryState
+    ) -> torch.FloatTensor:
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, memory_state=memory_state)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, memory_state=memory_state)
+
+        return hidden_states
+
+
+class UNetMidBlock3D(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        dropout: float = 0.0,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+    ):
+        super().__init__()
+        self.resnets = nn.ModuleList(
+            [
+                ResnetBlock3D(
+                    in_channels=channels,
+                    out_channels=channels,
+                    dropout=dropout,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                ),
+                ResnetBlock3D(
+                    in_channels=channels,
+                    out_channels=channels,
+                    dropout=dropout,
+                    inflation_mode=inflation_mode,
+                    time_receptive_field=time_receptive_field,
+                ),
+            ]
+        )
+
+    def forward(self, hidden_states: torch.Tensor, memory_state: MemoryState):
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, memory_state)
+        return hidden_states
+
+
+class Encoder3D(nn.Module):
+    r"""
+    The `Encoder` layer of a variational autoencoder that encodes
+    its input into a latent representation.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int, ...] = (64,),
+        layers_per_block: int = 2,
+        double_z: bool = True,
+        temporal_down_num: int = 2,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        selective_checkpointing: Tuple[_selective_checkpointing_t] = ("none",),
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+
+        self.temporal_down_num = temporal_down_num
+
+        self.conv_in = init_causal_conv3d(
+            in_channels,
+            block_out_channels[0],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        self.down_blocks = nn.ModuleList([])
+
+        # down
+        output_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            is_temporal_down_block = i >= len(block_out_channels) - self.temporal_down_num - 1
+            # Note: take the last one
+
+            down_block = DownEncoderBlock3D(
+                num_layers=self.layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                add_downsample=not is_final_block,
+                temporal_down=is_temporal_down_block,
+                spatial_down=True,
+                inflation_mode=inflation_mode,
+                time_receptive_field=time_receptive_field,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        self.mid_block = UNetMidBlock3D(
+            channels=block_out_channels[-1],
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(
+            num_channels=block_out_channels[-1], num_groups=32, eps=1e-6
+        )
+        self.conv_act = nn.SiLU()
+
+        conv_out_channels = 2 * out_channels if double_z else out_channels
+        self.conv_out = init_causal_conv3d(
+            block_out_channels[-1], conv_out_channels, 3, padding=1, inflation_mode=inflation_mode
+        )
+
+        assert len(selective_checkpointing) == len(self.down_blocks)
+        self.set_gradient_checkpointing(selective_checkpointing)
+
+    def set_gradient_checkpointing(self, checkpointing_types):
+        gradient_checkpointing = []
+        for down_block, sac_type in zip(self.down_blocks, checkpointing_types):
+            if sac_type == "coarse":
+                gradient_checkpointing.append(True)
+            elif sac_type == "fine":
+                for n, m in down_block.named_modules():
+                    if hasattr(m, "gradient_checkpointing"):
+                        m.gradient_checkpointing = True
+                        logger.debug(f"set gradient_checkpointing: {n}")
+                gradient_checkpointing.append(False)
+            else:
+                gradient_checkpointing.append(False)
+        self.gradient_checkpointing = gradient_checkpointing
+        logger.info(f"[Encoder3D] gradient_checkpointing: {checkpointing_types}")
+
+    def forward(self, sample: torch.FloatTensor, memory_state: MemoryState) -> torch.FloatTensor:
+        r"""The forward method of the `Encoder` class."""
+        sample = self.conv_in(sample, memory_state=memory_state)
+        # down
+        for down_block, sac in zip(self.down_blocks, self.gradient_checkpointing):
+            sample = gradient_checkpointing(
+                down_block,
+                sample,
+                memory_state=memory_state,
+                enabled=self.training and sac,
+            )
+
+        # middle
+        sample = self.mid_block(sample, memory_state=memory_state)
+
+        # post-process
+        sample = causal_norm_wrapper(self.conv_norm_out, sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample, memory_state=memory_state)
+
+        return sample
+
+
+class Decoder3D(nn.Module):
+    r"""
+    The `Decoder` layer of a variational autoencoder that
+    decodes its latent representation into an output sample.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int, ...] = (64,),
+        layers_per_block: int = 2,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        temporal_up_num: int = 2,
+        slicing_up_num: int = 0,
+        selective_checkpointing: Tuple[_selective_checkpointing_t] = ("none",),
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+        self.temporal_up_num = temporal_up_num
+
+        self.conv_in = init_causal_conv3d(
+            in_channels,
+            block_out_channels[-1],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inflation_mode=inflation_mode,
+        )
+
+        self.up_blocks = nn.ModuleList([])
+
+        # mid
+        self.mid_block = UNetMidBlock3D(
+            channels=block_out_channels[-1],
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        output_channel = reversed_block_out_channels[0]
+        for i in range(len(reversed_block_out_channels)):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+
+            is_final_block = i == len(block_out_channels) - 1
+            is_temporal_up_block = i < self.temporal_up_num
+            is_slicing_up_block = i >= len(block_out_channels) - slicing_up_num
+            # Note: Keep symmetric
+
+            up_block = UpDecoderBlock3D(
+                num_layers=self.layers_per_block + 1,
+                in_channels=prev_output_channel,
+                out_channels=output_channel,
+                add_upsample=not is_final_block,
+                temporal_up=is_temporal_up_block,
+                slicing=is_slicing_up_block,
+                inflation_mode=inflation_mode,
+                time_receptive_field=time_receptive_field,
+            )
+            self.up_blocks.append(up_block)
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(
+            num_channels=block_out_channels[0], num_groups=32, eps=1e-6
+        )
+        self.conv_act = nn.SiLU()
+        self.conv_out = init_causal_conv3d(
+            block_out_channels[0], out_channels, 3, padding=1, inflation_mode=inflation_mode
+        )
+
+        assert len(selective_checkpointing) == len(self.up_blocks)
+        self.set_gradient_checkpointing(selective_checkpointing)
+
+    def set_gradient_checkpointing(self, checkpointing_types):
+        gradient_checkpointing = []
+        for up_block, sac_type in zip(self.up_blocks, checkpointing_types):
+            if sac_type == "coarse":
+                gradient_checkpointing.append(True)
+            elif sac_type == "fine":
+                for n, m in up_block.named_modules():
+                    if hasattr(m, "gradient_checkpointing"):
+                        m.gradient_checkpointing = True
+                        logger.debug(f"set gradient_checkpointing: {n}")
+                gradient_checkpointing.append(False)
+            else:
+                gradient_checkpointing.append(False)
+        self.gradient_checkpointing = gradient_checkpointing
+        logger.info(f"[Decoder3D] gradient_checkpointing: {checkpointing_types}")
+
+    def forward(self, sample: torch.FloatTensor, memory_state: MemoryState) -> torch.FloatTensor:
+        r"""The forward method of the `Decoder` class."""
+
+        sample = self.conv_in(sample, memory_state=memory_state)
+
+        # middle
+        sample = self.mid_block(sample, memory_state=memory_state)
+
+        # up
+        for up_block, sac in zip(self.up_blocks, self.gradient_checkpointing):
+            sample = gradient_checkpointing(
+                up_block,
+                sample,
+                memory_state=memory_state,
+                enabled=self.training and sac,
+            )
+
+        # post-process
+        sample = causal_norm_wrapper(self.conv_norm_out, sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample, memory_state=memory_state)
+
+        return sample
+
+
+class VideoAutoencoderKL(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int] = (64,),
+        layers_per_block: int = 1,
+        latent_channels: int = 4,
+        use_quant_conv: bool = True,
+        use_post_quant_conv: bool = True,
+        enc_selective_checkpointing: Tuple[_selective_checkpointing_t] = ("none",),
+        dec_selective_checkpointing: Tuple[_selective_checkpointing_t] = ("none",),
+        temporal_scale_num: int = 3,
+        slicing_up_num: int = 0,
+        inflation_mode: _inflation_mode_t = "tail",
+        time_receptive_field: _receptive_field_t = "half",
+        slicing_sample_min_size: int = None,
+        spatial_downsample_factor: int = 16,
+        temporal_downsample_factor: int = 8,
+        freeze_encoder: bool = False,
+    ):
+        super().__init__()
+        self.spatial_downsample_factor = spatial_downsample_factor
+        self.temporal_downsample_factor = temporal_downsample_factor
+        self.freeze_encoder = freeze_encoder
+        if slicing_sample_min_size is None:
+            slicing_sample_min_size = temporal_downsample_factor
+        self.slicing_sample_min_size = slicing_sample_min_size
+        self.slicing_latent_min_size = slicing_sample_min_size // (2**temporal_scale_num)
+
+        # pass init params to Encoder
+        self.encoder = Encoder3D(
+            in_channels=in_channels,
+            out_channels=latent_channels,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            double_z=True,
+            temporal_down_num=temporal_scale_num,
+            selective_checkpointing=enc_selective_checkpointing,
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        # pass init params to Decoder
+        self.decoder = Decoder3D(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            # [Override] add temporal_up_num parameter
+            temporal_up_num=temporal_scale_num,
+            slicing_up_num=slicing_up_num,
+            selective_checkpointing=dec_selective_checkpointing,
+            inflation_mode=inflation_mode,
+            time_receptive_field=time_receptive_field,
+        )
+
+        self.quant_conv = (
+            init_causal_conv3d(
+                in_channels=2 * latent_channels,
+                out_channels=2 * latent_channels,
+                kernel_size=1,
+                inflation_mode=inflation_mode,
+            )
+            if use_quant_conv
+            else None
+        )
+        self.post_quant_conv = (
+            init_causal_conv3d(
+                in_channels=latent_channels,
+                out_channels=latent_channels,
+                kernel_size=1,
+                inflation_mode=inflation_mode,
+            )
+            if use_post_quant_conv
+            else None
+        )
+
+        self.use_slicing = False
+
+    def enable_slicing(self):
+        self.use_slicing = True
+
+    def disable_slicing(self):
+        self.use_slicing = False
+
+    def encode(self, x: torch.FloatTensor) -> CausalEncoderOutput:
+        if x.ndim == 4:
+            x = x.unsqueeze(2)
+        h = self.slicing_encode(x)
+        p = DiagonalGaussianDistribution(h)
+        z = p.sample()
+        return CausalEncoderOutput(z, p)
+
+    def decode(self, z: torch.FloatTensor) -> CausalDecoderOutput:
+        if z.ndim == 4:
+            z = z.unsqueeze(2)
+        x = self.slicing_decode(z)
+        return CausalDecoderOutput(x)
+
+    def _encode(self, x: torch.Tensor, memory_state: MemoryState) -> torch.Tensor:
+        x = causal_conv_slice_inputs(x, self.slicing_sample_min_size, memory_state=memory_state)
+        h = self.encoder(x, memory_state=memory_state)
+        h = self.quant_conv(h, memory_state=memory_state) if self.quant_conv is not None else h
+        h = causal_conv_gather_outputs(h)
+        return h
+
+    def _decode(self, z: torch.Tensor, memory_state: MemoryState) -> torch.Tensor:
+        z = causal_conv_slice_inputs(z, self.slicing_latent_min_size, memory_state=memory_state)
+        z = (
+            self.post_quant_conv(z, memory_state=memory_state)
+            if self.post_quant_conv is not None
+            else z
+        )
+        x = self.decoder(z, memory_state=memory_state)
+        x = causal_conv_gather_outputs(x)
+        return x
+
+    def slicing_encode(self, x: torch.Tensor) -> torch.Tensor:
+        sp_size = get_sequence_parallel_world_size()
+        if self.use_slicing and (x.shape[2] - 1) > self.slicing_sample_min_size * sp_size:
+            x_slices = x[:, :, 1:].split(split_size=self.slicing_sample_min_size * sp_size, dim=2)
+            encoded_slices = [
+                self._encode(
+                    torch.cat((x[:, :, :1], x_slices[0]), dim=2),
+                    memory_state=MemoryState.INITIALIZING,
+                )
+            ]
+            for x_idx in range(1, len(x_slices)):
+                encoded_slices.append(
+                    self._encode(x_slices[x_idx], memory_state=MemoryState.ACTIVE)
+                )
+            return torch.cat(encoded_slices, dim=2)
+        else:
+            return self._encode(x, memory_state=MemoryState.DISABLED)
+
+    def slicing_decode(self, z: torch.Tensor) -> torch.Tensor:
+        sp_size = get_sequence_parallel_world_size()
+        if self.use_slicing and (z.shape[2] - 1) > self.slicing_latent_min_size * sp_size:
+            z_slices = z[:, :, 1:].split(split_size=self.slicing_latent_min_size * sp_size, dim=2)
+            decoded_slices = [
+                self._decode(
+                    torch.cat((z[:, :, :1], z_slices[0]), dim=2),
+                    memory_state=MemoryState.INITIALIZING,
+                )
+            ]
+            for z_idx in range(1, len(z_slices)):
+                decoded_slices.append(
+                    self._decode(z_slices[z_idx], memory_state=MemoryState.ACTIVE)
+                )
+            return torch.cat(decoded_slices, dim=2)
+        else:
+            return self._decode(z, memory_state=MemoryState.DISABLED)
+
+    def forward(self, x: torch.FloatTensor) -> CausalAutoencoderOutput:
+        with torch.no_grad() if self.freeze_encoder else nullcontext():
+            z, p = self.encode(x)
+        x = self.decode(z).sample
+        return CausalAutoencoderOutput(x, z, p)
+
+    def preprocess(self, x: torch.Tensor):
+        # x should in [B, C, T, H, W], [B, C, H, W]
+        assert x.ndim == 4 or x.size(2) % self.temporal_downsample_factor == 1
+        return x
+
+    def postprocess(self, x: torch.Tensor):
+        # x should in [B, C, T, H, W], [B, C, H, W]
+        return x
+
+    def set_causal_slicing(
+        self,
+        *,
+        split_size: Optional[int],
+        memory_device: _memory_device_t,
+    ):
+        assert (
+            split_size is None or memory_device is not None
+        ), "if split_size is set, memory_device must not be None."
+        if split_size is not None:
+            self.enable_slicing()
+            self.slicing_sample_min_size = split_size
+            self.slicing_latent_min_size = split_size // self.temporal_downsample_factor
+        else:
+            self.disable_slicing()
+        for module in self.modules():
+            if isinstance(module, InflatedCausalConv3d):
+                module.set_memory_device(memory_device)
+
+    def set_memory_limit(self, conv_max_mem: Optional[float], norm_max_mem: Optional[float]):
+        set_norm_limit(norm_max_mem)
+        for m in self.modules():
+            if isinstance(m, InflatedCausalConv3d):
+                m.set_memory_limit(conv_max_mem if conv_max_mem is not None else float("inf"))
+
+
+class VideoAutoencoderKLWrapper(VideoAutoencoderKL):
+    def __init__(
+        self, *args, spatial_downsample_factor: int, temporal_downsample_factor: int, **kwargs
+    ):
+        self.spatial_downsample_factor = spatial_downsample_factor
+        self.temporal_downsample_factor = temporal_downsample_factor
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x) -> CausalAutoencoderOutput:
+        z, _, p = self.encode(x)
+        x, _ = self.decode(z)
+        return CausalAutoencoderOutput(x, z, None, p)
+
+    def encode(self, x) -> CausalEncoderOutput:
+        if x.ndim == 4:
+            x = x.unsqueeze(2)
+        p = super().encode(x).latent_dist
+        z = p.sample().squeeze(2)
+        return CausalEncoderOutput(z, None, p)
+
+    def decode(self, z) -> CausalDecoderOutput:
+        if z.ndim == 4:
+            z = z.unsqueeze(2)
+        x = super().decode(z).sample.squeeze(2)
+        return CausalDecoderOutput(x, None)
+
+    def preprocess(self, x):
+        # x should in [B, C, T, H, W], [B, C, H, W]
+        assert x.ndim == 4 or x.size(2) % 4 == 1
+        return x
+
+    def postprocess(self, x):
+        # x should in [B, C, T, H, W], [B, C, H, W]
+        return x
+
+    def set_causal_slicing(
+        self,
+        *,
+        split_size: Optional[int],
+        memory_device: Optional[Literal["cpu", "same"]],
+    ):
+        assert (
+            split_size is None or memory_device is not None
+        ), "if split_size is set, memory_device must not be None."
+        if split_size is not None:
+            self.enable_slicing()
+        else:
+            self.disable_slicing()
+        self.slicing_sample_min_size = split_size
+        if split_size is not None:
+            self.slicing_latent_min_size = split_size // self.temporal_downsample_factor
+        for module in self.modules():
+            if isinstance(module, InflatedCausalConv3d):
+                module.set_memory_device(memory_device)
diff --git a/modules/seedvr/src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml b/modules/seedvr/src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml
new file mode 100644
index 000000000..949122667
--- /dev/null
+++ b/modules/seedvr/src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml
@@ -0,0 +1,28 @@
+act_fn: silu
+block_out_channels:
+  - 128
+  - 256
+  - 512
+  - 512
+down_block_types:
+  - DownEncoderBlock3D
+  - DownEncoderBlock3D
+  - DownEncoderBlock3D
+  - DownEncoderBlock3D
+in_channels: 3
+latent_channels: 16
+layers_per_block: 2
+norm_num_groups: 32
+out_channels: 3
+slicing_sample_min_size: 4
+temporal_scale_num: 2
+inflation_mode: pad
+up_block_types:
+  - UpDecoderBlock3D
+  - UpDecoderBlock3D
+  - UpDecoderBlock3D
+  - UpDecoderBlock3D
+spatial_downsample_factor: 8
+temporal_downsample_factor: 4
+use_quant_conv: False
+use_post_quant_conv: False
diff --git a/modules/seedvr/src/optimization/__init__.py b/modules/seedvr/src/optimization/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/modules/seedvr/src/optimization/memory_manager.py b/modules/seedvr/src/optimization/memory_manager.py
new file mode 100644
index 000000000..47bc52101
--- /dev/null
+++ b/modules/seedvr/src/optimization/memory_manager.py
@@ -0,0 +1,131 @@
+"""
+Memory management module for SeedVR2
+Handles VRAM usage, cache management, and memory optimization
+
+Extracted from: seedvr2.py (lines 373-405, 607-626, 1016-1044)
+"""
+
+import os
+import torch
+import gc
+from typing import Tuple, Optional
+from src.common.cache import Cache
+from src.models.dit_v2.rope import RotaryEmbeddingBase
+
+
+def preinitialize_rope_cache(runner) -> None:
+    """
+    🚀 Pre-initialize RoPE cache to avoid OOM at first launch
+    
+    Args:
+        runner: The model runner containing DiT and VAE models
+    """
+
+    try:
+        # Create dummy tensors to simulate common shapes
+        # Format: [batch, channels, frames, height, width] for vid_shape
+        # Format: [batch, seq_len] for txt_shape
+        common_shapes = [
+            # Common video resolutions
+            (torch.tensor([[1, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 1 frame, 77 tokens
+            (torch.tensor([[4, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 4 frames
+            (torch.tensor([[5, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 5 frames (4n+1 format)
+            (torch.tensor([[1, 4, 4]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # Higher resolution
+        ]
+
+        # Create mock cache for pre-initialization
+
+        temp_cache = Cache()
+
+        # Access RoPE modules in DiT (recursive search)
+        def find_rope_modules(module):
+            rope_modules = []
+            for name, child in module.named_modules():
+                if hasattr(child, 'get_freqs') and callable(child.get_freqs):
+                    rope_modules.append((name, child))
+            return rope_modules
+
+        rope_modules = find_rope_modules(runner.dit)
+
+        # Pre-calculate for each RoPE module found
+        for name, rope_module in rope_modules:
+            # Temporarily move module to CPU if necessary
+            original_device = next(rope_module.parameters()).device if list(rope_module.parameters()) else torch.device('cpu')
+            rope_module.to('cpu')
+
+            try:
+                for vid_shape, txt_shape in common_shapes:
+                    cache_key = f"720pswin_by_size_bysize_{tuple(vid_shape[0].tolist())}_sd3.mmrope_freqs_3d"
+
+                    def compute_freqs():
+                        # Calculate with reduced dimensions to avoid OOM
+                        with torch.no_grad():
+                            # Detect RoPE module type
+                            module_type = type(rope_module).__name__
+
+                            if module_type == 'NaRotaryEmbedding3d':
+                                # NaRotaryEmbedding3d: only takes shape (vid_shape)
+                                return rope_module.get_freqs(vid_shape.cpu())
+                            else:
+                                # Standard RoPE: takes vid_shape and txt_shape
+                                return rope_module.get_freqs(vid_shape.cpu(), txt_shape.cpu())
+
+                    # Store in cache
+                    temp_cache(cache_key, compute_freqs)
+
+            except Exception as e:
+                print(f"    ❌ Error in module {name}: {e}")
+            finally:
+                # Restore to original device
+                rope_module.to(original_device)
+
+        # Copy temporary cache to runner cache
+        if hasattr(runner, 'cache'):
+            runner.cache.cache.update(temp_cache.cache)
+        else:
+            runner.cache = temp_cache
+
+    except Exception as e:
+        print(f"  ⚠️ Error during RoPE pre-init: {e}")
+        print("  🔄 Model will work but could have OOM at first launch")
+
+
+def clear_rope_cache(runner) -> None:
+    """
+    🧹 Clear RoPE cache to free VRAM
+    
+    Args:
+        runner: The model runner containing the cache
+    """
+    print("🧹 Cleaning RoPE cache...")
+
+    if hasattr(runner, 'cache') and hasattr(runner.cache, 'cache'):
+        # Count entries before cleanup
+        cache_size = len(runner.cache.cache)
+
+        # Free all tensors from cache
+        for key, value in runner.cache.cache.items():
+            if isinstance(value, (tuple, list)):
+                for item in value:
+                    if hasattr(item, 'cpu'):
+                        item.cpu()
+                        del item
+            elif hasattr(value, 'cpu'):
+                value.cpu()
+                del value
+
+        # Clear the cache
+        runner.cache.cache.clear()
+        print(f"  ✅ RoPE cache cleared ({cache_size} entries removed)")
+
+    if hasattr(runner, 'dit'):
+        cleared_lru_count = 0
+        for module in runner.dit.modules():
+            if isinstance(module, RotaryEmbeddingBase):
+                if hasattr(module.get_axial_freqs, 'cache_clear'):
+                    module.get_axial_freqs.cache_clear()
+                    cleared_lru_count += 1
+        if cleared_lru_count > 0:
+            print(f"  ✅ Cleared {cleared_lru_count} LRU caches from RoPE modules.")
+
+    print("🎯 RoPE cache cleanup completed!")
diff --git a/modules/seedvr/src/optimization/performance.py b/modules/seedvr/src/optimization/performance.py
new file mode 100644
index 000000000..73a95fe4d
--- /dev/null
+++ b/modules/seedvr/src/optimization/performance.py
@@ -0,0 +1,160 @@
+"""
+Performance optimization module for SeedVR2
+Contains optimized tensor operations and video processing functions
+
+Extracted from: seedvr2.py (lines 1633-1730)
+"""
+
+import torch
+from typing import List, Union
+
+
+def optimized_video_rearrange(video_tensors: List[torch.Tensor]) -> List[torch.Tensor]:
+    """
+    🚀 OPTIMIZED version of video rearrangement
+    Replaces slow loops with vectorized operations
+    
+    Transforms:
+    - 3D: c h w -> t c h w (with t=1)  
+    - 4D: c t h w -> t c h w
+    
+    Expected gains: 5-10x faster than naive loops
+    
+    Args:
+        video_tensors: List of video tensors to rearrange
+        
+    Returns:
+        List of rearranged tensors in t c h w format
+    """
+    if not video_tensors:
+        return []
+
+    # 🔍 Analyze dimensions to optimize processing
+    videos_3d = []
+    videos_4d = []
+    indices_3d = []
+    indices_4d = []
+
+    for i, video in enumerate(video_tensors):
+        if video.ndim == 3:
+            videos_3d.append(video)
+            indices_3d.append(i)
+        else:  # ndim == 4
+            videos_4d.append(video)
+            indices_4d.append(i)
+
+    # 🎯 Prepare final result
+    samples = [None] * len(video_tensors)
+
+    # 🚀 BATCH PROCESSING for 3D videos (c h w -> 1 c h w)
+    if videos_3d:
+        # Method 1: Stack + permute (faster than rearrange)
+        # c h w -> c 1 h w -> 1 c h w
+        batch_3d = torch.stack([v.unsqueeze(1) for v in videos_3d])  # [batch, c, 1, h, w]
+        batch_3d = batch_3d.permute(0, 2, 1, 3, 4)  # [batch, 1, c, h, w]
+
+        for i, idx in enumerate(indices_3d):
+            samples[idx] = batch_3d[i]  # [1, c, h, w]
+
+    # 🚀 BATCH PROCESSING for 4D videos (c t h w -> t c h w)
+    if videos_4d:
+        # Check if all 4D videos have the same shape for maximum optimization
+        shapes = [v.shape for v in videos_4d]
+        if len(set(shapes)) == 1:
+            # 🎯 MAXIMUM OPTIMIZATION: All shapes identical
+            # Stack + permute in single operation
+            batch_4d = torch.stack(videos_4d)  # [batch, c, t, h, w]
+            batch_4d = batch_4d.permute(0, 2, 1, 3, 4)  # [batch, t, c, h, w]
+
+            for i, idx in enumerate(indices_4d):
+                samples[idx] = batch_4d[i]  # [t, c, h, w]
+        else:
+            # 🔄 FALLBACK: Different shapes, optimized individual processing
+            for i, idx in enumerate(indices_4d):
+                # Use permute instead of rearrange (faster)
+                samples[idx] = videos_4d[i].permute(1, 0, 2, 3)  # c t h w -> t c h w
+
+    return samples
+
+
+def optimized_single_video_rearrange(video: torch.Tensor) -> torch.Tensor:
+    """
+    🚀 OPTIMIZED version for single video tensor
+    Replaces rearrange() with native PyTorch operations
+    
+    Transforms:
+    - 3D: c h w -> 1 c h w (add temporal dimension)
+    - 4D: c t h w -> t c h w (permute dimensions)
+    
+    Expected gains: 2-5x faster than rearrange()
+    
+    Args:
+        video: Input video tensor
+        
+    Returns:
+        Rearranged tensor with temporal dimension first
+    """
+    if video.ndim == 3:
+        # c h w -> 1 c h w (add temporal dimension t=1)
+        return video.unsqueeze(0)
+    else:  # ndim == 4
+        # c t h w -> t c h w (permute channels and temporal)
+        return video.permute(1, 0, 2, 3)
+
+
+def optimized_sample_to_image_format(sample: torch.Tensor) -> torch.Tensor:
+    """
+    🚀 OPTIMIZED version to convert sample to image format
+    Replaces rearrange() with native PyTorch operations
+    
+    Transforms:
+    - 3D: c h w -> 1 h w c (add temporal dimension + permute to image format)
+    - 4D: t c h w -> t h w c (permute to image format)
+    
+    Expected gains: 2-5x faster than rearrange()
+    
+    Args:
+        sample: Input sample tensor
+        
+    Returns:
+        Tensor in image format (channels last)
+    """
+    if sample.ndim == 3:
+        # c h w -> 1 h w c (add temporal dimension then permute)
+        return sample.unsqueeze(0).permute(0, 2, 3, 1)
+    else:  # ndim == 4
+        # t c h w -> t h w c (permute channels to last)
+        return sample.permute(0, 2, 3, 1)
+
+
+def temporal_latent_blending(latents1: torch.Tensor, latents2: torch.Tensor, blend_frames: int) -> torch.Tensor:
+    """
+    🎨 Temporal blending in latent space to avoid discontinuities
+    
+    Args:
+        latents1: Latents from previous batch (end frames)
+        latents2: Latents from current batch (start frames)
+        blend_frames: Number of frames to blend
+    
+    Returns:
+        Blended latents for smooth transition
+    """
+    if latents1.shape[0] != latents2.shape[0]:
+        # Adjust dimensions if necessary
+        min_frames = min(latents1.shape[0], latents2.shape[0])
+        latents1 = latents1[:min_frames]
+        latents2 = latents2[:min_frames]
+
+    # Create linear blending weights
+    # Frame 0: 100% latents1, 0% latents2
+    # Frame n: 0% latents1, 100% latents2
+    weights1 = torch.linspace(1.0, 0.0, blend_frames).view(-1, 1, 1, 1).to(latents1.device)
+    weights2 = torch.linspace(0.0, 1.0, blend_frames).view(-1, 1, 1, 1).to(latents2.device)
+
+    # Apply blending
+    blended_latents = weights1 * latents1 + weights2 * latents2
+
+    return blended_latents
+
+
+
diff --git a/modules/seedvr/src/utils/__init__.py b/modules/seedvr/src/utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/modules/seedvr/src/utils/color_fix.py b/modules/seedvr/src/utils/color_fix.py
new file mode 100644
index 000000000..8a042892b
--- /dev/null
+++ b/modules/seedvr/src/utils/color_fix.py
@@ -0,0 +1,138 @@
+import torch
+from PIL import Image
+from torch import Tensor
+from torch.nn import functional as F
+from src.common.half_precision_fixes import safe_pad_operation, safe_interpolate_operation
+from torchvision.transforms import ToTensor, ToPILImage
+
+def adain_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply adaptive instance normalization
+    result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def wavelet_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply wavelet reconstruction
+    result_tensor = wavelet_reconstruction(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def calc_mean_std(feat: Tensor, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.view(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(b, c, 1, 1)
+    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
+    return feat_mean, feat_std
+
+def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor):
+    """Adaptive instance normalization.
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+def wavelet_blur(image: Tensor, radius: int):
+    """
+    Apply wavelet blur to the input tensor.
+    """
+    # input shape: (1, 3, H, W)
+    # convolution kernel
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125, 0.25, 0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    # add channel dimensions to the kernel to make it a 4D tensor
+    kernel = kernel[None, None]
+    # repeat the kernel across all input channels
+    kernel = kernel.repeat(3, 1, 1, 1)
+    image = safe_pad_operation(image, (radius, radius, radius, radius), mode='replicate')
+    # apply convolution
+    output = F.conv2d(image, kernel, groups=3, dilation=radius)
+    return output
+
+def wavelet_decomposition(image: Tensor, levels=5):
+    """
+    Apply wavelet decomposition to the input tensor.
+    This function only returns the low frequency & the high frequency.
+    """
+    high_freq = torch.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+
+
+def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
+    """
+    Apply wavelet decomposition, so that the content will have the same color as the style.
+    """
+    # Vérifier et ajuster les dimensions si nécessaire
+    if content_feat.shape != style_feat.shape:
+        # Redimensionner style_feat pour correspondre à content_feat
+        target_shape = content_feat.shape
+        if len(target_shape) >= 3:  # Au moins 3 dimensions
+            # Utiliser interpolation pour ajuster les dimensions spatiales
+            style_feat = safe_interpolate_operation(
+                style_feat,
+                size=target_shape[-2:],  # Dernières 2 dimensions (H, W)
+                mode='bilinear',
+                align_corners=False
+            )
+
+    # calculate the wavelet decomposition of the content feature
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+    # calculate the wavelet decomposition of the style feature
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+
+    # Vérification finale avant addition
+    if content_high_freq.shape != style_low_freq.shape:
+        style_low_freq = safe_interpolate_operation(
+            style_low_freq,
+            size=content_high_freq.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    # reconstruct the content feature with the style's high frequency
+    return content_high_freq + style_low_freq
diff --git a/modules/seedvr/test.py b/modules/seedvr/test.py
new file mode 100644
index 000000000..6ecf022ab
--- /dev/null
+++ b/modules/seedvr/test.py
@@ -0,0 +1,58 @@
+import os
+import argparse
+import numpy as np
+import torch
+from PIL import Image
+from huggingface_hub import snapshot_download
+from torchvision.transforms import ToPILImage
+from src.core.generation import generation_loop
+from src.core.model_manager import configure_runner
+
+
+
+device = 'cuda'
+dtype = torch.bfloat16
+model_dir = 'seedvr2_models'
+model = 'seedvr2_ema_3b_fp16.safetensors'
+resolution = 1024
+seed = 100
+cfg = 1.0
+input_image = '/home/vlado/generative/Samples/cutie-512.png'
+
+to_pil = ToPILImage()
+runner = None
+loaded_model = None
+
+
+def upscale_image(model_name:str, image_path:str):
+    global runner, loaded_model
+    if (runner is None) or (loaded_model != model_name):
+        runner = configure_runner(model_name, model_dir, device=device, dtype=dtype)
+        loaded_model = model_name
+
+    image = Image.open(image_path).convert("RGB")
+    image_tensor = np.array(image)
+    image_tensor = torch.from_numpy(image_tensor).to(device=device, dtype=dtype).unsqueeze(0) / 255.0
+
+    result_tensor = generation_loop(
+        runner=runner,
+        images=image_tensor,
+        cfg_scale=cfg,
+        seed=seed,
+        res_w=resolution,
+        batch_size=1,
+        temporal_overlap=0,
+        device=device,
+    )
+    image = to_pil(result_tensor.squeeze().permute((2, 0, 1)))
+
+    output_path = os.path.join('/tmp', os.path.basename(image_path))
+
+    image.save(output_path, quality=95)
+    return image
+
+
+if __name__ == "__main__":
+    output_image = upscale_image(model, input_image)
+    print('input:', input_image)
+    print('output:', output_image)
diff --git a/modules/upscaler.py b/modules/upscaler.py
index 3494742e8..ec907ad8a 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -79,7 +79,7 @@ class Upscaler:
                 scalers.append(scaler)
                 loaded.append(model_path)
                 # shared.log.debug(f'Upscaler type={self.name} folder="{self.user_path}" model="{model[0]}" path="{model_path}"')
-        if not os.path.exists(self.user_path):
+        if self.user_path is None or not os.path.exists(self.user_path):
             return scalers
         self.find_folder(self.user_path, scalers, loaded)
         return scalers
@@ -148,7 +148,7 @@ class UpscalerData:
     scaler: Upscaler = None
     model: None
 
-    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
+    def __init__(self, name: str, path: str = None, upscaler: Upscaler = None, scale: int = 4, model=None):
         self.name = name
         self.data_path = path
         self.local_data_path = path

From 8d36a5aebb5fdf8db71982b4d584cebd4dfbb744 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 11 Oct 2025 18:51:07 -0400
Subject: [PATCH 206/230] lint

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_detect.py                          |  2 +-
 modules/seedvr/src/__init__.py                |  4 +--
 modules/seedvr/src/common/config.py           |  8 ++---
 modules/seedvr/src/common/seed.py             |  1 -
 modules/seedvr/src/core/__init__.py           |  8 ++---
 modules/seedvr/src/core/generation.py         | 24 +++++++-------
 modules/seedvr/src/models/dit_v2/attention.py |  1 -
 .../seedvr/src/optimization/memory_manager.py |  4 +--
 .../seedvr/src/optimization/performance.py    | 33 +++++++++----------
 9 files changed, 40 insertions(+), 45 deletions(-)

diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 9996537c1..0b2d3d19c 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -146,7 +146,7 @@ def guess_by_diffusers(fn, current_guess):
                 if folder.endswith('quantization_config.json'):
                     is_quant = True
                     break
-                elif os.path.isdir(folder):
+                if os.path.isdir(folder):
                     for f in os.listdir(folder):
                         if f.endswith('quantization_config.json'):
                             is_quant = True
diff --git a/modules/seedvr/src/__init__.py b/modules/seedvr/src/__init__.py
index ebff00ffb..a374bbd24 100644
--- a/modules/seedvr/src/__init__.py
+++ b/modules/seedvr/src/__init__.py
@@ -7,7 +7,7 @@ Version: 2.0.0 - Modular
 
 Available Modules:
 - utils: Download and path utilities
-- optimization: Memory, performance, and compatibility optimizations  
+- optimization: Memory, performance, and compatibility optimizations
 - core: Model management and generation pipeline (NEW)
 - processing: Video and tensor processing (coming next)
 - interfaces: ComfyUI integration
@@ -17,7 +17,7 @@ Available Modules:
 MODULES_AVAILABLE = {
     'downloads': True,          # ✅ Module 1 - Downloads and model management
     'memory_manager': True,     # ✅ Module 2 - Memory optimization
-    'performance': True,        # ✅ Module 3 - Performance optimizations  
+    'performance': True,        # ✅ Module 3 - Performance optimizations
     'compatibility': True,      # ✅ Module 4 - FP8/FP16 compatibility
     'model_manager': True,      # ✅ Module 5 - Model configuration and loading
     'generation': True,         # ✅ Module 6 - Generation loop and inference
diff --git a/modules/seedvr/src/common/config.py b/modules/seedvr/src/common/config.py
index 3667b4d2b..653685ccd 100644
--- a/modules/seedvr/src/common/config.py
+++ b/modules/seedvr/src/common/config.py
@@ -71,15 +71,15 @@ def resolve_inheritance(config: Union[DictConfig, ListConfig]) -> Any:
 def import_item(path: Union[str, List[str]], name: str) -> Any:
     """
     Import a python item with fallback support.
-    
+
     Args:
         path: Single path string or list of paths to try (fallback order)
         name: Class/function name to import
-        
+
     Returns:
         Imported object
-        
-    Example: 
+
+    Example:
         import_item("path.to.file", "MyClass") -> MyClass
         import_item(["path1.to.file", "path2.to.file"], "MyClass") -> MyClass (first working path)
     """
diff --git a/modules/seedvr/src/common/seed.py b/modules/seedvr/src/common/seed.py
index f00e33db6..f3e77f2bf 100644
--- a/modules/seedvr/src/common/seed.py
+++ b/modules/seedvr/src/common/seed.py
@@ -27,4 +27,3 @@ def set_seed(seed: Optional[int], same_across_ranks: bool = False):
         random.seed(seed)
         np.random.seed(seed)
         torch.manual_seed(seed)
-
diff --git a/modules/seedvr/src/core/__init__.py b/modules/seedvr/src/core/__init__.py
index 271ba39b8..e284d32ed 100644
--- a/modules/seedvr/src/core/__init__.py
+++ b/modules/seedvr/src/core/__init__.py
@@ -29,13 +29,13 @@ from .infer import VideoDiffusionInfer
 __all__ = [
     # Model management
     'configure_runner',
-    'load_quantized_state_dict', 
+    'load_quantized_state_dict',
     'configure_dit_model_inference',
     'configure_vae_model_inference',
-    
+
     # Generation logic
     'generation_step',
-    'generation_loop', 
+    'generation_loop',
     'cut_videos',
     'prepare_video_transforms',
     'load_text_embeddings',
@@ -43,5 +43,5 @@ __all__ = [
 
     # Infer
     'VideoDiffusionInfer'
-] 
+]
 '''
diff --git a/modules/seedvr/src/core/generation.py b/modules/seedvr/src/core/generation.py
index daac4020f..5f871cfe7 100644
--- a/modules/seedvr/src/core/generation.py
+++ b/modules/seedvr/src/core/generation.py
@@ -12,16 +12,16 @@ from src.utils.color_fix import wavelet_reconstruction
 def generation_step(runner, text_embeds_dict, cond_latents, temporal_overlap, device):
     """
     Execute a single generation step with adaptive dtype handling
-    
+
     Args:
         runner: VideoDiffusionInfer instance
         text_embeds_dict (dict): Text embeddings for positive and negative prompts
         cond_latents (list): Conditional latents for generation
         temporal_overlap (int): Number of frames for temporal overlap
-        
+
     Returns:
         tuple: (samples, last_latents) for potential temporal continuation
-        
+
     Features:
         - Adaptive dtype detection (FP8/FP16/BFloat16)
         - Optimal autocast configuration for each model type
@@ -111,7 +111,7 @@ def cut_videos(videos):
 def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_size=90, temporal_overlap=0, progress_callback=None, device:str='cpu'):
     """
     Main generation loop with context-aware temporal processing
-    
+
     Args:
         runner: VideoDiffusionInfer instance
         images (torch.Tensor): Input images for upscaling
@@ -121,10 +121,10 @@ def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_si
         batch_size (int): Batch size for processing
         temporal_overlap (int): Frames for temporal continuity
         progress_callback (callable): Optional callback for progress reporting
-        
+
     Returns:
         torch.Tensor: Generated video frames
-        
+
     Features:
         - Context-aware generation with temporal overlap
         - Adaptive dtype pipeline (FP8/FP16/BFloat16)
@@ -300,13 +300,13 @@ def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_si
 def prepare_video_transforms(res_w):
     """
     Prepare optimized video transformation pipeline
-    
+
     Args:
         res_w (int): Target resolution width
-        
+
     Returns:
         Compose: Configured transformation pipeline
-        
+
     Features:
         - Resolution-aware upscaling (no downsampling)
         - Proper normalization for model compatibility
@@ -328,15 +328,15 @@ def prepare_video_transforms(res_w):
 def calculate_optimal_batch_params(total_frames, batch_size, temporal_overlap):
     """
     Calculate optimal batch processing parameters
-    
+
     Args:
         total_frames (int): Total number of frames
         batch_size (int): Desired batch size
         temporal_overlap (int): Temporal overlap frames
-        
+
     Returns:
         dict: Optimized parameters and recommendations
-        
+
     Features:
         - 4n+1 constraint optimization
         - Padding waste calculation
diff --git a/modules/seedvr/src/models/dit_v2/attention.py b/modules/seedvr/src/models/dit_v2/attention.py
index 5a93ba30a..a88fd7d13 100644
--- a/modules/seedvr/src/models/dit_v2/attention.py
+++ b/modules/seedvr/src/models/dit_v2/attention.py
@@ -90,4 +90,3 @@ class FlashAttentionVarlen(nn.Module):
             return flash_attn_varlen_func(*args, **kwargs)
         except ImportError:
             return pytorch_varlen_attention(*args, **kwargs)
-
diff --git a/modules/seedvr/src/optimization/memory_manager.py b/modules/seedvr/src/optimization/memory_manager.py
index 47bc52101..3be323f67 100644
--- a/modules/seedvr/src/optimization/memory_manager.py
+++ b/modules/seedvr/src/optimization/memory_manager.py
@@ -16,7 +16,7 @@ from src.models.dit_v2.rope import RotaryEmbeddingBase
 def preinitialize_rope_cache(runner) -> None:
     """
     🚀 Pre-initialize RoPE cache to avoid OOM at first launch
-    
+
     Args:
         runner: The model runner containing DiT and VAE models
     """
@@ -93,7 +93,7 @@ def preinitialize_rope_cache(runner) -> None:
 def clear_rope_cache(runner) -> None:
     """
     🧹 Clear RoPE cache to free VRAM
-    
+
     Args:
         runner: The model runner containing the cache
     """
diff --git a/modules/seedvr/src/optimization/performance.py b/modules/seedvr/src/optimization/performance.py
index 73a95fe4d..83cba0462 100644
--- a/modules/seedvr/src/optimization/performance.py
+++ b/modules/seedvr/src/optimization/performance.py
@@ -13,16 +13,16 @@ def optimized_video_rearrange(video_tensors: List[torch.Tensor]) -> List[torch.T
     """
     🚀 OPTIMIZED version of video rearrangement
     Replaces slow loops with vectorized operations
-    
+
     Transforms:
-    - 3D: c h w -> t c h w (with t=1)  
+    - 3D: c h w -> t c h w (with t=1)
     - 4D: c t h w -> t c h w
-    
+
     Expected gains: 5-10x faster than naive loops
-    
+
     Args:
         video_tensors: List of video tensors to rearrange
-        
+
     Returns:
         List of rearranged tensors in t c h w format
     """
@@ -81,16 +81,16 @@ def optimized_single_video_rearrange(video: torch.Tensor) -> torch.Tensor:
     """
     🚀 OPTIMIZED version for single video tensor
     Replaces rearrange() with native PyTorch operations
-    
+
     Transforms:
     - 3D: c h w -> 1 c h w (add temporal dimension)
     - 4D: c t h w -> t c h w (permute dimensions)
-    
+
     Expected gains: 2-5x faster than rearrange()
-    
+
     Args:
         video: Input video tensor
-        
+
     Returns:
         Rearranged tensor with temporal dimension first
     """
@@ -106,16 +106,16 @@ def optimized_sample_to_image_format(sample: torch.Tensor) -> torch.Tensor:
     """
     🚀 OPTIMIZED version to convert sample to image format
     Replaces rearrange() with native PyTorch operations
-    
+
     Transforms:
     - 3D: c h w -> 1 h w c (add temporal dimension + permute to image format)
     - 4D: t c h w -> t h w c (permute to image format)
-    
+
     Expected gains: 2-5x faster than rearrange()
-    
+
     Args:
         sample: Input sample tensor
-        
+
     Returns:
         Tensor in image format (channels last)
     """
@@ -130,12 +130,12 @@ def optimized_sample_to_image_format(sample: torch.Tensor) -> torch.Tensor:
 def temporal_latent_blending(latents1: torch.Tensor, latents2: torch.Tensor, blend_frames: int) -> torch.Tensor:
     """
     🎨 Temporal blending in latent space to avoid discontinuities
-    
+
     Args:
         latents1: Latents from previous batch (end frames)
         latents2: Latents from current batch (start frames)
         blend_frames: Number of frames to blend
-    
+
     Returns:
         Blended latents for smooth transition
     """
@@ -155,6 +155,3 @@ def temporal_latent_blending(latents1: torch.Tensor, latents2: torch.Tensor, ble
     blended_latents = weights1 * latents1 + weights2 * latents2
 
     return blended_latents
-
-
-

From 2e4e741d47c61730d34e79ae8dae4f7995f7b4a7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 12 Oct 2025 15:35:08 -0400
Subject: [PATCH 207/230] seedvt2

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                                  |   9 +-
 modules/model_quant.py                        |   4 +-
 modules/postprocess/seedvr_model.py           | 131 +++++++++++++++---
 modules/sd_models_utils.py                    |   2 +
 modules/sd_offload.py                         |  10 +-
 modules/sdnq/quantizer.py                     |   5 +-
 modules/seedvr/config_3b.yaml                 |  12 +-
 modules/seedvr/config_7b.yaml                 |   8 +-
 modules/seedvr/src/__init__.py                |  28 +---
 modules/seedvr/src/common/config.py           |   2 +-
 modules/seedvr/src/common/decorators.py       |   2 +-
 modules/seedvr/src/common/distributed/ops.py  |  19 ++-
 modules/seedvr/src/common/logger.py           |   2 +-
 modules/seedvr/src/common/seed.py             |   1 -
 modules/seedvr/src/core/generation.py         |  41 +-----
 modules/seedvr/src/core/infer.py              |  68 +++------
 modules/seedvr/src/core/model_manager.py      |  32 ++---
 .../models/dit/blocks/mmdit_window_block.py   |   8 +-
 modules/seedvr/src/models/dit_v2/rope.py      |   3 +-
 .../video_vae_v3/modules/attn_video_vae.py    | 102 +++++++++-----
 .../modules/causal_inflation_lib.py           |  20 ---
 .../modules/context_parallel_lib.py           |   3 -
 .../video_vae_v3/modules/inflated_layers.py   |   8 +-
 .../video_vae_v3/modules/inflated_lib.py      |   2 +-
 .../{video_vae.py => video_vae.py.old}        |  34 +----
 .../seedvr/src/optimization/memory_manager.py | 116 +++++++---------
 modules/seedvr/src/utils/color_fix.py         |   2 +-
 modules/seedvr/test.py                        |   6 +-
 modules/shared.py                             |   4 +-
 modules/ui_settings.py                        |   2 +-
 30 files changed, 335 insertions(+), 351 deletions(-)
 rename modules/seedvr/src/models/video_vae_v3/modules/{video_vae.py => video_vae.py.old} (97%)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4f06fff89..860ba0038 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-11
+## Update for 2025-10-12
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -17,6 +17,13 @@
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
   - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
     updated version of E1 image editing model  
+  - [SeedVR2](https://iceclear.github.io/projects/seedvr/)  
+    originally designed for video restoration, seedvr works great for image detailing and upscaling!  
+    available in 3B, 7B and 7B-sharp variants  
+    use as any other upscaler!  
+    note: seedvr is a very large model (6.4GB and 16GB respectively) and not designed for lower-end hardware  
+    note: seedvr is highly sensitive to its cfg scale (set in settings -> postprocessing),  
+    lower values will result in smoother output while higher values add details  
   - [X-Omni SFT](https://x-omni-team.github.io/)  
     *experimental*: X-omni is a transformer-only discrete autoregressive image generative model trained with reinforcement learning  
 - **Features**
diff --git a/modules/model_quant.py b/modules/model_quant.py
index c3d2439ec..5f88d3174 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -482,7 +482,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
     from modules.sdnq import sdnq_post_load_quant
 
     if weights_dtype is None:
-        if op is not None and ("text_encoder" in op or op in {"TE", "LLM"}) and shared.opts.sdnq_quantize_weights_mode_te not in {"Same as model", "default"}:
+        if (op is not None) and ("text_encoder" in op or op in {"TE", "LLM"}) and (shared.opts.sdnq_quantize_weights_mode_te not in {"Same as model", "default"}):
             weights_dtype = shared.opts.sdnq_quantize_weights_mode_te
         else:
             weights_dtype = shared.opts.sdnq_quantize_weights_mode
@@ -588,6 +588,8 @@ def sdnq_quantize_weights(sd_model):
         log.info(f"Quantization: type=SDNQ time={t1-t0:.2f}")
     except Exception as e:
         log.warning(f"Quantization: type=SDNQ {e}")
+        from modules import errors
+        errors.display(e, 'Quantization')
     return sd_model
 
 
diff --git a/modules/postprocess/seedvr_model.py b/modules/postprocess/seedvr_model.py
index 1f833cbd5..fddeff557 100644
--- a/modules/postprocess/seedvr_model.py
+++ b/modules/postprocess/seedvr_model.py
@@ -1,9 +1,9 @@
 import time
+import random
 import numpy as np
 import torch
 from PIL import Image
 from torchvision.transforms import ToPILImage
-from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, TimeElapsedColumn
 from modules import devices
 from modules.shared import opts, log
 from modules.upscaler import Upscaler, UpscalerData
@@ -19,7 +19,7 @@ to_pil = ToPILImage()
 
 class UpscalerSeedVR(Upscaler):
     def __init__(self, dirname=None):
-        self.name = "SeedVR"
+        self.name = "SeedVR2"
         super().__init__()
         self.scalers = [
             UpscalerData(name="SeedVR2 3B", path=None, upscaler=self, model=None, scale=1),
@@ -32,45 +32,140 @@ class UpscalerSeedVR(Upscaler):
     def load_model(self, path: str):
         model_name = MODELS_MAP.get(path, None)
         if (self.model is None) or (self.model_loaded != model_name):
-            log.debug(f'Upscaler load: name="{self.name}" model="{model_name}"')
+            log.debug(f'Upscaler loading: name="{self.name}" model="{model_name}"')
+            t0 = time.time()
             from modules.seedvr.src.core.model_manager import configure_runner
+            from modules.seedvr.src.core import generation
             self.model = configure_runner(
                 model_name=model_name,
                 cache_dir=opts.hfcache_dir,
                 device=devices.device,
                 dtype=devices.dtype,
             )
+            self.model_loaded = model_name
+            self.model.dit.device = devices.device
+            self.model.dit.dtype = devices.dtype
+            self.model.vae_encode = self.vae_encode
+            self.model.vae_decode = self.vae_decode
+            self.model.model_step = generation.generation_step
+            generation.generation_step = self.model_step
+            self.model._internal_dict = {
+                'dit': self.model.dit,
+                'vae': self.model.vae,
+            }
+            t1 = time.time()
+            self.model.dit.config = self.model.config.dit
+            self.model.vae.tile_sample_min_size = 1024
+            self.model.vae.tile_latent_min_size = 128
+            # from modules.model_quant import do_post_load_quant
+            # from modules.sd_offload import set_diffuser_offload
+            # self.model = do_post_load_quant(self.model, allow=True)
+            # set_diffuser_offload(self.model)
+            log.info(f'Upscaler loaded: name="{self.name}" model="{model_name}" time={t1 - t0:.2f}')
+
+    def vae_encode(self, samples):
+        log.debug(f'Upscaler encode: samples={samples[0].shape if len(samples) > 0 else None} tile={self.model.vae.tile_sample_min_size} overlap={self.model.vae.tile_overlap_factor}')
+        latents = []
+        if len(samples) == 0:
+            return latents
+        self.model.dit = self.model.dit.to(device="cpu")
+        self.model.vae = self.model.vae.to(device=self.device)
+        devices.torch_gc()
+        from einops import rearrange
+        from modules.seedvr.src.optimization import memory_manager
+        memory_manager.clear_rope_cache(self.model)
+        scale = self.model.config.vae.scaling_factor
+        shift = self.model.config.vae.get("shifting_factor", 0.0)
+        batches = [sample.unsqueeze(0) for sample in samples]
+        for sample in batches:
+            sample = sample.to(self.device, self.model.vae.dtype)
+            sample = self.model.vae.preprocess(sample)
+            latent = self.model.vae.encode(sample).latent
+            latent = latent.unsqueeze(2) if latent.ndim == 4 else latent
+            latent = rearrange(latent, "b c ... -> b ... c")
+            latent = (latent - shift) * scale
+            latents.append(latent)
+        latents = [latent.squeeze(0) for latent in latents]
+        self.model.vae = self.model.vae.to(device="cpu")
+        devices.torch_gc()
+        return latents
+
+    def vae_decode(self, latents, target_dtype: torch.dtype = None):
+        log.debug(f'Upscaler decode: latents={latents[0].shape if len(latents) > 0 else None} tile={self.model.vae.tile_latent_min_size} overlap={self.model.vae.tile_overlap_factor}')
+        samples = []
+        if len(latents) == 0:
+            return samples
+        from einops import rearrange
+        from modules.seedvr.src.optimization import memory_manager
+        memory_manager.clear_rope_cache(self.model)
+        self.model.dit = self.model.dit.to(device="cpu")
+        self.model.vae = self.model.vae.to(device=self.device)
+        devices.torch_gc()
+        scale = self.model.config.vae.scaling_factor
+        shift = self.model.config.vae.get("shifting_factor", 0.0)
+        latents = [latent.unsqueeze(0) for latent in latents]
+        with devices.inference_context():
+            for _i, latent in enumerate(latents):
+                latent = latent.to(self.device, self.model.vae.dtype)
+                latent = latent / scale + shift
+                latent = rearrange(latent, "b ... c -> b c ...")
+                latent = latent.squeeze(2)
+                sample = self.model.vae.decode(latent).sample
+                sample = self.model.vae.postprocess(sample)
+                samples.append(sample)
+        samples = [sample.squeeze(0) for sample in samples]
+        self.model.vae = self.model.vae.to(device="cpu")
+        devices.torch_gc()
+        return samples
+
+    def model_step(self, *args, **kwargs):
+        from modules.seedvr.src.optimization import memory_manager
+        self.model.vae = self.model.vae.to(device="cpu")
+        self.model.dit = self.model.dit.to(device=self.device)
+        devices.torch_gc()
+        log.debug(f'Upscaler inference: args={len(args)} kwargs={list(kwargs.keys())}')
+        memory_manager.preinitialize_rope_cache(self.model)
+        with devices.inference_context():
+            result = self.model.model_step(*args, **kwargs)
+        self.model.dit = self.model.dit.to(device="cpu")
+        devices.torch_gc()
+        return result
 
     def do_upscale(self, img: Image.Image, selected_file):
-        devices.torch_gc()
         self.load_model(selected_file)
         if self.model is None:
             return img
 
-        from modules.seedvr.src.core.generation import generation_loop
+        from modules.seedvr.src.core import generation
 
         width = int(self.scale * img.width) // 8 * 8
         image_tensor = np.array(img)
         image_tensor = torch.from_numpy(image_tensor).to(device=devices.device, dtype=devices.dtype).unsqueeze(0) / 255.0
 
+        random.seed()
+        seed = int(random.randrange(4294967294))
+
         t0 = time.time()
-        result_tensor = generation_loop(
-            runner=self.model,
-            images=image_tensor,
-            cfg_scale=1.0,
-            seed=42,
-            res_w=width,
-            batch_size=1,
-            temporal_overlap=0,
-            device=devices.device,
-        )
+        with devices.inference_context():
+            result_tensor = generation.generation_loop(
+                runner=self.model,
+                images=image_tensor,
+                cfg_scale=opts.seedvt_cfg_scale,
+                seed=seed,
+                res_w=width,
+                batch_size=1,
+                temporal_overlap=0,
+                device=devices.device,
+            )
         t1 = time.time()
-        log.info(f'Upscaler: type="{self.name}" model="{selected_file}" scale={self.scale} time={t1 - t0:.2f}')
+        log.info(f'Upscaler: type="{self.name}" model="{selected_file}" scale={self.scale} cfg={opts.seedvt_cfg_scale} seed={seed} time={t1 - t0:.2f}')
         img = to_pil(result_tensor.squeeze().permute((2, 0, 1)))
-        devices.torch_gc()
 
         if opts.upscaler_unload:
+            self.model.dit = None
+            self.model.vae = None
+            self.model.cache = None
             self.model = None
             log.debug(f'Upscaler unload: type="{self.name}" model="{selected_file}"')
-            devices.torch_gc(force=True)
+        devices.torch_gc(force=True)
         return img
diff --git a/modules/sd_models_utils.py b/modules/sd_models_utils.py
index 637f8c585..05f44ef6c 100644
--- a/modules/sd_models_utils.py
+++ b/modules/sd_models_utils.py
@@ -164,6 +164,8 @@ def apply_function_to_model(sd_model, function, options, op=None):
             sd_model.unet = function(sd_model.unet, op="unet", sd_model=sd_model)
         if hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
             sd_model.transformer = function(sd_model.transformer, op="transformer", sd_model=sd_model)
+        if hasattr(sd_model, 'dit') and hasattr(sd_model.dit, 'config'):
+            sd_model.dit = function(sd_model.dit, op="dit", sd_model=sd_model)
         if hasattr(sd_model, 'transformer_2') and hasattr(sd_model.transformer_2, 'config'):
             sd_model.transformer_2 = function(sd_model.transformer_2, op="transformer_2", sd_model=sd_model)
         if hasattr(sd_model, 'transformer_3') and hasattr(sd_model.transformer_3, 'config'):
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index d20fb0371..0d9defd5d 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -274,7 +274,10 @@ class OffloadHook(accelerate.hooks.ModelHook):
 
 def get_pipe_variants(pipe=None):
     if pipe is None:
-        pipe = shared.sd_model
+        if shared.sd_loaded:
+            pipe = shared.sd_model
+        else:
+            return [pipe]
     variants = [pipe]
     if hasattr(pipe, "pipe"):
         variants.append(pipe.pipe)
@@ -287,7 +290,10 @@ def get_pipe_variants(pipe=None):
 
 def get_module_names(pipe=None, exclude=[]):
     if pipe is None:
-        pipe = shared.sd_model
+        if shared.sd_loaded:
+            pipe = shared.sd_model
+        else:
+            return []
     if hasattr(pipe, "_internal_dict"):
         modules_names = pipe._internal_dict.keys() # pylint: disable=protected-access
     else:
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 56c6c6d7f..499bc02b8 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -385,7 +385,6 @@ def sdnq_post_load_quant(
         modules_dtype_dict=modules_dtype_dict.copy(),
         op=op,
     )
-
     model.quantization_config = SDNQConfig(
         weights_dtype=weights_dtype,
         group_size=group_size,
@@ -402,8 +401,10 @@ def sdnq_post_load_quant(
         modules_dtype_dict=modules_dtype_dict.copy(),
     )
 
-    if hasattr(model, "config"):
+    try:
         model.config.quantization_config = model.quantization_config
+    except Exception:
+        pass
     model.quantization_method = QuantizationMethod.SDNQ
 
     return model
diff --git a/modules/seedvr/config_3b.yaml b/modules/seedvr/config_3b.yaml
index d2ffda469..711d4ec71 100644
--- a/modules/seedvr/config_3b.yaml
+++ b/modules/seedvr/config_3b.yaml
@@ -6,9 +6,9 @@ dit:
   model:
     __object__:
       path:
-        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit_v2.nadit"
-        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit_v2.nadit"
-        - "src.models.dit_v2.nadit"
+        - "SeedVR2_VideoUpscaler.src.models.dit_v2.nadit"
+        - "SeedVR2_VideoUpscaler.src.models.dit_v2.nadit"
+        - "modules.seedvr.src.models.dit_v2.nadit"
       name: "NaDiT"
       args: "as_params"
     vid_in_channels: 33
@@ -49,9 +49,9 @@ vae:
   model:
     __object__:
       path:
-        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
-        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
-        - "src.models.video_vae_v3.modules.attn_video_vae"
+        - "SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "modules.seedvr.src.models.video_vae_v3.modules.attn_video_vae"
       name: "VideoAutoencoderKLWrapper"
       args: "as_params"
     freeze_encoder: False
diff --git a/modules/seedvr/config_7b.yaml b/modules/seedvr/config_7b.yaml
index 3f80813d9..d3097c43a 100644
--- a/modules/seedvr/config_7b.yaml
+++ b/modules/seedvr/config_7b.yaml
@@ -6,8 +6,8 @@ dit:
   model:
     __object__:
       path:
-        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit.nadit"
-        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.dit.nadit"
+        - "SeedVR2_VideoUpscaler.src.models.dit.nadit"
+        - "SeedVR2_VideoUpscaler.src.models.dit.nadit"
         - "src.models.dit.nadit"
       name: "NaDiT"
       args: "as_params"
@@ -46,8 +46,8 @@ vae:
   model:
     __object__:
       path:
-        - "custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
-        - "ComfyUI.custom_nodes.ComfyUI-SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
+        - "SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
         - "src.models.video_vae_v3.modules.attn_video_vae"
       name: "VideoAutoencoderKLWrapper"
       args: "as_params"
diff --git a/modules/seedvr/src/__init__.py b/modules/seedvr/src/__init__.py
index a374bbd24..d9e24b028 100644
--- a/modules/seedvr/src/__init__.py
+++ b/modules/seedvr/src/__init__.py
@@ -1,31 +1,4 @@
 """
-SeedVR2 Video Upscaler - Modular Architecture
-Refactored from monolithic seedvr2.py for better maintainability
-
-Author: Refactored codebase
-Version: 2.0.0 - Modular
-
-Available Modules:
-- utils: Download and path utilities
-- optimization: Memory, performance, and compatibility optimizations
-- core: Model management and generation pipeline (NEW)
-- processing: Video and tensor processing (coming next)
-- interfaces: ComfyUI integration
-"""
-'''
-# Track which modules are available for progressive migration
-MODULES_AVAILABLE = {
-    'downloads': True,          # ✅ Module 1 - Downloads and model management
-    'memory_manager': True,     # ✅ Module 2 - Memory optimization
-    'performance': True,        # ✅ Module 3 - Performance optimizations
-    'compatibility': True,      # ✅ Module 4 - FP8/FP16 compatibility
-    'model_manager': True,      # ✅ Module 5 - Model configuration and loading
-    'generation': True,         # ✅ Module 6 - Generation loop and inference
-    'video_transforms': True,   # ✅ Module 7 - Video processing and transforms
-    'comfyui_node': True,       # ✅ Module 8 - ComfyUI node interface (COMPLETE!)
-    'infer': True,              # ✅ Module 9 - Infer
-}
-'''
 # Core imports (always available)
 import os
 import sys
@@ -35,3 +8,4 @@ current_dir = os.path.dirname(os.path.abspath(__file__))
 parent_dir = os.path.dirname(current_dir)
 if parent_dir not in sys.path:
     sys.path.insert(0, parent_dir)
+"""
\ No newline at end of file
diff --git a/modules/seedvr/src/common/config.py b/modules/seedvr/src/common/config.py
index 653685ccd..58c4e71f8 100644
--- a/modules/seedvr/src/common/config.py
+++ b/modules/seedvr/src/common/config.py
@@ -2,6 +2,7 @@ import importlib
 from typing import Any, Callable, List, Union
 from omegaconf import DictConfig, ListConfig, OmegaConf
 
+
 try:
     OmegaConf.register_new_resolver("eval", eval)
 except Exception as e:
@@ -9,7 +10,6 @@ except Exception as e:
         raise
 
 
-
 def load_config(path: str, argv: List[str] = None) -> Union[DictConfig, ListConfig]:
     """
     Load a configuration. Will resolve inheritance.
diff --git a/modules/seedvr/src/common/decorators.py b/modules/seedvr/src/common/decorators.py
index 6eacf8bd5..cf504b3bf 100644
--- a/modules/seedvr/src/common/decorators.py
+++ b/modules/seedvr/src/common/decorators.py
@@ -2,10 +2,10 @@ import functools
 import threading
 from typing import Callable
 import torch
-
 from .distributed import barrier_if_distributed, get_global_rank, get_local_rank
 from .logger import get_logger
 
+
 logger = get_logger(__name__)
 
 
diff --git a/modules/seedvr/src/common/distributed/ops.py b/modules/seedvr/src/common/distributed/ops.py
index 7c9256706..bba121b85 100644
--- a/modules/seedvr/src/common/distributed/ops.py
+++ b/modules/seedvr/src/common/distributed/ops.py
@@ -26,7 +26,6 @@ from ..cache import Cache
 from .advanced import (
     get_sequence_parallel_group,
     get_sequence_parallel_rank,
-    get_sequence_parallel_world_size,
 )
 
 from .basic import get_device
@@ -48,7 +47,7 @@ def single_all_to_all(
     """
     A function to do all-to-all on a tensor
     """
-    seq_world_size = dist.get_world_size(group)
+    seq_world_size = 1
     prev_scatter_dim = scatter_dim
     if scatter_dim != 0:
         local_input = local_input.transpose(0, scatter_dim)
@@ -80,7 +79,7 @@ def _all_to_all(
     gather_dim: int,
     group: dist.ProcessGroup,
 ):
-    seq_world_size = dist.get_world_size(group)
+    seq_world_size = 1
     input_list = [
         t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)
     ]
@@ -134,7 +133,7 @@ class Slice(torch.autograd.Function):
     def forward(ctx: Any, group: dist.ProcessGroup, local_input: Tensor, dim: int) -> Tensor:
         ctx.group = group
         ctx.rank = dist.get_rank(group)
-        seq_world_size = dist.get_world_size(group)
+        seq_world_size = 1
         ctx.seq_world_size = seq_world_size
         ctx.dim = dim
         dim_size = local_input.shape[dim]
@@ -163,7 +162,7 @@ class Gather(torch.autograd.Function):
         ctx.rank = dist.get_rank(group)
         ctx.dim = dim
         ctx.grad_scale = grad_scale
-        seq_world_size = dist.get_world_size(group)
+        seq_world_size = 1
         ctx.seq_world_size = seq_world_size
         dim_size = list(local_input.size())
         split_size = dim_size[0]
@@ -204,7 +203,7 @@ def gather_seq_scatter_heads_qkv(
     group = get_sequence_parallel_group()
     if not group:
         return qkv_tensor
-    world = get_sequence_parallel_world_size()
+    world = 1
     orig_shape = qkv_tensor.shape
     scatter_dim = qkv_tensor.dim()
     bef_all2all_shape = list(orig_shape)
@@ -237,7 +236,7 @@ def slice_inputs(x: Tensor, dim: int, padding: bool = True):
     if group is None:
         return x
     sp_rank = get_sequence_parallel_rank()
-    sp_world = get_sequence_parallel_world_size()
+    sp_world = 1
     dim_size = x.shape[dim]
     unit = (dim_size + sp_world - 1) // sp_world
     if padding and dim_size % sp_world:
@@ -255,7 +254,7 @@ def remove_seqeunce_parallel_padding(x: Tensor, dim: int, unpad_dim_size: int):
     group = get_sequence_parallel_group()
     if group is None:
         return x
-    sp_world = get_sequence_parallel_world_size()
+    sp_world = 1
     if unpad_dim_size % sp_world == 0:
         return x
     padding_size = sp_world - (unpad_dim_size % sp_world)
@@ -271,7 +270,7 @@ def gather_heads_scatter_seq(x: Tensor, head_dim: int, seq_dim: int) -> Tensor:
     if not group:
         return x
     dim_size = x.size(seq_dim)
-    sp_world = get_sequence_parallel_world_size()
+    sp_world = 1
     if dim_size % sp_world != 0:
         padding_size = sp_world - (dim_size % sp_world)
         x = _pad_tensor(x, seq_dim, padding_size)
@@ -424,7 +423,7 @@ class SPDistForward:
             yield inputs
         else:
             device = self.device
-            sp_world = get_sequence_parallel_world_size()
+            sp_world = 1
             sp_rank = get_sequence_parallel_rank()
             for local_step in range(sp_world):
                 src_rank = dist.get_global_rank(group, local_step)
diff --git a/modules/seedvr/src/common/logger.py b/modules/seedvr/src/common/logger.py
index ada368e79..48b6eab67 100644
--- a/modules/seedvr/src/common/logger.py
+++ b/modules/seedvr/src/common/logger.py
@@ -19,9 +19,9 @@ Logging utility functions.
 import logging
 import sys
 from typing import Optional
-
 from .distributed import get_global_rank, get_local_rank, get_world_size
 
+
 _default_handler = logging.StreamHandler(sys.stdout)
 _default_handler.setFormatter(
     logging.Formatter(
diff --git a/modules/seedvr/src/common/seed.py b/modules/seedvr/src/common/seed.py
index f3e77f2bf..2469ad944 100644
--- a/modules/seedvr/src/common/seed.py
+++ b/modules/seedvr/src/common/seed.py
@@ -16,7 +16,6 @@ import random
 from typing import Optional
 import numpy as np
 import torch
-
 from .distributed import get_global_rank
 
 
diff --git a/modules/seedvr/src/core/generation.py b/modules/seedvr/src/core/generation.py
index 5f871cfe7..401d8b874 100644
--- a/modules/seedvr/src/core/generation.py
+++ b/modules/seedvr/src/core/generation.py
@@ -1,11 +1,10 @@
 import torch
 from torchvision.transforms import Compose, Lambda, Normalize
-
-from src.optimization.performance import optimized_video_rearrange, optimized_single_video_rearrange, optimized_sample_to_image_format
-from src.common.seed import set_seed
-from src.data.image.transforms.divisible_crop import DivisibleCrop
-from src.data.image.transforms.na_resize import NaResize
-from src.utils.color_fix import wavelet_reconstruction
+from ..optimization.performance import optimized_video_rearrange, optimized_single_video_rearrange, optimized_sample_to_image_format
+from ..common.seed import set_seed
+from ..data.image.transforms.divisible_crop import DivisibleCrop
+from ..data.image.transforms.na_resize import NaResize
+from ..utils.color_fix import wavelet_reconstruction
 
 
 
@@ -67,8 +66,6 @@ def generation_step(runner, text_embeds_dict, cond_latents, temporal_overlap, de
         x = runner.schedule.forward(x, aug_noise, t)
         return x
 
-    # Generate conditions with memory optimization
-    runner.dit.to(device=device)
     condition = runner.get_condition(
         noises[0],
         task="sr",
@@ -76,8 +73,8 @@ def generation_step(runner, text_embeds_dict, cond_latents, temporal_overlap, de
     )
     conditions = [condition]
 
-    # Use adaptive autocast for optimal performance
     with torch.no_grad():
+        # Use adaptive autocast for optimal performance
         video_tensors = runner.inference(
             noises=noises,
             conditions=conditions,
@@ -92,6 +89,7 @@ def generation_step(runner, text_embeds_dict, cond_latents, temporal_overlap, de
     cond_latents = cond_latents[0].to("cpu")
     conditions = conditions[0].to("cpu")
     condition = condition.to("cpu")
+    del noises, aug_noises, cond_latents, conditions, condition
 
     return samples #, last_latents
 
@@ -133,7 +131,6 @@ def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_si
         - Intelligent VRAM management throughout process
         - Real-time progress reporting
     """
-
     model_dtype = None
     model_dtype = next(runner.dit.parameters()).dtype
     compute_dtype = model_dtype
@@ -230,7 +227,6 @@ def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_si
 
         # Apply color correction if available
         transformed_video = transformed_video.to(device)
-
         input_video = [optimized_single_video_rearrange(transformed_video)]
         del transformed_video
         sample = wavelet_reconstruction(sample, input_video[0][:sample.size(0)])
@@ -244,56 +240,33 @@ def generation_loop(runner, images, cfg_scale=1.0, seed=666, res_w=720, batch_si
         batch_samples.append(sample_cpu)
         #del sample
 
-        # Aggressive cleanup after each batch
-        # Progress callback - batch start
         if progress_callback:
             progress_callback(batch_count+1, total_batches, current_frames, "Processing batch...")
 
-    runner.vae.to(device="cpu")
-    runner.dit.to(device="cpu")
-    # OPTIMISATION ULTIME : Pré-allocation et copie directe (évite les torch.cat multiples)
 
     # 1. Calculer la taille totale finale
     total_frames = sum(batch.shape[0] for batch in batch_samples)
     if len(batch_samples) > 0:
         sample_shape = batch_samples[0].shape
         H, W, C = sample_shape[1], sample_shape[2], sample_shape[3]
-
-        # 2. Pré-allouer le tensor final directement sur CPU (évite concatenations)
         final_video_images = torch.empty((total_frames, H, W, C), dtype=torch.float16)
-
-        # 3. Copier par blocs directement dans le tensor final
         block_size = 500
         current_idx = 0
 
         for block_start in range(0, len(batch_samples), block_size):
             block_end = min(block_start + block_size, len(batch_samples))
-
-            # Charger le bloc en VRAM
             current_block = []
             for i in range(block_start, block_end):
                 current_block.append(batch_samples[i].to(device))
-
-            # Concatener en VRAM (rapide)
             block_result = torch.cat(current_block, dim=0)
-
-            # Convertir en Float16 sur GPU
-            #if block_result.dtype != torch.float16:
-            #    block_result = block_result.to(torch.float16)
-
-            # Copier directement dans le tensor final (pas de concatenation!)
             block_frames = block_result.shape[0]
             final_video_images[current_idx:current_idx + block_frames] = block_result.to("cpu")
             current_idx += block_frames
-
-            # Nettoyage immédiat VRAM
             del current_block, block_result
     else:
         print("SeedVR2: No batch_samples to process")
         final_video_images = torch.empty((0, 0, 0, 0), dtype=torch.float16)
 
-    # Cleanup batch_samples
-    #del batch_samples
     return final_video_images
 
 
diff --git a/modules/seedvr/src/core/infer.py b/modules/seedvr/src/core/infer.py
index fd8a595a2..7882d3a61 100644
--- a/modules/seedvr/src/core/infer.py
+++ b/modules/seedvr/src/core/infer.py
@@ -1,38 +1,12 @@
-# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
-# //
-# // Licensed under the Apache License, Version 2.0 (the "License");
-# // you may not use this file except in compliance with the License.
-# // You may obtain a copy of the License at
-# //
-# //     http://www.apache.org/licenses/LICENSE-2.0
-# //
-# // Unless required by applicable law or agreed to in writing, software
-# // distributed under the License is distributed on an "AS IS" BASIS,
-# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# // See the License for the specific language governing permissions and
-# // limitations under the License.
-
 from typing import List, Optional, Tuple, Union
 import torch
 from einops import rearrange
 from omegaconf import DictConfig, ListConfig
-from torch import Tensor
-from src.common.diffusion import (
-    classifier_free_guidance_dispatcher,
-    create_sampler_from_config,
-    create_sampling_timesteps_from_config,
-    create_schedule_from_config,
-)
-from src.common.distributed import (
-    get_device,
-)
-
-# from common.fs import download
-
-from src.models.dit_v2 import na
+from ..common.diffusion import classifier_free_guidance_dispatcher, create_sampler_from_config, create_sampling_timesteps_from_config, create_schedule_from_config
+from ..models.dit_v2 import na
 
 
-def optimized_channels_to_last(tensor):
+def optimized_channels_to_last(tensor: torch.Tensor) -> torch.Tensor:
     """🚀 Optimized replacement for rearrange(tensor, 'b c ... -> b ... c')
     Moves channels from position 1 to last position using PyTorch native operations.
     """
@@ -74,7 +48,7 @@ class VideoDiffusionInfer():
         self.dit = None
         self.sampler = None
         self.schedule = None
-    def get_condition(self, latent: Tensor, latent_blur: Tensor, task: str) -> Tensor:
+    def get_condition(self, latent: torch.Tensor, latent_blur: torch.Tensor, task: str) -> torch.Tensor:
         t, h, w, c = latent.shape
         cond = torch.zeros([t, h, w, c + 1], device=latent.device, dtype=latent.dtype)
         if task == "t2v" or t == 1:
@@ -118,22 +92,18 @@ class VideoDiffusionInfer():
     # -------------------------------- Helper ------------------------------- #
 
     @torch.no_grad()
-    def vae_encode(self, samples: List[Tensor]) -> List[Tensor]:
-        self.dit.to(device="cpu")
-        self.vae.to(device=self.device)
-
+    def vae_encode(self, samples: List[torch.Tensor]) -> List[torch.Tensor]:
         use_sample = self.config.vae.get("use_sample", True)
         latents = []
         if len(samples) > 0:
-            device = get_device()
             dtype = self.vae.dtype
             scale = self.config.vae.scaling_factor
             shift = self.config.vae.get("shifting_factor", 0.0)
 
             if isinstance(scale, ListConfig):
-                scale = torch.tensor(scale, device=device, dtype=dtype)
+                scale = torch.tensor(scale, device=self.device, dtype=dtype)
             if isinstance(shift, ListConfig):
-                shift = torch.tensor(shift, device=device, dtype=dtype)
+                shift = torch.tensor(shift, device=self.device, dtype=dtype)
 
             # Group samples of the same shape to batches if enabled.
             if self.config.vae.grouping:
@@ -143,7 +113,7 @@ class VideoDiffusionInfer():
 
             # Vae process by each group.
             for sample in batches:
-                sample = sample.to(device, dtype)
+                sample = sample.to(self.device, dtype)
                 if hasattr(self.vae, "preprocess"):
                     sample = self.vae.preprocess(sample)
                 if use_sample:
@@ -162,19 +132,15 @@ class VideoDiffusionInfer():
                 latents = na.unpack(latents, indices)
             else:
                 latents = [latent.squeeze(0) for latent in latents]
-
-        self.vae.to(device="cpu")
         return latents
 
 
     @torch.no_grad()
-    def vae_decode(self, latents: List[Tensor], target_dtype: torch.dtype = None) -> List[Tensor]:
+    def vae_decode(self, latents: List[torch.Tensor], target_dtype: torch.dtype = None) -> List[torch.Tensor]:
         """🚀 VAE decode optimisé - décodage direct sans chunking, compatible avec autocast externe"""
-        self.dit.to(device="cpu")
-        self.vae.to(device=self.device)
         samples = []
         if len(latents) > 0:
-            device = get_device()
+            device = self.device
             dtype = self.vae.dtype
             scale = self.config.vae.scaling_factor
             shift = self.config.vae.get("shifting_factor", 0.0)
@@ -218,10 +184,9 @@ class VideoDiffusionInfer():
                 samples = na.unpack(samples, indices)
             else:
                 samples = [sample.squeeze(0) for sample in samples]
-        self.vae.to(device="cpu")
         return samples
 
-    def timestep_transform(self, timesteps: Tensor, latents_shapes: Tensor):
+    def timestep_transform(self, timesteps: torch.Tensor, latents_shapes: torch.Tensor):
         # Skip if not needed.
         if not self.config.diffusion.timesteps.get("transform", False):
             return timesteps
@@ -256,13 +221,13 @@ class VideoDiffusionInfer():
     @torch.no_grad()
     def inference(
         self,
-        noises: List[Tensor],
-        conditions: List[Tensor],
-        texts_pos: Union[List[str], List[Tensor], List[Tuple[Tensor]]],
-        texts_neg: Union[List[str], List[Tensor], List[Tuple[Tensor]]],
+        noises: List[torch.Tensor],
+        conditions: List[torch.Tensor],
+        texts_pos: Union[List[str], List[torch.Tensor], List[Tuple[torch.Tensor]]],
+        texts_neg: Union[List[str], List[torch.Tensor], List[Tuple[torch.Tensor]]],
         cfg_scale: Optional[float] = None,
         temporal_overlap: int = 0, # pylint: disable=unused-argument
-    ) -> List[Tensor]:
+    ) -> List[torch.Tensor]:
         assert len(noises) == len(conditions) == len(texts_pos) == len(texts_neg)
         batch_size = len(noises)
 
@@ -316,6 +281,7 @@ class VideoDiffusionInfer():
         # Adapter les latents au dtype cible (compatible avec FP8)
         latents = latents.to(target_dtype) if latents.dtype != target_dtype else latents
         latents_cond = latents_cond.to(target_dtype) if latents_cond.dtype != target_dtype else latents_cond
+        self.dit = self.dit.to(device=self.device, dtype=target_dtype)
 
         latents = self.sampler.sample(
             x=latents,
diff --git a/modules/seedvr/src/core/model_manager.py b/modules/seedvr/src/core/model_manager.py
index 043e5ac61..3abade938 100644
--- a/modules/seedvr/src/core/model_manager.py
+++ b/modules/seedvr/src/core/model_manager.py
@@ -3,10 +3,9 @@ import torch
 from omegaconf import OmegaConf
 from safetensors.torch import load_file as load_safetensors_file
 from huggingface_hub import hf_hub_download
-
-from src.optimization.memory_manager import preinitialize_rope_cache
-from src.common.config import load_config, create_object
-from src.core.infer import VideoDiffusionInfer
+from ..optimization.memory_manager import preinitialize_rope_cache
+from ..common.config import load_config, create_object
+from ..core.infer import VideoDiffusionInfer
 
 
 def configure_runner(model_name, cache_dir, device:str='cpu', dtype:torch.dtype=None):
@@ -15,41 +14,39 @@ def configure_runner(model_name, cache_dir, device:str='cpu', dtype:torch.dtype=
     config_path = os.path.join(script_directory, './config_7b.yaml') if "7b" in model_name else os.path.join(script_directory, './config_3b.yaml')
     config = load_config(config_path)
 
-    vae_config_path = os.path.join(script_directory, 'src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml')
-    vae_config = OmegaConf.load(vae_config_path)
-    vae_config.spatial_downsample_factor = vae_config.get('spatial_downsample_factor', 8)
-    vae_config.temporal_downsample_factor = vae_config.get('temporal_downsample_factor', 4)
-    config.vae.model = OmegaConf.merge(config.vae.model, vae_config)
-
     runner = VideoDiffusionInfer(config, device=device, dtype=dtype)
     OmegaConf.set_readonly(runner.config, False)
 
     # load dit
     with torch.device("meta"):
         runner.dit = create_object(config.dit.model)
-        runner.dit.eval().to(dtype)
+        runner.dit.requires_grad_(False).eval()
         runner.dit.to_empty(device="cpu")
-
     model_file = hf_hub_download(repo_id=repo_id, filename=model_name, cache_dir=cache_dir)
     state_dict = load_safetensors_file(model_file)
     runner.dit.load_state_dict(state_dict, assign=True)
+    runner.dit = runner.dit.to(device="cpu", dtype=dtype)
     del state_dict
-    runner.dit = runner.dit.to(device=device, dtype=dtype)
 
     # load vae
+    vae_config_path = os.path.join(script_directory, 'src/models/video_vae_v3/s8_c16_t4_inflation_sd3.yaml')
+    vae_config = OmegaConf.load(vae_config_path)
+    config.vae.model = OmegaConf.merge(config.vae.model, vae_config)
+
     vae_file = hf_hub_download(repo_id=repo_id, filename=config.vae.checkpoint, cache_dir=cache_dir)
     with torch.device("meta"):
         runner.vae = create_object(config.vae.model)
         runner.vae.requires_grad_(False).eval()
         runner.vae.to_empty(device="cpu")
-
     state_dict = load_safetensors_file(vae_file)
     runner.vae.load_state_dict(state_dict)
-    del state_dict
-    runner.vae = runner.vae.to(device=device, dtype=dtype)
+    runner.vae = runner.vae.to(device="cpu", dtype=dtype)
     runner.config.vae.dtype = str(dtype)
-    runner.vae.set_causal_slicing(**config.vae.slicing)
+    runner.config.vae.slicing = {'split_size': 8, 'memory_device': 'same'}
+    runner.config.vae.memory_limit = {'conv_max_mem': 0.2, 'norm_max_mem': 0.2}
+    runner.vae.set_causal_slicing(**runner.config.vae.slicing)
     runner.vae.set_memory_limit(**runner.config.vae.memory_limit)
+    del state_dict
 
     # load embeds
     pos_embeds_file = hf_hub_download(repo_id=repo_id, filename='pos_emb.pt', cache_dir=cache_dir)
@@ -57,5 +54,4 @@ def configure_runner(model_name, cache_dir, device:str='cpu', dtype:torch.dtype=
     runner.text_pos_embeds = torch.load(pos_embeds_file).to(device=device, dtype=dtype)
     runner.text_neg_embeds = torch.load(neg_embeds_file).to(device=device, dtype=dtype)
 
-    preinitialize_rope_cache(runner)
     return runner
diff --git a/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py b/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py
index ee9ae9ae8..eb551aacc 100644
--- a/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py
+++ b/modules/seedvr/src/models/dit/blocks/mmdit_window_block.py
@@ -19,13 +19,7 @@ from torch import nn
 from torch.nn import functional as F
 from torch.nn.modules.utils import _triple
 from ....common.half_precision_fixes import safe_pad_operation
-from ....common.distributed.ops import (
-    gather_heads,
-    gather_heads_scatter_seq,
-    gather_seq_scatter_heads_qkv,
-    scatter_heads,
-)
-
+from ....common.distributed.ops import gather_heads, gather_heads_scatter_seq, gather_seq_scatter_heads_qkv, scatter_heads
 from ..attention import TorchAttention
 from ..mlp import get_mlp
 from ..mm import MMArg, MMModule
diff --git a/modules/seedvr/src/models/dit_v2/rope.py b/modules/seedvr/src/models/dit_v2/rope.py
index dde5b4756..89d851792 100644
--- a/modules/seedvr/src/models/dit_v2/rope.py
+++ b/modules/seedvr/src/models/dit_v2/rope.py
@@ -18,8 +18,7 @@ import torch
 from einops import rearrange
 from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
 from torch import nn
-
-from src.common.cache import Cache
+from ...common.cache import Cache
 
 
 class RotaryEmbeddingBase(nn.Module):
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py b/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
index 1f282dfa1..3aba37b66 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
@@ -15,7 +15,6 @@ from typing import Literal, Optional, Tuple, Union
 import diffusers
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from diffusers.models.attention_processor import Attention, SpatialNorm
 from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
 from diffusers.models.downsampling import Downsample2D
@@ -28,29 +27,12 @@ from diffusers.utils import is_torch_version
 from diffusers.utils.accelerate_utils import apply_forward_hook
 from einops import rearrange
 from ....common.half_precision_fixes import safe_pad_operation, safe_interpolate_operation
-
-from ....common.distributed.advanced import get_sequence_parallel_world_size
 from ....common.logger import get_logger
-from .causal_inflation_lib import (
-    InflatedCausalConv3d,
-    causal_norm_wrapper,
-    init_causal_conv3d,
-    remove_head,
-)
-from .context_parallel_lib import (
-    causal_conv_gather_outputs,
-    causal_conv_slice_inputs,
-)
+from .causal_inflation_lib import InflatedCausalConv3d, causal_norm_wrapper, init_causal_conv3d, remove_head
+from .context_parallel_lib import causal_conv_gather_outputs, causal_conv_slice_inputs
 from .global_config import set_norm_limit
-from .types import (
-    CausalAutoencoderOutput,
-    CausalDecoderOutput,
-    CausalEncoderOutput,
-    MemoryState,
-    _inflation_mode_t,
-    _memory_device_t,
-    _receptive_field_t,
-)
+from .types import CausalAutoencoderOutput, CausalDecoderOutput, CausalEncoderOutput, MemoryState, _inflation_mode_t, _memory_device_t,  _receptive_field_t
+
 
 logger = get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -1064,7 +1046,7 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
         scaling_factor: float = 0.18215,
         force_upcast: float = True,
         attention: bool = True,
-        temporal_scale_num: int = 2,
+        temporal_scale_num: int = 0,
         slicing_up_num: int = 0,
         gradient_checkpoint: bool = False,
         inflation_mode: _inflation_mode_t = "tail",
@@ -1164,7 +1146,8 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
 
     @apply_forward_hook
     def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
-        h = self.slicing_encode(x)
+        # h = self.slicing_encode(x)
+        h = self.tiled_encode(x)
         posterior = DiagonalGaussianDistribution(h)
 
         if not return_dict:
@@ -1176,7 +1159,8 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
     def decode(
         self, z: torch.Tensor, return_dict: bool = True
     ) -> Union[DecoderOutput, torch.Tensor]:
-        decoded = self.slicing_decode(z)
+        # decoded = self.slicing_decode(z)
+        decoded = self.tiled_decode(z)
 
         if not return_dict:
             return (decoded,)
@@ -1208,7 +1192,7 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
         return output.to(z.device)
 
     def slicing_encode(self, x: torch.Tensor) -> torch.Tensor:
-        sp_size = get_sequence_parallel_world_size()
+        sp_size = 1
         if self.use_slicing and (x.shape[2] - 1) > self.slicing_sample_min_size * sp_size:
             x_slices = x[:, :, 1:].split(split_size=self.slicing_sample_min_size * sp_size, dim=2)
             encoded_slices = [
@@ -1226,7 +1210,7 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
             return self._encode(x)
 
     def slicing_decode(self, z: torch.Tensor) -> torch.Tensor:
-        sp_size = get_sequence_parallel_world_size()
+        sp_size = 1
         if self.use_slicing and (z.shape[2] - 1) > self.slicing_latent_min_size * sp_size:
             z_slices = z[:, :, 1:].split(split_size=self.slicing_latent_min_size * sp_size, dim=2)
             decoded_slices = [
@@ -1243,12 +1227,68 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
         else:
             return self._decode(z)
 
-    def tiled_encode(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
-        raise NotImplementedError
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent)
+        return b
 
-    def tiled_decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
-        raise NotImplementedError
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[4], b.shape[4], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent)
+        return b
 
+    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
+        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_latent_min_size - blend_extent
+        rows = []
+        for i in range(0, x.shape[3], overlap_size):
+            row = []
+            for j in range(0, x.shape[4], overlap_size):
+                tile = x[:, :, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
+                tile = self._encode(tile)
+                row.append(tile)
+            rows.append(row)
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=4))
+        enc = torch.cat(result_rows, dim=3)
+        return enc
+
+    def tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
+        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_sample_min_size - blend_extent
+        rows = []
+        for i in range(0, z.shape[3], overlap_size):
+            row = []
+            for j in range(0, z.shape[4], overlap_size):
+                tile = z[:, :, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
+                decoded = self.decoder(tile)
+                row.append(decoded)
+            rows.append(row)
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=4))
+        dec = torch.cat(result_rows, dim=3)
+        return dec
+    
     def forward(
         self, x: torch.FloatTensor, mode: Literal["encode", "decode", "all"] = "all", **kwargs
     ):
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py b/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py
index 26f14739e..c6d35f0cb 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/causal_inflation_lib.py
@@ -21,30 +21,11 @@ from diffusers.models.normalization import RMSNorm
 from einops import rearrange
 from torch import Tensor, nn
 from torch.nn import Conv3d
-
 from .context_parallel_lib import cache_send_recv, get_cache_size
 from .global_config import get_norm_limit
 from .types import MemoryState, _inflation_mode_t, _memory_device_t
 from ....common.half_precision_fixes import safe_pad_operation
 
-# Single GPU inference - no distributed processing needed
-
-# Mock distributed functions for single GPU inference
-def get_sequence_parallel_group():
-    return None
-
-def get_sequence_parallel_rank():
-    return 0
-
-def get_sequence_parallel_world_size():
-    return 1
-
-def get_next_sequence_parallel_rank():
-    return 0
-
-def get_prev_sequence_parallel_rank():
-    return 0
-
 
 @contextmanager
 def ignore_padding(model):
@@ -172,7 +153,6 @@ class InflatedCausalConv3d(Conv3d):
         if (
             math.isinf(self.memory_limit)
             and torch.is_tensor(input)
-            and get_sequence_parallel_group() is None
         ):
             return self.basic_forward(input, memory_state)
         return self.slicing_forward(input, memory_state)
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py b/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py
index ffc16938a..830d18b87 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/context_parallel_lib.py
@@ -14,11 +14,8 @@
 
 from typing import List
 import torch
-import torch.nn.functional as F
 from torch import Tensor
 
-from .types import MemoryState
-
 # Single GPU inference - no distributed processing needed
 
 
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py b/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py
index 4b8e6dfb6..da8c1304f 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/inflated_layers.py
@@ -16,14 +16,8 @@ from functools import partial
 from typing import Literal, Optional
 from torch import Tensor
 from torch.nn import Conv3d
+from .inflated_lib import MemoryState, extend_head, inflate_bias, inflate_weight, modify_state_dict
 
-from .inflated_lib import (
-    MemoryState,
-    extend_head,
-    inflate_bias,
-    inflate_weight,
-    modify_state_dict,
-)
 
 _inflation_mode_t = Literal["none", "tail", "replicate"]
 _memory_device_t = Optional[Literal["cpu", "same"]]
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py b/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py
index 486c63ca1..6feda1751 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/inflated_lib.py
@@ -19,9 +19,9 @@ import torch
 from diffusers.models.normalization import RMSNorm
 from einops import rearrange
 from torch import Tensor, nn
-
 from ....common.logger import get_logger
 
+
 logger = get_logger(__name__)
 
 
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py b/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py.old
similarity index 97%
rename from modules/seedvr/src/models/video_vae_v3/modules/video_vae.py
rename to modules/seedvr/src/models/video_vae_v3/modules/video_vae.py.old
index 98147e82f..2696c32ca 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/video_vae.py.old
@@ -11,37 +11,17 @@
 
 from contextlib import nullcontext
 from typing import Optional, Tuple, Literal, Callable, Union
-
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from einops import rearrange
 from ....common.half_precision_fixes import safe_pad_operation
-
-from ....common.distributed.advanced import get_sequence_parallel_world_size
 from ....common.logger import get_logger
-from .causal_inflation_lib import (
-    InflatedCausalConv3d,
-    causal_norm_wrapper,
-    init_causal_conv3d,
-    remove_head,
-)
-from .context_parallel_lib import (
-    causal_conv_gather_outputs,
-    causal_conv_slice_inputs,
-)
+from .causal_inflation_lib import InflatedCausalConv3d, causal_norm_wrapper, init_causal_conv3d, remove_head
+from .context_parallel_lib import causal_conv_gather_outputs, causal_conv_slice_inputs
 from .global_config import set_norm_limit
-from .types import (
-    CausalAutoencoderOutput,
-    CausalDecoderOutput,
-    CausalEncoderOutput,
-    MemoryState,
-    _inflation_mode_t,
-    _memory_device_t,
-    _receptive_field_t,
-    _selective_checkpointing_t,
-)
+from .types import CausalAutoencoderOutput, CausalDecoderOutput, CausalEncoderOutput, MemoryState, _inflation_mode_t, _memory_device_t, _receptive_field_t, _selective_checkpointing_t
+
 
 logger = get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -717,7 +697,7 @@ class VideoAutoencoderKL(nn.Module):
         use_post_quant_conv: bool = True,
         enc_selective_checkpointing: Tuple[_selective_checkpointing_t] = ("none",),
         dec_selective_checkpointing: Tuple[_selective_checkpointing_t] = ("none",),
-        temporal_scale_num: int = 3,
+        temporal_scale_num: int = 0,
         slicing_up_num: int = 0,
         inflation_mode: _inflation_mode_t = "tail",
         time_receptive_field: _receptive_field_t = "half",
@@ -824,7 +804,7 @@ class VideoAutoencoderKL(nn.Module):
         return x
 
     def slicing_encode(self, x: torch.Tensor) -> torch.Tensor:
-        sp_size = get_sequence_parallel_world_size()
+        sp_size = 1
         if self.use_slicing and (x.shape[2] - 1) > self.slicing_sample_min_size * sp_size:
             x_slices = x[:, :, 1:].split(split_size=self.slicing_sample_min_size * sp_size, dim=2)
             encoded_slices = [
@@ -842,7 +822,7 @@ class VideoAutoencoderKL(nn.Module):
             return self._encode(x, memory_state=MemoryState.DISABLED)
 
     def slicing_decode(self, z: torch.Tensor) -> torch.Tensor:
-        sp_size = get_sequence_parallel_world_size()
+        sp_size = 1
         if self.use_slicing and (z.shape[2] - 1) > self.slicing_latent_min_size * sp_size:
             z_slices = z[:, :, 1:].split(split_size=self.slicing_latent_min_size * sp_size, dim=2)
             decoded_slices = [
diff --git a/modules/seedvr/src/optimization/memory_manager.py b/modules/seedvr/src/optimization/memory_manager.py
index 3be323f67..39feada97 100644
--- a/modules/seedvr/src/optimization/memory_manager.py
+++ b/modules/seedvr/src/optimization/memory_manager.py
@@ -5,12 +5,9 @@ Handles VRAM usage, cache management, and memory optimization
 Extracted from: seedvr2.py (lines 373-405, 607-626, 1016-1044)
 """
 
-import os
 import torch
-import gc
-from typing import Tuple, Optional
-from src.common.cache import Cache
-from src.models.dit_v2.rope import RotaryEmbeddingBase
+from ..common.cache import Cache
+from ..models.dit_v2.rope import RotaryEmbeddingBase
 
 
 def preinitialize_rope_cache(runner) -> None:
@@ -21,73 +18,63 @@ def preinitialize_rope_cache(runner) -> None:
         runner: The model runner containing DiT and VAE models
     """
 
-    try:
-        # Create dummy tensors to simulate common shapes
-        # Format: [batch, channels, frames, height, width] for vid_shape
-        # Format: [batch, seq_len] for txt_shape
-        common_shapes = [
-            # Common video resolutions
-            (torch.tensor([[1, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 1 frame, 77 tokens
-            (torch.tensor([[4, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 4 frames
-            (torch.tensor([[5, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 5 frames (4n+1 format)
-            (torch.tensor([[1, 4, 4]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # Higher resolution
-        ]
+    # Create dummy tensors to simulate common shapes
+    # Format: [batch, channels, frames, height, width] for vid_shape
+    # Format: [batch, seq_len] for txt_shape
+    common_shapes = [
+        # Common video resolutions
+        (torch.tensor([[1, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 1 frame, 77 tokens
+        (torch.tensor([[4, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 4 frames
+        (torch.tensor([[5, 3, 3]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # 5 frames (4n+1 format)
+        (torch.tensor([[1, 4, 4]], dtype=torch.long), torch.tensor([[77]], dtype=torch.long)),    # Higher resolution
+    ]
 
-        # Create mock cache for pre-initialization
+    # Create mock cache for pre-initialization
 
-        temp_cache = Cache()
+    temp_cache = Cache()
 
-        # Access RoPE modules in DiT (recursive search)
-        def find_rope_modules(module):
-            rope_modules = []
-            for name, child in module.named_modules():
-                if hasattr(child, 'get_freqs') and callable(child.get_freqs):
-                    rope_modules.append((name, child))
-            return rope_modules
+    # Access RoPE modules in DiT (recursive search)
+    def find_rope_modules(module):
+        rope_modules = []
+        for name, child in module.named_modules():
+            if hasattr(child, 'get_freqs') and callable(child.get_freqs):
+                rope_modules.append((name, child))
+        return rope_modules
 
-        rope_modules = find_rope_modules(runner.dit)
+    rope_modules = find_rope_modules(runner.dit)
 
-        # Pre-calculate for each RoPE module found
-        for name, rope_module in rope_modules:
-            # Temporarily move module to CPU if necessary
-            original_device = next(rope_module.parameters()).device if list(rope_module.parameters()) else torch.device('cpu')
-            rope_module.to('cpu')
+    # Pre-calculate for each RoPE module found
+    for _name, rope_module in rope_modules:
+        # Temporarily move module to CPU if necessary
+        original_device = next(rope_module.parameters()).device if list(rope_module.parameters()) else torch.device('cpu')
+        rope_module.to('cpu')
 
-            try:
-                for vid_shape, txt_shape in common_shapes:
-                    cache_key = f"720pswin_by_size_bysize_{tuple(vid_shape[0].tolist())}_sd3.mmrope_freqs_3d"
+        for vid_shape, txt_shape in common_shapes:
+            cache_key = f"720pswin_by_size_bysize_{tuple(vid_shape[0].tolist())}_sd3.mmrope_freqs_3d"
 
-                    def compute_freqs():
-                        # Calculate with reduced dimensions to avoid OOM
-                        with torch.no_grad():
-                            # Detect RoPE module type
-                            module_type = type(rope_module).__name__
+            def compute_freqs():
+                # Calculate with reduced dimensions to avoid OOM
+                with torch.no_grad():
+                    # Detect RoPE module type
+                    module_type = type(rope_module).__name__
 
-                            if module_type == 'NaRotaryEmbedding3d':
-                                # NaRotaryEmbedding3d: only takes shape (vid_shape)
-                                return rope_module.get_freqs(vid_shape.cpu())
-                            else:
-                                # Standard RoPE: takes vid_shape and txt_shape
-                                return rope_module.get_freqs(vid_shape.cpu(), txt_shape.cpu())
+                    if module_type == 'NaRotaryEmbedding3d':
+                        # NaRotaryEmbedding3d: only takes shape (vid_shape)
+                        return rope_module.get_freqs(vid_shape.cpu())
+                    else:
+                        # Standard RoPE: takes vid_shape and txt_shape
+                        return rope_module.get_freqs(vid_shape.cpu(), txt_shape.cpu())
 
-                    # Store in cache
-                    temp_cache(cache_key, compute_freqs)
+            # Store in cache
+            temp_cache(cache_key, compute_freqs)
 
-            except Exception as e:
-                print(f"    ❌ Error in module {name}: {e}")
-            finally:
-                # Restore to original device
-                rope_module.to(original_device)
+        rope_module.to(original_device)
 
-        # Copy temporary cache to runner cache
-        if hasattr(runner, 'cache'):
-            runner.cache.cache.update(temp_cache.cache)
-        else:
-            runner.cache = temp_cache
-
-    except Exception as e:
-        print(f"  ⚠️ Error during RoPE pre-init: {e}")
-        print("  🔄 Model will work but could have OOM at first launch")
+    # Copy temporary cache to runner cache
+    if hasattr(runner, 'cache'):
+        runner.cache.cache.update(temp_cache.cache)
+    else:
+        runner.cache = temp_cache
 
 
 def clear_rope_cache(runner) -> None:
@@ -97,8 +84,6 @@ def clear_rope_cache(runner) -> None:
     Args:
         runner: The model runner containing the cache
     """
-    print("🧹 Cleaning RoPE cache...")
-
     if hasattr(runner, 'cache') and hasattr(runner.cache, 'cache'):
         # Count entries before cleanup
         cache_size = len(runner.cache.cache)
@@ -116,7 +101,6 @@ def clear_rope_cache(runner) -> None:
 
         # Clear the cache
         runner.cache.cache.clear()
-        print(f"  ✅ RoPE cache cleared ({cache_size} entries removed)")
 
     if hasattr(runner, 'dit'):
         cleared_lru_count = 0
@@ -125,7 +109,3 @@ def clear_rope_cache(runner) -> None:
                 if hasattr(module.get_axial_freqs, 'cache_clear'):
                     module.get_axial_freqs.cache_clear()
                     cleared_lru_count += 1
-        if cleared_lru_count > 0:
-            print(f"  ✅ Cleared {cleared_lru_count} LRU caches from RoPE modules.")
-
-    print("🎯 RoPE cache cleanup completed!")
diff --git a/modules/seedvr/src/utils/color_fix.py b/modules/seedvr/src/utils/color_fix.py
index 8a042892b..a8b0da509 100644
--- a/modules/seedvr/src/utils/color_fix.py
+++ b/modules/seedvr/src/utils/color_fix.py
@@ -2,7 +2,7 @@ import torch
 from PIL import Image
 from torch import Tensor
 from torch.nn import functional as F
-from src.common.half_precision_fixes import safe_pad_operation, safe_interpolate_operation
+from ..common.half_precision_fixes import safe_pad_operation, safe_interpolate_operation
 from torchvision.transforms import ToTensor, ToPILImage
 
 def adain_color_fix(target: Image, source: Image):
diff --git a/modules/seedvr/test.py b/modules/seedvr/test.py
index 6ecf022ab..b8c53f874 100644
--- a/modules/seedvr/test.py
+++ b/modules/seedvr/test.py
@@ -1,12 +1,10 @@
 import os
-import argparse
 import numpy as np
 import torch
 from PIL import Image
-from huggingface_hub import snapshot_download
 from torchvision.transforms import ToPILImage
-from src.core.generation import generation_loop
-from src.core.model_manager import configure_runner
+from .src.core.generation import generation_loop
+from .src.core.model_manager import configure_runner
 
 
 
diff --git a/modules/shared.py b/modules/shared.py
index 16009b1e0..f987cdeb8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -620,7 +620,6 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "detailer_iou": OptionInfo(0.5, "Max overlap", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
     "detailer_sigma_adjust": OptionInfo(1.0, "Detailer sigma adjust", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
     "detailer_sigma_adjust_max": OptionInfo(1.0, "Detailer sigma end", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    # "detailer_resolution": OptionInfo(1024, "Detailer resolution", gr.Slider, {"minimum": 256, "maximum": 4096, "step": 8, "visible": False}),
     "detailer_min_size": OptionInfo(0.0, "Min object size", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
     "detailer_max_size": OptionInfo(1.0, "Max object size", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
     "detailer_padding": OptionInfo(20, "Item padding", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
@@ -631,6 +630,9 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "detailer_unload": OptionInfo(False, "Move detailer model to CPU when complete"),
     "detailer_augment": OptionInfo(True, "Detailer use model augment"),
 
+    "postprocessing_sep_seedvt": OptionInfo("<h2>SeedVT</h2>", "", gr.HTML),
+    "seedvt_cfg_scale": OptionInfo(3.5, "SeedVR CFG Scale", gr.Slider, {"minimum": 1, "maximum": 15, "step": 1}),
+
     "postprocessing_sep_face_restore": OptionInfo("<h2>Face Restore</h2>", "", gr.HTML),
     "face_restoration_model": OptionInfo("None", "Face restoration", gr.Radio, lambda: {"choices": ['None'] + [x.name() for x in face_restorers]}),
     "code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
diff --git a/modules/ui_settings.py b/modules/ui_settings.py
index 926b31762..673e45941 100644
--- a/modules/ui_settings.py
+++ b/modules/ui_settings.py
@@ -227,7 +227,7 @@ def create_ui():
                                     quicksettings_list.append((key, item))
                                     components.append(dummy_component)
                                 else:
-                                    with gr.Row(elem_id=f"settings_section_row_{section_id}"): # only so we can add dirty indicator at the start of the row
+                                    with gr.Row(elem_id=f"settings_section_row_{section_id}", elem_classes=["settings_section"]): # only so we can add dirty indicator at the start of the row
                                         component = create_setting_component(key)
                                         shared.settings_components[key] = component
                                         current_items.append(key)

From d4d24214b359256bffc29405932ffdffa5f4016a Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Mon, 13 Oct 2025 14:06:13 +0300
Subject: [PATCH 208/230] SDNQ use a better way of loading pre quants and
 cleanup

---
 modules/sdnq/quantizer.py | 219 +++++++++-----------------------------
 1 file changed, 53 insertions(+), 166 deletions(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 499bc02b8..68d1c355a 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -421,11 +421,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
     required_packages = None
     torch_dtype = None
 
-    def __init__(self, quantization_config, **kwargs):
-        super().__init__(quantization_config, **kwargs)
-        self.modules_to_not_convert = []
-        self.updated_expected_keys = False
-
     def check_if_quantized_param(
         self,
         model,
@@ -433,14 +428,16 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         param_name: str,
         *args, **kwargs, # pylint: disable=unused-argument
     ):
-        if self.pre_quantized and self.updated_expected_keys and (param_name.endswith(".scale") or param_name.endswith(".zero_point") or param_name.endswith(".svd_up") or param_name.endswith(".svd_down")):
-            return True
-        if param_name.endswith(".weight"):
+        if self.pre_quantized:
+            layer, tensor_name = get_module_from_name(model, param_name)
+            if hasattr(layer, "sdnq_dequantizer"):
+                return True
+        elif param_name.endswith(".weight"):
             split_param_name = param_name.split(".")
             if (
-                param_name not in self.modules_to_not_convert
-                and not any(param in split_param_name for param in self.modules_to_not_convert)
-                and not any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in self.modules_to_not_convert)
+                param_name not in self.quantization_config.modules_to_not_convert
+                and not any(param in split_param_name for param in self.quantization_config.modules_to_not_convert)
+                and not any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in self.quantization_config.modules_to_not_convert)
             ):
                 layer_class_name = get_module_from_name(model, param_name)[0].__class__.__name__
                 if layer_class_name in allowed_types:
@@ -449,7 +446,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
                             return True
                     else:
                         return True
-        if param_value is not None:
+        if param_value is not None and param_value.device.type == "cpu":
             with devices.inference_context():
                 param_value.data = param_value.clone() # safetensors is unable to release the cpu memory without this
         return False
@@ -476,10 +473,10 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         state_dict: Dict[str, Any],
         *args, **kwargs, # pylint: disable=unused-argument
     ):
-        if self.pre_quantized and self.updated_expected_keys and (param_name.endswith(".scale") or param_name.endswith(".zero_point") or param_name.endswith(".svd_up") or param_name.endswith(".svd_down")):
+        if self.pre_quantized:
             layer, tensor_name = get_module_from_name(model, param_name)
-            return_dtype = torch.float32 if self.quantization_config.dequantize_fp32 else self.torch_dtype if self.torch_dtype is not None else param_value.dtype
             if param_value is not None:
+                return_dtype = param_value.dtype if tensor_name == "weight" else torch.float32 if self.quantization_config.dequantize_fp32 else self.torch_dtype if self.torch_dtype is not None else param_value.dtype
                 if param_value.dtype == return_dtype and devices.same_device(param_value.device, target_device):
                     param_value = param_value.clone()
                 else:
@@ -519,88 +516,30 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         if self.quantization_config.quantization_device is not None:
             target_device = self.quantization_config.quantization_device
 
-        if not self.pre_quantized:
-            torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
-            if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
-                param_value = param_value.clone()
-            else:
-                param_value = param_value.to(target_device, non_blocking=self.quantization_config.non_blocking).to(dtype=torch.float32)
-
-            layer, _ = get_module_from_name(model, param_name)
-            layer.weight = torch.nn.Parameter(param_value, requires_grad=False)
-            layer = sdnq_quantize_layer(
-                layer,
-                weights_dtype=weights_dtype,
-                torch_dtype=torch_dtype,
-                group_size=self.quantization_config.group_size,
-                svd_rank=self.quantization_config.svd_rank,
-                use_svd=self.quantization_config.use_svd,
-                quant_conv=self.quantization_config.quant_conv,
-                use_quantized_matmul=self.quantization_config.use_quantized_matmul,
-                use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
-                dequantize_fp32=self.quantization_config.dequantize_fp32,
-                non_blocking=self.quantization_config.non_blocking,
-                quantization_device=None,
-                return_device=return_device,
-                param_name=param_name,
-            )
+        torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
+        if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
+            param_value = param_value.clone()
         else:
-            from accelerate import init_empty_weights
-            layer, _ = get_module_from_name(model, param_name)
-            torch_dtype = layer.weight.dtype if self.torch_dtype is None else self.torch_dtype
+            param_value = param_value.to(target_device, non_blocking=self.quantization_config.non_blocking).to(dtype=torch.float32)
 
-            if self.updated_expected_keys: # prevent overwrite to meta
-                scale, zero_point, svd_up, svd_down = None, None, None, None
-                if hasattr(layer, "scale") and layer.scale.device.type != "meta":
-                    scale = layer.scale
-                    del layer.scale
-                if hasattr(layer, "zero_point") and layer.zero_point.device.type != "meta":
-                    zero_point = layer.zero_point
-                    del layer.zero_point
-                if hasattr(layer, "svd_up") and layer.svd_up.device.type != "meta":
-                    svd_up = layer.svd_up
-                    del layer.svd_up
-                if hasattr(layer, "svd_down") and layer.svd_down.device.type != "meta":
-                    svd_down = layer.svd_down
-                    del layer.svd_down
-
-            with init_empty_weights():
-                # add sdnq_dequantizer
-                layer = sdnq_quantize_layer(
-                    layer,
-                    weights_dtype=weights_dtype,
-                    torch_dtype=torch_dtype,
-                    group_size=self.quantization_config.group_size,
-                    svd_rank=self.quantization_config.svd_rank,
-                    use_svd=self.quantization_config.use_svd,
-                    quant_conv=self.quantization_config.quant_conv,
-                    use_quantized_matmul=self.quantization_config.use_quantized_matmul,
-                    use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
-                    dequantize_fp32=self.quantization_config.dequantize_fp32,
-                    non_blocking=self.quantization_config.non_blocking,
-                    quantization_device="meta",
-                    return_device="meta",
-                    param_name=param_name,
-                )
-
-            layer.weight = torch.nn.Parameter(param_value.clone().to(target_device), requires_grad=False)
-            if self.updated_expected_keys: # Transformers
-                if scale is not None:
-                    layer.scale = torch.nn.Parameter(scale, requires_grad=False)
-                if zero_point is not None:
-                    layer.zero_point = torch.nn.Parameter(zero_point, requires_grad=False)
-                if svd_up is not None:
-                    layer.svd_up = torch.nn.Parameter(svd_up, requires_grad=False)
-                if svd_down is not None:
-                    layer.svd_down = torch.nn.Parameter(svd_down, requires_grad=False)
-            else: # Diffusers doesn't have the API for updating expected keys
-                layer_key = param_name.removesuffix(".weight")
-                layer.scale = torch.nn.Parameter(state_dict[layer_key + ".scale"].clone().to(target_device), requires_grad=False)
-                if layer.zero_point is not None:
-                    layer.zero_point = torch.nn.Parameter(state_dict[layer_key + ".zero_point"].clone().to(target_device), requires_grad=False)
-                if layer.svd_up is not None:
-                    layer.svd_up = torch.nn.Parameter(state_dict[layer_key + ".svd_up"].clone().to(target_device), requires_grad=False)
-                    layer.svd_down = torch.nn.Parameter(state_dict[layer_key + ".svd_down"].clone().to(target_device), requires_grad=False)
+        layer, _ = get_module_from_name(model, param_name)
+        layer.weight = torch.nn.Parameter(param_value, requires_grad=False)
+        layer = sdnq_quantize_layer(
+            layer,
+            weights_dtype=weights_dtype,
+            torch_dtype=torch_dtype,
+            group_size=self.quantization_config.group_size,
+            svd_rank=self.quantization_config.svd_rank,
+            use_svd=self.quantization_config.use_svd,
+            quant_conv=self.quantization_config.quant_conv,
+            use_quantized_matmul=self.quantization_config.use_quantized_matmul,
+            use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
+            dequantize_fp32=self.quantization_config.dequantize_fp32,
+            non_blocking=self.quantization_config.non_blocking,
+            quantization_device=None,
+            return_device=return_device,
+            param_name=param_name,
+        )
 
     def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
         max_memory = {key: val * 0.80 for key, val in max_memory.items()}
@@ -620,31 +559,33 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         keep_in_fp32_modules: List[str] = None,
         **kwargs, # pylint: disable=unused-argument
     ):
+        if self.pre_quantized:
+            from accelerate import init_empty_weights
+            self.quantization_config.quantization_device = None
+            self.quantization_config.return_device = None
+            self.quantization_config.non_blocking = False
+            self.quantization_config.add_skip_keys = False
+
+            quantization_config_dict = self.quantization_config.to_dict()
+            quantization_config_dict.pop("is_integer", None)
+            quantization_config_dict.pop("quant_method", None)
+            quantization_config_dict.pop("quantization_device", None)
+            quantization_config_dict.pop("return_device", None)
+            quantization_config_dict.pop("non_blocking", None)
+            quantization_config_dict.pop("add_skip_keys", None)
+            with init_empty_weights():
+                model = sdnq_post_load_quant(model, add_skip_keys=False, **quantization_config_dict)
+
         if self.quantization_config.add_skip_keys:
             if keep_in_fp32_modules is not None:
-                self.modules_to_not_convert.extend(keep_in_fp32_modules)
-            model, self.modules_to_not_convert, self.quantization_config.modules_dtype_dict = add_module_skip_keys(
-                model, self.modules_to_not_convert, self.quantization_config.modules_dtype_dict
+                self.quantization_config.modules_to_not_convert.extend(keep_in_fp32_modules)
+            model, self.quantization_config.modules_to_not_convert, self.quantization_config.modules_dtype_dict = add_module_skip_keys(
+                model, self.quantization_config.modules_to_not_convert, self.quantization_config.modules_dtype_dict
             )
-        self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
-        self.quantization_config.modules_to_not_convert = self.modules_to_not_convert
         if hasattr(model, "config"):
             model.config.quantization_config = self.quantization_config
         model.quantization_config = self.quantization_config
 
-        if self.pre_quantized and hasattr(model, "get_parameter_or_buffer"):
-            from functools import wraps
-            @wraps(model.get_parameter_or_buffer)
-            def get_parameter_or_buffer(self, target: str):
-                try:
-                    return self.original_get_parameter_or_buffer(target)
-                except Exception as e:
-                    if target.endswith(".scale") or target.endswith(".zero_point") or target.endswith(".svd_up") or target.endswith(".svd_down"):
-                        return None
-                    raise e
-            model.original_get_parameter_or_buffer = model.get_parameter_or_buffer
-            model.get_parameter_or_buffer = get_parameter_or_buffer.__get__(model, model.__class__)
-
     def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument
         if shared.opts.diffusers_offload_mode != "none":
             model = model.to(devices.cpu)
@@ -660,60 +601,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         """
         return self.get_accelerator_warm_up_factor()
 
-    def update_unexpected_keys(self, model, unexpected_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        if not self.pre_quantized:
-            return unexpected_keys
-        new_unexpected_keys = []
-        for key in unexpected_keys:
-            if not (key.endswith(".scale") or key.endswith(".zero_point") or key.endswith(".svd_up") or key.endswith(".svd_down")):
-                new_unexpected_keys.append(key)
-        return new_unexpected_keys
-
-    def update_expected_keys(self, model, expected_keys: List[str], loaded_keys: list[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        if not self.pre_quantized:
-            return expected_keys
-        self.updated_expected_keys = True
-        for key in loaded_keys:
-            if key.endswith(".scale") or key.endswith(".zero_point") or key.endswith(".svd_up") or key.endswith(".svd_down"):
-                expected_keys.append(key)
-        return expected_keys
-
-    def update_tp_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return config
-
-    def update_ep_plan(self, config, *args, **kwargs): # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return config
-
-    def update_missing_keys_after_loading(self, model, missing_keys: List[str], *args, **kwargs) -> List[str]: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return missing_keys
-
-    def update_state_dict_with_metadata(self, state_dict: dict, *args, **kwargs) -> dict: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return state_dict
-
-    def update_param_name(self, param_name: str, *args, **kwargs) -> str: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return param_name
-
-    def update_dtype(self, dtype: torch.dtype, *args, **kwargs) -> torch.dtype: # pylint: disable=unused-argument
-        """
-        needed for transformers compatibilty, no-op function
-        """
-        return dtype
-
     def _dequantize(self, model):
         model = dequantize_sdnq_model(model)
         if hasattr(model, "quantization_method"):

From 32014fbb9d12f24685fcc52281a5d1271a560503 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 13 Oct 2025 10:03:59 -0400
Subject: [PATCH 209/230] seedvr requirements

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/postprocess/seedvr_model.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/postprocess/seedvr_model.py b/modules/postprocess/seedvr_model.py
index fddeff557..84a49c22b 100644
--- a/modules/postprocess/seedvr_model.py
+++ b/modules/postprocess/seedvr_model.py
@@ -4,6 +4,7 @@ import numpy as np
 import torch
 from PIL import Image
 from torchvision.transforms import ToPILImage
+from installer import install
 from modules import devices
 from modules.shared import opts, log
 from modules.upscaler import Upscaler, UpscalerData
@@ -32,6 +33,7 @@ class UpscalerSeedVR(Upscaler):
     def load_model(self, path: str):
         model_name = MODELS_MAP.get(path, None)
         if (self.model is None) or (self.model_loaded != model_name):
+            install('rotary_embedding_torch')
             log.debug(f'Upscaler loading: name="{self.name}" model="{model_name}"')
             t0 = time.time()
             from modules.seedvr.src.core.model_manager import configure_runner

From f3b4ef2551b3bb3cb21a604c20da5a49b478f4a3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 13 Oct 2025 10:20:42 -0400
Subject: [PATCH 210/230] simplify seedvr depedencies

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/postprocess/seedvr_model.py           |   2 -
 modules/seedvr/rotary_embedding.py            | 346 ++++++++++++++++++
 .../src/common/distributed/meta_init_utils.py |   3 +-
 modules/seedvr/src/models/dit/rope.py         |   3 +-
 modules/seedvr/src/models/dit_v2/rope.py      |   2 +-
 5 files changed, 349 insertions(+), 7 deletions(-)
 create mode 100644 modules/seedvr/rotary_embedding.py

diff --git a/modules/postprocess/seedvr_model.py b/modules/postprocess/seedvr_model.py
index 84a49c22b..fddeff557 100644
--- a/modules/postprocess/seedvr_model.py
+++ b/modules/postprocess/seedvr_model.py
@@ -4,7 +4,6 @@ import numpy as np
 import torch
 from PIL import Image
 from torchvision.transforms import ToPILImage
-from installer import install
 from modules import devices
 from modules.shared import opts, log
 from modules.upscaler import Upscaler, UpscalerData
@@ -33,7 +32,6 @@ class UpscalerSeedVR(Upscaler):
     def load_model(self, path: str):
         model_name = MODELS_MAP.get(path, None)
         if (self.model is None) or (self.model_loaded != model_name):
-            install('rotary_embedding_torch')
             log.debug(f'Upscaler loading: name="{self.name}" model="{model_name}"')
             t0 = time.time()
             from modules.seedvr.src.core.model_manager import configure_runner
diff --git a/modules/seedvr/rotary_embedding.py b/modules/seedvr/rotary_embedding.py
new file mode 100644
index 000000000..021ae9e52
--- /dev/null
+++ b/modules/seedvr/rotary_embedding.py
@@ -0,0 +1,346 @@
+from __future__ import annotations
+from typing import Literal
+from math import pi
+import torch
+from torch.amp import autocast
+from torch.nn import Module
+from torch import nn, einsum, broadcast_tensors, is_tensor, Tensor
+from einops import rearrange, repeat
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+# broadcat, as tortoise-tts was using it
+
+def broadcat(tensors, dim = -1):
+    broadcasted_tensors = broadcast_tensors(*tensors)
+    return torch.cat(broadcasted_tensors, dim = dim)
+
+def slice_at_dim(t, dim_slice: slice, *, dim):
+    dim += (t.ndim if dim < 0 else 0)
+    colons = [slice(None)] * t.ndim
+    colons[dim] = dim_slice
+    return t[tuple(colons)]
+
+# rotary embedding helper functions
+
+def rotate_half(x):
+    x = rearrange(x, '... (d r) -> ... d r', r = 2)
+    x1, x2 = x.unbind(dim = -1)
+    x = torch.stack((-x2, x1), dim = -1)
+    return rearrange(x, '... d r -> ... (d r)')
+
+@autocast('cuda', enabled = False)
+def apply_rotary_emb(
+    freqs,
+    t,
+    start_index = 0,
+    scale = 1.,
+    seq_dim = -2,
+    freqs_seq_dim = None
+):
+    dtype = t.dtype
+
+    if not exists(freqs_seq_dim):
+        if freqs.ndim == 2 or t.ndim == 3:
+            freqs_seq_dim = 0
+
+    if t.ndim == 3 or exists(freqs_seq_dim):
+        seq_len = t.shape[seq_dim]
+        freqs = slice_at_dim(freqs, slice(-seq_len, None), dim = freqs_seq_dim)
+
+    rot_dim = freqs.shape[-1]
+    end_index = start_index + rot_dim
+
+    assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}'
+
+    # Split t into three parts: left, middle (to be transformed), and right
+    t_left = t[..., :start_index]
+    t_middle = t[..., start_index:end_index]
+    t_right = t[..., end_index:]
+
+    # Apply rotary embeddings without modifying t in place
+    t_transformed = (t_middle * freqs.cos() * scale) + (rotate_half(t_middle) * freqs.sin() * scale)
+    out = torch.cat((t_left, t_transformed, t_right), dim=-1)
+
+    return out.type(dtype)
+
+# learned rotation helpers
+
+def apply_learned_rotations(rotations, t, start_index = 0, freq_ranges = None):
+    if exists(freq_ranges):
+        rotations = einsum('..., f -> ... f', rotations, freq_ranges)
+        rotations = rearrange(rotations, '... r f -> ... (r f)')
+
+    rotations = repeat(rotations, '... n -> ... (n r)', r = 2)
+    return apply_rotary_emb(rotations, t, start_index = start_index)
+
+# classes
+
+class RotaryEmbedding(Module):
+    def __init__(
+        self,
+        dim,
+        custom_freqs: Tensor | None = None,
+        freqs_for:  Literal['lang', 'pixel', 'constant'] = 'lang',
+        theta = 10000,
+        max_freq = 10,
+        num_freqs = 1,
+        learned_freq = False,
+        use_xpos = False,
+        xpos_scale_base = 512,
+        interpolate_factor = 1.,
+        theta_rescale_factor = 1.,
+        seq_before_head_dim = False,
+        cache_if_possible = True,
+        cache_max_seq_len = 8192
+    ):
+        super().__init__()
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+
+        theta *= theta_rescale_factor ** (dim / (dim - 2))
+
+        self.freqs_for = freqs_for
+
+        if exists(custom_freqs):
+            freqs = custom_freqs
+        elif freqs_for == 'lang':
+            freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
+        elif freqs_for == 'pixel':
+            freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi
+        elif freqs_for == 'constant':
+            freqs = torch.ones(num_freqs).float()
+
+        self.cache_if_possible = cache_if_possible
+        self.cache_max_seq_len = cache_max_seq_len
+
+        self.register_buffer('cached_freqs', torch.zeros(cache_max_seq_len, dim), persistent = False)
+        self.cached_freqs_seq_len = 0
+
+        self.freqs = nn.Parameter(freqs, requires_grad = learned_freq) # pylint: disable=possibly-used-before-assignment
+
+        self.learned_freq = learned_freq
+
+        # dummy for device
+
+        self.register_buffer('dummy', torch.tensor(0), persistent = False)
+
+        # default sequence dimension
+
+        self.seq_before_head_dim = seq_before_head_dim
+        self.default_seq_dim = -3 if seq_before_head_dim else -2
+
+        # interpolation factors
+
+        assert interpolate_factor >= 1.
+        self.interpolate_factor = interpolate_factor
+
+        # xpos
+
+        self.use_xpos = use_xpos
+
+        if not use_xpos:
+            return
+
+        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+        self.scale_base = xpos_scale_base
+
+        self.register_buffer('scale', scale, persistent = False)
+        self.register_buffer('cached_scales', torch.zeros(cache_max_seq_len, dim), persistent = False)
+        self.cached_scales_seq_len = 0
+
+        # add apply_rotary_emb as static method
+
+        self.apply_rotary_emb = staticmethod(apply_rotary_emb)
+
+    @property
+    def device(self):
+        return self.dummy.device
+
+    def get_seq_pos(self, seq_len, device = None, dtype = None, offset = 0):
+        device = default(device, self.device)
+        dtype = default(dtype, self.cached_freqs.dtype)
+
+        return (torch.arange(seq_len, device = device, dtype = dtype) + offset) / self.interpolate_factor
+
+    def rotate_queries_or_keys(self, t, seq_dim = None, offset = 0, scale = None):
+        seq_dim = default(seq_dim, self.default_seq_dim)
+
+        assert not self.use_xpos or exists(scale), 'you must use `.rotate_queries_and_keys` method instead and pass in both queries and keys, for length extrapolatable rotary embeddings'
+
+        device, dtype, seq_len = t.device, t.dtype, t.shape[seq_dim]
+
+        seq = self.get_seq_pos(seq_len, device = device, dtype = dtype, offset = offset)
+
+        freqs = self.forward(seq, seq_len = seq_len, offset = offset)
+
+        if seq_dim == -3:
+            freqs = rearrange(freqs, 'n d -> n 1 d')
+
+        return apply_rotary_emb(freqs, t, scale = default(scale, 1.), seq_dim = seq_dim)
+
+    def rotate_queries_with_cached_keys(self, q, k, seq_dim = None, offset = 0):
+        dtype, device, seq_dim = q.dtype, q.device, default(seq_dim, self.default_seq_dim)
+
+        q_len, k_len = q.shape[seq_dim], k.shape[seq_dim]
+        assert q_len <= k_len
+
+        q_scale = k_scale = 1.
+
+        if self.use_xpos:
+            seq = self.get_seq_pos(k_len, dtype = dtype, device = device)
+
+            q_scale = self.get_scale(seq[-q_len:]).type(dtype)
+            k_scale = self.get_scale(seq).type(dtype)
+
+        rotated_q = self.rotate_queries_or_keys(q, seq_dim = seq_dim, scale = q_scale, offset = k_len - q_len + offset)
+        rotated_k = self.rotate_queries_or_keys(k, seq_dim = seq_dim, scale = k_scale ** -1)
+
+        rotated_q = rotated_q.type(q.dtype)
+        rotated_k = rotated_k.type(k.dtype)
+
+        return rotated_q, rotated_k
+
+    def rotate_queries_and_keys(self, q, k, seq_dim = None):
+        seq_dim = default(seq_dim, self.default_seq_dim)
+
+        assert self.use_xpos
+        device, dtype, seq_len = q.device, q.dtype, q.shape[seq_dim]
+
+        seq = self.get_seq_pos(seq_len, dtype = dtype, device = device)
+
+        freqs = self.forward(seq, seq_len = seq_len)
+        scale = self.get_scale(seq, seq_len = seq_len).to(dtype)
+
+        if seq_dim == -3:
+            freqs = rearrange(freqs, 'n d -> n 1 d')
+            scale = rearrange(scale, 'n d -> n 1 d')
+
+        rotated_q = apply_rotary_emb(freqs, q, scale = scale, seq_dim = seq_dim)
+        rotated_k = apply_rotary_emb(freqs, k, scale = scale ** -1, seq_dim = seq_dim)
+
+        rotated_q = rotated_q.type(q.dtype)
+        rotated_k = rotated_k.type(k.dtype)
+
+        return rotated_q, rotated_k
+
+    def get_scale(
+        self,
+        t: Tensor,
+        seq_len: int | None = None,
+        offset = 0
+    ):
+        assert self.use_xpos
+
+        should_cache = (
+            self.cache_if_possible and
+            exists(seq_len) and
+            (offset + seq_len) <= self.cache_max_seq_len
+        )
+
+        if (
+            should_cache and \
+            exists(self.cached_scales) and \
+            (seq_len + offset) <= self.cached_scales_seq_len
+        ):
+            return self.cached_scales[offset:(offset + seq_len)]
+
+        scale = 1.
+        if self.use_xpos:
+            power = (t - len(t) // 2) / self.scale_base
+            scale = self.scale ** rearrange(power, 'n -> n 1')
+            scale = repeat(scale, 'n d -> n (d r)', r = 2)
+
+        if should_cache and offset == 0:
+            self.cached_scales[:seq_len] = scale.detach()
+            self.cached_scales_seq_len = seq_len
+
+        return scale
+
+    def get_axial_freqs(
+        self,
+        *dims,
+        offsets: (
+            tuple[int | float, ...] |
+            Tensor |
+            None
+        ) = None
+    ):
+        Colon = slice(None)
+        all_freqs = []
+
+        # handle offset
+
+        if exists(offsets):
+            if not is_tensor(offsets):
+                offsets = torch.tensor(offsets)
+
+            assert len(offsets) == len(dims)
+
+        # get frequencies for each axis
+
+        for ind, dim in enumerate(dims):
+
+            offset = 0
+            if exists(offsets):
+                offset = offsets[ind]
+
+            if self.freqs_for == 'pixel':
+                pos = torch.linspace(-1, 1, steps = dim, device = self.device)
+            else:
+                pos = torch.arange(dim, device = self.device)
+
+            pos = pos + offset
+
+            freqs = self.forward(pos, seq_len = dim)
+
+            all_axis = [None] * len(dims)
+            all_axis[ind] = Colon
+
+            new_axis_slice = (Ellipsis, *all_axis, Colon)
+            all_freqs.append(freqs[new_axis_slice])
+
+        # concat all freqs
+
+        all_freqs = broadcast_tensors(*all_freqs)
+        return torch.cat(all_freqs, dim = -1)
+
+    @autocast('cuda', enabled = False)
+    def forward(
+        self,
+        t: Tensor,
+        seq_len: int | None = None,
+        offset = 0
+    ):
+        should_cache = (
+            self.cache_if_possible and
+            not self.learned_freq and
+            exists(seq_len) and
+            self.freqs_for != 'pixel' and
+            (offset + seq_len) <= self.cache_max_seq_len
+        )
+
+        if (
+            should_cache and \
+            exists(self.cached_freqs) and \
+            (offset + seq_len) <= self.cached_freqs_seq_len
+        ):
+            return self.cached_freqs[offset:(offset + seq_len)].detach()
+
+        freqs = self.freqs
+
+        freqs = einsum('..., f -> ... f', t.type(freqs.dtype), freqs)
+        freqs = repeat(freqs, '... n -> ... (n r)', r = 2)
+
+        if should_cache and offset == 0:
+            self.cached_freqs[:seq_len] = freqs.detach()
+            self.cached_freqs_seq_len = seq_len
+
+        return freqs
diff --git a/modules/seedvr/src/common/distributed/meta_init_utils.py b/modules/seedvr/src/common/distributed/meta_init_utils.py
index 794cd0b81..9a6570646 100644
--- a/modules/seedvr/src/common/distributed/meta_init_utils.py
+++ b/modules/seedvr/src/common/distributed/meta_init_utils.py
@@ -13,9 +13,8 @@
 # // limitations under the License.
 
 import torch
-from rotary_embedding_torch import RotaryEmbedding
 from torch import nn
-from torch.distributed.fsdp._common_utils import _is_fsdp_flattened
+from ....rotary_embedding import RotaryEmbedding
 
 __all__ = ["meta_non_persistent_buffer_init_fn"]
 
diff --git a/modules/seedvr/src/models/dit/rope.py b/modules/seedvr/src/models/dit/rope.py
index 3aded8fed..35b91ea8b 100644
--- a/modules/seedvr/src/models/dit/rope.py
+++ b/modules/seedvr/src/models/dit/rope.py
@@ -16,10 +16,9 @@ from functools import lru_cache
 from typing import Tuple
 import torch
 from einops import rearrange
-from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
 from torch import nn
-
 from ...common.cache import Cache
+from ....rotary_embedding import RotaryEmbedding
 
 
 class RotaryEmbeddingBase(nn.Module):
diff --git a/modules/seedvr/src/models/dit_v2/rope.py b/modules/seedvr/src/models/dit_v2/rope.py
index 89d851792..3d294621c 100644
--- a/modules/seedvr/src/models/dit_v2/rope.py
+++ b/modules/seedvr/src/models/dit_v2/rope.py
@@ -16,9 +16,9 @@ from functools import lru_cache
 from typing import Optional, Tuple
 import torch
 from einops import rearrange
-from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
 from torch import nn
 from ...common.cache import Cache
+from ....rotary_embedding import RotaryEmbedding, apply_rotary_emb
 
 
 class RotaryEmbeddingBase(nn.Module):

From b601f0d402ec3838e03f9980e3bcb6db16daa2c2 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 14 Oct 2025 00:15:09 +0300
Subject: [PATCH 211/230] SDNQ expose svd_steps and update module skip keys

---
 modules/lora/lora_apply.py |   1 +
 modules/model_quant.py     |   8 +-
 modules/sdnq/common.py     |  16 ++++
 modules/sdnq/loader.py     |  39 ++++-----
 modules/sdnq/quantizer.py  | 173 ++++++++++++++++++++-----------------
 modules/shared.py          |   1 +
 pipelines/model_qwen.py    |   3 +-
 7 files changed, 132 insertions(+), 109 deletions(-)

diff --git a/modules/lora/lora_apply.py b/modules/lora/lora_apply.py
index 2221fbf3e..f18922040 100644
--- a/modules/lora/lora_apply.py
+++ b/modules/lora/lora_apply.py
@@ -196,6 +196,7 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
                 torch_dtype=devices.dtype,
                 group_size=shared.opts.sdnq_quantize_weights_group_size,
                 svd_rank=shared.opts.sdnq_svd_rank,
+                svd_steps=shared.opts.sdnq_svd_steps,
                 use_svd=shared.opts.sdnq_use_svd,
                 quant_conv=shared.opts.sdnq_quantize_conv_layers,
                 use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
diff --git a/modules/model_quant.py b/modules/model_quant.py
index 5f88d3174..0bb72a75a 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -206,6 +206,7 @@ def create_sdnq_config(kwargs = None, allow: bool = True, module: str = 'Model',
             weights_dtype=weights_dtype,
             group_size=shared.opts.sdnq_quantize_weights_group_size,
             svd_rank=shared.opts.sdnq_svd_rank,
+            svd_steps=shared.opts.sdnq_svd_steps,
             use_svd=shared.opts.sdnq_use_svd,
             quant_conv=shared.opts.sdnq_quantize_conv_layers,
             use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
@@ -217,7 +218,7 @@ def create_sdnq_config(kwargs = None, allow: bool = True, module: str = 'Model',
             modules_to_not_convert=modules_to_not_convert,
             modules_dtype_dict=modules_dtype_dict.copy(),
         )
-        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} use_svd={shared.opts.sdnq_use_svd} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
+        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} svd_steps={shared.opts.sdnq_svd_steps} use_svd={shared.opts.sdnq_use_svd} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
         if kwargs is None:
             return sdnq_config
         else:
@@ -525,6 +526,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         torch_dtype=devices.dtype,
         group_size=shared.opts.sdnq_quantize_weights_group_size,
         svd_rank=shared.opts.sdnq_svd_rank,
+        svd_steps=shared.opts.sdnq_svd_steps,
         use_svd=shared.opts.sdnq_use_svd,
         quant_conv=shared.opts.sdnq_quantize_conv_layers,
         use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
@@ -562,7 +564,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
     if do_gc:
         devices.torch_gc(force=True, reason='sdnq')
 
-    log.debug(f'Quantization: module="{op if op is not None else model.__class__}" type=sdnq mode=post dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} use_svd={shared.opts.sdnq_use_svd} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
+    log.debug(f'Quantization: module="{op if op is not None else model.__class__}" type=sdnq mode=post dtype={weights_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} svd_steps={shared.opts.sdnq_svd_steps} use_svd={shared.opts.sdnq_use_svd} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
     return model
 
 
@@ -570,7 +572,7 @@ def sdnq_quantize_weights(sd_model):
     try:
         t0 = time.time()
         from modules import shared, devices, sd_models
-        log.debug(f"Quantization: type=SDNQ modules={shared.opts.sdnq_quantize_weights} dtype={shared.opts.sdnq_quantize_weights_mode} dtype_te={shared.opts.sdnq_quantize_weights_mode_te} matmul={shared.opts.sdnq_use_quantized_matmul} svd_rank={shared.opts.sdnq_svd_rank} use_svd={shared.opts.sdnq_use_svd} group_size={shared.opts.sdnq_quantize_weights_group_size} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} pre_forward={shared.opts.diffusers_offload_pre}")
+        log.debug(f"Quantization: type=SDNQ modules={shared.opts.sdnq_quantize_weights} dtype={shared.opts.sdnq_quantize_weights_mode} dtype_te={shared.opts.sdnq_quantize_weights_mode_te} matmul={shared.opts.sdnq_use_quantized_matmul} svd_rank={shared.opts.sdnq_svd_rank} svd_steps={shared.opts.sdnq_svd_steps} use_svd={shared.opts.sdnq_use_svd} group_size={shared.opts.sdnq_quantize_weights_group_size} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} pre_forward={shared.opts.diffusers_offload_pre}")
         global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
 
         sd_model = sd_models.apply_function_to_model(sd_model, sdnq_quantize_model, shared.opts.sdnq_quantize_weights, op="sdnq")
diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 57a4f0aaf..698a35633 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -80,3 +80,19 @@ if use_torch_compile:
 else:
     def compile_func(fn, **kwargs): # pylint: disable=unused-argument
         return fn
+
+
+module_skip_keys_dict = {
+    "FluxTransformer2DModel": [
+        ["single_transformer_blocks.0.norm.linear.weight", ".time_text_embed", ".context_embedder", ".x_embedder", ".proj_out", ".norm_out", "pos_embed"],
+        {}
+    ],
+    "ChromaTransformer2DModel": [
+        ["distilled_guidance_layer", ".time_text_embed", ".context_embedder", ".x_embedder", ".proj_out", ".norm_out", "pos_embed"],
+        {}
+    ],
+    "QwenImageTransformer2DModel": [
+        ["transformer_blocks.0.img_mod.1.weight", ".time_text_embed", ".txt_in", ".img_in", ".proj_out", ".norm_out", "pos_embed"],
+        {}
+    ],
+}
diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index e2ecce092..30a23dc70 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -10,46 +10,39 @@ from .dequantizer import dequantize_symmetric, re_quantize_int8, re_quantize_fp8
 
 def get_module_names(model: ModelMixin) -> list:
     modules_names = model._internal_dict.keys() # pylint: disable=protected-access
-    modules_names = [m for m in modules_names if not m.startswith('_')]
+    modules_names = [m for m in modules_names if not m.startswith("_")]
     modules_names = [m for m in modules_names if isinstance(getattr(model, m, None), torch.nn.Module)]
     modules_names = sorted(set(modules_names))
     return modules_names
 
 
+def unset_config_on_save(config: SDNQConfig) -> SDNQConfig:
+    config.quantization_config.quantization_device = None
+    config.quantization_config.return_device = None
+    config.quantization_config.non_blocking = False
+    config.quantization_config.add_skip_keys = False
+    return config
+
+
 def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", is_pipeline: bool = False, sdnq_config: SDNQConfig = None) -> None:
     if is_pipeline:
         for module_name in get_module_names(model):
             module = getattr(model, module_name, None)
             if hasattr(module, "config") and hasattr(module.config, "quantization_config") and isinstance(module.config.quantization_config, SDNQConfig):
-                module.config.quantization_config.quantization_device = None
-                module.config.quantization_config.return_device = None
-                module.config.quantization_config.non_blocking = False
-                module.config.quantization_config.add_skip_keys = False
+                module.config.quantization_config = unset_config_on_save(module.config.quantization_config)
             if hasattr(module, "quantization_config") and isinstance(module.quantization_config, SDNQConfig):
-                module.quantization_config.quantization_device = None
-                module.quantization_config.return_device = None
-                module.quantization_config.non_blocking = False
-                module.quantization_config.add_skip_keys = False
+                module.quantization_config = unset_config_on_save(module.quantization_config)
     else:
         if hasattr(model, "config") and hasattr(model.config, "quantization_config") and isinstance(model.config.quantization_config, SDNQConfig):
-            model.config.quantization_config.quantization_device = None
-            model.config.quantization_config.return_device = None
-            model.config.quantization_config.non_blocking = False
-            model.config.quantization_config.add_skip_keys = False
+            model.config.quantization_config = unset_config_on_save(model.config.quantization_config)
         if hasattr(model, "quantization_config") and isinstance(model.quantization_config, SDNQConfig):
-            model.quantization_config.quantization_device = None
-            model.quantization_config.return_device = None
-            model.quantization_config.non_blocking = False
-            model.quantization_config.add_skip_keys = False
+            model.quantization_config = unset_config_on_save(model.quantization_config)
 
     model.save_pretrained(model_path, max_shard_size=max_shard_size) # actual save
 
     quantization_config_path = os.path.join(model_path, "quantization_config.json")
     if sdnq_config is not None: # if provided, save global config
-        sdnq_config.quantization_device = None
-        sdnq_config.return_device = None
-        sdnq_config.non_blocking = False
-        sdnq_config.add_skip_keys = False
+        sdnq_config = unset_config_on_save(sdnq_config)
         sdnq_config.to_json_file(quantization_config_path)
 
     if is_pipeline:
@@ -69,7 +62,7 @@ def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "1
             model.config.quantization_config.to_json_file(quantization_config_path)
 
 
-def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, device: torch.device = 'cpu', dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:
+def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: str = None, dtype: torch.dtype = None, device: torch.device = "cpu", dequantize_fp32: bool = None, use_quantized_matmul: bool = None, model_config: dict = None, quantization_config: dict = None) -> ModelMixin:
     from accelerate import init_empty_weights
     from safetensors.torch import safe_open
 
@@ -83,7 +76,7 @@ def load_sdnq_model(model_path: str, model_cls: ModelMixin = None, file_name: st
 
         if model_config is None:
             try:
-                with open(os.path.join(model_path, 'config.json'), "r", encoding="utf-8") as f:
+                with open(os.path.join(model_path, "config.json"), "r", encoding="utf-8") as f:
                     model_config = json.load(f)
             except Exception:
                 model_config = {}
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 68d1c355a..b4e60a704 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -12,7 +12,7 @@ from diffusers.quantizers.quantization_config import QuantizationConfigMixin
 from diffusers.utils import get_module_from_name
 from modules import devices, shared
 
-from .common import dtype_dict, accepted_weights, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
+from .common import dtype_dict, module_skip_keys_dict, accepted_weights, use_tensorwise_fp8_matmul, allowed_types, conv_types, conv_transpose_types, use_contiguous_mm
 from .dequantizer import dequantizer_dict, dequantize_sdnq_model
 from .forward import get_forward_func
 
@@ -49,13 +49,13 @@ def quantize_weight(weight: torch.FloatTensor, reduction_axes: Union[int, List[i
     return quantized_weight, scale, zero_point
 
 
-def apply_svdquant(weight: torch.FloatTensor, rank: int = 32) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+def apply_svdquant(weight: torch.FloatTensor, rank: int = 32, niter: int = 8) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
     reshape_weight = False
     if weight.ndim > 2: # convs
         reshape_weight = True
         weight_shape = weight.shape
         weight = weight.flatten(1,-1)
-    U, S, svd_down = torch.svd_lowrank(weight, q=rank)
+    U, S, svd_down = torch.svd_lowrank(weight, q=rank, niter=niter)
     svd_up = torch.mul(U, S.unsqueeze(0))
     svd_down = svd_down.t_()
     weight = weight.sub_(torch.mm(svd_up, svd_down))
@@ -64,8 +64,79 @@ def apply_svdquant(weight: torch.FloatTensor, rank: int = 32) -> Tuple[torch.Flo
     return weight, svd_up, svd_down
 
 
+def check_param_name_in(param_name: str, param_list: List[str]) -> bool:
+    split_param_name = param_name.split(".")
+    for param in param_list:
+        if param.startswith("."):
+            if param_name.startswith(param[1:]):
+                return True
+            else:
+                continue
+        if (
+            param_name == param
+            or param in split_param_name
+            or ("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name))
+        ):
+            return True
+    return False
+
+
+def get_minimum_dtype(weights_dtype: str, param_name: str, modules_dtype_dict: Dict[str, List[str]]):
+    if len(modules_dtype_dict.keys()) > 0:
+        for key, value in modules_dtype_dict.items():
+            if check_param_name_in(param_name, value):
+                key = key.lower()
+                if key in {"8bit", "8bits"}:
+                    if dtype_dict[weights_dtype]["num_bits"] != 8:
+                        return "int8"
+                elif key.startswith("minimum_"):
+                    minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
+                    if minimum_bits_str.startswith("uint"):
+                        is_unsigned = True
+                        minimum_bits_str = minimum_bits_str.removeprefix("uint")
+                    else:
+                        is_unsigned = False
+                        minimum_bits_str = minimum_bits_str.removeprefix("int")
+                    minimum_bits = int(minimum_bits_str)
+                    if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
+                        if is_unsigned or minimum_bits <= 4:
+                            return "uint" + minimum_bits_str
+                        else:
+                            return "int" + minimum_bits_str
+                else:
+                    return key
+    return weights_dtype
+
+
+def add_module_skip_keys(model, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None):
+    if modules_to_not_convert is None:
+        modules_to_not_convert = []
+    if modules_dtype_dict is None:
+        modules_dtype_dict = {}
+    if getattr(model, "_keep_in_fp32_modules", None) is not None:
+        modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
+
+    skip_key_list = module_skip_keys_dict.get(model.__class__.__name__, None)
+    if skip_key_list is not None:
+        modules_to_not_convert.extend(skip_key_list[0])
+        for key, value in skip_key_list[1].items():
+            if key in modules_dtype_dict.keys():
+                modules_dtype_dict[key].extend(value)
+            else:
+                modules_dtype_dict[key] = value
+    elif getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
+        modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
+
+    # dedupe
+    modules_to_not_convert = list(set(modules_to_not_convert))
+    for key, value in modules_dtype_dict.items():
+        modules_dtype_dict[key] = list(set(value))
+
+    return model, modules_to_not_convert, modules_dtype_dict
+
+
 @devices.inference_context()
-def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
+def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, svd_steps=8, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, param_name=None): # pylint: disable=unused-argument
     layer_class_name = layer.__class__.__name__
     if layer_class_name in allowed_types:
         num_of_groups = 1
@@ -128,7 +199,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             layer.weight.data = layer.weight.to(dtype=torch.float32)
 
         if use_svd:
-            layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank)
+            layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank, niter=svd_steps)
             if use_quantized_matmul:
                 svd_up = svd_up.t_()
                 svd_down = svd_down.t_()
@@ -237,7 +308,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
     return layer
 
 
-def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, full_param_name="", op=None): # pylint: disable=unused-argument
+def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, svd_rank=32, svd_steps=8, use_svd=False, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, dequantize_fp32=False, non_blocking=False, quantization_device=None, return_device=None, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None, full_param_name="", op=None): # pylint: disable=unused-argument
     has_children = list(model.children())
     if not has_children:
         return model
@@ -252,12 +323,7 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
             param_name = full_param_name + "." + param_name
         if hasattr(module, "weight") and module.weight is not None:
             param_name = param_name + ".weight"
-            split_param_name = param_name.split(".")
-            if (
-                param_name in modules_to_not_convert
-                or any(param in split_param_name for param in modules_to_not_convert)
-                or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in modules_to_not_convert)
-            ):
+            if check_param_name_in(param_name, modules_to_not_convert):
                 continue
             layer_class_name = module.__class__.__name__
             if layer_class_name in allowed_types:
@@ -265,33 +331,15 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
                     continue
             else:
                 continue
-            if len(modules_dtype_dict.keys()) > 0:
-                for key, value in modules_dtype_dict.items():
-                    if (
-                        param_name in value
-                        or any(param in split_param_name for param in value)
-                        or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in value)
-                    ):
-                        key = key.lower()
-                        if key in {"8bit", "8bits"}:
-                            if dtype_dict[weights_dtype]["num_bits"] != 8:
-                                weights_dtype = "int8"
-                        elif key.startswith("minimum_"):
-                            minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
-                            minimum_bits = int(minimum_bits_str)
-                            if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
-                                weights_dtype = "int" + minimum_bits_str
-                                if minimum_bits <= 4:
-                                    weights_dtype = "u" + weights_dtype
-                        else:
-                            weights_dtype = key
 
+            weights_dtype = get_minimum_dtype(weights_dtype, param_name, modules_dtype_dict)
             module = sdnq_quantize_layer(
                 module,
                 weights_dtype=weights_dtype,
                 torch_dtype=torch_dtype,
                 group_size=group_size,
                 svd_rank=svd_rank,
+                svd_steps=svd_steps,
                 use_svd=use_svd,
                 quant_conv=quant_conv,
                 use_quantized_matmul=use_quantized_matmul,
@@ -308,6 +356,7 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
                 torch_dtype=torch_dtype,
                 group_size=group_size,
                 svd_rank=svd_rank,
+                svd_steps=svd_steps,
                 use_svd=use_svd,
                 quant_conv=quant_conv,
                 use_quantized_matmul=use_quantized_matmul,
@@ -324,32 +373,13 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
     return model
 
 
-def add_module_skip_keys(model, modules_to_not_convert: List[str] = None, modules_dtype_dict: Dict[str, List[str]] = None):
-    if modules_to_not_convert is None:
-        modules_to_not_convert = []
-    if modules_dtype_dict is None:
-        modules_dtype_dict = {}
-    if getattr(model, "_keep_in_fp32_modules", None) is not None:
-        modules_to_not_convert.extend(model._keep_in_fp32_modules) # pylint: disable=protected-access
-    if getattr(model, "_skip_layerwise_casting_patterns", None) is not None:
-        modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
-    if model.__class__.__name__ == "ChromaTransformer2DModel":
-        modules_to_not_convert.append("distilled_guidance_layer")
-    elif model.__class__.__name__ == "QwenImageTransformer2DModel":
-        modules_to_not_convert.extend(["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"])
-        if "minimum_6bit" not in modules_dtype_dict.keys():
-            modules_dtype_dict["minimum_6bit"] = ["img_mod"]
-        else:
-            modules_dtype_dict["minimum_6bit"].append("img_mod")
-    return model, modules_to_not_convert, modules_dtype_dict
-
-
 def sdnq_post_load_quant(
     model,
     weights_dtype="int8",
     torch_dtype: torch.dtype = None,
     group_size: int = 0,
     svd_rank: int = 32,
+    svd_steps: int = 8,
     use_svd: bool = False,
     quant_conv: bool = False,
     use_quantized_matmul: bool = False,
@@ -373,6 +403,7 @@ def sdnq_post_load_quant(
         torch_dtype=torch_dtype,
         group_size=group_size,
         svd_rank=svd_rank,
+        svd_steps=svd_steps,
         use_svd=use_svd,
         quant_conv=quant_conv,
         use_quantized_matmul=use_quantized_matmul,
@@ -389,6 +420,7 @@ def sdnq_post_load_quant(
         weights_dtype=weights_dtype,
         group_size=group_size,
         svd_rank=svd_rank,
+        svd_steps=svd_steps,
         use_svd=use_svd,
         quant_conv=quant_conv,
         use_quantized_matmul=use_quantized_matmul,
@@ -433,12 +465,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
             if hasattr(layer, "sdnq_dequantizer"):
                 return True
         elif param_name.endswith(".weight"):
-            split_param_name = param_name.split(".")
-            if (
-                param_name not in self.quantization_config.modules_to_not_convert
-                and not any(param in split_param_name for param in self.quantization_config.modules_to_not_convert)
-                and not any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in self.quantization_config.modules_to_not_convert)
-            ):
+            if not check_param_name_in(param_name, self.quantization_config.modules_to_not_convert):
                 layer_class_name = get_module_from_name(model, param_name)[0].__class__.__name__
                 if layer_class_name in allowed_types:
                     if layer_class_name in conv_types or layer_class_name in conv_transpose_types:
@@ -485,28 +512,8 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
             setattr(layer, tensor_name, param_value)
             return
 
-        weights_dtype = self.quantization_config.weights_dtype
-        if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
-            split_param_name = param_name.split(".")
-            for key, value in self.quantization_config.modules_dtype_dict.items():
-                if (
-                    param_name in value
-                    or any(param in split_param_name for param in value)
-                    or any("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name) for param in value)
-                ):
-                    key = key.lower()
-                    if key in {"8bit", "8bits"}:
-                        if dtype_dict[weights_dtype]["num_bits"] != 8:
-                            weights_dtype = "int8"
-                    elif key.startswith("minimum_"):
-                        minimum_bits_str = key.removeprefix("minimum_").removesuffix("bits").removesuffix("bit")
-                        minimum_bits = int(minimum_bits_str)
-                        if dtype_dict[weights_dtype]["num_bits"] < minimum_bits:
-                            weights_dtype = "int" + minimum_bits_str
-                            if minimum_bits <= 4:
-                                weights_dtype = "u" + weights_dtype
-                    else:
-                        weights_dtype = key
+        torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
+        weights_dtype = get_minimum_dtype(self.quantization_config.weights_dtype, param_name, self.quantization_config.modules_dtype_dict)
 
         if self.quantization_config.return_device is not None:
             return_device = self.quantization_config.return_device
@@ -516,7 +523,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         if self.quantization_config.quantization_device is not None:
             target_device = self.quantization_config.quantization_device
 
-        torch_dtype = param_value.dtype if self.torch_dtype is None else self.torch_dtype
         if param_value.dtype == torch.float32 and devices.same_device(param_value.device, target_device):
             param_value = param_value.clone()
         else:
@@ -530,6 +536,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
             torch_dtype=torch_dtype,
             group_size=self.quantization_config.group_size,
             svd_rank=self.quantization_config.svd_rank,
+            svd_steps=self.quantization_config.svd_steps,
             use_svd=self.quantization_config.use_svd,
             quant_conv=self.quantization_config.quant_conv,
             use_quantized_matmul=self.quantization_config.use_quantized_matmul,
@@ -638,6 +645,8 @@ class SDNQConfig(QuantizationConfigMixin):
             group_size = 0 will automatically select a group size based on weights_dtype.
         svd_rank (`int`, *optional*, defaults to `32`):
             The rank size used for the SVDQuant algorithm.
+        svd_steps (`int`, *optional*, defaults to `8`):
+            The number of iterations to use in svd lowrank estimation.
         use_svd (`bool`, *optional*, defaults to `False`):
             Enabling this option will use SVDQuant algorithm on top of SDNQ quantization.
         quant_conv (`bool`, *optional*, defaults to `False`):
@@ -668,6 +677,7 @@ class SDNQConfig(QuantizationConfigMixin):
         weights_dtype: str = "int8",
         group_size: int = 0,
         svd_rank: int = 32,
+        svd_steps: int = 8,
         use_svd: bool = False,
         quant_conv: bool = False,
         use_quantized_matmul: bool = False,
@@ -685,6 +695,7 @@ class SDNQConfig(QuantizationConfigMixin):
         self.quant_method = QuantizationMethod.SDNQ
         self.group_size = group_size
         self.svd_rank = svd_rank
+        self.svd_steps = svd_steps
         self.use_svd = use_svd
         self.quant_conv = quant_conv
         self.use_quantized_matmul = use_quantized_matmul
diff --git a/modules/shared.py b/modules/shared.py
index f987cdeb8..0c3315f26 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -202,6 +202,7 @@ options_templates.update(options_section(("quantization", "Model Quantization"),
     "sdnq_modules_dtype_dict": OptionInfo("{}", "Modules dtype dict"),
     "sdnq_quantize_weights_group_size": OptionInfo(0, "Group size", gr.Slider, {"minimum": -1, "maximum": 4096, "step": 1}),
     "sdnq_svd_rank": OptionInfo(32, "SVDQuant Rank size", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
+    "sdnq_svd_steps": OptionInfo(8, "SVDQuant quantization steps", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "sdnq_use_svd": OptionInfo(False, "Use SVDQuant quantization", gr.Checkbox),
     "sdnq_quantize_conv_layers": OptionInfo(False, "Quantize convolutional layers", gr.Checkbox),
     "sdnq_dequantize_compile": OptionInfo(devices.has_triton(), "Dequantize using torch.compile", gr.Checkbox),
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index d9a98d9be..08faf5624 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -49,8 +49,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
             subfolder=transformer_subfolder,
             cls_name=diffusers.QwenImageTransformer2DModel,
             load_config=diffusers_load_config,
-            modules_dtype_dict={"minimum_6bit": ["img_mod"]},
-            modules_to_not_convert=["transformer_blocks.0.img_mod.1.weight", "time_text_embed", "img_in", "txt_in", "proj_out", "norm_out", "pos_embed"],
+            modules_to_not_convert=["transformer_blocks.0.img_mod.1.weight"],
         )
 
     repo_te = 'Qwen/Qwen-Image'

From db9297244c52bf54ff9f07fbf283eb2cd7edb980 Mon Sep 17 00:00:00 2001
From: Nolan GILBERT <115945238+nolbert82@users.noreply.github.com>
Date: Tue, 14 Oct 2025 01:07:48 +0200
Subject: [PATCH 212/230] Fixed prompt scheduling + uses TE once per batch

---
 modules/prompt_parser_diffusers.py | 68 ++++++++++++++++++++++--------
 1 file changed, 50 insertions(+), 18 deletions(-)

diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 31947ee7b..738737443 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -59,12 +59,12 @@ class PromptEmbedder:
         self.steps = steps
         self.clip_skip = clip_skip
         # All embeds are nested lists, outer list batch length, inner schedule length
-        self.prompt_embeds = [[]] * self.batchsize
-        self.positive_pooleds = [[]] * self.batchsize
-        self.negative_prompt_embeds = [[]] * self.batchsize
-        self.negative_pooleds = [[]] * self.batchsize
-        self.prompt_attention_masks = [[]] * self.batchsize
-        self.negative_prompt_attention_masks = [[]] * self.batchsize
+        self.prompt_embeds = [[] for _ in range(self.batchsize)]
+        self.positive_pooleds = [[] for _ in range(self.batchsize)]
+        self.negative_prompt_embeds = [[] for _ in range(self.batchsize)]
+        self.negative_pooleds = [[] for _ in range(self.batchsize)]
+        self.prompt_attention_masks = [[] for _ in range(self.batchsize)]
+        self.negative_prompt_attention_masks = [[] for _ in range(self.batchsize)]
         self.positive_schedule = None
         self.negative_schedule = None
         self.scheduled_prompt = False
@@ -77,13 +77,25 @@ class PromptEmbedder:
         if self.pipe is None:
             shared.log.error("Prompt encode: cannot find text encoder in model")
             return
+        seen_prompts = {}
         # per prompt in batch
         for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts)):
             self.prepare_schedule(prompt, negative_prompt)
+            schedule_key = (
+                tuple(self.positive_schedule) if self.positive_schedule is not None else None,
+                tuple(self.negative_schedule) if self.negative_schedule is not None else None,
+                self.scheduled_prompt,
+            )
+            cache_key = (prompt, negative_prompt, schedule_key)
+            cached_idx = seen_prompts.get(cache_key)
+            if cached_idx is not None:
+                self.clone_embeds(batchidx, cached_idx)
+                continue
             if self.scheduled_prompt:
                 self.scheduled_encode(self.pipe, batchidx)
             else:
                 self.encode(self.pipe, prompt, negative_prompt, batchidx)
+            seen_prompts[cache_key] = batchidx
         self.checkcache(p)
         debug(f"Prompt encode: time={(time.time() - t0):.3f}")
 
@@ -207,22 +219,42 @@ class PromptEmbedder:
                 negative_pooled,
                 negative_prompt_attention_mask
             ) = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, self.clip_skip)
-        if prompt_embed is not None:
-            self.prompt_embeds[batchidx] = [prompt_embed]
-        if negative_embed is not None:
-            self.negative_prompt_embeds[batchidx] = [negative_embed]
-        if positive_pooled is not None:
-            self.positive_pooleds[batchidx] = [positive_pooled]
-        if negative_pooled is not None:
-            self.negative_pooleds[batchidx] = [negative_pooled]
-        if prompt_attention_mask is not None:
-            self.prompt_attention_masks[batchidx] = [prompt_attention_mask]
-        if negative_prompt_attention_mask is not None:
-            self.negative_prompt_attention_masks[batchidx] = [negative_prompt_attention_mask]
+        def _store(target, value):
+            if value is None:
+                return
+            # scheduled prompts need to keep all slices, unscheduled can overwrite
+            if self.scheduled_prompt and len(target[batchidx]) > 0:
+                target[batchidx].append(value)
+            else:
+                target[batchidx] = [value]
+
+        _store(self.prompt_embeds, prompt_embed)
+        _store(self.negative_prompt_embeds, negative_embed)
+        _store(self.positive_pooleds, positive_pooled)
+        _store(self.negative_pooleds, negative_pooled)
+        _store(self.prompt_attention_masks, prompt_attention_mask)
+        _store(self.negative_prompt_attention_masks, negative_prompt_attention_mask)
         if debug_enabled:
             get_tokens(pipe, 'positive', positive_prompt)
             get_tokens(pipe, 'negative', negative_prompt)
 
+    def clone_embeds(self, batchidx, idx):
+        def _clone(target):
+            if len(target) <= idx:
+                return
+            src = target[idx]
+            if isinstance(src, list):
+                target[batchidx] = [item if not isinstance(item, list) else list(item) for item in src]
+            else:
+                target[batchidx] = src
+
+        _clone(self.prompt_embeds)
+        _clone(self.negative_prompt_embeds)
+        _clone(self.positive_pooleds)
+        _clone(self.negative_pooleds)
+        _clone(self.prompt_attention_masks)
+        _clone(self.negative_prompt_attention_masks)
+
     def __call__(self, key, step=0):
         batch = getattr(self, key)
         res = []

From b626ce059e99001917a7007f21df434a8a4fa298 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 14 Oct 2025 09:24:18 -0400
Subject: [PATCH 213/230] update chainner

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sd-extension-chainner | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extensions-builtin/sd-extension-chainner b/extensions-builtin/sd-extension-chainner
index 3e0108fed..da7a2bd55 160000
--- a/extensions-builtin/sd-extension-chainner
+++ b/extensions-builtin/sd-extension-chainner
@@ -1 +1 @@
-Subproject commit 3e0108fedbec300f72c3ca6e06236419d45eb660
+Subproject commit da7a2bd553b81d66fab21c2f12559439a36e844c

From a0e8b04ec67b2021c99fea3f06ce6ee5ecf94f30 Mon Sep 17 00:00:00 2001
From: Nolan GILBERT <115945238+nolbert82@users.noreply.github.com>
Date: Tue, 14 Oct 2025 15:25:28 +0200
Subject: [PATCH 214/230] Fix empty prompt overriding

---
 modules/generation_parameters_copypaste.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index a2f38b94e..1427e53cc 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -229,6 +229,11 @@ def connect_paste(button, local_paste_fields, input_comp, override_settings_comp
                 res.append(v)
                 applied[key] = v
             else:
+                if isinstance(v, str) and v.strip() == '' and key in {'Prompt', 'Negative prompt'}:
+                    debug(f'Paste skip empty: "{key}"')
+                    res.append(gr.update())
+                    skipped[key] = v
+                    continue
                 if should_skip(key):
                     debug(f'Paste skip: "{key}"="{v}"')
                     res.append(gr.update())

From 57230bbf3a3e711cdab07aefbb91cc2d6b36d937 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 14 Oct 2025 09:35:00 -0400
Subject: [PATCH 215/230] update seedvr-7b config

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                  | 3 ++-
 modules/seedvr/config_7b.yaml | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 860ba0038..e2dd66d08 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-12
+## Update for 2025-10-14
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -134,6 +134,7 @@
   - **Microsoft Florence 2** both base and large variants  
     *note* this will trigger download of the new variant of the model, feel free to delete older variant in `huggingface` folder  
   - **MiaoshouAI PromptGen** 1.5/2.0 in both base and large variants  
+  - fix prompt scheduling, thanks @nolbert82  
   - ui: fix image metadata display when switching selected image in control tab  
   - framepack: add explicit hf-login before framepack load  
   - framepack: patch solver for unsupported gpus  
diff --git a/modules/seedvr/config_7b.yaml b/modules/seedvr/config_7b.yaml
index d3097c43a..61803415d 100644
--- a/modules/seedvr/config_7b.yaml
+++ b/modules/seedvr/config_7b.yaml
@@ -48,7 +48,7 @@ vae:
       path:
         - "SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
         - "SeedVR2_VideoUpscaler.src.models.video_vae_v3.modules.attn_video_vae"
-        - "src.models.video_vae_v3.modules.attn_video_vae"
+        - "modules.seedvr.src.models.video_vae_v3.modules.attn_video_vae"
       name: "VideoAutoencoderKLWrapper"
       args: "as_params"
     freeze_encoder: False

From 4aee524ddfebfd280ba22484a3d283e40c8c31de Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 14 Oct 2025 17:18:58 +0300
Subject: [PATCH 216/230] SDNQ add NaDiT keys

---
 modules/sdnq/common.py        | 6 ++++++
 modules/seedvr/config_7b.yaml | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 698a35633..c5ff8ab9a 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -95,4 +95,10 @@ module_skip_keys_dict = {
         ["transformer_blocks.0.img_mod.1.weight", ".time_text_embed", ".txt_in", ".img_in", ".proj_out", ".norm_out", "pos_embed"],
         {}
     ],
+    "NaDiT": [
+        [".emb_in", ".txt_in", ".vid_in", ".emb_scale", ".vid_out", ".vid_out_norm", ".vid_out_ada"],
+        {}
+    ],
 }
+
+module_skip_keys_dict["NaDiTUpscaler"] = module_skip_keys_dict["NaDiT"]
diff --git a/modules/seedvr/config_7b.yaml b/modules/seedvr/config_7b.yaml
index 61803415d..0e5cb146c 100644
--- a/modules/seedvr/config_7b.yaml
+++ b/modules/seedvr/config_7b.yaml
@@ -8,7 +8,7 @@ dit:
       path:
         - "SeedVR2_VideoUpscaler.src.models.dit.nadit"
         - "SeedVR2_VideoUpscaler.src.models.dit.nadit"
-        - "src.models.dit.nadit"
+        - "modules.seedvr.src.models.dit.nadit"
       name: "NaDiT"
       args: "as_params"
     vid_in_channels: 33

From 85a58ed5bf1d9854dd24f969cb4b72a69afc7b8a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 14 Oct 2025 10:23:12 -0400
Subject: [PATCH 217/230] seedvr enable quant

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/postprocess/seedvr_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/postprocess/seedvr_model.py b/modules/postprocess/seedvr_model.py
index fddeff557..577d8609f 100644
--- a/modules/postprocess/seedvr_model.py
+++ b/modules/postprocess/seedvr_model.py
@@ -57,9 +57,9 @@ class UpscalerSeedVR(Upscaler):
             self.model.dit.config = self.model.config.dit
             self.model.vae.tile_sample_min_size = 1024
             self.model.vae.tile_latent_min_size = 128
-            # from modules.model_quant import do_post_load_quant
+            from modules.model_quant import do_post_load_quant
+            self.model = do_post_load_quant(self.model, allow=True)
             # from modules.sd_offload import set_diffuser_offload
-            # self.model = do_post_load_quant(self.model, allow=True)
             # set_diffuser_offload(self.model)
             log.info(f'Upscaler loaded: name="{self.name}" model="{model_name}" time={t1 - t0:.2f}')
 

From ac4c607d2952f04d2610436b430ec89290d03705 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 14 Oct 2025 10:35:15 -0400
Subject: [PATCH 218/230] update changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 24 +++++++++++++++++++-----
 1 file changed, 19 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e2dd66d08..2a0af3ba9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,21 @@
 
 ## Update for 2025-10-14
 
+### Highlights for 2025-10-14
+
+It's been a month since the last release and number of changes is yet again massive with over 250 commits!  
+Highlight are:  
+- **Torch**: ROCm on Windows for AMD GPUs  
+  if you have a compatible GPU, performance gains are significant!  
+- **Models**:  
+  new WAN variants, a lot of new stuff with Qwen Image Edit including multi-image edits, expanded Nunchaku support and new SOTA upscaler with SeedVR2  
+  plus improved video support in general, including new methods of video encoding  
+- **Quantization**:  
+  new **SVD**-style quantization using SDNQ ofers almost zero loss even with **4bit** quantization  
+  and now you can also test your favorite quantization on-the-fly and then save/load model for future use  
+
+### Details for 2025-10-14
+
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
     available for *text-to-image* and *text-to-video* and *image-to-video* workflows  
@@ -19,10 +34,9 @@
     updated version of E1 image editing model  
   - [SeedVR2](https://iceclear.github.io/projects/seedvr/)  
     originally designed for video restoration, seedvr works great for image detailing and upscaling!  
-    available in 3B, 7B and 7B-sharp variants  
-    use as any other upscaler!  
-    note: seedvr is a very large model (6.4GB and 16GB respectively) and not designed for lower-end hardware  
-    note: seedvr is highly sensitive to its cfg scale (set in settings -> postprocessing),  
+    available in 3B, 7B and 7B-sharp variants, use as any other upscaler!  
+    note: seedvr is a very large model (6.4GB and 16GB respectively) and not designed for lower-end hardware, quantization is highly recommended  
+    note: seedvr is highly sensitive to its cfg scale, set in *settings -> postprocessing*  
     lower values will result in smoother output while higher values add details  
   - [X-Omni SFT](https://x-omni-team.github.io/)  
     *experimental*: X-omni is a transformer-only discrete autoregressive image generative model trained with reinforcement learning  
@@ -101,7 +115,7 @@
   - **video** new LTX model selection  
   - replace `pynvml` with `nvidia-ml-py` for gpu monitoring  
   - update **loopback** script with radon seed option, thanks @rabanti  
-  - **vae** slicing enable for lowvram/medvram, tiling for lowvram, both disabled otherwise  
+  - **vae** slicing enable for *lowvram/medvram*, tiling for *lowvram*, both disabled otherwise  
   - **attention** remove split-attention and add explicitly attention slicing enable/disable option  
     enable in *settings -> compute settings*  
     can be combined with sdp, enabling may improve stability when used on iGPU or shared memory systems  

From 6d64f4a2fde7da3a546d6b6629cb279a1fcc6763 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 14 Oct 2025 17:48:01 +0300
Subject: [PATCH 219/230] rename svd options

---
 modules/shared.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 0c3315f26..3cca72294 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -201,9 +201,9 @@ options_templates.update(options_section(("quantization", "Model Quantization"),
     "sdnq_modules_to_not_convert": OptionInfo("", "Modules to not convert"),
     "sdnq_modules_dtype_dict": OptionInfo("{}", "Modules dtype dict"),
     "sdnq_quantize_weights_group_size": OptionInfo(0, "Group size", gr.Slider, {"minimum": -1, "maximum": 4096, "step": 1}),
-    "sdnq_svd_rank": OptionInfo(32, "SVDQuant Rank size", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
-    "sdnq_svd_steps": OptionInfo(8, "SVDQuant quantization steps", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-    "sdnq_use_svd": OptionInfo(False, "Use SVDQuant quantization", gr.Checkbox),
+    "sdnq_svd_rank": OptionInfo(32, "SVD rank size", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
+    "sdnq_svd_steps": OptionInfo(8, "SVD steps", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+    "sdnq_use_svd": OptionInfo(False, "Use SVD quantization", gr.Checkbox),
     "sdnq_quantize_conv_layers": OptionInfo(False, "Quantize convolutional layers", gr.Checkbox),
     "sdnq_dequantize_compile": OptionInfo(devices.has_triton(), "Dequantize using torch.compile", gr.Checkbox),
     "sdnq_use_quantized_matmul": OptionInfo(False, "Use quantized MatMul", gr.Checkbox),

From 845869079d9ab56e3d3bff68e260ac2f0e86d2f1 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Tue, 14 Oct 2025 17:58:09 +0300
Subject: [PATCH 220/230] Fix sdnq unset config

---
 modules/sdnq/loader.py | 12 ++++++------
 wiki                   |  2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/modules/sdnq/loader.py b/modules/sdnq/loader.py
index 30a23dc70..88d8140a7 100644
--- a/modules/sdnq/loader.py
+++ b/modules/sdnq/loader.py
@@ -16,12 +16,12 @@ def get_module_names(model: ModelMixin) -> list:
     return modules_names
 
 
-def unset_config_on_save(config: SDNQConfig) -> SDNQConfig:
-    config.quantization_config.quantization_device = None
-    config.quantization_config.return_device = None
-    config.quantization_config.non_blocking = False
-    config.quantization_config.add_skip_keys = False
-    return config
+def unset_config_on_save(quantization_config: SDNQConfig) -> SDNQConfig:
+    quantization_config.quantization_device = None
+    quantization_config.return_device = None
+    quantization_config.non_blocking = False
+    quantization_config.add_skip_keys = False
+    return quantization_config
 
 
 def save_sdnq_model(model: ModelMixin, model_path: str, max_shard_size: str = "10GB", is_pipeline: bool = False, sdnq_config: SDNQConfig = None) -> None:
diff --git a/wiki b/wiki
index be337fb3e..bf77a802d 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit be337fb3e02048113e97515aceced6b3c67eb3d9
+Subproject commit bf77a802db032e2f5a48c379088f35cdadd76a8b

From 61f8f32cb3261651a5c20ef331bfb6fabdd694de Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 15 Oct 2025 11:09:36 -0400
Subject: [PATCH 221/230] update changelog and readme

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md | 48 +++++++++++++++++++++++++-----------------------
 README.md    | 10 +++++-----
 wiki         |  2 +-
 3 files changed, 31 insertions(+), 29 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2a0af3ba9..0b41b3816 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,21 +1,22 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-14
+## Update for 2025-10-15
 
-### Highlights for 2025-10-14
+### Highlights for 2025-10-15
 
-It's been a month since the last release and number of changes is yet again massive with over 250 commits!  
+It's been a month since the last release and number of changes is yet again massive with over 300 commits!  
 Highlight are:  
 - **Torch**: ROCm on Windows for AMD GPUs  
   if you have a compatible GPU, performance gains are significant!  
 - **Models**:  
-  new WAN variants, a lot of new stuff with Qwen Image Edit including multi-image edits, expanded Nunchaku support and new SOTA upscaler with SeedVR2  
+  a lot of new stuff with **Qwen-Image-Edit** including multi-image edits and distilled variants,  
+  new **Flux**, **WAN**, **HiDream** variants, expanded **Nunchaku** support and new SOTA upscaler with **SeedVR2**  
   plus improved video support in general, including new methods of video encoding  
 - **Quantization**:  
-  new **SVD**-style quantization using SDNQ ofers almost zero loss even with **4bit** quantization  
+  new **SVD**-style quantization using SDNQ offers almost zero-loss even with **4bit** quantization  
   and now you can also test your favorite quantization on-the-fly and then save/load model for future use  
 
-### Details for 2025-10-14
+### Details for 2025-10-15
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -28,7 +29,7 @@ Highlight are:
     SRPO is trained by Tencent with specific technique: directly aligning the full diffusion trajectory with fine-grained human preference  
   - [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)  
     impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline  
-    note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes  
+    note that nunchaku optimized and pre-quantized unet is replacement for base unet, so its only applicable to base models, not any of fine-tunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
   - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
     updated version of E1 image editing model  
@@ -39,13 +40,13 @@ Highlight are:
     note: seedvr is highly sensitive to its cfg scale, set in *settings -> postprocessing*  
     lower values will result in smoother output while higher values add details  
   - [X-Omni SFT](https://x-omni-team.github.io/)  
-    *experimental*: X-omni is a transformer-only discrete autoregressive image generative model trained with reinforcement learning  
+    *experimental*: X-omni is a transformer-only discrete auto-regressive image generative model trained with reinforcement learning  
 - **Features**
   - **Model save**: ability to save currently loaded model as a new standalone model  
     why? SD.Next always prefers to start with full model and quantize on-demand during load  
     however, when you find your exact preferred quantization settings that work well for you,  
     saving such model as a new model allows for faster loads and reduced disk space usage  
-    so its best of both worlds: you can experiment and test different quantizations and once you find the one that works for you, save it as a new model  
+    so its best of both worlds: you can experiment and test different quantization methods and once you find the one that works for you, save it as a new model  
     saved models appear in network tab as normal models and can be loaded as such  
     available in *models* tab  
   - [Qwen Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) multi-image editing
@@ -56,15 +57,15 @@ Highlight are:
   - [Cache-DiT](https://github.com/vipshop/cache-dit)  
     cache-dit is a unified, flexible and training-free cache acceleration framework  
     compatible with many dit-based models such as FLUX.1, Qwen, HunyuanImage, Wan2.2, Chroma, etc.  
-    enable in *settings -> pipeline modifers -> cache-dit*  
+    enable in *settings -> pipeline modifiers -> cache-dit*  
   - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
     automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
 - **Compute**
   - **ROCm** for Windows  
-    support for both official torch preview release of `torch-rocm` for windows and **TheRock** unoffical `torch-rocm` builds for windows  
+    support for both official torch preview release of `torch-rocm` for windows and **TheRock** unofficial `torch-rocm` builds for windows  
     note that rocm for windows is still in preview and has limited gpu support, please check rocm docs for details  
   - **DirectML** warn as *end-of-life*  
-    `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
+    `torch-directml` received no updates in over 1 year and its currently superseded by `rocm` or `zluda`  
   - command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible  
     previously sdnext was performing a fallback to `torch-cpu` which is not desired  
   - **installer** if `--use-cuda` or `--use-rocm` are specified and `torch-cpu` is installed, installer will attempt to reinstall correct torch package  
@@ -95,11 +96,11 @@ Highlight are:
 - **SDNQ**
   - add `SVDQuant` quantization method support  
   - make sdnq scales compatible with balanced offload  
-  - add int8 matmul support for RDNA2 GPUs via triton  
-  - improve int8 mamtul performance on Intel GPUs  
+  - add int8 `matmul` support for RDNA2 GPUs via triton  
+  - improve int8 `matmul` performance on Intel GPUs  
 - **Other**
   - server will note when restart is recommended due to package updates  
-  - **interrrupt** will now show last known preview image  
+  - **interrupt** will now show last known preview image  
     *keep incomplete* setting is now *save interrupted*  
   - **logging** enable `debug`, `docs` and `api-docs` by default  
   - **logging** add detailed ram/vram utilization info to log  
@@ -143,7 +144,8 @@ Highlight are:
     allows for using many different guidance methods:  
     *CFG, CFGZero, PAG, APG, SLG, SEG, TCFG, FDG*  
 - **Wiki**
-  - updates to *AMD-ROCm, ZLUDA, LoRA, DirectML, SDNQ* pages
+  - updates to *AMD-ROCm, ZLUDA, LoRA, DirectML, SDNQ, Quantization, Prompting, LoRA* pages  
+  - new *Stability-Matrix* page  
 - **Fixes**
   - **Microsoft Florence 2** both base and large variants  
     *note* this will trigger download of the new variant of the model, feel free to delete older variant in `huggingface` folder  
@@ -168,7 +170,7 @@ Highlight are:
 **StandardUI** is still available and can be selected in settings, but ModernUI is now the default for new installs  
 
 *What's else*? **Chroma** is in its final form, there are several new **Qwen-Image** variants and **Nunchaku** hit version 1.0!  
-Also, there are quite a few offloading improvements and many quality-of-life changes to UI and overal workflows  
+Also, there are quite a few offloading improvements and many quality-of-life changes to UI and overall workflows  
 And check out new **history** tab in the right panel, it now shows visualization of entire processing timeline!  
 
 ![Screenshot](https://github.com/user-attachments/assets/d6119a63-6ee5-4597-95f6-29ed0701d3b5)
@@ -182,13 +184,13 @@ And check out new **history** tab in the right panel, it now shows visualization
   - **Qwen-Image** [InstantX ControlNet Union](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union) support  
     *note* qwen-image is already a very large model and controlnet adds 3.5GB on top of that so quantization and offloading are highly recommended!  
   - [Qwen-Lightning-Edit](https://huggingface.co/vladmandic/Qwen-Lightning-Edit) and [Qwen-Image-Distill](https://huggingface.co/SahilCarterr/Qwen-Image-Distill-Full) variants  
-  - **Nuchaku** variants of [Qwen-Image-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image), [Qwen-Image-Edit](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit), [Nunchaku-Qwen-Image-Edit-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit)
+  - **Nunchaku** variants of [Qwen-Image-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image), [Qwen-Image-Edit](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit), [Nunchaku-Qwen-Image-Edit-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit)
   - **Nunchaku** variant of [Flux.1-Krea-Dev](https://huggingface.co/nunchaku-tech/nunchaku-flux.1-krea-dev)  
     if you have a compatible nVidia GPU, Nunchaku is the fastest quantization & inference engine  
   - [HunyuanDiT ControlNet](https://huggingface.co/Tencent-Hunyuan/HYDiT-ControlNet-v1.2) Canny, Depth, Pose  
   - [KBlueLeaf/HDM-xut-340M-anime](https://huggingface.co/KBlueLeaf/HDM-xut-340M-anime)  
     highly experimental: HDM *Home-made-Diffusion-Model* is a project to investigate specialized training recipe/scheme  
-    for pretraining T2I model at home based on super-light architecture  
+    for pre-training T2I model at home based on super-light architecture  
     *requires*: generator=cpu, dtype=float16, offload=none, both positive and negative prompts are required and must be long & detailed  
   - [Apple FastVLM](https://huggingface.co/apple/FastVLM-0.5B) in 0.5B, 1.5B and 7B variants  
     available in captioning tab  
@@ -207,13 +209,13 @@ And check out new **history** tab in the right panel, it now shows visualization
   - additional artwork for reference models in networks, thanks @liutyi  
   - improve ui hints display  
   - restyled all toolbuttons to be modernui native  
-  - reodered system settings  
+  - reordered system settings  
   - dynamic direction of dropdowns  
   - improve process tab layout  
   - improve detection of active tab  
   - configurable horizontal vs vertical panel layout  
     in settings -> user interface -> panel min width  
-    *example*: if panel width is less than specified value, layout switches to verical  
+    *example*: if panel width is less than specified value, layout switches to vertical  
   - configurable grid images size  
     in *settings -> user interface -> grid image size*  
   - gallery now includes reference model images  
@@ -224,10 +226,10 @@ And check out new **history** tab in the right panel, it now shows visualization
     - improve offloading of models with multiple dits  
     - improve offloading of models with impliciy vae processing  
     - improve offloading of models with controlnet  
-    - more aggressive offloading of controlnets with lowvram flag  
+    - more aggressive offloading of controlnet with lowvram flag  
   - **group**
     - new offloading method, using *type=leaf* works on a similar level as sequential offloading  
-      and can present siginificant savings on low-vram gpus, but comes at the higher performace cost  
+      and can present significant savings on low-vram gpus, but comes at the higher performance cost  
 - **Quantization**
   - option to specify models types not to quantize: *settings -> quantization*  
     allows for having quantization enabled, but skipping specific model types that do not need it  
diff --git a/README.md b/README.md
index f57feb916..b81f1a8aa 100644
--- a/README.md
+++ b/README.md
@@ -31,12 +31,12 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
   ▹ **Standard | Modern**  
 - Multiple [diffusion models](https://vladmandic.github.io/sdnext-docs/Model-Support/)!  
 - Built-in Control for Text, Image, Batch and Video processing!  
-- Multiplatform!  
+- Multi-platform!  
  ▹ **Windows | Linux | MacOS | nVidia CUDA | AMD ROCm | Intel Arc / IPEX XPU | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
-- Platform specific autodetection and tuning performed on install  
+- Platform specific auto-detection and tuning performed on install  
 - Optimized processing with latest `torch` developments with built-in support for model compile and quantize  
   Compile backends: *Triton | StableFast | DeepCache | OneDiff | TeaCache | etc.*  
-  Quantization methods: *SDNQ | BitsAndBytes | Optimum-Quanto | TorchAO*  
+  Quantization methods: *SDNQ | BitsAndBytes | Optimum-Quanto | TorchAO / LayerWise*  
 - **Interrogate/Captioning** with 150+ **OpenCLiP** models and 20+ built-in **VLMs**  
 - Built-in queue management  
 - Built in installer with automatic updates and dependency management  
@@ -54,7 +54,7 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
 <img src="https://github.com/user-attachments/assets/ced9fe0c-d2c2-46d1-94a7-8f9f2307ce38" alt="screenshot-modernui-mobile" width="35%">
 </div>
 
-For screenshots and informations on other available themes, see [Themes](https://vladmandic.github.io/sdnext-docs/Themes/)
+For screenshots and information on other available themes, see [Themes](https://vladmandic.github.io/sdnext-docs/Themes/)
 
 <br>
 
@@ -75,7 +75,7 @@ SD.Next supports broad range of models: [supported models](https://vladmandic.gi
 - *ONNX/Olive*  
 - *AMD* GPUs on Windows using **ZLUDA** libraries  
 
-Plus Docker container receipes for: [CUDA, ROCm, Intel IPEX and OpenVINO](https://vladmandic.github.io/sdnext-docs/Docker/)
+Plus Docker container recipes for: [CUDA, ROCm, Intel IPEX and OpenVINO](https://vladmandic.github.io/sdnext-docs/Docker/)
 
 ## Getting started
 
diff --git a/wiki b/wiki
index bf77a802d..ae55ef9b4 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit bf77a802db032e2f5a48c379088f35cdadd76a8b
+Subproject commit ae55ef9b423918e096d516ee1646c0b59fbc9528

From 4452e03221f3ccdc939d0aa3e9c6322330864af7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 15 Oct 2025 13:39:56 -0400
Subject: [PATCH 222/230] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8a133599e..7c9ea0953 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -528,7 +528,7 @@ def load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_con
             diffusers_load_config.pop('local_files_only', None)
             shared.log.debug(f'Setting {op}: pipeline={sd_model.__class__.__name__} config={diffusers_load_config}') # pylint: disable=protected-access
     except Exception as e:
-        shared.log.error(f'Load {op}: file="{checkpoint_info.path}" pipeline={shared.opts.diffusers_pipeline}/{sd_model.__class__.__name__} config={diffusers_load_config} {e}')
+        shared.log.error(f'Load {op}: file="{checkpoint_info.path}" pipeline={shared.opts.diffusers_pipeline} config={diffusers_load_config} {e}')
         if 'Weights for this component appear to be missing in the checkpoint' in str(e):
             shared.log.error(f'Load {op}: file="{checkpoint_info.path}" is not a complete model')
         else:

From ffe2a9d1480ba31939d9892a1062a5e5a78103ca Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 15 Oct 2025 14:49:31 -0400
Subject: [PATCH 223/230] add ltxvideo-0.9.8

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 6 ++++--
 installer.py                       | 2 +-
 modules/video_models/models_def.py | 6 ++++++
 3 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0b41b3816..a4159fa5f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,7 +10,7 @@ Highlight are:
   if you have a compatible GPU, performance gains are significant!  
 - **Models**:  
   a lot of new stuff with **Qwen-Image-Edit** including multi-image edits and distilled variants,  
-  new **Flux**, **WAN**, **HiDream** variants, expanded **Nunchaku** support and new SOTA upscaler with **SeedVR2**  
+  new **Flux**, **WAN**, **LTX**, **HiDream** variants, expanded **Nunchaku** support and new SOTA upscaler with **SeedVR2**  
   plus improved video support in general, including new methods of video encoding  
 - **Quantization**:  
   new **SVD**-style quantization using SDNQ offers almost zero-loss even with **4bit** quantization  
@@ -32,7 +32,9 @@ Highlight are:
     note that nunchaku optimized and pre-quantized unet is replacement for base unet, so its only applicable to base models, not any of fine-tunes  
     *how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models  
   - [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)  
-    updated version of E1 image editing model  
+    updated version of HiDream-E1 image editing model  
+  - [LTXVideo 0.9.8](https://huggingface.co/Lightricks/LTX-Video-0.9.8-13B-distilled)  
+    updated version of LTXVideo t2v/i2iv model  
   - [SeedVR2](https://iceclear.github.io/projects/seedvr/)  
     originally designed for video restoration, seedvr works great for image detailing and upscaling!  
     available in 3B, 7B and 7B-sharp variants, use as any other upscaler!  
diff --git a/installer.py b/installer.py
index e668f3c1a..3e2c06e72 100644
--- a/installer.py
+++ b/installer.py
@@ -608,7 +608,7 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = 'a9df12ab451be65060f6f69e201e3a6cb2ee15e8' # diffusers commit hash
+    sha = 'f072c64bf22c6a259d38435b19a8ef3eb67c96a8' # diffusers commit hash
     # if args.use_rocm or args.use_zluda or args.use_directml:
     #     sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
diff --git a/modules/video_models/models_def.py b/modules/video_models/models_def.py
index e2605ae50..e1cc76f71 100644
--- a/modules/video_models/models_def.py
+++ b/modules/video_models/models_def.py
@@ -74,6 +74,12 @@ models = {
     ],
     'LTX Video': [
         Model(name='None'),
+        Model(name='LTXVideo 0.9.8 13B',
+              url='https://huggingface.co/Lightricks/LTX-Video-0.9.8-13B-distilled',
+              repo='Lightricks/LTX-Video-0.9.8-13B-distilled',
+              repo_cls=diffusers.LTXConditionPipeline,
+              te_cls=transformers.T5EncoderModel,
+              dit_cls=diffusers.LTXVideoTransformer3DModel),
         Model(name='LTXVideo 0.9.7 13B',
               url='https://huggingface.co/Lightricks/LTX-Video-0.9.7-dev',
               repo='a-r-r-o-w/LTX-Video-0.9.7-diffusers',

From 070edb20b07b98bb9c188ba17fb40514a6ee5f77 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 16 Oct 2025 09:21:20 -0400
Subject: [PATCH 224/230] update transformers and fix quant params

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py                       | 2 +-
 modules/ltx/ltx_process.py         | 9 ++++++++-
 modules/ltx/ltx_util.py            | 6 ++++++
 modules/sdnq/quantizer.py          | 2 +-
 modules/video_models/video_load.py | 2 +-
 requirements.txt                   | 4 ++--
 6 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/installer.py b/installer.py
index 3e2c06e72..8b87eafa9 100644
--- a/installer.py
+++ b/installer.py
@@ -637,7 +637,7 @@ def check_transformers():
         target_transformers = '4.52.4'
         target_tokenizers = '0.21.4'
     else:
-        target_transformers = '4.56.2'
+        target_transformers = '4.57.1'
         target_tokenizers = '0.22.1'
     if (pkg_transformers is None) or ((pkg_transformers.version != target_transformers) or (pkg_tokenizers is None) or ((pkg_tokenizers.version != target_tokenizers) and (not args.experimental))):
         if pkg_transformers is None:
diff --git a/modules/ltx/ltx_process.py b/modules/ltx/ltx_process.py
index 46374d77d..7d9e6ae84 100644
--- a/modules/ltx/ltx_process.py
+++ b/modules/ltx/ltx_process.py
@@ -66,7 +66,10 @@ def run_ltx(task_id,
         progress.finish_task(task_id)
         yield None, f'LTX Error: {str(e)}'
 
-    from diffusers import LTXConditionPipeline # pylint: disable=unused-import
+    if model is None or len(model) == 0:
+        yield from abort('Video: no model selected', ok=True)
+        return
+    # from diffusers import LTXConditionPipeline # pylint: disable=unused-import
     check_av()
     progress.add_task_to_queue(task_id)
     with queue_lock:
@@ -76,6 +79,10 @@ def run_ltx(task_id,
         yield None, 'LTX: Loading...'
         engine = 'LTX Video'
         load_model(engine, model)
+        debug(f'Video: cls={shared.sd_model.__class__.__name__} op=init model="{model}"')
+        if not shared.sd_model.__class__.__name__.startswith("LTX"):
+            yield from abort(f'Video: cls={shared.sd_model.__class__.__name__} selected model is not LTX model', ok=True)
+            return
 
         videojob = shared.state.begin('Video', task_id=task_id)
         shared.state.job_count = 1
diff --git a/modules/ltx/ltx_util.py b/modules/ltx/ltx_util.py
index 9cca59306..ddc53a323 100644
--- a/modules/ltx/ltx_util.py
+++ b/modules/ltx/ltx_util.py
@@ -8,6 +8,8 @@ loaded_model: str = None
 
 
 def get_bucket(size: int):
+    if not hasattr(shared.sd_model, 'vae_temporal_compression_ratio'):
+        return int(size) - (int(size) % 16)
     return int(size) - (int(size) % shared.sd_model.vae_temporal_compression_ratio)
 
 
@@ -19,6 +21,10 @@ def load_model(engine: str, model: str):
     global loaded_model # pylint: disable=global-statement
     if loaded_model == model:
         return
+    if model is None or model == '' or model=='None':
+        loaded_model = None
+        shared.sd_model = None
+        return
     t0 = time.time()
     from modules.video_models import models_def, video_load
     selected: models_def.Model = [m for m in models_def.models[engine] if m.name == model][0]
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index b4e60a704..78eb7d333 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -497,7 +497,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         param_value: torch.FloatTensor,
         param_name: str,
         target_device: torch.device,
-        state_dict: Dict[str, Any],
+        # state_dict: Dict[str, Any], # removed in transformers==4.57
         *args, **kwargs, # pylint: disable=unused-argument
     ):
         if self.pre_quantized:
diff --git a/modules/video_models/video_load.py b/modules/video_models/video_load.py
index e8d616c69..d7f47d5f2 100644
--- a/modules/video_models/video_load.py
+++ b/modules/video_models/video_load.py
@@ -8,7 +8,7 @@ loaded_model = None
 
 
 def load_model(selected: models_def.Model):
-    if selected is None:
+    if selected is None or selected.te_cls is None or selected.dit_cls is None:
         return ''
     global loaded_model # pylint: disable=global-statement
     if loaded_model == selected.name:
diff --git a/requirements.txt b/requirements.txt
index f41e599e2..66803df33 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,8 +35,8 @@ rich==14.1.0
 safetensors==0.6.2
 tensordict==0.8.3
 peft==0.17.1
-httpx==0.24.1
-compel==2.1.1
+httpx==0.28.1
+compel==2.2.1
 torchsde==0.2.6
 antlr4-python3-runtime==4.9.3
 requests==2.32.4

From 63aad89676c7d02163a39ce28066359e0b6c1407 Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Thu, 16 Oct 2025 16:29:23 +0300
Subject: [PATCH 225/230] remove the unused state_dict arg

---
 modules/sdnq/quantizer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 78eb7d333..d56dafea0 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -497,7 +497,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         param_value: torch.FloatTensor,
         param_name: str,
         target_device: torch.device,
-        # state_dict: Dict[str, Any], # removed in transformers==4.57
         *args, **kwargs, # pylint: disable=unused-argument
     ):
         if self.pre_quantized:

From 45c5091aa9859f41f9ef0b42e5f880e39f705654 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 16 Oct 2025 11:23:29 -0400
Subject: [PATCH 226/230] fix wan and add hf mirror setting

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                        | 10 +++++++---
 modules/modelloader.py              |  2 ++
 modules/shared.py                   |  1 +
 modules/video_models/models_def.py  | 24 ++++++++++++------------
 modules/video_models/video_load.py  |  4 ++++
 modules/video_models/video_save.py  |  9 ++++-----
 modules/video_models/video_utils.py |  3 +++
 7 files changed, 33 insertions(+), 20 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a4159fa5f..be679abcc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,8 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-15
+## Update for 2025-10-16
 
-### Highlights for 2025-10-15
+### Highlights for 2025-10-16
 
 It's been a month since the last release and number of changes is yet again massive with over 300 commits!  
 Highlight are:  
@@ -15,8 +15,9 @@ Highlight are:
 - **Quantization**:  
   new **SVD**-style quantization using SDNQ offers almost zero-loss even with **4bit** quantization  
   and now you can also test your favorite quantization on-the-fly and then save/load model for future use  
+- Other: support for **Huggingface** mirrors, changes to installer to prevent unwanted `torch-cpu` operations, improved previews, etc.  
 
-### Details for 2025-10-15
+### Details for 2025-10-16
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
@@ -62,6 +63,9 @@ Highlight are:
     enable in *settings -> pipeline modifiers -> cache-dit*  
   - [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)  
     automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled  
+  - **Huggingface mirror** in *settings -> huggingface*  
+    if you're working from location with limited access to huggingface, you can now specify a mirror site  
+    for example enter, `https://hf-mirror.com`  
 - **Compute**
   - **ROCm** for Windows  
     support for both official torch preview release of `torch-rocm` for windows and **TheRock** unofficial `torch-rocm` builds for windows  
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 5018d5cb9..0f8c502c3 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -27,6 +27,8 @@ def hf_login(token=None):
     if token is None or len(token) <= 4:
         log.debug('HF login: no token provided')
         return False
+    if len(shared.opts.huggingface_mirror.strip()) > 0 and os.environ.get('HF_ENDPOINT', None) is None:
+        os.environ['HF_ENDPOINT'] = shared.opts.huggingface_mirror.strip()
     if os.environ.get('HUGGING_FACE_HUB_TOKEN', None) is not None:
         os.environ.pop('HUGGING_FACE_HUB_TOKEN', None)
         os.unsetenv('HUGGING_FACE_HUB_TOKEN')
diff --git a/modules/shared.py b/modules/shared.py
index 3cca72294..fcaa05f1b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -688,6 +688,7 @@ options_templates.update(options_section(('huggingface', "Huggingface"), {
     "diffuser_cache_config": OptionInfo(True, "Use cached model config when available"),
     "huggingface_token": OptionInfo('', 'HuggingFace token', gr.Textbox, {"lines": 2}),
     "hf_transfer_mode": OptionInfo("rust", "HuggingFace download method", gr.Radio, {"choices": ['requests', 'rust', 'xet']}),
+    "huggingface_mirror": OptionInfo('', 'HuggingFace mirror', gr.Textbox),
 
     "diffusers_model_load_variant": OptionInfo("default", "Preferred Model variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
     "diffusers_vae_load_variant": OptionInfo("default", "Preferred VAE variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
diff --git a/modules/video_models/models_def.py b/modules/video_models/models_def.py
index e1cc76f71..14f0eb8e0 100644
--- a/modules/video_models/models_def.py
+++ b/modules/video_models/models_def.py
@@ -153,76 +153,76 @@ models = {
               url='https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers',
               repo='Wan-AI/Wan2.2-TI2V-5B-Diffusers',
               repo_cls=diffusers.WanPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.2 5B I2V',
               url='https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers',
               repo='Wan-AI/Wan2.2-TI2V-5B-Diffusers',
               repo_cls=diffusers.WanImageToVideoPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.2 A14B T2V',
               url='https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers',
               repo='Wan-AI/Wan2.2-T2V-A14B-Diffusers',
               repo_cls=diffusers.WanPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel,
               dit_folder=("transformer", "transformer_2")),
         Model(name='WAN 2.2 A14B I2V',
               url='https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers',
               repo='Wan-AI/Wan2.2-I2V-A14B-Diffusers',
               repo_cls=diffusers.WanImageToVideoPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel,
               dit_folder=("transformer", "transformer_2")),
         Model(name='WAN 2.2 14B VACE',
               url='https://huggingface.co/linoyts/Wan2.2-VACE-Fun-14B-diffusers',
               repo='linoyts/Wan2.2-VACE-Fun-14B-diffusers',
               repo_cls=diffusers.WanVACEPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanVACETransformer3DModel,
               dit_folder=("transformer", "transformer_2")),
         Model(name='WAN 2.1 1.3B T2V',
               url='https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
               repo='Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
               repo_cls=diffusers.WanPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.1 14B T2V',
               url='https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers',
               repo='Wan-AI/Wan2.1-T2V-14B-Diffusers',
               repo_cls=diffusers.WanPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.1 14B I2V 480p',
               url='https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers',
               repo='Wan-AI/Wan2.1-I2V-14B-480P-Diffusers',
               repo_cls=diffusers.WanImageToVideoPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.1 14B I2V 720p',
               url='https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers',
               repo='Wan-AI/Wan2.1-I2V-14B-720P-Diffusers',
               repo_cls=diffusers.WanImageToVideoPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.1 14B FLF2V 720p',
               url='https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P',
               repo='Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers',
               repo_cls=diffusers.WanImageToVideoPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel),
         Model(name='WAN 2.1 VACE 1.3B',
               url='https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B-diffusers',
               repo='Wan-AI/Wan2.1-VACE-1.3B-diffusers',
               repo_cls=diffusers.WanVACEPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanVACETransformer3DModel),
         Model(name='WAN 2.1 VACE 14B',
               url='https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-diffusers',
               repo='Wan-AI/Wan2.1-VACE-14B-diffusers',
               repo_cls=diffusers.WanVACEPipeline,
-              te_cls=transformers.T5EncoderModel,
+              te_cls=transformers.UMT5EncoderModel,
               dit_cls=diffusers.WanVACETransformer3DModel),
     ],
     'SkyReels V2': [
diff --git a/modules/video_models/video_load.py b/modules/video_models/video_load.py
index d7f47d5f2..d41e3d463 100644
--- a/modules/video_models/video_load.py
+++ b/modules/video_models/video_load.py
@@ -31,6 +31,10 @@ def load_model(selected: models_def.Model):
             selected.te = 'Disty0/t5-xxl'
             selected.te_folder = ''
             selected.te_revision = None
+        if selected.te_cls.__name__ == 'UMT5EncoderModel' and shared.opts.te_shared_t5:
+            selected.te = 'Wan-AI/Wan2.2-TI2V-5B-Diffusers'
+            selected.te_folder = 'text_encoder'
+            selected.te_revision = None
         if selected.te_cls.__name__ == 'LlamaModel' and shared.opts.te_shared_t5:
             selected.te = 'hunyuanvideo-community/HunyuanVideo'
             selected.te_folder = 'text_encoder'
diff --git a/modules/video_models/video_save.py b/modules/video_models/video_save.py
index 41095922f..24775c5a1 100644
--- a/modules/video_models/video_save.py
+++ b/modules/video_models/video_save.py
@@ -6,6 +6,7 @@ import numpy as np
 import torch
 import einops
 from modules import shared, errors ,timer, rife
+from modules.video_models.video_utils import check_av
 
 
 def get_video_filename(frames:int, codec:str):
@@ -27,11 +28,9 @@ def images_to_tensor(images):
 
 
 def atomic_save_video(filename, tensor:torch.Tensor, fps:float=24, codec:str='libx264', pix_fmt:str='yuv420p', options:str='', metadata:dict={}, pbar=None):
-    try:
-        import av
-        av.logging.set_level(av.logging.ERROR) # pylint: disable=c-extension-no-member
-    except Exception as e:
-        shared.log.error(f'Video: {e}')
+    av = check_av()
+    if av is None or av is False:
+        shared.log.error('Video: ffmpeg/av not available')
         return
 
     savejob = shared.state.begin('Save video')
diff --git a/modules/video_models/video_utils.py b/modules/video_models/video_utils.py
index 47faeac13..f29c43906 100644
--- a/modules/video_models/video_utils.py
+++ b/modules/video_models/video_utils.py
@@ -2,6 +2,7 @@ import os
 import sys
 import time
 from PIL import Image
+from installer import install
 from modules import shared, sd_models, timer, errors, devices
 
 
@@ -18,8 +19,10 @@ def get_url(url):
 
 
 def check_av():
+    install('av')
     try:
         import av
+        av.logging.set_level(av.logging.ERROR) # pylint: disable=c-extension-no-member
     except Exception as e:
         shared.log.error(f'av package: {e}')
         return False

From 2cf9938d97549a28f5010746ad5edea843af859c Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Fri, 17 Oct 2025 00:08:12 +0300
Subject: [PATCH 227/230] SDNQ fix sdxl unet quant config not getting saved

---
 modules/sdnq/quantizer.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index d56dafea0..87239aa19 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -433,10 +433,12 @@ def sdnq_post_load_quant(
         modules_dtype_dict=modules_dtype_dict.copy(),
     )
 
-    try:
-        model.config.quantization_config = model.quantization_config
-    except Exception:
-        pass
+    if hasattr(model, "config"):
+        try:
+            model.config.quantization_config = model.quantization_config
+            model.config["quantization_config"] = model.quantization_config.to_dict()
+        except Exception:
+            pass
     model.quantization_method = QuantizationMethod.SDNQ
 
     return model
@@ -589,7 +591,11 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
                 model, self.quantization_config.modules_to_not_convert, self.quantization_config.modules_dtype_dict
             )
         if hasattr(model, "config"):
-            model.config.quantization_config = self.quantization_config
+            try:
+                model.config.quantization_config = self.quantization_config
+                model.config["quantization_config"] = self.quantization_config.to_dict()
+            except Exception:
+                pass
         model.quantization_config = self.quantization_config
 
     def _process_model_after_weight_loading(self, model, **kwargs): # pylint: disable=unused-argument

From a4bc61919d78d6c696b369d086eae66f02f0bac6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 16 Oct 2025 19:25:12 -0400
Subject: [PATCH 228/230] add explicit sync for vae preview

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    | 1 +
 modules/processing_callbacks.py | 2 +-
 wiki                            | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index be679abcc..29ae7a851 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -167,6 +167,7 @@ Highlight are:
   - fix hf token with extra chars  
   - image viewer refocus on gallery after returning from full screen mode  
   - fix attention guidance metadata save/restore  
+  - vae preview add explicity cuda.sync  
 
 ## Update for 2025-09-15
 
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 46067e0c2..1fce953ee 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -56,7 +56,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = {}
     t0 = time.time()
     if devices.backend == "ipex": # xe driver on linux needs this
         torch.xpu.synchronize(devices.device)
-    elif devices.backend == "zluda" or (devices.backend == "rocm" and sys.platform == "win32"):
+    elif (devices.backend == "zluda") or (devices.backend == "rocm") or (devices.backend == "cuda"):
         torch.cuda.synchronize(devices.device)
     latents = kwargs.get('latents', None)
     if debug:
diff --git a/wiki b/wiki
index ae55ef9b4..78e4e6b94 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit ae55ef9b423918e096d516ee1646c0b59fbc9528
+Subproject commit 78e4e6b94adac848c118b85d7a4c552babcb1acd

From 4f336d3aab8f95809cc8a5007598a03043362d2a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 16 Oct 2025 19:39:05 -0400
Subject: [PATCH 229/230] linting

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                                     | 1 -
 launch.py                                                     | 2 +-
 modules/modelloader.py                                        | 2 +-
 modules/processing_callbacks.py                               | 1 -
 modules/safe.py                                               | 4 ++--
 modules/sdnq/quantizer.py                                     | 4 ++--
 modules/seedvr/src/__init__.py                                | 2 +-
 .../seedvr/src/models/video_vae_v3/modules/attn_video_vae.py  | 2 +-
 pipelines/model_stablecascade.py                              | 4 ++--
 9 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/.pylintrc b/.pylintrc
index ff0af298f..5dc22b99c 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -76,7 +76,6 @@ persistent=yes
 py-version=3.9
 recursive=no
 source-roots=
-suggestion-mode=yes
 unsafe-load-any-extension=no
 
 [BASIC]
diff --git a/launch.py b/launch.py
index 1f55a848d..40e1946fc 100755
--- a/launch.py
+++ b/launch.py
@@ -133,7 +133,7 @@ def run_pip(pkg, desc=None): # compatbility function
         desc = pkg
     for f in forbidden:
         if f in pkg:
-            debug_install('Blocked package installation: package={f}')
+            debug_install(f'Blocked package installation: package={f}')
             return True
     index_url_line = f' --index-url {index_url}' if index_url != '' else ''
     return run(f'"{sys.executable}" -m pip {pkg} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 0f8c502c3..c448caad2 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -306,7 +306,7 @@ def download_url_to_file(url: str, dst: str):
             continue
         break
     else:
-        shared.log.error('Error downloading: url={url} no usable temporary filename found')
+        shared.log.error(f'Error downloading: url={url} no usable temporary filename found')
         return
     try:
         with Progress(TextColumn('[cyan]{task.description}'), BarColumn(), TaskProgressColumn(), TimeRemainingColumn(), TimeElapsedColumn(), console=shared.console) as progress:
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 1fce953ee..62f9b624b 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -1,6 +1,5 @@
 import typing
 import os
-import sys
 import time
 import torch
 import numpy as np
diff --git a/modules/safe.py b/modules/safe.py
index 21b8c08b2..82d4ae77a 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -21,8 +21,8 @@ def encode(*args):
 class RestrictedUnpickler(pickle.Unpickler):
     extra_handler = None
 
-    def persistent_load(self, saved_id):
-        assert saved_id[0] == 'storage'
+    def persistent_load(self, pid):
+        assert pid[0] == 'storage'
         try:
             return TypedStorage(_internal=True)
         except TypeError:
diff --git a/modules/sdnq/quantizer.py b/modules/sdnq/quantizer.py
index 87239aa19..29f3b61d8 100644
--- a/modules/sdnq/quantizer.py
+++ b/modules/sdnq/quantizer.py
@@ -1,6 +1,6 @@
 # pylint: disable=redefined-builtin,no-member,protected-access
 
-from typing import Any, Dict, List, Tuple, Optional, Union
+from typing import Dict, List, Tuple, Optional, Union
 from dataclasses import dataclass
 from enum import Enum
 
@@ -463,7 +463,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
         *args, **kwargs, # pylint: disable=unused-argument
     ):
         if self.pre_quantized:
-            layer, tensor_name = get_module_from_name(model, param_name)
+            layer, _tensor_name = get_module_from_name(model, param_name)
             if hasattr(layer, "sdnq_dequantizer"):
                 return True
         elif param_name.endswith(".weight"):
diff --git a/modules/seedvr/src/__init__.py b/modules/seedvr/src/__init__.py
index d9e24b028..e2a119280 100644
--- a/modules/seedvr/src/__init__.py
+++ b/modules/seedvr/src/__init__.py
@@ -8,4 +8,4 @@ current_dir = os.path.dirname(os.path.abspath(__file__))
 parent_dir = os.path.dirname(current_dir)
 if parent_dir not in sys.path:
     sys.path.insert(0, parent_dir)
-"""
\ No newline at end of file
+"""
diff --git a/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py b/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
index 3aba37b66..2b07fb1d3 100644
--- a/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
+++ b/modules/seedvr/src/models/video_vae_v3/modules/attn_video_vae.py
@@ -1288,7 +1288,7 @@ class VideoAutoencoderKL(diffusers.AutoencoderKL):
             result_rows.append(torch.cat(result_row, dim=4))
         dec = torch.cat(result_rows, dim=3)
         return dec
-    
+
     def forward(
         self, x: torch.FloatTensor, mode: Literal["encode", "decode", "all"] = "all", **kwargs
     ):
diff --git a/pipelines/model_stablecascade.py b/pipelines/model_stablecascade.py
index 60d8ca87c..40d141364 100644
--- a/pipelines/model_stablecascade.py
+++ b/pipelines/model_stablecascade.py
@@ -162,10 +162,10 @@ def load_cascade_combined(checkpoint_info, diffusers_load_config):
 
 # Balanced offload hooks:
 class StableCascadeDecoderPipelineFixed(diffusers.StableCascadeDecoderPipeline):
-    def guidance_scale(self):
+    def guidance_scale(self): # pylint: disable=invalid-overridden-method
         return self._guidance_scale
 
-    def do_classifier_free_guidance(self):
+    def do_classifier_free_guidance(self): # pylint: disable=invalid-overridden-method
         return self._guidance_scale > 1
 
     @torch.no_grad()

From e66d78f5563f69d9b047810e9db97b36d23d4d58 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 17 Oct 2025 08:53:26 -0400
Subject: [PATCH 230/230] pre-merge cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                             | 6 +++---
 extensions-builtin/sd-extension-chainner | 2 +-
 extensions-builtin/sdnext-modernui       | 2 +-
 installer.py                             | 2 +-
 modules/processing.py                    | 2 +-
 package.json                             | 2 +-
 6 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 29ae7a851..a175bfdb8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,8 @@
 # Change Log for SD.Next
 
-## Update for 2025-10-16
+## Update for 2025-10-17
 
-### Highlights for 2025-10-16
+### Highlights for 2025-10-17
 
 It's been a month since the last release and number of changes is yet again massive with over 300 commits!  
 Highlight are:  
@@ -17,7 +17,7 @@ Highlight are:
   and now you can also test your favorite quantization on-the-fly and then save/load model for future use  
 - Other: support for **Huggingface** mirrors, changes to installer to prevent unwanted `torch-cpu` operations, improved previews, etc.  
 
-### Details for 2025-10-16
+### Details for 2025-10-17
 
 - **Models**
   - [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)  
diff --git a/extensions-builtin/sd-extension-chainner b/extensions-builtin/sd-extension-chainner
index da7a2bd55..c6dc85eb2 160000
--- a/extensions-builtin/sd-extension-chainner
+++ b/extensions-builtin/sd-extension-chainner
@@ -1 +1 @@
-Subproject commit da7a2bd553b81d66fab21c2f12559439a36e844c
+Subproject commit c6dc85eb28a02bc7af268497b7a5a596770c5d7b
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 638a07d70..db79b18f6 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 638a07d70998cc4ffc1fd9fe8a51de180c1c9e08
+Subproject commit db79b18f6f3f5a247e710b507d10cb39b01cc371
diff --git a/installer.py b/installer.py
index 8b87eafa9..835478690 100644
--- a/installer.py
+++ b/installer.py
@@ -608,7 +608,7 @@ def check_diffusers():
     if args.skip_git:
         install('diffusers')
         return
-    sha = 'f072c64bf22c6a259d38435b19a8ef3eb67c96a8' # diffusers commit hash
+    sha = 'af769881d37fe916afef2c47279f66c79f5f2714' # diffusers commit hash
     # if args.use_rocm or args.use_zluda or args.use_directml:
     #     sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
diff --git a/modules/processing.py b/modules/processing.py
index d42929b23..aaf681840 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,7 +46,7 @@ class Processed:
 
         self.sampler_name = p.sampler_name or ''
         self.cfg_scale = p.cfg_scale if p.cfg_scale > 1 else None
-        self.cfg_end = p.cfg_end if p.cfg_end < 0 else None
+        self.cfg_end = p.cfg_end if p.cfg_end < 1 else None
         self.image_cfg_scale = p.image_cfg_scale or 0
         self.steps = p.steps or 0
         self.batch_size = max(1, p.batch_size)
diff --git a/package.json b/package.json
index 1b0b95bca..e42d5a2fd 100644
--- a/package.json
+++ b/package.json
@@ -19,7 +19,7 @@
     "venv": ". venv/bin/activate",
     "start": ". venv/bin/activate; python launch.py --debug",
     "localize": "node cli/localize.js",
-    "packages": ". venv/bin/activate && pip install --upgrade transformers accelerate huggingface_hub safetensors tokenizers peft compel pytorch_lightning pylint ruff",
+    "packages": ". venv/bin/activate && pip install --upgrade transformers accelerate huggingface_hub safetensors tokenizers peft pytorch_lightning pylint ruff",
     "eslint": "eslint . javascript/ extensions-builtin/sdnext-modernui/javascript/",
     "ruff": ". venv/bin/activate && ruff check",
     "pylint": ". venv/bin/activate && pylint *.py modules/ pipelines/ scripts/ extensions-builtin/ | grep -v '^*'",