From 90e12b78803e8336be148e43393bf6f0729e51d0 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Sat, 26 Oct 2024 12:17:58 -0500
Subject: [PATCH 001/119] Initial Prompt Refactor

---
 modules/processing_args.py         |  37 ++++-----
 modules/processing_callbacks.py    |  16 ++--
 modules/processing_class.py        |  11 +--
 modules/prompt_parser_diffusers.py | 126 +++++++++++++++++++++++++++++
 4 files changed, 159 insertions(+), 31 deletions(-)

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 1ea91fb08..8dd47bd87 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -117,7 +117,8 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
         'Flux' in model.__class__.__name__
     ):
         try:
-            prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, steps=steps, clip_skip=clip_skip)
+            # prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, steps=steps, clip_skip=clip_skip)
+            p.embedder = prompt_parser_diffusers.PromptEmbedder(prompts, negative_prompts, clip_skip, p)
             parser = shared.opts.prompt_attention
         except Exception as e:
             shared.log.error(f'Prompt parser encode: {e}')
@@ -128,27 +129,27 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if 'prompt' in possible:
         if 'OmniGen' in model.__class__.__name__:
             prompts = [p.replace('|image|', '<|image_1|>') for p in prompts]
-        if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and len(p.prompt_embeds) > 0 and p.prompt_embeds[0] is not None:
-            args['prompt_embeds'] = p.prompt_embeds[0]
+        if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and p.embedder is not None:
+            args['prompt_embeds'] = p.embedder('prompt_embeds')
             if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
-                args['prompt_embeds_pooled'] = p.positive_pooleds[0].unsqueeze(0)
-            elif 'XL' in model.__class__.__name__ and len(getattr(p, 'positive_pooleds', [])) > 0:
-                args['pooled_prompt_embeds'] = p.positive_pooleds[0]
-            elif 'StableDiffusion3' in model.__class__.__name__ and len(getattr(p, 'positive_pooleds', [])) > 0:
-                args['pooled_prompt_embeds'] = p.positive_pooleds[0]
-            elif 'Flux' in model.__class__.__name__ and len(getattr(p, 'positive_pooleds', [])) > 0:
-                args['pooled_prompt_embeds'] = p.positive_pooleds[0]
+                args['prompt_embeds_pooled'] = p.embedder('positive_pooleds').unsqueeze(0)
+            elif 'XL' in model.__class__.__name__ and p.embedder is not None:
+                args['pooled_prompt_embeds'] = p.embedder('positive_pooleds')
+            elif 'StableDiffusion3' in model.__class__.__name__ and p.embedder is not None:
+                args['pooled_prompt_embeds'] = p.embedder('positive_pooleds')
+            elif 'Flux' in model.__class__.__name__ and p.embedder is not None:
+                args['pooled_prompt_embeds'] = p.embedder('positive_pooleds')
         else:
             args['prompt'] = prompts
     if 'negative_prompt' in possible:
-        if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and len(p.negative_embeds) > 0 and p.negative_embeds[0] is not None:
-            args['negative_prompt_embeds'] = p.negative_embeds[0]
-            if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
-                args['negative_prompt_embeds_pooled'] = p.negative_pooleds[0].unsqueeze(0)
-            if 'XL' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
-                args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0]
-            if 'StableDiffusion3' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
-                args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0]
+        if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and p.embedder is not None:
+            args['negative_prompt_embeds'] = p.embedder('negative_embeds')
+            if 'StableCascade' in model.__class__.__name__ and p.embedder is not None:
+                args['negative_prompt_embeds_pooled'] = p.embedder('negative_pooleds').unsqueeze(0)
+            if 'XL' in model.__class__.__name__ and p.embedder is not None:
+                args['negative_pooled_prompt_embeds'] = p.embedder('negative_pooleds')
+            if 'StableDiffusion3' in model.__class__.__name__ and p.embedder is not None:
+                args['negative_pooled_prompt_embeds'] = p.embedder('negative_pooleds')
         else:
             if 'PixArtSigmaPipeline' in model.__class__.__name__: # pixart-sigma pipeline throws list-of-list for negative prompt
                 args['negative_prompt'] = negative_prompts[0]
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 47c8e8827..2584ee796 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -67,14 +67,14 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
             pipe.set_ip_adapter_scale(ip_adapter_scales)
     if step != getattr(pipe, 'num_timesteps', 0):
         kwargs = processing_correction.correction_callback(p, timestep, kwargs)
-    if p.scheduled_prompt and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
-        try:
-            i = (step + 1) % len(p.prompt_embeds)
-            kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].expand(kwargs["prompt_embeds"].shape)
-            j = (step + 1) % len(p.negative_embeds)
-            kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].expand(kwargs["negative_prompt_embeds"].shape)
-        except Exception as e:
-            shared.log.debug(f"Callback: {e}")
+    # if p.scheduled_prompt and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
+    #     try:
+    #         i = (step + 1) % len(p.prompt_embeds)
+    #         kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].expand(kwargs["prompt_embeds"].shape)
+    #         j = (step + 1) % len(p.negative_embeds)
+    #         kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].expand(kwargs["negative_prompt_embeds"].shape)
+    #     except Exception as e:
+    #         shared.log.debug(f"Callback: {e}")
     if step == int(getattr(pipe, 'num_timesteps', 100) * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
         if "PAG" in shared.sd_model.__class__.__name__:
             pipe._guidance_scale = 1.001 if pipe._guidance_scale > 1 else pipe._guidance_scale  # pylint: disable=protected-access
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 9265ea3cf..d38aae790 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -204,11 +204,12 @@ class StableDiffusionProcessing:
         self.hdr_color_picker=hdr_color_picker
         self.hdr_tint_ratio=hdr_tint_ratio
         # globals
-        self.scheduled_prompt: bool = False
-        self.prompt_embeds = []
-        self.positive_pooleds = []
-        self.negative_embeds = []
-        self.negative_pooleds = []
+        self.embedder = None
+        # self.scheduled_prompt: bool = False
+        # self.prompt_embeds = []
+        # self.positive_pooleds = []
+        # self.negative_embeds = []
+        # self.negative_pooleds = []
 
     @property
     def sd_model(self):
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index cc814f379..42a6bcbbd 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -7,6 +7,7 @@ from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsPr
 from transformers import PreTrainedTokenizer
 from modules import shared, prompt_parser, devices, sd_models
 from modules.prompt_parser_xhinker import get_weighted_text_embeddings_sd15, get_weighted_text_embeddings_sdxl_2p, get_weighted_text_embeddings_sd3, get_weighted_text_embeddings_flux1
+from modules.processing_helpers import fix_prompts
 
 debug_enabled = os.environ.get('SD_PROMPT_DEBUG', None)
 debug = shared.log.trace if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -17,6 +18,131 @@ token_type = None # used by helper get_tokens
 cache = {}
 
 
+def prompt_compatible():
+    if (
+            'StableDiffusion' not in shared.sd_model.__class__.__name__ and
+            'DemoFusion' not in shared.sd_model.__class__.__name__ and
+            'StableCascade' not in shared.sd_model.__class__.__name__ and
+            'Flux' not in shared.sd_model.__class__.__name__
+    ):
+        shared.log.warning(f"Prompt parser not supported: {shared.sd_model.__class__.__name__}")
+        return False
+    return True
+
+
+def prepare_model():
+    pipe = shared.sd_model
+    if shared.opts.diffusers_offload_mode == "balanced":
+        pipe = sd_models.apply_balanced_offload(pipe)
+    elif hasattr(pipe, "maybe_free_model_hooks"):
+        pipe.maybe_free_model_hooks()
+        devices.torch_gc()
+    return pipe
+
+
+class PromptEmbedder:
+    def __init__(self, prompts, negative_prompts, clip_skip, p):
+        t0 = time.time()
+        # self.prompts, self.negative_prompts, _, _ = fix_prompts(prompts, negative_prompts, None, None)
+        self.prompts = prompts
+        self.negative_prompts = negative_prompts
+        self.batchsize = len(self.prompts)
+        self.allsame = self.compare_prompts()  # collapses batched prompts to single prompt if same
+        self.steps = p.steps
+        self.clip_skip = clip_skip
+        self.prompt_embeds = [[]] * self.batchsize
+        self.positive_pooleds = [[]] * self.batchsize
+        self.negative_embeds = [[]] * self.batchsize
+        self.negative_pooleds = [[]] * self.batchsize
+        self.positive_schedule = None
+        self.negative_schedule = None
+        self.scheduled_prompt = False
+        pipe = prepare_model()
+        # per prompt in batch
+        for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts)):
+            self.prepare_schedule(prompt, negative_prompt)
+            if self.scheduled_prompt:
+                self.scheduled_encode(pipe, batchidx)
+            else:
+                self.encode(pipe, prompt, negative_prompt, batchidx)
+        if self.allsame:
+            self.duplicate_embeds()
+        debug(f"Prompt encode: time={(time.time() - t0):.3f}")
+
+    def compare_prompts(self):
+        same = (self.prompts == [self.prompts[0]] * len(self.prompts) and
+                self.negative_prompts == [self.negative_prompts[0]] * len(self.negative_prompts))
+        if same:
+            self.prompts = [self.prompts[0]]
+            self.negative_prompts = [self.negative_prompts[0]]
+        return same
+
+    def prepare_schedule(self, prompt, negative_prompt):
+        self.positive_schedule, scheduled = get_prompt_schedule(prompt, self.steps)
+        self.negative_schedule, neg_scheduled = get_prompt_schedule(negative_prompt, self.steps)
+        self.scheduled_prompt = scheduled or neg_scheduled
+
+    def scheduled_encode(self, pipe, batchidx):
+        prompt_dict = {}
+        for i in range(max(len(self.positive_schedule), len(self.negative_schedule))):
+            positive_prompt = self.positive_schedule[i % len(self.positive_schedule)]
+            negative_prompt = self.negative_schedule[i % len(self.negative_schedule)]
+            # skip repeated scheduled subprompts
+            idx = prompt_dict.get(positive_prompt+negative_prompt)
+            if idx is not None:
+                self.extend_embeds(batchidx, idx)
+                continue
+            self.encode(pipe, positive_prompt, negative_prompt, batchidx)
+            prompt_dict[positive_prompt+negative_prompt] = i
+
+    def extend_embeds(self, batchidx, idx):
+        self.prompt_embeds[batchidx].append(self.prompt_embeds[batchidx][idx])
+        self.negative_embeds[batchidx].append(self.negative_embeds[batchidx][idx])
+        if len(self.positive_pooleds[batchidx]) > 0:
+            self.positive_pooleds[batchidx].append(self.positive_pooleds[batchidx][idx])
+        if len(self.negative_pooleds[batchidx]) > 0:
+            self.negative_pooleds[batchidx].append(self.negative_pooleds[batchidx][idx])
+
+    def duplicate_embeds(self):
+        self.prompt_embeds = self.prompt_embeds[0] * self.batchsize
+        self.positive_pooleds = self.positive_pooleds[0] * self.batchsize
+        self.negative_embeds = self.negative_embeds[0] * self.batchsize
+        self.negative_pooleds = self.negative_pooleds[0] * self.batchsize
+
+    def encode(self, pipe, positive_prompt, negative_prompt, batchidx):
+        if shared.opts.prompt_attention == "xhinker parser" or 'Flux' in pipe.__class__.__name__:
+            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(
+                pipe, positive_prompt, negative_prompt, self.clip_skip)
+        else:
+            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(
+                pipe, positive_prompt, negative_prompt, self.clip_skip)
+        if prompt_embed is not None:
+            self.prompt_embeds[batchidx].append(prompt_embed)
+        if negative_embed is not None:
+            self.negative_embeds[batchidx].append(negative_embed)
+        if positive_pooled is not None:
+            self.positive_pooleds[batchidx].append(positive_pooled)
+        if negative_pooled is not None:
+            self.negative_pooleds[batchidx].append(negative_pooled)
+
+        if debug_enabled:
+            get_tokens('positive', positive_prompt)
+            get_tokens('negative', negative_prompt)
+        pipe = prepare_model()
+
+    def __call__(self, key, step=0):
+        batch = getattr(self, key)
+        res = []
+        for embed in batch:
+            if len(embed) == 0:
+                return None
+            if len(embed) == 1:
+                res.append(embed[0])
+            else:
+                res.append(embed[step])
+        return torch.stack(res)
+
+
 def compel_hijack(self, token_ids: torch.Tensor, attention_mask: typing.Optional[torch.Tensor] = None) -> torch.Tensor:
     if not devices.same_device(self.text_encoder.device, devices.device):
         sd_models.move_model(self.text_encoder, devices.device)

From f3442abc929c966581a48e74cb4e782bfda2a39d Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Sat, 26 Oct 2024 18:47:11 -0500
Subject: [PATCH 002/119] Prompt LRU Cache

---
 modules/extra_networks.py          |  2 +-
 modules/prompt_parser_diffusers.py | 44 +++++++++++++++++++++++++++---
 modules/shared.py                  |  3 +-
 3 files changed, 43 insertions(+), 6 deletions(-)

diff --git a/modules/extra_networks.py b/modules/extra_networks.py
index a574e8469..673549b6b 100644
--- a/modules/extra_networks.py
+++ b/modules/extra_networks.py
@@ -102,8 +102,8 @@ def activate(p, extra_network_data, step=0):
             except Exception as e:
                 errors.display(e, f"Activating network: type={extra_network_name}")
 
+    p.extra_network_data = extra_network_data
     if stepwise:
-        p.extra_network_data = extra_network_data
         shared.opts.data['lora_functional'] = functional
 
 
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 42a6bcbbd..ecf0cebd5 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -3,11 +3,11 @@ import math
 import time
 import typing
 import torch
+from collections import OrderedDict
 from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
 from transformers import PreTrainedTokenizer
 from modules import shared, prompt_parser, devices, sd_models
 from modules.prompt_parser_xhinker import get_weighted_text_embeddings_sd15, get_weighted_text_embeddings_sdxl_2p, get_weighted_text_embeddings_sd3, get_weighted_text_embeddings_flux1
-from modules.processing_helpers import fix_prompts
 
 debug_enabled = os.environ.get('SD_PROMPT_DEBUG', None)
 debug = shared.log.trace if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -15,7 +15,7 @@ debug('Trace: PROMPT')
 orig_encode_token_ids_to_embeddings = EmbeddingsProvider._encode_token_ids_to_embeddings # pylint: disable=protected-access
 token_dict = None # used by helper get_tokens
 token_type = None # used by helper get_tokens
-cache = {}
+cache = OrderedDict()
 
 
 def prompt_compatible():
@@ -57,6 +57,9 @@ class PromptEmbedder:
         self.positive_schedule = None
         self.negative_schedule = None
         self.scheduled_prompt = False
+        earlyout = self.checkcache(p)
+        if earlyout:
+            return
         pipe = prepare_model()
         # per prompt in batch
         for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts)):
@@ -66,8 +69,41 @@ class PromptEmbedder:
             else:
                 self.encode(pipe, prompt, negative_prompt, batchidx)
         if self.allsame:
-            self.duplicate_embeds()
+            self.fix_batch_embeds()
         debug(f"Prompt encode: time={(time.time() - t0):.3f}")
+        self.checkcache(p)
+
+    def checkcache(self, p):
+        if shared.opts.sd_textencoder_cache_size == 0:
+            return False
+        def flatten(xss):
+            return [x for xs in xss for x in xs]
+
+        # unpack EN data in case of TE LoRA
+        en_data = p.extra_network_data
+        en_data = [idx.items for item in en_data.values() for idx in item]
+        key = str([self.prompts, self.negative_prompts, self.batchsize, self.clip_skip, self.steps, en_data])
+        item = cache.get(key)
+        if not item:
+            if not any([flatten(emb) for emb in [self.prompt_embeds,
+                                                 self.negative_embeds,
+                                                 self.positive_pooleds,
+                                                 self.negative_pooleds]]):
+                return False
+            else:
+                cache[key] = {'prompt_embeds': self.prompt_embeds,
+                              'negative_embeds': self.negative_embeds,
+                              'positive_pooleds': self.positive_pooleds,
+                              'negative_pooleds': self.negative_pooleds,
+                              }
+                debug(f"Prompt cache: Adding {key}")
+                while len(cache) > int(shared.opts.sd_textencoder_cache_size):
+                    cache.popitem(last=False)
+        if item:
+            self.__dict__.update(cache[key])
+            cache.move_to_end(key)
+            debug(f"Prompt cache: Retrieving {key}")
+            return True
 
     def compare_prompts(self):
         same = (self.prompts == [self.prompts[0]] * len(self.prompts) and
@@ -103,7 +139,7 @@ class PromptEmbedder:
         if len(self.negative_pooleds[batchidx]) > 0:
             self.negative_pooleds[batchidx].append(self.negative_pooleds[batchidx][idx])
 
-    def duplicate_embeds(self):
+    def fix_batch_embeds(self):
         self.prompt_embeds = self.prompt_embeds[0] * self.batchsize
         self.positive_pooleds = self.positive_pooleds[0] * self.batchsize
         self.negative_embeds = self.negative_embeds[0] * self.batchsize
diff --git a/modules/shared.py b/modules/shared.py
index f7be44390..4622f1d2c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -435,7 +435,8 @@ options_templates.update(options_section(('sd', "Execution & Models"), {
     "sd_model_dict": OptionInfo('None', "Use separate base dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_autoload": OptionInfo(True, "Model autoload on start"),
     "sd_checkpoint_autodownload": OptionInfo(True, "Model auto-download on demand"),
-    "sd_textencoder_cache": OptionInfo(True, "Cache text encoder results"),
+    "sd_textencoder_cache": OptionInfo(True, "Cache text encoder results", gr.Checkbox, {"visible": False}),
+    "sd_textencoder_cache_size": OptionInfo(4, "Text encoder results LRU cache size", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "stream_load": OptionInfo(False, "Load models using stream loading method", gr.Checkbox, {"visible": not native }),
     "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
     "prompt_mean_norm": OptionInfo(False, "Prompt attention normalization", gr.Checkbox),

From 39dfa9cbdbe147f8cc0698cab955b55467ca1918 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Mon, 28 Oct 2024 22:32:26 -0500
Subject: [PATCH 003/119] Scheduling and cleanup

---
 modules/processing_args.py         |   3 +-
 modules/processing_callbacks.py    |  16 +--
 modules/prompt_parser_diffusers.py | 178 +++++------------------------
 3 files changed, 39 insertions(+), 158 deletions(-)

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 8dd47bd87..34dd97a1c 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -117,7 +117,6 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
         'Flux' in model.__class__.__name__
     ):
         try:
-            # prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, steps=steps, clip_skip=clip_skip)
             p.embedder = prompt_parser_diffusers.PromptEmbedder(prompts, negative_prompts, clip_skip, p)
             parser = shared.opts.prompt_attention
         except Exception as e:
@@ -143,7 +142,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
             args['prompt'] = prompts
     if 'negative_prompt' in possible:
         if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and p.embedder is not None:
-            args['negative_prompt_embeds'] = p.embedder('negative_embeds')
+            args['negative_prompt_embeds'] = p.embedder('negative_prompt_embeds')
             if 'StableCascade' in model.__class__.__name__ and p.embedder is not None:
                 args['negative_prompt_embeds_pooled'] = p.embedder('negative_pooleds').unsqueeze(0)
             if 'XL' in model.__class__.__name__ and p.embedder is not None:
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 2584ee796..5c24aead0 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -67,14 +67,14 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
             pipe.set_ip_adapter_scale(ip_adapter_scales)
     if step != getattr(pipe, 'num_timesteps', 0):
         kwargs = processing_correction.correction_callback(p, timestep, kwargs)
-    # if p.scheduled_prompt and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
-    #     try:
-    #         i = (step + 1) % len(p.prompt_embeds)
-    #         kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].expand(kwargs["prompt_embeds"].shape)
-    #         j = (step + 1) % len(p.negative_embeds)
-    #         kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].expand(kwargs["negative_prompt_embeds"].shape)
-    #     except Exception as e:
-    #         shared.log.debug(f"Callback: {e}")
+    if p.embedder is not None:
+        try:
+            if 'prompt_embeds' in kwargs:
+                kwargs["prompt_embeds"] = p.embedder("prompt_embeds", step + 1)
+            if 'negative_prompt_embeds' in kwargs:
+                kwargs["negative_prompt_embeds"] = p.embedder("negative_prompt_embeds", step + 1)
+        except Exception as e:
+            shared.log.debug(f"Callback: {e}")
     if step == int(getattr(pipe, 'num_timesteps', 100) * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
         if "PAG" in shared.sd_model.__class__.__name__:
             pipe._guidance_scale = 1.001 if pipe._guidance_scale > 1 else pipe._guidance_scale  # pylint: disable=protected-access
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index ecf0cebd5..5ba0e8a74 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -2,8 +2,8 @@ import os
 import math
 import time
 import typing
-import torch
 from collections import OrderedDict
+import torch
 from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
 from transformers import PreTrainedTokenizer
 from modules import shared, prompt_parser, devices, sd_models
@@ -43,16 +43,16 @@ def prepare_model():
 class PromptEmbedder:
     def __init__(self, prompts, negative_prompts, clip_skip, p):
         t0 = time.time()
-        # self.prompts, self.negative_prompts, _, _ = fix_prompts(prompts, negative_prompts, None, None)
         self.prompts = prompts
         self.negative_prompts = negative_prompts
         self.batchsize = len(self.prompts)
-        self.allsame = self.compare_prompts()  # collapses batched prompts to single prompt if same
+        self.allsame = self.compare_prompts()  # collapses batched prompts to single prompt if possible
         self.steps = p.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_embeds = [[]] * self.batchsize
+        self.negative_prompt_embeds = [[]] * self.batchsize
         self.negative_pooleds = [[]] * self.batchsize
         self.positive_schedule = None
         self.negative_schedule = None
@@ -68,31 +68,31 @@ class PromptEmbedder:
                 self.scheduled_encode(pipe, batchidx)
             else:
                 self.encode(pipe, prompt, negative_prompt, batchidx)
-        if self.allsame:
-            self.fix_batch_embeds()
-        debug(f"Prompt encode: time={(time.time() - t0):.3f}")
         self.checkcache(p)
+        debug(f"Prompt encode: time={(time.time() - t0):.3f}")
 
     def checkcache(self, p):
         if shared.opts.sd_textencoder_cache_size == 0:
             return False
+
         def flatten(xss):
             return [x for xs in xss for x in xs]
 
         # unpack EN data in case of TE LoRA
         en_data = p.extra_network_data
         en_data = [idx.items for item in en_data.values() for idx in item]
-        key = str([self.prompts, self.negative_prompts, self.batchsize, self.clip_skip, self.steps, en_data])
+        effective_batch = 1 if self.allsame else self.batchsize
+        key = str([self.prompts, self.negative_prompts, effective_batch, self.clip_skip, self.steps, en_data])
         item = cache.get(key)
         if not item:
-            if not any([flatten(emb) for emb in [self.prompt_embeds,
-                                                 self.negative_embeds,
-                                                 self.positive_pooleds,
-                                                 self.negative_pooleds]]):
+            if not any(flatten(emb) for emb in [self.prompt_embeds,
+                                                self.negative_prompt_embeds,
+                                                self.positive_pooleds,
+                                                self.negative_pooleds]):
                 return False
             else:
                 cache[key] = {'prompt_embeds': self.prompt_embeds,
-                              'negative_embeds': self.negative_embeds,
+                              'negative_prompt_embeds': self.negative_prompt_embeds,
                               'positive_pooleds': self.positive_pooleds,
                               'negative_pooleds': self.negative_pooleds,
                               }
@@ -102,6 +102,11 @@ class PromptEmbedder:
         if item:
             self.__dict__.update(cache[key])
             cache.move_to_end(key)
+            if self.allsame and len(self.prompt_embeds) < self.batchsize:  # If current batch larger than cached
+                self.prompt_embeds = [self.prompt_embeds[0]] * self.batchsize
+                self.positive_pooleds = [self.positive_pooleds[0]] * self.batchsize
+                self.negative_prompt_embeds = [self.negative_prompt_embeds[0]] * self.batchsize
+                self.negative_pooleds = [self.negative_pooleds[0]] * self.batchsize
             debug(f"Prompt cache: Retrieving {key}")
             return True
 
@@ -119,7 +124,7 @@ class PromptEmbedder:
         self.scheduled_prompt = scheduled or neg_scheduled
 
     def scheduled_encode(self, pipe, batchidx):
-        prompt_dict = {}
+        prompt_dict = {}  # index cache
         for i in range(max(len(self.positive_schedule), len(self.negative_schedule))):
             positive_prompt = self.positive_schedule[i % len(self.positive_schedule)]
             negative_prompt = self.negative_schedule[i % len(self.negative_schedule)]
@@ -131,20 +136,14 @@ class PromptEmbedder:
             self.encode(pipe, positive_prompt, negative_prompt, batchidx)
             prompt_dict[positive_prompt+negative_prompt] = i
 
-    def extend_embeds(self, batchidx, idx):
+    def extend_embeds(self, batchidx, idx):  # Extends scheduled prompt via index
         self.prompt_embeds[batchidx].append(self.prompt_embeds[batchidx][idx])
-        self.negative_embeds[batchidx].append(self.negative_embeds[batchidx][idx])
+        self.negative_prompt_embeds[batchidx].append(self.negative_prompt_embeds[batchidx][idx])
         if len(self.positive_pooleds[batchidx]) > 0:
             self.positive_pooleds[batchidx].append(self.positive_pooleds[batchidx][idx])
         if len(self.negative_pooleds[batchidx]) > 0:
             self.negative_pooleds[batchidx].append(self.negative_pooleds[batchidx][idx])
 
-    def fix_batch_embeds(self):
-        self.prompt_embeds = self.prompt_embeds[0] * self.batchsize
-        self.positive_pooleds = self.positive_pooleds[0] * self.batchsize
-        self.negative_embeds = self.negative_embeds[0] * self.batchsize
-        self.negative_pooleds = self.negative_pooleds[0] * self.batchsize
-
     def encode(self, pipe, positive_prompt, negative_prompt, batchidx):
         if shared.opts.prompt_attention == "xhinker parser" or 'Flux' in pipe.__class__.__name__:
             prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(
@@ -155,7 +154,7 @@ class PromptEmbedder:
         if prompt_embed is not None:
             self.prompt_embeds[batchidx].append(prompt_embed)
         if negative_embed is not None:
-            self.negative_embeds[batchidx].append(negative_embed)
+            self.negative_prompt_embeds[batchidx].append(negative_embed)
         if positive_pooled is not None:
             self.positive_pooleds[batchidx].append(positive_pooled)
         if negative_pooled is not None:
@@ -169,14 +168,14 @@ class PromptEmbedder:
     def __call__(self, key, step=0):
         batch = getattr(self, key)
         res = []
-        for embed in batch:
-            if len(embed) == 0:
+        for i in range(self.batchsize):
+            if len(batch[i]) == 0:
                 return None
-            if len(embed) == 1:
-                res.append(embed[0])
             else:
-                res.append(embed[step])
-        return torch.stack(res)
+                res.append(batch[i][step])
+                if step != 0:  # For Callback
+                    res.append(batch[i][step])  # Diffusers internally doubles batch dimension
+        return torch.cat(res)
 
 
 def compel_hijack(self, token_ids: torch.Tensor, attention_mask: typing.Optional[torch.Tensor] = None) -> torch.Tensor:
@@ -221,9 +220,9 @@ def insert_parser_highjack(pipename):
         debug("Load Standard Parser hijack")
 
 
-
 insert_parser_highjack("Initialize")
 
+
 # from https://github.com/damian0815/compel/blob/main/src/compel/diffusers_textual_inversion_manager.py
 class DiffusersTextualInversionManager(BaseTextualInversionManager):
     def __init__(self, pipe, tokenizer):
@@ -270,12 +269,6 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
 
 def get_prompt_schedule(prompt, steps):
     t0 = time.time()
-    if shared.native:
-        # TODO prompt scheduling
-        # prompt schedule returns array of prompts which would require that each prompt is fed to the model per-step
-        # prompt scheduling should instead interpolate between each prompt in schedule
-        # this temporarily disables prompt scheduling
-        return [prompt], False
     temp = []
     schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
     if all(x == schedule[0] for x in schedule):
@@ -319,118 +312,6 @@ def get_tokens(msg, prompt):
         debug(f'Prompt tokenizer: type={msg} tokens={token_count} {tokens}')
 
 
-def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, clip_skip: typing.Optional[int] = None):
-    params_match = prompts == cache.get('prompts', None) and negative_prompts == cache.get('negative_prompts', None) and clip_skip == cache.get('clip_skip', None) and steps == cache.get('steps', None)
-    if (
-        'StableDiffusion' not in pipe.__class__.__name__ and
-        'DemoFusion' not in pipe.__class__.__name__ and
-        'StableCascade' not in pipe.__class__.__name__ and
-        'Flux' not in pipe.__class__.__name__
-    ):
-        shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
-        return
-    elif shared.opts.sd_textencoder_cache and cache.get('model_type', None) == shared.sd_model_type and params_match:
-        p.prompt_embeds = cache.get('prompt_embeds', None)
-        p.positive_pooleds = cache.get('positive_pooleds', None)
-        p.negative_embeds = cache.get('negative_embeds', None)
-        p.negative_pooleds = cache.get('negative_pooleds', None)
-        p.scheduled_prompt = cache.get('scheduled_prompt', None)
-        debug("Prompt encode: cached")
-        return
-    else:
-        t0 = time.time()
-        if shared.opts.diffusers_offload_mode == "balanced":
-            pipe = sd_models.apply_balanced_offload(pipe)
-        elif hasattr(pipe, "maybe_free_model_hooks"):
-            pipe.maybe_free_model_hooks()
-            devices.torch_gc()
-
-        prompt_embeds, positive_pooleds, negative_embeds, negative_pooleds = [], [], [], []
-        last_prompt, last_negative = None, None
-        for prompt, negative in zip(prompts, negative_prompts):
-            prompt_embed, positive_pooled, negative_embed, negative_pooled = None, None, None, None
-            if last_prompt == prompt and last_negative == negative:
-                prompt_embeds.append(prompt_embeds[-1])
-                negative_embeds.append(negative_embeds[-1])
-                if len(positive_pooleds) > 0:
-                    positive_pooleds.append(positive_pooleds[-1])
-                if len(negative_pooleds) > 0:
-                    negative_pooleds.append(negative_pooleds[-1])
-                continue
-            positive_schedule, scheduled = get_prompt_schedule(prompt, steps)
-            negative_schedule, neg_scheduled = get_prompt_schedule(negative, steps)
-            p.scheduled_prompt = scheduled or neg_scheduled
-            p.prompt_embeds = []
-            p.positive_pooleds = []
-            p.negative_embeds = []
-            p.negative_pooleds = []
-
-            for i in range(max(len(positive_schedule), len(negative_schedule))):
-                positive_prompt = positive_schedule[i % len(positive_schedule)]
-                negative_prompt = negative_schedule[i % len(negative_schedule)]
-                if shared.opts.prompt_attention == "xhinker parser" or 'Flux' in pipe.__class__.__name__:
-                    prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
-                else:
-                    prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
-                if prompt_embed is not None:
-                    prompt_embeds.append(prompt_embed)
-                if negative_embed is not None:
-                    negative_embeds.append(negative_embed)
-                if positive_pooled is not None:
-                    positive_pooleds.append(positive_pooled)
-                if negative_pooled is not None:
-                    negative_pooleds.append(negative_pooled)
-            last_prompt, last_negative = prompt, negative
-            # TODO prompt scheduling
-            # interpolation should happen here and then we can re-enable prompt scheduling
-            # ive tried simple torch.mean and its not good-enough
-
-        def fix_length(embeds):
-            max_len = max([e.shape[1] for e in embeds if e is not None])
-            for i, e in enumerate(embeds):
-                if e is not None and e.shape[1] < max_len:
-                    expanded = torch.zeros((e.shape[0], max_len, e.shape[2]), device=e.device, dtype=e.dtype)
-                    expanded[:, :e.shape[1], :] = e
-                    embeds[i] = expanded
-            return torch.cat(embeds, dim=0).to(devices.device, dtype=devices.dtype)
-
-        if len(prompt_embeds) > 0:
-            p.prompt_embeds.append(fix_length(prompt_embeds))
-        if len(negative_embeds) > 0:
-            p.negative_embeds.append(fix_length(negative_embeds))
-        if len(positive_pooleds) > 0:
-            p.positive_pooleds.append(fix_length(positive_pooleds))
-        if len(negative_pooleds) > 0:
-            p.negative_pooleds.append(fix_length(negative_pooleds))
-
-        if shared.opts.sd_textencoder_cache and p.batch_size == 1:
-            cache.update({
-                'prompt_embeds': p.prompt_embeds,
-                'negative_embeds': p.negative_embeds,
-                'positive_pooleds': p.positive_pooleds,
-                'negative_pooleds': p.negative_pooleds,
-                'scheduled_prompt': p.scheduled_prompt,
-                'prompts': prompts,
-                'negative_prompts': negative_prompts,
-                'clip_skip': clip_skip,
-                'steps': steps,
-                'model_type': shared.sd_model_type
-            })
-        else:
-            cache.clear()
-        if debug_enabled:
-            get_tokens('positive', prompts[0])
-            get_tokens('negative', negative_prompts[0])
-        if shared.opts.diffusers_offload_mode == "balanced":
-            pipe = sd_models.apply_balanced_offload(pipe)
-        elif hasattr(pipe, "maybe_free_model_hooks"):
-            # text encoder will stay in the vram and cause oom, send everything back to cpu before continuing
-            pipe.maybe_free_model_hooks()
-        debug(f"Prompt encode: time={(time.time() - t0):.3f}")
-        devices.torch_gc()
-        return
-
-
 def normalize_prompt(pairs: list):
     num_words = 0
     total_weight = 0
@@ -516,6 +397,7 @@ def pad_to_same_length(pipe, embeds, empty_embedding_providers=None):
             embeds[i] = embed
     return embeds
 
+
 def split_prompts(prompt, SD3 = False):
     if prompt.find("TE2:") != -1:
         prompt, prompt2 = prompt.split("TE2:")

From 38303f0c6138e01f36fc14e000a7916734069210 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Tue, 29 Oct 2024 21:24:10 -0500
Subject: [PATCH 004/119] Cache unload

---
 modules/sd_models.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index bc293f5fc..11f601260 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1377,8 +1377,9 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
         sd_model.embedding_db.load_textual_inversion_embeddings(force_reload=True)
         timer.record("embeddings")
 
-        from modules.prompt_parser_diffusers import insert_parser_highjack
-        insert_parser_highjack(sd_model.__class__.__name__)
+        from modules import prompt_parser_diffusers
+        prompt_parser_diffusers.insert_parser_highjack(sd_model.__class__.__name__)
+        prompt_parser_diffusers.cache.clear()
 
         set_diffuser_options(sd_model, vae, op, offload=False)
         if shared.opts.nncf_compress_weights and not ('Model' in shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx"):

From 3932d5fd1b2f69a119217871bf6b286321fdcac5 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Wed, 30 Oct 2024 23:24:50 -0500
Subject: [PATCH 005/119] Move embedder object, cleanup stepwise lora

---
 modules/extra_networks.py          |  1 +
 modules/processing_args.py         | 36 +++++++++++++++---------------
 modules/processing_callbacks.py    | 11 +++++----
 modules/prompt_parser_diffusers.py |  5 +++--
 4 files changed, 27 insertions(+), 26 deletions(-)

diff --git a/modules/extra_networks.py b/modules/extra_networks.py
index 673549b6b..b464bd349 100644
--- a/modules/extra_networks.py
+++ b/modules/extra_networks.py
@@ -104,6 +104,7 @@ def activate(p, extra_network_data, step=0):
 
     p.extra_network_data = extra_network_data
     if stepwise:
+        p.stepwise_lora = True
         shared.opts.data['lora_functional'] = functional
 
 
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 34dd97a1c..5cdf290a7 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -117,7 +117,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
         'Flux' in model.__class__.__name__
     ):
         try:
-            p.embedder = prompt_parser_diffusers.PromptEmbedder(prompts, negative_prompts, clip_skip, p)
+            prompt_parser_diffusers.embedder = prompt_parser_diffusers.PromptEmbedder(prompts, negative_prompts, steps, clip_skip, p)
             parser = shared.opts.prompt_attention
         except Exception as e:
             shared.log.error(f'Prompt parser encode: {e}')
@@ -128,27 +128,27 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if 'prompt' in possible:
         if 'OmniGen' in model.__class__.__name__:
             prompts = [p.replace('|image|', '<|image_1|>') for p in prompts]
-        if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and p.embedder is not None:
-            args['prompt_embeds'] = p.embedder('prompt_embeds')
+        if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_parser_diffusers.embedder is not None:
+            args['prompt_embeds'] = prompt_parser_diffusers.embedder('prompt_embeds')
             if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
-                args['prompt_embeds_pooled'] = p.embedder('positive_pooleds').unsqueeze(0)
-            elif 'XL' in model.__class__.__name__ and p.embedder is not None:
-                args['pooled_prompt_embeds'] = p.embedder('positive_pooleds')
-            elif 'StableDiffusion3' in model.__class__.__name__ and p.embedder is not None:
-                args['pooled_prompt_embeds'] = p.embedder('positive_pooleds')
-            elif 'Flux' in model.__class__.__name__ and p.embedder is not None:
-                args['pooled_prompt_embeds'] = p.embedder('positive_pooleds')
+                args['prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('positive_pooleds').unsqueeze(0)
+            elif 'XL' in model.__class__.__name__ and prompt_parser_diffusers.embedder is not None:
+                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+            elif 'StableDiffusion3' in model.__class__.__name__ and prompt_parser_diffusers.embedder is not None:
+                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+            elif 'Flux' in model.__class__.__name__ and prompt_parser_diffusers.embedder is not None:
+                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
         else:
             args['prompt'] = prompts
     if 'negative_prompt' in possible:
-        if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and p.embedder is not None:
-            args['negative_prompt_embeds'] = p.embedder('negative_prompt_embeds')
-            if 'StableCascade' in model.__class__.__name__ and p.embedder is not None:
-                args['negative_prompt_embeds_pooled'] = p.embedder('negative_pooleds').unsqueeze(0)
-            if 'XL' in model.__class__.__name__ and p.embedder is not None:
-                args['negative_pooled_prompt_embeds'] = p.embedder('negative_pooleds')
-            if 'StableDiffusion3' in model.__class__.__name__ and p.embedder is not None:
-                args['negative_pooled_prompt_embeds'] = p.embedder('negative_pooleds')
+        if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and prompt_parser_diffusers.embedder is not None:
+            args['negative_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_prompt_embeds')
+            if 'StableCascade' in model.__class__.__name__ and prompt_parser_diffusers.embedder is not None:
+                args['negative_prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('negative_pooleds').unsqueeze(0)
+            if 'XL' in model.__class__.__name__ and prompt_parser_diffusers.embedder is not None:
+                args['negative_pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_pooleds')
+            if 'StableDiffusion3' in model.__class__.__name__ and prompt_parser_diffusers.embedder is not None:
+                args['negative_pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_pooleds')
         else:
             if 'PixArtSigmaPipeline' in model.__class__.__name__: # pixart-sigma pipeline throws list-of-list for negative prompt
                 args['negative_prompt'] = negative_prompts[0]
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 5c24aead0..3ace64ed8 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -3,8 +3,7 @@ import os
 import time
 import torch
 import numpy as np
-from modules import shared, processing_correction, extra_networks, timer
-
+from modules import shared, processing_correction, extra_networks, timer, prompt_parser_diffusers
 
 p = None
 debug_callback = shared.log.trace if os.environ.get('SD_CALLBACK_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -49,7 +48,7 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
             if shared.state.interrupted or shared.state.skipped:
                 raise AssertionError('Interrupted...')
             time.sleep(0.1)
-    if hasattr(p, "extra_network_data"):
+    if hasattr(p, "stepwise_lora"):
         extra_networks.activate(p, p.extra_network_data, step=step)
     if latents is None:
         return kwargs
@@ -67,12 +66,12 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
             pipe.set_ip_adapter_scale(ip_adapter_scales)
     if step != getattr(pipe, 'num_timesteps', 0):
         kwargs = processing_correction.correction_callback(p, timestep, kwargs)
-    if p.embedder is not None:
+    if prompt_parser_diffusers.embedder is not None:
         try:
             if 'prompt_embeds' in kwargs:
-                kwargs["prompt_embeds"] = p.embedder("prompt_embeds", step + 1)
+                kwargs["prompt_embeds"] = prompt_parser_diffusers.embedder("prompt_embeds", step + 1)
             if 'negative_prompt_embeds' in kwargs:
-                kwargs["negative_prompt_embeds"] = p.embedder("negative_prompt_embeds", step + 1)
+                kwargs["negative_prompt_embeds"] = prompt_parser_diffusers.embedder("negative_prompt_embeds", step + 1)
         except Exception as e:
             shared.log.debug(f"Callback: {e}")
     if step == int(getattr(pipe, 'num_timesteps', 100) * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 5ba0e8a74..678649a66 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -16,6 +16,7 @@ orig_encode_token_ids_to_embeddings = EmbeddingsProvider._encode_token_ids_to_em
 token_dict = None # used by helper get_tokens
 token_type = None # used by helper get_tokens
 cache = OrderedDict()
+embedder = None
 
 
 def prompt_compatible():
@@ -41,13 +42,13 @@ def prepare_model():
 
 
 class PromptEmbedder:
-    def __init__(self, prompts, negative_prompts, clip_skip, p):
+    def __init__(self, prompts, negative_prompts, steps, clip_skip, p):
         t0 = time.time()
         self.prompts = prompts
         self.negative_prompts = negative_prompts
         self.batchsize = len(self.prompts)
         self.allsame = self.compare_prompts()  # collapses batched prompts to single prompt if possible
-        self.steps = p.steps
+        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

From da9550dd68cb94fbed64c5cfee5f42e4290870e3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 2 Nov 2024 14:00:33 -0400
Subject: [PATCH 006/119] print model components for safetensors load

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/{load_unet.py => load-unet.py} |  0
 cli/model-keys.py                  | 83 ++++++++++++++++++++++++++++++
 modules/model_tools.py             | 23 ++++++++-
 modules/sd_detect.py               | 10 +++-
 wiki                               |  2 +-
 5 files changed, 114 insertions(+), 4 deletions(-)
 rename cli/{load_unet.py => load-unet.py} (100%)
 create mode 100755 cli/model-keys.py

diff --git a/cli/load_unet.py b/cli/load-unet.py
similarity index 100%
rename from cli/load_unet.py
rename to cli/load-unet.py
diff --git a/cli/model-keys.py b/cli/model-keys.py
new file mode 100755
index 000000000..df66179b3
--- /dev/null
+++ b/cli/model-keys.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+import os
+import sys
+from rich import print as pprint
+
+
+def has(obj, attr, *args):
+    import functools
+    if not isinstance(obj, dict):
+        return False
+    def _getattr(obj, attr):
+        return obj.get(attr, args) if isinstance(obj, dict) else False
+    return functools.reduce(_getattr, [obj] + attr.split('.'))
+
+
+def remove_entries_after_depth(d, depth, current_depth=0):
+    if current_depth >= depth:
+        return None
+    if isinstance(d, dict):
+        return {k: remove_entries_after_depth(v, depth, current_depth + 1) for k, v in d.items() if remove_entries_after_depth(v, depth, current_depth + 1) is not None}
+    return d
+
+
+def list_to_dict(flat_list):
+    result_dict = {}
+    for item in flat_list:
+        keys = item.split('.')
+        d = result_dict
+        for key in keys[:-1]:
+            d = d.setdefault(key, {})
+        d[keys[-1]] = None
+    return result_dict
+
+
+def guess_dct(dct: dict):
+    # if has(dct, 'model.diffusion_model.input_blocks') and has(dct, 'model.diffusion_model.label_emb'):
+    #    return 'sdxl'
+    if has(dct, 'model.diffusion_model.input_blocks') and len(list(has(dct, 'model.diffusion_model.input_blocks'))) == 12:
+        return 'sd15'
+    if has(dct, 'model.diffusion_model.input_blocks') and len(list(has(dct, 'model.diffusion_model.input_blocks'))) == 9:
+        return 'sdxl'
+    if has(dct, 'model.diffusion_model.joint_blocks') and len(list(has(dct, 'model.diffusion_model.joint_blocks'))) == 24:
+        return 'sd35-medium'
+    if has(dct, 'model.diffusion_model.joint_blocks') and len(list(has(dct, 'model.diffusion_model.joint_blocks'))) == 38:
+        return 'sd35-large'
+    if has(dct, 'model.diffusion_model.double_blocks') and len(list(has(dct, 'model.diffusion_model.double_blocks'))) == 19:
+        return 'flux-dev'
+    return None
+
+
+def read_keys(fn):
+    if not fn.lower().endswith(".safetensors"):
+        return
+    from safetensors.torch import safe_open
+    keys = []
+    try:
+        with safe_open(fn, framework="pt", device="cpu") as f:
+            keys = f.keys()
+    except Exception as e:
+        pprint(e)
+    dct = list_to_dict(keys)
+    pprint(f'file: {fn}')
+    pprint(remove_entries_after_depth(dct, 3))
+    pprint(remove_entries_after_depth(dct, 6))
+    guess = guess_dct(dct)
+    pprint(f'guess: {guess}')
+    return keys
+
+
+def main():
+    if len(sys.argv) == 0:
+        print('metadata:', 'no files specified')
+    for fn in sys.argv:
+        if os.path.isfile(fn):
+            read_keys(fn)
+        elif os.path.isdir(fn):
+            for root, _dirs, files in os.walk(fn):
+                for file in files:
+                    read_keys(os.path.join(root, file))
+
+if __name__ == '__main__':
+    sys.argv.pop(0)
+    main()
diff --git a/modules/model_tools.py b/modules/model_tools.py
index 1212da244..42cc9cebe 100644
--- a/modules/model_tools.py
+++ b/modules/model_tools.py
@@ -5,13 +5,32 @@ import safetensors.torch
 from modules import shared, devices, model_quant
 
 
+def remove_entries_after_depth(d, depth, current_depth=0):
+    if current_depth >= depth:
+        return None
+    if isinstance(d, dict):
+        return {k: remove_entries_after_depth(v, depth, current_depth + 1) for k, v in d.items() if remove_entries_after_depth(v, depth, current_depth + 1) is not None}
+    return d
+
+
+def list_to_dict(flat_list):
+    result_dict = {}
+    for item in flat_list:
+        keys = item.split('.')
+        d = result_dict
+        for key in keys[:-1]:
+            d = d.setdefault(key, {})
+        d[keys[-1]] = None
+    return result_dict
+
+
 def get_safetensor_keys(filename):
     keys = []
     try:
         with safetensors.torch.safe_open(filename, framework="pt", device="cpu") as f:
             keys = f.keys()
-    except Exception as e:
-        shared.log.error(f'Load dict: path="{filename}" {e}')
+    except Exception:
+        pass
     return keys
 
 
diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 148e788d5..b191b04f4 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -1,7 +1,8 @@
 import os
+import time
 import torch
 import diffusers
-from modules import shared, shared_items, devices, errors
+from modules import shared, shared_items, devices, errors, model_tools
 
 
 debug_load = os.environ.get('SD_LOAD_DEBUG', None)
@@ -103,6 +104,13 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
             pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline
             if not quiet:
                 shared.log.info(f'Autodetect {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB')
+                t0 = time.time()
+                keys = model_tools.get_safetensor_keys(f)
+                if keys is not None:
+                    modules = model_tools.list_to_dict(keys)
+                    modules = model_tools.remove_entries_after_depth(modules, 3)
+                    t1 = time.time()
+                    shared.log.debug(f'Autodetect {op}: modules={modules} time={t1-t0:.2f}')
         except Exception as e:
             shared.log.error(f'Autodetect {op}: file="{f}" {e}')
             if debug_load:
diff --git a/wiki b/wiki
index b36c2e1a4..2b88684b1 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit b36c2e1a4cb85338f20061639d4130255d10bf48
+Subproject commit 2b88684b1dab51ca02625e7046c7dcf79a8ba877

From 430902c9481b795cee2e643e79e30c66b460fedd Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 2 Nov 2024 14:02:29 -0400
Subject: [PATCH 007/119] module print logging

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

diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index b191b04f4..567031b71 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -110,7 +110,7 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
                     modules = model_tools.list_to_dict(keys)
                     modules = model_tools.remove_entries_after_depth(modules, 3)
                     t1 = time.time()
-                    shared.log.debug(f'Autodetect {op}: modules={modules} time={t1-t0:.2f}')
+                    shared.log.debug(f'Autodetect modules: {modules} time={t1-t0:.2f}')
         except Exception as e:
             shared.log.error(f'Autodetect {op}: file="{f}" {e}')
             if debug_load:

From 358f1897a88274906bea69daf7879d0132f9a4cc Mon Sep 17 00:00:00 2001
From: Disty0 <disty@disty.xyz>
Date: Sun, 3 Nov 2024 01:04:49 +0300
Subject: [PATCH 008/119] Don't uninstall flash-attn

---
 installer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/installer.py b/installer.py
index 492961439..da1817508 100644
--- a/installer.py
+++ b/installer.py
@@ -595,11 +595,11 @@ def install_rocm_zluda():
             install(ort_package, 'onnxruntime-training')
 
         if installed("torch") and device is not None:
-            if 'Flash attention' in opts.get('sdp_options'):
+            if 'Flash attention' in opts.get('sdp_options', ''):
                 if not installed('flash-attn'):
                     install(rocm.get_flash_attention_command(device), reinstall=True)
-            elif not args.experimental:
-                uninstall('flash-attn')
+            #elif not args.experimental:
+            #    uninstall('flash-attn')
 
         if device is not None and rocm.version != "6.2" and rocm.version == rocm.version_torch and rocm.get_blaslt_enabled():
             log.debug(f'ROCm hipBLASLt: arch={device.name} available={device.blaslt_supported}')

From f63464ec97c38f7c064e24e521e5097e06b8473f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 04:46:35 -0500
Subject: [PATCH 009/119] fix k-diff

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 scripts/k_diff.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/scripts/k_diff.py b/scripts/k_diff.py
index 757f2ea2d..354df5d4b 100644
--- a/scripts/k_diff.py
+++ b/scripts/k_diff.py
@@ -7,7 +7,6 @@ from modules import scripts, processing, shared, sd_models
 class Script(scripts.Script):
     supported_models = ['sd', 'sdxl']
     orig_pipe = None
-    library = None
 
     def title(self):
         return 'K-Diffusion'
@@ -34,6 +33,8 @@ class Script(scripts.Script):
         _step = d['i']
 
     def run(self, p: processing.StableDiffusionProcessing, sampler: str): # pylint: disable=arguments-differ
+        if sampler is None or len(sampler) == 0:
+            return None
         if shared.sd_model_type not in self.supported_models:
             shared.log.warning(f'K-Diffusion: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={self.supported_models}')
             return None
@@ -44,12 +45,16 @@ class Script(scripts.Script):
             cls = diffusers.pipelines.StableDiffusionXLKDiffusionPipeline
         if cls is None:
             return None
+        from modules import sd_samplers_kdiffusion
+
+        sampler_fn = getattr(sd_samplers_kdiffusion.k_sampling, f'sample_{sampler}', None)
+        if sampler_fn is None:
+            shared.log.warning(f'K-Diffusion: sampler={sampler} not found')
+            return None
+
         self.orig_pipe = shared.sd_model
         shared.sd_model = sd_models.switch_pipe(cls, shared.sd_model)
-        sampler = 'sample_' + sampler
-
-        sampling = getattr(self.library, "sampling", None)
-        shared.sd_model.sampler = getattr(sampling, sampler)
+        shared.sd_model.sampler = sampler_fn
 
         params = inspect.signature(shared.sd_model.sampler).parameters.values()
         params = {param.name: param.default for param in params if param.default != inspect.Parameter.empty}

From 766a74267b692a366fbf3c35eb83b189319b97f6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 05:05:23 -0500
Subject: [PATCH 010/119] handle lora bad tags

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .../Lora/ui_extra_networks_lora.py            | 71 ++++++++++---------
 wiki                                          |  2 +-
 2 files changed, 39 insertions(+), 34 deletions(-)

diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index 170c3c7d3..4220b8e02 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -5,7 +5,7 @@ import networks
 from modules import shared, ui_extra_networks
 
 
-debug = os.environ.get('SD_LOAD_DEBUG', None) is not None
+debug = os.environ.get('SD_LORA_DEBUG', None) is not None
 
 
 class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
@@ -16,34 +16,18 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         networks.list_available_networks()
 
-    def create_item(self, name):
-        l = networks.available_networks.get(name)
-        if l is None:
-            shared.log.warning(f'Networks: type=lora registered={len(list(networks.available_networks))} file="{name}" not registered')
-            return None
+    def get_tags(self, l, info):
+        tags = {}
         try:
-            # path, _ext = os.path.splitext(l.filename)
-            name = os.path.splitext(os.path.relpath(l.filename, shared.cmd_opts.lora_dir))[0]
-            item = {
-                "type": 'Lora',
-                "name": name,
-                "filename": l.filename,
-                "hash": l.shorthash,
-                "prompt": json.dumps(f" <lora:{l.get_alias()}:{shared.opts.extra_networks_default_multiplier}>"),
-                "metadata": json.dumps(l.metadata, indent=4) if l.metadata else None,
-                "mtime": os.path.getmtime(l.filename),
-                "size": os.path.getsize(l.filename),
-                "version": l.sd_version,
-            }
-            info = self.find_info(l.filename)
-
-            tags = {}
             if l.metadata is not None:
                 modelspec_tags = l.metadata.get('modelspec.tags', {})
                 possible_tags = l.metadata.get('ss_tag_frequency', {}) # tags from model metedata
-                possible_tags.update(modelspec_tags)
                 if isinstance(possible_tags, str):
                     possible_tags = {}
+                if isinstance(modelspec_tags, str):
+                    modelspec_tags = {}
+                if len(list(modelspec_tags)) > 0:
+                    possible_tags.update(modelspec_tags)
                 for k, v in possible_tags.items():
                     words = k.split('_', 1) if '_' in k else [v, k]
                     words = [str(w).replace('.json', '') for w in words]
@@ -80,20 +64,41 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
                 tag = tag.strip().lower()
                 if tag not in tags:
                     tags[tag] = 0
+        except Exception:
+            pass
+        bad_chars = [';', ':', '<', ">", "*", '?', '\'', '\"', '(', ')', '[', ']', '{', '}', '\\', '/']
+        clean_tags = {}
+        for k, v in tags.items():
+            tag = ''.join(i for i in k if i not in bad_chars).strip()
+            clean_tags[tag] = v
 
-            bad_chars = [';', ':', '<', ">", "*", '?', '\'', '\"', '(', ')', '[', ']', '{', '}', '\\', '/']
-            clean_tags = {}
-            for k, v in tags.items():
-                tag = ''.join(i for i in k if i not in bad_chars).strip()
-                clean_tags[tag] = v
-
-            clean_tags.pop('img', None)
-            clean_tags.pop('dataset', None)
+        clean_tags.pop('img', None)
+        clean_tags.pop('dataset', None)
+        return clean_tags
 
+    def create_item(self, name):
+        l = networks.available_networks.get(name)
+        if l is None:
+            shared.log.warning(f'Networks: type=lora registered={len(list(networks.available_networks))} file="{name}" not registered')
+            return None
+        try:
+            # path, _ext = os.path.splitext(l.filename)
+            name = os.path.splitext(os.path.relpath(l.filename, shared.cmd_opts.lora_dir))[0]
+            item = {
+                "type": 'Lora',
+                "name": name,
+                "filename": l.filename,
+                "hash": l.shorthash,
+                "prompt": json.dumps(f" <lora:{l.get_alias()}:{shared.opts.extra_networks_default_multiplier}>"),
+                "metadata": json.dumps(l.metadata, indent=4) if l.metadata else None,
+                "mtime": os.path.getmtime(l.filename),
+                "size": os.path.getsize(l.filename),
+                "version": l.sd_version,
+            }
+            info = self.find_info(l.filename)
             item["info"] = info
             item["description"] = self.find_description(l.filename, info) # use existing info instead of double-read
-            item["tags"] = clean_tags
-
+            item["tags"] = self.get_tags(l, info)
             return item
         except Exception as e:
             shared.log.error(f'Networks: type=lora file="{name}" {e}')
diff --git a/wiki b/wiki
index 2b88684b1..4a90ecebd 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 2b88684b1dab51ca02625e7046c7dcf79a8ba877
+Subproject commit 4a90ecebda962316705e4a630ae28d636a6e9c68

From a5f112172d52395cf3ab7da888c6991d61627cae Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 05:37:08 -0500
Subject: [PATCH 011/119] fix t2v

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    | 41 ++++++++++++++++++---------------
 modules/processing_diffusers.py |  3 ++-
 modules/sd_detect.py            |  2 +-
 scripts/text2video.py           |  2 +-
 4 files changed, 26 insertions(+), 22 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bb56efec5..27d0d3076 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,36 +1,37 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-02
+## Update for 2024-11-03
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
 
-- Docs:
+- Docs:  
   - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
-    since changelog is the best up-to-date source of info
-    go to system -> changelog and search/highligh/navigate directly in UI!
-- SD3: ControlNets:
-  - *InstantX Canny, Pose, Depth, Tile*
-  - *Alimama Inpainting, SoftEdge*
+    since changelog is the best up-to-date source of info  
+    go to system -> changelog and search/highligh/navigate directly in UI!  
+- SD3: ControlNets:  
+  - *InstantX Canny, Pose, Depth, Tile*  
+  - *Alimama Inpainting, SoftEdge*  
   - *note*: that just like with FLUX.1 or any large model, ControlNet are also large and can push your system over the limit  
     e.g. SD3 controlnets vary from 1GB to over 4GB in size  
-- SD3: all-in-one safetensors
-  - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)
+- SD3: all-in-one safetensors  
+  - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
   - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
-- UI:
-  - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)
-  - add show networks on startup setting
+- UI:  
+  - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
+  - add show networks on startup setting  
   - better mapping of networks previews  
   - optimize networks display load  
-- XYZ grid:
-  - optional per-image time benchmark info
-- CLI:
+- XYZ grid:  
+  - optional per-image time benchmark info  
+- CLI:  
   - refactor command line params  
     run `webui.sh`/`webui.bat` with `--help` to see all options  
-- Other:
-  - Repo: move screenshots to GH pages
-  - Update requirements
-- Fixes:
+- Other:  
+  - Model loader: Report modules included in safetensors when attempting to load a model  
+  - Repo: move screenshots to GH pages  
+  - Requirements: update  
+- Fixes:  
   - custom watermark add alphablending  
   - detailer min/max size as fractions of image size  
   - ipadapter load on-demand  
@@ -43,6 +44,8 @@ This release can be considered an LTS release before we kick off the next round
   - fix vqa models ignoring hfcache folder setting  
   - fix network height in standard vs modern ui  
   - fix k-diff enum on startup  
+  - fix text2video scripts  
+  - dont uninstall flash-attn
   - move downloads of some auxillary models to hfcache instead of models folder  
 
 ## Update for 2024-10-29
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 12fa4bc53..7ec0dd08a 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -101,7 +101,8 @@ def process_base(p: processing.StableDiffusionProcessing):
             output = SimpleNamespace(**output)
         if isinstance(output, list):
             output = SimpleNamespace(images=output)
-        shared.history.add(output.images, info=processing.create_infotext(p), ops=p.ops)
+        if hasattr(output, 'images'):
+            shared.history.add(output.images, info=processing.create_infotext(p), ops=p.ops)
         timer.process.record('pipeline')
         hidiffusion.unapply()
         sd_models_compile.openvino_post_compile(op="base") # only executes on compiled vino models
diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 567031b71..78052e0e1 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -106,7 +106,7 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
                 shared.log.info(f'Autodetect {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB')
                 t0 = time.time()
                 keys = model_tools.get_safetensor_keys(f)
-                if keys is not None:
+                if keys is not None and len(keys) > 0:
                     modules = model_tools.list_to_dict(keys)
                     modules = model_tools.remove_entries_after_depth(modules, 3)
                     t1 = time.time()
diff --git a/scripts/text2video.py b/scripts/text2video.py
index 2c93abf27..8dec9bd0e 100644
--- a/scripts/text2video.py
+++ b/scripts/text2video.py
@@ -84,7 +84,7 @@ class Script(scripts.Script):
                 shared.log.error(f'Text2Video: failed to find model={model["path"]}')
                 return
             shared.log.debug(f'Text2Video loading: model={checkpoint}')
-            shared.opts.sd_model_checkpoint = checkpoint
+            shared.opts.sd_model_checkpoint = checkpoint.name
             sd_models.reload_model_weights(op='model')
 
         p.ops.append('text2video')

From 7a24c898b6041743e7318ac56f9cc6af2d5cb6b0 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 10:38:37 -0500
Subject: [PATCH 012/119] fix min-max size

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

diff --git a/modules/postprocess/yolo.py b/modules/postprocess/yolo.py
index 316984207..14d6558be 100644
--- a/modules/postprocess/yolo.py
+++ b/modules/postprocess/yolo.py
@@ -18,7 +18,7 @@ PREDEFINED = [ # <https://huggingface.co/vladmandic/yolo-detailers/tree/main>
 
 
 class YoloResult:
-    def __init__(self, cls: int, label: str, score: float, box: list[int], mask: Image.Image = None, item: Image.Image = None, size: float = 0, width = 0, height = 0, args = {}):
+    def __init__(self, cls: int, label: str, score: float, box: list[int], mask: Image.Image = None, item: Image.Image = None, width = 0, height = 0, args = {}):
         self.cls = cls
         self.label = label
         self.score = score
@@ -330,9 +330,9 @@ class YoloRestorer(Detailer):
                 iou = gr.Slider(label="Max overlap", elem_id=f"{tab}_detailer_iou", value=shared.opts.detailer_iou, minimum=0, maximum=1.0, step=0.05)
             with gr.Row():
                 min_size = shared.opts.detailer_min_size if shared.opts.detailer_min_size < 1 else 0.0
-                min_size = gr.Slider(label="Min size", elem_id=f"{tab}_detailer_min_size", value=min_size, minimum=0.1, maximum=1.0, step=0.05)
-                max_size = shared.opts.detailer_min_size if shared.opts.detailer_min_size < 1 and shared.opts.detailer_min_size > 0 else 1.0
-                max_size = gr.Slider(label="Max size", elem_id=f"{tab}_detailer_max_size", value=max_size, minimum=0.1, maximum=1.0, step=0.05)
+                min_size = gr.Slider(label="Min size", elem_id=f"{tab}_detailer_min_size", value=min_size, minimum=0.0, maximum=1.0, step=0.05)
+                max_size = shared.opts.detailer_max_size if shared.opts.detailer_max_size < 1 and shared.opts.detailer_max_size > 0 else 1.0
+                max_size = gr.Slider(label="Max size", elem_id=f"{tab}_detailer_max_size", value=max_size, minimum=0.0, maximum=1.0, step=0.05)
             detailers.change(fn=ui_settings_change, inputs=[detailers, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou], outputs=[])
             classes.change(fn=ui_settings_change, inputs=[detailers, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou], outputs=[])
             strength.change(fn=ui_settings_change, inputs=[detailers, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou], outputs=[])

From 51d6e1d5591690add2a0f102388622d056860142 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 11:14:51 -0500
Subject: [PATCH 013/119] update notes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md           |  3 +++
 cli/README.md          | 56 ++++++++++++++++++++++++------------------
 cli/model-keys.py      | 26 ++++++++++++--------
 modules/model_tools.py | 15 ++++++-----
 modules/sd_detect.py   |  3 +++
 modules/sd_models.py   |  3 +++
 6 files changed, 66 insertions(+), 40 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 27d0d3076..e7ca479f8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -31,6 +31,9 @@ This release can be considered an LTS release before we kick off the next round
   - Model loader: Report modules included in safetensors when attempting to load a model  
   - Repo: move screenshots to GH pages  
   - Requirements: update  
+- CLI:
+  - added `cli/model-metadata.py` to display metadata in any safetensors file
+  - added `cli/model-keys.py` to quicky display content of any safetensors file
 - Fixes:  
   - custom watermark add alphablending  
   - detailer min/max size as fractions of image size  
diff --git a/cli/README.md b/cli/README.md
index 70de255b1..838db7a50 100644
--- a/cli/README.md
+++ b/cli/README.md
@@ -1,16 +1,43 @@
 # Stable-Diffusion Productivity Scripts
 
-Note: All scripts have built-in `--help` parameter that can be used to get more information
+## API Examples
 
-<br>
+### Run Generate
 
-## Main Scripts
+- `cli/api-txt2img.py`
+- `cli/api-img2img.py`
+- `cli/api-control.py`
 
-### Generate
+### Monitor
+
+- `cli/api-progress.py`
+
+### Generic
+
+- `cli/api-json.py`
+
+### Process
+
+- `cli/api-info.py`
+- `cli/api-upscale.py`
+- `cli/api-vqa.py`
+- `cli/api-preprocess.py`
+
+### Other
+
+- `cli/api-faceid.py`
+- `cli/api-faces.py`
+- `cli/api-mask.py`
+
+### JavaScript
+
+- `cli/api-txt2img.js`
+
+## Generate
 
 Text-to-image with all of the possible parameters  
 Supports upsampling, face restoration and grid creation  
-> python generate.py
+> python cli/generate.py
 
 By default uses parameters from  `generate.json`
 
@@ -20,25 +47,6 @@ Parameters that are not specified will be randomized:
 - Sampler/Scheduler will be randomly picked from available ones
 - CFG Scale set to 5-10
 
-### Train
-
-Combined pipeline for **embeddings**, **lora**, **lycoris**, **dreambooth** and **hypernetwork**  
-Optionally runs several image processing steps before training:
-
-- keep original image
-- detect and extract face
-- detect and extract body
-- detect blur
-- detect dynamic range
-- attempt to upscale low resolution images
-- attempt to restore quality of low quality images
-- automatically generate captions using interrogate
-- resize image
-- square image
-- run image segmentation to remove background
-
-> python train.py
-
 <br>
 
 ## Auxiliary Scripts
diff --git a/cli/model-keys.py b/cli/model-keys.py
index df66179b3..bd4a91551 100755
--- a/cli/model-keys.py
+++ b/cli/model-keys.py
@@ -14,21 +14,27 @@ def has(obj, attr, *args):
 
 
 def remove_entries_after_depth(d, depth, current_depth=0):
-    if current_depth >= depth:
-        return None
-    if isinstance(d, dict):
-        return {k: remove_entries_after_depth(v, depth, current_depth + 1) for k, v in d.items() if remove_entries_after_depth(v, depth, current_depth + 1) is not None}
+    try:
+        if current_depth >= depth:
+            return None
+        if isinstance(d, dict):
+            return {k: remove_entries_after_depth(v, depth, current_depth + 1) for k, v in d.items() if remove_entries_after_depth(v, depth, current_depth + 1) is not None}
+    except Exception:
+        pass
     return d
 
 
 def list_to_dict(flat_list):
     result_dict = {}
-    for item in flat_list:
-        keys = item.split('.')
-        d = result_dict
-        for key in keys[:-1]:
-            d = d.setdefault(key, {})
-        d[keys[-1]] = None
+    try:
+        for item in flat_list:
+            keys = item.split('.')
+            d = result_dict
+            for key in keys[:-1]:
+                d = d.setdefault(key, {})
+            d[keys[-1]] = None
+    except Exception:
+        pass
     return result_dict
 
 
diff --git a/modules/model_tools.py b/modules/model_tools.py
index 42cc9cebe..1d016a19e 100644
--- a/modules/model_tools.py
+++ b/modules/model_tools.py
@@ -15,12 +15,15 @@ def remove_entries_after_depth(d, depth, current_depth=0):
 
 def list_to_dict(flat_list):
     result_dict = {}
-    for item in flat_list:
-        keys = item.split('.')
-        d = result_dict
-        for key in keys[:-1]:
-            d = d.setdefault(key, {})
-        d[keys[-1]] = None
+    try:
+        for item in flat_list:
+            keys = item.split('.')
+            d = result_dict
+            for key in keys[:-1]:
+                d = d.setdefault(key, {})
+            d[keys[-1]] = None
+    except Exception:
+        pass
     return result_dict
 
 
diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 78052e0e1..31f773607 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -82,6 +82,9 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
             if 'meissonic' in f.lower():
                 guess = 'Meissonic'
                 pipeline = 'custom'
+            if 'monetico' in f.lower():
+                guess = 'Monetico'
+                pipeline = 'custom'
             if 'omnigen' in f.lower():
                 guess = 'OmniGen'
                 pipeline = 'custom'
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7743ce27d..610f5a248 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -797,6 +797,9 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
         # preload vae so it can be used as param
         vae = None
         sd_vae.loaded_vae_file = None
+        if model_type is None:
+            shared.log.error(f'Load {op}: pipeline={shared.opts.diffusers_pipeline} not detected')
+            return
         if model_type.startswith('Stable Diffusion') and (op == 'model' or op == 'refiner'): # preload vae for sd models
             vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
             vae = sd_vae.load_vae_diffusers(checkpoint_info.path, vae_file, vae_source)

From 73a1de3deb510593034bc327e81a0b9903f55285 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 16:20:22 -0500
Subject: [PATCH 014/119] add pulid

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                     |   5 +-
 .ruff.toml                                    |   1 +
 CHANGELOG.md                                  |   6 +-
 modules/processing.py                         |   6 +-
 modules/pulid/__init__.py                     |  10 +
 modules/pulid/attention_processor.py          | 423 ++++++++++
 modules/pulid/encoders_transformer.py         | 208 +++++
 modules/pulid/eva_clip/__init__.py            |  11 +
 .../eva_clip/bpe_simple_vocab_16e6.txt.gz     | Bin 0 -> 1356917 bytes
 modules/pulid/eva_clip/constants.py           |   2 +
 modules/pulid/eva_clip/eva_vit_model.py       | 548 +++++++++++++
 modules/pulid/eva_clip/factory.py             | 517 +++++++++++++
 modules/pulid/eva_clip/hf_configs.py          |  57 ++
 modules/pulid/eva_clip/hf_model.py            | 248 ++++++
 modules/pulid/eva_clip/loss.py                | 138 ++++
 modules/pulid/eva_clip/model.py               | 432 +++++++++++
 .../model_configs/EVA01-CLIP-B-16.json        |  19 +
 .../model_configs/EVA01-CLIP-g-14-plus.json   |  24 +
 .../model_configs/EVA01-CLIP-g-14.json        |  24 +
 .../model_configs/EVA02-CLIP-B-16.json        |  29 +
 .../model_configs/EVA02-CLIP-L-14-336.json    |  29 +
 .../model_configs/EVA02-CLIP-L-14.json        |  29 +
 .../EVA02-CLIP-bigE-14-plus.json              |  25 +
 .../model_configs/EVA02-CLIP-bigE-14.json     |  25 +
 modules/pulid/eva_clip/modified_resnet.py     | 181 +++++
 modules/pulid/eva_clip/openai.py              | 144 ++++
 modules/pulid/eva_clip/pretrained.py          | 332 ++++++++
 modules/pulid/eva_clip/rope.py                | 137 ++++
 modules/pulid/eva_clip/timm_model.py          | 119 +++
 modules/pulid/eva_clip/tokenizer.py           | 201 +++++
 modules/pulid/eva_clip/transform.py           | 103 +++
 modules/pulid/eva_clip/transformer.py         | 721 ++++++++++++++++++
 modules/pulid/eva_clip/utils.py               | 326 ++++++++
 modules/pulid/pipe_sdxl.py                    | 315 ++++++++
 modules/pulid/pulid_utils.py                  | 337 ++++++++
 scripts/pulid_ext.py                          | 151 ++++
 wiki                                          |   2 +-
 37 files changed, 5879 insertions(+), 6 deletions(-)
 create mode 100644 modules/pulid/__init__.py
 create mode 100644 modules/pulid/attention_processor.py
 create mode 100644 modules/pulid/encoders_transformer.py
 create mode 100644 modules/pulid/eva_clip/__init__.py
 create mode 100644 modules/pulid/eva_clip/bpe_simple_vocab_16e6.txt.gz
 create mode 100644 modules/pulid/eva_clip/constants.py
 create mode 100644 modules/pulid/eva_clip/eva_vit_model.py
 create mode 100644 modules/pulid/eva_clip/factory.py
 create mode 100644 modules/pulid/eva_clip/hf_configs.py
 create mode 100644 modules/pulid/eva_clip/hf_model.py
 create mode 100644 modules/pulid/eva_clip/loss.py
 create mode 100644 modules/pulid/eva_clip/model.py
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA01-CLIP-B-16.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA02-CLIP-B-16.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14-336.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json
 create mode 100644 modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14.json
 create mode 100644 modules/pulid/eva_clip/modified_resnet.py
 create mode 100644 modules/pulid/eva_clip/openai.py
 create mode 100644 modules/pulid/eva_clip/pretrained.py
 create mode 100644 modules/pulid/eva_clip/rope.py
 create mode 100644 modules/pulid/eva_clip/timm_model.py
 create mode 100644 modules/pulid/eva_clip/tokenizer.py
 create mode 100644 modules/pulid/eva_clip/transform.py
 create mode 100644 modules/pulid/eva_clip/transformer.py
 create mode 100644 modules/pulid/eva_clip/utils.py
 create mode 100644 modules/pulid/pipe_sdxl.py
 create mode 100644 modules/pulid/pulid_utils.py
 create mode 100644 scripts/pulid_ext.py

diff --git a/.pylintrc b/.pylintrc
index 45869a8c3..37b812ffe 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -31,6 +31,7 @@ ignore-paths=/usr/lib/.*$,
              modules/xadapter,
              modules/meissonic,
              modules/omnigen,
+             modules/pulid/eva_clip,
              repositories,
              extensions-builtin/sd-webui-agent-scheduler,
              extensions-builtin/sd-extension-chainner/nodes,
@@ -130,7 +131,8 @@ confidence=HIGH,
            INFERENCE_FAILURE,
            UNDEFINED
 # disable=C,R,W
-disable=bad-inline-option,
+disable=abstract-method,
+        bad-inline-option,
         bare-except,
         broad-exception-caught,
         chained-comparison,
@@ -174,6 +176,7 @@ disable=bad-inline-option,
         unnecessary-dict-index-lookup,
         unnecessary-dunder-call,
         unnecessary-lambda,
+        unnecessary-lambda-assigment,
         use-dict-literal,
         use-symbolic-message-instead,
         unknown-option-value,
diff --git a/.ruff.toml b/.ruff.toml
index fe8ac4f87..28439c73c 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -26,6 +26,7 @@ exclude = [
     "modules/xadapter",
     "modules/meissonic",
     "modules/omnigen",
+    "modules/pulid/eva_clip",
     "repositories",
     "extensions-builtin/sd-extension-chainner/nodes",
     "extensions-builtin/sd-webui-agent-scheduler",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index e7ca479f8..cf496f81c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-03
+## Update for 2024-11-04
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -9,6 +9,10 @@ This release can be considered an LTS release before we kick off the next round
   - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to system -> changelog and search/highligh/navigate directly in UI!  
+- [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
+  - advanced method of face transfer with better quality as well as control over identity and appearance  
+  - compatible with *sdxl*  
+  - select in *scripts -> pulid*  
 - SD3: ControlNets:  
   - *InstantX Canny, Pose, Depth, Tile*  
   - *Alimama Inpainting, SoftEdge*  
diff --git a/modules/processing.py b/modules/processing.py
index 99d0cb351..0d557e64e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -34,14 +34,14 @@ images_tensor_to_samples = processing_helpers.images_tensor_to_samples
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info=None, subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
         self.images = images_list
         self.prompt = p.prompt or ''
         self.negative_prompt = p.negative_prompt or ''
         self.seed = seed if seed != -1 else p.seed
         self.subseed = subseed
         self.subseed_strength = p.subseed_strength
-        self.info = info
+        self.info = info or create_infotext(p)
         self.comments = comments or ''
         self.width = p.width if hasattr(p, 'width') else (self.images[0].width if len(self.images) > 0 else 0)
         self.height = p.height if hasattr(p, 'height') else (self.images[0].height if len(self.images) > 0 else 0)
@@ -80,7 +80,7 @@ class Processed:
         self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
         self.all_seeds = all_seeds or p.all_seeds or [self.seed]
         self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
-        self.infotexts = infotexts or [info]
+        self.infotexts = infotexts or [self.info]
 
     def js(self):
         obj = {
diff --git a/modules/pulid/__init__.py b/modules/pulid/__init__.py
new file mode 100644
index 000000000..77a1bae8f
--- /dev/null
+++ b/modules/pulid/__init__.py
@@ -0,0 +1,10 @@
+"""
+Credit and original implementation: <https://github.com/ToTheBeginning/PuLID>
+"""
+
+import os
+import sys
+sys.path.append(os.path.dirname(__file__))
+from pipe_sdxl import PuLIDPipeline as PuLIDPipelineXL
+from pulid_utils import resize_numpy_image_long as resize
+import attention_processor as attention
diff --git a/modules/pulid/attention_processor.py b/modules/pulid/attention_processor.py
new file mode 100644
index 000000000..9756decc1
--- /dev/null
+++ b/modules/pulid/attention_processor.py
@@ -0,0 +1,423 @@
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+NUM_ZERO = 0
+ORTHO = False
+ORTHO_v2 = False
+
+
+class AttnProcessor(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        id_embedding=None,
+        id_scale=1.0,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(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)
+        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)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class IDAttnProcessor(nn.Module):
+    r"""
+    Attention processor for ID-Adapater.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None):
+        super().__init__()
+        self.id_to_k = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.id_to_v = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        id_embedding=None,
+        id_scale=1.0,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(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)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # for id-adapter
+        if id_embedding is not None:
+            if NUM_ZERO == 0:
+                id_key = self.id_to_k(id_embedding)
+                id_value = self.id_to_v(id_embedding)
+            else:
+                zero_tensor = torch.zeros(
+                    (id_embedding.size(0), NUM_ZERO, id_embedding.size(-1)),
+                    dtype=id_embedding.dtype,
+                    device=id_embedding.device,
+                )
+                id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1))
+                id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1))
+
+            id_key = attn.head_to_batch_dim(id_key).to(query.dtype)
+            id_value = attn.head_to_batch_dim(id_value).to(query.dtype)
+
+            id_attention_probs = attn.get_attention_scores(query, id_key, None)
+            id_hidden_states = torch.bmm(id_attention_probs, id_value)
+            id_hidden_states = attn.batch_to_head_dim(id_hidden_states)
+
+            if not ORTHO:
+                hidden_states = hidden_states + id_scale * id_hidden_states
+            else:
+                projection = (
+                    torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True)
+                    / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True)
+                    * hidden_states
+                )
+                orthogonal = id_hidden_states - projection
+                hidden_states = hidden_states + id_scale * orthogonal
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AttnProcessor2_0(nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        id_embedding=None,
+        id_scale=1.0,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class IDAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for ID-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.id_to_k = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.id_to_v = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        id_embedding=None,
+        id_scale=1.0,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # for id embedding
+        if id_embedding is not None:
+            if NUM_ZERO == 0:
+                id_key = self.id_to_k(id_embedding).to(query.dtype)
+                id_value = self.id_to_v(id_embedding).to(query.dtype)
+            else:
+                zero_tensor = torch.zeros(
+                    (id_embedding.size(0), NUM_ZERO, id_embedding.size(-1)),
+                    dtype=id_embedding.dtype,
+                    device=id_embedding.device,
+                )
+                id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
+                id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
+
+            id_key = id_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+            id_value = id_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+            # the output of sdp = (batch, num_heads, seq_len, head_dim)
+            id_hidden_states = F.scaled_dot_product_attention(
+                query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False
+            )
+
+            id_hidden_states = id_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+            id_hidden_states = id_hidden_states.to(query.dtype)
+
+            if not ORTHO and not ORTHO_v2:
+                hidden_states = hidden_states + id_scale * id_hidden_states
+            elif ORTHO_v2:
+                orig_dtype = hidden_states.dtype
+                hidden_states = hidden_states.to(torch.float32)
+                id_hidden_states = id_hidden_states.to(torch.float32)
+                attn_map = query @ id_key.transpose(-2, -1)
+                attn_mean = attn_map.softmax(dim=-1).mean(dim=1)
+                attn_mean = attn_mean[:, :, :5].sum(dim=-1, keepdim=True)
+                projection = (
+                    torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True)
+                    / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True)
+                    * hidden_states
+                )
+                orthogonal = id_hidden_states + (attn_mean - 1) * projection
+                hidden_states = hidden_states + id_scale * orthogonal
+                hidden_states = hidden_states.to(orig_dtype)
+            else:
+                orig_dtype = hidden_states.dtype
+                hidden_states = hidden_states.to(torch.float32)
+                id_hidden_states = id_hidden_states.to(torch.float32)
+                projection = (
+                    torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True)
+                    / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True)
+                    * hidden_states
+                )
+                orthogonal = id_hidden_states - projection
+                hidden_states = hidden_states + id_scale * orthogonal
+                hidden_states = hidden_states.to(orig_dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
diff --git a/modules/pulid/encoders_transformer.py b/modules/pulid/encoders_transformer.py
new file mode 100644
index 000000000..d1ecef2c6
--- /dev/null
+++ b/modules/pulid/encoders_transformer.py
@@ -0,0 +1,208 @@
+import math
+import torch
+import torch.nn as nn
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, _width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttentionCA(nn.Module):
+    def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, seq_len, _ = latents.shape
+
+        q = self.to_q(latents)
+        k, v = self.to_kv(x).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
+
+        return self.to_out(out)
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, seq_len, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
+
+        return self.to_out(out)
+
+
+class IDFormer(nn.Module):
+    """
+    - perceiver resampler like arch (compared with previous MLP-like arch)
+    - we concat id embedding (generated by arcface) and query tokens as latents
+    - latents will attend each other and interact with vit features through cross-attention
+    - vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two
+      IDFormer layers
+    """
+    def __init__(
+            self,
+            dim=1024,
+            depth=10,
+            dim_head=64,
+            heads=16,
+            num_id_token=5,
+            num_queries=32,
+            output_dim=2048,
+            ff_mult=4,
+    ):
+        super().__init__()
+
+        self.num_id_token = num_id_token
+        self.dim = dim
+        self.num_queries = num_queries
+        assert depth % 5 == 0
+        self.depth = depth // 5
+        scale = dim ** -0.5
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale)
+        self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim))
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+        for i in range(5):
+            setattr(
+                self,
+                f'mapping_{i}',
+                nn.Sequential(
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, dim),
+                ),
+            )
+
+        self.id_embedding_mapping = nn.Sequential(
+            nn.Linear(1280, 1024),
+            nn.LayerNorm(1024),
+            nn.LeakyReLU(),
+            nn.Linear(1024, 1024),
+            nn.LayerNorm(1024),
+            nn.LeakyReLU(),
+            nn.Linear(1024, dim * num_id_token),
+        )
+
+    def forward(self, x, y):
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        num_duotu = x.shape[1] if x.ndim == 3 else 1
+
+        x = self.id_embedding_mapping(x)
+        x = x.reshape(-1, self.num_id_token * num_duotu, self.dim)
+
+        latents = torch.cat((latents, x), dim=1)
+
+        for i in range(5):
+            vit_feature = getattr(self, f'mapping_{i}')(y[i])
+            ctx_feature = torch.cat((x, vit_feature), dim=1)
+            for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]:
+                latents = attn(ctx_feature, latents) + latents
+                latents = ff(latents) + latents
+
+        latents = latents[:, :self.num_queries]
+        latents = latents @ self.proj_out
+        return latents
diff --git a/modules/pulid/eva_clip/__init__.py b/modules/pulid/eva_clip/__init__.py
new file mode 100644
index 000000000..fa2d014bb
--- /dev/null
+++ b/modules/pulid/eva_clip/__init__.py
@@ -0,0 +1,11 @@
+from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
+from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_transforms
+from .factory import list_models, add_model_config, get_model_config, load_checkpoint
+from .loss import ClipLoss
+from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg,\
+    convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype
+from .openai import load_openai_model, list_openai_models
+from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\
+    get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained
+from .tokenizer import SimpleTokenizer, tokenize
+from .transform import image_transform
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/bpe_simple_vocab_16e6.txt.gz b/modules/pulid/eva_clip/bpe_simple_vocab_16e6.txt.gz
new file mode 100644
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diff --git a/modules/pulid/eva_clip/constants.py b/modules/pulid/eva_clip/constants.py
new file mode 100644
index 000000000..a670bb3fa
--- /dev/null
+++ b/modules/pulid/eva_clip/constants.py
@@ -0,0 +1,2 @@
+OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
diff --git a/modules/pulid/eva_clip/eva_vit_model.py b/modules/pulid/eva_clip/eva_vit_model.py
new file mode 100644
index 000000000..51db88cf0
--- /dev/null
+++ b/modules/pulid/eva_clip/eva_vit_model.py
@@ -0,0 +1,548 @@
+# --------------------------------------------------------
+# Adapted from  https://github.com/microsoft/unilm/tree/master/beit
+# --------------------------------------------------------
+import math
+import os
+from functools import partial
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+try:
+    from timm.models.layers import drop_path, to_2tuple, trunc_normal_
+except:
+    from timm.layers import drop_path, to_2tuple, trunc_normal_
+    
+from .transformer import PatchDropout
+from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast
+
+if os.getenv('ENV_TYPE') == 'deepspeed':
+    try:
+        from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
+    except:
+        from torch.utils.checkpoint import checkpoint
+else:
+    from torch.utils.checkpoint import checkpoint
+
+try:
+    import xformers
+    import xformers.ops as xops
+    XFORMERS_IS_AVAILBLE = True
+except:
+    XFORMERS_IS_AVAILBLE = False
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+    
+    def extra_repr(self) -> str:
+        return 'p={}'.format(self.drop_prob)
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self, 
+        in_features, 
+        hidden_features=None, 
+        out_features=None, 
+        act_layer=nn.GELU, 
+        norm_layer=nn.LayerNorm, 
+        drop=0.,
+        subln=False,
+
+        ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+
+        self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
+
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        # x = self.drop(x)
+        # commit this for the orignal BERT implement 
+        x = self.ffn_ln(x)
+
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+class SwiGLU(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., 
+                norm_layer=nn.LayerNorm, subln=False):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+
+        self.w1 = nn.Linear(in_features, hidden_features)
+        self.w2 = nn.Linear(in_features, hidden_features)
+
+        self.act = act_layer()
+        self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
+        self.w3 = nn.Linear(hidden_features, out_features)
+        
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x1 = self.w1(x)
+        x2 = self.w2(x)
+        hidden = self.act(x1) * x2
+        x = self.ffn_ln(hidden)
+        x = self.w3(x)
+        x = self.drop(x)
+        return x
+
+class Attention(nn.Module):
+    def __init__(
+            self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
+            proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.subln = subln
+        if self.subln:
+            self.q_proj = nn.Linear(dim, all_head_dim, bias=False)
+            self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
+            self.v_proj = nn.Linear(dim, all_head_dim, bias=False)
+        else:
+            self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        if window_size:
+            self.window_size = window_size
+            self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+            # cls to token & token 2 cls & cls to cls
+
+            # get pair-wise relative position index for each token inside the window
+            coords_h = torch.arange(window_size[0])
+            coords_w = torch.arange(window_size[1])
+            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+            relative_coords[:, :, 1] += window_size[1] - 1
+            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+            relative_position_index = \
+                torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
+            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+            relative_position_index[0, 0:] = self.num_relative_distance - 3
+            relative_position_index[0:, 0] = self.num_relative_distance - 2
+            relative_position_index[0, 0] = self.num_relative_distance - 1
+
+            self.register_buffer("relative_position_index", relative_position_index)
+        else:
+            self.window_size = None
+            self.relative_position_bias_table = None
+            self.relative_position_index = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity()
+        # self.proj = nn.Linear(all_head_dim, all_head_dim)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.xattn = xattn
+        self.xattn_drop = attn_drop
+
+        self.rope = rope
+
+    def forward(self, x, rel_pos_bias=None, attn_mask=None):
+        B, N, C = x.shape
+        if self.subln: 
+            q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias)
+            k = F.linear(input=x, weight=self.k_proj.weight, bias=None)
+            v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias)
+
+            q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)     # B, num_heads, N, C
+            k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)  
+            v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) 
+        else: 
+
+            qkv_bias = None
+            if self.q_bias is not None:
+                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+            
+            qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+            qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)   # 3, B, num_heads, N, C
+            q, k, v = qkv[0], qkv[1], qkv[2]
+
+        if self.rope:
+            # slightly fast impl
+            q_t = q[:, :, 1:, :]
+            ro_q_t = self.rope(q_t)
+            q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)
+
+            k_t = k[:, :, 1:, :]
+            ro_k_t = self.rope(k_t)
+            k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
+
+        if self.xattn:
+            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 = xops.memory_efficient_attention(
+                q, k, v,
+                p=self.xattn_drop,
+                scale=self.scale,
+                )
+            x = x.reshape(B, N, -1)
+            x = self.inner_attn_ln(x)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+        else:
+            q = q * self.scale
+            attn = (q @ k.transpose(-2, -1))
+
+            if self.relative_position_bias_table is not None:
+                relative_position_bias = \
+                    self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+                        self.window_size[0] * self.window_size[1] + 1,
+                        self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
+                relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+                attn = attn + relative_position_bias.unsqueeze(0).type_as(attn)
+
+            if rel_pos_bias is not None:
+                attn = attn + rel_pos_bias.type_as(attn)
+
+            if attn_mask is not None:
+                attn_mask = attn_mask.bool()
+                attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
+            
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+
+            x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+            x = self.inner_attn_ln(x)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+                 window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False,
+                 subln=False, naiveswiglu=False):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
+            attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim,
+            xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+
+        if naiveswiglu:
+            self.mlp = SwiGLU(
+                in_features=dim, 
+                hidden_features=mlp_hidden_dim, 
+                subln=subln,
+                norm_layer=norm_layer,
+            )
+        else:
+            self.mlp = Mlp(
+                in_features=dim, 
+                hidden_features=mlp_hidden_dim, 
+                act_layer=act_layer,
+                subln=subln,
+                drop=drop
+            )
+
+        if init_values is not None and init_values > 0:
+            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
+            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
+        else:
+            self.gamma_1, self.gamma_2 = None, None
+
+        self.postnorm = postnorm
+
+    def forward(self, x, rel_pos_bias=None, attn_mask=None):
+        if self.gamma_1 is None:
+            if self.postnorm:
+                x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
+                x = x + self.drop_path(self.norm2(self.mlp(x)))
+            else:
+                x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
+                x = x + self.drop_path(self.mlp(self.norm2(x)))
+        else:
+            if self.postnorm:
+                x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
+                x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
+            else:
+                x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
+                x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x, **kwargs):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class RelativePositionBias(nn.Module):
+
+    def __init__(self, window_size, num_heads):
+        super().__init__()
+        self.window_size = window_size
+        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+        # cls to token & token 2 cls & cls to cls
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(window_size[0])
+        coords_w = torch.arange(window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+        relative_position_index = \
+            torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
+        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        relative_position_index[0, 0:] = self.num_relative_distance - 3
+        relative_position_index[0:, 0] = self.num_relative_distance - 2
+        relative_position_index[0, 0] = self.num_relative_distance - 1
+
+        self.register_buffer("relative_position_index", relative_position_index)
+
+    def forward(self):
+        relative_position_bias = \
+            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1] + 1,
+                self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
+        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+
+
+class EVAVisionTransformer(nn.Module):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0.,
+                 use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False,
+                 use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False,
+                 pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False):
+        super().__init__()
+
+        if not XFORMERS_IS_AVAILBLE:
+            xattn = False
+
+        self.image_size = img_size
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        if use_abs_pos_emb:
+            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        else:
+            self.pos_embed = None
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        if use_shared_rel_pos_bias:
+            self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
+        else:
+            self.rel_pos_bias = None
+
+        if rope:
+            half_head_dim = embed_dim // num_heads // 2
+            hw_seq_len = img_size // patch_size
+            self.rope = VisionRotaryEmbeddingFast(
+                dim=half_head_dim,
+                pt_seq_len=pt_hw_seq_len,
+                ft_seq_len=hw_seq_len if intp_freq else None,
+                # patch_dropout=patch_dropout
+            )
+        else: 
+            self.rope = None
+
+        self.naiveswiglu = naiveswiglu
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.use_rel_pos_bias = use_rel_pos_bias
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
+                init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None,
+                xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu)
+            for i in range(depth)])
+        self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
+        self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        if self.pos_embed is not None:
+            trunc_normal_(self.pos_embed, std=.02)
+
+        trunc_normal_(self.cls_token, std=.02)
+        # trunc_normal_(self.mask_token, std=.02)
+
+        self.apply(self._init_weights)
+        self.fix_init_weight()
+
+        if isinstance(self.head, nn.Linear):
+            trunc_normal_(self.head.weight, std=.02)
+            self.head.weight.data.mul_(init_scale)
+            self.head.bias.data.mul_(init_scale)
+
+        # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
+        self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
+
+        self.grad_checkpointing = grad_checkpointing
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            if self.naiveswiglu:
+                rescale(layer.mlp.w3.weight.data, layer_id + 1)
+            else:
+                rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def get_cast_dtype(self) -> torch.dtype:
+        return self.blocks[0].mlp.fc2.weight.dtype
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def get_num_layers(self):
+        return len(self.blocks)
+    
+    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
+        assert unlocked_groups == 0, 'partial locking not currently supported for this model'
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x, return_all_features=False, return_hidden=False, shuffle=False):
+        
+        x = self.patch_embed(x)
+        batch_size, seq_len, _ = x.size()
+
+        if shuffle:
+            idx = torch.randperm(x.shape[1]) + 1
+            zero = torch.LongTensor([0, ])
+            idx = torch.cat([zero, idx])
+            pos_embed = self.pos_embed[:, idx]
+
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        if shuffle:
+            x = x + pos_embed
+        elif self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
+        if os.getenv('RoPE') == '1':
+            if self.training and not isinstance(self.patch_dropout, nn.Identity):
+                x, patch_indices_keep = self.patch_dropout(x)
+                self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep)
+            else:
+                self.rope.forward = partial(self.rope.forward, patch_indices_keep=None)
+                x = self.patch_dropout(x)
+        else:
+            x = self.patch_dropout(x)
+
+        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        hidden_states = []
+        for idx, blk in enumerate(self.blocks):
+            if (0 < idx <= 20) and (idx % 4 == 0) and return_hidden:
+                hidden_states.append(x)
+            if self.grad_checkpointing:
+                x = checkpoint(blk, x, (rel_pos_bias,))
+            else:
+                x = blk(x, rel_pos_bias=rel_pos_bias)
+
+        if not return_all_features:
+            x = self.norm(x)
+            if self.fc_norm is not None:
+                return self.fc_norm(x.mean(1)), hidden_states
+            else:
+                return x[:, 0], hidden_states
+        return x
+
+    def forward(self, x, return_all_features=False, return_hidden=False, shuffle=False):
+        if return_all_features:
+            return self.forward_features(x, return_all_features, return_hidden, shuffle)
+        x, hidden_states = self.forward_features(x, return_all_features, return_hidden, shuffle)
+        x = self.head(x)
+        if return_hidden:
+            return x, hidden_states
+        return x
diff --git a/modules/pulid/eva_clip/factory.py b/modules/pulid/eva_clip/factory.py
new file mode 100644
index 000000000..ced899999
--- /dev/null
+++ b/modules/pulid/eva_clip/factory.py
@@ -0,0 +1,517 @@
+import json
+import logging
+import os
+import pathlib
+import re
+from copy import deepcopy
+from pathlib import Path
+from typing import Optional, Tuple, Union, Dict, Any
+import torch
+
+from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
+from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
+    get_cast_dtype
+from .openai import load_openai_model
+from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model
+from .transform import image_transform
+from .tokenizer import HFTokenizer, tokenize
+from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed
+
+
+_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
+_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
+
+
+def _natural_key(string_):
+    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
+
+
+def _rescan_model_configs():
+    global _MODEL_CONFIGS
+
+    config_ext = ('.json',)
+    config_files = []
+    for config_path in _MODEL_CONFIG_PATHS:
+        if config_path.is_file() and config_path.suffix in config_ext:
+            config_files.append(config_path)
+        elif config_path.is_dir():
+            for ext in config_ext:
+                config_files.extend(config_path.glob(f'*{ext}'))
+
+    for cf in config_files:
+        with open(cf, "r", encoding="utf8") as f:
+            model_cfg = json.load(f)
+            if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
+                _MODEL_CONFIGS[cf.stem] = model_cfg
+
+    _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0])))
+
+
+_rescan_model_configs()  # initial populate of model config registry
+
+
+def list_models():
+    """ enumerate available model architectures based on config files """
+    return list(_MODEL_CONFIGS.keys())
+
+
+def add_model_config(path):
+    """ add model config path or file and update registry """
+    if not isinstance(path, Path):
+        path = Path(path)
+    _MODEL_CONFIG_PATHS.append(path)
+    _rescan_model_configs()
+
+
+def get_model_config(model_name):
+    if model_name in _MODEL_CONFIGS:
+        return deepcopy(_MODEL_CONFIGS[model_name])
+    else:
+        return None
+
+
+def get_tokenizer(model_name):
+    config = get_model_config(model_name)
+    tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize
+    return tokenizer
+
+
+# loading openai CLIP weights when is_openai=True for training
+def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]):
+    if is_openai:
+        model = torch.jit.load(checkpoint_path, map_location="cpu").eval()
+        state_dict = model.state_dict()
+        for key in ["input_resolution", "context_length", "vocab_size"]:
+            state_dict.pop(key, None)
+    else:
+        checkpoint = torch.load(checkpoint_path, map_location=map_location)
+        for mk in model_key.split('|'):
+            if isinstance(checkpoint, dict) and mk in checkpoint:
+                state_dict = checkpoint[mk]
+                break
+            else:
+                state_dict = checkpoint
+        if next(iter(state_dict.items()))[0].startswith('module'):
+            state_dict = {k[7:]: v for k, v in state_dict.items()}
+    
+    for k in skip_list:
+        if k in list(state_dict.keys()):
+            logging.info(f"Removing key {k} from pretrained checkpoint")
+            del state_dict[k]
+
+    if os.getenv('RoPE') == '1':
+        for k in list(state_dict.keys()):
+            if 'freqs_cos' in k or 'freqs_sin' in k:
+                del state_dict[k]
+    return state_dict
+
+
+
+def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True):
+    state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False)
+    # detect old format and make compatible with new format
+    if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
+        state_dict = convert_to_custom_text_state_dict(state_dict)
+    if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'):
+        state_dict['logit_scale'] = state_dict['text.logit_scale']
+        del state_dict['text.logit_scale']
+
+    # resize_clip_pos_embed for CLIP and open CLIP
+    if 'visual.positional_embedding' in state_dict:
+        resize_clip_pos_embed(state_dict, model)
+    # specified to eva_vit_model
+    elif 'visual.pos_embed' in state_dict:
+        resize_evaclip_pos_embed(state_dict, model)
+
+    # resize_clip_pos_embed(state_dict, model)
+    incompatible_keys = model.load_state_dict(state_dict, strict=strict)
+    logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}")
+    return incompatible_keys
+
+def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]):
+    state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list)
+
+    for k in list(state_dict.keys()):
+        if not k.startswith('visual.'):
+            del state_dict[k]
+    for k in list(state_dict.keys()):
+        if k.startswith('visual.'):
+            new_k = k[7:]
+            state_dict[new_k] = state_dict[k]
+            del state_dict[k]
+    return state_dict
+
+def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]):
+    state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list)
+
+    for k in list(state_dict.keys()):
+        if k.startswith('visual.'):
+            del state_dict[k]
+    return state_dict
+
+def get_pretrained_tag(pretrained_model):
+    pretrained_model = pretrained_model.lower()
+    if "laion" in pretrained_model or "open_clip" in pretrained_model:
+        return "open_clip"
+    elif "openai" in pretrained_model:
+        return "clip"
+    elif "eva" in pretrained_model and "clip" in pretrained_model:
+        return "eva_clip"
+    else:
+        return "other"
+
+def load_pretrained_checkpoint(
+        model,
+        visual_checkpoint_path,
+        text_checkpoint_path,
+        strict=True,
+        visual_model=None,
+        text_model=None,
+        model_key="model|module|state_dict",
+        skip_list=[]):
+    visual_tag = get_pretrained_tag(visual_model)
+    text_tag = get_pretrained_tag(text_model)
+
+    logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}")
+    visual_incompatible_keys, text_incompatible_keys = None, None
+    if visual_checkpoint_path:
+        if visual_tag == "eva_clip" or visual_tag == "open_clip":
+            visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list)
+        elif visual_tag == "clip":
+            visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list)
+        else:
+            visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list)
+    
+        # resize_clip_pos_embed for CLIP and open CLIP
+        if 'positional_embedding' in visual_state_dict:
+            resize_visual_pos_embed(visual_state_dict, model)
+        # specified to EVA model
+        elif 'pos_embed' in visual_state_dict:
+            resize_eva_pos_embed(visual_state_dict, model)
+
+        visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict)
+        logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}")
+        logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}")
+
+    if text_checkpoint_path:
+        if text_tag == "eva_clip" or text_tag == "open_clip":
+            text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list)
+        elif text_tag == "clip":
+            text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list)
+        else:
+            text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list)
+
+        text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict)
+        
+        logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}")
+        logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}")
+
+    return visual_incompatible_keys, text_incompatible_keys
+
+def create_model(
+        model_name: str,
+        pretrained: Optional[str] = None,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_clip: bool = False,
+        force_patch_dropout: Optional[float] = None,
+        pretrained_image: str = '',
+        pretrained_text: str = '',
+        pretrained_hf: bool = True,
+        pretrained_visual_model: str = None,
+        pretrained_text_model: str = None,
+        cache_dir: Optional[str] = None,
+        skip_list: list  = [],
+):
+    model_name = model_name.replace('/', '-')  # for callers using old naming with / in ViT names
+    if isinstance(device, str):
+        device = torch.device(device)
+
+    if pretrained and pretrained.lower() == 'openai':
+        logging.info(f'Loading pretrained {model_name} from OpenAI.')
+        model = load_openai_model(
+            model_name,
+            precision=precision,
+            device=device,
+            jit=jit,
+            cache_dir=cache_dir,
+        )
+    else:
+        model_cfg = get_model_config(model_name)
+        if model_cfg is not None:
+            logging.info(f'Loaded {model_name} model config.')
+        else:
+            logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
+            raise RuntimeError(f'Model config for {model_name} not found.')
+
+        if 'rope' in model_cfg.get('vision_cfg', {}):
+            if model_cfg['vision_cfg']['rope']:
+                os.environ['RoPE'] = "1"
+        else:
+            os.environ['RoPE'] = "0"
+
+        if force_quick_gelu:
+            # override for use of QuickGELU on non-OpenAI transformer models
+            model_cfg["quick_gelu"] = True
+        
+        if force_patch_dropout is not None:
+            # override the default patch dropout value
+            model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout
+
+        cast_dtype = get_cast_dtype(precision)
+        custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg'])
+
+
+        if custom_clip:
+            if 'hf_model_name' in model_cfg.get('text_cfg', {}):
+                model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
+            model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype)
+        else:
+            model = CLIP(**model_cfg, cast_dtype=cast_dtype)
+
+        pretrained_cfg = {}
+        if pretrained:
+            checkpoint_path = ''
+            pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
+            if pretrained_cfg:
+                checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
+            elif os.path.exists(pretrained):
+                checkpoint_path = pretrained
+
+            if checkpoint_path:
+                logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
+                load_checkpoint(model,
+                               checkpoint_path,
+                               model_key="model|module|state_dict",
+                               strict=False
+                               ) 
+            else:
+                error_str = (
+                    f'Pretrained weights ({pretrained}) not found for model {model_name}.'
+                    f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
+                logging.warning(error_str)
+                raise RuntimeError(error_str)
+        else:
+            visual_checkpoint_path = ''
+            text_checkpoint_path = ''
+            
+            if pretrained_image:
+                pretrained_visual_model = pretrained_visual_model.replace('/', '-')  # for callers using old naming with / in ViT names
+                pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image)
+                if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
+                    # pretrained weight loading for timm models set via vision_cfg
+                    model_cfg['vision_cfg']['timm_model_pretrained'] = True
+                elif pretrained_image_cfg:
+                    visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir)
+                elif os.path.exists(pretrained_image):
+                    visual_checkpoint_path = pretrained_image
+                else:
+                    logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.')
+                    raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.')
+
+            if pretrained_text:
+                pretrained_text_model = pretrained_text_model.replace('/', '-')  # for callers using old naming with / in ViT names
+                pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text)
+                if pretrained_image_cfg:
+                    text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir)
+                elif os.path.exists(pretrained_text):
+                    text_checkpoint_path = pretrained_text
+                else:
+                    logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.')
+                    raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.')
+            
+            if visual_checkpoint_path:
+                logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).')
+            if text_checkpoint_path:
+                logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).')
+
+            if visual_checkpoint_path or text_checkpoint_path:
+                load_pretrained_checkpoint(
+                    model,
+                    visual_checkpoint_path,
+                    text_checkpoint_path,
+                    strict=False,
+                    visual_model=pretrained_visual_model,
+                    text_model=pretrained_text_model,
+                    model_key="model|module|state_dict",
+                    skip_list=skip_list
+                )
+        
+        if "fp16" in precision or "bf16" in precision:
+            logging.info(f'convert precision to {precision}')
+            model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16)
+
+        model.to(device=device)
+
+        # set image / mean metadata from pretrained_cfg if available, or use default
+        model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
+        model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
+
+        if jit:
+            model = torch.jit.script(model)
+
+    return model
+
+
+def create_model_and_transforms(
+        model_name: str,
+        pretrained: Optional[str] = None,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_clip: bool = False,
+        force_patch_dropout: Optional[float] = None,
+        pretrained_image: str = '',
+        pretrained_text: str = '',
+        pretrained_hf: bool = True,
+        pretrained_visual_model: str = None,
+        pretrained_text_model: str = None,
+        image_mean: Optional[Tuple[float, ...]] = None,
+        image_std: Optional[Tuple[float, ...]] = None,
+        cache_dir: Optional[str] = None,
+        skip_list: list = [],
+):
+    model = create_model(
+        model_name,
+        pretrained,
+        precision=precision,
+        device=device,
+        jit=jit,
+        force_quick_gelu=force_quick_gelu,
+        force_custom_clip=force_custom_clip,
+        force_patch_dropout=force_patch_dropout,
+        pretrained_image=pretrained_image,
+        pretrained_text=pretrained_text,
+        pretrained_hf=pretrained_hf,
+        pretrained_visual_model=pretrained_visual_model,
+        pretrained_text_model=pretrained_text_model,
+        cache_dir=cache_dir,
+        skip_list=skip_list,
+    )
+
+    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
+    image_std = image_std or getattr(model.visual, 'image_std', None)
+    preprocess_train = image_transform(
+        model.visual.image_size,
+        is_train=True,
+        mean=image_mean,
+        std=image_std
+    )
+    preprocess_val = image_transform(
+        model.visual.image_size,
+        is_train=False,
+        mean=image_mean,
+        std=image_std
+    )
+
+    return model, preprocess_train, preprocess_val
+
+
+def create_transforms(
+        model_name: str,
+        pretrained: Optional[str] = None,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_clip: bool = False,
+        force_patch_dropout: Optional[float] = None,
+        pretrained_image: str = '',
+        pretrained_text: str = '',
+        pretrained_hf: bool = True,
+        pretrained_visual_model: str = None,
+        pretrained_text_model: str = None,
+        image_mean: Optional[Tuple[float, ...]] = None,
+        image_std: Optional[Tuple[float, ...]] = None,
+        cache_dir: Optional[str] = None,
+        skip_list: list = [],
+):
+    model = create_model(
+        model_name,
+        pretrained,
+        precision=precision,
+        device=device,
+        jit=jit,
+        force_quick_gelu=force_quick_gelu,
+        force_custom_clip=force_custom_clip,
+        force_patch_dropout=force_patch_dropout,
+        pretrained_image=pretrained_image,
+        pretrained_text=pretrained_text,
+        pretrained_hf=pretrained_hf,
+        pretrained_visual_model=pretrained_visual_model,
+        pretrained_text_model=pretrained_text_model,
+        cache_dir=cache_dir,
+        skip_list=skip_list,
+    )
+
+
+    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
+    image_std = image_std or getattr(model.visual, 'image_std', None)
+    preprocess_train = image_transform(
+        model.visual.image_size,
+        is_train=True,
+        mean=image_mean,
+        std=image_std
+    )
+    preprocess_val = image_transform(
+        model.visual.image_size,
+        is_train=False,
+        mean=image_mean,
+        std=image_std
+    )
+    del model
+
+    return preprocess_train, preprocess_val
+
+def create_model_from_pretrained(
+        model_name: str,
+        pretrained: str,
+        precision: str = 'fp32',
+        device: Union[str, torch.device] = 'cpu',
+        jit: bool = False,
+        force_quick_gelu: bool = False,
+        force_custom_clip: bool = False,
+        force_patch_dropout: Optional[float] = None,
+        return_transform: bool = True,
+        image_mean: Optional[Tuple[float, ...]] = None,
+        image_std: Optional[Tuple[float, ...]] = None,
+        cache_dir: Optional[str] = None,
+        is_frozen: bool = False,
+):
+    if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained):
+        raise RuntimeError(
+            f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.'
+            f' Use open_clip.list_pretrained() to find one.')
+
+    model = create_model(
+        model_name,
+        pretrained,
+        precision=precision,
+        device=device,
+        jit=jit,
+        force_quick_gelu=force_quick_gelu,
+        force_custom_clip=force_custom_clip,
+        force_patch_dropout=force_patch_dropout,
+        cache_dir=cache_dir,
+    )
+
+    if is_frozen:
+        for param in model.parameters():
+            param.requires_grad = False
+
+    if not return_transform:
+        return model
+
+    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
+    image_std = image_std or getattr(model.visual, 'image_std', None)
+    preprocess = image_transform(
+        model.visual.image_size,
+        is_train=False,
+        mean=image_mean,
+        std=image_std
+    )
+
+    return model, preprocess
diff --git a/modules/pulid/eva_clip/hf_configs.py b/modules/pulid/eva_clip/hf_configs.py
new file mode 100644
index 000000000..a8c9b704d
--- /dev/null
+++ b/modules/pulid/eva_clip/hf_configs.py
@@ -0,0 +1,57 @@
+# HF architecture dict:
+arch_dict = {
+  # https://huggingface.co/docs/transformers/model_doc/roberta#roberta
+  "roberta": {
+      "config_names": {
+          "context_length": "max_position_embeddings",
+          "vocab_size": "vocab_size",
+          "width": "hidden_size",
+          "heads": "num_attention_heads",
+          "layers": "num_hidden_layers",
+          "layer_attr": "layer",
+          "token_embeddings_attr": "embeddings"
+      },
+      "pooler": "mean_pooler",
+  },
+  # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig
+  "xlm-roberta": {
+      "config_names": {
+          "context_length": "max_position_embeddings",
+          "vocab_size": "vocab_size",
+          "width": "hidden_size",
+          "heads": "num_attention_heads",
+          "layers": "num_hidden_layers",
+          "layer_attr": "layer",
+          "token_embeddings_attr": "embeddings"
+      },
+      "pooler": "mean_pooler",
+  },
+  # https://huggingface.co/docs/transformers/model_doc/mt5#mt5
+  "mt5": {
+      "config_names": {
+          # unlimited seqlen
+          # https://github.com/google-research/text-to-text-transfer-transformer/issues/273
+          # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374
+          "context_length": "",
+          "vocab_size": "vocab_size",
+          "width": "d_model",
+          "heads": "num_heads",
+          "layers": "num_layers",
+          "layer_attr": "block",
+          "token_embeddings_attr": "embed_tokens"
+      },
+      "pooler": "mean_pooler",
+  },
+  "bert": {
+    "config_names": {
+      "context_length": "max_position_embeddings",
+      "vocab_size": "vocab_size",
+      "width": "hidden_size",
+      "heads": "num_attention_heads",
+      "layers": "num_hidden_layers",
+      "layer_attr": "layer",
+      "token_embeddings_attr": "embeddings"
+    },
+    "pooler": "mean_pooler",
+  }
+}
diff --git a/modules/pulid/eva_clip/hf_model.py b/modules/pulid/eva_clip/hf_model.py
new file mode 100644
index 000000000..c4b9fd85b
--- /dev/null
+++ b/modules/pulid/eva_clip/hf_model.py
@@ -0,0 +1,248 @@
+""" huggingface model adapter
+
+Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model.
+"""
+
+import re
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch import TensorType
+try:
+    import transformers
+    from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig
+    from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \
+        BaseModelOutputWithPoolingAndCrossAttentions
+except ImportError as e:
+    transformers = None
+
+
+    class BaseModelOutput:
+        pass
+
+
+    class PretrainedConfig:
+        pass
+
+from .hf_configs import arch_dict
+
+# utils
+def _camel2snake(s):
+    return re.sub(r'(?<!^)(?=[A-Z])', '_', s).lower()
+
+# TODO: ?last - for gpt-like models
+_POOLERS = {}
+
+def register_pooler(cls):
+    """Decorator registering pooler class"""
+    _POOLERS[_camel2snake(cls.__name__)] = cls
+    return cls
+
+
+@register_pooler
+class MeanPooler(nn.Module):
+    """Mean pooling"""
+    def forward(self, x:BaseModelOutput, attention_mask:TensorType):
+        masked_output = x.last_hidden_state * attention_mask.unsqueeze(-1)
+        return masked_output.sum(dim=1) / attention_mask.sum(-1, keepdim=True)
+
+@register_pooler
+class MaxPooler(nn.Module):
+    """Max pooling"""
+    def forward(self, x:BaseModelOutput, attention_mask:TensorType):
+        masked_output = x.last_hidden_state.masked_fill(attention_mask.unsqueeze(-1), -torch.inf)
+        return masked_output.max(1).values
+
+@register_pooler
+class ClsPooler(nn.Module):
+    """CLS token pooling"""
+    def __init__(self, use_pooler_output=True):
+        super().__init__()
+        self.cls_token_position = 0
+        self.use_pooler_output = use_pooler_output
+
+    def forward(self, x:BaseModelOutput, attention_mask:TensorType):
+        
+        if (self.use_pooler_output and 
+            isinstance(x, (BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions)) and
+            (x.pooler_output is not None)
+            ):
+            return x.pooler_output
+        
+        return x.last_hidden_state[:, self.cls_token_position, :]
+
+class HFTextEncoder(nn.Module):
+    """HuggingFace model adapter"""
+    def __init__(
+            self, 
+            model_name_or_path: str,
+            output_dim: int,
+            tokenizer_name: str = None,
+            config: PretrainedConfig = None,
+            pooler_type: str = None,
+            proj: str = None,
+            pretrained: bool = True,
+            masked_language_modeling: bool = False):
+        super().__init__()
+
+        self.output_dim = output_dim
+
+        # TODO: find better way to get this information
+        uses_transformer_pooler = (pooler_type == "cls_pooler")
+
+        if transformers is None:
+            raise RuntimeError("Please `pip install transformers` to use pre-trained HuggingFace models")
+        if config is None:
+            self.config = AutoConfig.from_pretrained(model_name_or_path)
+            if masked_language_modeling:
+                create_func, model_args = (AutoModelForMaskedLM.from_pretrained, model_name_or_path) if pretrained else (
+                    AutoModelForMaskedLM.from_config, self.config)
+            else:
+                create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (
+                    AutoModel.from_config, self.config)
+            # TODO: do all model configs have this attribute? PretrainedConfig does so yes??
+            if hasattr(self.config, "is_encoder_decoder") and self.config.is_encoder_decoder:
+                self.transformer = create_func(model_args)
+                self.transformer = self.transformer.encoder
+            else:
+                self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)
+        else:
+            self.config = config
+            if masked_language_modeling:
+                self.transformer = AutoModelForMaskedLM.from_config(config)
+            else:
+                self.transformer = AutoModel.from_config(config)
+
+        if pooler_type is None: # get default arch pooler
+            self.pooler = _POOLERS[(arch_dict[self.config.model_type]["pooler"])]()
+        else:
+            self.pooler = _POOLERS[pooler_type]()
+
+        d_model = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["width"])
+        if (d_model == output_dim) and (proj is None): # do we always need a proj?
+            self.proj = nn.Identity()
+        elif proj == 'linear':
+            self.proj = nn.Linear(d_model, output_dim, bias=False)
+        elif proj == 'mlp':
+            hidden_size = (d_model + output_dim) // 2
+            self.proj = nn.Sequential(
+                nn.Linear(d_model, hidden_size, bias=False),
+                nn.GELU(),
+                nn.Linear(hidden_size, output_dim, bias=False),
+            )
+
+        # self.itm_proj = nn.Linear(d_model, 2, bias=False)
+        # self.mlm_proj = nn.Linear(d_model, self.config.vocab_size), bias=False)
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+
+    # def forward_itm(self, x:TensorType, image_embeds:TensorType) -> TensorType:
+    #     image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(x.device)  
+    #     attn_mask = (x != self.config.pad_token_id).long()
+    #     out = self.transformer(
+    #         input_ids=x, 
+    #         attention_mask=attn_mask,
+    #         encoder_hidden_states = image_embeds,
+    #         encoder_attention_mask = image_atts,
+    #         )
+    #     pooled_out = self.pooler(out, attn_mask)
+
+    #     return self.itm_proj(pooled_out)
+
+    def mask(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None):
+        if masked_indices is None:                                       
+            masked_indices = torch.bernoulli(probability_matrix).bool()
+                                               
+        masked_indices[input_ids == self.tokenizer.pad_token_id] = False
+        masked_indices[input_ids == self.tokenizer.cls_token_id] = False
+        
+        if targets is not None:
+            targets[~masked_indices] = -100 # We only compute loss on masked tokens            
+
+        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
+        indices_replaced = torch.bernoulli(torch.full(input_ids.shape, 0.8)).bool() & masked_indices
+        input_ids[indices_replaced] = self.tokenizer.mask_token_id
+
+        # 10% of the time, we replace masked input tokens with random word
+        indices_random = torch.bernoulli(torch.full(input_ids.shape, 0.5)).bool() & masked_indices & ~indices_replaced
+        random_words = torch.randint(vocab_size, input_ids.shape, dtype=torch.long).to(device)
+        input_ids[indices_random] = random_words[indices_random]                     
+        # The rest of the time (10% of the time) we keep the masked input tokens unchanged   
+        
+        if targets is not None:
+            return input_ids, targets
+        else:
+            return input_ids
+
+    def forward_mlm(self, input_ids, image_embeds, mlm_probability=0.25):
+        labels = input_ids.clone()
+        attn_mask = (input_ids != self.config.pad_token_id).long()
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(input_ids.device) 
+        vocab_size = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["vocab_size"])
+        probability_matrix = torch.full(labels.shape, mlm_probability)
+        input_ids, labels = self.mask(input_ids, vocab_size, input_ids.device, targets=labels,
+                                      probability_matrix = probability_matrix)
+        mlm_output = self.transformer(input_ids,
+                        attention_mask = attn_mask,
+                        encoder_hidden_states = image_embeds,
+                        encoder_attention_mask = image_atts,
+                        return_dict = True,
+                        labels = labels,
+                    )
+        return mlm_output.loss
+        # mlm_output = self.transformer(input_ids,
+        #                 attention_mask = attn_mask,
+        #                 encoder_hidden_states = image_embeds,
+        #                 encoder_attention_mask = image_atts,
+        #                 return_dict = True,
+        #             ).last_hidden_state
+        # logits = self.mlm_proj(mlm_output)
+
+        # # logits = logits[:, :-1, :].contiguous().view(-1, vocab_size)
+        # logits = logits[:, 1:, :].contiguous().view(-1, vocab_size)
+        # labels = labels[:, 1:].contiguous().view(-1)
+
+        # mlm_loss = F.cross_entropy(
+        #     logits,
+        #     labels,
+        #     # label_smoothing=0.1,
+        # )
+        # return mlm_loss
+
+
+    def forward(self, x:TensorType) -> TensorType:
+        attn_mask = (x != self.config.pad_token_id).long()
+        out = self.transformer(input_ids=x, attention_mask=attn_mask)
+        pooled_out = self.pooler(out, attn_mask)
+
+        return self.proj(pooled_out)
+
+    def lock(self, unlocked_layers:int=0, freeze_layer_norm:bool=True):
+        if not unlocked_layers: # full freezing
+             for n, p in self.transformer.named_parameters():
+                 p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
+             return
+
+        encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer
+        layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"])
+        print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model")
+        embeddings = getattr(
+            self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"])
+        modules = [embeddings, *layer_list][:-unlocked_layers]
+        # freeze layers
+        for module in modules:
+            for n, p in module.named_parameters():
+                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
+
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.transformer.gradient_checkpointing_enable()
+
+    def get_num_layers(self):
+        encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer
+        layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"])
+        return len(layer_list)
+
+    def init_parameters(self):
+        pass
diff --git a/modules/pulid/eva_clip/loss.py b/modules/pulid/eva_clip/loss.py
new file mode 100644
index 000000000..473f60d98
--- /dev/null
+++ b/modules/pulid/eva_clip/loss.py
@@ -0,0 +1,138 @@
+import math
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+try:
+    import torch.distributed.nn
+    from torch import distributed as dist
+    has_distributed = True
+except ImportError:
+    has_distributed = False
+
+try:
+    import horovod.torch as hvd
+except ImportError:
+    hvd = None
+
+from timm.loss import LabelSmoothingCrossEntropy
+
+
+def gather_features(
+        image_features,
+        text_features,
+        local_loss=False,
+        gather_with_grad=False,
+        rank=0,
+        world_size=1,
+        use_horovod=False
+):
+    assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
+    if use_horovod:
+        assert hvd is not None, 'Please install horovod'
+        if gather_with_grad:
+            all_image_features = hvd.allgather(image_features)
+            all_text_features = hvd.allgather(text_features)
+        else:
+            with torch.no_grad():
+                all_image_features = hvd.allgather(image_features)
+                all_text_features = hvd.allgather(text_features)
+            if not local_loss:
+                # ensure grads for local rank when all_* features don't have a gradient
+                gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
+                gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
+                gathered_image_features[rank] = image_features
+                gathered_text_features[rank] = text_features
+                all_image_features = torch.cat(gathered_image_features, dim=0)
+                all_text_features = torch.cat(gathered_text_features, dim=0)
+    else:
+        # We gather tensors from all gpus
+        if gather_with_grad:
+            all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
+            all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
+            # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0)
+            # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0)
+        else:
+            gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
+            gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
+            dist.all_gather(gathered_image_features, image_features)
+            dist.all_gather(gathered_text_features, text_features)
+            if not local_loss:
+                # ensure grads for local rank when all_* features don't have a gradient
+                gathered_image_features[rank] = image_features
+                gathered_text_features[rank] = text_features
+            all_image_features = torch.cat(gathered_image_features, dim=0)
+            all_text_features = torch.cat(gathered_text_features, dim=0)
+
+    return all_image_features, all_text_features
+
+
+class ClipLoss(nn.Module):
+
+    def __init__(
+            self,
+            local_loss=False,
+            gather_with_grad=False,
+            cache_labels=False,
+            rank=0,
+            world_size=1,
+            use_horovod=False,
+            smoothing=0.,
+    ):
+        super().__init__()
+        self.local_loss = local_loss
+        self.gather_with_grad = gather_with_grad
+        self.cache_labels = cache_labels
+        self.rank = rank
+        self.world_size = world_size
+        self.use_horovod = use_horovod
+        self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None
+
+        # cache state
+        self.prev_num_logits = 0
+        self.labels = {}
+
+    def forward(self, image_features, text_features, logit_scale=1.):
+        device = image_features.device
+        if self.world_size > 1:
+            all_image_features, all_text_features = gather_features(
+                image_features, text_features,
+                self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod)
+
+            if self.local_loss:
+                logits_per_image = logit_scale * image_features @ all_text_features.T
+                logits_per_text = logit_scale * text_features @ all_image_features.T
+            else:
+                logits_per_image = logit_scale * all_image_features @ all_text_features.T
+                logits_per_text = logits_per_image.T
+        else:
+            logits_per_image = logit_scale * image_features @ text_features.T
+            logits_per_text = logit_scale * text_features @ image_features.T
+        # calculated ground-truth and cache if enabled
+        num_logits = logits_per_image.shape[0]
+        if self.prev_num_logits != num_logits or device not in self.labels:
+            labels = torch.arange(num_logits, device=device, dtype=torch.long)
+            if self.world_size > 1 and self.local_loss:
+                labels = labels + num_logits * self.rank
+            if self.cache_labels:
+                self.labels[device] = labels
+                self.prev_num_logits = num_logits
+        else:
+            labels = self.labels[device]
+        
+        if self.label_smoothing_cross_entropy:
+            total_loss = (
+                self.label_smoothing_cross_entropy(logits_per_image, labels) +
+                self.label_smoothing_cross_entropy(logits_per_text, labels)
+                ) / 2
+        else:
+            total_loss = (
+                F.cross_entropy(logits_per_image, labels) +
+                F.cross_entropy(logits_per_text, labels)
+                ) / 2
+            
+        acc = None
+        i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image)
+        t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text)
+        acc = {"i2t": i2t_acc, "t2i": t2i_acc}
+        return total_loss, acc
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model.py b/modules/pulid/eva_clip/model.py
new file mode 100644
index 000000000..abd8c02db
--- /dev/null
+++ b/modules/pulid/eva_clip/model.py
@@ -0,0 +1,432 @@
+""" CLIP Model
+
+Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
+"""
+import os
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+from functools import partial
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+try:
+    from .hf_model import HFTextEncoder
+except:
+    HFTextEncoder = None
+from .modified_resnet import ModifiedResNet
+from .timm_model import TimmModel
+from .eva_vit_model import EVAVisionTransformer
+from .transformer import LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer
+
+try:
+    from apex.normalization import FusedLayerNorm
+except:
+    FusedLayerNorm = LayerNorm
+
+@dataclass
+class CLIPVisionCfg:
+    layers: Union[Tuple[int, int, int, int], int] = 12
+    width: int = 768
+    head_width: int = 64
+    mlp_ratio: float = 4.0
+    patch_size: int = 16
+    image_size: Union[Tuple[int, int], int] = 224
+    ls_init_value: Optional[float] = None  # layer scale initial value
+    patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
+    global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
+    drop_path_rate: Optional[float] = None  # drop path rate
+    timm_model_name: str = None  # a valid model name overrides layers, width, patch_size
+    timm_model_pretrained: bool = False  # use (imagenet) pretrained weights for named model
+    timm_pool: str = 'avg'  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
+    timm_proj: str = 'linear'  # linear projection for timm model output ('linear', 'mlp', '')
+    timm_proj_bias: bool = False  # enable bias final projection
+    eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size
+    qkv_bias: bool = True
+    fusedLN: bool = False
+    xattn: bool = False
+    postnorm: bool = False
+    rope: bool = False
+    pt_hw_seq_len: int = 16   # 224/14
+    intp_freq: bool = False
+    naiveswiglu: bool = False
+    subln: bool = False
+
+
+@dataclass
+class CLIPTextCfg:
+    context_length: int = 77
+    vocab_size: int = 49408
+    width: int = 512
+    heads: int = 8
+    layers: int = 12
+    ls_init_value: Optional[float] = None  # layer scale initial value
+    hf_model_name: str = None
+    hf_tokenizer_name: str = None
+    hf_model_pretrained: bool = True
+    proj: str = 'mlp'
+    pooler_type: str = 'mean_pooler'
+    masked_language_modeling: bool = False
+    fusedLN: bool = False
+    xattn: bool = False
+    attn_mask: bool = True
+
+def get_cast_dtype(precision: str):
+    cast_dtype = None
+    if precision == 'bf16':
+        cast_dtype = torch.bfloat16
+    elif precision == 'fp16':
+        cast_dtype = torch.float16
+    return cast_dtype
+
+
+def _build_vision_tower(
+        embed_dim: int,
+        vision_cfg: CLIPVisionCfg,
+        quick_gelu: bool = False,
+        cast_dtype: Optional[torch.dtype] = None
+):
+    if isinstance(vision_cfg, dict):
+        vision_cfg = CLIPVisionCfg(**vision_cfg)
+
+    # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
+    # memory efficient in recent PyTorch releases (>= 1.10).
+    # NOTE: timm models always use native GELU regardless of quick_gelu flag.
+    act_layer = QuickGELU if quick_gelu else nn.GELU
+
+    if vision_cfg.eva_model_name:
+        vision_heads = vision_cfg.width // vision_cfg.head_width
+        norm_layer = LayerNorm
+
+        visual = EVAVisionTransformer(
+            img_size=vision_cfg.image_size,
+            patch_size=vision_cfg.patch_size,
+            num_classes=embed_dim,
+            use_mean_pooling=vision_cfg.global_average_pool, #False
+            init_values=vision_cfg.ls_init_value,
+            patch_dropout=vision_cfg.patch_dropout,
+            embed_dim=vision_cfg.width,
+            depth=vision_cfg.layers,
+            num_heads=vision_heads,
+            mlp_ratio=vision_cfg.mlp_ratio,
+            qkv_bias=vision_cfg.qkv_bias,
+            drop_path_rate=vision_cfg.drop_path_rate,
+            norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6),
+            xattn=vision_cfg.xattn,
+            rope=vision_cfg.rope,
+            postnorm=vision_cfg.postnorm,
+            pt_hw_seq_len= vision_cfg.pt_hw_seq_len,   # 224/14
+            intp_freq= vision_cfg.intp_freq,
+            naiveswiglu= vision_cfg.naiveswiglu,
+            subln= vision_cfg.subln
+        )
+    elif vision_cfg.timm_model_name:
+        visual = TimmModel(
+            vision_cfg.timm_model_name,
+            pretrained=vision_cfg.timm_model_pretrained,
+            pool=vision_cfg.timm_pool,
+            proj=vision_cfg.timm_proj,
+            proj_bias=vision_cfg.timm_proj_bias,
+            embed_dim=embed_dim,
+            image_size=vision_cfg.image_size
+        )
+        act_layer = nn.GELU  # so that text transformer doesn't use QuickGELU w/ timm models
+    elif isinstance(vision_cfg.layers, (tuple, list)):
+        vision_heads = vision_cfg.width * 32 // vision_cfg.head_width
+        visual = ModifiedResNet(
+            layers=vision_cfg.layers,
+            output_dim=embed_dim,
+            heads=vision_heads,
+            image_size=vision_cfg.image_size,
+            width=vision_cfg.width
+        )
+    else:
+        vision_heads = vision_cfg.width // vision_cfg.head_width
+        norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
+        visual = VisionTransformer(
+            image_size=vision_cfg.image_size,
+            patch_size=vision_cfg.patch_size,
+            width=vision_cfg.width,
+            layers=vision_cfg.layers,
+            heads=vision_heads,
+            mlp_ratio=vision_cfg.mlp_ratio,
+            ls_init_value=vision_cfg.ls_init_value,
+            patch_dropout=vision_cfg.patch_dropout,
+            global_average_pool=vision_cfg.global_average_pool,
+            output_dim=embed_dim,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+        )
+
+    return visual
+
+
+def _build_text_tower(
+        embed_dim: int,
+        text_cfg: CLIPTextCfg,
+        quick_gelu: bool = False,
+        cast_dtype: Optional[torch.dtype] = None,
+):
+    if isinstance(text_cfg, dict):
+        text_cfg = CLIPTextCfg(**text_cfg)
+
+    if text_cfg.hf_model_name:
+        text = HFTextEncoder(
+            text_cfg.hf_model_name,
+            output_dim=embed_dim,
+            tokenizer_name=text_cfg.hf_tokenizer_name,
+            proj=text_cfg.proj,
+            pooler_type=text_cfg.pooler_type,
+            masked_language_modeling=text_cfg.masked_language_modeling
+       )
+    else:
+        act_layer = QuickGELU if quick_gelu else nn.GELU
+        norm_layer = LayerNorm
+
+        text = TextTransformer(
+            context_length=text_cfg.context_length,
+            vocab_size=text_cfg.vocab_size,
+            width=text_cfg.width,
+            heads=text_cfg.heads,
+            layers=text_cfg.layers,
+            ls_init_value=text_cfg.ls_init_value,
+            output_dim=embed_dim,
+            act_layer=act_layer,
+            norm_layer= FusedLayerNorm if text_cfg.fusedLN else norm_layer,
+            xattn=text_cfg.xattn,
+            attn_mask=text_cfg.attn_mask,
+        )
+    return text
+
+class CLIP(nn.Module):
+    def __init__(
+            self,
+            embed_dim: int,
+            vision_cfg: CLIPVisionCfg,
+            text_cfg: CLIPTextCfg,
+            quick_gelu: bool = False,
+            cast_dtype: Optional[torch.dtype] = None,
+    ):
+        super().__init__()
+        self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
+
+        text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
+        self.transformer = text.transformer
+        self.vocab_size = text.vocab_size
+        self.token_embedding = text.token_embedding
+        self.positional_embedding = text.positional_embedding
+        self.ln_final = text.ln_final
+        self.text_projection = text.text_projection
+        self.register_buffer('attn_mask', text.attn_mask, persistent=False)
+
+        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+    def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
+        # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
+        self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.visual.set_grad_checkpointing(enable)
+        self.transformer.grad_checkpointing = enable
+    
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'logit_scale'}
+
+    def encode_image(self, image, normalize: bool = False):
+        features = self.visual(image)
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def encode_text(self, text, normalize: bool = False):
+        cast_dtype = self.transformer.get_cast_dtype()
+
+        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.to(cast_dtype)
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x, attn_mask=self.attn_mask)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.ln_final(x)  # [batch_size, n_ctx, transformer.width]
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
+        return F.normalize(x, dim=-1) if normalize else x
+
+    def forward(self, image, text):
+        image_features = self.encode_image(image, normalize=True)
+        text_features = self.encode_text(text, normalize=True)
+        return image_features, text_features, self.logit_scale.exp()
+
+
+class CustomCLIP(nn.Module):
+    def __init__(
+            self,
+            embed_dim: int,
+            vision_cfg: CLIPVisionCfg,
+            text_cfg: CLIPTextCfg,
+            quick_gelu: bool = False,
+            cast_dtype: Optional[torch.dtype] = None,
+            itm_task: bool = False,
+    ):
+        super().__init__()
+        self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
+        self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
+        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+    def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
+        # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
+        self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
+
+    def lock_text_tower(self, unlocked_layers:int=0, freeze_layer_norm:bool=True):
+        self.text.lock(unlocked_layers, freeze_layer_norm)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.visual.set_grad_checkpointing(enable)
+        self.text.set_grad_checkpointing(enable)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'logit_scale'}
+
+    def encode_image(self, image, normalize: bool = False):
+        features = self.visual(image)
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def encode_text(self, text, normalize: bool = False):
+        features = self.text(text)
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def forward(self, image, text):
+        image_features = self.encode_image(image, normalize=True)
+        text_features = self.encode_text(text, normalize=True)
+        return image_features, text_features, self.logit_scale.exp()
+
+
+def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
+    """Convert applicable model parameters to low-precision (bf16 or fp16)"""
+
+    def _convert_weights(l):
+        
+        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+            l.weight.data = l.weight.data.to(dtype)
+            if l.bias is not None:
+                l.bias.data = l.bias.data.to(dtype)
+
+        if isinstance(l, (nn.MultiheadAttention, Attention)):
+            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+                tensor = getattr(l, attr, None)
+                if tensor is not None:
+                    tensor.data = tensor.data.to(dtype)
+
+        if isinstance(l, nn.Parameter):
+            l.data = l.data.to(dtype)
+
+        for name in ["text_projection", "proj"]:
+            if hasattr(l, name) and isinstance(l, nn.Parameter):
+                attr = getattr(l, name, None)
+                if attr is not None:
+                    attr.data = attr.data.to(dtype)
+
+    model.apply(_convert_weights)
+
+
+convert_weights_to_fp16 = convert_weights_to_lp  # backwards compat
+
+
+# used to maintain checkpoint compatibility
+def convert_to_custom_text_state_dict(state_dict: dict):
+    if 'text_projection' in state_dict:
+        # old format state_dict, move text tower -> .text
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            if any(k.startswith(p) for p in (
+                'text_projection',
+                'positional_embedding',
+                'token_embedding',
+                'transformer',
+                'ln_final',
+                'logit_scale'
+            )):
+                k = 'text.' + k
+            new_state_dict[k] = v
+        return new_state_dict
+    return state_dict
+
+
+def build_model_from_openai_state_dict(
+        state_dict: dict,
+        quick_gelu=True,
+        cast_dtype=torch.float16,
+):
+    vit = "visual.proj" in state_dict
+
+    if vit:
+        vision_width = state_dict["visual.conv1.weight"].shape[0]
+        vision_layers = len(
+            [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
+        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
+        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
+        image_size = vision_patch_size * grid_size
+    else:
+        counts: list = [
+            len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
+        vision_layers = tuple(counts)
+        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
+        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
+        vision_patch_size = None
+        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
+        image_size = output_width * 32
+
+    embed_dim = state_dict["text_projection"].shape[1]
+    context_length = state_dict["positional_embedding"].shape[0]
+    vocab_size = state_dict["token_embedding.weight"].shape[0]
+    transformer_width = state_dict["ln_final.weight"].shape[0]
+    transformer_heads = transformer_width // 64
+    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
+
+    vision_cfg = CLIPVisionCfg(
+        layers=vision_layers,
+        width=vision_width,
+        patch_size=vision_patch_size,
+        image_size=image_size,
+    )
+    text_cfg = CLIPTextCfg(
+        context_length=context_length,
+        vocab_size=vocab_size,
+        width=transformer_width,
+        heads=transformer_heads,
+        layers=transformer_layers
+    )
+    model = CLIP(
+        embed_dim,
+        vision_cfg=vision_cfg,
+        text_cfg=text_cfg,
+        quick_gelu=quick_gelu,  # OpenAI models were trained with QuickGELU
+        cast_dtype=cast_dtype,
+    )
+
+    for key in ["input_resolution", "context_length", "vocab_size"]:
+        state_dict.pop(key, None)
+
+    convert_weights_to_fp16(model)  # OpenAI state dicts are partially converted to float16
+    model.load_state_dict(state_dict)
+    return model.eval()
+
+
+def trace_model(model, batch_size=256, device=torch.device('cpu')):
+    model.eval()
+    image_size = model.visual.image_size
+    example_images = torch.ones((batch_size, 3, image_size, image_size), device=device)
+    example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device)
+    model = torch.jit.trace_module(
+        model,
+        inputs=dict(
+            forward=(example_images, example_text),
+            encode_text=(example_text,),
+            encode_image=(example_images,)
+        ))
+    model.visual.image_size = image_size
+    return model
diff --git a/modules/pulid/eva_clip/model_configs/EVA01-CLIP-B-16.json b/modules/pulid/eva_clip/model_configs/EVA01-CLIP-B-16.json
new file mode 100644
index 000000000..aad205800
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA01-CLIP-B-16.json
@@ -0,0 +1,19 @@
+{
+    "embed_dim": 512,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 12,
+        "width": 768,
+        "patch_size": 16,
+        "eva_model_name": "eva-clip-b-16",
+        "ls_init_value": 0.1,
+        "drop_path_rate": 0.0
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 512,
+        "heads": 8,
+        "layers": 12
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json b/modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json
new file mode 100644
index 000000000..100279572
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14-plus.json
@@ -0,0 +1,24 @@
+{
+    "embed_dim": 1024,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 40,
+        "width": 1408,
+        "head_width": 88,
+        "mlp_ratio": 4.3637,
+        "patch_size": 14,
+        "eva_model_name": "eva-clip-g-14-x",
+        "drop_path_rate": 0,
+        "xattn": true,
+        "fusedLN": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 1024,
+        "heads": 16,
+        "layers": 24,
+        "xattn": false,
+        "fusedLN": true
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14.json b/modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14.json
new file mode 100644
index 000000000..5d338b4e6
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA01-CLIP-g-14.json
@@ -0,0 +1,24 @@
+{
+    "embed_dim": 1024,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 40,
+        "width": 1408,
+        "head_width": 88,
+        "mlp_ratio": 4.3637,
+        "patch_size": 14,
+        "eva_model_name": "eva-clip-g-14-x",
+        "drop_path_rate": 0.4,
+        "xattn": true,
+        "fusedLN": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 768,
+        "heads": 12,
+        "layers": 12,
+        "xattn": false,
+        "fusedLN": true
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model_configs/EVA02-CLIP-B-16.json b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-B-16.json
new file mode 100644
index 000000000..e4a6e723f
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-B-16.json
@@ -0,0 +1,29 @@
+{
+    "embed_dim": 512,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 12,
+        "width": 768,
+        "head_width": 64,
+        "patch_size": 16,
+        "mlp_ratio": 2.6667,
+        "eva_model_name": "eva-clip-b-16-X",
+        "drop_path_rate": 0.0,
+        "xattn": true,
+        "fusedLN": true,
+        "rope": true,
+        "pt_hw_seq_len": 16,
+        "intp_freq": true,
+        "naiveswiglu": true,
+        "subln": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 512,
+        "heads": 8,
+        "layers": 12,
+        "xattn": true,
+        "fusedLN": true
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14-336.json b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14-336.json
new file mode 100644
index 000000000..3e1d124e1
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14-336.json
@@ -0,0 +1,29 @@
+{
+    "embed_dim": 768,
+    "vision_cfg": {
+        "image_size": 336,
+        "layers": 24,
+        "width": 1024,
+        "drop_path_rate": 0,
+        "head_width": 64,
+        "mlp_ratio": 2.6667,
+        "patch_size": 14,
+        "eva_model_name": "eva-clip-l-14-336",
+        "xattn": true,
+        "fusedLN": true,
+        "rope": true,
+        "pt_hw_seq_len": 16,
+        "intp_freq": true,
+        "naiveswiglu": true,
+        "subln": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 768,
+        "heads": 12,
+        "layers": 12,
+        "xattn": false,
+        "fusedLN": true
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14.json b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14.json
new file mode 100644
index 000000000..03b22ad3c
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-L-14.json
@@ -0,0 +1,29 @@
+{
+    "embed_dim": 768,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 24,
+        "width": 1024,
+        "drop_path_rate": 0,
+        "head_width": 64,
+        "mlp_ratio": 2.6667,
+        "patch_size": 14,
+        "eva_model_name": "eva-clip-l-14",
+        "xattn": true,
+        "fusedLN": true,
+        "rope": true,
+        "pt_hw_seq_len": 16,
+        "intp_freq": true,
+        "naiveswiglu": true,
+        "subln": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 768,
+        "heads": 12,
+        "layers": 12,
+        "xattn": false,
+        "fusedLN": true
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json
new file mode 100644
index 000000000..aa04e2545
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14-plus.json
@@ -0,0 +1,25 @@
+{
+    "embed_dim": 1024,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 64,
+        "width": 1792,
+        "head_width": 112,
+        "mlp_ratio": 8.571428571428571,
+        "patch_size": 14,
+        "eva_model_name": "eva-clip-4b-14-x",
+        "drop_path_rate": 0,
+        "xattn": true,
+        "postnorm": true,
+        "fusedLN": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 1280,
+        "heads": 20,
+        "layers": 32,
+        "xattn": false,
+        "fusedLN": true
+    }
+}
diff --git a/modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14.json b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14.json
new file mode 100644
index 000000000..747ffccc8
--- /dev/null
+++ b/modules/pulid/eva_clip/model_configs/EVA02-CLIP-bigE-14.json
@@ -0,0 +1,25 @@
+{
+    "embed_dim": 1024,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 64,
+        "width": 1792,
+        "head_width": 112,
+        "mlp_ratio": 8.571428571428571,
+        "patch_size": 14,
+        "eva_model_name": "eva-clip-4b-14-x",
+        "drop_path_rate": 0,
+        "xattn": true,
+        "postnorm": true,
+        "fusedLN": true
+    },
+    "text_cfg": {
+        "context_length": 77,
+        "vocab_size": 49408,
+        "width": 1024,
+        "heads": 16,
+        "layers": 24,
+        "xattn": false,
+        "fusedLN": true
+    }
+}
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/modified_resnet.py b/modules/pulid/eva_clip/modified_resnet.py
new file mode 100644
index 000000000..151bfdd0b
--- /dev/null
+++ b/modules/pulid/eva_clip/modified_resnet.py
@@ -0,0 +1,181 @@
+from collections import OrderedDict
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from eva_clip.utils import freeze_batch_norm_2d
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1):
+        super().__init__()
+
+        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
+        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.act1 = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.act2 = nn.ReLU(inplace=True)
+
+        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
+
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.act3 = nn.ReLU(inplace=True)
+
+        self.downsample = None
+        self.stride = stride
+
+        if stride > 1 or inplanes != planes * Bottleneck.expansion:
+            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
+            self.downsample = nn.Sequential(OrderedDict([
+                ("-1", nn.AvgPool2d(stride)),
+                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
+                ("1", nn.BatchNorm2d(planes * self.expansion))
+            ]))
+
+    def forward(self, x: torch.Tensor):
+        identity = x
+
+        out = self.act1(self.bn1(self.conv1(x)))
+        out = self.act2(self.bn2(self.conv2(out)))
+        out = self.avgpool(out)
+        out = self.bn3(self.conv3(out))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.act3(out)
+        return out
+
+
+class AttentionPool2d(nn.Module):
+    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
+        self.k_proj = nn.Linear(embed_dim, embed_dim)
+        self.q_proj = nn.Linear(embed_dim, embed_dim)
+        self.v_proj = nn.Linear(embed_dim, embed_dim)
+        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
+        self.num_heads = num_heads
+
+    def forward(self, x):
+        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
+        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
+        x, _ = F.multi_head_attention_forward(
+            query=x, key=x, value=x,
+            embed_dim_to_check=x.shape[-1],
+            num_heads=self.num_heads,
+            q_proj_weight=self.q_proj.weight,
+            k_proj_weight=self.k_proj.weight,
+            v_proj_weight=self.v_proj.weight,
+            in_proj_weight=None,
+            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
+            bias_k=None,
+            bias_v=None,
+            add_zero_attn=False,
+            dropout_p=0.,
+            out_proj_weight=self.c_proj.weight,
+            out_proj_bias=self.c_proj.bias,
+            use_separate_proj_weight=True,
+            training=self.training,
+            need_weights=False
+        )
+
+        return x[0]
+
+
+class ModifiedResNet(nn.Module):
+    """
+    A ResNet class that is similar to torchvision's but contains the following changes:
+    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
+    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
+    - The final pooling layer is a QKV attention instead of an average pool
+    """
+
+    def __init__(self, layers, output_dim, heads, image_size=224, width=64):
+        super().__init__()
+        self.output_dim = output_dim
+        self.image_size = image_size
+
+        # the 3-layer stem
+        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width // 2)
+        self.act1 = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(width // 2)
+        self.act2 = nn.ReLU(inplace=True)
+        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(width)
+        self.act3 = nn.ReLU(inplace=True)
+        self.avgpool = nn.AvgPool2d(2)
+
+        # residual layers
+        self._inplanes = width  # this is a *mutable* variable used during construction
+        self.layer1 = self._make_layer(width, layers[0])
+        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
+        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
+
+        embed_dim = width * 32  # the ResNet feature dimension
+        self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
+
+        self.init_parameters()
+
+    def _make_layer(self, planes, blocks, stride=1):
+        layers = [Bottleneck(self._inplanes, planes, stride)]
+
+        self._inplanes = planes * Bottleneck.expansion
+        for _ in range(1, blocks):
+            layers.append(Bottleneck(self._inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def init_parameters(self):
+        if self.attnpool is not None:
+            std = self.attnpool.c_proj.in_features ** -0.5
+            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
+            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
+            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
+            nn.init.normal_(self.attnpool.c_proj.weight, std=std)
+
+        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
+            for name, param in resnet_block.named_parameters():
+                if name.endswith("bn3.weight"):
+                    nn.init.zeros_(param)
+
+    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
+        assert unlocked_groups == 0, 'partial locking not currently supported for this model'
+        for param in self.parameters():
+            param.requires_grad = False
+        if freeze_bn_stats:
+            freeze_batch_norm_2d(self)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        # FIXME support for non-transformer
+        pass
+
+    def stem(self, x):
+        x = self.act1(self.bn1(self.conv1(x)))
+        x = self.act2(self.bn2(self.conv2(x)))
+        x = self.act3(self.bn3(self.conv3(x)))
+        x = self.avgpool(x)
+        return x
+
+    def forward(self, x):
+        x = self.stem(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.attnpool(x)
+
+        return x
diff --git a/modules/pulid/eva_clip/openai.py b/modules/pulid/eva_clip/openai.py
new file mode 100644
index 000000000..cc4e13e87
--- /dev/null
+++ b/modules/pulid/eva_clip/openai.py
@@ -0,0 +1,144 @@
+""" OpenAI pretrained model functions
+
+Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
+"""
+
+import os
+import warnings
+from typing import List, Optional, Union
+
+import torch
+
+from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype
+from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url
+
+__all__ = ["list_openai_models", "load_openai_model"]
+
+
+def list_openai_models() -> List[str]:
+    """Returns the names of available CLIP models"""
+    return list_pretrained_models_by_tag('openai')
+
+
+def load_openai_model(
+        name: str,
+        precision: Optional[str] = None,
+        device: Optional[Union[str, torch.device]] = None,
+        jit: bool = True,
+        cache_dir: Optional[str] = None,
+):
+    """Load a CLIP model
+
+    Parameters
+    ----------
+    name : str
+        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
+    precision: str
+        Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'.
+    device : Union[str, torch.device]
+        The device to put the loaded model
+    jit : bool
+        Whether to load the optimized JIT model (default) or more hackable non-JIT model.
+    cache_dir : Optional[str]
+        The directory to cache the downloaded model weights
+
+    Returns
+    -------
+    model : torch.nn.Module
+        The CLIP model
+    preprocess : Callable[[PIL.Image], torch.Tensor]
+        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
+    """
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    if precision is None:
+        precision = 'fp32' if device == 'cpu' else 'fp16'
+
+    if get_pretrained_url(name, 'openai'):
+        model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir)
+    elif os.path.isfile(name):
+        model_path = name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}")
+
+    try:
+        # loading JIT archive
+        model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
+        state_dict = None
+    except RuntimeError:
+        # loading saved state dict
+        if jit:
+            warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
+            jit = False
+        state_dict = torch.load(model_path, map_location="cpu")
+
+    if not jit:
+        # Build a non-jit model from the OpenAI jitted model state dict
+        cast_dtype = get_cast_dtype(precision)
+        try:
+            model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype)
+        except KeyError:
+            sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
+            model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype)
+
+        # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use
+        model = model.to(device)
+        if precision.startswith('amp') or precision == 'fp32':
+            model.float()
+        elif precision == 'bf16':
+            convert_weights_to_lp(model, dtype=torch.bfloat16)
+
+        return model
+
+    # patch the device names
+    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
+    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
+
+    def patch_device(module):
+        try:
+            graphs = [module.graph] if hasattr(module, "graph") else []
+        except RuntimeError:
+            graphs = []
+
+        if hasattr(module, "forward1"):
+            graphs.append(module.forward1.graph)
+
+        for graph in graphs:
+            for node in graph.findAllNodes("prim::Constant"):
+                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
+                    node.copyAttributes(device_node)
+
+    model.apply(patch_device)
+    patch_device(model.encode_image)
+    patch_device(model.encode_text)
+
+    # patch dtype to float32 (typically for CPU)
+    if precision == 'fp32':
+        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
+        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
+        float_node = float_input.node()
+
+        def patch_float(module):
+            try:
+                graphs = [module.graph] if hasattr(module, "graph") else []
+            except RuntimeError:
+                graphs = []
+
+            if hasattr(module, "forward1"):
+                graphs.append(module.forward1.graph)
+
+            for graph in graphs:
+                for node in graph.findAllNodes("aten::to"):
+                    inputs = list(node.inputs())
+                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
+                        if inputs[i].node()["value"] == 5:
+                            inputs[i].node().copyAttributes(float_node)
+
+        model.apply(patch_float)
+        patch_float(model.encode_image)
+        patch_float(model.encode_text)
+        model.float()
+
+    # ensure image_size attr available at consistent location for both jit and non-jit
+    model.visual.image_size = model.input_resolution.item()
+    return model
diff --git a/modules/pulid/eva_clip/pretrained.py b/modules/pulid/eva_clip/pretrained.py
new file mode 100644
index 000000000..a1e55dcf3
--- /dev/null
+++ b/modules/pulid/eva_clip/pretrained.py
@@ -0,0 +1,332 @@
+import hashlib
+import os
+import urllib
+import warnings
+from functools import partial
+from typing import Dict, Union
+
+from tqdm import tqdm
+
+try:
+    from huggingface_hub import hf_hub_download
+    _has_hf_hub = True
+except ImportError:
+    hf_hub_download = None
+    _has_hf_hub = False
+
+
+def _pcfg(url='', hf_hub='', filename='', mean=None, std=None):
+    return dict(
+        url=url,
+        hf_hub=hf_hub,
+        mean=mean,
+        std=std,
+    )
+
+_VITB32 = dict(
+    openai=_pcfg(
+        "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
+    laion400m_e31=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
+    laion400m_e32=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
+    laion2b_e16=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"),
+    laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/')
+)
+
+_VITB32_quickgelu = dict(
+    openai=_pcfg(
+        "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
+    laion400m_e31=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
+    laion400m_e32=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
+)
+
+_VITB16 = dict(
+    openai=_pcfg(
+        "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"),
+    laion400m_e31=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"),
+    laion400m_e32=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"),
+    laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'),
+)
+
+_EVAB16 = dict(
+    eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'),
+    eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'),
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'),
+    eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'),
+)
+
+_VITB16_PLUS_240 = dict(
+    laion400m_e31=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"),
+    laion400m_e32=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"),
+)
+
+_VITL14 = dict(
+    openai=_pcfg(
+        "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"),
+    laion400m_e31=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"),
+    laion400m_e32=_pcfg(
+        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"),
+    laion2b_s32b_b82k=_pcfg(
+        hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+)
+
+_EVAL14 = dict(
+    eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'),
+    eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'),
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'),
+    eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'),
+)
+
+_VITL14_336 = dict(
+    openai=_pcfg(
+        "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"),
+)
+
+_EVAL14_336 = dict(
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'),
+    eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'),
+    eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'),
+    eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'),
+)
+
+_VITH14 = dict(
+    laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'),
+)
+
+_VITg14 = dict(
+    laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'),
+    laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'),
+)
+
+_EVAg14 = dict(
+    eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'),
+    eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'),
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'),
+    eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'),
+)
+
+_EVAg14_PLUS = dict(
+    eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'),
+    eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'),
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'),
+    eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'),
+)
+
+_VITbigG14 = dict(
+    laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'),
+)
+
+_EVAbigE14 = dict(
+    eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'),
+    eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'),
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'),
+    eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'),
+)
+
+_EVAbigE14_PLUS = dict(
+    eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'),
+    eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'),
+    eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'),
+    eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'),
+)
+
+
+_PRETRAINED = {
+    # "ViT-B-32": _VITB32,
+    "OpenaiCLIP-B-32": _VITB32,
+    "OpenCLIP-B-32": _VITB32,
+
+    # "ViT-B-32-quickgelu": _VITB32_quickgelu,
+    "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu,
+    "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu,
+
+    # "ViT-B-16": _VITB16,
+    "OpenaiCLIP-B-16": _VITB16,
+    "OpenCLIP-B-16": _VITB16,
+
+    "EVA02-B-16": _EVAB16,
+    "EVA02-CLIP-B-16": _EVAB16,
+
+    # "ViT-B-16-plus-240": _VITB16_PLUS_240,
+    "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240,
+
+    # "ViT-L-14": _VITL14,
+    "OpenaiCLIP-L-14": _VITL14,
+    "OpenCLIP-L-14": _VITL14,
+
+    "EVA02-L-14": _EVAL14,
+    "EVA02-CLIP-L-14": _EVAL14,
+
+    # "ViT-L-14-336": _VITL14_336,
+    "OpenaiCLIP-L-14-336": _VITL14_336,
+
+    "EVA02-CLIP-L-14-336": _EVAL14_336,
+
+    # "ViT-H-14": _VITH14,
+    # "ViT-g-14": _VITg14,
+    "OpenCLIP-H-14": _VITH14,
+    "OpenCLIP-g-14": _VITg14,
+
+    "EVA01-CLIP-g-14": _EVAg14,
+    "EVA01-CLIP-g-14-plus": _EVAg14_PLUS,
+
+    # "ViT-bigG-14": _VITbigG14,
+    "OpenCLIP-bigG-14": _VITbigG14,
+
+    "EVA02-CLIP-bigE-14": _EVAbigE14,
+    "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS,
+}
+
+
+def _clean_tag(tag: str):
+    # normalize pretrained tags
+    return tag.lower().replace('-', '_')
+
+
+def list_pretrained(as_str: bool = False):
+    """ returns list of pretrained models
+    Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True
+    """
+    return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()]
+
+
+def list_pretrained_models_by_tag(tag: str):
+    """ return all models having the specified pretrain tag """
+    models = []
+    tag = _clean_tag(tag)
+    for k in _PRETRAINED.keys():
+        if tag in _PRETRAINED[k]:
+            models.append(k)
+    return models
+
+
+def list_pretrained_tags_by_model(model: str):
+    """ return all pretrain tags for the specified model architecture """
+    tags = []
+    if model in _PRETRAINED:
+        tags.extend(_PRETRAINED[model].keys())
+    return tags
+
+
+def is_pretrained_cfg(model: str, tag: str):
+    if model not in _PRETRAINED:
+        return False
+    return _clean_tag(tag) in _PRETRAINED[model]
+
+
+def get_pretrained_cfg(model: str, tag: str):
+    if model not in _PRETRAINED:
+        return {}
+    model_pretrained = _PRETRAINED[model]
+    return model_pretrained.get(_clean_tag(tag), {})
+
+
+def get_pretrained_url(model: str, tag: str):
+    cfg = get_pretrained_cfg(model, _clean_tag(tag))
+    return cfg.get('url', '')
+
+
+def download_pretrained_from_url(
+        url: str,
+        cache_dir: Union[str, None] = None,
+):
+    if not cache_dir:
+        cache_dir = os.path.expanduser("~/.cache/clip")
+    os.makedirs(cache_dir, exist_ok=True)
+    filename = os.path.basename(url)
+
+    if 'openaipublic' in url:
+        expected_sha256 = url.split("/")[-2]
+    elif 'mlfoundations' in url:
+        expected_sha256 = os.path.splitext(filename)[0].split("-")[-1]
+    else:
+        expected_sha256 = ''
+
+    download_target = os.path.join(cache_dir, filename)
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        if expected_sha256:
+            if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
+                return download_target
+            else:
+                warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+        else:
+            return download_target
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
+        raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
+
+    return download_target
+
+
+def has_hf_hub(necessary=False):
+    if not _has_hf_hub and necessary:
+        # if no HF Hub module installed, and it is necessary to continue, raise error
+        raise RuntimeError(
+            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
+    return _has_hf_hub
+
+
+def download_pretrained_from_hf(
+        model_id: str,
+        filename: str = 'open_clip_pytorch_model.bin',
+        revision=None,
+        cache_dir: Union[str, None] = None,
+):
+    has_hf_hub(True)
+    cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir)
+    return cached_file
+
+
+def download_pretrained(
+        cfg: Dict,
+        force_hf_hub: bool = False,
+        cache_dir: Union[str, None] = None,
+):
+    target = ''
+    if not cfg:
+        return target
+
+    download_url = cfg.get('url', '')
+    download_hf_hub = cfg.get('hf_hub', '')
+    if download_hf_hub and force_hf_hub:
+        # use HF hub even if url exists
+        download_url = ''
+
+    if download_url:
+        target = download_pretrained_from_url(download_url, cache_dir=cache_dir)
+    elif download_hf_hub:
+        has_hf_hub(True)
+        # we assume the hf_hub entries in pretrained config combine model_id + filename in
+        # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and
+        # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'.
+        model_id, filename = os.path.split(download_hf_hub)
+        if filename:
+            target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir)
+        else:
+            target = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
+
+    return target
diff --git a/modules/pulid/eva_clip/rope.py b/modules/pulid/eva_clip/rope.py
new file mode 100644
index 000000000..69030c35e
--- /dev/null
+++ b/modules/pulid/eva_clip/rope.py
@@ -0,0 +1,137 @@
+from math import pi
+import torch
+from torch import nn
+from einops import rearrange, repeat
+import logging
+
+def broadcat(tensors, dim = -1):
+    num_tensors = len(tensors)
+    shape_lens = set(list(map(lambda t: len(t.shape), tensors)))
+    assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions'
+    shape_len = list(shape_lens)[0]
+    dim = (dim + shape_len) if dim < 0 else dim
+    dims = list(zip(*map(lambda t: list(t.shape), tensors)))
+    expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
+    assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation'
+    max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims))
+    expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims))
+    expanded_dims.insert(dim, (dim, dims[dim]))
+    expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims)))
+    tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes)))
+    return torch.cat(tensors, dim = dim)
+
+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)')
+
+
+class VisionRotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        pt_seq_len,
+        ft_seq_len=None,
+        custom_freqs = None,
+        freqs_for = 'lang',
+        theta = 10000,
+        max_freq = 10,
+        num_freqs = 1,
+    ):
+        super().__init__()
+        if 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()
+        else:
+            raise ValueError(f'unknown modality {freqs_for}')
+
+        if ft_seq_len is None: ft_seq_len = pt_seq_len
+        t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len
+
+        freqs_h = torch.einsum('..., f -> ... f', t, freqs)
+        freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2)
+
+        freqs_w = torch.einsum('..., f -> ... f', t, freqs)
+        freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2)
+
+        freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) 
+
+        self.register_buffer("freqs_cos", freqs.cos())
+        self.register_buffer("freqs_sin", freqs.sin())
+
+        logging.info(f'Shape of rope freq: {self.freqs_cos.shape}')
+
+    def forward(self, t, start_index = 0):
+        rot_dim = self.freqs_cos.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}'
+        t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:]
+        t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin)
+
+        return torch.cat((t_left, t, t_right), dim = -1)
+
+class VisionRotaryEmbeddingFast(nn.Module):
+    def __init__(
+        self,
+        dim,
+        pt_seq_len,
+        ft_seq_len=None,
+        custom_freqs = None,
+        freqs_for = 'lang',
+        theta = 10000,
+        max_freq = 10,
+        num_freqs = 1,
+        patch_dropout = 0.
+    ):
+        super().__init__()
+        if 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()
+        else:
+            raise ValueError(f'unknown modality {freqs_for}')
+
+        if ft_seq_len is None: ft_seq_len = pt_seq_len
+        t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len
+
+        freqs = torch.einsum('..., f -> ... f', t, freqs)
+        freqs = repeat(freqs, '... n -> ... (n r)', r = 2)
+        freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1)
+
+        freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
+        freqs_sin = freqs.sin().view(-1, freqs.shape[-1])
+
+        self.patch_dropout = patch_dropout
+
+        self.register_buffer("freqs_cos", freqs_cos)
+        self.register_buffer("freqs_sin", freqs_sin)
+
+        logging.info(f'Shape of rope freq: {self.freqs_cos.shape}')
+
+    def forward(self, t, patch_indices_keep=None):
+        if patch_indices_keep is not None:
+            batch = t.size()[0]
+            batch_indices = torch.arange(batch)
+            batch_indices = batch_indices[..., None]
+
+            freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
+            freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
+
+            freqs_cos = freqs_cos[batch_indices, patch_indices_keep]
+            freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j')
+            freqs_sin = freqs_sin[batch_indices, patch_indices_keep]
+            freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j')
+
+            return  t * freqs_cos + rotate_half(t) * freqs_sin
+
+        return  t * self.freqs_cos + rotate_half(t) * self.freqs_sin
\ No newline at end of file
diff --git a/modules/pulid/eva_clip/timm_model.py b/modules/pulid/eva_clip/timm_model.py
new file mode 100644
index 000000000..53bc4d469
--- /dev/null
+++ b/modules/pulid/eva_clip/timm_model.py
@@ -0,0 +1,119 @@
+""" timm model adapter
+
+Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
+"""
+import logging
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+try:
+    import timm
+    from timm.models.layers import Mlp, to_2tuple
+    try:
+        # old timm imports < 0.8.1
+        from timm.models.layers.attention_pool2d import RotAttentionPool2d
+        from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d
+    except ImportError:
+        # new timm imports >= 0.8.1
+        from timm.layers import RotAttentionPool2d
+        from timm.layers import AttentionPool2d as AbsAttentionPool2d
+except ImportError:
+    timm = None
+
+from .utils import freeze_batch_norm_2d
+
+
+class TimmModel(nn.Module):
+    """ timm model adapter
+    # FIXME this adapter is a work in progress, may change in ways that break weight compat
+    """
+
+    def __init__(
+            self,
+            model_name,
+            embed_dim,
+            image_size=224,
+            pool='avg',
+            proj='linear',
+            proj_bias=False,
+            drop=0.,
+            pretrained=False):
+        super().__init__()
+
+        self.image_size = to_2tuple(image_size)
+        self.trunk = timm.create_model(model_name, pretrained=pretrained)
+        feat_size = self.trunk.default_cfg.get('pool_size', None)
+        feature_ndim = 1 if not feat_size else 2
+        if pool in ('abs_attn', 'rot_attn'):
+            assert feature_ndim == 2
+            # if attn pooling used, remove both classifier and default pool
+            self.trunk.reset_classifier(0, global_pool='')
+        else:
+            # reset global pool if pool config set, otherwise leave as network default
+            reset_kwargs = dict(global_pool=pool) if pool else {}
+            self.trunk.reset_classifier(0, **reset_kwargs)
+        prev_chs = self.trunk.num_features
+
+        head_layers = OrderedDict()
+        if pool == 'abs_attn':
+            head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)
+            prev_chs = embed_dim
+        elif pool == 'rot_attn':
+            head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)
+            prev_chs = embed_dim
+        else:
+            assert proj, 'projection layer needed if non-attention pooling is used.'
+
+        # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
+        if proj == 'linear':
+            head_layers['drop'] = nn.Dropout(drop)
+            head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)
+        elif proj == 'mlp':
+            head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias))
+
+        self.head = nn.Sequential(head_layers)
+
+    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
+        """ lock modules
+        Args:
+            unlocked_groups (int): leave last n layer groups unlocked (default: 0)
+        """
+        if not unlocked_groups:
+            # lock full model
+            for param in self.trunk.parameters():
+                param.requires_grad = False
+            if freeze_bn_stats:
+                freeze_batch_norm_2d(self.trunk)
+        else:
+            # NOTE: partial freeze requires latest timm (master) branch and is subject to change
+            try:
+                # FIXME import here until API stable and in an official release
+                from timm.models.helpers import group_parameters, group_modules
+            except ImportError:
+                raise RuntimeError('Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')
+            matcher = self.trunk.group_matcher()
+            gparams = group_parameters(self.trunk, matcher)
+            max_layer_id = max(gparams.keys())
+            max_layer_id = max_layer_id - unlocked_groups
+            for group_idx in range(max_layer_id + 1):
+                group = gparams[group_idx]
+                for param in group:
+                    self.trunk.get_parameter(param).requires_grad = False
+            if freeze_bn_stats:
+                gmodules = group_modules(self.trunk, matcher, reverse=True)
+                gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}
+                freeze_batch_norm_2d(self.trunk, gmodules)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        try:
+            self.trunk.set_grad_checkpointing(enable)
+        except Exception as e:
+            logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')
+
+    def forward(self, x):
+        x = self.trunk(x)
+        x = self.head(x)
+        return x
diff --git a/modules/pulid/eva_clip/tokenizer.py b/modules/pulid/eva_clip/tokenizer.py
new file mode 100644
index 000000000..41482f82a
--- /dev/null
+++ b/modules/pulid/eva_clip/tokenizer.py
@@ -0,0 +1,201 @@
+""" CLIP tokenizer
+
+Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
+"""
+import gzip
+import html
+import os
+from functools import lru_cache
+from typing import Union, List
+
+import ftfy
+import regex as re
+import torch
+
+# https://stackoverflow.com/q/62691279
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+@lru_cache()
+def default_bpe():
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8+n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r'\s+', ' ', text)
+    text = text.strip()
+    return text
+
+
+class SimpleTokenizer(object):
+    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
+        merges = merges[1:49152-256-2+1]
+        merges = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v+'</w>' for v in vocab]
+        for merge in merges:
+            vocab.append(''.join(merge))
+        if not special_tokens:
+            special_tokens = ['<start_of_text>', '<end_of_text>']
+        else:
+            special_tokens = ['<start_of_text>', '<end_of_text>'] + special_tokens
+        vocab.extend(special_tokens)
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {t:t for t in special_tokens}
+        special = "|".join(special_tokens)
+        self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
+
+        self.vocab_size = len(self.encoder)
+        self.all_special_ids = [self.encoder[t] for t in special_tokens]
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token+'</w>'
+
+        while True:
+            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word)-1 and word[i+1] == second:
+                    new_word.append(first+second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = ' '.join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = ''.join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
+        return text
+
+
+_tokenizer = SimpleTokenizer()
+
+
+def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
+    """
+    Returns the tokenized representation of given input string(s)
+
+    Parameters
+    ----------
+    texts : Union[str, List[str]]
+        An input string or a list of input strings to tokenize
+    context_length : int
+        The context length to use; all CLIP models use 77 as the context length
+
+    Returns
+    -------
+    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
+    """
+    if isinstance(texts, str):
+        texts = [texts]
+
+    sot_token = _tokenizer.encoder["<start_of_text>"]
+    eot_token = _tokenizer.encoder["<end_of_text>"]
+    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
+    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+
+    for i, tokens in enumerate(all_tokens):
+        if len(tokens) > context_length:
+            tokens = tokens[:context_length]  # Truncate
+            tokens[-1] = eot_token
+        result[i, :len(tokens)] = torch.tensor(tokens)
+
+    return result
+
+
+class HFTokenizer:
+    "HuggingFace tokenizer wrapper"
+    def __init__(self, tokenizer_name:str):
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+
+    def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor:
+        # same cleaning as for default tokenizer, except lowercasing
+        # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance
+        if isinstance(texts, str):
+            texts = [texts]
+        texts = [whitespace_clean(basic_clean(text)) for text in texts]
+        input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids
+        return input_ids
diff --git a/modules/pulid/eva_clip/transform.py b/modules/pulid/eva_clip/transform.py
new file mode 100644
index 000000000..39f3e4cf6
--- /dev/null
+++ b/modules/pulid/eva_clip/transform.py
@@ -0,0 +1,103 @@
+from typing import Optional, Sequence, Tuple
+
+import torch
+import torch.nn as nn
+import torchvision.transforms.functional as F
+
+from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
+    CenterCrop
+
+from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
+
+
+class ResizeMaxSize(nn.Module):
+
+    def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):
+        super().__init__()
+        if not isinstance(max_size, int):
+            raise TypeError(f"Size should be int. Got {type(max_size)}")
+        self.max_size = max_size
+        self.interpolation = interpolation
+        self.fn = min if fn == 'min' else min
+        self.fill = fill
+
+    def forward(self, img):
+        if isinstance(img, torch.Tensor):
+            height, width = img.shape[:2]
+        else:
+            width, height = img.size
+        scale = self.max_size / float(max(height, width))
+        if scale != 1.0:
+            new_size = tuple(round(dim * scale) for dim in (height, width))
+            img = F.resize(img, new_size, self.interpolation)
+            pad_h = self.max_size - new_size[0]
+            pad_w = self.max_size - new_size[1]
+            img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)
+        return img
+
+
+def _convert_to_rgb(image):
+    return image.convert('RGB')
+
+
+# class CatGen(nn.Module):
+#     def __init__(self, num=4):
+#         self.num = num
+#     def mixgen_batch(image, text):
+#         batch_size = image.shape[0]
+#         index = np.random.permutation(batch_size)
+
+#         cat_images = []
+#         for i in range(batch_size):
+#             # image mixup
+#             image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:]
+#             # text concat
+#             text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0]
+#         text = torch.stack(text)
+#         return image, text
+
+
+def image_transform(
+        image_size: int,
+        is_train: bool,
+        mean: Optional[Tuple[float, ...]] = None,
+        std: Optional[Tuple[float, ...]] = None,
+        resize_longest_max: bool = False,
+        fill_color: int = 0,
+):
+    mean = mean or OPENAI_DATASET_MEAN
+    if not isinstance(mean, (list, tuple)):
+        mean = (mean,) * 3
+
+    std = std or OPENAI_DATASET_STD
+    if not isinstance(std, (list, tuple)):
+        std = (std,) * 3
+
+    if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
+        # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
+        image_size = image_size[0]
+
+    normalize = Normalize(mean=mean, std=std)
+    if is_train:
+        return Compose([
+            RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC),
+            _convert_to_rgb,
+            ToTensor(),
+            normalize,
+        ])
+    else:
+        if resize_longest_max:
+            transforms = [
+                ResizeMaxSize(image_size, fill=fill_color)
+            ]
+        else:
+            transforms = [
+                Resize(image_size, interpolation=InterpolationMode.BICUBIC),
+                CenterCrop(image_size),
+            ]
+        transforms.extend([
+            _convert_to_rgb,
+            ToTensor(),
+            normalize,
+        ])
+        return Compose(transforms)
diff --git a/modules/pulid/eva_clip/transformer.py b/modules/pulid/eva_clip/transformer.py
new file mode 100644
index 000000000..1e0a52ceb
--- /dev/null
+++ b/modules/pulid/eva_clip/transformer.py
@@ -0,0 +1,721 @@
+import os
+import logging
+from collections import OrderedDict
+import math
+from typing import Callable, Optional, Sequence
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+try:
+    from timm.models.layers import trunc_normal_
+except:
+    from timm.layers import trunc_normal_
+    
+from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast
+from .utils import to_2tuple
+
+
+class LayerNormFp32(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back)."""
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x: torch.Tensor):
+        output = F.layer_norm(
+            x.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(x)
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm (with cast back to input dtype)."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+        return x.to(orig_type)
+
+class QuickGELU(nn.Module):
+    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class LayerScale(nn.Module):
+    def __init__(self, dim, init_values=1e-5, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+class PatchDropout(nn.Module):
+    """
+    https://arxiv.org/abs/2212.00794
+    """
+
+    def __init__(self, prob, exclude_first_token=True):
+        super().__init__()
+        assert 0 <= prob < 1.
+        self.prob = prob
+        self.exclude_first_token = exclude_first_token  # exclude CLS token
+        logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}")
+
+    def forward(self, x):
+        if not self.training or self.prob == 0.:
+            return x
+
+        if self.exclude_first_token:
+            cls_tokens, x = x[:, :1], x[:, 1:]
+        else:
+            cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
+
+        batch = x.size()[0]
+        num_tokens = x.size()[1]
+
+        batch_indices = torch.arange(batch)
+        batch_indices = batch_indices[..., None]
+
+        keep_prob = 1 - self.prob
+        num_patches_keep = max(1, int(num_tokens * keep_prob))
+
+        rand = torch.randn(batch, num_tokens)
+        patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
+
+        x = x[batch_indices, patch_indices_keep]
+
+        if self.exclude_first_token:
+            x = torch.cat((cls_tokens, x), dim=1)
+
+        if self.training and os.getenv('RoPE') == '1':
+            return x, patch_indices_keep
+
+        return x
+
+
+def _in_projection_packed(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    w: torch.Tensor,
+    b: Optional[torch.Tensor] = None,
+    ):
+    """
+    https://github.com/pytorch/pytorch/blob/db2a237763eb8693a20788be94f8c192e762baa8/torch/nn/functional.py#L4726
+    """
+    E = q.size(-1)
+    if k is v:
+        if q is k:
+            # self-attention
+            return F.linear(q, w, b).chunk(3, dim=-1)
+        else:
+            # encoder-decoder attention
+            w_q, w_kv = w.split([E, E * 2])
+            if b is None:
+                b_q = b_kv = None
+            else:
+                b_q, b_kv = b.split([E, E * 2])
+            return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1)
+    else:
+        w_q, w_k, w_v = w.chunk(3)
+        if b is None:
+            b_q = b_k = b_v = None
+        else:
+            b_q, b_k, b_v = b.chunk(3)
+        return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
+
+class Attention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=True,
+            scaled_cosine=False,
+            scale_heads=False,
+            logit_scale_max=math.log(1. / 0.01),
+            attn_drop=0.,
+            proj_drop=0.,
+            xattn=False,
+            rope=False
+    ):
+        super().__init__()
+        self.scaled_cosine = scaled_cosine
+        self.scale_heads = scale_heads
+        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.logit_scale_max = logit_scale_max
+
+        # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original
+        self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)
+        if qkv_bias:
+            self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))
+        else:
+            self.in_proj_bias = None
+
+        if self.scaled_cosine:
+            self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
+        else:
+            self.logit_scale = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        if self.scale_heads:
+            self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))
+        else:
+            self.head_scale = None
+        self.out_proj = nn.Linear(dim, dim)
+        self.out_drop = nn.Dropout(proj_drop)
+        self.xattn = xattn
+        self.xattn_drop = attn_drop
+        self.rope = rope
+
+    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
+        L, N, C = x.shape
+        q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)
+        if self.xattn:
+            q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1)
+            k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1)
+            v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1)
+
+            x = xops.memory_efficient_attention(
+                q, k, v,
+                p=self.xattn_drop,
+                scale=self.scale if self.logit_scale is None else None,
+                attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None,
+                )
+        else:
+            q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
+            k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
+            v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
+
+            if self.logit_scale is not None:
+                attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))
+                logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
+                attn = attn.view(N, self.num_heads, L, L) * logit_scale
+                attn = attn.view(-1, L, L)
+            else:
+                q = q * self.scale
+                attn = torch.bmm(q, k.transpose(-1, -2))
+
+            if attn_mask is not None:
+                if attn_mask.dtype == torch.bool:
+                    new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
+                    new_attn_mask.masked_fill_(attn_mask, float("-inf"))
+                    attn_mask = new_attn_mask
+                attn += attn_mask
+
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+
+            x = torch.bmm(attn, v)
+
+        if self.head_scale is not None:
+            x = x.view(N, self.num_heads, L, C) * self.head_scale
+            x = x.view(-1, L, C)
+        x = x.transpose(0, 1).reshape(L, N, C)
+        x = self.out_proj(x)
+        x = self.out_drop(x)
+        return x
+
+class CustomAttention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=True,
+            scaled_cosine=True,
+            scale_heads=False,
+            logit_scale_max=math.log(1. / 0.01),
+            attn_drop=0.,
+            proj_drop=0.,
+            xattn=False
+    ):
+        super().__init__()
+        self.scaled_cosine = scaled_cosine
+        self.scale_heads = scale_heads
+        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.logit_scale_max = logit_scale_max
+
+        # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original
+        self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)
+        if qkv_bias:
+            self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))
+        else:
+            self.in_proj_bias = None
+
+        if self.scaled_cosine:
+            self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
+        else:
+            self.logit_scale = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        if self.scale_heads:
+            self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))
+        else:
+            self.head_scale = None
+        self.out_proj = nn.Linear(dim, dim)
+        self.out_drop = nn.Dropout(proj_drop)
+        self.xattn = xattn
+        self.xattn_drop = attn_drop
+
+    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias)
+        N_q, B_q, C_q = q.shape
+        N_k, B_k, C_k = k.shape
+        N_v, B_v, C_v = v.shape
+        if self.xattn:
+            # B, N, C -> B, N, num_heads, C
+            q = q.permute(1, 0, 2).reshape(B_q, N_q, self.num_heads, -1)
+            k = k.permute(1, 0, 2).reshape(B_k, N_k, self.num_heads, -1)
+            v = v.permute(1, 0, 2).reshape(B_v, N_v, self.num_heads, -1)
+
+            x = xops.memory_efficient_attention(
+                q, k, v,
+                p=self.xattn_drop,
+                scale=self.scale if self.logit_scale is None else None,
+                attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None
+                )
+        else:
+            # B*H, L, C
+            q = q.contiguous().view(N_q, B_q * self.num_heads, -1).transpose(0, 1)
+            k = k.contiguous().view(N_k, B_k * self.num_heads, -1).transpose(0, 1)
+            v = v.contiguous().view(N_v, B_v * self.num_heads, -1).transpose(0, 1)
+
+            if self.logit_scale is not None:
+                # B*H, N_q, N_k
+                attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))
+                logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
+                attn = attn.view(B_q, self.num_heads, N_q, N_k) * logit_scale
+                attn = attn.view(-1, N_q, N_k)
+            else:
+                q = q * self.scale
+                attn = torch.bmm(q, k.transpose(-1, -2))
+
+            if attn_mask is not None:
+                if attn_mask.dtype == torch.bool:
+                    new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
+                    new_attn_mask.masked_fill_(attn_mask, float("-inf"))
+                    attn_mask = new_attn_mask
+                attn += attn_mask
+
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+
+            x = torch.bmm(attn, v)
+            
+        if self.head_scale is not None:
+            x = x.view(B_q, self.num_heads, N_q, C_q) * self.head_scale
+            x = x.view(-1, N_q, C_q)
+        x = x.transpose(0, 1).reshape(N_q, B_q, C_q)
+        x = self.out_proj(x)
+        x = self.out_drop(x)
+        return x
+
+class CustomResidualAttentionBlock(nn.Module):
+    def __init__(
+            self,
+            d_model: int,
+            n_head: int,
+            mlp_ratio: float = 4.0,
+            ls_init_value: float = None,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = LayerNorm,
+            scale_cosine_attn: bool = False,
+            scale_heads: bool = False,
+            scale_attn: bool = False,
+            scale_fc: bool = False,
+            cross_attn: bool = False,
+            xattn: bool = False,
+    ):
+        super().__init__()
+
+        self.ln_1 = norm_layer(d_model)
+        self.ln_1_k = norm_layer(d_model) if cross_attn else self.ln_1
+        self.ln_1_v = norm_layer(d_model) if cross_attn else self.ln_1
+        self.attn = CustomAttention(
+            d_model, n_head,
+            qkv_bias=True,
+            attn_drop=0.,
+            proj_drop=0.,
+            scaled_cosine=scale_cosine_attn,
+            scale_heads=scale_heads,
+            xattn=xattn
+        )
+
+        self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity()
+        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
+
+        self.ln_2 = norm_layer(d_model)
+        mlp_width = int(d_model * mlp_ratio)
+        self.mlp = nn.Sequential(OrderedDict([
+            ("c_fc", nn.Linear(d_model, mlp_width)),
+            ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()),
+            ("gelu", act_layer()),
+            ("c_proj", nn.Linear(mlp_width, d_model))
+        ]))
+
+        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        q = q + self.ls_1(self.ln_attn(self.attn(self.ln_1(q), self.ln_1_k(k), self.ln_1_v(v), attn_mask=attn_mask)))
+        q = q + self.ls_2(self.mlp(self.ln_2(q)))
+        return q
+
+class CustomTransformer(nn.Module):
+    def __init__(
+            self,
+            width: int,
+            layers: int,
+            heads: int,
+            mlp_ratio: float = 4.0,
+            ls_init_value: float = None,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = LayerNorm,
+            scale_cosine_attn: bool = True,
+            scale_heads: bool = False,
+            scale_attn: bool = False,
+            scale_fc: bool = False,
+            cross_attn: bool = False,
+            xattn: bool = False,
+    ):
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.grad_checkpointing = False
+        self.xattn = xattn
+
+        self.resblocks = nn.ModuleList([
+            CustomResidualAttentionBlock(
+                width,
+                heads,
+                mlp_ratio,
+                ls_init_value=ls_init_value,
+                act_layer=act_layer,
+                norm_layer=norm_layer,
+                scale_cosine_attn=scale_cosine_attn,
+                scale_heads=scale_heads,
+                scale_attn=scale_attn,
+                scale_fc=scale_fc,
+                cross_attn=cross_attn,
+                xattn=xattn)
+            for _ in range(layers)
+        ])
+
+    def get_cast_dtype(self) -> torch.dtype:
+        return self.resblocks[0].mlp.c_fc.weight.dtype 
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor = None, v: torch.Tensor = None, attn_mask: Optional[torch.Tensor] = None):
+        if k is None and v is None:
+            k = v = q
+        for r in self.resblocks:
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                q = checkpoint(r, q, k, v, attn_mask)
+            else:
+                q = r(q, k, v, attn_mask=attn_mask)
+        return q
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+            self,
+            d_model: int,
+            n_head: int,
+            mlp_ratio: float = 4.0,
+            ls_init_value: float = None,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = LayerNorm,
+            xattn: bool = False,
+    ):
+        super().__init__()
+
+        self.ln_1 = norm_layer(d_model)
+        if xattn:
+            self.attn = Attention(d_model, n_head, xattn=True)
+        else:
+            self.attn = nn.MultiheadAttention(d_model, n_head)
+        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
+
+        self.ln_2 = norm_layer(d_model)
+        mlp_width = int(d_model * mlp_ratio)
+        self.mlp = nn.Sequential(OrderedDict([
+            ("c_fc", nn.Linear(d_model, mlp_width)),
+            ("gelu", act_layer()),
+            ("c_proj", nn.Linear(mlp_width, d_model))
+        ]))
+
+        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
+        self.xattn = xattn
+
+    def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None
+        if self.xattn:
+            return self.attn(x, attn_mask=attn_mask)
+        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
+
+    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask))
+        x = x + self.ls_2(self.mlp(self.ln_2(x)))
+        return x
+
+class Transformer(nn.Module):
+    def __init__(
+            self,
+            width: int,
+            layers: int,
+            heads: int,
+            mlp_ratio: float = 4.0,
+            ls_init_value: float = None,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = LayerNorm,
+            xattn: bool = False,
+    ):
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.grad_checkpointing = False
+
+        self.resblocks = nn.ModuleList([
+            ResidualAttentionBlock(
+                width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, xattn=xattn)
+            for _ in range(layers)
+        ])
+
+    def get_cast_dtype(self) -> torch.dtype:
+        return self.resblocks[0].mlp.c_fc.weight.dtype
+
+    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        for r in self.resblocks:
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint(r, x, attn_mask)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        return x
+
+
+class VisionTransformer(nn.Module):
+    def __init__(
+            self,
+            image_size: int,
+            patch_size: int,
+            width: int,
+            layers: int,
+            heads: int,
+            mlp_ratio: float,
+            ls_init_value: float = None,
+            patch_dropout: float = 0.,
+            global_average_pool: bool = False,
+            output_dim: int = 512,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = LayerNorm,
+            xattn: bool = False,
+    ):
+        super().__init__()
+        self.image_size = to_2tuple(image_size)
+        self.patch_size = to_2tuple(patch_size)
+        self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1])
+        self.output_dim = output_dim
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
+
+        scale = width ** -0.5
+        self.class_embedding = nn.Parameter(scale * torch.randn(width))
+        self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))
+
+        # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
+        self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
+        self.ln_pre = norm_layer(width)
+        
+        self.transformer = Transformer(
+            width,
+            layers,
+            heads,
+            mlp_ratio,
+            ls_init_value=ls_init_value,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            xattn=xattn
+        )
+
+        self.global_average_pool = global_average_pool
+        self.ln_post = norm_layer(width)
+        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
+
+    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
+        for param in self.parameters():
+            param.requires_grad = False
+        
+        if unlocked_groups != 0:
+            groups = [
+                [
+                    self.conv1,
+                    self.class_embedding,
+                    self.positional_embedding,
+                    self.ln_pre,
+                ],
+                *self.transformer.resblocks[:-1],
+                [
+                    self.transformer.resblocks[-1],
+                    self.ln_post,
+                ],
+                self.proj,
+            ]
+
+            def _unlock(x):
+                if isinstance(x, Sequence):
+                    for g in x:
+                        _unlock(g)
+                else:
+                    if isinstance(x, torch.nn.Parameter):
+                        x.requires_grad = True
+                    else:
+                        for p in x.parameters():
+                            p.requires_grad = True
+
+            _unlock(groups[-unlocked_groups:])
+
+    def get_num_layers(self):
+        return self.transformer.layers
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.transformer.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'positional_embedding', 'class_embedding'}
+
+    def forward(self, x: torch.Tensor, return_all_features: bool=False):
+        x = self.conv1(x)  # shape = [*, width, grid, grid]
+        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+        x = torch.cat(
+            [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+             x], dim=1)  # shape = [*, grid ** 2 + 1, width]
+        x = x + self.positional_embedding.to(x.dtype)
+
+        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
+        x = self.patch_dropout(x)
+        x = self.ln_pre(x)
+
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+
+        if not return_all_features:
+            if self.global_average_pool:
+                x = x.mean(dim=1) #x = x[:,1:,:].mean(dim=1)
+            else:
+                x = x[:, 0]
+
+            x = self.ln_post(x)
+
+            if self.proj is not None:
+                x = x @ self.proj
+
+        return x
+
+
+class TextTransformer(nn.Module):
+    def __init__(
+            self,
+            context_length: int = 77,
+            vocab_size: int = 49408,
+            width: int = 512,
+            heads: int = 8,
+            layers: int = 12,
+            ls_init_value: float = None,
+            output_dim: int = 512,
+            act_layer: Callable = nn.GELU,
+            norm_layer: Callable = LayerNorm,
+            xattn: bool= False,
+            attn_mask: bool = True
+    ):
+        super().__init__()
+        self.context_length = context_length
+        self.vocab_size = vocab_size
+        self.width = width
+        self.output_dim = output_dim
+
+        self.token_embedding = nn.Embedding(vocab_size, width)
+        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))
+        self.transformer = Transformer(
+            width=width,
+            layers=layers,
+            heads=heads,
+            ls_init_value=ls_init_value,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            xattn=xattn
+        )
+        
+        self.xattn = xattn
+        self.ln_final = norm_layer(width)
+        self.text_projection = nn.Parameter(torch.empty(width, output_dim))
+
+        if attn_mask:
+            self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
+        else:
+            self.attn_mask = None
+
+        self.init_parameters()
+
+    def init_parameters(self):
+        nn.init.normal_(self.token_embedding.weight, std=0.02)
+        nn.init.normal_(self.positional_embedding, std=0.01)
+
+        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
+        attn_std = self.transformer.width ** -0.5
+        fc_std = (2 * self.transformer.width) ** -0.5
+        for block in self.transformer.resblocks:
+            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
+            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
+            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
+            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
+
+        if self.text_projection is not None:
+            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.transformer.grad_checkpointing = enable
+    
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        # return {'positional_embedding', 'token_embedding'}
+        return {'positional_embedding'}
+
+    def get_num_layers(self):
+        return self.transformer.layers
+
+    def build_attention_mask(self):
+        # lazily create causal attention mask, with full attention between the vision tokens
+        # pytorch uses additive attention mask; fill with -inf
+        mask = torch.empty(self.context_length, self.context_length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        return mask
+
+    def forward(self, text, return_all_features: bool=False):
+        cast_dtype = self.transformer.get_cast_dtype()
+        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.to(cast_dtype)
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x, attn_mask=self.attn_mask)
+        # x = self.transformer(x) # no attention mask is applied
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.ln_final(x)
+
+        if not return_all_features:
+            # x.shape = [batch_size, n_ctx, transformer.width]
+            # take features from the eot embedding (eot_token is the highest number in each sequence)
+            x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
+        return x
diff --git a/modules/pulid/eva_clip/utils.py b/modules/pulid/eva_clip/utils.py
new file mode 100644
index 000000000..bdc5a7a45
--- /dev/null
+++ b/modules/pulid/eva_clip/utils.py
@@ -0,0 +1,326 @@
+from itertools import repeat
+import collections.abc
+import logging
+import math
+import numpy as np
+
+import torch
+from torch import nn as nn
+from torchvision.ops.misc import FrozenBatchNorm2d
+import torch.nn.functional as F
+
+# open CLIP
+def resize_clip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1):
+    # Rescale the grid of position embeddings when loading from state_dict
+    old_pos_embed = state_dict.get('visual.positional_embedding', None)
+    if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):
+        return
+    grid_size = to_2tuple(model.visual.grid_size)
+    extra_tokens = 1  # FIXME detect different token configs (ie no class token, or more)
+    new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
+    if new_seq_len == old_pos_embed.shape[0]:
+        return
+
+    if extra_tokens:
+        pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
+    else:
+        pos_emb_tok, pos_emb_img = None, old_pos_embed
+    old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
+
+    logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
+    pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
+    pos_emb_img = F.interpolate(
+        pos_emb_img,
+        size=grid_size,
+        mode=interpolation,
+        align_corners=True,
+    )
+    pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
+    if pos_emb_tok is not None:
+        new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
+    else:
+        new_pos_embed = pos_emb_img
+    state_dict['visual.positional_embedding'] = new_pos_embed
+
+
+def resize_visual_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1):
+    # Rescale the grid of position embeddings when loading from state_dict
+    old_pos_embed = state_dict.get('positional_embedding', None)
+    if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):
+        return
+    grid_size = to_2tuple(model.visual.grid_size)
+    extra_tokens = 1  # FIXME detect different token configs (ie no class token, or more)
+    new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
+    if new_seq_len == old_pos_embed.shape[0]:
+        return
+
+    if extra_tokens:
+        pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
+    else:
+        pos_emb_tok, pos_emb_img = None, old_pos_embed
+    old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
+
+    logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
+    pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
+    pos_emb_img = F.interpolate(
+        pos_emb_img,
+        size=grid_size,
+        mode=interpolation,
+        align_corners=True,
+    )
+    pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
+    if pos_emb_tok is not None:
+        new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
+    else:
+        new_pos_embed = pos_emb_img
+    state_dict['positional_embedding'] = new_pos_embed
+
+def resize_evaclip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1):
+    all_keys = list(state_dict.keys())
+    # interpolate position embedding
+    if 'visual.pos_embed' in state_dict:
+        pos_embed_checkpoint = state_dict['visual.pos_embed']
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.visual.patch_embed.num_patches
+        num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            state_dict['visual.pos_embed'] = new_pos_embed
+
+            patch_embed_proj = state_dict['visual.patch_embed.proj.weight']
+            patch_size = model.visual.patch_embed.patch_size
+            state_dict['visual.patch_embed.proj.weight'] = torch.nn.functional.interpolate(
+                patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False)
+
+
+def resize_eva_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1):
+    all_keys = list(state_dict.keys())
+    # interpolate position embedding
+    if 'pos_embed' in state_dict:
+        pos_embed_checkpoint = state_dict['pos_embed']
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.visual.patch_embed.num_patches
+        num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            state_dict['pos_embed'] = new_pos_embed
+
+            patch_embed_proj = state_dict['patch_embed.proj.weight']
+            patch_size = model.visual.patch_embed.patch_size
+            state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate(
+                patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False)
+                
+
+def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1):
+    all_keys = list(state_dict.keys())
+    for key in all_keys:
+        if "relative_position_index" in key:
+            state_dict.pop(key)
+
+        if "relative_position_bias_table" in key:
+            rel_pos_bias = state_dict[key]
+            src_num_pos, num_attn_heads = rel_pos_bias.size()
+            dst_num_pos, _ = model.visual.state_dict()[key].size()
+            dst_patch_shape = model.visual.patch_embed.patch_shape
+            if dst_patch_shape[0] != dst_patch_shape[1]:
+                raise NotImplementedError()
+            num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)
+            src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
+            dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
+            if src_size != dst_size:
+                print("Position interpolate for %s from %dx%d to %dx%d" % (
+                    key, src_size, src_size, dst_size, dst_size))
+                extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
+                rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
+
+                def geometric_progression(a, r, n):
+                    return a * (1.0 - r ** n) / (1.0 - r)
+
+                left, right = 1.01, 1.5
+                while right - left > 1e-6:
+                    q = (left + right) / 2.0
+                    gp = geometric_progression(1, q, src_size // 2)
+                    if gp > dst_size // 2:
+                        right = q
+                    else:
+                        left = q
+
+                # if q > 1.090307:
+                #     q = 1.090307
+
+                dis = []
+                cur = 1
+                for i in range(src_size // 2):
+                    dis.append(cur)
+                    cur += q ** (i + 1)
+
+                r_ids = [-_ for _ in reversed(dis)]
+
+                x = r_ids + [0] + dis
+                y = r_ids + [0] + dis
+
+                t = dst_size // 2.0
+                dx = np.arange(-t, t + 0.1, 1.0)
+                dy = np.arange(-t, t + 0.1, 1.0)
+
+                print("Original positions = %s" % str(x))
+                print("Target positions = %s" % str(dx))
+
+                all_rel_pos_bias = []
+
+                for i in range(num_attn_heads):
+                    z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
+                    f = F.interpolate.interp2d(x, y, z, kind='cubic')
+                    all_rel_pos_bias.append(
+                        torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
+
+                rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
+
+                new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
+                state_dict[key] = new_rel_pos_bias
+
+    # interpolate position embedding
+    if 'pos_embed' in state_dict:
+        pos_embed_checkpoint = state_dict['pos_embed']
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.visual.patch_embed.num_patches
+        num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            state_dict['pos_embed'] = new_pos_embed
+
+            patch_embed_proj = state_dict['patch_embed.proj.weight']
+            patch_size = model.visual.patch_embed.patch_size
+            state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate(
+                patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False)
+
+
+def freeze_batch_norm_2d(module, module_match={}, name=''):
+    """
+    Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
+    itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and
+    returned. Otherwise, the module is walked recursively and submodules are converted in place.
+
+    Args:
+        module (torch.nn.Module): Any PyTorch module.
+        module_match (dict): Dictionary of full module names to freeze (all if empty)
+        name (str): Full module name (prefix)
+
+    Returns:
+        torch.nn.Module: Resulting module
+
+    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
+    """
+    res = module
+    is_match = True
+    if module_match:
+        is_match = name in module_match
+    if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)):
+        res = FrozenBatchNorm2d(module.num_features)
+        res.num_features = module.num_features
+        res.affine = module.affine
+        if module.affine:
+            res.weight.data = module.weight.data.clone().detach()
+            res.bias.data = module.bias.data.clone().detach()
+        res.running_mean.data = module.running_mean.data
+        res.running_var.data = module.running_var.data
+        res.eps = module.eps
+    else:
+        for child_name, child in module.named_children():
+            full_child_name = '.'.join([name, child_name]) if name else child_name
+            new_child = freeze_batch_norm_2d(child, module_match, full_child_name)
+            if new_child is not child:
+                res.add_module(child_name, new_child)
+    return res
+
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = lambda n, x: _ntuple(n)(x)
+
+
+def is_logging(args):
+    def is_global_master(args):
+        return args.rank == 0
+
+    def is_local_master(args):
+        return args.local_rank == 0
+
+    def is_master(args, local=False):
+        return is_local_master(args) if local else is_global_master(args)
+    return is_master
+
+
+class AllGather(torch.autograd.Function):
+    """An autograd function that performs allgather on a tensor.
+    Performs all_gather operation on the provided tensors.
+    *** Warning ***: torch.distributed.all_gather has no gradient.
+    """
+
+    @staticmethod
+    def forward(ctx, tensor, rank, world_size):
+        tensors_gather = [torch.empty_like(tensor) for _ in range(world_size)]
+        torch.distributed.all_gather(tensors_gather, tensor)
+        ctx.rank = rank
+        ctx.batch_size = tensor.shape[0]
+        return torch.cat(tensors_gather, 0)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return (
+            grad_output[ctx.batch_size * ctx.rank: ctx.batch_size * (ctx.rank + 1)],
+            None,
+            None
+        )
+
+allgather = AllGather.apply
\ No newline at end of file
diff --git a/modules/pulid/pipe_sdxl.py b/modules/pulid/pipe_sdxl.py
new file mode 100644
index 000000000..247a2067e
--- /dev/null
+++ b/modules/pulid/pipe_sdxl.py
@@ -0,0 +1,315 @@
+import os
+import cv2
+import insightface
+import numpy as np
+import torch
+import torch.nn as nn
+from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
+
+from huggingface_hub import hf_hub_download, snapshot_download
+from safetensors.torch import load_file
+from torchvision.transforms import InterpolationMode
+from torchvision.transforms.functional import normalize, resize
+
+from basicsr.utils import img2tensor, tensor2img
+from facexlib.parsing import init_parsing_model
+from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+from insightface.app import FaceAnalysis
+
+from eva_clip import create_model_and_transforms
+from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
+from encoders_transformer import IDFormer
+from pulid_utils import sample_dpmpp_2m, sample_dpmpp_sde
+from attention_processor import AttnProcessor2_0 as AttnProcessor
+from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
+
+
+class PuLIDPipeline:
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler='dpmpp_sde', cache_dir=None):
+        super().__init__()
+        self.device = device
+        self.pipe = pipe
+        self.cache_dir = cache_dir
+        self.hack_unet_attn_layers(self.pipe.unet)
+        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        self.id_adapter = IDFormer().to(self.device)
+
+        # preprocessors
+        # face align and parsing
+        self.face_helper = FaceRestoreHelper(
+            upscale_factor=1,
+            face_size=512,
+            crop_ratio=(1, 1),
+            det_model='retinaface_resnet50',
+            save_ext='png',
+            device=self.device,
+        )
+        self.face_helper.face_parse = None
+        self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
+
+        # clip-vit backbone
+        model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True)
+        model = model.visual
+        self.clip_vision_model = model.to(self.device)
+        eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
+        eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
+        if not isinstance(eva_transform_mean, (list, tuple)):
+            eva_transform_mean = (eva_transform_mean,) * 3
+        if not isinstance(eva_transform_std, (list, tuple)):
+            eva_transform_std = (eva_transform_std,) * 3
+        self.eva_transform_mean = eva_transform_mean
+        self.eva_transform_std = eva_transform_std
+
+        # antelopev2
+        # snapshot_download('DIAMONIK7777/antelopev2', local_dir='models/antelopev2')
+        local_dir = os.path.join(self.cache_dir, 'pulid', 'models', 'antelopev2')
+        _loc = snapshot_download('DIAMONIK7777/antelopev2', local_dir=local_dir)
+        self.app = FaceAnalysis(
+            name='antelopev2',
+            root=os.path.join(self.cache_dir, 'pulid'),
+            providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
+        )
+        self.app.prepare(ctx_id=0, det_size=(640, 640))
+        self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
+        self.handler_ante.prepare(ctx_id=0)
+
+        torch.cuda.empty_cache()
+        self.load_pretrain()
+
+        # other configs
+        self.debug_img_list = []
+
+        # karras schedule related code, borrow from lllyasviel/Omost
+        linear_start = 0.00085
+        linear_end = 0.012
+        timesteps = 1000
+        betas = torch.linspace(linear_start**0.5, linear_end**0.5, timesteps, dtype=torch.float64) ** 2
+        alphas = 1.0 - betas
+        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
+
+        self.sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        self.log_sigmas = self.sigmas.log()
+        self.sigma_data = 1.0
+
+        if sampler == 'dpmpp_sde':
+            self.sampler = sample_dpmpp_sde
+        elif sampler == 'dpmpp_2m':
+            self.sampler = sample_dpmpp_2m
+        else:
+            raise NotImplementedError(f'sampler {sampler} not implemented')
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        log_sigma = sigma.log()
+        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
+        return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)
+
+    def get_sigmas_karras(self, n, rho=7.0):
+        ramp = torch.linspace(0, 1, n)
+        min_inv_rho = self.sigma_min ** (1 / rho)
+        max_inv_rho = self.sigma_max ** (1 / rho)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        return torch.cat([sigmas, sigmas.new_zeros([1])])
+
+    def hack_unet_attn_layers(self, unet):
+        id_adapter_attn_procs = {}
+        for name, _ in unet.attn_processors.items():
+            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = unet.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = unet.config.block_out_channels[block_id]
+            else:
+                hidden_size = None
+            if cross_attention_dim is not None:
+                id_adapter_attn_procs[name] = IDAttnProcessor(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                ).to(unet.device)
+            else:
+                id_adapter_attn_procs[name] = AttnProcessor()
+        unet.set_attn_processor(id_adapter_attn_procs)
+        self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
+
+    def load_pretrain(self):
+        ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.1.safetensors', local_dir=os.path.join(self.cache_dir, 'pulid'))
+        state_dict = load_file(ckpt_path)
+        state_dict_dict = {}
+        for k, v in state_dict.items():
+            module = k.split('.')[0]
+            state_dict_dict.setdefault(module, {})
+            new_k = k[len(module) + 1 :]
+            state_dict_dict[module][new_k] = v
+
+        for module in state_dict_dict:
+            print(f'loading from {module}')
+            getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)
+
+    def to_gray(self, img):
+        x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3]
+        x = x.repeat(1, 3, 1, 1)
+        return x
+
+    def get_id_embedding(self, image_list):
+        """
+        Args:
+            image in image_list: numpy rgb image, range [0, 255]
+        """
+        id_cond_list = []
+        id_vit_hidden_list = []
+        for _ii, image in enumerate(image_list):
+            self.face_helper.clean_all()
+            image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+            # get antelopev2 embedding
+            face_info = self.app.get(image_bgr)
+            if len(face_info) > 0:
+                face_info = sorted(
+                    face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1])
+                )[
+                    -1
+                ]  # only use the maximum face
+                id_ante_embedding = face_info['embedding']
+                self.debug_img_list.append(
+                    image[
+                        int(face_info['bbox'][1]) : int(face_info['bbox'][3]),
+                        int(face_info['bbox'][0]) : int(face_info['bbox'][2]),
+                    ]
+                )
+            else:
+                id_ante_embedding = None
+
+            # using facexlib to detect and align face
+            self.face_helper.read_image(image_bgr)
+            self.face_helper.get_face_landmarks_5(only_center_face=True)
+            self.face_helper.align_warp_face()
+            if len(self.face_helper.cropped_faces) == 0:
+                raise RuntimeError('facexlib align face fail')
+            align_face = self.face_helper.cropped_faces[0]
+            # incase insightface didn't detect face
+            if id_ante_embedding is None:
+                print('fail to detect face using insightface, extract embedding on align face')
+                id_ante_embedding = self.handler_ante.get_feat(align_face)
+
+            id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device)
+            if id_ante_embedding.ndim == 1:
+                id_ante_embedding = id_ante_embedding.unsqueeze(0)
+
+            # parsing
+            input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0 # pylint: disable=redefined-builtin
+            input = input.to(self.device)
+            parsing_out = self.face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0]
+            parsing_out = parsing_out.argmax(dim=1, keepdim=True)
+            bg_label = [0, 16, 18, 7, 8, 9, 14, 15]
+            bg = sum(parsing_out == i for i in bg_label).bool()
+            white_image = torch.ones_like(input)
+            # only keep the face features
+            face_features_image = torch.where(bg, white_image, self.to_gray(input))
+            self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False))
+
+            # transform img before sending to eva-clip-vit
+            face_features_image = resize(
+                face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC
+            )
+            face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std)
+            id_cond_vit, id_vit_hidden = self.clip_vision_model(
+                face_features_image, return_all_features=False, return_hidden=True, shuffle=False
+            )
+            id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
+            id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)
+
+            id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1)
+
+            id_cond_list.append(id_cond)
+            id_vit_hidden_list.append(id_vit_hidden)
+
+        id_uncond = torch.zeros_like(id_cond_list[0])
+        id_vit_hidden_uncond = []
+        for layer_idx in range(0, len(id_vit_hidden_list[0])):
+            id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden_list[0][layer_idx]))
+
+        id_cond = torch.stack(id_cond_list, dim=1)
+        id_vit_hidden = id_vit_hidden_list[0]
+        for i in range(1, len(image_list)):
+            for j, x in enumerate(id_vit_hidden_list[i]):
+                id_vit_hidden[j] = torch.cat([id_vit_hidden[j], x], dim=1)
+        id_embedding = self.id_adapter(id_cond, id_vit_hidden)
+        uncond_id_embedding = self.id_adapter(id_uncond, id_vit_hidden_uncond)
+
+        # return id_embedding
+        return uncond_id_embedding, id_embedding
+
+    def __call__(self, x, sigma, **extra_args):
+        x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
+        t = self.timestep(sigma)
+        cfg_scale = extra_args['cfg_scale']
+        eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
+        eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
+        noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
+        return x - noise_pred * sigma[:, None, None, None]
+
+    def inference(
+        self,
+        prompt,
+        size,
+        prompt_n='',
+        id_embedding=None,
+        uncond_id_embedding=None,
+        id_scale=1.0,
+        guidance_scale=1.2,
+        steps=4,
+        seed=-1,
+    ):
+
+        # sigmas
+        sigmas = self.get_sigmas_karras(steps).to(self.device)
+
+        # latents
+        noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
+        noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
+        latents = noise * sigmas[0].to(noise)
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.pipe.encode_prompt(
+            prompt=prompt,
+            negative_prompt=prompt_n,
+        )
+
+        add_time_ids = list((size[1], size[2]) + (0, 0) + (size[1], size[2]))
+        add_time_ids = torch.tensor([add_time_ids], dtype=self.pipe.unet.dtype, device=self.device)
+        add_neg_time_ids = add_time_ids.clone()
+
+        sampler_kwargs = dict(
+            cfg_scale=guidance_scale,
+            positive=dict(
+                encoder_hidden_states=prompt_embeds,
+                added_cond_kwargs={"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids},
+                cross_attention_kwargs={'id_embedding': id_embedding, 'id_scale': id_scale},
+            ),
+            negative=dict(
+                encoder_hidden_states=negative_prompt_embeds,
+                added_cond_kwargs={"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_neg_time_ids},
+                cross_attention_kwargs={'id_embedding': uncond_id_embedding, 'id_scale': id_scale},
+            ),
+        )
+
+        latents = self.sampler(self, latents, sigmas, extra_args=sampler_kwargs, disable=False)
+        latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
+        images = self.pipe.vae.decode(latents).sample
+        images = self.pipe.image_processor.postprocess(images, output_type='pil')
+
+        return images
diff --git a/modules/pulid/pulid_utils.py b/modules/pulid/pulid_utils.py
new file mode 100644
index 000000000..1a8d3ff06
--- /dev/null
+++ b/modules/pulid/pulid_utils.py
@@ -0,0 +1,337 @@
+import importlib
+import math
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchsde
+from torchvision.utils import make_grid
+from tqdm.auto import trange
+from transformers import PretrainedConfig
+
+
+def seed_everything(seed):
+    os.environ["PL_GLOBAL_SEED"] = str(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def is_torch2_available():
+    return hasattr(F, "scaled_dot_product_attention")
+
+
+def instantiate_from_config(config):
+    if "target" not in config:
+        if config == '__is_first_stage__' or config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", {}))
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def drop_seq_token(seq, drop_rate=0.5):
+    idx = torch.randperm(seq.size(1))
+    num_keep_tokens = int(len(idx) * (1 - drop_rate))
+    idx = idx[:num_keep_tokens]
+    seq = seq[:, idx]
+    return seq
+
+
+def import_model_class_from_model_name_or_path(
+    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
+):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "CLIPTextModelWithProjection":
+        from transformers import CLIPTextModelWithProjection
+
+        return CLIPTextModelWithProjection
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def resize_numpy_image_long(image, resize_long_edge=768):
+    h, w = image.shape[:2]
+    if max(h, w) <= resize_long_edge:
+        return image
+    k = resize_long_edge / max(h, w)
+    h = int(h * k)
+    w = int(w * k)
+    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
+    return image
+
+
+# from basicsr
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+    """Numpy array to tensor.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Input images.
+        bgr2rgb (bool): Whether to change bgr to rgb.
+        float32 (bool): Whether to change to float32.
+
+    Returns:
+        list[tensor] | tensor: Tensor images. If returned results only have
+            one element, just return tensor.
+    """
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    return _totensor(imgs, bgr2rgb, float32)
+
+
+def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
+    """Convert torch Tensors into image numpy arrays.
+
+    After clamping to [min, max], values will be normalized to [0, 1].
+
+    Args:
+        tensor (Tensor or list[Tensor]): Accept shapes:
+            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
+            2) 3D Tensor of shape (3/1 x H x W);
+            3) 2D Tensor of shape (H x W).
+            Tensor channel should be in RGB order.
+        rgb2bgr (bool): Whether to change rgb to bgr.
+        out_type (numpy type): output types. If ``np.uint8``, transform outputs
+            to uint8 type with range [0, 255]; otherwise, float type with
+            range [0, 1]. Default: ``np.uint8``.
+        min_max (tuple[int]): min and max values for clamp.
+
+    Returns:
+        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
+        shape (H x W). The channel order is BGR.
+    """
+    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
+        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
+
+    if torch.is_tensor(tensor):
+        tensor = [tensor]
+    result = []
+    for _tensor in tensor:
+        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
+        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
+
+        n_dim = _tensor.dim()
+        if n_dim == 4:
+            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if rgb2bgr:
+                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 3:
+            img_np = _tensor.numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if img_np.shape[2] == 1:  # gray image
+                img_np = np.squeeze(img_np, axis=2)
+            else:
+                if rgb2bgr:
+                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 2:
+            img_np = _tensor.numpy()
+        else:
+            raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
+        if out_type == np.uint8:
+            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
+            img_np = (img_np * 255.0).round()
+        img_np = img_np.astype(out_type)
+        result.append(img_np)
+    if len(result) == 1:
+        result = result[0]
+    return result
+
+
+# We didn't find a correct configuration to make the diffusers scheduler align with dpm++2m (karras) in ComfyUI,
+# so we copied the ComfyUI code directly.
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    expanded = x[(...,) + (None,) * dims_to_append]
+    # MPS will get inf values if it tries to index into the new axes, but detaching fixes this.
+    # https://github.com/pytorch/pytorch/issues/84364
+    return expanded.detach().clone() if expanded.device.type == 'mps' else expanded
+
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / append_dims(sigma, x.ndim)
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.0):
+    """Calculates the noise level (sigma_down) to step down to and the amount
+    of noise to add (sigma_up) when doing an ancestral sampling step."""
+    if not eta:
+        return sigma_to, 0.0
+    sigma_up = min(sigma_to, eta * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
+    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
+    return sigma_down, sigma_up
+
+
+class BatchedBrownianTree:
+    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
+
+    def __init__(self, x, t0, t1, seed=None, **kwargs):
+        self.cpu_tree = True
+        if "cpu" in kwargs:
+            self.cpu_tree = kwargs.pop("cpu")
+        t0, t1, self.sign = self.sort(t0, t1)
+        w0 = kwargs.get('w0', torch.zeros_like(x))
+        if seed is None:
+            seed = torch.randint(0, 2**63 - 1, []).item()
+        self.batched = True
+        try:
+            assert len(seed) == x.shape[0]
+            w0 = w0[0]
+        except TypeError:
+            seed = [seed]
+            self.batched = False
+        if self.cpu_tree:
+            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
+        else:
+            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+
+    @staticmethod
+    def sort(a, b):
+        return (a, b, 1) if a < b else (b, a, -1)
+
+    def __call__(self, t0, t1):
+        t0, t1, sign = self.sort(t0, t1)
+        if self.cpu_tree:
+            w = torch.stack(
+                [tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]
+            ) * (self.sign * sign)
+        else:
+            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+
+        return w if self.batched else w[0]
+
+
+class BrownianTreeNoiseSampler:
+    """A noise sampler backed by a torchsde.BrownianTree.
+
+    Args:
+        x (Tensor): The tensor whose shape, device and dtype to use to generate
+            random samples.
+        sigma_min (float): The low end of the valid interval.
+        sigma_max (float): The high end of the valid interval.
+        seed (int or List[int]): The random seed. If a list of seeds is
+            supplied instead of a single integer, then the noise sampler will
+            use one BrownianTree per batch item, each with its own seed.
+        transform (callable): A function that maps sigma to the sampler's
+            internal timestep.
+    """
+
+    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
+        self.transform = transform
+        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
+        self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
+
+    def __call__(self, sigma, sigma_next):
+        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
+        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
+
+
+@torch.no_grad()
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=unnecessary-lambda-assignment
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=unnecessary-lambda-assignment
+    old_denoised = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_denoised is None or sigmas[i + 1] == 0:
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
+        old_denoised = denoised
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_sde(
+    model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None, r=1 / 2
+):
+    """DPM-Solver++ (stochastic)."""
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    seed = extra_args.get("seed", None)
+    noise_sampler = (
+        BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
+        if noise_sampler is None
+        else noise_sampler
+    )
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=unnecessary-lambda-assignment
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=unnecessary-lambda-assignment
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigmas[i + 1] - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver++
+            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+            h = t_next - t
+            s = t + h * r
+            fac = 1 / (2 * r)
+
+            # Step 1
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
+            s_ = t_fn(sd)
+            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
+            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+
+            # Step 2
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
+            t_next_ = t_fn(sd)
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
+            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+    return x
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
new file mode 100644
index 000000000..2c74dcd09
--- /dev/null
+++ b/scripts/pulid_ext.py
@@ -0,0 +1,151 @@
+import time
+import gradio as gr
+import numpy as np
+from PIL import Image
+from modules import shared, devices, scripts, processing, processing_helpers
+
+
+pulid = None
+
+
+class Script(scripts.Script):
+    def title(self):
+        return 'PuLID'
+
+    def show(self, _is_img2img):
+        return not _is_img2img
+
+    def dependencies(self):
+        from installer import install, installed
+        # if not installed('apex', reload=False, quiet=True):
+        #     install('apex', 'apex', ignore=False)
+        if not installed('insightface', reload=False, quiet=True):
+            install('insightface', 'insightface', ignore=False)
+            install('albumentations==1.4.3', 'albumentations', ignore=False, reinstall=True)
+            install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
+
+    def load_images(self, files):
+        init_images = []
+        for file in files or []:
+            try:
+                if isinstance(file, str):
+                    from modules.api.api import decode_base64_to_image
+                    image = decode_base64_to_image(file)
+                elif isinstance(file, Image.Image):
+                    image = file
+                elif isinstance(file, dict) and 'name' in file:
+                    image = Image.open(file['name']) # _TemporaryFileWrapper from gr.Files
+                elif hasattr(file, 'name'):
+                    image = Image.open(file.name) # _TemporaryFileWrapper from gr.Files
+                else:
+                    raise ValueError(f'IP adapter unknown input: {file}')
+                init_images.append(image)
+            except Exception as e:
+                shared.log.warning(f'IP adapter failed to load image: {e}')
+        return gr.update(value=init_images, visible=len(init_images) > 0)
+
+    # return signature is array of gradio components
+    def ui(self, _is_img2img):
+        with gr.Row():
+            gr.HTML('<a href="https://github.com/ToTheBeginning/PuLID">&nbsp PuLID: Pure and Lightning ID Customization</a><br>')
+        with gr.Row():
+            strength = gr.Slider(label = 'Strength', value = 0.8, mininimum = 0, maximum = 1, step = 0.01)
+            zero = gr.Slider(label = 'Zero', value = 20, mininimum = 0, maximum = 80, step = 1)
+        with gr.Row():
+            sampler = gr.Dropdown(label="Sampler", choices=['dpmpp_sde', 'dpmpp_2m'], value='dpmpp_sde', visible=True)
+            ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2', visible=True)
+        with gr.Row():
+            files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
+        with gr.Row():
+            gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
+        files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
+        return [strength, zero, sampler, ortho, gallery]
+
+    def run(self, p: processing.StableDiffusionProcessing, strength, zero, sampler, ortho, gallery): # pylint: disable=arguments-differ
+        global pulid # pylint: disable=global-statement
+        images = []
+        try:
+            images = [Image.open(f['name']) for f in gallery]
+            images = [np.array(image) for image in images]
+        except Exception as e:
+            shared.log.error(f'PuLID: failed to load images: {e}')
+            return None
+        if len(images) == 0:
+            shared.log.error('PuLID: no images loaded')
+            return None
+        supported_model_list = ['sdxl']
+        if shared.sd_model_type not in supported_model_list:
+            shared.log.error(f'PuLID: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
+            return None
+        if pulid is None:
+            self.dependencies()
+            from modules import pulid # pylint: disable=redefined-outer-name
+            # import os
+            # import importlib
+            # module_path = os.path.join(os.path.dirname(__file__), '..', 'pulid', '__init__.py')
+            # module_spec = importlib.util.spec_from_file_location('pulid', module_path)
+            # pulid = importlib.util.module_from_spec(module_spec)
+            # module_spec.loader.exec_module(pulid)
+        if pulid is None:
+            shared.log.error('PuLID: failed to load PuLID library')
+            return None
+        if p.batch_size > 1:
+            shared.log.warning('PuLID: batch size not supported')
+            p.batch_size = 1
+
+        processing.fix_seed(p)
+        pipe = None
+        if shared.sd_model_type == 'sdxl':
+            pipe = pulid.PuLIDPipelineXL(
+                pipe =shared.sd_model,
+                device=devices.device,
+                sampler=sampler,
+                cache_dir=shared.opts.hfcache_dir,
+            )
+        if pipe is None:
+            return None
+        shared.state.begin('PuLID')
+        shared.log.info(f'PuLID: class={pipe.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
+
+        pipe.debug_img_list = []
+        pulid.attention.NUM_ZERO = zero
+        if ortho == 'v2':
+            pulid.attention.ORTHO = False
+            pulid.attention.ORTHO_v2 = True
+        elif ortho == 'v1':
+            pulid.attention.ORTHO = True
+            pulid.attention.ORTHO_v2 = False
+        else:
+            pulid.attention.ORTHO = False
+            pulid.attention.ORTHO_v2 = False
+
+        t0 = time.time()
+        images = [pulid.resize(image, 1024) for image in images]
+        outputs = []
+        infotexts = []
+        seeds = []
+        prompts = []
+        negative_prompts = []
+
+        for _n in range(p.n_iter):
+            seed = processing_helpers.get_fixed_seed(p.seed)
+            prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)
+            negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
+            with devices.inference_context():
+                uncond_id_embedding, id_embedding = pipe.get_id_embedding(images)
+                output = pipe.inference(prompt, (1, p.height, p.width), negative_prompt, id_embedding, uncond_id_embedding, strength, p.cfg_scale, p.steps, seed)[0]
+            outputs.append(output)
+            infotexts.append(processing.create_infotext(p))
+            seeds.append(seed)
+            prompts.append(prompt)
+            negative_prompts.append(negative_prompt)
+
+        interim = [Image.fromarray(face) for face in pipe.debug_img_list]
+        t1 = time.time()
+        shared.log.debug(f'PuLID: output={output} interim={interim} time={t1-t0:.2f}')
+
+        p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
+        processed = processing.Processed(p, outputs, infotexts=infotexts, all_seeds=seeds, all_prompts=prompts, all_negative_prompts=negative_prompts)
+
+        shared.state.end('PuLID')
+        return processed
diff --git a/wiki b/wiki
index 4a90ecebd..2dba58a69 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 4a90ecebda962316705e4a630ae28d636a6e9c68
+Subproject commit 2dba58a6962b70e92a077dcda8f178f5e811f175

From 363e7baca3cbbcc847d2dcfdfdea3513cff21930 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 3 Nov 2024 17:32:42 -0500
Subject: [PATCH 015/119] cleanup pulid

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 scripts/pulid_ext.py | 84 +++++++++++++++++++++++++++++++++-----------
 1 file changed, 64 insertions(+), 20 deletions(-)

diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 2c74dcd09..4ee696538 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -2,13 +2,18 @@ import time
 import gradio as gr
 import numpy as np
 from PIL import Image
-from modules import shared, devices, scripts, processing, processing_helpers
+from modules import shared, devices, errors, sd_models, scripts, processing, processing_helpers
 
 
 pulid = None
 
 
 class Script(scripts.Script):
+    def __init__(self):
+        self.images = []
+        super().__init__()
+        # self.register() # pulid is script with processing override so xyz doesnt execute
+
     def title(self):
         return 'PuLID'
 
@@ -24,8 +29,20 @@ class Script(scripts.Script):
             install('albumentations==1.4.3', 'albumentations', ignore=False, reinstall=True)
             install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
 
+    def register(self): # register xyz grid elements
+        def apply_field(field):
+            def fun(p, x, xs): # pylint: disable=unused-argument
+                setattr(p, field, x)
+                self.run(p)
+            return fun
+
+        import sys
+        xyz_classes = [v for k, v in sys.modules.items() if 'xyz_grid_classes' in k][0]
+        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Strength", float, apply_field("pulid_strength")))
+        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Zero", float, apply_field("pulid_zero")))
+
     def load_images(self, files):
-        init_images = []
+        self.images = []
         for file in files or []:
             try:
                 if isinstance(file, str):
@@ -39,10 +56,10 @@ class Script(scripts.Script):
                     image = Image.open(file.name) # _TemporaryFileWrapper from gr.Files
                 else:
                     raise ValueError(f'IP adapter unknown input: {file}')
-                init_images.append(image)
+                self.images.append(image)
             except Exception as e:
                 shared.log.warning(f'IP adapter failed to load image: {e}')
-        return gr.update(value=init_images, visible=len(init_images) > 0)
+        return gr.update(value=self.images, visible=len(self.images) > 0)
 
     # return signature is array of gradio components
     def ui(self, _is_img2img):
@@ -61,11 +78,13 @@ class Script(scripts.Script):
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
         return [strength, zero, sampler, ortho, gallery]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength, zero, sampler, ortho, gallery): # pylint: disable=arguments-differ
+    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = []): # pylint: disable=arguments-differ
         global pulid # pylint: disable=global-statement
         images = []
         try:
-            images = [Image.open(f['name']) for f in gallery]
+            if len(gallery) == 0:
+                gallery = self.images
+            images = [Image.open(f['name']) for f in gallery if isinstance(f, dict)]
             images = [np.array(image) for image in images]
         except Exception as e:
             shared.log.error(f'PuLID: failed to load images: {e}')
@@ -79,7 +98,11 @@ class Script(scripts.Script):
             return None
         if pulid is None:
             self.dependencies()
-            from modules import pulid # pylint: disable=redefined-outer-name
+            try:
+                from modules import pulid # pylint: disable=redefined-outer-name
+            except Exception as e:
+                shared.log.error(f'PuLID: failed to import library: {e}')
+                return None
             # import os
             # import importlib
             # module_path = os.path.join(os.path.dirname(__file__), '..', 'pulid', '__init__.py')
@@ -92,16 +115,29 @@ class Script(scripts.Script):
         if p.batch_size > 1:
             shared.log.warning('PuLID: batch size not supported')
             p.batch_size = 1
+        strength = getattr(p, 'pulid_strength', strength)
+        zero = getattr(p, 'pulid_zero', zero)
 
         processing.fix_seed(p)
         pipe = None
         if shared.sd_model_type == 'sdxl':
-            pipe = pulid.PuLIDPipelineXL(
-                pipe =shared.sd_model,
-                device=devices.device,
-                sampler=sampler,
-                cache_dir=shared.opts.hfcache_dir,
-            )
+            # TODO pulid has monolithic inference so not really working with offloading
+            sd_models.move_model(shared.sd_model, devices.device)
+            sd_models.move_model(shared.sd_model.vae, devices.device)
+            sd_models.move_model(shared.sd_model.unet, devices.device)
+            sd_models.move_model(shared.sd_model.text_encoder, devices.device)
+            sd_models.move_model(shared.sd_model.text_encoder_2, devices.device)
+            try:
+                pipe = pulid.PuLIDPipelineXL(
+                    pipe =shared.sd_model,
+                    device=devices.device,
+                    sampler=sampler,
+                    cache_dir=shared.opts.hfcache_dir,
+                )
+            except Exception as e:
+                shared.log.error(f'PuLID: failed to create pipeline: {e}')
+                errors.display(e, 'PuLID')
+                return None
         if pipe is None:
             return None
         shared.state.begin('PuLID')
@@ -134,18 +170,26 @@ class Script(scripts.Script):
             with devices.inference_context():
                 uncond_id_embedding, id_embedding = pipe.get_id_embedding(images)
                 output = pipe.inference(prompt, (1, p.height, p.width), negative_prompt, id_embedding, uncond_id_embedding, strength, p.cfg_scale, p.steps, seed)[0]
-            outputs.append(output)
-            infotexts.append(processing.create_infotext(p))
-            seeds.append(seed)
-            prompts.append(prompt)
-            negative_prompts.append(negative_prompt)
+            if output is not None:
+                outputs.append(output)
+                infotexts.append(processing.create_infotext(p))
+                seeds.append(seed)
+                prompts.append(prompt)
+                negative_prompts.append(negative_prompt)
 
         interim = [Image.fromarray(face) for face in pipe.debug_img_list]
         t1 = time.time()
         shared.log.debug(f'PuLID: output={output} interim={interim} time={t1-t0:.2f}')
 
-        p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
-        processed = processing.Processed(p, outputs, infotexts=infotexts, all_seeds=seeds, all_prompts=prompts, all_negative_prompts=negative_prompts)
+        if len(outputs) > 0:
+            p.prompt = prompts[0]
+            p.negative_prompt = negative_prompts[0]
+            p.seed = seeds[0]
+            p.all_prompts = prompts
+            p.all_negative_prompts = negative_prompts
+            p.all_seeds = seeds
+            p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
+        processed = processing.Processed(p, outputs, infotexts=infotexts)
 
         shared.state.end('PuLID')
         return processed

From 6306aab1e4984c56b3ccdbb4bd191b3578aad7af Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 4 Nov 2024 08:04:25 -0500
Subject: [PATCH 016/119] detailer add ultralytics version

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

diff --git a/modules/postprocess/yolo.py b/modules/postprocess/yolo.py
index 14d6558be..1a52e1334 100644
--- a/modules/postprocess/yolo.py
+++ b/modules/postprocess/yolo.py
@@ -156,10 +156,10 @@ class YoloRestorer(Detailer):
             try:
                 model_file = modelloader.load_file_from_url(url=model_url, model_dir=shared.opts.yolo_dir, file_name=file_name)
                 if model_file is not None:
-                    from ultralytics import YOLO # pylint: disable=import-outside-toplevel
-                    model = YOLO(model_file)
+                    import ultralytics
+                    model = ultralytics.YOLO(model_file)
                     classes = list(model.names.values())
-                    shared.log.info(f'Load: type=Detailer name="{model_name}" model="{model_file}" classes={classes}')
+                    shared.log.info(f'Load: type=Detailer name="{model_name}" model="{model_file}" ultralytics={ultralytics.__version__} classes={classes}')
                     self.models[model_name] = model
                     return model_name, model
             except Exception as e:

From a2f9a4dbb0cab116e549cbae387de86f4d9779ee Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 4 Nov 2024 12:31:39 -0500
Subject: [PATCH 017/119] refactor pulid

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                                  |   3 +-
 modules/processing_args.py                    |   7 +-
 modules/processing_callbacks.py               |   2 +-
 modules/prompt_parser_diffusers.py            |   2 +
 modules/pulid/__init__.py                     |   2 +-
 modules/pulid/{pipe_sdxl.py => pulid_sdxl.py} |  76 ++++++--
 modules/sd_models.py                          |   1 +
 scripts/pulid_ext.py                          | 183 ++++++++++--------
 scripts/xyz_grid.py                           |   1 +
 scripts/xyz_grid_classes.py                   |   2 +-
 scripts/xyz_grid_on.py                        |   1 +
 11 files changed, 169 insertions(+), 111 deletions(-)
 rename modules/pulid/{pipe_sdxl.py => pulid_sdxl.py} (83%)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cf496f81c..8e91157bb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,8 +11,9 @@ This release can be considered an LTS release before we kick off the next round
     go to system -> changelog and search/highligh/navigate directly in UI!  
 - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
   - advanced method of face transfer with better quality as well as control over identity and appearance  
-  - compatible with *sdxl*  
   - select in *scripts -> pulid*  
+  - compatible with *sdxl*  
+  - can be used in xyz grid  
 - SD3: ControlNets:  
   - *InstantX Canny, Pose, Depth, Tile*  
   - *Alimama Inpainting, SoftEdge*  
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 1ea91fb08..002655cd5 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -100,10 +100,11 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if hasattr(model, "set_progress_bar_config"):
         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')
     args = {}
-    if hasattr(model, 'pipe'): # recurse
+    if hasattr(model, 'pipe') and not hasattr(model, 'no_recurse'): # recurse
         model = model.pipe
     signature = inspect.signature(type(model).__call__, follow_wrapped=True)
     possible = list(signature.parameters)
+
     debug(f'Diffusers pipeline possible: {possible}')
     prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
     parser = 'Fixed attention'
@@ -128,7 +129,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if 'prompt' in possible:
         if 'OmniGen' in model.__class__.__name__:
             prompts = [p.replace('|image|', '<|image_1|>') for p in prompts]
-        if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and len(p.prompt_embeds) > 0 and p.prompt_embeds[0] is not None:
+        if hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'prompt_embeds' in possible and len(p.prompt_embeds) > 0 and p.prompt_embeds[0] is not None:
             args['prompt_embeds'] = p.prompt_embeds[0]
             if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
                 args['prompt_embeds_pooled'] = p.positive_pooleds[0].unsqueeze(0)
@@ -141,7 +142,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
         else:
             args['prompt'] = prompts
     if 'negative_prompt' in possible:
-        if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and len(p.negative_embeds) > 0 and p.negative_embeds[0] is not None:
+        if hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'negative_prompt_embeds' in possible and len(p.negative_embeds) > 0 and p.negative_embeds[0] is not None:
             args['negative_prompt_embeds'] = p.negative_embeds[0]
             if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
                 args['negative_prompt_embeds_pooled'] = p.negative_pooleds[0].unsqueeze(0)
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 47c8e8827..45a0724fb 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -33,7 +33,7 @@ def diffusers_callback_legacy(step: int, timestep: int, latents: typing.Union[to
             time.sleep(0.1)
 
 
-def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
+def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = {}):
     t0 = time.time()
     if p is None:
         return kwargs
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index cc814f379..2994d6ef5 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -158,6 +158,8 @@ def get_tokens(msg, prompt):
 
 
 def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, clip_skip: typing.Optional[int] = None):
+    if not hasattr(pipe, 'text_encoder') or not hasattr(pipe, 'tokenizer'):
+        return
     params_match = prompts == cache.get('prompts', None) and negative_prompts == cache.get('negative_prompts', None) and clip_skip == cache.get('clip_skip', None) and steps == cache.get('steps', None)
     if (
         'StableDiffusion' not in pipe.__class__.__name__ and
diff --git a/modules/pulid/__init__.py b/modules/pulid/__init__.py
index 77a1bae8f..000f45293 100644
--- a/modules/pulid/__init__.py
+++ b/modules/pulid/__init__.py
@@ -5,6 +5,6 @@ Credit and original implementation: <https://github.com/ToTheBeginning/PuLID>
 import os
 import sys
 sys.path.append(os.path.dirname(__file__))
-from pipe_sdxl import PuLIDPipeline as PuLIDPipelineXL
+from pulid_sdxl import StableDiffusionXLPuLIDPipeline
 from pulid_utils import resize_numpy_image_long as resize
 import attention_processor as attention
diff --git a/modules/pulid/pipe_sdxl.py b/modules/pulid/pulid_sdxl.py
similarity index 83%
rename from modules/pulid/pipe_sdxl.py
rename to modules/pulid/pulid_sdxl.py
index 247a2067e..a4c3bf06c 100644
--- a/modules/pulid/pipe_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -24,7 +24,7 @@ from attention_processor import AttnProcessor2_0 as AttnProcessor
 from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
 
 
-class PuLIDPipeline:
+class StableDiffusionXLPuLIDPipeline:
     def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler='dpmpp_sde', cache_dir=None):
         super().__init__()
         self.device = device
@@ -73,7 +73,6 @@ class PuLIDPipeline:
         self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
         self.handler_ante.prepare(ctx_id=0)
 
-        torch.cuda.empty_cache()
         self.load_pretrain()
 
         # other configs
@@ -249,34 +248,75 @@ class PuLIDPipeline:
         # return id_embedding
         return uncond_id_embedding, id_embedding
 
-    def __call__(self, x, sigma, **extra_args):
+    def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
+        import functools
+        from tqdm.auto import trange as trange_orig
+        import pulid_utils
+        pulid_utils.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
+
+    def sample(self, x, sigma, **extra_args):
         x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
         t = self.timestep(sigma)
         cfg_scale = extra_args['cfg_scale']
         eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
         eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
         noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
-        return x - noise_pred * sigma[:, None, None, None]
+        latent = x - noise_pred * sigma[:, None, None, None]
+        if self.callback_on_step_end is not None:
+            self.step += 1
+            self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
+        return latent
 
-    def inference(
+    def init_latent(self, seed, size, image, strength): # pylint: disable=unused-argument
+        if image is not None and strength > 0:
+            # TODO pulid img2img
+            # input can be PIL.Image or np.ndarray so it needs to be converted to rgb tensor
+            # image must be resized, encoded and noised according to denoising strength
+            # see below for example from StableDiffusionXLImg2ImgPipeline
+            latents = None
+            """
+            image = self.image_processor.preprocess(image)
+            latents = self.prepare_latents(
+                image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                add_noise,
+            )
+            """
+            raise NotImplementedError('pulid: img2img')
+        else:
+            latents = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
+            latents = latents.to(dtype=self.pipe.unet.dtype, device=self.device)
+        return latents
+
+    def __call__(
         self,
-        prompt,
-        size,
-        prompt_n='',
+        prompt: str='',
+        negative_prompt: str='',
+        width: int=1024,
+        height: int=1024,
+        guidance_scale: float=7.0,
+        num_inference_steps: int=50,
+        seed: int=-1,
+        image: np.ndarray=None,
+        strength: float=0.3,
         id_embedding=None,
         uncond_id_embedding=None,
-        id_scale=1.0,
-        guidance_scale=1.2,
-        steps=4,
-        seed=-1,
+        id_scale: float=1.0,
+        callback_on_step_end=None,
     ):
-
+        self.step = 0 # pylint: disable=attribute-defined-outside-init
+        self.callback_on_step_end = callback_on_step_end # pylint: disable=attribute-defined-outside-init
+        size = (1, height, width)
         # sigmas
-        sigmas = self.get_sigmas_karras(steps).to(self.device)
+        sigmas = self.get_sigmas_karras(num_inference_steps).to(self.device)
 
         # latents
-        noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
-        noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
+        noise = self.init_latent(seed, size, image, strength)
         latents = noise * sigmas[0].to(noise)
 
         (
@@ -286,7 +326,7 @@ class PuLIDPipeline:
             negative_pooled_prompt_embeds,
         ) = self.pipe.encode_prompt(
             prompt=prompt,
-            negative_prompt=prompt_n,
+            negative_prompt=negative_prompt,
         )
 
         add_time_ids = list((size[1], size[2]) + (0, 0) + (size[1], size[2]))
@@ -307,7 +347,7 @@ class PuLIDPipeline:
             ),
         )
 
-        latents = self.sampler(self, latents, sigmas, extra_args=sampler_kwargs, disable=False)
+        latents = self.sampler(self.sample, latents, sigmas, extra_args=sampler_kwargs, disable=False)
         latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
         images = self.pipe.vae.decode(latents).sample
         images = self.pipe.image_processor.postprocess(images, output_type='pil')
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 610f5a248..575e9318a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1052,6 +1052,7 @@ def set_diffuser_pipe(pipe, new_pipe_type):
         'AnimateDiffSDXLPipeline',
         'OmniGenPipeline',
         'StableDiffusion3ControlNetPipeline',
+        'StableDiffusionXLPuLIDPipeline',
     ]
 
     n = getattr(pipe.__class__, '__name__', '')
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 4ee696538..d31c18164 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -1,33 +1,37 @@
-import time
+import io
+import os
+import contextlib
 import gradio as gr
 import numpy as np
 from PIL import Image
-from modules import shared, devices, errors, sd_models, scripts, processing, processing_helpers
+from modules import shared, devices, errors, scripts, processing, processing_helpers, sd_models
 
 
-pulid = None
+debug = os.environ.get('SD_PULID_DEBUG', None) is not None
 
 
 class Script(scripts.Script):
     def __init__(self):
         self.images = []
+        self.pulid = None
+        self.cache = None
         super().__init__()
-        # self.register() # pulid is script with processing override so xyz doesnt execute
+        self.register() # pulid is script with processing override so xyz doesnt execute
 
     def title(self):
         return 'PuLID'
 
     def show(self, _is_img2img):
-        return not _is_img2img
+        return shared.native
 
     def dependencies(self):
         from installer import install, installed
-        # if not installed('apex', reload=False, quiet=True):
-        #     install('apex', 'apex', ignore=False)
         if not installed('insightface', reload=False, quiet=True):
             install('insightface', 'insightface', ignore=False)
             install('albumentations==1.4.3', 'albumentations', ignore=False, reinstall=True)
             install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
+        # if not installed('apex', reload=False, quiet=True):
+        #     install('apex', 'apex', ignore=False)
 
     def register(self): # register xyz grid elements
         def apply_field(field):
@@ -39,7 +43,8 @@ class Script(scripts.Script):
         import sys
         xyz_classes = [v for k, v in sys.modules.items() if 'xyz_grid_classes' in k][0]
         xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Strength", float, apply_field("pulid_strength")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Zero", float, apply_field("pulid_zero")))
+        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Zero", int, apply_field("pulid_zero")))
+        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Ortho", str, apply_field("pulid_ortho"), choices=lambda: ['off', 'v1', 'v2']))
 
     def load_images(self, files):
         self.images = []
@@ -79,117 +84,123 @@ class Script(scripts.Script):
         return [strength, zero, sampler, ortho, gallery]
 
     def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = []): # pylint: disable=arguments-differ
-        global pulid # pylint: disable=global-statement
         images = []
         try:
             if len(gallery) == 0:
-                gallery = self.images
-            images = [Image.open(f['name']) for f in gallery if isinstance(f, dict)]
+                from modules.api.api import decode_base64_to_image
+                images = getattr(p, 'pulid_images', self.images)
+                images = [decode_base64_to_image(image) if isinstance(image, str) else image for image in images]
+            else:
+                images = [Image.open(f['name']) if isinstance(f, dict) else f for f in gallery]
             images = [np.array(image) for image in images]
         except Exception as e:
             shared.log.error(f'PuLID: failed to load images: {e}')
             return None
         if len(images) == 0:
-            shared.log.error('PuLID: no images loaded')
+            shared.log.error('PuLID: no images')
             return None
         supported_model_list = ['sdxl']
         if shared.sd_model_type not in supported_model_list:
             shared.log.error(f'PuLID: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
             return None
-        if pulid is None:
+        if self.pulid is None:
             self.dependencies()
             try:
                 from modules import pulid # pylint: disable=redefined-outer-name
+                self.pulid = pulid
+                # from diffusers import pipelines
+                # pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["pilid"] = pulid.StableDiffusionXLPuLIDPipeline
+                # pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["omnigen"] = pulid.StableDiffusionXLPuLIDPipelineImg2Img
             except Exception as e:
                 shared.log.error(f'PuLID: failed to import library: {e}')
                 return None
-            # import os
-            # import importlib
-            # module_path = os.path.join(os.path.dirname(__file__), '..', 'pulid', '__init__.py')
-            # module_spec = importlib.util.spec_from_file_location('pulid', module_path)
-            # pulid = importlib.util.module_from_spec(module_spec)
-            # module_spec.loader.exec_module(pulid)
-        if pulid is None:
-            shared.log.error('PuLID: failed to load PuLID library')
-            return None
+            if self.pulid is None:
+                shared.log.error('PuLID: failed to load PuLID library')
+                return None
         if p.batch_size > 1:
             shared.log.warning('PuLID: batch size not supported')
             p.batch_size = 1
+
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
+        ortho = getattr(p, 'pulid_ortho', ortho)
 
-        processing.fix_seed(p)
-        pipe = None
-        if shared.sd_model_type == 'sdxl':
-            # TODO pulid has monolithic inference so not really working with offloading
-            sd_models.move_model(shared.sd_model, devices.device)
-            sd_models.move_model(shared.sd_model.vae, devices.device)
-            sd_models.move_model(shared.sd_model.unet, devices.device)
-            sd_models.move_model(shared.sd_model.text_encoder, devices.device)
-            sd_models.move_model(shared.sd_model.text_encoder_2, devices.device)
+        if shared.sd_model_type == 'sdxl' and not hasattr(shared.sd_model, 'pipe'):
             try:
-                pipe = pulid.PuLIDPipelineXL(
-                    pipe =shared.sd_model,
-                    device=devices.device,
-                    sampler=sampler,
-                    cache_dir=shared.opts.hfcache_dir,
-                )
+                stdout = io.StringIO()
+                ctx = contextlib.nullcontext if debug else contextlib.redirect_stdout(stdout)
+                with ctx:
+                    shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
+                        pipe =shared.sd_model,
+                        device=devices.device,
+                        sampler=sampler,
+                        cache_dir=shared.opts.hfcache_dir,
+                    )
+                shared.sd_model.no_recurse = True
+                sd_models.copy_diffuser_options(shared.sd_model, shared.sd_model.pipe)
+                sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
+                sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
+                devices.torch_gc()
             except Exception as e:
                 shared.log.error(f'PuLID: failed to create pipeline: {e}')
                 errors.display(e, 'PuLID')
                 return None
-        if pipe is None:
-            return None
-        shared.state.begin('PuLID')
-        shared.log.info(f'PuLID: class={pipe.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
 
-        pipe.debug_img_list = []
-        pulid.attention.NUM_ZERO = zero
-        if ortho == 'v2':
-            pulid.attention.ORTHO = False
-            pulid.attention.ORTHO_v2 = True
-        elif ortho == 'v1':
-            pulid.attention.ORTHO = True
-            pulid.attention.ORTHO_v2 = False
-        else:
-            pulid.attention.ORTHO = False
-            pulid.attention.ORTHO_v2 = False
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
+        self.pulid.attention.NUM_ZERO = zero
+        self.pulid.attention.ORTHO = ortho == 'v1'
+        self.pulid.attention.ORTHO_v2 = ortho == 'v2'
+        images = [self.pulid.resize(image, 1024) for image in images]
+        shared.sd_model.debug_img_list = []
+        uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
 
-        t0 = time.time()
-        images = [pulid.resize(image, 1024) for image in images]
-        outputs = []
-        infotexts = []
-        seeds = []
-        prompts = []
-        negative_prompts = []
-
-        for _n in range(p.n_iter):
-            seed = processing_helpers.get_fixed_seed(p.seed)
-            prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)
-            negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
+        if debug: # run pipeline directly
+            shared.state.begin('PuLID')
+            processing.fix_seed(p)
+            p.seed = processing_helpers.get_fixed_seed(p.seed)
+            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)
             with devices.inference_context():
-                uncond_id_embedding, id_embedding = pipe.get_id_embedding(images)
-                output = pipe.inference(prompt, (1, p.height, p.width), negative_prompt, id_embedding, uncond_id_embedding, strength, p.cfg_scale, p.steps, seed)[0]
-            if output is not None:
-                outputs.append(output)
-                infotexts.append(processing.create_infotext(p))
-                seeds.append(seed)
-                prompts.append(prompt)
-                negative_prompts.append(negative_prompt)
-
-        interim = [Image.fromarray(face) for face in pipe.debug_img_list]
-        t1 = time.time()
-        shared.log.debug(f'PuLID: output={output} interim={interim} time={t1-t0:.2f}')
-
-        if len(outputs) > 0:
-            p.prompt = prompts[0]
-            p.negative_prompt = negative_prompts[0]
-            p.seed = seeds[0]
-            p.all_prompts = prompts
-            p.all_negative_prompts = negative_prompts
-            p.all_seeds = seeds
+                output = shared.sd_model(
+                    prompt=p.prompt,
+                    negative_prompt=p.negative_prompt,
+                    width=p.width,
+                    height=p.height,
+                    seed=p.seed,
+                    num_inference_steps=p.steps,
+                    guidance_scale=p.cfg_scale,
+                    id_embedding=id_embedding,
+                    uncond_id_embedding=uncond_id_embedding,
+                    id_scale=strength,
+                    )[0]
+            info = processing.create_infotext(p)
+            processed = processing.Processed(p, [output], info=info)
+            shared.state.end('PuLID')
+        else: # let processing run the pipeline
+            p.task_args['id_embedding'] = id_embedding
+            p.task_args['uncond_id_embedding'] = uncond_id_embedding
+            p.task_args['id_scale'] = strength
+            if len(getattr(p, 'init_images', [])) > 0:
+                p.task_args['image'] = p.init_images[0]
+                p.task_args['strength'] = p.denoising_strength
             p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
-        processed = processing.Processed(p, outputs, infotexts=infotexts)
+            if getattr(p, 'xyz', False): # xyz will run its own processing
+                return None
+            processed: processing.Processed = processing.process_images(p) # runs processing using main loop
 
-        shared.state.end('PuLID')
+        # 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 after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
+        if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
+            if hasattr(shared.sd_model, 'app'):
+                shared.sd_model.app = None
+                shared.sd_model.ip_adapter = None
+                shared.sd_model.face_helper = None
+                shared.sd_model.clip_vision_model = None
+                shared.sd_model.handler_ante = None
+                devices.torch_gc(force=True)
+            shared.sd_model = shared.sd_model.pipe
+            # shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
         return processed
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index e8ecb5bd4..62a722bb3 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -258,6 +258,7 @@ class Script(scripts.Script):
         def cell(x, y, z, ix, iy, iz):
             if shared.state.interrupted:
                 return processing.Processed(p, [], p.seed, "")
+            p.xyz = True
             pc = copy(p)
             pc.override_settings_restore_afterwards = False
             pc.styles = pc.styles[:]
diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py
index 202482157..4898c6b73 100644
--- a/scripts/xyz_grid_classes.py
+++ b/scripts/xyz_grid_classes.py
@@ -1,4 +1,4 @@
-from scripts.xyz_grid_shared import apply_field, apply_task_args, apply_setting, apply_prompt, apply_order, apply_sampler, apply_hr_sampler_name, confirm_samplers, apply_checkpoint, apply_refiner, apply_unet, apply_dict, apply_clip_skip, apply_vae, list_lora, apply_lora, apply_te, apply_styles, apply_upscaler, apply_context, apply_detailer, apply_override, apply_processing, apply_options, apply_seed, format_value_add_label, format_value, format_value_join_list, do_nothing, format_nothing, str_permutations # pylint: disable=no-name-in-module
+from scripts.xyz_grid_shared import apply_field, apply_task_args, apply_setting, apply_prompt, apply_order, apply_sampler, apply_hr_sampler_name, confirm_samplers, apply_checkpoint, apply_refiner, apply_unet, apply_dict, apply_clip_skip, apply_vae, list_lora, apply_lora, apply_te, apply_styles, apply_upscaler, apply_context, apply_detailer, apply_override, apply_processing, apply_options, apply_seed, format_value_add_label, format_value, format_value_join_list, do_nothing, format_nothing, str_permutations # pylint: disable=no-name-in-module, unused-import
 from modules import shared, shared_items, sd_samplers, ipadapter, sd_models, sd_vae, sd_unet
 
 
diff --git a/scripts/xyz_grid_on.py b/scripts/xyz_grid_on.py
index cdef01e60..6affde005 100644
--- a/scripts/xyz_grid_on.py
+++ b/scripts/xyz_grid_on.py
@@ -273,6 +273,7 @@ class Script(scripts.Script):
         def cell(x, y, z, ix, iy, iz):
             if shared.state.interrupted:
                 return processing.Processed(p, [], p.seed, "")
+            p.xyz = True
             pc = copy(p)
             pc.override_settings_restore_afterwards = False
             pc.styles = pc.styles[:]

From bb80286aab77f9fa4869b2789a646927ee3d8366 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 4 Nov 2024 12:35:38 -0500
Subject: [PATCH 018/119] add note

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

diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index a4c3bf06c..0651efab4 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -289,6 +289,7 @@ class StableDiffusionXLPuLIDPipeline:
             """
             raise NotImplementedError('pulid: img2img')
         else:
+            # standard txt2img will full noise
             latents = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
             latents = latents.to(dtype=self.pipe.unet.dtype, device=self.device)
         return latents

From 3df0fb8008b58d5cfe4009d1bf9148b422187273 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 4 Nov 2024 12:42:16 -0500
Subject: [PATCH 019/119] apply balanced to recursive pipe

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

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 575e9318a..1a17660a3 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -416,6 +416,8 @@ def apply_balanced_offload(sd_model):
                 devices.torch_gc(fast=True)
 
     apply_balanced_offload_to_module(sd_model)
+    if hasattr(sd_model, "pipe"):
+        apply_balanced_offload_to_module(sd_model.pipe)
     if hasattr(sd_model, "prior_pipe"):
         apply_balanced_offload_to_module(sd_model.prior_pipe)
     if hasattr(sd_model, "decoder_pipe"):

From f933663afab375aaa6f58364c5fd0e78ddf7ccc9 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 4 Nov 2024 14:00:38 -0500
Subject: [PATCH 020/119] allow new ext via experimental

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

diff --git a/installer.py b/installer.py
index da1817508..431e75462 100644
--- a/installer.py
+++ b/installer.py
@@ -377,10 +377,11 @@ def update(folder, keep_branch = False, rebase = True):
     else:
         res = git(f'pull origin {b} {arg}', folder)
         debug(f'Install update: folder={folder} branch={b} args={arg} {res}')
-    commit = extensions_commit.get(os.path.basename(folder), None)
-    if commit is not None:
-        res = git(f'checkout {commit}', folder)
-        debug(f'Install update: folder={folder} branch={b} args={arg} commit={commit} {res}')
+    if not args.experimental:
+        commit = extensions_commit.get(os.path.basename(folder), None)
+        if commit is not None:
+            res = git(f'checkout {commit}', folder)
+            debug(f'Install update: folder={folder} branch={b} args={arg} commit={commit} {res}')
     return res
 
 
@@ -547,7 +548,6 @@ def install_rocm_zluda():
             os.environ['HIP_VISIBLE_DEVICES'] = args.device_id
             del args.device_id
 
-        log.warning("ZLUDA support: experimental")
         error = None
         from modules import zluda_installer
         zluda_installer.set_default_agent(device)

From f593efe966e749146545c6d6e1e25189616eed36 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 5 Nov 2024 09:06:49 -0500
Subject: [PATCH 021/119] xyz grid create video

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md             |  12 +++--
 modules/images.py        |   4 +-
 scripts/xyz_grid.py      |  86 ++++++++++++++++++++++++------
 scripts/xyz_grid_draw.py |  14 ++++-
 scripts/xyz_grid_on.py   | 112 ++++++++++++++++++++++++---------------
 5 files changed, 161 insertions(+), 67 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8e91157bb..1429c707a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-04
+## Update for 2024-11-05
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -11,6 +11,7 @@ This release can be considered an LTS release before we kick off the next round
     go to system -> changelog and search/highligh/navigate directly in UI!  
 - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
   - advanced method of face transfer with better quality as well as control over identity and appearance  
+    try it out, likely the best quality available for sdxl models  
   - select in *scripts -> pulid*  
   - compatible with *sdxl*  
   - can be used in xyz grid  
@@ -22,13 +23,16 @@ This release can be considered an LTS release before we kick off the next round
 - SD3: all-in-one safetensors  
   - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
   - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
+- XYZ grid:  
+  - optional time benchmark info to individual images  
+  - optional add params to individual images  
+  - create video from generated grid images  
+    supports all standard video types and interpolation  
 - UI:  
   - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
   - add show networks on startup setting  
   - better mapping of networks previews  
   - optimize networks display load  
-- XYZ grid:  
-  - optional per-image time benchmark info  
 - CLI:  
   - refactor command line params  
     run `webui.sh`/`webui.bat` with `--help` to see all options  
@@ -49,6 +53,8 @@ This release can be considered an LTS release before we kick off the next round
   - fix diffusers load from folder  
   - fix lora enum logging on windows  
   - fix xyz grid with batch count  
+  - fix xyz grid include images  
+  - fix xyz skip on interrupted  
   - fix vqa models ignoring hfcache folder setting  
   - fix network height in standard vs modern ui  
   - fix k-diff enum on startup  
diff --git a/modules/images.py b/modules/images.py
index e6a334034..fb9cc9652 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -361,11 +361,11 @@ def flatten(img, bgcolor):
     return img.convert('RGB')
 
 
-def draw_overlay(im, text):
+def draw_overlay(im, text: str = '', y_offset: int = 0):
     d = ImageDraw.Draw(im)
     fontsize = (im.width + im.height) // 50
     font = get_font(fontsize)
-    d.text((fontsize//2, fontsize//2), text, font=font, fill=shared.opts.font_color)
+    d.text((fontsize//2, fontsize//2 + y_offset), text, font=font, fill=shared.opts.font_color)
     return im
 
 
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index 62a722bb3..60e608c76 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -1,4 +1,5 @@
 # xyz grid that shows as selectable script
+import os
 import csv
 import random
 from collections import namedtuple
@@ -16,6 +17,9 @@ from modules.ui_components import ToolButton
 import modules.ui_symbols as symbols
 
 
+debug = shared.log.trace if os.environ.get('SD_XYZ_DEBUG', None) is not None else lambda *args, **kwargs: None
+
+
 class Script(scripts.Script):
     current_axis_options = []
 
@@ -26,6 +30,7 @@ class Script(scripts.Script):
         self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
         with gr.Row():
             gr.HTML('<span">&nbsp XYZ Grid</span><br>')
+
         with gr.Row():
             with gr.Column():
                 with gr.Row(variant='compact'):
@@ -43,18 +48,42 @@ class Script(scripts.Script):
                     z_values = gr.Textbox(label="Z values", container=True, lines=1, elem_id=self.elem_id("z_values"))
                     z_values_dropdown = gr.Dropdown(label="Z values", container=True, visible=False, multiselect=True, interactive=True)
                     fill_z_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_z_tool_button", visible=False)
+
         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)
                 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():
                 include_grid = gr.Checkbox(label='Include main grid', value=True, elem_id=self.elem_id("no_xyz_grid"), container=False)
                 include_subgrids = gr.Checkbox(label='Include sub grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False)
                 include_images = gr.Checkbox(label='Include images', value=False, elem_id=self.elem_id("include_lone_images"), container=False)
+                create_video = gr.Checkbox(label='Create video', value=False, elem_id=self.elem_id("xyz_create_video"), container=False)
+
+        with gr.Row(visible=False) as ui_video:
+            def video_type_change(video_type):
+                return [
+                    gr.update(visible=video_type != 'None'),
+                    gr.update(visible=video_type == 'GIF' or video_type == 'PNG'),
+                    gr.update(visible=video_type == 'MP4'),
+                    gr.update(visible=video_type == 'MP4'),
+                ]
+
+            with gr.Column():
+                video_type = gr.Dropdown(label='Video type', choices=['None', 'GIF', 'PNG', 'MP4'], value='None')
+            with gr.Column():
+                video_duration = gr.Slider(label='Duration', minimum=0.25, maximum=300, step=0.25, value=2, visible=False)
+                video_loop = gr.Checkbox(label='Loop', value=True, visible=False, elem_id="control_video_loop")
+                video_pad = gr.Slider(label='Pad frames', minimum=0, maximum=24, step=1, value=1, visible=False)
+                video_interpolate = gr.Slider(label='Interpolate frames', minimum=0, maximum=24, step=1, value=0, visible=False)
+            video_type.change(fn=video_type_change, inputs=[video_type], outputs=[video_duration, video_loop, video_pad, video_interpolate])
+        create_video.change(fn=lambda x: gr.update(visible=x), inputs=[create_video], outputs=[ui_video])
+
         with gr.Row():
             margin_size = gr.Slider(label="Grid margins", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
+
         with gr.Row():
             swap_xy_axes_button = gr.Button(value="Swap X/Y", elem_id="xy_grid_swap_axes_button", variant="secondary")
             swap_yz_axes_button = gr.Button(value="Swap Y/Z", elem_id="yz_grid_swap_axes_button", variant="secondary")
@@ -131,9 +160,25 @@ class Script(scripts.Script):
             (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
         )
 
-        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size]
+        return [
+            x_type, x_values, x_values_dropdown,
+            y_type, y_values, y_values_dropdown,
+            z_type, z_values, z_values_dropdown,
+            csv_mode, draw_legend, no_fixed_seeds,
+            include_grid, include_subgrids, include_images,
+            include_time, include_text, margin_size,
+            create_video, video_type, video_duration, video_loop, video_pad, video_interpolate,
+        ]
 
-    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size): # pylint: disable=W0221
+    def run(self, p,
+            x_type, x_values, x_values_dropdown,
+            y_type, y_values, y_values_dropdown,
+            z_type, z_values, z_values_dropdown,
+            csv_mode, draw_legend, no_fixed_seeds,
+            include_grid, include_subgrids, include_images,
+            include_time, include_text, margin_size,
+            create_video, video_type, video_duration, video_loop, video_pad, video_interpolate,
+           ): # pylint: disable=W0221
         if not no_fixed_seeds:
             processing.fix_seed(p)
         if not shared.opts.return_grid:
@@ -315,31 +360,40 @@ class Script(scripts.Script):
                 margin_size=margin_size,
                 no_grid=not include_grid,
                 include_time=include_time,
+                include_text=include_text,
             )
 
         if not processed.images:
             return processed # something broke, no further handling needed.
-        z_count = len(zs)
-        processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] # set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
-        if not include_images: # dont need sub-images anymore, drop from list:
-            if not include_grid and include_subgrids:
-                processed.images = processed.images[:z_count] # we don't have the main grid image, and need zero additional sub-images
-            else:
-                processed.images = processed.images[:z_count+1] # we either have the main grid image, or need one sub-images
-        if shared.opts.grid_save: # auto-save main and sub-grids:
-            grid_count = z_count + ( 1 if include_grid and z_count > 1 else 0 )
-            for g in range(grid_count):
+        # processed.images = (1)*grid + (z > 1 ? z : 0)*subgrids + (x*y*z)*images
+        have_grid = 1 if include_grid else 0
+        have_subgrids = len(zs) if len(zs) > 1 and include_subgrids else 0
+        have_images = processed.images[have_grid+have_subgrids:]
+        processed.infotexts[:have_grid+have_subgrids] = grid_infotext[:have_grid+have_subgrids] # update infotexts with grid and subgrid info
+        shared.log.debug(f'XYZ grid: grid={have_grid} subgrids={have_subgrids} images={len(have_images)} total={len(processed.images)}')
+
+        if not include_images: # dont need images anymore, drop from list:
+            processed.images = processed.images[:have_grid+have_subgrids]
+            debug(f'XYZ grid remove images: total={processed.images}')
+
+        if shared.opts.grid_save and not shared.state.interrupted: # auto-save main and sub-grids:
+            for g in range(have_grid + have_subgrids):
                 adj_g = g-1 if g > 0 else g
                 info = processed.infotexts[g]
                 prompt = processed.all_prompts[adj_g]
                 seed = processed.all_seeds[adj_g]
+                debug(f'XYZ grid save grid: i={g+1}')
                 images.save_image(processed.images[g], p.outpath_grids, "grid", info=info, extension=shared.opts.grid_format, prompt=prompt, seed=seed, grid=True, p=processed)
-        if not include_subgrids: # done with sub-grids, drop all related information:
-            for _sg in range(z_count):
+
+        if not include_subgrids and have_subgrids > 0: # done with sub-grids, drop all related information:
+            for _sg in range(have_subgrids):
                 del processed.images[1]
                 del processed.all_prompts[1]
                 del processed.all_seeds[1]
                 del processed.infotexts[1]
-        elif include_grid:
-            del processed.infotexts[0]
+            debug(f'XYZ grid remove subgrids: total={processed.images}')
+
+        if create_video and video_type != 'None' and not shared.state.interrupted:
+            images.save_video(p, filename=None, images=have_images, video_type=video_type, duration=video_duration, loop=video_loop, pad=video_pad, interpolate=video_interpolate)
+
         return processed
diff --git a/scripts/xyz_grid_draw.py b/scripts/xyz_grid_draw.py
index 9a9f2246c..80336fa73 100644
--- a/scripts/xyz_grid_draw.py
+++ b/scripts/xyz_grid_draw.py
@@ -4,7 +4,7 @@ from PIL import Image
 from modules import shared, images, processing
 
 
-def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, no_grid: False, include_time: False): # pylint: disable=unused-argument
+def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, no_grid: False, include_time: False, include_text: False): # pylint: disable=unused-argument
     x_texts = [[images.GridAnnotation(x)] for x in x_labels]
     y_texts = [[images.GridAnnotation(y)] for y in y_labels]
     z_texts = [[images.GridAnnotation(z)] for z in z_labels]
@@ -40,8 +40,18 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
         idx = index(ix, iy, iz)
         if processed is not None and processed.images:
             processed_result.images[idx] = processed.images[0]
+            overlay_text = ''
+            if include_text:
+                if len(x_labels[ix]) > 0:
+                    overlay_text += f'{x_labels[ix]}\n'
+                if len(y_labels[iy]) > 0:
+                    overlay_text += f'{y_labels[iy]}\n'
+                if len(z_labels[iz]) > 0:
+                    overlay_text += f'{z_labels[iz]}\n'
             if include_time:
-                processed_result.images[idx] = images.draw_overlay(processed_result.images[idx], f'time: {p1 - p0:.2f}')
+                overlay_text += f'Time: {p1 - p0:.2f}'
+            if len(overlay_text) > 0:
+                processed_result.images[idx] = images.draw_overlay(processed_result.images[idx], overlay_text)
             processed_result.all_prompts[idx] = processed.prompt
             processed_result.all_seeds[idx] = processed.seed
             processed_result.infotexts[idx] = processed.infotexts[0]
diff --git a/scripts/xyz_grid_on.py b/scripts/xyz_grid_on.py
index 6affde005..f0455f0f1 100644
--- a/scripts/xyz_grid_on.py
+++ b/scripts/xyz_grid_on.py
@@ -1,4 +1,5 @@
 # xyz grid that shows up as alwayson script
+import os
 import csv
 import random
 from collections import namedtuple
@@ -18,6 +19,7 @@ import modules.ui_symbols as symbols
 
 active = False
 cache = None
+debug = shared.log.trace if os.environ.get('SD_XYZ_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
 class Script(scripts.Script):
@@ -35,6 +37,7 @@ class Script(scripts.Script):
         with gr.Accordion('XYZ Grid', open = False, elem_id='xyz_grid'):
             with gr.Row():
                 enabled = gr.Checkbox(label = 'Enabled', value = False)
+
             with gr.Row():
                 with gr.Column():
                     with gr.Row(variant='compact'):
@@ -52,18 +55,42 @@ class Script(scripts.Script):
                         z_values = gr.Textbox(label="Z values", container=True, lines=1, elem_id=self.elem_id("z_values"))
                         z_values_dropdown = gr.Dropdown(label="Z values", container=True, visible=False, multiselect=True, interactive=True)
                         fill_z_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_z_tool_button", visible=False)
+
             with gr.Row():
                 with gr.Column():
                     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():
                     include_grid = gr.Checkbox(label='Include main grid', value=True, elem_id=self.elem_id("no_xyz_grid"), container=False)
                     include_subgrids = gr.Checkbox(label='Include sub grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False)
                     include_images = gr.Checkbox(label='Include images', value=False, elem_id=self.elem_id("include_lone_images"), container=False)
+                    create_video = gr.Checkbox(label='Create video', value=False, elem_id=self.elem_id("xyz_create_video"), container=False)
+
+            with gr.Row(visible=False) as ui_video:
+                def video_type_change(video_type):
+                    return [
+                        gr.update(visible=video_type != 'None'),
+                        gr.update(visible=video_type == 'GIF' or video_type == 'PNG'),
+                        gr.update(visible=video_type == 'MP4'),
+                        gr.update(visible=video_type == 'MP4'),
+                    ]
+
+                with gr.Column():
+                    video_type = gr.Dropdown(label='Video type', choices=['None', 'GIF', 'PNG', 'MP4'], value='None')
+                with gr.Column():
+                    video_duration = gr.Slider(label='Duration', minimum=0.25, maximum=300, step=0.25, value=2, visible=False)
+                    video_loop = gr.Checkbox(label='Loop', value=True, visible=False, elem_id="control_video_loop")
+                    video_pad = gr.Slider(label='Pad frames', minimum=0, maximum=24, step=1, value=1, visible=False)
+                    video_interpolate = gr.Slider(label='Interpolate frames', minimum=0, maximum=24, step=1, value=0, visible=False)
+                video_type.change(fn=video_type_change, inputs=[video_type], outputs=[video_duration, video_loop, video_pad, video_interpolate])
+            create_video.change(fn=lambda x: gr.update(visible=x), inputs=[create_video], outputs=[ui_video])
+
             with gr.Row():
                 margin_size = gr.Slider(label="Grid margins", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
+
             with gr.Row():
                 swap_xy_axes_button = gr.Button(value="Swap X/Y", elem_id="xy_grid_swap_axes_button", variant="secondary")
                 swap_yz_axes_button = gr.Button(value="Swap Y/Z", elem_id="yz_grid_swap_axes_button", variant="secondary")
@@ -140,9 +167,27 @@ class Script(scripts.Script):
             (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
         )
 
-        return [enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size]
+        return [
+            enabled,
+            x_type, x_values, x_values_dropdown,
+            y_type, y_values, y_values_dropdown,
+            z_type, z_values, z_values_dropdown,
+            csv_mode, draw_legend, no_fixed_seeds,
+            include_grid, include_subgrids, include_images,
+            include_time, include_text, margin_size,
+            create_video, video_type, video_duration, video_loop, video_pad, video_interpolate,
+        ]
 
-    def process(self, p, enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size): # pylint: disable=W0221
+    def process(self, p,
+                enabled,
+                x_type, x_values, x_values_dropdown,
+                y_type, y_values, y_values_dropdown,
+                z_type, z_values, z_values_dropdown,
+                csv_mode, draw_legend, no_fixed_seeds,
+                include_grid, include_subgrids, include_images,
+                include_time, include_text, margin_size,
+                create_video, video_type, video_duration, video_loop, video_pad, video_interpolate,
+               ): # pylint: disable=W0221
         global active, cache # pylint: disable=W0603
         cache = None
         if not enabled or active:
@@ -330,62 +375,41 @@ class Script(scripts.Script):
                 margin_size=margin_size,
                 no_grid=not include_grid,
                 include_time=include_time,
+                include_text=include_text,
             )
 
-        """
         if not processed.images:
-            active = False
-            return processed # It broke, no further handling needed.
-        # images stucture: main-grid, sub-grid1, sub-grid2, ..., image-1, image-2, ...
-        z_count = len(processed.images) - (len(zs) * len(ys) * len(xs)) # how many grids are there: main grid + sub-grids
-        processed.infotexts[:z_count] = grid_infotext[:z_count] # replace grid info texts
-        if not include_images:
-            processed.images = processed.images[:z_count]
-        if shared.opts.grid_save: # auto-save main and sub-grids:
-            for i in range(z_count):
-                info = processed.infotexts[i]
-                prompt = processed.all_prompts[i]
-                seed = processed.all_seeds[i]
-                _fn, _txt, _exif = images.save_image(processed.images[i], p.outpath_grids, "grid", info=info, extension=shared.opts.grid_format, prompt=prompt, seed=seed, grid=True, p=processed)
-        if not include_subgrids and z_count > 1: # delete sub-grids
-            for _sg in range(z_count - 1):
-                del processed.images[1]
-                del processed.all_prompts[1]
-                del processed.all_seeds[1]
-                del processed.infotexts[1]
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
-        active = False
-        cache = processed
-        return processed
-        """
+            return processed # something broke, no further handling needed.
+        # processed.images = (1)*grid + (z > 1 ? z : 0)*subgrids + (x*y*z)*images
+        have_grid = 1 if include_grid else 0
+        have_subgrids = len(zs) if len(zs) > 1 and include_subgrids else 0
+        have_images = processed.images[have_grid+have_subgrids:]
+        processed.infotexts[:have_grid+have_subgrids] = grid_infotext[:have_grid+have_subgrids] # update infotexts with grid and subgrid info
+        shared.log.debug(f'XYZ grid: grid={have_grid} subgrids={have_subgrids} images={len(have_images)} total={len(processed.images)}')
 
-        if not processed.images:
-            return processed # It broke, no further handling needed.
-        z_count = len(zs)
-        processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] # set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
-        if not include_images: # dont need sub-images anymore, drop from list:
-            if not include_grid and include_subgrids:
-                processed.images = processed.images[:z_count] # we don't have the main grid image, and need zero additional sub-images
-            else:
-                processed.images = processed.images[:z_count+1] # we either have the main grid image, or need one sub-images
+        if not include_images: # dont need images anymore, drop from list:
+            processed.images = processed.images[:have_grid+have_subgrids]
+            debug(f'XYZ grid remove images: total={processed.images}')
 
-        if shared.opts.grid_save: # auto-save main and sub-grids:
-            grid_count = z_count + ( 1 if include_grid and z_count > 1 else 0 )
-            for g in range(grid_count):
+        if shared.opts.grid_save and not shared.state.interrupted: # auto-save main and sub-grids:
+            for g in range(have_grid + have_subgrids):
                 adj_g = g-1 if g > 0 else g
                 info = processed.infotexts[g]
                 prompt = processed.all_prompts[adj_g]
                 seed = processed.all_seeds[adj_g]
+                debug(f'XYZ grid save grid: i={g+1}')
                 images.save_image(processed.images[g], p.outpath_grids, "grid", info=info, extension=shared.opts.grid_format, prompt=prompt, seed=seed, grid=True, p=processed)
-        if not include_subgrids: # done with sub-grids, drop all related information:
-            for _sg in range(z_count):
+
+        if not include_subgrids and have_subgrids > 0: # done with sub-grids, drop all related information:
+            for _sg in range(have_subgrids):
                 del processed.images[1]
                 del processed.all_prompts[1]
                 del processed.all_seeds[1]
                 del processed.infotexts[1]
-        elif include_grid:
-            del processed.infotexts[0]
+            debug(f'XYZ grid remove subgrids: total={processed.images}')
+
+        if create_video and video_type != 'None' and not shared.state.interrupted:
+            images.save_video(p, filename=None, images=have_images, video_type=video_type, duration=video_duration, loop=video_loop, pad=video_pad, interpolate=video_interpolate)
 
         p.do_not_save_grid = True
         p.do_not_save_samples = True

From 9b567e8ad187880f01803ced21a6014e94f49ac8 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 5 Nov 2024 09:40:27 -0500
Subject: [PATCH 022/119] installer verify and remove venv pgks

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md |  4 ++++
 installer.py | 22 ++++++++++++++++++++++
 launch.py    |  1 +
 3 files changed, 27 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1429c707a..ee7199296 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -33,6 +33,10 @@ This release can be considered an LTS release before we kick off the next round
   - add show networks on startup setting  
   - better mapping of networks previews  
   - optimize networks display load  
+- Installer:
+  - Log `venv` and package search paths
+  - Auto-remove invalid packages from `venv/site-packages`  
+    e.g. packages starting with `~` which are left-over due to windows access violation  
 - CLI:  
   - refactor command line params  
     run `webui.sh`/`webui.bat` with `--help` to see all options  
diff --git a/installer.py b/installer.py
index 431e75462..b19eae87e 100644
--- a/installer.py
+++ b/installer.py
@@ -1139,6 +1139,28 @@ def check_ui(ver):
         os.chdir(cwd)
 
 
+def check_venv():
+    import site
+    pkg_path = [os.path.relpath(p) for p in site.getsitepackages() if os.path.exists(p)]
+    log.debug(f'Packages: venv={os.path.relpath(sys.prefix)} site={pkg_path}')
+    for p in pkg_path:
+        invalid = []
+        for f in os.listdir(p):
+            if f.startswith('~'):
+                invalid.append(f)
+        if len(invalid) > 0:
+            log.warning(f'Packages: site="{p}" invalid={invalid} removing')
+        for f in invalid:
+            fn = os.path.join(p, f)
+            try:
+                if os.path.isdir(fn):
+                    shutil.rmtree(fn)
+                elif os.path.isfile(fn):
+                    os.unlink(fn)
+            except Exception as e:
+                log.error(f'Packages: site={p} invalid={f} error={e}')
+
+
 # check version of the main repo and optionally upgrade it
 def check_version(offline=False, reset=True): # pylint: disable=unused-argument
     if args.skip_all:
diff --git a/launch.py b/launch.py
index 8379a5d6d..903234490 100755
--- a/launch.py
+++ b/launch.py
@@ -208,6 +208,7 @@ def main():
         installer.log.info('Skipping GIT operations')
     installer.check_version()
     installer.log.info(f'Platform: {installer.print_dict(installer.get_platform())}')
+    installer.check_venv()
     installer.log.info(f'Args: {sys.argv[1:]}')
     if not args.skip_env:
         installer.set_environment()

From 0a0c5cd85a37a9dd7919cbdea716e9b69ddbf1f6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 5 Nov 2024 18:20:38 -0500
Subject: [PATCH 023/119] add instantir

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                     |    1 +
 .ruff.toml                                    |    1 +
 CHANGELOG.md                                  |   10 +-
 modules/img2img.py                            |    1 +
 modules/instantir/__init__.py                 |    3 +
 modules/instantir/aggregator.py               |  983 ++++++++++
 modules/instantir/ip_adapter/__init__.py      |    0
 .../ip_adapter/attention_processor.py         | 1467 ++++++++++++++
 modules/instantir/ip_adapter/ip_adapter.py    |  236 +++
 modules/instantir/ip_adapter/resampler.py     |  158 ++
 modules/instantir/ip_adapter/utils.py         |  248 +++
 .../instantir/lcm_single_step_scheduler.py    |  537 +++++
 modules/instantir/sdxl_instantir.py           | 1738 +++++++++++++++++
 modules/sd_models.py                          |    1 +
 scripts/instantir.py                          |   97 +
 15 files changed, 5478 insertions(+), 3 deletions(-)
 create mode 100644 modules/instantir/__init__.py
 create mode 100644 modules/instantir/aggregator.py
 create mode 100644 modules/instantir/ip_adapter/__init__.py
 create mode 100644 modules/instantir/ip_adapter/attention_processor.py
 create mode 100644 modules/instantir/ip_adapter/ip_adapter.py
 create mode 100644 modules/instantir/ip_adapter/resampler.py
 create mode 100644 modules/instantir/ip_adapter/utils.py
 create mode 100644 modules/instantir/lcm_single_step_scheduler.py
 create mode 100644 modules/instantir/sdxl_instantir.py
 create mode 100644 scripts/instantir.py

diff --git a/.pylintrc b/.pylintrc
index 37b812ffe..f541638e7 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -31,6 +31,7 @@ ignore-paths=/usr/lib/.*$,
              modules/xadapter,
              modules/meissonic,
              modules/omnigen,
+             modules/instantir,
              modules/pulid/eva_clip,
              repositories,
              extensions-builtin/sd-webui-agent-scheduler,
diff --git a/.ruff.toml b/.ruff.toml
index 28439c73c..154d9a2c1 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -26,6 +26,7 @@ exclude = [
     "modules/xadapter",
     "modules/meissonic",
     "modules/omnigen",
+    "modules/instantir",
     "modules/pulid/eva_clip",
     "repositories",
     "extensions-builtin/sd-extension-chainner/nodes",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index ee7199296..4bb10f5de 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,11 @@ This release can be considered an LTS release before we kick off the next round
   - select in *scripts -> pulid*  
   - compatible with *sdxl*  
   - can be used in xyz grid  
+- [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
+  - alternative to traditional `img2img` with more control over restoration process  
+  - select in *image -> scripts -> instantir*  
+  - compatible with *sdxl*  
+  - *note*: after used once it cannot be unloaded without reloading base model  
 - SD3: ControlNets:  
   - *InstantX Canny, Pose, Depth, Tile*  
   - *Alimama Inpainting, SoftEdge*  
@@ -40,13 +45,12 @@ This release can be considered an LTS release before we kick off the next round
 - CLI:  
   - refactor command line params  
     run `webui.sh`/`webui.bat` with `--help` to see all options  
+  - added `cli/model-metadata.py` to display metadata in any safetensors file
+  - added `cli/model-keys.py` to quicky display content of any safetensors file
 - Other:  
   - Model loader: Report modules included in safetensors when attempting to load a model  
   - Repo: move screenshots to GH pages  
   - Requirements: update  
-- CLI:
-  - added `cli/model-metadata.py` to display metadata in any safetensors file
-  - added `cli/model-keys.py` to quicky display content of any safetensors file
 - Fixes:  
   - custom watermark add alphablending  
   - detailer min/max size as fractions of image size  
diff --git a/modules/img2img.py b/modules/img2img.py
index faf65161a..f3bec5b02 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -267,6 +267,7 @@ def img2img(id_task: str, state: str, mode: int,
         processed = modules.scripts.scripts_img2img.run(p, *args)
         if processed is None:
             processed = processing.process_images(p)
+        processed = modules.scripts.scripts_img2img.after(p, processed, *args)
     p.close()
     generation_info_js = processed.js() if processed is not None else ''
     if processed is None:
diff --git a/modules/instantir/__init__.py b/modules/instantir/__init__.py
new file mode 100644
index 000000000..fdd6cf8a0
--- /dev/null
+++ b/modules/instantir/__init__.py
@@ -0,0 +1,3 @@
+from .sdxl_instantir import InstantIRPipeline
+from .lcm_single_step_scheduler import LCMSingleStepScheduler
+from .ip_adapter.utils import init_adapter_in_unet, load_adapter_to_pipe
diff --git a/modules/instantir/aggregator.py b/modules/instantir/aggregator.py
new file mode 100644
index 000000000..fd6151003
--- /dev/null
+++ b/modules/instantir/aggregator.py
@@ -0,0 +1,983 @@
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders.single_file_model import FromOriginalModelMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unets.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    DownBlock2D,
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+)
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class ZeroConv(nn.Module):
+    def __init__(self, label_nc, norm_nc, mask=False):
+        super().__init__()
+        self.zero_conv = zero_module(nn.Conv2d(label_nc+norm_nc, norm_nc, 1, 1, 0))
+        self.mask = mask
+
+    def forward(self, hidden_states, h_ori=None):
+        # with torch.cuda.amp.autocast(enabled=False, dtype=torch.float32):
+        c, h = hidden_states
+        if not self.mask:
+            h = self.zero_conv(torch.cat([c, h], dim=1))
+        else:
+            h = self.zero_conv(torch.cat([c, h], dim=1)) * torch.zeros_like(h)
+        if h_ori is not None:
+            h = torch.cat([h_ori, h], dim=1)
+        return h
+
+
+class SFT(nn.Module):
+    def __init__(self, label_nc, norm_nc, mask=False):
+        super().__init__()
+
+        # param_free_norm_type = str(parsed.group(1))
+        ks = 3
+        pw = ks // 2
+
+        self.mask = mask
+
+        nhidden = 128
+
+        self.mlp_shared = nn.Sequential(
+            nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw),
+            nn.SiLU()
+        )
+        self.mul = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
+        self.add = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
+
+    def forward(self, hidden_states, mask=False):
+        
+        c, h = hidden_states
+        mask = mask or self.mask
+        assert mask is False
+
+        actv = self.mlp_shared(c)
+        gamma = self.mul(actv)
+        beta = self.add(actv)
+
+        if self.mask:
+            gamma = gamma * torch.zeros_like(gamma)
+            beta = beta * torch.zeros_like(beta)
+        # gamma_ori, gamma_res = torch.split(gamma, [h_ori_c, h_c], dim=1)
+        # beta_ori, beta_res = torch.split(beta, [h_ori_c, h_c], dim=1)
+        # print(gamma_ori.mean(), gamma_res.mean(), beta_ori.mean(), beta_res.mean())
+        h = h * (gamma + 1) + beta
+        # sample_ori, sample_res = torch.split(h, [h_ori_c, h_c], dim=1)
+        # print(sample_ori.mean(), sample_res.mean())
+
+        return h
+
+
+@dataclass
+class AggregatorOutput(BaseOutput):
+    """
+    The output of [`Aggregator`].
+
+    Args:
+        down_block_res_samples (`tuple[torch.Tensor]`):
+            A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
+            be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
+            used to condition the original UNet's downsampling activations.
+        mid_down_block_re_sample (`torch.Tensor`):
+            The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
+            `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
+            Output can be used to condition the original UNet's middle block activation.
+    """
+
+    down_block_res_samples: Tuple[torch.Tensor]
+    mid_block_res_sample: torch.Tensor
+
+
+class ConditioningEmbedding(nn.Module):
+    """
+    Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
+    [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
+    training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
+    convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
+    (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
+    model) to encode image-space conditions ... into feature maps ..."
+    """
+
+    def __init__(
+        self,
+        conditioning_embedding_channels: int,
+        conditioning_channels: int = 3,
+        block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
+    ):
+        super().__init__()
+
+        self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
+
+        self.blocks = nn.ModuleList([])
+
+        for i in range(len(block_out_channels) - 1):
+            channel_in = block_out_channels[i]
+            channel_out = block_out_channels[i + 1]
+            self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
+            self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
+
+        self.conv_out = zero_module(
+            nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
+        )
+
+    def forward(self, conditioning):
+        embedding = self.conv_in(conditioning)
+        embedding = F.silu(embedding)
+
+        for block in self.blocks:
+            embedding = block(embedding)
+            embedding = F.silu(embedding)
+
+        embedding = self.conv_out(embedding)
+
+        return embedding
+
+
+class Aggregator(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+    """
+    Aggregator model.
+
+    Args:
+        in_channels (`int`, defaults to 4):
+            The number of channels in the input sample.
+        flip_sin_to_cos (`bool`, defaults to `True`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, defaults to 0):
+            The frequency shift to apply to the time embedding.
+        down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
+        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, defaults to 2):
+            The number of layers per block.
+        downsample_padding (`int`, defaults to 1):
+            The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, defaults to 1):
+            The scale factor to use for the mid block.
+        act_fn (`str`, defaults to "silu"):
+            The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use for the normalization. If None, normalization and activation layers is skipped
+            in post-processing.
+        norm_eps (`float`, defaults to 1e-5):
+            The epsilon to use for the normalization.
+        cross_attention_dim (`int`, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
+            The dimension of the attention heads.
+        use_linear_projection (`bool`, defaults to `False`):
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        num_class_embeds (`int`, *optional*, defaults to 0):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        upcast_attention (`bool`, defaults to `False`):
+        resnet_time_scale_shift (`str`, defaults to `"default"`):
+            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
+        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
+            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
+            `class_embed_type="projection"`.
+        controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
+            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
+        conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
+            The tuple of output channel for each block in the `conditioning_embedding` layer.
+        global_pool_conditions (`bool`, defaults to `False`):
+            TODO(Patrick) - unused parameter.
+        addition_embed_type_num_heads (`int`, defaults to 64):
+            The number of heads to use for the `TextTimeEmbedding` layer.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 4,
+        conditioning_channels: int = 3,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str, ...] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
+        encoder_hid_dim: Optional[int] = None,
+        encoder_hid_dim_type: Optional[str] = None,
+        attention_head_dim: Union[int, Tuple[int, ...]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        controlnet_conditioning_channel_order: str = "rgb",
+        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
+        global_pool_conditions: bool = False,
+        addition_embed_type_num_heads: int = 64,
+        pad_concat: bool = False,
+    ):
+        super().__init__()
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+        self.pad_concat = pad_concat
+
+        # Check inputs
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        # input
+        conv_in_kernel = 3
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        time_embed_dim = block_out_channels[0] * 4
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
+
+        # control net conditioning embedding
+        self.ref_conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.controlnet_down_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+
+        # controlnet_block = ZeroConv(output_channel, output_channel)
+        controlnet_block = nn.Sequential(
+            SFT(output_channel, output_channel),
+            zero_module(nn.Conv2d(output_channel, output_channel, kernel_size=1))
+        )
+        self.controlnet_down_blocks.append(controlnet_block)
+
+        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
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                num_attention_heads=num_attention_heads[i],
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                downsample_padding=downsample_padding,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.down_blocks.append(down_block)
+
+            for _ in range(layers_per_block):
+                # controlnet_block = ZeroConv(output_channel, output_channel)
+                controlnet_block = nn.Sequential(
+                    SFT(output_channel, output_channel),
+                    zero_module(nn.Conv2d(output_channel, output_channel, kernel_size=1))
+                )
+                self.controlnet_down_blocks.append(controlnet_block)
+
+            if not is_final_block:
+                # controlnet_block = ZeroConv(output_channel, output_channel)
+                controlnet_block = nn.Sequential(
+                    SFT(output_channel, output_channel),
+                    zero_module(nn.Conv2d(output_channel, output_channel, kernel_size=1))
+                )
+                self.controlnet_down_blocks.append(controlnet_block)
+
+        # mid
+        mid_block_channel = block_out_channels[-1]
+
+        # controlnet_block = ZeroConv(mid_block_channel, mid_block_channel)
+        controlnet_block = nn.Sequential(
+            SFT(mid_block_channel, mid_block_channel),
+            zero_module(nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1))
+        )
+        self.controlnet_mid_block = controlnet_block
+
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=mid_block_channel,
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+            )
+        elif mid_block_type == "UNetMidBlock2D":
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                num_layers=0,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                add_attention=False,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+    @classmethod
+    def from_unet(
+        cls,
+        unet: UNet2DConditionModel,
+        controlnet_conditioning_channel_order: str = "rgb",
+        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
+        load_weights_from_unet: bool = True,
+        conditioning_channels: int = 3,
+    ):
+        r"""
+        Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
+
+        Parameters:
+            unet (`UNet2DConditionModel`):
+                The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
+                where applicable.
+        """
+        transformer_layers_per_block = (
+            unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
+        )
+        encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
+        encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
+        addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
+        addition_time_embed_dim = (
+            unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
+        )
+
+        controlnet = cls(
+            encoder_hid_dim=encoder_hid_dim,
+            encoder_hid_dim_type=encoder_hid_dim_type,
+            addition_embed_type=addition_embed_type,
+            addition_time_embed_dim=addition_time_embed_dim,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=unet.config.in_channels,
+            flip_sin_to_cos=unet.config.flip_sin_to_cos,
+            freq_shift=unet.config.freq_shift,
+            down_block_types=unet.config.down_block_types,
+            only_cross_attention=unet.config.only_cross_attention,
+            block_out_channels=unet.config.block_out_channels,
+            layers_per_block=unet.config.layers_per_block,
+            downsample_padding=unet.config.downsample_padding,
+            mid_block_scale_factor=unet.config.mid_block_scale_factor,
+            act_fn=unet.config.act_fn,
+            norm_num_groups=unet.config.norm_num_groups,
+            norm_eps=unet.config.norm_eps,
+            cross_attention_dim=unet.config.cross_attention_dim,
+            attention_head_dim=unet.config.attention_head_dim,
+            num_attention_heads=unet.config.num_attention_heads,
+            use_linear_projection=unet.config.use_linear_projection,
+            class_embed_type=unet.config.class_embed_type,
+            num_class_embeds=unet.config.num_class_embeds,
+            upcast_attention=unet.config.upcast_attention,
+            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
+            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
+            mid_block_type=unet.config.mid_block_type,
+            controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
+            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
+            conditioning_channels=conditioning_channels,
+        )
+
+        if load_weights_from_unet:
+            controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
+            controlnet.ref_conv_in.load_state_dict(unet.conv_in.state_dict())
+            controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
+            controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
+
+            if controlnet.class_embedding:
+                controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
+
+            if hasattr(controlnet, "add_embedding"):
+                controlnet.add_embedding.load_state_dict(unet.add_embedding.state_dict())
+
+            controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
+            controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
+
+        return controlnet
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
+    def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def process_encoder_hidden_states(
+        self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
+    ) -> torch.Tensor:
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            image_embeds = self.encoder_hid_proj(image_embeds)
+            encoder_hidden_states = (encoder_hidden_states, image_embeds)
+        return encoder_hidden_states
+    
+    def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
+        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        controlnet_cond: torch.FloatTensor,
+        cat_dim: int = -2,
+        conditioning_scale: float = 1.0,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[AggregatorOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
+        """
+        The [`Aggregator`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor.
+            timestep (`Union[torch.Tensor, float, int]`):
+                The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.Tensor`):
+                The encoder hidden states.
+            controlnet_cond (`torch.FloatTensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
+                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
+                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
+                embeddings.
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            added_cond_kwargs (`dict`):
+                Additional conditions for the Stable Diffusion XL UNet.
+            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
+                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
+            return_dict (`bool`, defaults to `True`):
+                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
+                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
+                returned where the first element is the sample tensor.
+        """
+        # check channel order
+        channel_order = self.config.controlnet_conditioning_channel_order
+
+        if channel_order == "rgb":
+            # in rgb order by default
+            ...
+        else:
+            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        if self.config.addition_embed_type is not None:
+            if self.config.addition_embed_type == "text":
+                aug_emb = self.add_embedding(encoder_hidden_states)
+
+            elif self.config.addition_embed_type == "text_time":
+                if "text_embeds" not in added_cond_kwargs:
+                    raise ValueError(
+                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                    )
+                text_embeds = added_cond_kwargs.get("text_embeds")
+                if "time_ids" not in added_cond_kwargs:
+                    raise ValueError(
+                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                    )
+                time_ids = added_cond_kwargs.get("time_ids")
+                time_embeds = self.add_time_proj(time_ids.flatten())
+                time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+                add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+                add_embeds = add_embeds.to(emb.dtype)
+                aug_emb = self.add_embedding(add_embeds)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        encoder_hidden_states = self.process_encoder_hidden_states(
+            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
+        )
+
+        # 2. prepare input
+        cond_latent = self.conv_in(sample)
+        ref_latent = self.ref_conv_in(controlnet_cond)
+        batch_size, channel, height, width = cond_latent.shape
+        if self.pad_concat:
+            if cat_dim == -2 or cat_dim == 2:
+                concat_pad = torch.zeros(batch_size, channel, 1, width)
+            elif cat_dim == -1 or cat_dim == 3:
+                concat_pad = torch.zeros(batch_size, channel, height, 1)
+            else:
+                raise ValueError(f"Aggregator shall concat along spatial dimension, but is asked to concat dim: {cat_dim}.")
+            concat_pad = concat_pad.to(cond_latent.device, dtype=cond_latent.dtype)
+            sample = torch.cat([cond_latent, concat_pad, ref_latent], dim=cat_dim)
+        else:
+            sample = torch.cat([cond_latent, ref_latent], dim=cat_dim)
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            sample, res_samples = downsample_block(
+                hidden_states=sample,
+                temb=emb,
+                cross_attention_kwargs=cross_attention_kwargs,
+            )
+
+            # rebuild sample: split and concat
+            if self.pad_concat:
+                batch_size, channel, height, width = sample.shape
+                if cat_dim == -2 or cat_dim == 2:
+                    cond_latent = sample[:, :, :height//2, :]
+                    ref_latent = sample[:, :, -(height//2):, :]
+                    concat_pad = torch.zeros(batch_size, channel, 1, width)
+                elif cat_dim == -1 or cat_dim == 3:
+                    cond_latent = sample[:, :, :, :width//2]
+                    ref_latent = sample[:, :, :, -(width//2):]
+                    concat_pad = torch.zeros(batch_size, channel, height, 1)
+                concat_pad = concat_pad.to(cond_latent.device, dtype=cond_latent.dtype)
+                sample = torch.cat([cond_latent, concat_pad, ref_latent], dim=cat_dim)
+                res_samples = res_samples[:-1] + (sample,)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                cross_attention_kwargs=cross_attention_kwargs,
+            )
+
+        # 5. split samples and SFT.
+        controlnet_down_block_res_samples = ()
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+            batch_size, channel, height, width = down_block_res_sample.shape
+            if cat_dim == -2 or cat_dim == 2:
+                cond_latent = down_block_res_sample[:, :, :height//2, :]
+                ref_latent = down_block_res_sample[:, :, -(height//2):, :]
+            elif cat_dim == -1 or cat_dim == 3:
+                cond_latent = down_block_res_sample[:, :, :, :width//2]
+                ref_latent = down_block_res_sample[:, :, :, -(width//2):]
+            down_block_res_sample = controlnet_block((cond_latent, ref_latent), )
+            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
+
+        down_block_res_samples = controlnet_down_block_res_samples
+
+        batch_size, channel, height, width = sample.shape
+        if cat_dim == -2 or cat_dim == 2:
+            cond_latent = sample[:, :, :height//2, :]
+            ref_latent = sample[:, :, -(height//2):, :]
+        elif cat_dim == -1 or cat_dim == 3:
+            cond_latent = sample[:, :, :, :width//2]
+            ref_latent = sample[:, :, :, -(width//2):]
+        mid_block_res_sample = self.controlnet_mid_block((cond_latent, ref_latent), )
+
+        # 6. scaling
+        down_block_res_samples = [sample*conditioning_scale for sample in down_block_res_samples]
+        mid_block_res_sample = mid_block_res_sample*conditioning_scale
+
+        if self.config.global_pool_conditions:
+            down_block_res_samples = [
+                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
+            ]
+            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
+
+        if not return_dict:
+            return (down_block_res_samples, mid_block_res_sample)
+
+        return AggregatorOutput(
+            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
+        )
+
+
+def zero_module(module):
+    for p in module.parameters():
+        nn.init.zeros_(p)
+    return module
diff --git a/modules/instantir/ip_adapter/__init__.py b/modules/instantir/ip_adapter/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/modules/instantir/ip_adapter/attention_processor.py b/modules/instantir/ip_adapter/attention_processor.py
new file mode 100644
index 000000000..ed6cf755f
--- /dev/null
+++ b/modules/instantir/ip_adapter/attention_processor.py
@@ -0,0 +1,1467 @@
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class AdaLayerNorm(nn.Module):
+    def __init__(self, embedding_dim: int, time_embedding_dim: int = None):
+        super().__init__()
+
+        if time_embedding_dim is None:
+            time_embedding_dim = embedding_dim
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(time_embedding_dim, 2 * embedding_dim, bias=True)
+        nn.init.zeros_(self.linear.weight)
+        nn.init.zeros_(self.linear.bias)
+
+        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(
+        self, x: torch.Tensor, timestep_embedding: torch.Tensor
+    ):
+        emb = self.linear(self.silu(timestep_embedding))
+        shift, scale = emb.view(len(x), 1, -1).chunk(2, dim=-1)
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class AttnProcessor(nn.Module):
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(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)
+        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)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class IPAttnProcessor(nn.Module):
+    r"""
+    Attention processor for IP-Adapater.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(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)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        ip_key = attn.head_to_batch_dim(ip_key)
+        ip_value = attn.head_to_batch_dim(ip_value)
+
+        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
+        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
+        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class TA_IPAttnProcessor(nn.Module):
+    r"""
+    Attention processor for IP-Adapater.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+        self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim)
+        self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."
+
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(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)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        # time-dependent adaLN
+        ip_key = self.ln_k_ip(ip_key, temb)
+        ip_value = self.ln_v_ip(ip_value, temb)
+
+        ip_key = attn.head_to_batch_dim(ip_key)
+        ip_value = attn.head_to_batch_dim(ip_value)
+
+        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
+        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
+        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        external_kv=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        if external_kv:
+            key = torch.cat([key, external_kv.k], axis=1)
+            value = torch.cat([value, external_kv.v], axis=1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class split_AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+        time_embedding_dim=None,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        external_kv=None,
+        temb=None,
+        cat_dim=-2,
+        original_shape=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            # 2d to sequence.
+            height, width = hidden_states.shape[-2:]
+            if cat_dim==-2 or cat_dim==2:
+                hidden_states_0 = hidden_states[:, :, :height//2, :]
+                hidden_states_1 = hidden_states[:, :, -(height//2):, :]
+            elif cat_dim==-1 or cat_dim==3:
+                hidden_states_0 = hidden_states[:, :, :, :width//2]
+                hidden_states_1 = hidden_states[:, :, :, -(width//2):]
+            batch_size, channel, height, width = hidden_states_0.shape
+            hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2)
+            hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2)
+        else:
+            # directly split sqeuence according to concat dim.
+            single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1]
+            hidden_states_0 = hidden_states[:, :single_dim*single_dim,:]
+            hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:]
+
+        hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=1)
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        if external_kv:
+            key = torch.cat([key, external_kv.k], dim=1)
+            value = torch.cat([value, external_kv.v], dim=1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        # spatially split.
+        hidden_states_0, hidden_states_1 = hidden_states.chunk(2, dim=1)
+
+        if input_ndim == 4:
+            hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width)
+            hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+            if cat_dim==-2 or cat_dim==2:
+                hidden_states_pad = torch.zeros(batch_size, channel, 1, width)
+            elif cat_dim==-1 or cat_dim==3:
+                hidden_states_pad = torch.zeros(batch_size, channel, height, 1)
+            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
+            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim)
+            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"
+        else:
+            batch_size, sequence_length, inner_dim = hidden_states.shape
+            hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim)
+            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
+            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1)
+            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class sep_split_AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+        time_embedding_dim=None,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.ln_k_ref = AdaLayerNorm(hidden_size, time_embedding_dim)
+        self.ln_v_ref = AdaLayerNorm(hidden_size, time_embedding_dim)
+        # self.hidden_size = hidden_size
+        # self.cross_attention_dim = cross_attention_dim
+        # self.scale = scale
+        # self.num_tokens = num_tokens
+
+        # self.to_q_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        # self.to_k_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        # self.to_v_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        external_kv=None,
+        temb=None,
+        cat_dim=-2,
+        original_shape=None,
+        ref_scale=1.0,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            # 2d to sequence.
+            height, width = hidden_states.shape[-2:]
+            if cat_dim==-2 or cat_dim==2:
+                hidden_states_0 = hidden_states[:, :, :height//2, :]
+                hidden_states_1 = hidden_states[:, :, -(height//2):, :]
+            elif cat_dim==-1 or cat_dim==3:
+                hidden_states_0 = hidden_states[:, :, :, :width//2]
+                hidden_states_1 = hidden_states[:, :, :, -(width//2):]
+            batch_size, channel, height, width = hidden_states_0.shape
+            hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2)
+            hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2)
+        else:
+            # directly split sqeuence according to concat dim.
+            single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1]
+            hidden_states_0 = hidden_states[:, :single_dim*single_dim,:]
+            hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:]
+
+        batch_size, sequence_length, _ = (
+            hidden_states_0.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states_0 = attn.group_norm(hidden_states_0.transpose(1, 2)).transpose(1, 2)
+            hidden_states_1 = attn.group_norm(hidden_states_1.transpose(1, 2)).transpose(1, 2)
+
+        query_0 = attn.to_q(hidden_states_0)
+        query_1 = attn.to_q(hidden_states_1)
+        key_0 = attn.to_k(hidden_states_0)
+        key_1 = attn.to_k(hidden_states_1)
+        value_0 = attn.to_v(hidden_states_0)
+        value_1 = attn.to_v(hidden_states_1)
+
+        # time-dependent adaLN
+        key_1 = self.ln_k_ref(key_1, temb)
+        value_1 = self.ln_v_ref(value_1, temb)
+
+        if external_kv:
+            key_1 = torch.cat([key_1, external_kv.k], dim=1)
+            value_1 = torch.cat([value_1, external_kv.v], dim=1)
+
+        inner_dim = key_0.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query_0 = query_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        query_1 = query_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key_0 = key_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key_1 = key_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value_0 = value_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value_1 = value_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states_0 = F.scaled_dot_product_attention(
+            query_0, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+        hidden_states_1 = F.scaled_dot_product_attention(
+            query_1, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        # cross-attn
+        _hidden_states_0 = F.scaled_dot_product_attention(
+            query_0, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+        hidden_states_0 = hidden_states_0 + ref_scale * _hidden_states_0 * 10
+
+        # TODO: drop this cross-attn
+        _hidden_states_1 = F.scaled_dot_product_attention(
+            query_1, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+        hidden_states_1 = hidden_states_1 + ref_scale * _hidden_states_1
+
+        hidden_states_0 = hidden_states_0.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states_1 = hidden_states_1.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states_0 = hidden_states_0.to(query_0.dtype)
+        hidden_states_1 = hidden_states_1.to(query_1.dtype)
+
+
+        # linear proj
+        hidden_states_0 = attn.to_out[0](hidden_states_0)
+        hidden_states_1 = attn.to_out[0](hidden_states_1)
+        # dropout
+        hidden_states_0 = attn.to_out[1](hidden_states_0)
+        hidden_states_1 = attn.to_out[1](hidden_states_1)
+
+
+        if input_ndim == 4:
+            hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width)
+            hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+            if cat_dim==-2 or cat_dim==2:
+                hidden_states_pad = torch.zeros(batch_size, channel, 1, width)
+            elif cat_dim==-1 or cat_dim==3:
+                hidden_states_pad = torch.zeros(batch_size, channel, height, 1)
+            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
+            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim)
+            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"
+        else:
+            batch_size, sequence_length, inner_dim = hidden_states.shape
+            hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim)
+            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
+            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1)
+            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AdditiveKV_AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(
+        self,
+        hidden_size: int = None,
+        cross_attention_dim: int = None,
+        time_embedding_dim: int = None,
+        additive_scale: float = 1.0,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.additive_scale = additive_scale
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        external_kv=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."
+        
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+
+        if external_kv:
+            key = external_kv.k
+            value = external_kv.v
+
+            key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+            value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+            external_attn_output = F.scaled_dot_product_attention(
+                query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+            )
+
+            external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+            hidden_states = hidden_states + self.additive_scale * external_attn_output
+
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class TA_AdditiveKV_AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(
+        self,
+        hidden_size: int = None,
+        cross_attention_dim: int = None,
+        time_embedding_dim: int = None,
+        additive_scale: float = 1.0,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.ln_k = AdaLayerNorm(hidden_size, time_embedding_dim)
+        self.ln_v = AdaLayerNorm(hidden_size, time_embedding_dim)
+        self.additive_scale = additive_scale
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        external_kv=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."
+        
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+
+        if external_kv:
+            key = external_kv.k
+            value = external_kv.v
+
+            # time-dependent adaLN
+            key = self.ln_k(key, temb)
+            value = self.ln_v(value, temb)
+
+            key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+            value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+            external_attn_output = F.scaled_dot_product_attention(
+                query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+            )
+
+            external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+            hidden_states = hidden_states + self.additive_scale * external_attn_output
+
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        if isinstance(encoder_hidden_states, tuple):
+            # FIXME: now hard coded to single image prompt.
+            batch_size, _, hid_dim = encoder_hidden_states[0].shape
+            ip_tokens = encoder_hidden_states[1][0]
+            encoder_hidden_states = torch.cat([encoder_hidden_states[0], ip_tokens], dim=1)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        ip_hidden_states = F.scaled_dot_product_attention(
+            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+        )
+
+        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class TA_IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim)
+        self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        external_kv=None,
+        temb=None,
+    ):
+        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."
+
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        if not isinstance(encoder_hidden_states, tuple):
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+        else:
+            # FIXME: now hard coded to single image prompt.
+            batch_size, _, hid_dim = encoder_hidden_states[0].shape
+            ip_hidden_states = encoder_hidden_states[1][0]
+            encoder_hidden_states = encoder_hidden_states[0]
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        if external_kv:
+            key = torch.cat([key, external_kv.k], axis=1)
+            value = torch.cat([value, external_kv.v], axis=1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+        
+        # time-dependent adaLN
+        ip_key = self.ln_k_ip(ip_key, temb)
+        ip_value = self.ln_v_ip(ip_value, temb)
+
+        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        ip_hidden_states = F.scaled_dot_product_attention(
+            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+        )
+
+        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+## for controlnet
+class CNAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __init__(self, num_tokens=4):
+        self.num_tokens = num_tokens
+
+    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(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)
+        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)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class CNAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self, num_tokens=4):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.num_tokens = num_tokens
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+def init_attn_proc(unet, ip_adapter_tokens=16, use_lcm=False, use_adaln=True, use_external_kv=False):
+    attn_procs = {}
+    unet_sd = unet.state_dict()
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+        if cross_attention_dim is None:
+            if use_external_kv:
+                attn_procs[name] = AdditiveKV_AttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    time_embedding_dim=1280,
+                ) if hasattr(F, "scaled_dot_product_attention") else AdditiveKV_AttnProcessor()
+            else:
+                attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
+        else:
+            if use_adaln:
+                layer_name = name.split(".processor")[0]
+                if use_lcm:
+                    weights = {
+                        "to_k_ip.weight": unet_sd[layer_name + ".to_k.base_layer.weight"],
+                        "to_v_ip.weight": unet_sd[layer_name + ".to_v.base_layer.weight"],
+                    }
+                else:
+                    weights = {
+                        "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
+                        "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
+                    }
+                attn_procs[name] = TA_IPAttnProcessor2_0(
+                        hidden_size=hidden_size,
+                        cross_attention_dim=cross_attention_dim,
+                        num_tokens=ip_adapter_tokens,
+                        time_embedding_dim=1280,
+                    ) if hasattr(F, "scaled_dot_product_attention") else \
+                    TA_IPAttnProcessor(
+                        hidden_size=hidden_size,
+                        cross_attention_dim=cross_attention_dim,
+                        num_tokens=ip_adapter_tokens,
+                        time_embedding_dim=1280,
+                    )
+                attn_procs[name].load_state_dict(weights, strict=False)
+            else:
+                attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
+
+    return attn_procs
+
+
+def init_aggregator_attn_proc(unet, use_adaln=False, split_attn=False):
+    attn_procs = {}
+    unet_sd = unet.state_dict()
+    for name in unet.attn_processors.keys():
+        # get layer name and hidden dim
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+        # init attn proc
+        if split_attn:
+            # layer_name = name.split(".processor")[0]
+            # weights = {
+            #     "to_q_ref.weight": unet_sd[layer_name + ".to_q.weight"],
+            #     "to_k_ref.weight": unet_sd[layer_name + ".to_k.weight"],
+            #     "to_v_ref.weight": unet_sd[layer_name + ".to_v.weight"],
+            # }
+            attn_procs[name] = (
+                sep_split_AttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=hidden_size,
+                    time_embedding_dim=1280,
+                )
+                if use_adaln
+                else split_AttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    time_embedding_dim=1280,
+                )
+            )
+            # attn_procs[name].load_state_dict(weights, strict=False)
+        else:
+            attn_procs[name] = (
+                AttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=hidden_size,
+                )
+                if hasattr(F, "scaled_dot_product_attention")
+                else AttnProcessor(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=hidden_size,
+                )
+            )
+
+    return attn_procs
diff --git a/modules/instantir/ip_adapter/ip_adapter.py b/modules/instantir/ip_adapter/ip_adapter.py
new file mode 100644
index 000000000..10f01d4f3
--- /dev/null
+++ b/modules/instantir/ip_adapter/ip_adapter.py
@@ -0,0 +1,236 @@
+import os
+import torch
+from typing import List
+from collections import namedtuple, OrderedDict
+
+def is_torch2_available():
+    return hasattr(torch.nn.functional, "scaled_dot_product_attention")
+
+if is_torch2_available():
+    from .attention_processor import (
+        AttnProcessor2_0 as AttnProcessor,
+    )
+    from .attention_processor import (
+        CNAttnProcessor2_0 as CNAttnProcessor,
+    )
+    from .attention_processor import (
+        IPAttnProcessor2_0 as IPAttnProcessor,
+    )
+    from .attention_processor import (
+        TA_IPAttnProcessor2_0 as TA_IPAttnProcessor,
+    )
+else:
+    from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor, TA_IPAttnProcessor
+
+
+class ImageProjModel(torch.nn.Module):
+    """Projection Model"""
+
+    def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4):
+        super().__init__()
+
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+
+    def forward(self, image_embeds):
+        embeds = image_embeds
+        clip_extra_context_tokens = self.proj(embeds).reshape(
+            -1, self.clip_extra_context_tokens, self.cross_attention_dim
+        )
+        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+        return clip_extra_context_tokens
+
+
+class MLPProjModel(torch.nn.Module):
+    """SD model with image prompt"""
+    def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280):
+        super().__init__()
+        
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+            torch.nn.GELU(),
+            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
+            torch.nn.LayerNorm(cross_attention_dim)
+        )
+        
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
+class MultiIPAdapterImageProjection(torch.nn.Module):
+    def __init__(self, IPAdapterImageProjectionLayers):
+        super().__init__()
+        self.image_projection_layers = torch.nn.ModuleList(IPAdapterImageProjectionLayers)
+
+    def forward(self, image_embeds: List[torch.FloatTensor]):
+        projected_image_embeds = []
+
+        # currently, we accept `image_embeds` as
+        #  1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim]
+        #  2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim]
+        if not isinstance(image_embeds, list):
+            image_embeds = [image_embeds.unsqueeze(1)]
+
+        if len(image_embeds) != len(self.image_projection_layers):
+            raise ValueError(
+                f"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}"
+            )
+
+        for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers):
+            batch_size, num_images = image_embed.shape[0], image_embed.shape[1]
+            image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:])
+            image_embed = image_projection_layer(image_embed)
+            # image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:])
+
+            projected_image_embeds.append(image_embed)
+
+        return projected_image_embeds
+
+
+class IPAdapter(torch.nn.Module):
+    """IP-Adapter"""
+    def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
+        super().__init__()
+        self.unet = unet
+        self.image_proj = image_proj_model
+        self.ip_adapter = adapter_modules
+
+        if ckpt_path is not None:
+            self.load_from_checkpoint(ckpt_path)
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
+        ip_tokens = self.image_proj(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
+        return noise_pred
+
+    def load_from_checkpoint(self, ckpt_path: str):
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+        keys = list(state_dict.keys())
+        if keys != ["image_proj", "ip_adapter"]:
+            state_dict = revise_state_dict(state_dict)
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj.load_state_dict(state_dict["image_proj"], strict=True)
+        self.ip_adapter.load_state_dict(state_dict["ip_adapter"], strict=True)
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
+
+
+class IPAdapterPlus(torch.nn.Module):
+    """IP-Adapter"""
+    def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
+        super().__init__()
+        self.unet = unet
+        self.image_proj = image_proj_model
+        self.ip_adapter = adapter_modules
+
+        if ckpt_path is not None:
+            self.load_from_checkpoint(ckpt_path)
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
+        ip_tokens = self.image_proj(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
+        return noise_pred
+
+    def load_from_checkpoint(self, ckpt_path: str):
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
+        org_unet_sum = []
+        for attn_name, attn_proc in self.unet.attn_processors.items():
+            if isinstance(attn_proc, (TA_IPAttnProcessor, IPAttnProcessor)):
+                org_unet_sum.append(torch.sum(torch.stack([torch.sum(p) for p in attn_proc.parameters()])))
+        org_unet_sum = torch.sum(torch.stack(org_unet_sum))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+        keys = list(state_dict.keys())
+        if keys != ["image_proj", "ip_adapter"]:
+            state_dict = revise_state_dict(state_dict)
+
+        # Check if 'latents' exists in both the saved state_dict and the current model's state_dict
+        strict_load_image_proj_model = True
+        if "latents" in state_dict["image_proj"] and "latents" in self.image_proj.state_dict():
+            # Check if the shapes are mismatched
+            if state_dict["image_proj"]["latents"].shape != self.image_proj.state_dict()["latents"].shape:
+                print(f"Shapes of 'image_proj.latents' in checkpoint {ckpt_path} and current model do not match.")
+                print("Removing 'latents' from checkpoint and loading the rest of the weights.")
+                del state_dict["image_proj"]["latents"]
+                strict_load_image_proj_model = False
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj.load_state_dict(state_dict["image_proj"], strict=strict_load_image_proj_model)
+        missing_key, unexpected_key = self.ip_adapter.load_state_dict(state_dict["ip_adapter"], strict=False)
+        if len(missing_key) > 0:
+            for ms in missing_key:
+                if "ln" not in ms:
+                    raise ValueError(f"Missing key in adapter_modules: {len(missing_key)}")
+        if len(unexpected_key) > 0:
+            raise ValueError(f"Unexpected key in adapter_modules: {len(unexpected_key)}")
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
+
+        # Verify if the weights loaded to unet
+        unet_sum = []
+        for attn_name, attn_proc in self.unet.attn_processors.items():
+            if isinstance(attn_proc, (TA_IPAttnProcessor, IPAttnProcessor)):
+                unet_sum.append(torch.sum(torch.stack([torch.sum(p) for p in attn_proc.parameters()])))
+        unet_sum = torch.sum(torch.stack(unet_sum))
+
+        assert org_unet_sum != unet_sum, "Weights of adapter_modules in unet did not change!"
+        assert (unet_sum - new_adapter_sum < 1e-4), "Weights of adapter_modules did not load to unet!"
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_mod`ules did not change!"
+
+
+class IPAdapterXL(IPAdapter):
+    """SDXL"""
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds):
+        ip_tokens = self.image_proj(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs).sample
+        return noise_pred
+
+
+class IPAdapterPlusXL(IPAdapterPlus):
+    """IP-Adapter with fine-grained features"""
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds):
+        ip_tokens = self.image_proj(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs).sample
+        return noise_pred
+
+
+class IPAdapterFull(IPAdapterPlus):
+    """IP-Adapter with full features"""
+
+    def init_proj(self):
+        image_proj_model = MLPProjModel(
+            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+            clip_embeddings_dim=self.image_encoder.config.hidden_size,
+        ).to(self.device, dtype=torch.float16)
+        return image_proj_model
diff --git a/modules/instantir/ip_adapter/resampler.py b/modules/instantir/ip_adapter/resampler.py
new file mode 100644
index 000000000..72295f90b
--- /dev/null
+++ b/modules/instantir/ip_adapter/resampler.py
@@ -0,0 +1,158 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1280,
+        depth=4,
+        dim_head=64,
+        heads=20,
+        num_queries=64,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+        max_seq_len: int = 257,  # CLIP tokens + CLS token
+        apply_pos_emb: bool = False,
+        num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
+    ):
+        super().__init__()
+        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.to_latents_from_mean_pooled_seq = (
+            nn.Sequential(
+                nn.LayerNorm(dim),
+                nn.Linear(dim, dim * num_latents_mean_pooled),
+                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+            )
+            if num_latents_mean_pooled > 0
+            else None
+        )
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        if self.pos_emb is not None:
+            n, device = x.shape[1], x.device
+            pos_emb = self.pos_emb(torch.arange(n, device=device))
+            x = x + pos_emb
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        if self.to_latents_from_mean_pooled_seq:
+            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+            latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+    if mask is None:
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, "b n -> b n 1")
+    masked_t = t.masked_fill(~mask, 0.0)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
diff --git a/modules/instantir/ip_adapter/utils.py b/modules/instantir/ip_adapter/utils.py
new file mode 100644
index 000000000..64c45cd85
--- /dev/null
+++ b/modules/instantir/ip_adapter/utils.py
@@ -0,0 +1,248 @@
+import torch
+from collections import namedtuple, OrderedDict
+from safetensors import safe_open
+from .attention_processor import init_attn_proc
+from .ip_adapter import MultiIPAdapterImageProjection
+from .resampler import Resampler
+from transformers import (
+    AutoModel, AutoImageProcessor,
+    CLIPVisionModelWithProjection, CLIPImageProcessor)
+
+
+def init_adapter_in_unet(
+        unet,
+        image_proj_model=None,
+        pretrained_model_path_or_dict=None,
+        adapter_tokens=64,
+        embedding_dim=None,
+        use_lcm=False,
+        use_adaln=True,
+    ):
+        device = unet.device
+        dtype = unet.dtype
+        if image_proj_model is None:
+            assert embedding_dim is not None, "embedding_dim must be provided if image_proj_model is None."
+            image_proj_model = Resampler(
+                embedding_dim=embedding_dim,
+                output_dim=unet.config.cross_attention_dim,
+                num_queries=adapter_tokens,
+            )
+        if pretrained_model_path_or_dict is not None:
+            if not isinstance(pretrained_model_path_or_dict, dict):
+                if pretrained_model_path_or_dict.endswith(".safetensors"):
+                    state_dict = {"image_proj": {}, "ip_adapter": {}}
+                    with safe_open(pretrained_model_path_or_dict, framework="pt", device=unet.device) as f:
+                        for key in f.keys():
+                            if key.startswith("image_proj."):
+                                state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
+                            elif key.startswith("ip_adapter."):
+                                state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
+                else:
+                    state_dict = torch.load(pretrained_model_path_or_dict, map_location=unet.device)
+            else:
+                state_dict = pretrained_model_path_or_dict
+            keys = list(state_dict.keys())
+            if "image_proj" not in keys and "ip_adapter" not in keys:
+                state_dict = revise_state_dict(state_dict)
+
+        # Creat IP cross-attention in unet.
+        attn_procs = init_attn_proc(unet, adapter_tokens, use_lcm, use_adaln)
+        unet.set_attn_processor(attn_procs)
+
+        # Load pretrinaed model if needed.
+        if pretrained_model_path_or_dict is not None:
+            if "ip_adapter" in state_dict.keys():
+                adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
+                missing, unexpected = adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=False)
+                for mk in missing:
+                    if "ln" not in mk:
+                        raise ValueError(f"Missing keys in adapter_modules: {missing}")
+            if "image_proj" in state_dict.keys():
+                image_proj_model.load_state_dict(state_dict["image_proj"])
+
+        # Load image projectors into iterable ModuleList.
+        image_projection_layers = []
+        image_projection_layers.append(image_proj_model)
+        unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
+
+        # Adjust unet config to handle addtional ip hidden states.
+        unet.config.encoder_hid_dim_type = "ip_image_proj"
+        unet.to(dtype=dtype, device=device)
+
+
+def load_adapter_to_pipe(
+        pipe,
+        pretrained_model_path_or_dict,
+        image_encoder_or_path=None,
+        feature_extractor_or_path=None,
+        use_clip_encoder=False,
+        adapter_tokens=64,
+        use_lcm=False,
+        use_adaln=True,
+    ):
+
+        if not isinstance(pretrained_model_path_or_dict, dict):
+            if pretrained_model_path_or_dict.endswith(".safetensors"):
+                state_dict = {"image_proj": {}, "ip_adapter": {}}
+                with safe_open(pretrained_model_path_or_dict, framework="pt", device=pipe.device) as f:
+                    for key in f.keys():
+                        if key.startswith("image_proj."):
+                            state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
+                        elif key.startswith("ip_adapter."):
+                            state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
+            else:
+                state_dict = torch.load(pretrained_model_path_or_dict, map_location=pipe.device)
+        else:
+            state_dict = pretrained_model_path_or_dict
+        keys = list(state_dict.keys())
+        if "image_proj" not in keys and "ip_adapter" not in keys:
+            state_dict = revise_state_dict(state_dict)
+
+        # load CLIP image encoder here if it has not been registered to the pipeline yet
+        if image_encoder_or_path is not None:
+            if isinstance(image_encoder_or_path, str):
+                feature_extractor_or_path = image_encoder_or_path if feature_extractor_or_path is None else feature_extractor_or_path
+
+                image_encoder_or_path = (
+                    CLIPVisionModelWithProjection.from_pretrained(
+                        image_encoder_or_path
+                    ) if use_clip_encoder else
+                    AutoModel.from_pretrained(image_encoder_or_path)
+                )
+
+        if feature_extractor_or_path is not None:
+            if isinstance(feature_extractor_or_path, str):
+                feature_extractor_or_path = (
+                    CLIPImageProcessor() if use_clip_encoder else
+                    AutoImageProcessor.from_pretrained(feature_extractor_or_path)
+                )
+
+        # create image encoder if it has not been registered to the pipeline yet
+        if hasattr(pipe, "image_encoder") and getattr(pipe, "image_encoder", None) is None:
+            image_encoder = image_encoder_or_path.to(pipe.device, dtype=pipe.dtype)
+            pipe.register_modules(image_encoder=image_encoder)
+        else:
+            image_encoder = pipe.image_encoder
+
+        # create feature extractor if it has not been registered to the pipeline yet
+        if hasattr(pipe, "feature_extractor") and getattr(pipe, "feature_extractor", None) is None:
+            feature_extractor = feature_extractor_or_path
+            pipe.register_modules(feature_extractor=feature_extractor)
+        else:
+            feature_extractor = pipe.feature_extractor
+
+        # load adapter into unet
+        unet = getattr(pipe, pipe.unet_name) if not hasattr(pipe, "unet") else pipe.unet
+        attn_procs = init_attn_proc(unet, adapter_tokens, use_lcm, use_adaln)
+        unet.set_attn_processor(attn_procs)
+        image_proj_model = Resampler(
+            embedding_dim=image_encoder.config.hidden_size,
+            output_dim=unet.config.cross_attention_dim,
+            num_queries=adapter_tokens,
+        )
+
+        # Load pretrinaed model if needed.
+        if "ip_adapter" in state_dict.keys():
+            adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
+            missing, unexpected = adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=False)
+            for mk in missing:
+                if "ln" not in mk:
+                    raise ValueError(f"Missing keys in adapter_modules: {missing}")
+        if "image_proj" in state_dict.keys():
+            image_proj_model.load_state_dict(state_dict["image_proj"])
+
+        # convert IP-Adapter Image Projection layers to diffusers
+        image_projection_layers = []
+        image_projection_layers.append(image_proj_model)
+        unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
+
+        # Adjust unet config to handle addtional ip hidden states.
+        unet.config.encoder_hid_dim_type = "ip_image_proj"
+        unet.to(dtype=pipe.dtype, device=pipe.device)
+
+
+def revise_state_dict(old_state_dict_or_path, map_location="cpu"):
+    new_state_dict = OrderedDict()
+    new_state_dict["image_proj"] = OrderedDict()
+    new_state_dict["ip_adapter"] = OrderedDict()
+    if isinstance(old_state_dict_or_path, str):
+        old_state_dict = torch.load(old_state_dict_or_path, map_location=map_location)
+    else:
+        old_state_dict = old_state_dict_or_path
+    for name, weight in old_state_dict.items():
+        if name.startswith("image_proj_model."):
+            new_state_dict["image_proj"][name[len("image_proj_model."):]] = weight
+        elif name.startswith("adapter_modules."):
+            new_state_dict["ip_adapter"][name[len("adapter_modules."):]] = weight
+    return new_state_dict
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+def encode_image(image_encoder, feature_extractor, image, device, num_images_per_prompt, output_hidden_states=None):
+    dtype = next(image_encoder.parameters()).dtype
+
+    if not isinstance(image, torch.Tensor):
+        image = feature_extractor(image, return_tensors="pt").pixel_values
+
+    image = image.to(device=device, dtype=dtype)
+    if output_hidden_states:
+        image_enc_hidden_states = image_encoder(image, output_hidden_states=True).hidden_states[-2]
+        image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        return image_enc_hidden_states
+    else:
+        if isinstance(image_encoder, CLIPVisionModelWithProjection):
+            # CLIP image encoder.
+            image_embeds = image_encoder(image).image_embeds
+        else:
+            # DINO image encoder.
+            image_embeds = image_encoder(image).last_hidden_state
+        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+        return image_embeds
+
+
+def prepare_training_image_embeds(
+    image_encoder, feature_extractor,
+    ip_adapter_image, ip_adapter_image_embeds,
+    device, drop_rate, output_hidden_state, idx_to_replace=None
+):
+    if ip_adapter_image_embeds is None:
+        if not isinstance(ip_adapter_image, list):
+            ip_adapter_image = [ip_adapter_image]
+
+        # if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers):
+        #     raise ValueError(
+        #         f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+        #     )
+
+        image_embeds = []
+        for single_ip_adapter_image in ip_adapter_image:
+            if idx_to_replace is None:
+                idx_to_replace = torch.rand(len(single_ip_adapter_image)) < drop_rate
+            zero_ip_adapter_image = torch.zeros_like(single_ip_adapter_image)
+            single_ip_adapter_image[idx_to_replace] = zero_ip_adapter_image[idx_to_replace]
+            single_image_embeds = encode_image(
+                image_encoder, feature_extractor, single_ip_adapter_image, device, 1, output_hidden_state
+            )
+            single_image_embeds = torch.stack([single_image_embeds], dim=1) # FIXME
+
+            image_embeds.append(single_image_embeds)
+    else:
+        repeat_dims = [1]
+        image_embeds = []
+        for single_image_embeds in ip_adapter_image_embeds:
+            if do_classifier_free_guidance:
+                single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
+                single_image_embeds = single_image_embeds.repeat(
+                    num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                )
+                single_negative_image_embeds = single_negative_image_embeds.repeat(
+                    num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
+                )
+                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+            else:
+                single_image_embeds = single_image_embeds.repeat(
+                    num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                )
+            image_embeds.append(single_image_embeds)
+
+    return image_embeds
\ No newline at end of file
diff --git a/modules/instantir/lcm_single_step_scheduler.py b/modules/instantir/lcm_single_step_scheduler.py
new file mode 100644
index 000000000..a32affdc2
--- /dev/null
+++ b/modules/instantir/lcm_single_step_scheduler.py
@@ -0,0 +1,537 @@
+# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
+# and https://github.com/hojonathanho/diffusion
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import BaseOutput, logging
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class LCMSingleStepSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    denoised: Optional[torch.FloatTensor] = None
+
+
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(
+    num_diffusion_timesteps,
+    max_beta=0.999,
+    alpha_transform_type="cosine",
+):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
+                     Choose from `cosine` or `exp`
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+    if alpha_transform_type == "cosine":
+
+        def alpha_bar_fn(t):
+            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    elif alpha_transform_type == "exp":
+
+        def alpha_bar_fn(t):
+            return math.exp(t * -12.0)
+
+    else:
+        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
+# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
+def rescale_zero_terminal_snr(betas: torch.FloatTensor) -> torch.FloatTensor:
+    """
+    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
+
+
+    Args:
+        betas (`torch.FloatTensor`):
+            the betas that the scheduler is being initialized with.
+
+    Returns:
+        `torch.FloatTensor`: rescaled betas with zero terminal SNR
+    """
+    # Convert betas to alphas_bar_sqrt
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= alphas_bar_sqrt_T
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
+    alphas = torch.cat([alphas_bar[0:1], alphas])
+    betas = 1 - alphas
+
+    return betas
+
+
+class LCMSingleStepScheduler(SchedulerMixin, ConfigMixin):
+    """
+    `LCMSingleStepScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
+    non-Markovian guidance.
+
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all config
+    attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be
+    accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving
+    functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions.
+
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        beta_start (`float`, defaults to 0.0001):
+            The starting `beta` value of inference.
+        beta_end (`float`, defaults to 0.02):
+            The final `beta` value.
+        beta_schedule (`str`, defaults to `"linear"`):
+            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
+        trained_betas (`np.ndarray`, *optional*):
+            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
+        original_inference_steps (`int`, *optional*, defaults to 50):
+            The default number of inference steps used to generate a linearly-spaced timestep schedule, from which we
+            will ultimately take `num_inference_steps` evenly spaced timesteps to form the final timestep schedule.
+        clip_sample (`bool`, defaults to `True`):
+            Clip the predicted sample for numerical stability.
+        clip_sample_range (`float`, defaults to 1.0):
+            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
+        set_alpha_to_one (`bool`, defaults to `True`):
+            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
+            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
+            otherwise it uses the alpha value at step 0.
+        steps_offset (`int`, defaults to 0):
+            An offset added to the inference steps. You can use a combination of `offset=1` and
+            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
+            Diffusion.
+        prediction_type (`str`, defaults to `epsilon`, *optional*):
+            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
+            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
+            Video](https://imagen.research.google/video/paper.pdf) paper).
+        thresholding (`bool`, defaults to `False`):
+            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
+            as Stable Diffusion.
+        dynamic_thresholding_ratio (`float`, defaults to 0.995):
+            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
+        sample_max_value (`float`, defaults to 1.0):
+            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
+        timestep_spacing (`str`, defaults to `"leading"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
+        timestep_scaling (`float`, defaults to 10.0):
+            The factor the timesteps will be multiplied by when calculating the consistency model boundary conditions
+            `c_skip` and `c_out`. Increasing this will decrease the approximation error (although the approximation
+            error at the default of `10.0` is already pretty small).
+        rescale_betas_zero_snr (`bool`, defaults to `False`):
+            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
+            dark samples instead of limiting it to samples with medium brightness. Loosely related to
+            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
+    """
+
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.00085,
+        beta_end: float = 0.012,
+        beta_schedule: str = "scaled_linear",
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
+        original_inference_steps: int = 50,
+        clip_sample: bool = False,
+        clip_sample_range: float = 1.0,
+        set_alpha_to_one: bool = True,
+        steps_offset: int = 0,
+        prediction_type: str = "epsilon",
+        thresholding: bool = False,
+        dynamic_thresholding_ratio: float = 0.995,
+        sample_max_value: float = 1.0,
+        timestep_spacing: str = "leading",
+        timestep_scaling: float = 10.0,
+        rescale_betas_zero_snr: bool = False,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
+        elif beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = (
+                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        # Rescale for zero SNR
+        if rescale_betas_zero_snr:
+            self.betas = rescale_zero_terminal_snr(self.betas)
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        # At every step in ddim, we are looking into the previous alphas_cumprod
+        # For the final step, there is no previous alphas_cumprod because we are already at 0
+        # `set_alpha_to_one` decides whether we set this parameter simply to one or
+        # whether we use the final alpha of the "non-previous" one.
+        self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = 1.0
+
+        # setable values
+        self.num_inference_steps = None
+        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
+
+        self._step_index = None
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
+    def _init_step_index(self, timestep):
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+
+        index_candidates = (self.timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        if len(index_candidates) > 1:
+            step_index = index_candidates[1]
+        else:
+            step_index = index_candidates[0]
+
+        self._step_index = step_index.item()
+
+    @property
+    def step_index(self):
+        return self._step_index
+
+    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The input sample.
+            timestep (`int`, *optional*):
+                The current timestep in the diffusion chain.
+        Returns:
+            `torch.FloatTensor`:
+                A scaled input sample.
+        """
+        return sample
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
+    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
+        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
+        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
+        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
+        photorealism as well as better image-text alignment, especially when using very large guidance weights."
+
+        https://arxiv.org/abs/2205.11487
+        """
+        dtype = sample.dtype
+        batch_size, channels, *remaining_dims = sample.shape
+
+        if dtype not in (torch.float32, torch.float64):
+            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
+
+        # Flatten sample for doing quantile calculation along each image
+        sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
+
+        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
+
+        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
+        s = torch.clamp(
+            s, min=1, max=self.config.sample_max_value
+        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
+        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
+        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
+
+        sample = sample.reshape(batch_size, channels, *remaining_dims)
+        sample = sample.to(dtype)
+
+        return sample
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        original_inference_steps: Optional[int] = None,
+        strength: int = 1.0,
+        timesteps: Optional[list] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+            original_inference_steps (`int`, *optional*):
+                The original number of inference steps, which will be used to generate a linearly-spaced timestep
+                schedule (which is different from the standard `diffusers` implementation). We will then take
+                `num_inference_steps` timesteps from this schedule, evenly spaced in terms of indices, and use that as
+                our final timestep schedule. If not set, this will default to the `original_inference_steps` attribute.
+        """
+
+        if num_inference_steps is not None and timesteps is not None:
+            raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.")
+
+        if timesteps is not None:
+            for i in range(1, len(timesteps)):
+                if timesteps[i] >= timesteps[i - 1]:
+                    raise ValueError("`custom_timesteps` must be in descending order.")
+
+            if timesteps[0] >= self.config.num_train_timesteps:
+                raise ValueError(
+                    f"`timesteps` must start before `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps}."
+                )
+
+            timesteps = np.array(timesteps, dtype=np.int64)
+        else:
+            if num_inference_steps > self.config.num_train_timesteps:
+                raise ValueError(
+                    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
+                    f" maximal {self.config.num_train_timesteps} timesteps."
+                )
+
+            self.num_inference_steps = num_inference_steps
+            original_steps = (
+                original_inference_steps if original_inference_steps is not None else self.config.original_inference_steps
+            )
+
+            if original_steps > self.config.num_train_timesteps:
+                raise ValueError(
+                    f"`original_steps`: {original_steps} cannot be larger than `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
+                    f" maximal {self.config.num_train_timesteps} timesteps."
+                )
+
+            if num_inference_steps > original_steps:
+                raise ValueError(
+                    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `original_inference_steps`:"
+                    f" {original_steps} because the final timestep schedule will be a subset of the"
+                    f" `original_inference_steps`-sized initial timestep schedule."
+                )
+
+            # LCM Timesteps Setting
+            # Currently, only linear spacing is supported.
+            c = self.config.num_train_timesteps // original_steps
+            # LCM Training Steps Schedule
+            lcm_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * c - 1
+            skipping_step = len(lcm_origin_timesteps) // num_inference_steps
+            # LCM Inference Steps Schedule
+            timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps]
+
+        self.timesteps = torch.from_numpy(timesteps.copy()).to(device=device, dtype=torch.long)
+
+        self._step_index = None
+
+    def get_scalings_for_boundary_condition_discrete(self, timestep):
+        self.sigma_data = 0.5  # Default: 0.5
+        scaled_timestep = timestep * self.config.timestep_scaling
+
+        c_skip = self.sigma_data**2 / (scaled_timestep**2 + self.sigma_data**2)
+        c_out = scaled_timestep / (scaled_timestep**2 + self.sigma_data**2) ** 0.5
+        return c_skip, c_out
+
+    def append_dims(self, x, target_dims):
+        """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+        dims_to_append = target_dims - x.ndim
+        if dims_to_append < 0:
+            raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+        return x[(...,) + (None,) * dims_to_append]
+
+    def extract_into_tensor(self, a, t, x_shape):
+        b, *_ = t.shape
+        out = a.gather(-1, t)
+        return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: torch.Tensor,
+        sample: torch.FloatTensor,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[LCMSingleStepSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
+        Returns:
+            [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
+                tuple is returned where the first element is the sample tensor.
+        """
+        # 0. make sure everything is on the same device
+        alphas_cumprod = self.alphas_cumprod.to(sample.device)
+
+        # 1. compute alphas, betas
+        if timestep.ndim == 0:
+            timestep = timestep.unsqueeze(0)
+        alpha_prod_t = self.extract_into_tensor(alphas_cumprod, timestep, sample.shape)
+        beta_prod_t = 1 - alpha_prod_t
+
+        # 2. Get scalings for boundary conditions
+        c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
+        c_skip, c_out = [self.append_dims(x, sample.ndim) for x in [c_skip, c_out]]
+
+        # 3. Compute the predicted original sample x_0 based on the model parameterization
+        if self.config.prediction_type == "epsilon":  # noise-prediction
+            predicted_original_sample = (sample - torch.sqrt(beta_prod_t) * model_output) / torch.sqrt(alpha_prod_t)
+        elif self.config.prediction_type == "sample":  # x-prediction
+            predicted_original_sample = model_output
+        elif self.config.prediction_type == "v_prediction":  # v-prediction
+            predicted_original_sample = torch.sqrt(alpha_prod_t) * sample - torch.sqrt(beta_prod_t) * model_output
+        else:
+            raise ValueError(
+                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
+                " `v_prediction` for `LCMScheduler`."
+            )
+
+        # 4. Clip or threshold "predicted x_0"
+        if self.config.thresholding:
+            predicted_original_sample = self._threshold_sample(predicted_original_sample)
+        elif self.config.clip_sample:
+            predicted_original_sample = predicted_original_sample.clamp(
+                -self.config.clip_sample_range, self.config.clip_sample_range
+            )
+
+        # 5. Denoise model output using boundary conditions
+        denoised = c_out * predicted_original_sample + c_skip * sample
+
+        if not return_dict:
+            return (denoised, )
+
+        return LCMSingleStepSchedulerOutput(denoised=denoised)
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
+
+        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
+        return noisy_samples
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
+    def get_velocity(
+        self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
+    ) -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as sample
+        alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
+        timesteps = timesteps.to(sample.device)
+
+        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(sample.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
+        return velocity
+
+    def __len__(self):
+        return self.config.num_train_timesteps
diff --git a/modules/instantir/sdxl_instantir.py b/modules/instantir/sdxl_instantir.py
new file mode 100644
index 000000000..b279d8445
--- /dev/null
+++ b/modules/instantir/sdxl_instantir.py
@@ -0,0 +1,1738 @@
+# Copyright 2024 The InstantX Team. All rights reserved.
+#
+# 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 inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.utils.import_utils import is_invisible_watermark_available
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers, LCMScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+    convert_unet_state_dict_to_peft
+)
+from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+
+
+if is_invisible_watermark_available():
+    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+from peft import LoraConfig, set_peft_model_state_dict
+from .aggregator import Aggregator
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install diffusers pillow transformers accelerate
+        >>> import torch
+        >>> from PIL import Image
+
+        >>> from diffusers import DDPMScheduler
+        >>> from schedulers.lcm_single_step_scheduler import LCMSingleStepScheduler
+
+        >>> from module.ip_adapter.utils import load_adapter_to_pipe
+        >>> from pipelines.sdxl_instantir import InstantIRPipeline
+
+        >>> # download models under ./models
+        >>> dcp_adapter = f'./models/adapter.pt'
+        >>> previewer_lora_path = f'./models'
+        >>> instantir_path = f'./models/aggregator.pt'
+
+        >>> # load pretrained models
+        >>> pipe = InstantIRPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16
+        ... )
+        >>> # load adapter
+        >>> load_adapter_to_pipe(
+        ...     pipe,
+        ...     dcp_adapter,
+        ...     image_encoder_or_path = 'facebook/dinov2-large',
+        ... )
+        >>> # load previewer lora
+        >>> pipe.prepare_previewers(previewer_lora_path)
+        >>> pipe.scheduler = DDPMScheduler.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', subfolder="scheduler")
+        >>> lcm_scheduler = LCMSingleStepScheduler.from_config(pipe.scheduler.config)
+
+        >>> # load aggregator weights
+        >>> pretrained_state_dict = torch.load(instantir_path)
+        >>> pipe.aggregator.load_state_dict(pretrained_state_dict)
+
+        >>> # send to GPU and fp16
+        >>> pipe.to(device="cuda", dtype=torch.float16)
+        >>> pipe.aggregator.to(device="cuda", dtype=torch.float16)
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> # load a broken image
+        >>> low_quality_image = Image.open('path/to/your-image').convert("RGB")
+
+        >>> # restoration
+        >>> image = pipe(
+        ...     image=low_quality_image,
+        ...     previewer_scheduler=lcm_scheduler,
+        ... ).images[0]
+        ```
+"""
+
+LCM_LORA_MODULES = [
+    "to_q",
+    "to_k",
+    "to_v",
+    "to_out.0",
+    "proj_in",
+    "proj_out",
+    "ff.net.0.proj",
+    "ff.net.2",
+    "conv1",
+    "conv2",
+    "conv_shortcut",
+    "downsamplers.0.conv",
+    "upsamplers.0.conv",
+    "time_emb_proj",
+]
+PREVIEWER_LORA_MODULES = [
+    "to_q",
+    "to_kv",
+    "0.to_out",
+    "attn1.to_k",
+    "attn1.to_v",
+    "to_k_ip",
+    "to_v_ip",
+    "ln_k_ip.linear",
+    "ln_v_ip.linear",
+    "to_out.0",
+    "proj_in",
+    "proj_out",
+    "ff.net.0.proj",
+    "ff.net.2",
+    "conv1",
+    "conv2",
+    "conv_shortcut",
+    "downsamplers.0.conv",
+    "upsamplers.0.conv",
+    "time_emb_proj",
+]
+
+
+def remove_attn2(model):
+    def recursive_find_module(name, module):
+        if not "up_blocks" in name and not "down_blocks" in name and not "mid_block" in name: return
+        elif "resnets" in name: return
+        if hasattr(module, "attn2"):
+            setattr(module, "attn2", None)
+            setattr(module, "norm2", None)
+            return
+        for sub_name, sub_module in module.named_children():
+            recursive_find_module(f"{name}.{sub_name}", sub_module)
+
+    for name, module in model.named_children():
+        recursive_find_module(name, module)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
+                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+                must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class InstantIRPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    IPAdapterMixin,
+    FromSingleFileMixin,
+):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        text_encoder ([`~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
+            Second frozen text-encoder
+            ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        tokenizer_2 ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A `UNet2DConditionModel` to denoise the encoded image latents.
+        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
+            Provides additional conditioning to the `unet` during the denoising process. If you set multiple
+            ControlNets as a list, the outputs from each ControlNet are added together to create one combined
+            additional conditioning.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+            Whether the negative prompt embeddings should always be set to 0. Also see the config of
+            `stabilityai/stable-diffusion-xl-base-1-0`.
+        add_watermarker (`bool`, *optional*):
+            Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
+            watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
+            watermarker is used.
+    """
+
+    # leave controlnet out on purpose because it iterates with unet
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+        "feature_extractor",
+        "image_encoder",
+    ]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        aggregator: Aggregator = None,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+        feature_extractor: CLIPImageProcessor = None,
+        image_encoder: CLIPVisionModelWithProjection = None,
+    ):
+        super().__init__()
+
+        if aggregator is None:
+            aggregator = Aggregator.from_unet(unet)
+        remove_attn2(aggregator)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            aggregator=aggregator,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=True
+        )
+        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+        if add_watermarker:
+            self.watermark = StableDiffusionXLWatermarker()
+        else:
+            self.watermark = None
+
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+
+    def prepare_previewers(self, previewer_lora_path: str, use_lcm=False):
+        if use_lcm:
+            lora_state_dict, alpha_dict = self.lora_state_dict(
+                previewer_lora_path,
+            )
+        else:
+            lora_state_dict, alpha_dict = self.lora_state_dict(
+                previewer_lora_path,
+                weight_name="previewer_lora_weights.bin"
+            )
+        unet_state_dict = {
+            f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")
+        }
+        unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
+        lora_state_dict = dict()
+        for k, v in unet_state_dict.items():
+            if "ip" in k:
+                k = k.replace("attn2", "attn2.processor")
+                lora_state_dict[k] = v
+            else:
+                lora_state_dict[k] = v
+        if alpha_dict:
+            lora_alpha = next(iter(alpha_dict.values()))
+        else:
+            lora_alpha = 1
+        logger.info(f"use lora alpha {lora_alpha}")
+        lora_config = LoraConfig(
+            r=64,
+            target_modules=LCM_LORA_MODULES if use_lcm else PREVIEWER_LORA_MODULES,
+            lora_alpha=lora_alpha,
+            lora_dropout=0.0,
+        )
+
+        adapter_name = "lcm" if use_lcm else "previewer"
+        self.unet.add_adapter(lora_config, adapter_name)
+        incompatible_keys = set_peft_model_state_dict(self.unet, lora_state_dict, adapter_name=adapter_name)
+        if incompatible_keys is not None:
+            # check only for unexpected keys
+            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+            missing_keys = getattr(incompatible_keys, "missing_keys", None)
+            if unexpected_keys:
+                raise ValueError(
+                    f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                    f" {unexpected_keys}. "
+                )
+        self.unet.disable_adapters()
+
+        return lora_alpha
+    
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            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 `guidance_scale` is
+                less 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 both text-encoders
+            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.
+            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.
+            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.
+            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.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        if output_hidden_states:
+            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_enc_hidden_states = self.image_encoder(
+                torch.zeros_like(image), output_hidden_states=True
+            ).hidden_states[-2]
+            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+                num_images_per_prompt, dim=0
+            )
+            return image_enc_hidden_states, uncond_image_enc_hidden_states
+        else:
+            if isinstance(self.image_encoder, CLIPVisionModelWithProjection):
+                # CLIP image encoder.
+                image_embeds = self.image_encoder(image).image_embeds
+                image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+                uncond_image_embeds = torch.zeros_like(image_embeds)
+            else:
+                # DINO image encoder.
+                image_embeds = self.image_encoder(image).last_hidden_state
+                image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+                uncond_image_embeds = self.image_encoder(
+                    torch.zeros_like(image)
+                ).last_hidden_state
+                uncond_image_embeds = uncond_image_embeds.repeat_interleave(
+                    num_images_per_prompt, dim=0
+                )
+
+            return image_embeds, uncond_image_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+    ):
+        if ip_adapter_image_embeds is None:
+            if not isinstance(ip_adapter_image, list):
+                ip_adapter_image = [ip_adapter_image]
+
+            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+                if isinstance(ip_adapter_image[0], list):
+                    raise ValueError(
+                        f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                    )
+                else:
+                    logger.warning(
+                        f"Got {len(ip_adapter_image)} images for {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                        " By default, these images will be sent to each IP-Adapter. If this is not your use-case, please specify `ip_adapter_image` as a list of image-list, with"
+                        f" length equals to the number of IP-Adapters."
+                    )
+                    ip_adapter_image = [ip_adapter_image] * len(self.unet.encoder_hid_proj.image_projection_layers)
+
+            image_embeds = []
+            for single_ip_adapter_image, image_proj_layer in zip(
+                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+            ):
+                output_hidden_state = isinstance(self.image_encoder, CLIPVisionModelWithProjection) and not isinstance(image_proj_layer, ImageProjection)
+                single_image_embeds, single_negative_image_embeds = self.encode_image(
+                    single_ip_adapter_image, device, 1, output_hidden_state
+                )
+                single_image_embeds = torch.stack([single_image_embeds] * (num_images_per_prompt//single_image_embeds.shape[0]), dim=0)
+                single_negative_image_embeds = torch.stack(
+                    [single_negative_image_embeds] * (num_images_per_prompt//single_negative_image_embeds.shape[0]), dim=0
+                )
+
+                if do_classifier_free_guidance:
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                    single_image_embeds = single_image_embeds.to(device)
+
+                image_embeds.append(single_image_embeds)
+        else:
+            repeat_dims = [1]
+            image_embeds = []
+            for single_image_embeds in ip_adapter_image_embeds:
+                if do_classifier_free_guidance:
+                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
+                    single_image_embeds = single_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                    )
+                    single_negative_image_embeds = single_negative_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
+                    )
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                else:
+                    single_image_embeds = single_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                    )
+                image_embeds.append(single_image_embeds)
+
+        return image_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        image,
+        callback_steps,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        ip_adapter_image=None,
+        ip_adapter_image_embeds=None,
+        negative_pooled_prompt_embeds=None,
+        controlnet_conditioning_scale=1.0,
+        control_guidance_start=0.0,
+        control_guidance_end=1.0,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        if prompt_embeds is not None and pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+
+        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        # Check `image`
+        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
+            self.aggregator, torch._dynamo.eval_frame.OptimizedModule
+        )
+        if (
+            isinstance(self.aggregator, Aggregator)
+            or is_compiled
+            and isinstance(self.aggregator._orig_mod, Aggregator)
+        ):
+            self.check_image(image, prompt, prompt_embeds)
+        else:
+            assert False
+
+        if control_guidance_start >= control_guidance_end:
+            raise ValueError(
+                f"control guidance start: {control_guidance_start} cannot be larger or equal to control guidance end: {control_guidance_end}."
+            )
+        if control_guidance_start < 0.0:
+            raise ValueError(f"control guidance start: {control_guidance_start} can't be smaller than 0.")
+        if control_guidance_end > 1.0:
+            raise ValueError(f"control guidance end: {control_guidance_end} can't be larger than 1.0.")
+
+        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
+            raise ValueError(
+                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
+            )
+
+        if ip_adapter_image_embeds is not None:
+            if not isinstance(ip_adapter_image_embeds, list):
+                raise ValueError(
+                    f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
+                )
+            elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
+                raise ValueError(
+                    f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
+                )
+
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
+    def check_image(self, image, prompt, prompt_embeds):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        image_is_tensor = isinstance(image, torch.Tensor)
+        image_is_np = isinstance(image, np.ndarray)
+        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
+        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
+        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
+
+        if (
+            not image_is_pil
+            and not image_is_tensor
+            and not image_is_np
+            and not image_is_pil_list
+            and not image_is_tensor_list
+            and not image_is_np_list
+        ):
+            raise TypeError(
+                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        else:
+            image_batch_size = len(image)
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+
+        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+    ):
+        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        return image
+
+    @torch.no_grad()
+    def init_latents(self, latents, generator, timestep):
+        noise = torch.randn(latents.shape, generator=generator[0] if isinstance(generator, list) else generator, device=self.vae.device, dtype=self.vae.dtype, layout=torch.strided)
+        bsz = latents.shape[0]
+        timestep = torch.tensor([timestep]*bsz, device=self.vae.device)
+        # Note that the latents will be scaled aleady by scheduler.add_noise
+        latents = self.scheduler.add_noise(latents, noise, timestep)
+        return latents
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
+    ):
+        add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
+        )
+        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+        if expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        return add_time_ids
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+    def upcast_vae(self):
+        dtype = self.vae.dtype
+        self.vae.to(dtype=torch.float32)
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.FloatTensor:
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
+            embedding_dim (`int`, *optional*, defaults to 512):
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
+
+        Returns:
+            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def denoising_end(self):
+        return self._denoising_end
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 30,
+        timesteps: List[int] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 7.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        save_preview_row: bool = False,
+        init_latents_with_lq: bool = True,
+        multistep_restore: bool = False,
+        adastep_restore: bool = False,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        controlnet_conditioning_scale: float = 1.0,
+        control_guidance_start: float = 0.0,
+        control_guidance_end: float = 1.0,
+        preview_start: float = 0.0,
+        preview_end: float = 1.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        previewer_scheduler: KarrasDiffusionSchedulers = None,
+        reference_latents: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            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
+                used in both text-encoders.
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
+                specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
+                accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
+                and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
+                `init`, images must be passed as a list such that each element of the list can be correctly batched for
+                input to a single ControlNet.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
+                and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](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 is 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 (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, pooled text embeddings are generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
+                weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
+                argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *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)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
+                the corresponding scale as a list.
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            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.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned containing the output images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+
+        aggregator = self.aggregator._orig_mod if is_compiled_module(self.aggregator) else self.aggregator
+        if not isinstance(ip_adapter_image, list):
+            ip_adapter_image = [ip_adapter_image] if ip_adapter_image is not None else [image]
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            negative_pooled_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            if not isinstance(image, PIL.Image.Image):
+                batch_size = len(image)
+            else:
+                batch_size = 1
+            prompt = [prompt] * batch_size
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+            assert batch_size == len(image) or (isinstance(image, PIL.Image.Image) or len(image) == 1)
+        else:
+            batch_size = prompt_embeds.shape[0]
+            assert batch_size == len(image) or (isinstance(image, PIL.Image.Image) or len(image) == 1)
+
+        device = self._execution_device
+
+        # 3.1 Encode input prompt
+        text_encoder_lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            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,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+        # 3.2 Encode ip_adapter_image
+        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,
+                self.do_classifier_free_guidance,
+            )
+
+        # 4. Prepare image
+        image = self.prepare_image(
+            image=image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=aggregator.dtype,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+        )
+        height, width = image.shape[-2:]
+        if image.shape[1] != 4:
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            if needs_upcasting:
+                image = image.float()
+                self.vae.to(dtype=torch.float32)
+            image = self.vae.encode(image).latent_dist.sample()
+            image = image * self.vae.config.scaling_factor
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+                image = image.to(dtype=torch.float16)
+        else:
+            height = int(height * self.vae_scale_factor)
+            width = int(width * self.vae_scale_factor)
+
+        # 5. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+
+        # 6. Prepare latent variables
+        if init_latents_with_lq:
+            latents = self.init_latents(image, generator, timesteps[0])
+        else:
+            num_channels_latents = self.unet.config.in_channels
+            latents = self.prepare_latents(
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+            )
+
+        # 6.5 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7.1 Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        previewing = []
+        for i in range(len(timesteps)):
+            keeps = 1.0 - float(i / len(timesteps) < control_guidance_start or (i + 1) / len(timesteps) > control_guidance_end)
+            controlnet_keep.append(keeps)
+            use_preview = 1.0 - float(i / len(timesteps) < preview_start or (i + 1) / len(timesteps) > preview_end)
+            previewing.append(use_preview)
+        if isinstance(controlnet_conditioning_scale, list):
+            assert len(controlnet_conditioning_scale) == len(timesteps), f"{len(controlnet_conditioning_scale)} controlnet scales do not match number of sampling steps {len(timesteps)}"
+        else:
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet_keep)
+
+        # 7.2 Prepare added time ids & embeddings
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+            image = torch.cat([image] * 2, dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 8.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        is_unet_compiled = is_compiled_module(self.unet)
+        is_aggregator_compiled = is_compiled_module(self.aggregator)
+        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
+        previewer_mean = torch.zeros_like(latents)
+        unet_mean = torch.zeros_like(latents)
+        preview_factor = torch.ones(
+            (latents.shape[0], *((1,) * (len(latents.shape) - 1))), dtype=latents.dtype, device=latents.device
+        )
+
+        self._num_timesteps = len(timesteps)
+        preview_row = []
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Relevant thread:
+                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
+                if (is_unet_compiled and is_aggregator_compiled) and is_torch_higher_equal_2_1:
+                    torch._inductor.cudagraph_mark_step_begin()
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                prev_t = t
+                unet_model_input = latent_model_input
+
+                added_cond_kwargs = {
+                    "text_embeds": add_text_embeds,
+                    "time_ids": add_time_ids,
+                    "image_embeds": image_embeds
+                }
+                aggregator_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                # prepare time_embeds in advance as adapter input
+                cross_attention_t_emb = self.unet.get_time_embed(sample=latent_model_input, timestep=t)
+                cross_attention_emb = self.unet.time_embedding(cross_attention_t_emb, timestep_cond)
+                cross_attention_aug_emb = None
+
+                cross_attention_aug_emb = self.unet.get_aug_embed(
+                    emb=cross_attention_emb,
+                    encoder_hidden_states=prompt_embeds,
+                    added_cond_kwargs=added_cond_kwargs
+                )
+
+                cross_attention_emb = cross_attention_emb + cross_attention_aug_emb if cross_attention_aug_emb is not None else cross_attention_emb
+
+                if self.unet.time_embed_act is not None:
+                    cross_attention_emb = self.unet.time_embed_act(cross_attention_emb)
+
+                current_cross_attention_kwargs = {"temb": cross_attention_emb}
+                if cross_attention_kwargs is not None:
+                    for k,v in cross_attention_kwargs.items():
+                        current_cross_attention_kwargs[k] = v
+                self._cross_attention_kwargs = current_cross_attention_kwargs
+
+                # adaptive restoration factors
+                adaRes_scale = preview_factor.to(latent_model_input.dtype).clamp(0.0, controlnet_conditioning_scale[i])
+                cond_scale = adaRes_scale * controlnet_keep[i]
+                cond_scale = torch.cat([cond_scale] * 2) if self.do_classifier_free_guidance else cond_scale
+
+                if (cond_scale>0.1).sum().item() > 0:
+                    if previewing[i] > 0:
+                        # preview with LCM
+                        self.unet.enable_adapters()
+                        preview_noise = self.unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds,
+                            timestep_cond=timestep_cond,
+                            cross_attention_kwargs=self.cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+                        preview_latent = previewer_scheduler.step(
+                            preview_noise,
+                            t.to(dtype=torch.int64),
+                            # torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,
+                            latent_model_input,     # scaled latents here for compatibility
+                            return_dict=False
+                        )[0]
+                        self.unet.disable_adapters()
+
+                        if self.do_classifier_free_guidance:
+                            preview_row.append(preview_latent.chunk(2)[1].to('cpu'))
+                        else:
+                            preview_row.append(preview_latent.to('cpu'))
+                        # Prepare 2nd order step.
+                        if multistep_restore and i+1 < len(timesteps):
+                            noise_preview = preview_noise.chunk(2)[1] if self.do_classifier_free_guidance else preview_noise
+                            first_step = self.scheduler.step(
+                                noise_preview, t, latents,
+                                **extra_step_kwargs, return_dict=True, step_forward=False
+                            )
+                            prev_t = timesteps[i + 1]
+                            unet_model_input = torch.cat([first_step.prev_sample] * 2) if self.do_classifier_free_guidance else first_step.prev_sample
+                            unet_model_input = self.scheduler.scale_model_input(unet_model_input, prev_t, heun_step=True)
+
+                    elif reference_latents is not None:
+                        preview_latent = torch.cat([reference_latents] * 2) if self.do_classifier_free_guidance else reference_latents
+                    else:
+                        preview_latent = image
+
+                    # Add fresh noise
+                    # preview_noise = torch.randn_like(preview_latent)
+                    # preview_latent = self.scheduler.add_noise(preview_latent, preview_noise, t)
+
+                    preview_latent=preview_latent.to(dtype=next(aggregator.parameters()).dtype)
+
+                    # Aggregator inference
+                    down_block_res_samples, mid_block_res_sample = aggregator(
+                        image,
+                        prev_t,
+                        encoder_hidden_states=prompt_embeds,
+                        controlnet_cond=preview_latent,
+                        # conditioning_scale=cond_scale,
+                        added_cond_kwargs=aggregator_added_cond_kwargs,
+                        return_dict=False,
+                    )
+
+                # aggregator features scaling
+                down_block_res_samples = [sample*cond_scale for sample in down_block_res_samples]
+                mid_block_res_sample = mid_block_res_sample*cond_scale
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    unet_model_input,
+                    prev_t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                unet_step = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=True)
+                latents = unet_step.prev_sample
+
+                # Update adaRes factors
+                unet_pred_latent = unet_step.pred_original_sample
+
+                # Adaptive restoration.
+                if adastep_restore:
+                    pred_x0_l2 = ((preview_latent[latents.shape[0]:].float()-unet_pred_latent.float())).pow(2).sum(dim=(1,2,3))
+                    previewer_l2 = ((preview_latent[latents.shape[0]:].float()-previewer_mean.float())).pow(2).sum(dim=(1,2,3))
+                    # unet_l2 = ((unet_pred_latent.float()-unet_mean.float())).pow(2).sum(dim=(1,2,3)).sqrt()
+                    # l2_error = (((preview_latent[latents.shape[0]:]-previewer_mean) - (unet_pred_latent-unet_mean))).pow(2).mean(dim=(1,2,3))
+                    # preview_error = torch.nn.functional.cosine_similarity(preview_latent[latents.shape[0]:].reshape(latents.shape[0], -1), unet_pred_latent.reshape(latents.shape[0],-1))
+                    previewer_mean = preview_latent[latents.shape[0]:]
+                    unet_mean = unet_pred_latent
+                    preview_factor = (pred_x0_l2 / previewer_l2).reshape(-1, 1, 1, 1)
+
+                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)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_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()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        if save_preview_row:
+            preview_image_row = []
+            if needs_upcasting:
+                self.upcast_vae()
+            for preview_latents in preview_row:
+                preview_latents = preview_latents.to(device=self.device, dtype=next(iter(self.vae.post_quant_conv.parameters())).dtype)
+                if has_latents_mean and has_latents_std:
+                    latents_mean = (
+                        torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(preview_latents.device, preview_latents.dtype)
+                    )
+                    latents_std = (
+                        torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(preview_latents.device, preview_latents.dtype)
+                    )
+                    preview_latents = preview_latents * latents_std / self.vae.config.scaling_factor + latents_mean
+                else:
+                    preview_latents = preview_latents / self.vae.config.scaling_factor
+
+                preview_image = self.vae.decode(preview_latents, return_dict=False)[0]
+                preview_image = self.image_processor.postprocess(preview_image, output_type=output_type)
+                preview_image_row.append(preview_image)
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            if save_preview_row:
+                return (image, preview_image_row)
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1a17660a3..354ceea7d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1055,6 +1055,7 @@ def set_diffuser_pipe(pipe, new_pipe_type):
         'OmniGenPipeline',
         'StableDiffusion3ControlNetPipeline',
         'StableDiffusionXLPuLIDPipeline',
+        'InstantIRPipeline',
     ]
 
     n = getattr(pipe.__class__, '__name__', '')
diff --git a/scripts/instantir.py b/scripts/instantir.py
new file mode 100644
index 000000000..4c7ce77b7
--- /dev/null
+++ b/scripts/instantir.py
@@ -0,0 +1,97 @@
+import gradio as gr
+import torch
+import diffusers
+from huggingface_hub import hf_hub_download
+from modules import scripts, processing, shared, sd_models, devices, ipadapter
+
+
+class Script(scripts.Script):
+    def __init__(self):
+        super().__init__()
+        self.orig_pipe = None
+        self.orig_ip_unapply = None
+
+    def title(self):
+        return 'InstantIR'
+
+    def show(self, is_img2img):
+        return is_img2img if shared.native else False
+
+    def ui(self, _is_img2img): # ui elements
+        with gr.Row():
+            gr.HTML('<a href="https://github.com/instantX-research/InstantIR">&nbsp InstantIR: Image Restoration</a><br>')
+        with gr.Row():
+            start = gr.Slider(label='Preview start', minimum=0.0, maximum=1.0, step=0.01, value=0.0)
+            end = gr.Slider(label='Preview end', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
+        with gr.Row():
+            hq = gr.Checkbox(label='HQ init latents', value=False)
+            multistep = gr.Checkbox(label='Multistep restore', value=False)
+            adastep = gr.Checkbox(label='Adaptive restore', value=False)
+        with gr.Row():
+            image = gr.Image(label='Override guidance image')
+        return [start, end, hq, multistep, adastep, image]
+
+    def run(self, p: processing.StableDiffusionProcessing, *args): # pylint: disable=arguments-differ
+        supported_model_list = ['sdxl']
+        if not hasattr(p, 'init_images') or len(p.init_images) == 0:
+            shared.log.warning('InstantIR: no image')
+            return None
+        if shared.sd_model_type not in supported_model_list and shared.sd_model.__class__.__name__ != "InstantIRPipeline":
+            shared.log.warning(f'InstantIR: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
+            return None
+        start, end, hq, multistep, adastep, image = args
+        from modules import instantir as ir
+        if shared.sd_model_type == "sdxl":
+            if shared.sd_model.__class__.__name__ != "InstantIRPipeline":
+                self.orig_pipe = shared.sd_model
+                self.orig_ip_unapply = ipadapter.unapply
+                shared.sd_model = sd_models.switch_pipe(ir.InstantIRPipeline, shared.sd_model)
+                adapter_file = hf_hub_download('InstantX/InstantIR', subfolder='models', filename='adapter.pt', cache_dir=shared.opts.hfcache_dir)
+                aggregator_file = hf_hub_download('InstantX/InstantIR', subfolder='models', filename='aggregator.pt', cache_dir=shared.opts.hfcache_dir)
+                previewer_file = hf_hub_download('InstantX/InstantIR', subfolder='models', filename='previewer_lora_weights.bin', cache_dir=shared.opts.hfcache_dir)
+                shared.log.debug(f'InstantIR: adapter="{adapter_file}" aggregator="{aggregator_file}" previewer="{previewer_file}"')
+                ir.load_adapter_to_pipe(
+                    pipe=shared.sd_model,
+                    pretrained_model_path_or_dict=adapter_file,
+                    image_encoder_or_path='facebook/dinov2-large',
+                    use_lcm=False,
+                    use_adaln=True,
+                    )
+                shared.sd_model.prepare_previewers(previewer_file)
+                shared.sd_model.scheduler = diffusers.DDPMScheduler.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', subfolder="scheduler")
+                pretrained_state_dict = torch.load(aggregator_file)
+                shared.sd_model.aggregator.load_state_dict(pretrained_state_dict)
+                shared.sd_model.aggregator.to(device=devices.device, dtype=devices.dtype)
+
+        shared.log.info(f'InstantIR: class={shared.sd_model.__class__.__name__} start={start} end={end} multistep={multistep} adastep={adastep} hq={hq} cache={shared.opts.hfcache_dir}')
+        p.sampler_name = 'Default' # ir has its own sampler
+        p.init() # run init early to take care of resizing
+        p.task_args['previewer_scheduler'] = ir.LCMSingleStepScheduler.from_config(shared.sd_model.scheduler.config)
+        p.task_args['image'] = p.init_images
+        p.task_args['save_preview_row'] = False
+        p.task_args['init_latents_with_lq'] = not hq
+        p.task_args['multistep_restore'] = multistep
+        p.task_args['adastep_restore'] = adastep
+        p.task_args['preview_start'] = start
+        p.task_args['preview_end'] = end
+        p.task_args['ip_adapter_image'] = image
+        p.extra_generation_params["InstantIR"] = f'Start={start} End={end} HQ={hq} Multistep={multistep} Adastep={adastep}'
+        ipadapter.unapply = lambda x: x # disable as main processing unloads ipadapter as it thinks its not needed
+        devices.torch_gc()
+
+    def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=arguments-differ, unused-argument
+        # TODO instantir is a mess to unload
+        """
+        if self.orig_pipe is None:
+            return processed
+        if hasattr(shared.sd_model, 'aggregator'):
+            shared.sd_model.aggregator = None
+        shared.log.debug(f'InstantIR restore: class={shared.sd_model.__class__.__name__}')
+        shared.sd_model = self.orig_pipe
+        self.orig_pipe = None
+        shared.sd_model.unet.register_to_config(encoder_hid_dim_type=None)
+        ipadapter.unapply = self.orig_ip_unapply
+        ipadapter.unapply(shared.sd_model)
+        devices.torch_gc()
+        """
+        return processed

From e8a25a7271c4b66d6c17a2d4ce3007e33c984883 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 5 Nov 2024 18:31:58 -0500
Subject: [PATCH 024/119] add promptgen-v2

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md     | 7 +++++++
 modules/vqa.py   | 2 ++
 requirements.txt | 4 ++--
 3 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4bb10f5de..724960169 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,6 +28,13 @@ This release can be considered an LTS release before we kick off the next round
 - SD3: all-in-one safetensors  
   - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
   - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
+- [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) base and large:
+  - *in process -> visual query*
+  - caption modes:
+    `<GENERATE_TAGS>` generate tags  
+    `<CAPTION>`, `<DETAILED_CAPTION>`, `<MORE_DETAILED_CAPTION>` caption image  
+    `<ANALYZE>` image composition  
+    `<MIXED_CAPTION>`, `<MIXED_CAPTION_PLUS>` detailed caption and tags with optional analyze  
 - XYZ grid:  
   - optional time benchmark info to individual images  
   - optional add params to individual images  
diff --git a/modules/vqa.py b/modules/vqa.py
index 64ba83696..a0a5d147c 100644
--- a/modules/vqa.py
+++ b/modules/vqa.py
@@ -14,6 +14,8 @@ MODELS = {
     "MS Florence 2 Large": "microsoft/Florence-2-large", # 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
     "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
diff --git a/requirements.txt b/requirements.txt
index c6fcf97ec..de0223bd5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -49,8 +49,8 @@ scipy
 pandas
 protobuf==4.25.3
 pytorch_lightning==1.9.4
-tokenizers==0.20.1
-transformers==4.46.1
+tokenizers==0.20.3
+transformers==4.46.2
 urllib3==1.26.19
 Pillow==10.4.0
 timm==0.9.16

From d95cfc937622e5eb5f24d6893f7c469fc7101a03 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 5 Nov 2024 23:13:08 -0500
Subject: [PATCH 025/119] consistory prototype

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                  |    1 +
 .ruff.toml                                 |    1 +
 modules/consistory/__init__.py             |    6 +
 modules/consistory/attention_processor.py  |  289 +++++
 modules/consistory/consistory_cache.py     |  217 ++++
 modules/consistory/consistory_pipeline.py  |  519 +++++++++
 modules/consistory/consistory_run.py       |  280 +++++
 modules/consistory/consistory_unet_sdxl.py | 1173 ++++++++++++++++++++
 modules/consistory/consistory_utils.py     |  200 ++++
 modules/consistory/utils/general_utils.py  |  133 +++
 modules/consistory/utils/ptp_utils.py      |  194 ++++
 scripts/consistory_ext.py                  |  105 ++
 12 files changed, 3118 insertions(+)
 create mode 100644 modules/consistory/__init__.py
 create mode 100644 modules/consistory/attention_processor.py
 create mode 100644 modules/consistory/consistory_cache.py
 create mode 100644 modules/consistory/consistory_pipeline.py
 create mode 100644 modules/consistory/consistory_run.py
 create mode 100644 modules/consistory/consistory_unet_sdxl.py
 create mode 100644 modules/consistory/consistory_utils.py
 create mode 100644 modules/consistory/utils/general_utils.py
 create mode 100644 modules/consistory/utils/ptp_utils.py
 create mode 100644 scripts/consistory_ext.py

diff --git a/.pylintrc b/.pylintrc
index f541638e7..2d8e4869b 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -32,6 +32,7 @@ ignore-paths=/usr/lib/.*$,
              modules/meissonic,
              modules/omnigen,
              modules/instantir,
+             modules/consistory,
              modules/pulid/eva_clip,
              repositories,
              extensions-builtin/sd-webui-agent-scheduler,
diff --git a/.ruff.toml b/.ruff.toml
index 154d9a2c1..2a29fb089 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -27,6 +27,7 @@ exclude = [
     "modules/meissonic",
     "modules/omnigen",
     "modules/instantir",
+    "modules/consistory",
     "modules/pulid/eva_clip",
     "repositories",
     "extensions-builtin/sd-extension-chainner/nodes",
diff --git a/modules/consistory/__init__.py b/modules/consistory/__init__.py
new file mode 100644
index 000000000..2a06b53a6
--- /dev/null
+++ b/modules/consistory/__init__.py
@@ -0,0 +1,6 @@
+"""
+original code from <https://github.com/NVlabs/consistory>
+"""
+from .consistory_pipeline import ConsistoryExtendAttnSDXLPipeline
+from .consistory_unet_sdxl import ConsistorySDXLUNet2DConditionModel
+from .consistory_run import run_anchor_generation, run_extra_generation
diff --git a/modules/consistory/attention_processor.py b/modules/consistory/attention_processor.py
new file mode 100644
index 000000000..d1fd10cc3
--- /dev/null
+++ b/modules/consistory/attention_processor.py
@@ -0,0 +1,289 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+# Not a contribution
+# Changes made by NVIDIA CORPORATION & AFFILIATES enabling ConsiStory or otherwise documented as NVIDIA-proprietary
+# are not a contribution and subject to the license under the LICENSE file located at the root directory.
+
+
+from diffusers.utils import USE_PEFT_BACKEND
+from typing import Callable, Optional
+import torch
+import torch.nn.functional as F
+from diffusers.models.attention_processor import Attention
+
+from .consistory_utils import AnchorCache, FeatureInjector, QueryStore
+
+
+class ConsistoryAttnStoreProcessor:
+    def __init__(self, attnstore, place_in_unet):
+        super().__init__()
+        self.attnstore = attnstore
+        self.place_in_unet = place_in_unet
+
+    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, record_attention=True, **kwargs):
+        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)
+
+        is_cross = encoder_hidden_states is not None
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else 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)
+
+        # only need to store attention maps during the Attend and Excite process
+        # if attention_probs.requires_grad:
+        if record_attention:
+            self.attnstore(attention_probs, is_cross, self.place_in_unet, attn.heads)
+
+        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
+
+
+class ConsistoryExtendedAttnXFormersAttnProcessor:
+    r"""
+    Processor for implementing memory efficient attention using xFormers.
+
+    Args:
+        attention_op (`Callable`, *optional*, defaults to `None`):
+            The base
+            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
+            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
+            operator.
+    """
+
+    def __init__(self, place_in_unet, attnstore, extended_attn_kwargs, attention_op: Optional[Callable] = None):
+        self.attention_op = attention_op
+        self.t_range = extended_attn_kwargs.get('t_range', [])
+        self.extend_kv_unet_parts = extended_attn_kwargs.get('extend_kv_unet_parts', ['down', 'mid', 'up'])
+
+        self.place_in_unet = place_in_unet
+        self.curr_unet_part = self.place_in_unet.split('_')[0]
+        self.attnstore = attnstore
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        scale: float = 1.0,
+        perform_extend_attn: bool = False,
+        query_store: Optional[QueryStore] = None,
+        feature_injector: Optional[FeatureInjector] = None,
+        anchors_cache: Optional[AnchorCache] = None,
+        **kwargs
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        args = () if USE_PEFT_BACKEND else (scale,)
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+        else:
+            batch_size, wh, channel = hidden_states.shape
+            height = width = int(wh ** 0.5)
+
+        is_cross = encoder_hidden_states is not None
+        perform_extend_attn = perform_extend_attn and (not is_cross) and \
+                              any([self.attnstore.curr_iter >= x[0] and self.attnstore.curr_iter <= x[1] for x in self.t_range]) and \
+                              self.curr_unet_part in self.extend_kv_unet_parts
+
+        batch_size, key_tokens, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
+        if attention_mask is not None:
+            # expand our mask's singleton query_tokens dimension:
+            #   [batch*heads,            1, key_tokens] ->
+            #   [batch*heads, query_tokens, key_tokens]
+            # so that it can be added as a bias onto the attention scores that xformers computes:
+            #   [batch*heads, query_tokens, key_tokens]
+            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
+            _, query_tokens, _ = hidden_states.shape
+            attention_mask = attention_mask.expand(-1, query_tokens, -1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states, *args)
+
+        if (self.curr_unet_part in self.extend_kv_unet_parts) and query_store and query_store.mode == 'cache':
+            query_store.cache_query(query, self.place_in_unet)
+        elif perform_extend_attn and query_store and query_store.mode == 'inject':
+            query = query_store.inject_query(query, self.place_in_unet, self.attnstore.curr_iter)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states, *args)
+        value = attn.to_v(encoder_hidden_states, *args)
+
+        query = attn.head_to_batch_dim(query).contiguous()
+
+        if perform_extend_attn:
+            # Anchor Caching
+            if anchors_cache and anchors_cache.is_cache_mode():
+                if self.place_in_unet not in anchors_cache.input_h_cache:
+                    anchors_cache.input_h_cache[self.place_in_unet] = {}
+
+                # Hidden states inside the mask, for uncond (index 0) and cond (index 1) prompts
+                subjects_hidden_states = torch.stack([x[self.attnstore.last_mask_dropout[width]] for x in hidden_states.chunk(2)])
+                anchors_cache.input_h_cache[self.place_in_unet][self.attnstore.curr_iter] = subjects_hidden_states
+
+            if anchors_cache and anchors_cache.is_inject_mode():
+                # We make extended key and value by concatenating the original key and value with the query.
+                anchors_hidden_states = anchors_cache.input_h_cache[self.place_in_unet][self.attnstore.curr_iter]
+
+                anchors_keys = attn.to_k(anchors_hidden_states, *args)
+                anchors_values = attn.to_v(anchors_hidden_states, *args)
+
+                extended_key = torch.cat([torch.cat([key.chunk(2, dim=0)[x], anchors_keys[x].unsqueeze(0)], dim=1) for x in range(2)])
+                extended_value = torch.cat([torch.cat([value.chunk(2, dim=0)[x], anchors_values[x].unsqueeze(0)], dim=1) for x in range(2)])
+
+                extended_key = attn.head_to_batch_dim(extended_key).contiguous()
+                extended_value = attn.head_to_batch_dim(extended_value).contiguous()
+
+                # attn_masks needs to be of shape [batch_size, query_tokens, key_tokens]
+                # hidden_states = xformers.ops.memory_efficient_attention(query, extended_key, extended_value,  op=self.attention_op, scale=attn.scale)
+                hidden_states = F.scaled_dot_product_attention(query, extended_key, extended_value, scale=attn.scale)
+            else:
+                # # We make extended key and value by concatenating the original key and value with the query.
+                # attention_mask_bias = self.attnstore.get_attn_mask_bias(tgt_size = width, bsz = batch_size)
+
+                # if attention_mask_bias is not None:
+                #     attention_mask_bias = torch.cat([x.unsqueeze(0).expand(attn.heads, -1, -1) for x in attention_mask_bias])
+
+                # Pre-allocate the output tensor
+                ex_out = torch.empty_like(query)
+
+                for i in range(batch_size):
+                    start_idx = i * attn.heads
+                    end_idx = start_idx + attn.heads
+
+                    attention_mask = self.attnstore.get_extended_attn_mask_instance(width, i%(batch_size//2))
+
+                    curr_q = query[start_idx:end_idx]
+
+                    if i < batch_size//2:
+                        curr_k = key[:batch_size//2]
+                        curr_v = value[:batch_size//2]
+                    else:
+                        curr_k = key[batch_size//2:]
+                        curr_v = value[batch_size//2:]
+
+                    curr_k = curr_k.flatten(0,1)[attention_mask].unsqueeze(0)
+                    curr_v = curr_v.flatten(0,1)[attention_mask].unsqueeze(0)
+
+                    curr_k = attn.head_to_batch_dim(curr_k).contiguous()
+                    curr_v = attn.head_to_batch_dim(curr_v).contiguous()
+
+                    # hidden_states = xformers.ops.memory_efficient_attention(curr_q, curr_k, curr_v, op=self.attention_op, scale=attn.scale)
+                    hidden_states = F.scaled_dot_product_attention(curr_q, curr_k, curr_v, scale=attn.scale)
+
+                    ex_out[start_idx:end_idx] = hidden_states
+
+                hidden_states = ex_out
+        else:
+            key = attn.head_to_batch_dim(key).contiguous()
+            value = attn.head_to_batch_dim(value).contiguous()
+
+            # attn_masks needs to be of shape [batch_size, query_tokens, key_tokens]
+            # hidden_states = xformers.ops.memory_efficient_attention(query, key, value, op=self.attention_op, scale=attn.scale)
+            hidden_states = F.scaled_dot_product_attention(query, key, value, scale=attn.scale)
+
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states, *args)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if (feature_injector is not None):
+            output_res = int(hidden_states.shape[1] ** 0.5)
+
+            if anchors_cache and anchors_cache.is_inject_mode():
+                hidden_states[batch_size//2:] = feature_injector.inject_anchors(hidden_states[batch_size//2:], self.attnstore.curr_iter, output_res, self.attnstore.extended_mapping, self.place_in_unet, anchors_cache)
+            else:
+                hidden_states[batch_size//2:] = feature_injector.inject_outputs(hidden_states[batch_size//2:], self.attnstore.curr_iter, output_res, self.attnstore.extended_mapping, self.place_in_unet, anchors_cache)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+def register_extended_self_attn(unet, attnstore, extended_attn_kwargs):
+    DICT_PLACE_TO_RES = {'down_0': 64, 'down_1': 64, 'down_2': 64, 'down_3': 64, 'down_4': 64, 'down_5': 64, 'down_6': 64, 'down_7': 64,
+                         'down_8': 32, 'down_9': 32, 'down_10': 32, 'down_11': 32, 'down_12': 32, 'down_13': 32, 'down_14': 32, 'down_15': 32,
+                         'down_16': 32, 'down_17': 32, 'down_18': 32, 'down_19': 32, 'down_20': 32, 'down_21': 32, 'down_22': 32, 'down_23': 32,
+                         'down_24': 32, 'down_25': 32, 'down_26': 32, 'down_27': 32, 'down_28': 32, 'down_29': 32, 'down_30': 32, 'down_31': 32,
+                         'down_32': 32, 'down_33': 32, 'down_34': 32, 'down_35': 32, 'down_36': 32, 'down_37': 32, 'down_38': 32, 'down_39': 32,
+                         'down_40': 32, 'down_41': 32, 'down_42': 32, 'down_43': 32, 'down_44': 32, 'down_45': 32, 'down_46': 32, 'down_47': 32,
+                         'mid_120': 32, 'mid_121': 32, 'mid_122': 32, 'mid_123': 32, 'mid_124': 32, 'mid_125': 32, 'mid_126': 32, 'mid_127': 32,
+                         'mid_128': 32, 'mid_129': 32, 'mid_130': 32, 'mid_131': 32, 'mid_132': 32, 'mid_133': 32, 'mid_134': 32, 'mid_135': 32,
+                         'mid_136': 32, 'mid_137': 32, 'mid_138': 32, 'mid_139': 32, 'up_49': 32, 'up_51': 32, 'up_53': 32, 'up_55': 32, 'up_57': 32,
+                         'up_59': 32, 'up_61': 32, 'up_63': 32, 'up_65': 32, 'up_67': 32, 'up_69': 32, 'up_71': 32, 'up_73': 32, 'up_75': 32,
+                         'up_77': 32, 'up_79': 32, 'up_81': 32, 'up_83': 32, 'up_85': 32, 'up_87': 32, 'up_89': 32, 'up_91': 32, 'up_93': 32,
+                         'up_95': 32, 'up_97': 32, 'up_99': 32, 'up_101': 32, 'up_103': 32, 'up_105': 32, 'up_107': 32, 'up_109': 64, 'up_111': 64,
+                         'up_113': 64, 'up_115': 64, 'up_117': 64, 'up_119': 64}
+    attn_procs = {}
+    for i, name in enumerate(unet.attn_processors.keys()):
+        is_self_attn = (i % 2 == 0)
+
+        if name.startswith("mid_block"):
+            place_in_unet = f"mid_{i}"
+        elif name.startswith("up_blocks"):
+            place_in_unet = f"up_{i}"
+        elif name.startswith("down_blocks"):
+            place_in_unet = f"down_{i}"
+        else:
+            continue
+
+        if is_self_attn:
+            attn_procs[name] = ConsistoryExtendedAttnXFormersAttnProcessor(place_in_unet, attnstore, extended_attn_kwargs)
+        else:
+            attn_procs[name] = ConsistoryAttnStoreProcessor(attnstore, place_in_unet)
+
+    unet.set_attn_processor(attn_procs)
\ No newline at end of file
diff --git a/modules/consistory/consistory_cache.py b/modules/consistory/consistory_cache.py
new file mode 100644
index 000000000..66312e4e1
--- /dev/null
+++ b/modules/consistory/consistory_cache.py
@@ -0,0 +1,217 @@
+# Copyright (C) 2024 NVIDIA Corporation.  All rights reserved.
+#
+# This work is licensed under the LICENSE file
+# located at the root directory.
+
+import torch
+from diffusers import DDIMScheduler
+from .consistory_unet_sdxl import ConsistorySDXLUNet2DConditionModel
+from .consistory_pipeline import ConsistoryExtendAttnSDXLPipeline
+from .consistory_utils import FeatureInjector, AnchorCache
+from .utils.general_utils import *
+
+
+def load_pipeline(gpu_id=0):
+    float_type = torch.float16
+    sd_id = "stabilityai/stable-diffusion-xl-base-1.0"
+
+    device = torch.device(f'cuda:{gpu_id}') if torch.cuda.is_available() else torch.device('cpu')
+    unet = ConsistorySDXLUNet2DConditionModel.from_pretrained(sd_id, subfolder="unet", torch_dtype=float_type)
+    scheduler = DDIMScheduler.from_pretrained(sd_id, subfolder="scheduler")
+
+    story_pipeline = ConsistoryExtendAttnSDXLPipeline.from_pretrained(
+        sd_id, unet=unet, torch_dtype=float_type, variant="fp16", use_safetensors=True, scheduler=scheduler
+    ).to(device)
+    story_pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+
+    return story_pipeline
+
+def create_anchor_mapping(bsz, anchor_indices=[0]):
+    anchor_mapping = torch.eye(bsz, dtype=torch.bool)
+    for anchor_idx in anchor_indices:
+        anchor_mapping[:, anchor_idx] = True
+
+    return anchor_mapping
+
+def create_token_indices(prompts, batch_size, concept_token, tokenizer):
+    if isinstance(concept_token, str):
+        concept_token = [concept_token]
+
+    concept_token_id = [tokenizer.encode(x, add_special_tokens=False)[0] for x in concept_token]
+    tokens = tokenizer.batch_encode_plus(prompts, padding=True, return_tensors='pt')['input_ids']
+
+    token_indices = torch.full((len(concept_token), batch_size), -1, dtype=torch.int64)
+    for i, token_id in enumerate(concept_token_id):
+        batch_loc, token_loc = torch.where(tokens == token_id)
+        token_indices[i, batch_loc] = token_loc
+
+    return token_indices
+
+def create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type):
+    # if seed is int
+    if isinstance(seed, int):
+        g = torch.Generator('cuda').manual_seed(seed)
+        shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
+        latents = randn_tensor(shape, generator=g, device=device, dtype=float_type)
+    elif isinstance(seed, list):
+        shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
+        latents = torch.empty(shape, device=device, dtype=float_type)
+        for i, seed_i in enumerate(seed):
+            g = torch.Generator('cuda').manual_seed(seed_i)
+            curr_latent = randn_tensor(shape, generator=g, device=device, dtype=float_type)
+            latents[i] = curr_latent[i]
+
+    if same_latent:
+        latents = latents[:1].repeat(batch_size, 1, 1, 1)
+
+    return latents, g
+
+def run_anchor_generation(story_pipeline, prompts, concept_token,
+                   seed=40, n_steps=50, mask_dropout=0.5,
+                   same_latent=False, share_queries=True,
+                   perform_sdsa=True, perform_injection=True):
+    latent_resolutions = [32, 64]
+
+    device = story_pipeline.device
+    tokenizer = story_pipeline.tokenizer
+    float_type = story_pipeline.dtype
+    unet = story_pipeline.unet
+
+    batch_size = len(prompts)
+
+    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
+
+    default_attention_store_kwargs = {
+        'token_indices': token_indices,
+        'mask_dropout': mask_dropout
+    }
+
+    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
+    query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
+
+    latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
+
+    anchor_cache_first_stage = AnchorCache()
+    anchor_cache_second_stage = AnchorCache()
+
+    # ------------------ #
+    # Extended attention First Run #
+
+    if perform_sdsa:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
+    else:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
+
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                        attention_store_kwargs=default_attention_store_kwargs,
+                        extended_attn_kwargs=extended_attn_kwargs,
+                        share_queries=share_queries,
+                        query_store_kwargs=query_store_kwargs,
+                        anchors_cache=anchor_cache_first_stage,
+                        num_inference_steps=n_steps)
+    last_masks = story_pipeline.attention_store.last_mask
+
+    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
+    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
+
+    anchor_cache_first_stage.dift_cache = dift_features
+    anchor_cache_first_stage.anchors_last_mask = last_masks
+
+    nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, latent_resolutions, device)
+
+    # ------------------ #
+    # Extended attention with nn_map #
+
+    if perform_injection:
+        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
+                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
+
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                            attention_store_kwargs=default_attention_store_kwargs,
+                            extended_attn_kwargs=extended_attn_kwargs,
+                            share_queries=share_queries,
+                            query_store_kwargs=query_store_kwargs,
+                            feature_injector=feature_injector,
+                            anchors_cache=anchor_cache_second_stage,
+                            num_inference_steps=n_steps)
+        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
+        anchor_cache_second_stage.dift_cache = dift_features
+        anchor_cache_second_stage.anchors_last_mask = last_masks
+
+    return out.images, anchor_cache_first_stage, anchor_cache_second_stage
+
+def run_extra_generation(story_pipeline, prompts, concept_token, 
+                         anchor_cache_first_stage, anchor_cache_second_stage,
+                         seed=40, n_steps=50, mask_dropout=0.5,
+                         same_latent=False, share_queries=True,
+                         perform_sdsa=True, perform_injection=True):
+    latent_resolutions = [32, 64]
+
+    device = story_pipeline.device
+    tokenizer = story_pipeline.tokenizer
+    float_type = story_pipeline.dtype
+    unet = story_pipeline.unet
+
+    batch_size = len(prompts)
+
+    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
+
+    default_attention_store_kwargs = {
+        'token_indices': token_indices,
+        'mask_dropout': mask_dropout
+    }
+
+    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
+    query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
+
+    extra_batch_size = batch_size + 2
+    if isinstance(seed, list):
+        seed = [seed[0], seed[0], *seed]
+
+    latents, g = create_latents(story_pipeline, seed, extra_batch_size, same_latent, device, float_type)
+    latents = latents[2:]
+
+    anchor_cache_first_stage.set_mode_inject()
+    anchor_cache_second_stage.set_mode_inject()
+
+    # ------------------ #
+    # Extended attention First Run #
+
+    if perform_sdsa:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
+    else:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
+
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                        attention_store_kwargs=default_attention_store_kwargs,
+                        extended_attn_kwargs=extended_attn_kwargs,
+                        share_queries=share_queries,
+                        query_store_kwargs=query_store_kwargs,
+                        anchors_cache=anchor_cache_first_stage,
+                        num_inference_steps=n_steps)
+    last_masks = story_pipeline.attention_store.last_mask
+
+    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
+    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
+
+    anchor_dift_features = anchor_cache_first_stage.dift_cache
+    anchor_last_masks = anchor_cache_first_stage.anchors_last_mask
+
+    nn_map, nn_distances = anchor_nn_map(dift_features, anchor_dift_features, last_masks, anchor_last_masks, latent_resolutions, device)
+
+    # ------------------ #
+    # Extended attention with nn_map #
+    if perform_injection:
+        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
+                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
+
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                            attention_store_kwargs=default_attention_store_kwargs,
+                            extended_attn_kwargs=extended_attn_kwargs,
+                            share_queries=share_queries,
+                            query_store_kwargs=query_store_kwargs,
+                            feature_injector=feature_injector,
+                            anchors_cache=anchor_cache_second_stage,
+                            num_inference_steps=n_steps)
+        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
+    return out.images
\ No newline at end of file
diff --git a/modules/consistory/consistory_pipeline.py b/modules/consistory/consistory_pipeline.py
new file mode 100644
index 000000000..b17fb4143
--- /dev/null
+++ b/modules/consistory/consistory_pipeline.py
@@ -0,0 +1,519 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+# Not a contribution
+# Changes made by NVIDIA CORPORATION & AFFILIATES enabling ConsiStory or otherwise documented as NVIDIA-proprietary
+# are not a contribution and subject to the license under the LICENSE file located at the root directory.
+
+import torch
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import StableDiffusionXLPipeline, \
+    rescale_noise_cfg, EXAMPLE_DOC_STRING
+from diffusers.utils import (
+    deprecate,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+)
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from .attention_processor import register_extended_self_attn
+from .consistory_utils import FeatureInjector, AnchorCache, QueryStore
+from .utils.ptp_utils import AttentionStore
+
+if is_torch_xla_available():
+    # import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+T = torch.Tensor
+
+class ConsistoryExtendAttnSDXLPipeline(
+    StableDiffusionXLPipeline
+):
+
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        
+        attention_store_kwargs: Optional[Dict] = None,
+        extended_attn_kwargs: Optional[Dict] = None,
+        share_queries: bool = False,
+        query_store_kwargs: Optional[Dict] = {},
+        feature_injector: Optional[FeatureInjector] = None,
+        anchors_cache: Optional[AnchorCache] = None,
+
+        instance_latents: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        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 the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            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.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            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.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            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.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                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.
+            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 `guidance_scale` is
+                less 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 both text-encoders
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            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.
+            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.
+            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.
+            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.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            cross_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).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            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 pipeine class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        # 0. Default height and width to unet
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+
+        # 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
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            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,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        if share_queries:
+            query_store = QueryStore(**query_store_kwargs)
+        else:
+            query_store = None
+
+        self.attention_store = AttentionStore(attention_store_kwargs)
+        register_extended_self_attn(self.unet, self.attention_store, extended_attn_kwargs)
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 8.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        # 9. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+
+        if instance_latents is not None:
+            n_instances = instance_latents.shape[0]
+            instance_noise = latents[:n_instances].clone()
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                self.attention_store.curr_iter = i
+
+                if instance_latents is not None:
+                    noised_instances = self.scheduler.add_noise(instance_latents, instance_noise, t.repeat(n_instances).long())
+                    latents[:n_instances] = noised_instances
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                if share_queries and (i >= query_store.t_range[0] and i <= query_store.t_range[1]):
+                    query_store.set_mode('cache')
+                    noise_pred_vanilla = self.unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        timestep_cond=timestep_cond,
+                        cross_attention_kwargs={'query_store': query_store, 
+                                                'perform_extend_attn': False, 
+                                                'record_attention': False},
+                        added_cond_kwargs=added_cond_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                    query_store.set_mode('inject')
+
+                noise_pred = self.unet(
+                       latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        timestep_cond=timestep_cond,
+                        cross_attention_kwargs={'query_store': query_store, 
+                                                'perform_extend_attn': True, 
+                                                'record_attention': True, 
+                                                'feature_injector': feature_injector,
+                                                'anchors_cache': anchors_cache},
+                        added_cond_kwargs=added_cond_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                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)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
+
+                # 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()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+                if XLA_AVAILABLE:
+                    # xm.mark_step()
+                    pass
+                
+                # Update attention store mask
+                self.attention_store.aggregate_last_steps_attention()
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            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 StableDiffusionXLPipelineOutput(images=image)
\ No newline at end of file
diff --git a/modules/consistory/consistory_run.py b/modules/consistory/consistory_run.py
new file mode 100644
index 000000000..d7bcdd080
--- /dev/null
+++ b/modules/consistory/consistory_run.py
@@ -0,0 +1,280 @@
+# Copyright (C) 2024 NVIDIA Corporation.  All rights reserved.
+#
+# This work is licensed under the LICENSE file
+# located at the root directory.
+
+import torch
+from diffusers import DDIMScheduler
+from .consistory_unet_sdxl import ConsistorySDXLUNet2DConditionModel
+from .consistory_pipeline import ConsistoryExtendAttnSDXLPipeline
+from .consistory_utils import FeatureInjector, AnchorCache
+from .utils.general_utils import *
+
+
+LATENT_RESOLUTIONS = [32, 64]
+
+def load_pipeline(gpu_id=0):
+    float_type = torch.float16
+    sd_id = "stabilityai/stable-diffusion-xl-base-1.0"
+
+    device = torch.device(f'cuda:{gpu_id}') if torch.cuda.is_available() else torch.device('cpu')
+    unet = ConsistorySDXLUNet2DConditionModel.from_pretrained(sd_id, subfolder="unet", torch_dtype=float_type)
+    scheduler = DDIMScheduler.from_pretrained(sd_id, subfolder="scheduler")
+
+    story_pipeline = ConsistoryExtendAttnSDXLPipeline.from_pretrained(
+        sd_id, unet=unet, torch_dtype=float_type, variant="fp16", use_safetensors=True, scheduler=scheduler
+    ).to(device)
+    story_pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+
+    return story_pipeline
+
+def create_anchor_mapping(bsz, anchor_indices=[0]):
+    anchor_mapping = torch.eye(bsz, dtype=torch.bool)
+    for anchor_idx in anchor_indices:
+        anchor_mapping[:, anchor_idx] = True
+
+    return anchor_mapping
+
+def create_token_indices(prompts, batch_size, concept_token, tokenizer):
+    if isinstance(concept_token, str):
+        concept_token = [concept_token]
+
+    concept_token_id = [tokenizer.encode(x, add_special_tokens=False)[0] for x in concept_token]
+    tokens = tokenizer.batch_encode_plus(prompts, padding=True, return_tensors='pt')['input_ids']
+
+    token_indices = torch.full((len(concept_token), batch_size), -1, dtype=torch.int64)
+    for i, token_id in enumerate(concept_token_id):
+        batch_loc, token_loc = torch.where(tokens == token_id)
+        token_indices[i, batch_loc] = token_loc
+
+    return token_indices
+
+def create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type):
+    # if seed is int
+    if isinstance(seed, int):
+        g = torch.Generator('cuda').manual_seed(seed)
+        shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
+        latents = randn_tensor(shape, generator=g, device=device, dtype=float_type)
+    elif isinstance(seed, list):
+        shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
+        latents = torch.empty(shape, device=device, dtype=float_type)
+        for i, seed_i in enumerate(seed):
+            g = torch.Generator('cuda').manual_seed(seed_i)
+            curr_latent = randn_tensor(shape, generator=g, device=device, dtype=float_type)
+            latents[i] = curr_latent[i]
+
+    if same_latent:
+        latents = latents[:1].repeat(batch_size, 1, 1, 1)
+
+    return latents, g
+
+# Batch inference
+def run_batch_generation(story_pipeline, prompts, concept_token,
+                        seed=40, n_steps=50, mask_dropout=0.5,
+                        same_latent=False, share_queries=True,
+                        perform_sdsa=True, perform_injection=True,
+                        n_achors=2):
+    device = story_pipeline.device
+    tokenizer = story_pipeline.tokenizer
+    float_type = story_pipeline.dtype
+    unet = story_pipeline.unet
+
+    batch_size = len(prompts)
+
+    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
+    anchor_mappings = create_anchor_mapping(batch_size, anchor_indices=list(range(n_achors)))
+
+    default_attention_store_kwargs = {
+        'token_indices': token_indices,
+        'mask_dropout': mask_dropout,
+        'extended_mapping': anchor_mappings
+    }
+
+    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
+    query_store_kwargs= {'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
+
+    latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
+
+    # ------------------ #
+    # Extended attention First Run #
+
+    if perform_sdsa:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
+    else:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
+
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                        attention_store_kwargs=default_attention_store_kwargs,
+                        extended_attn_kwargs=extended_attn_kwargs,
+                        share_queries=share_queries,
+                        query_store_kwargs=query_store_kwargs,
+                        num_inference_steps=n_steps)
+    last_masks = story_pipeline.attention_store.last_mask
+
+    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
+    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
+
+    nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, LATENT_RESOLUTIONS, device)
+
+    # ------------------ #
+    # Extended attention with nn_map #
+
+    if perform_injection:
+        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
+                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
+
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                            attention_store_kwargs=default_attention_store_kwargs,
+                            extended_attn_kwargs=extended_attn_kwargs,
+                            share_queries=share_queries,
+                            query_store_kwargs=query_store_kwargs,
+                            feature_injector=feature_injector,
+                            num_inference_steps=n_steps)
+        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
+    return out.images
+
+# Anchors
+def run_anchor_generation(story_pipeline, prompts, concept_token,
+                        seed=40, n_steps=50, mask_dropout=0.5,
+                        same_latent=False, share_queries=True,
+                        perform_sdsa=True, perform_injection=True):
+    device = story_pipeline.device
+    tokenizer = story_pipeline.tokenizer
+    float_type = story_pipeline.dtype
+    unet = story_pipeline.unet
+
+    batch_size = len(prompts)
+
+    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
+
+    default_attention_store_kwargs = {
+        'token_indices': token_indices,
+        'mask_dropout': mask_dropout
+    }
+
+    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
+    query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
+
+    latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
+
+    anchor_cache_first_stage = AnchorCache()
+    anchor_cache_second_stage = AnchorCache()
+
+    # ------------------ #
+    # Extended attention First Run #
+
+    if perform_sdsa:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
+    else:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
+
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                        attention_store_kwargs=default_attention_store_kwargs,
+                        extended_attn_kwargs=extended_attn_kwargs,
+                        share_queries=share_queries,
+                        query_store_kwargs=query_store_kwargs,
+                        anchors_cache=anchor_cache_first_stage,
+                        num_inference_steps=n_steps)
+    last_masks = story_pipeline.attention_store.last_mask
+
+    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
+    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
+
+    anchor_cache_first_stage.dift_cache = dift_features
+    anchor_cache_first_stage.anchors_last_mask = last_masks
+
+    nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, LATENT_RESOLUTIONS, device)
+
+    # ------------------ #
+    # Extended attention with nn_map #
+
+    if perform_injection:
+        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
+                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
+
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                            attention_store_kwargs=default_attention_store_kwargs,
+                            extended_attn_kwargs=extended_attn_kwargs,
+                            share_queries=share_queries,
+                            query_store_kwargs=query_store_kwargs,
+                            feature_injector=feature_injector,
+                            anchors_cache=anchor_cache_second_stage,
+                            num_inference_steps=n_steps)
+        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
+        anchor_cache_second_stage.dift_cache = dift_features
+        anchor_cache_second_stage.anchors_last_mask = last_masks
+    return out.images, anchor_cache_first_stage, anchor_cache_second_stage
+
+def run_extra_generation(story_pipeline, prompts, concept_token, 
+                         anchor_cache_first_stage, anchor_cache_second_stage,
+                         seed=40, n_steps=50, mask_dropout=0.5,
+                         same_latent=False, share_queries=True,
+                         perform_sdsa=True, perform_injection=True):
+    device = story_pipeline.device
+    tokenizer = story_pipeline.tokenizer
+    float_type = story_pipeline.dtype
+    unet = story_pipeline.unet
+
+    batch_size = len(prompts)
+
+    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
+
+    default_attention_store_kwargs = {
+        'token_indices': token_indices,
+        'mask_dropout': mask_dropout
+    }
+
+    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
+    query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
+
+    extra_batch_size = batch_size + 2
+    if isinstance(seed, list):
+        seed = [seed[0], seed[0], *seed]
+
+    latents, g = create_latents(story_pipeline, seed, extra_batch_size, same_latent, device, float_type)
+    latents = latents[2:]
+
+    anchor_cache_first_stage.set_mode_inject()
+    anchor_cache_second_stage.set_mode_inject()
+
+    # ------------------ #
+    # Extended attention First Run #
+
+    if perform_sdsa:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
+    else:
+        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
+
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                        attention_store_kwargs=default_attention_store_kwargs,
+                        extended_attn_kwargs=extended_attn_kwargs,
+                        share_queries=share_queries,
+                        query_store_kwargs=query_store_kwargs,
+                        anchors_cache=anchor_cache_first_stage,
+                        num_inference_steps=n_steps)
+    last_masks = story_pipeline.attention_store.last_mask
+
+    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
+    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
+
+    anchor_dift_features = anchor_cache_first_stage.dift_cache
+    anchor_last_masks = anchor_cache_first_stage.anchors_last_mask
+
+    nn_map, nn_distances = anchor_nn_map(dift_features, anchor_dift_features, last_masks, anchor_last_masks, LATENT_RESOLUTIONS, device)
+
+    # ------------------ #
+    # Extended attention with nn_map #
+    if perform_injection:
+        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
+                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
+
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+                            attention_store_kwargs=default_attention_store_kwargs,
+                            extended_attn_kwargs=extended_attn_kwargs,
+                            share_queries=share_queries,
+                            query_store_kwargs=query_store_kwargs,
+                            feature_injector=feature_injector,
+                            anchors_cache=anchor_cache_second_stage,
+                            num_inference_steps=n_steps)
+        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
+    return out.images
diff --git a/modules/consistory/consistory_unet_sdxl.py b/modules/consistory/consistory_unet_sdxl.py
new file mode 100644
index 000000000..4dd9b42d2
--- /dev/null
+++ b/modules/consistory/consistory_unet_sdxl.py
@@ -0,0 +1,1173 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+# Not a contribution
+# Changes made by NVIDIA CORPORATION & AFFILIATES enabling ConsiStory or otherwise documented as NVIDIA-proprietary
+# are not a contribution and subject to the license under the LICENSE file located at the root directory.
+
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    ImageHintTimeEmbedding,
+    ImageProjection,
+    ImageTimeEmbedding,
+    PositionNet,
+    TextImageProjection,
+    TextImageTimeEmbedding,
+    TextTimeEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unets.unet_2d_blocks import (
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    UNetMidBlock2DSimpleCrossAttn,
+    get_down_block,
+    get_up_block,
+)
+
+from .consistory_utils import DIFTLatentStore
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet2DConditionOutput(BaseOutput):
+    """
+    The output of [`UNet2DConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+
+
+class ConsistorySDXLUNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
+            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+       reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
+            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+            Dimension for the timestep embeddings.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
+        *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
+        *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+            otherwise.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: Union[int, Tuple[int]] = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        dropout: float = 0.0,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: Union[int, Tuple[int]] = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
+        encoder_hid_dim: Optional[int] = None,
+        encoder_hid_dim_type: Optional[str] = None,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        resnet_skip_time_act: bool = False,
+        resnet_out_scale_factor: int = 1.0,
+        time_embedding_type: str = "positional",
+        time_embedding_dim: Optional[int] = None,
+        time_embedding_act_fn: Optional[str] = None,
+        timestep_post_act: Optional[str] = None,
+        time_cond_proj_dim: Optional[int] = None,
+        conv_in_kernel: int = 3,
+        conv_out_kernel: int = 3,
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        attention_type: str = "default",
+        class_embeddings_concat: bool = False,
+        mid_block_only_cross_attention: Optional[bool] = None,
+        cross_attention_norm: Optional[str] = None,
+        addition_embed_type_num_heads=64,
+    ):
+        super().__init__()
+
+        self.latent_store = DIFTLatentStore(steps=[261], up_ft_indices=[0])
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
+            for layer_number_per_block in transformer_layers_per_block:
+                if isinstance(layer_number_per_block, list):
+                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            if mid_block_only_cross_attention is None:
+                mid_block_only_cross_attention = only_cross_attention
+
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if mid_block_only_cross_attention is None:
+            mid_block_only_cross_attention = False
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # 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
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                dropout=dropout,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                attention_type=attention_type,
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                cross_attention_dim=cross_attention_dim[-1],
+                attention_head_dim=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                skip_time_act=resnet_skip_time_act,
+                only_cross_attention=mid_block_only_cross_attention,
+                cross_attention_norm=cross_attention_norm,
+            )
+        elif mid_block_type == "UNetMidBlock2D":
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                num_layers=0,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                add_attention=False,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = (
+            list(reversed(transformer_layers_per_block))
+            if reverse_transformer_layers_per_block is None
+            else reverse_transformer_layers_per_block
+        )
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resolution_idx=i,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                dropout=dropout,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if norm_num_groups is not None:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+            )
+
+            self.conv_act = get_activation(act_fn)
+
+        else:
+            self.conv_norm_out = None
+            self.conv_act = None
+
+        conv_out_padding = (conv_out_kernel - 1) // 2
+        self.conv_out = nn.Conv2d(
+            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
+        )
+
+        if attention_type in ["gated", "gated-text-image"]:
+            positive_len = 768
+            if isinstance(cross_attention_dim, int):
+                positive_len = cross_attention_dim
+            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
+                positive_len = cross_attention_dim[0]
+
+            feature_type = "text-only" if attention_type == "gated" else "text-image"
+            self.position_net = PositionNet(
+                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
+            )
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def enable_freeu(self, s1, s2, b1, b2):
+        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
+
+        The suffixes after the scaling factors represent the stage blocks where they are being applied.
+
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
+        are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+        Args:
+            s1 (`float`):
+                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+                mitigate the "oversmoothing effect" in the enhanced denoising process.
+            s2 (`float`):
+                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+                mitigate the "oversmoothing effect" in the enhanced denoising process.
+            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+        """
+        for i, upsample_block in enumerate(self.up_blocks):
+            setattr(upsample_block, "s1", s1)
+            setattr(upsample_block, "s2", s2)
+            setattr(upsample_block, "b1", b1)
+            setattr(upsample_block, "b2", b2)
+
+    def disable_freeu(self):
+        """Disables the FreeU mechanism."""
+        freeu_keys = {"s1", "s2", "b1", "b2"}
+        for i, upsample_block in enumerate(self.up_blocks):
+            for k in freeu_keys:
+                if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
+                    setattr(upsample_block, k, None)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        r"""
+        The [`UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
+                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
+                through the `self.time_embedding` layer to obtain the timestep embeddings.
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            cross_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).
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
+                A tuple of tensors that if specified are added to the residuals of down unet blocks.
+            mid_block_additional_residual: (`torch.Tensor`, *optional*):
+                A tensor that if specified is added to the residual of the middle unet block.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+            down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+                additional residuals to be added to UNet long skip connections from down blocks to up blocks for
+                example from ControlNet side model(s)
+            mid_block_additional_residual (`torch.Tensor`, *optional*):
+                additional residual to be added to UNet mid block output, for example from ControlNet side model
+            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        for dim in sample.shape[-2:]:
+            if dim % default_overall_up_factor != 0:
+                # Forward upsample size to force interpolation output size.
+                forward_upsample_size = True
+                break
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
+            sample = torch.cat([sample, hint], dim=1)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 2.5 GLIGEN position net
+        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
+            cross_attention_kwargs = cross_attention_kwargs.copy()
+            gligen_args = cross_attention_kwargs.pop("gligen")
+            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
+
+        # 3. down
+        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+
+        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
+        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
+        is_adapter = down_intrablock_additional_residuals is not None
+        # maintain backward compatibility for legacy usage, where
+        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
+        #       but can only use one or the other
+        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
+            deprecate(
+                "T2I should not use down_block_additional_residuals",
+                "1.3.0",
+                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
+                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
+                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
+                standard_warn=False,
+            )
+            down_intrablock_additional_residuals = down_block_additional_residuals
+            is_adapter = True
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+                if is_adapter and len(down_intrablock_additional_residuals) > 0:
+                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)
+                if is_adapter and len(down_intrablock_additional_residuals) > 0:
+                    sample += down_intrablock_additional_residuals.pop(0)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = down_block_res_sample + down_block_additional_residual
+                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
+                sample = self.mid_block(
+                    sample,
+                    emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = self.mid_block(sample, emb)
+
+            # To support T2I-Adapter-XL
+            if (
+                is_adapter
+                and len(down_intrablock_additional_residuals) > 0
+                and sample.shape == down_intrablock_additional_residuals[0].shape
+            ):
+                sample += down_intrablock_additional_residuals.pop(0)
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    upsample_size=upsample_size,
+                    scale=lora_scale,
+                )
+            
+            self.latent_store(sample.detach(), t=timestep, layer_index=i)
+
+        # 6. post-process
+        if self.conv_norm_out:
+            sample = self.conv_norm_out(sample)
+            sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet2DConditionOutput(sample=sample)
diff --git a/modules/consistory/consistory_utils.py b/modules/consistory/consistory_utils.py
new file mode 100644
index 000000000..c9526ecb0
--- /dev/null
+++ b/modules/consistory/consistory_utils.py
@@ -0,0 +1,200 @@
+# Copyright (C) 2024 NVIDIA Corporation.  All rights reserved.
+#
+# This work is licensed under the LICENSE file
+# located at the root directory.
+
+import numpy as np
+import torch
+from collections import defaultdict
+from diffusers.utils.import_utils import is_xformers_available
+from typing import Optional, List
+
+from .utils.general_utils import get_dynamic_threshold
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+class FeatureInjector:
+    def __init__(self, nn_map, nn_distances, attn_masks, inject_range_alpha=[(10,20,0.8)], swap_strategy='min', dist_thr='dynamic', inject_unet_parts=['up']):
+        self.nn_map = nn_map
+        self.nn_distances = nn_distances
+        self.attn_masks = attn_masks
+
+        self.inject_range_alpha = inject_range_alpha if isinstance(inject_range_alpha, list) else [inject_range_alpha]
+        self.swap_strategy = swap_strategy # 'min / 'mean' / 'first'
+        self.dist_thr = dist_thr
+        self.inject_unet_parts = inject_unet_parts
+        self.inject_res = [64]
+
+    def inject_outputs(self, output, curr_iter, output_res, extended_mapping, place_in_unet, anchors_cache=None):
+        curr_unet_part = place_in_unet.split('_')[0]
+
+        # Inject only in the specified unet parts (up, mid, down)
+        if (curr_unet_part not in self.inject_unet_parts) or output_res not in self.inject_res:
+            return output
+
+        bsz = output.shape[0]
+        nn_map = self.nn_map[output_res]
+        nn_distances = self.nn_distances[output_res]
+        attn_masks = self.attn_masks[output_res]
+        vector_dim = output_res**2
+
+        alpha = next((alpha for min_range, max_range, alpha in self.inject_range_alpha if min_range <= curr_iter <= max_range), None)
+        if alpha:
+            old_output = output#.clone()
+            for i in range(bsz):
+                other_outputs = []
+
+                if self.swap_strategy == 'min':
+                    curr_mapping = extended_mapping[i]
+
+                    # If the current image is not mapped to any other image, skip
+                    if not torch.any(torch.cat([curr_mapping[:i], curr_mapping[i+1:]])):
+                        continue
+
+                    min_dists = nn_distances[i][curr_mapping].argmin(dim=0)
+                    curr_nn_map = nn_map[i][curr_mapping][min_dists, torch.arange(vector_dim)]
+
+                    curr_nn_distances = nn_distances[i][curr_mapping][min_dists, torch.arange(vector_dim)]
+                    dist_thr = get_dynamic_threshold(curr_nn_distances) if self.dist_thr == 'dynamic' else self.dist_thr
+                    dist_mask = curr_nn_distances < dist_thr
+                    final_mask_tgt = attn_masks[i] & dist_mask
+
+                    other_outputs = old_output[curr_mapping][min_dists, curr_nn_map][final_mask_tgt]
+
+                    output[i][final_mask_tgt] = alpha * other_outputs + (1 - alpha)*old_output[i][final_mask_tgt]
+
+            if anchors_cache and anchors_cache.is_cache_mode():
+                if place_in_unet not in anchors_cache.h_out_cache:
+                    anchors_cache.h_out_cache[place_in_unet] = {}
+
+                anchors_cache.h_out_cache[place_in_unet][curr_iter] = output
+
+        return output
+
+    def inject_anchors(self, output, curr_iter, output_res, extended_mapping, place_in_unet, anchors_cache):
+        curr_unet_part = place_in_unet.split('_')[0]
+
+        # Inject only in the specified unet parts (up, mid, down)
+        if (curr_unet_part not in self.inject_unet_parts) or output_res not in self.inject_res:
+            return output
+
+        bsz = output.shape[0]
+        nn_map = self.nn_map[output_res]
+        nn_distances = self.nn_distances[output_res]
+        attn_masks = self.attn_masks[output_res]
+        vector_dim = output_res**2
+
+        alpha = next((alpha for min_range, max_range, alpha in self.inject_range_alpha if min_range <= curr_iter <= max_range), None)
+        if alpha:
+
+            anchor_outputs = anchors_cache.h_out_cache[place_in_unet][curr_iter]
+
+            old_output = output#.clone()
+            for i in range(bsz):
+                other_outputs = []
+
+                if self.swap_strategy == 'min':
+                    min_dists = nn_distances[i].argmin(dim=0)
+                    curr_nn_map = nn_map[i][min_dists, torch.arange(vector_dim)]
+
+                    curr_nn_distances = nn_distances[i][min_dists, torch.arange(vector_dim)]
+                    dist_thr = get_dynamic_threshold(curr_nn_distances) if self.dist_thr == 'dynamic' else self.dist_thr
+                    dist_mask = curr_nn_distances < dist_thr
+                    final_mask_tgt = attn_masks[i] & dist_mask
+
+                    other_outputs = anchor_outputs[min_dists, curr_nn_map][final_mask_tgt]
+
+                    output[i][final_mask_tgt] = alpha * other_outputs + (1 - alpha)*old_output[i][final_mask_tgt]
+
+        return output
+
+
+class AnchorCache:
+    def __init__(self):
+        self.input_h_cache = {} # place_in_unet, iter, h_in
+        self.h_out_cache = {} # place_in_unet, iter, h_out
+        self.anchors_last_mask = None
+        self.dift_cache = None
+
+        self.mode = 'cache' # mode can be 'cache' or 'inject'
+
+    def set_mode(self, mode):
+        self.mode = mode
+
+    def set_mode_inject(self):
+        self.mode = 'inject'
+
+    def set_mode_cache(self):
+        self.mode = 'cache'
+
+    def is_inject_mode(self):
+        return self.mode == 'inject'
+
+    def is_cache_mode(self):
+        return self.mode == 'cache'
+
+
+    def to_device(self, device):
+        for key, value in self.input_h_cache.items():
+            self.input_h_cache[key] = {k: v.to(device) for k, v in value.items()}
+
+        for key, value in self.h_out_cache.items():
+            self.h_out_cache[key] = {k: v.to(device) for k, v in value.items()}
+
+        if self.anchors_last_mask:
+            self.anchors_last_mask = {k: v.to(device) for k, v in self.anchors_last_mask.items()}
+
+        if self.dift_cache is not None:
+            self.dift_cache = self.dift_cache.to(device)
+
+
+class QueryStore:
+    def __init__(self, mode='store', t_range=[0, 1000], strength_start=1, strength_end=1):
+        """
+        Initialize an empty ActivationsStore
+        """
+        self.query_store = defaultdict(list)
+        self.mode = mode
+        self.t_range = t_range
+        self.strengthes = np.linspace(strength_start, strength_end, (t_range[1] - t_range[0])+1)
+
+    def set_mode(self, mode): # mode can be 'cache' or 'inject'
+        self.mode = mode
+
+    def cache_query(self, query, place_in_unet: str):
+        self.query_store[place_in_unet] = query
+
+    def inject_query(self, query, place_in_unet, t):
+        if t >= self.t_range[0] and t <= self.t_range[1]:
+            relative_t = t - self.t_range[0]
+            strength = self.strengthes[relative_t]
+            new_query = strength * self.query_store[place_in_unet] + (1 - strength) * query
+        else:
+            new_query = query
+
+        return new_query
+
+class DIFTLatentStore:
+    def __init__(self, steps: List[int], up_ft_indices: List[int]):
+        self.steps = steps
+        self.up_ft_indices = up_ft_indices
+        self.dift_features = {}
+
+    def __call__(self, features: torch.Tensor, t: int, layer_index: int):
+        if t in self.steps and layer_index in self.up_ft_indices:
+            self.dift_features[f'{int(t)}_{layer_index}'] = features
+
+    def copy(self):
+        copy_dift = DIFTLatentStore(self.steps, self.up_ft_indices)
+
+        for key, value in self.dift_features.items():
+            copy_dift.dift_features[key] = value.clone()
+
+        return copy_dift
+
+    def reset(self):
+        self.dift_features = {}
diff --git a/modules/consistory/utils/general_utils.py b/modules/consistory/utils/general_utils.py
new file mode 100644
index 000000000..f328ba906
--- /dev/null
+++ b/modules/consistory/utils/general_utils.py
@@ -0,0 +1,133 @@
+# Copyright (C) 2024 NVIDIA Corporation.  All rights reserved.
+#
+# This work is licensed under the LICENSE file
+# located at the root directory.
+
+from typing import List, Dict
+import torch
+
+import sys 
+sys.path.append(".")
+sys.path.append("..")
+
+import numpy as np
+from typing import List
+from diffusers.utils.torch_utils import randn_tensor
+import torch.nn.functional as F
+from skimage import filters
+
+from tqdm import tqdm
+
+## Attention Utils
+def get_dynamic_threshold(tensor):
+    return filters.threshold_otsu(tensor.float().cpu().numpy())
+
+def attn_map_to_binary(attention_map, scaler=1.):
+    attention_map_np = attention_map.float().cpu().numpy()
+    threshold_value = filters.threshold_otsu(attention_map_np) * scaler
+    binary_mask = (attention_map_np > threshold_value).astype(np.uint8)
+
+    return binary_mask
+
+
+## Features
+
+def gaussian_smooth(input_tensor, kernel_size=3, sigma=1):
+    """
+    Function to apply Gaussian smoothing on each 2D slice of a 3D tensor.
+    """
+
+    kernel = np.fromfunction(
+        lambda x, y: (1/ (2 * np.pi * sigma ** 2)) * 
+                      np.exp(-((x - (kernel_size - 1) / 2) ** 2 + (y - (kernel_size - 1) / 2) ** 2) / (2 * sigma ** 2)),
+        (kernel_size, kernel_size)
+    )
+    kernel = torch.Tensor(kernel / kernel.sum()).to(input_tensor.dtype).to(input_tensor.device)
+    
+    # Add batch and channel dimensions to the kernel
+    kernel = kernel.unsqueeze(0).unsqueeze(0)
+    
+    # Iterate over each 2D slice and apply convolution
+    smoothed_slices = []
+    for i in range(input_tensor.size(0)):
+        slice_tensor = input_tensor[i, :, :]
+        slice_tensor = F.conv2d(slice_tensor.unsqueeze(0).unsqueeze(0), kernel, padding=kernel_size // 2)[0, 0]
+        smoothed_slices.append(slice_tensor)
+    
+    # Stack the smoothed slices to get the final tensor
+    smoothed_tensor = torch.stack(smoothed_slices, dim=0)
+
+    return smoothed_tensor
+
+## Dense correspondence utils
+
+def cos_dist(a, b):
+    a_norm = F.normalize(a, dim=-1)
+    b_norm = F.normalize(b, dim=-1)
+    res = a_norm @ b_norm.T
+
+    return 1 - res
+
+def gen_nn_map(src_features, src_mask,  tgt_features, tgt_mask, device, batch_size=100, tgt_size=768):
+    resized_src_features = F.interpolate(src_features.unsqueeze(0), size=tgt_size, mode='bilinear', align_corners=False).squeeze(0)
+    resized_src_features = resized_src_features.permute(1,2,0).view(tgt_size**2, -1)
+    resized_tgt_features = F.interpolate(tgt_features.unsqueeze(0), size=tgt_size, mode='bilinear', align_corners=False).squeeze(0)
+    resized_tgt_features = resized_tgt_features.permute(1,2,0).view(tgt_size**2, -1)
+
+    nearest_neighbor_indices = torch.zeros(tgt_size**2, dtype=torch.long, device=device)
+    nearest_neighbor_distances = torch.zeros(tgt_size**2, dtype=src_features.dtype, device=device)
+
+    if not batch_size:
+        batch_size = tgt_size**2
+
+    for i in range(0, tgt_size**2, batch_size):
+        distances = cos_dist(resized_src_features, resized_tgt_features[i:i+batch_size])
+        distances[~src_mask] = 2.
+        min_distances, min_indices = torch.min(distances, dim=0)
+        nearest_neighbor_indices[i:i+batch_size] = min_indices
+        nearest_neighbor_distances[i:i+batch_size] = min_distances
+
+    return nearest_neighbor_indices, nearest_neighbor_distances
+
+def cyclic_nn_map(features, masks, latent_resolutions, device):
+    bsz = features.shape[0]
+    nn_map_dict = {}
+    nn_distances_dict = {}
+
+    for tgt_size in latent_resolutions:
+        nn_map = torch.empty(bsz, bsz, tgt_size**2, dtype=torch.long, device=device)
+        nn_distances = torch.full((bsz, bsz, tgt_size**2), float('inf'), dtype=features.dtype, device=device)
+
+        for i in range(bsz):
+            for j in range(bsz):
+                if i != j:
+                    nearest_neighbor_indices, nearest_neighbor_distances = gen_nn_map(features[j], masks[tgt_size][j], features[i], masks[tgt_size][i], device, batch_size=None, tgt_size=tgt_size)
+                    nn_map[i,j] = nearest_neighbor_indices
+                    nn_distances[i,j] = nearest_neighbor_distances
+
+        nn_map_dict[tgt_size] = nn_map
+        nn_distances_dict[tgt_size] = nn_distances
+    
+    return nn_map_dict, nn_distances_dict
+
+def anchor_nn_map(features, anchor_features, masks, anchor_masks, latent_resolutions, device):
+    bsz = features.shape[0]
+    anchor_bsz = anchor_features.shape[0]
+    nn_map_dict = {}
+    nn_distances_dict = {}
+
+    for tgt_size in latent_resolutions:
+        nn_map = torch.empty(bsz, anchor_bsz, tgt_size**2, dtype=torch.long, device=device)
+        nn_distances = torch.full((bsz, anchor_bsz, tgt_size**2), float('inf'), dtype=features.dtype, device=device)
+
+        for i in range(bsz):
+            for j in range(anchor_bsz):
+                nearest_neighbor_indices, nearest_neighbor_distances = gen_nn_map(anchor_features[j], anchor_masks[tgt_size][j], features[i], masks[tgt_size][i], device, batch_size=None, tgt_size=tgt_size)
+                nn_map[i,j] = nearest_neighbor_indices
+                nn_distances[i,j] = nearest_neighbor_distances
+        
+        nn_map_dict[tgt_size] = nn_map
+        nn_distances_dict[tgt_size] = nn_distances
+    
+    return nn_map_dict, nn_distances_dict
+
diff --git a/modules/consistory/utils/ptp_utils.py b/modules/consistory/utils/ptp_utils.py
new file mode 100644
index 000000000..0ab2d8d93
--- /dev/null
+++ b/modules/consistory/utils/ptp_utils.py
@@ -0,0 +1,194 @@
+# Copyright 2022 Google LLC
+#
+# 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.
+
+# MIT License
+# 
+# Copyright (c) 2023 AttendAndExcite
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# Copyright 2022 Google LLC
+#
+# 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.
+
+# Not a contribution
+# Changes made by NVIDIA CORPORATION & AFFILIATES enabling ConsiStory or otherwise documented as NVIDIA-proprietary
+# are not a contribution and subject to the license under the LICENSE file located at the root directory.
+
+import torch
+from collections import defaultdict
+import numpy as np
+from typing import Union, List
+from PIL import Image
+
+from modules.consistory.utils.general_utils import attn_map_to_binary
+import torch.nn.functional as F
+
+
+class AttentionStore:
+    def __init__(self, attention_store_kwargs):
+        """
+        Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion
+        process
+        """
+        self.attn_res = attention_store_kwargs.get('attn_res', (32,32))
+        self.token_indices = attention_store_kwargs['token_indices']
+        bsz = self.token_indices.size(1)
+        self.mask_background_query = attention_store_kwargs.get('mask_background_query', False)
+        self.original_attn_masks = attention_store_kwargs.get('original_attn_masks', None)
+        self.extended_mapping = attention_store_kwargs.get('extended_mapping', torch.ones(bsz, bsz).bool())
+        self.mask_dropout = attention_store_kwargs.get('mask_dropout', 0.0)
+        torch.manual_seed(0) # For dropout mask reproducibility
+
+        self.curr_iter = 0
+        self.ALL_RES = [32, 64]
+        self.step_store = defaultdict(list)
+        self.attn_masks = {res: None for res in self.ALL_RES}
+        self.last_mask = {res: None for res in self.ALL_RES}
+        self.last_mask_dropout = {res: None for res in self.ALL_RES}
+
+    def __call__(self, attn, is_cross: bool, place_in_unet: str, attn_heads: int):
+        if is_cross and attn.shape[1] == np.prod(self.attn_res):
+            guidance_attention = attn[attn.size(0)//2:]
+            batched_guidance_attention = guidance_attention.reshape([guidance_attention.shape[0]//attn_heads, attn_heads, *guidance_attention.shape[1:]])
+            batched_guidance_attention = batched_guidance_attention.mean(dim=1)
+            self.step_store[place_in_unet].append(batched_guidance_attention)
+
+    def reset(self):
+        self.step_store = defaultdict(list)
+        self.attn_masks = {res: None for res in self.ALL_RES}
+        self.last_mask = {res: None for res in self.ALL_RES}
+        self.last_mask_dropout = {res: None for res in self.ALL_RES}
+
+        torch.cuda.empty_cache()
+
+    def aggregate_last_steps_attention(self) -> torch.Tensor:
+        """Aggregates the attention across the different layers and heads at the specified resolution."""
+        attention_maps = torch.cat([torch.stack(x[-20:]) for x in self.step_store.values()]).mean(dim=0)
+        bsz, wh, _ = attention_maps.shape
+
+        # Create attention maps for each concept token, for each batch item
+        agg_attn_maps = []
+        for i in range(bsz):
+            curr_prompt_indices = []
+
+            for concept_token_indices in self.token_indices:
+                if concept_token_indices[i] != -1:
+                    curr_prompt_indices.append(attention_maps[i, :, concept_token_indices[i]].view(*self.attn_res))
+
+            agg_attn_maps.append(torch.stack(curr_prompt_indices))
+
+        # Upsample the attention maps to the target resolution
+        # and create the attention masks, unifying masks across the different concepts
+        for tgt_size in self.ALL_RES:
+            pixels = tgt_size ** 2
+            tgt_agg_attn_maps = [F.interpolate(x.unsqueeze(1), size=tgt_size, mode='bilinear').squeeze(1) for x in agg_attn_maps]
+
+            attn_masks = []
+            for batch_item_map in tgt_agg_attn_maps:
+                concept_attn_masks = []
+
+                for concept_maps in batch_item_map:
+                    concept_attn_masks.append(torch.from_numpy(attn_map_to_binary(concept_maps, 1.)).to(attention_maps.device).bool().view(-1))
+
+                concept_attn_masks = torch.stack(concept_attn_masks, dim=0).max(dim=0).values
+                attn_masks.append(concept_attn_masks)
+
+            attn_masks = torch.stack(attn_masks)
+            self.last_mask[tgt_size] = attn_masks.clone()
+
+            # Add mask dropout
+            if self.curr_iter < 1000:
+                rand_mask = (torch.rand_like(attn_masks.float()) < self.mask_dropout)
+                attn_masks[rand_mask] = False
+
+            self.last_mask_dropout[tgt_size] = attn_masks.clone()
+
+            # # Create subject driven extended self attention masks
+            # output_attn_mask = torch.zeros((bsz, tgt_size**2, attn_masks.view(-1).size(0)), device=attn_masks.device).bool()
+
+            # for i in range(bsz):
+            #     for j in range(bsz):
+            #         if i==j:
+            #             output_attn_mask[i, :, j*pixels:(j+1)*pixels] = 1
+            #         else:
+            #             if self.extended_mapping[i,j]:
+            #                 if not self.mask_background_query:
+            #                     output_attn_mask[i, :, j*pixels:(j+1)*pixels] = attn_masks[j].unsqueeze(0).expand(pixels, -1)
+            #                 else:
+            #                     output_attn_mask[i, attn_masks[i], j*pixels:(j+1)*pixels] = attn_masks[j].unsqueeze(0).expand(attn_masks[i].sum(), -1)
+
+            # self.attn_masks[tgt_size] = output_attn_mask
+
+    def get_attn_mask_bias(self, tgt_size, bsz=None):
+        attn_mask = self.attn_masks[tgt_size] if self.original_attn_masks is None else self.original_attn_masks[tgt_size]
+
+        if attn_mask is None:
+            return None
+
+        attn_bias = torch.zeros_like(attn_mask, dtype=torch.float16)
+        attn_bias[~attn_mask] = float('-inf')
+
+        if bsz and bsz != attn_bias.shape[0]:
+            attn_bias = attn_bias.repeat(bsz // attn_bias.shape[0], 1, 1)
+
+        return attn_bias
+
+    def get_extended_attn_mask_instance(self, width, i):
+        attn_mask = self.last_mask_dropout[width]
+        if attn_mask is None:
+            return None
+        
+        n_patches = width**2
+        
+
+        output_attn_mask = torch.zeros((attn_mask.shape[0] * attn_mask.shape[1],), device=attn_mask.device, dtype=torch.bool)
+        for j in range(attn_mask.shape[0]):
+            if i==j:
+                output_attn_mask[j*n_patches:(j+1)*n_patches] = 1
+            else:
+                if self.extended_mapping[i,j]:
+                    if not self.mask_background_query:
+                        output_attn_mask[j*n_patches:(j+1)*n_patches] = attn_mask[j].unsqueeze(0) #.expand(n_patches, -1)
+                    else:
+                        raise NotImplementedError('mask_background_query is not supported anymore')
+                        output_attn_mask[0, attn_mask[i], k*n_patches:(k+1)*n_patches] = attn_mask[j].unsqueeze(0).expand(attn_mask[i].sum(), -1)
+
+        return output_attn_mask
\ No newline at end of file
diff --git a/scripts/consistory_ext.py b/scripts/consistory_ext.py
new file mode 100644
index 000000000..316528c18
--- /dev/null
+++ b/scripts/consistory_ext.py
@@ -0,0 +1,105 @@
+"""
+original code from <https://github.com/NVlabs/consistory>
+ported to modules/consistory
+- do not load pipeline and unet, use existing model
+- uses diffusers class definitions from 0.25 needed updates
+- forces uses of xformers, converted attention calls to sdp
+- unsafe tensor to numpy breaks with bfloat16
+- removed debug print statements
+"""
+import gradio as gr
+import diffusers
+from modules import scripts, processing, shared, sd_models, devices
+
+
+class Script(scripts.Script):
+    def __init__(self):
+        super().__init__()
+        self.orig_pipe = None
+
+    def title(self):
+        return 'ConsiStory'
+
+    def show(self, is_img2img):
+        return not is_img2img if shared.native and shared.cmd_opts.experimental else False
+
+    def ui(self, _is_img2img): # ui elements
+        with gr.Row():
+            gr.HTML('<a href="https://github.com/NVlabs/consistory">&nbsp ConsiStory: Consistent Image Generation</a><br>')
+        with gr.Row():
+            pass
+        return []
+
+    def run(self, p: processing.StableDiffusionProcessing, *args): # pylint: disable=arguments-differ
+        supported_model_list = ['sdxl']
+        if shared.sd_model_type not in supported_model_list:
+            shared.log.warning(f'ConsiStory: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
+            return None
+        diffusers.models.embeddings.PositionNet = diffusers.models.embeddings.GLIGENTextBoundingboxProjection # patch as renamed in https://github.com/huggingface/diffusers/pull/6244/files
+        import modules.consistory as cs
+        if shared.sd_model_type == "sdxl":
+            self.orig_pipe = shared.sd_model
+            state_dict = shared.sd_model.unet.state_dict()
+            pipe = sd_models.switch_pipe(cs.ConsistoryExtendAttnSDXLPipeline, shared.sd_model)
+            pipe.unet = cs.ConsistorySDXLUNet2DConditionModel.from_config(pipe.unet.config)
+            pipe.unet.load_state_dict(state_dict)
+            pipe.unet.to(device=devices.device, dtype=devices.dtype)
+            # sd_models.set_diffuser_options(pipe)
+            devices.torch_gc(force=True)
+
+        processing.fix_seed(p)
+        subject="digital image of a cute robot"
+        concept_token=['robot']
+        settings=["sitting in the beach", "standing in the snow", "playing on the beach", "dancing in the meadow"]
+        prompts = [f'{subject} {setting}' for setting in settings]
+        anchor_prompts = prompts[:1]
+        extra_prompts = prompts[1:]
+
+        p.steps = 50
+
+        images = []
+        anchor_out_images, anchor_cache_first_stage, anchor_cache_second_stage = cs.run_anchor_generation(
+            story_pipeline=pipe,
+            prompts=anchor_prompts,
+            concept_token=concept_token,
+            seed=p.seed,
+            n_steps=p.steps,
+            mask_dropout=0.5,
+            same_latent=False,
+            share_queries=True,
+            perform_sdsa=True,
+            perform_injection=True,
+        )
+        devices.torch_gc(force=True)
+        for i, image in enumerate(anchor_out_images):
+            image.save(f'/tmp/anchor_image_{i}.png')
+            images.append(image)
+
+        extra_out_images = cs.run_extra_generation(
+            story_pipeline=pipe,
+            prompts=extra_prompts,
+            concept_token=concept_token,
+            anchor_cache_first_stage=anchor_cache_first_stage,
+            anchor_cache_second_stage=anchor_cache_second_stage,
+            seed=p.seed,
+            n_steps=p.steps,
+            mask_dropout=0.5,
+            same_latent=False,
+            share_queries=True,
+            perform_sdsa=True,
+            perform_injection=True,
+        )
+        for j, image in enumerate(extra_out_images):
+            image.save(f'/tmp/extra_image_{j}.png')
+            images.append(image)
+
+        devices.torch_gc(force=True)
+        processed = processing.Processed(p, images_list=images)
+        return processed
+
+
+    def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=arguments-differ, unused-argument
+        if self.orig_pipe is None:
+            return processed
+        shared.sd_model = self.orig_pipe
+        return processed

From f9076a253a9baf283454b2f0b65793f9a45b8766 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 10:47:23 -0500
Subject: [PATCH 026/119] add consistory

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                              | 114 +++++-----
 modules/consistory/attention_processor.py |  10 +-
 modules/consistory/consistory_cache.py    | 217 -------------------
 modules/consistory/consistory_run.py      | 106 ++++------
 modules/consistory/consistory_utils.py    |  12 +-
 modules/consistory/utils/general_utils.py |  34 +--
 scripts/consistory_ext.py                 | 240 ++++++++++++++++------
 7 files changed, 298 insertions(+), 435 deletions(-)
 delete mode 100644 modules/consistory/consistory_cache.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 724960169..2726a1864 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-05
+## Update for 2024-11-06
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -9,55 +9,71 @@ This release can be considered an LTS release before we kick off the next round
   - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to system -> changelog and search/highligh/navigate directly in UI!  
-- [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
-  - advanced method of face transfer with better quality as well as control over identity and appearance  
-    try it out, likely the best quality available for sdxl models  
-  - select in *scripts -> pulid*  
-  - compatible with *sdxl*  
-  - can be used in xyz grid  
-- [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
-  - alternative to traditional `img2img` with more control over restoration process  
-  - select in *image -> scripts -> instantir*  
-  - compatible with *sdxl*  
-  - *note*: after used once it cannot be unloaded without reloading base model  
-- SD3: ControlNets:  
-  - *InstantX Canny, Pose, Depth, Tile*  
-  - *Alimama Inpainting, SoftEdge*  
-  - *note*: that just like with FLUX.1 or any large model, ControlNet are also large and can push your system over the limit  
-    e.g. SD3 controlnets vary from 1GB to over 4GB in size  
-- SD3: all-in-one safetensors  
-  - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
-  - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
-- [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) base and large:
-  - *in process -> visual query*
-  - caption modes:
-    `<GENERATE_TAGS>` generate tags  
-    `<CAPTION>`, `<DETAILED_CAPTION>`, `<MORE_DETAILED_CAPTION>` caption image  
-    `<ANALYZE>` image composition  
-    `<MIXED_CAPTION>`, `<MIXED_CAPTION_PLUS>` detailed caption and tags with optional analyze  
-- XYZ grid:  
-  - optional time benchmark info to individual images  
-  - optional add params to individual images  
-  - create video from generated grid images  
-    supports all standard video types and interpolation  
-- UI:  
-  - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
-  - add show networks on startup setting  
-  - better mapping of networks previews  
-  - optimize networks display load  
-- Installer:
-  - Log `venv` and package search paths
-  - Auto-remove invalid packages from `venv/site-packages`  
-    e.g. packages starting with `~` which are left-over due to windows access violation  
-- CLI:  
-  - refactor command line params  
-    run `webui.sh`/`webui.bat` with `--help` to see all options  
-  - added `cli/model-metadata.py` to display metadata in any safetensors file
-  - added `cli/model-keys.py` to quicky display content of any safetensors file
+
+- Integrations:
+  - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
+    - advanced method of face transfer with better quality as well as control over identity and appearance  
+      try it out, likely the best quality available for sdxl models  
+    - select in *scripts -> pulid*  
+    - compatible with *sdxl*  
+    - can be used in xyz grid  
+  - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
+    - alternative to traditional `img2img` with more control over restoration process  
+    - select in *image -> scripts -> instantir*  
+    - compatible with *sdxl*  
+    - *note*: after used once it cannot be unloaded without reloading base model  
+  - [ConsiStory](https://github.com/NVlabs/consistory): Consistent Image Generation  
+    - create consistent anchor image and then generate images that are consistent with anchor  
+    - select in *scripts -> consistory*  
+    - compatible with *sdxl*
+    - *note*: very resource intensive and not compatible with model offloading  
+    - *note*: changing default parameters can lead to unexpected results and/or failures  
+    - *note*: after used once it cannot be unloaded without reloading base model  
+  - [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) base and large:
+    - *in process -> visual query*
+    - caption modes:
+      `<GENERATE_TAGS>` generate tags  
+      `<CAPTION>`, `<DETAILED_CAPTION>`, `<MORE_DETAILED_CAPTION>` caption image  
+      `<ANALYZE>` image composition  
+      `<MIXED_CAPTION>`, `<MIXED_CAPTION_PLUS>` detailed caption and tags with optional analyze  
+
+- Model improvements:
+  - SD3: ControlNets:  
+    - *InstantX Canny, Pose, Depth, Tile*  
+    - *Alimama Inpainting, SoftEdge*  
+    - *note*: that just like with FLUX.1 or any large model, ControlNet are also large and can push your system over the limit  
+      e.g. SD3 controlnets vary from 1GB to over 4GB in size  
+  - SD3: all-in-one safetensors  
+    - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
+    - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
+
+- Workflow improvements:
+  - XYZ grid:  
+    - optional time benchmark info to individual images  
+    - optional add params to individual images  
+    - create video from generated grid images  
+      supports all standard video types and interpolation  
+  - UI:  
+    - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
+    - add show networks on startup setting  
+    - better mapping of networks previews  
+    - optimize networks display load  
 - Other:  
-  - Model loader: Report modules included in safetensors when attempting to load a model  
-  - Repo: move screenshots to GH pages  
-  - Requirements: update  
+  - Installer:
+    - Log `venv` and package search paths
+    - Auto-remove invalid packages from `venv/site-packages`  
+      e.g. packages starting with `~` which are left-over due to windows access violation  
+    - Requirements: update  
+  - Model loader:
+    - Report modules included in safetensors when attempting to load a model  
+  - CLI:  
+    - refactor command line params  
+      run `webui.sh`/`webui.bat` with `--help` to see all options  
+    - added `cli/model-metadata.py` to display metadata in any safetensors file
+    - added `cli/model-keys.py` to quicky display content of any safetensors file
+  - Internal:
+    - Repo: move screenshots to GH pages  
+
 - Fixes:  
   - custom watermark add alphablending  
   - detailer min/max size as fractions of image size  
diff --git a/modules/consistory/attention_processor.py b/modules/consistory/attention_processor.py
index d1fd10cc3..04985ee4f 100644
--- a/modules/consistory/attention_processor.py
+++ b/modules/consistory/attention_processor.py
@@ -17,12 +17,11 @@
 # are not a contribution and subject to the license under the LICENSE file located at the root directory.
 
 
-from diffusers.utils import USE_PEFT_BACKEND
 from typing import Callable, Optional
 import torch
 import torch.nn.functional as F
+from diffusers.utils import USE_PEFT_BACKEND
 from diffusers.models.attention_processor import Attention
-
 from .consistory_utils import AnchorCache, FeatureInjector, QueryStore
 
 
@@ -235,7 +234,7 @@ class ConsistoryExtendedAttnXFormersAttnProcessor:
         # dropout
         hidden_states = attn.to_out[1](hidden_states)
 
-        if (feature_injector is not None):
+        if feature_injector is not None:
             output_res = int(hidden_states.shape[1] ** 0.5)
 
             if anchors_cache and anchors_cache.is_inject_mode():
@@ -270,8 +269,7 @@ def register_extended_self_attn(unet, attnstore, extended_attn_kwargs):
                          'up_113': 64, 'up_115': 64, 'up_117': 64, 'up_119': 64}
     attn_procs = {}
     for i, name in enumerate(unet.attn_processors.keys()):
-        is_self_attn = (i % 2 == 0)
-
+        is_self_attn = i % 2 == 0
         if name.startswith("mid_block"):
             place_in_unet = f"mid_{i}"
         elif name.startswith("up_blocks"):
@@ -286,4 +284,4 @@ def register_extended_self_attn(unet, attnstore, extended_attn_kwargs):
         else:
             attn_procs[name] = ConsistoryAttnStoreProcessor(attnstore, place_in_unet)
 
-    unet.set_attn_processor(attn_procs)
\ No newline at end of file
+    unet.set_attn_processor(attn_procs)
diff --git a/modules/consistory/consistory_cache.py b/modules/consistory/consistory_cache.py
deleted file mode 100644
index 66312e4e1..000000000
--- a/modules/consistory/consistory_cache.py
+++ /dev/null
@@ -1,217 +0,0 @@
-# Copyright (C) 2024 NVIDIA Corporation.  All rights reserved.
-#
-# This work is licensed under the LICENSE file
-# located at the root directory.
-
-import torch
-from diffusers import DDIMScheduler
-from .consistory_unet_sdxl import ConsistorySDXLUNet2DConditionModel
-from .consistory_pipeline import ConsistoryExtendAttnSDXLPipeline
-from .consistory_utils import FeatureInjector, AnchorCache
-from .utils.general_utils import *
-
-
-def load_pipeline(gpu_id=0):
-    float_type = torch.float16
-    sd_id = "stabilityai/stable-diffusion-xl-base-1.0"
-
-    device = torch.device(f'cuda:{gpu_id}') if torch.cuda.is_available() else torch.device('cpu')
-    unet = ConsistorySDXLUNet2DConditionModel.from_pretrained(sd_id, subfolder="unet", torch_dtype=float_type)
-    scheduler = DDIMScheduler.from_pretrained(sd_id, subfolder="scheduler")
-
-    story_pipeline = ConsistoryExtendAttnSDXLPipeline.from_pretrained(
-        sd_id, unet=unet, torch_dtype=float_type, variant="fp16", use_safetensors=True, scheduler=scheduler
-    ).to(device)
-    story_pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
-
-    return story_pipeline
-
-def create_anchor_mapping(bsz, anchor_indices=[0]):
-    anchor_mapping = torch.eye(bsz, dtype=torch.bool)
-    for anchor_idx in anchor_indices:
-        anchor_mapping[:, anchor_idx] = True
-
-    return anchor_mapping
-
-def create_token_indices(prompts, batch_size, concept_token, tokenizer):
-    if isinstance(concept_token, str):
-        concept_token = [concept_token]
-
-    concept_token_id = [tokenizer.encode(x, add_special_tokens=False)[0] for x in concept_token]
-    tokens = tokenizer.batch_encode_plus(prompts, padding=True, return_tensors='pt')['input_ids']
-
-    token_indices = torch.full((len(concept_token), batch_size), -1, dtype=torch.int64)
-    for i, token_id in enumerate(concept_token_id):
-        batch_loc, token_loc = torch.where(tokens == token_id)
-        token_indices[i, batch_loc] = token_loc
-
-    return token_indices
-
-def create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type):
-    # if seed is int
-    if isinstance(seed, int):
-        g = torch.Generator('cuda').manual_seed(seed)
-        shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
-        latents = randn_tensor(shape, generator=g, device=device, dtype=float_type)
-    elif isinstance(seed, list):
-        shape = (batch_size, story_pipeline.unet.config.in_channels, 128, 128)
-        latents = torch.empty(shape, device=device, dtype=float_type)
-        for i, seed_i in enumerate(seed):
-            g = torch.Generator('cuda').manual_seed(seed_i)
-            curr_latent = randn_tensor(shape, generator=g, device=device, dtype=float_type)
-            latents[i] = curr_latent[i]
-
-    if same_latent:
-        latents = latents[:1].repeat(batch_size, 1, 1, 1)
-
-    return latents, g
-
-def run_anchor_generation(story_pipeline, prompts, concept_token,
-                   seed=40, n_steps=50, mask_dropout=0.5,
-                   same_latent=False, share_queries=True,
-                   perform_sdsa=True, perform_injection=True):
-    latent_resolutions = [32, 64]
-
-    device = story_pipeline.device
-    tokenizer = story_pipeline.tokenizer
-    float_type = story_pipeline.dtype
-    unet = story_pipeline.unet
-
-    batch_size = len(prompts)
-
-    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
-
-    default_attention_store_kwargs = {
-        'token_indices': token_indices,
-        'mask_dropout': mask_dropout
-    }
-
-    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
-    query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
-
-    latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
-
-    anchor_cache_first_stage = AnchorCache()
-    anchor_cache_second_stage = AnchorCache()
-
-    # ------------------ #
-    # Extended attention First Run #
-
-    if perform_sdsa:
-        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
-    else:
-        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
-
-    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
-                        attention_store_kwargs=default_attention_store_kwargs,
-                        extended_attn_kwargs=extended_attn_kwargs,
-                        share_queries=share_queries,
-                        query_store_kwargs=query_store_kwargs,
-                        anchors_cache=anchor_cache_first_stage,
-                        num_inference_steps=n_steps)
-    last_masks = story_pipeline.attention_store.last_mask
-
-    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
-    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
-
-    anchor_cache_first_stage.dift_cache = dift_features
-    anchor_cache_first_stage.anchors_last_mask = last_masks
-
-    nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, latent_resolutions, device)
-
-    # ------------------ #
-    # Extended attention with nn_map #
-
-    if perform_injection:
-        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
-                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
-
-        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
-                            attention_store_kwargs=default_attention_store_kwargs,
-                            extended_attn_kwargs=extended_attn_kwargs,
-                            share_queries=share_queries,
-                            query_store_kwargs=query_store_kwargs,
-                            feature_injector=feature_injector,
-                            anchors_cache=anchor_cache_second_stage,
-                            num_inference_steps=n_steps)
-        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
-        anchor_cache_second_stage.dift_cache = dift_features
-        anchor_cache_second_stage.anchors_last_mask = last_masks
-
-    return out.images, anchor_cache_first_stage, anchor_cache_second_stage
-
-def run_extra_generation(story_pipeline, prompts, concept_token, 
-                         anchor_cache_first_stage, anchor_cache_second_stage,
-                         seed=40, n_steps=50, mask_dropout=0.5,
-                         same_latent=False, share_queries=True,
-                         perform_sdsa=True, perform_injection=True):
-    latent_resolutions = [32, 64]
-
-    device = story_pipeline.device
-    tokenizer = story_pipeline.tokenizer
-    float_type = story_pipeline.dtype
-    unet = story_pipeline.unet
-
-    batch_size = len(prompts)
-
-    token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
-
-    default_attention_store_kwargs = {
-        'token_indices': token_indices,
-        'mask_dropout': mask_dropout
-    }
-
-    default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
-    query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
-
-    extra_batch_size = batch_size + 2
-    if isinstance(seed, list):
-        seed = [seed[0], seed[0], *seed]
-
-    latents, g = create_latents(story_pipeline, seed, extra_batch_size, same_latent, device, float_type)
-    latents = latents[2:]
-
-    anchor_cache_first_stage.set_mode_inject()
-    anchor_cache_second_stage.set_mode_inject()
-
-    # ------------------ #
-    # Extended attention First Run #
-
-    if perform_sdsa:
-        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
-    else:
-        extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
-
-    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
-                        attention_store_kwargs=default_attention_store_kwargs,
-                        extended_attn_kwargs=extended_attn_kwargs,
-                        share_queries=share_queries,
-                        query_store_kwargs=query_store_kwargs,
-                        anchors_cache=anchor_cache_first_stage,
-                        num_inference_steps=n_steps)
-    last_masks = story_pipeline.attention_store.last_mask
-
-    dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
-    dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
-
-    anchor_dift_features = anchor_cache_first_stage.dift_cache
-    anchor_last_masks = anchor_cache_first_stage.anchors_last_mask
-
-    nn_map, nn_distances = anchor_nn_map(dift_features, anchor_dift_features, last_masks, anchor_last_masks, latent_resolutions, device)
-
-    # ------------------ #
-    # Extended attention with nn_map #
-    if perform_injection:
-        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
-                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
-
-        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
-                            attention_store_kwargs=default_attention_store_kwargs,
-                            extended_attn_kwargs=extended_attn_kwargs,
-                            share_queries=share_queries,
-                            query_store_kwargs=query_store_kwargs,
-                            feature_injector=feature_injector,
-                            anchors_cache=anchor_cache_second_stage,
-                            num_inference_steps=n_steps)
-        # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
-    return out.images
\ No newline at end of file
diff --git a/modules/consistory/consistory_run.py b/modules/consistory/consistory_run.py
index d7bcdd080..c51bf08b9 100644
--- a/modules/consistory/consistory_run.py
+++ b/modules/consistory/consistory_run.py
@@ -5,50 +5,47 @@
 
 import torch
 from diffusers import DDIMScheduler
+from diffusers.utils.torch_utils import randn_tensor
 from .consistory_unet_sdxl import ConsistorySDXLUNet2DConditionModel
 from .consistory_pipeline import ConsistoryExtendAttnSDXLPipeline
 from .consistory_utils import FeatureInjector, AnchorCache
-from .utils.general_utils import *
+# from .utils.general_utils import *
+from .utils.general_utils import gaussian_smooth, cyclic_nn_map, anchor_nn_map
 
 
 LATENT_RESOLUTIONS = [32, 64]
 
+
 def load_pipeline(gpu_id=0):
     float_type = torch.float16
     sd_id = "stabilityai/stable-diffusion-xl-base-1.0"
-
     device = torch.device(f'cuda:{gpu_id}') if torch.cuda.is_available() else torch.device('cpu')
     unet = ConsistorySDXLUNet2DConditionModel.from_pretrained(sd_id, subfolder="unet", torch_dtype=float_type)
     scheduler = DDIMScheduler.from_pretrained(sd_id, subfolder="scheduler")
-
-    story_pipeline = ConsistoryExtendAttnSDXLPipeline.from_pretrained(
-        sd_id, unet=unet, torch_dtype=float_type, variant="fp16", use_safetensors=True, scheduler=scheduler
-    ).to(device)
+    story_pipeline = ConsistoryExtendAttnSDXLPipeline.from_pretrained(sd_id, unet=unet, torch_dtype=float_type, variant="fp16", use_safetensors=True, scheduler=scheduler).to(device)
     story_pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
-
     return story_pipeline
 
+
 def create_anchor_mapping(bsz, anchor_indices=[0]):
     anchor_mapping = torch.eye(bsz, dtype=torch.bool)
     for anchor_idx in anchor_indices:
         anchor_mapping[:, anchor_idx] = True
-
     return anchor_mapping
 
+
 def create_token_indices(prompts, batch_size, concept_token, tokenizer):
     if isinstance(concept_token, str):
         concept_token = [concept_token]
-
     concept_token_id = [tokenizer.encode(x, add_special_tokens=False)[0] for x in concept_token]
     tokens = tokenizer.batch_encode_plus(prompts, padding=True, return_tensors='pt')['input_ids']
-
     token_indices = torch.full((len(concept_token), batch_size), -1, dtype=torch.int64)
     for i, token_id in enumerate(concept_token_id):
         batch_loc, token_loc = torch.where(tokens == token_id)
         token_indices[i, batch_loc] = token_loc
-
     return token_indices
 
+
 def create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type):
     # if seed is int
     if isinstance(seed, int):
@@ -62,68 +59,61 @@ def create_latents(story_pipeline, seed, batch_size, same_latent, device, float_
             g = torch.Generator('cuda').manual_seed(seed_i)
             curr_latent = randn_tensor(shape, generator=g, device=device, dtype=float_type)
             latents[i] = curr_latent[i]
-
     if same_latent:
         latents = latents[:1].repeat(batch_size, 1, 1, 1)
-
     return latents, g
 
+
 # Batch inference
 def run_batch_generation(story_pipeline, prompts, concept_token,
                         seed=40, n_steps=50, mask_dropout=0.5,
                         same_latent=False, share_queries=True,
                         perform_sdsa=True, perform_injection=True,
+                        inject_range_alpha=(10,20,0.8),
                         n_achors=2):
     device = story_pipeline.device
     tokenizer = story_pipeline.tokenizer
     float_type = story_pipeline.dtype
     unet = story_pipeline.unet
-
     batch_size = len(prompts)
-
     token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
     anchor_mappings = create_anchor_mapping(batch_size, anchor_indices=list(range(n_achors)))
-
     default_attention_store_kwargs = {
         'token_indices': token_indices,
         'mask_dropout': mask_dropout,
         'extended_mapping': anchor_mappings
     }
-
     default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
     query_store_kwargs= {'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
-
     latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
 
     # ------------------ #
     # Extended attention First Run #
-
     if perform_sdsa:
         extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
     else:
         extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
-
-    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents,
                         attention_store_kwargs=default_attention_store_kwargs,
                         extended_attn_kwargs=extended_attn_kwargs,
                         share_queries=share_queries,
                         query_store_kwargs=query_store_kwargs,
                         num_inference_steps=n_steps)
     last_masks = story_pipeline.attention_store.last_mask
-
     dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
     dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
-
     nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, LATENT_RESOLUTIONS, device)
 
     # ------------------ #
     # Extended attention with nn_map #
-
     if perform_injection:
-        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
-                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
-
-        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+        feature_injector = FeatureInjector(
+            nn_map,
+            nn_distances,
+            last_masks,
+            inject_range_alpha=[inject_range_alpha],
+            swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents,
                             attention_store_kwargs=default_attention_store_kwargs,
                             extended_attn_kwargs=extended_attn_kwargs,
                             share_queries=share_queries,
@@ -133,42 +123,36 @@ def run_batch_generation(story_pipeline, prompts, concept_token,
         # display_attn_maps(story_pipeline.attention_store.last_mask, out.images)
     return out.images
 
+
 # Anchors
 def run_anchor_generation(story_pipeline, prompts, concept_token,
                         seed=40, n_steps=50, mask_dropout=0.5,
+                        inject_range_alpha=(10,20,0.8),
                         same_latent=False, share_queries=True,
                         perform_sdsa=True, perform_injection=True):
     device = story_pipeline.device
     tokenizer = story_pipeline.tokenizer
     float_type = story_pipeline.dtype
     unet = story_pipeline.unet
-
     batch_size = len(prompts)
-
     token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
-
     default_attention_store_kwargs = {
         'token_indices': token_indices,
         'mask_dropout': mask_dropout
     }
-
     default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
     query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
-
     latents, g = create_latents(story_pipeline, seed, batch_size, same_latent, device, float_type)
-
     anchor_cache_first_stage = AnchorCache()
     anchor_cache_second_stage = AnchorCache()
 
     # ------------------ #
     # Extended attention First Run #
-
     if perform_sdsa:
         extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
     else:
         extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
-
-    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents,
                         attention_store_kwargs=default_attention_store_kwargs,
                         extended_attn_kwargs=extended_attn_kwargs,
                         share_queries=share_queries,
@@ -176,23 +160,24 @@ def run_anchor_generation(story_pipeline, prompts, concept_token,
                         anchors_cache=anchor_cache_first_stage,
                         num_inference_steps=n_steps)
     last_masks = story_pipeline.attention_store.last_mask
-
     dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
     dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
-
     anchor_cache_first_stage.dift_cache = dift_features
     anchor_cache_first_stage.anchors_last_mask = last_masks
-
     nn_map, nn_distances = cyclic_nn_map(dift_features, last_masks, LATENT_RESOLUTIONS, device)
 
     # ------------------ #
     # Extended attention with nn_map #
-
     if perform_injection:
-        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
-                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
-
-        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+        feature_injector = FeatureInjector(
+            nn_map,
+            nn_distances,
+            last_masks,
+            inject_range_alpha=[inject_range_alpha],
+            swap_strategy='min',
+            inject_unet_parts=['up', 'down'],
+            dist_thr='dynamic')
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents,
                             attention_store_kwargs=default_attention_store_kwargs,
                             extended_attn_kwargs=extended_attn_kwargs,
                             share_queries=share_queries,
@@ -205,47 +190,40 @@ def run_anchor_generation(story_pipeline, prompts, concept_token,
         anchor_cache_second_stage.anchors_last_mask = last_masks
     return out.images, anchor_cache_first_stage, anchor_cache_second_stage
 
-def run_extra_generation(story_pipeline, prompts, concept_token, 
+
+def run_extra_generation(story_pipeline, prompts, concept_token,
                          anchor_cache_first_stage, anchor_cache_second_stage,
                          seed=40, n_steps=50, mask_dropout=0.5,
+                         inject_range_alpha=(10,20,0.8),
                          same_latent=False, share_queries=True,
                          perform_sdsa=True, perform_injection=True):
     device = story_pipeline.device
     tokenizer = story_pipeline.tokenizer
     float_type = story_pipeline.dtype
     unet = story_pipeline.unet
-
     batch_size = len(prompts)
-
     token_indices = create_token_indices(prompts, batch_size, concept_token, tokenizer)
-
     default_attention_store_kwargs = {
         'token_indices': token_indices,
         'mask_dropout': mask_dropout
     }
-
     default_extended_attn_kwargs = {'extend_kv_unet_parts': ['up']}
     query_store_kwargs={'t_range': [0,n_steps//10], 'strength_start': 0.9, 'strength_end': 0.81836735}
-
     extra_batch_size = batch_size + 2
     if isinstance(seed, list):
         seed = [seed[0], seed[0], *seed]
-
     latents, g = create_latents(story_pipeline, seed, extra_batch_size, same_latent, device, float_type)
     latents = latents[2:]
-
     anchor_cache_first_stage.set_mode_inject()
     anchor_cache_second_stage.set_mode_inject()
 
     # ------------------ #
     # Extended attention First Run #
-
     if perform_sdsa:
         extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': [(1, n_steps)]}
     else:
         extended_attn_kwargs = {**default_extended_attn_kwargs, 't_range': []}
-
-    out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+    out = story_pipeline(prompt=prompts, generator=g, latents=latents,
                         attention_store_kwargs=default_attention_store_kwargs,
                         extended_attn_kwargs=extended_attn_kwargs,
                         share_queries=share_queries,
@@ -253,22 +231,24 @@ def run_extra_generation(story_pipeline, prompts, concept_token,
                         anchors_cache=anchor_cache_first_stage,
                         num_inference_steps=n_steps)
     last_masks = story_pipeline.attention_store.last_mask
-
     dift_features = unet.latent_store.dift_features['261_0'][batch_size:]
     dift_features = torch.stack([gaussian_smooth(x, kernel_size=3, sigma=1) for x in dift_features], dim=0)
-
     anchor_dift_features = anchor_cache_first_stage.dift_cache
     anchor_last_masks = anchor_cache_first_stage.anchors_last_mask
-
     nn_map, nn_distances = anchor_nn_map(dift_features, anchor_dift_features, last_masks, anchor_last_masks, LATENT_RESOLUTIONS, device)
 
     # ------------------ #
     # Extended attention with nn_map #
     if perform_injection:
-        feature_injector = FeatureInjector(nn_map, nn_distances, last_masks, inject_range_alpha=[(n_steps//10, n_steps//3,0.8)], 
-                                        swap_strategy='min', inject_unet_parts=['up', 'down'], dist_thr='dynamic')
-
-        out = story_pipeline(prompt=prompts, generator=g, latents=latents, 
+        feature_injector = FeatureInjector(
+            nn_map,
+            nn_distances,
+            last_masks,
+            inject_range_alpha=[inject_range_alpha],
+            swap_strategy='min',
+            inject_unet_parts=['up', 'down'],
+            dist_thr='dynamic')
+        out = story_pipeline(prompt=prompts, generator=g, latents=latents,
                             attention_store_kwargs=default_attention_store_kwargs,
                             extended_attn_kwargs=extended_attn_kwargs,
                             share_queries=share_queries,
diff --git a/modules/consistory/consistory_utils.py b/modules/consistory/consistory_utils.py
index c9526ecb0..4255ac3ed 100644
--- a/modules/consistory/consistory_utils.py
+++ b/modules/consistory/consistory_utils.py
@@ -3,26 +3,18 @@
 # This work is licensed under the LICENSE file
 # located at the root directory.
 
+from typing import List
+from collections import defaultdict
 import numpy as np
 import torch
-from collections import defaultdict
-from diffusers.utils.import_utils import is_xformers_available
-from typing import Optional, List
-
 from .utils.general_utils import get_dynamic_threshold
 
-if is_xformers_available():
-    import xformers
-    import xformers.ops
-else:
-    xformers = None
 
 class FeatureInjector:
     def __init__(self, nn_map, nn_distances, attn_masks, inject_range_alpha=[(10,20,0.8)], swap_strategy='min', dist_thr='dynamic', inject_unet_parts=['up']):
         self.nn_map = nn_map
         self.nn_distances = nn_distances
         self.attn_masks = attn_masks
-
         self.inject_range_alpha = inject_range_alpha if isinstance(inject_range_alpha, list) else [inject_range_alpha]
         self.swap_strategy = swap_strategy # 'min / 'mean' / 'first'
         self.dist_thr = dist_thr
diff --git a/modules/consistory/utils/general_utils.py b/modules/consistory/utils/general_utils.py
index f328ba906..4493fa96e 100644
--- a/modules/consistory/utils/general_utils.py
+++ b/modules/consistory/utils/general_utils.py
@@ -3,25 +3,17 @@
 # This work is licensed under the LICENSE file
 # located at the root directory.
 
-from typing import List, Dict
 import torch
-
-import sys 
-sys.path.append(".")
-sys.path.append("..")
-
-import numpy as np
-from typing import List
-from diffusers.utils.torch_utils import randn_tensor
 import torch.nn.functional as F
+import numpy as np
 from skimage import filters
 
-from tqdm import tqdm
 
 ## Attention Utils
 def get_dynamic_threshold(tensor):
     return filters.threshold_otsu(tensor.float().cpu().numpy())
 
+
 def attn_map_to_binary(attention_map, scaler=1.):
     attention_map_np = attention_map.float().cpu().numpy()
     threshold_value = filters.threshold_otsu(attention_map_np) * scaler
@@ -36,59 +28,52 @@ def gaussian_smooth(input_tensor, kernel_size=3, sigma=1):
     """
     Function to apply Gaussian smoothing on each 2D slice of a 3D tensor.
     """
-
     kernel = np.fromfunction(
-        lambda x, y: (1/ (2 * np.pi * sigma ** 2)) * 
+        lambda x, y: (1/ (2 * np.pi * sigma ** 2)) *
                       np.exp(-((x - (kernel_size - 1) / 2) ** 2 + (y - (kernel_size - 1) / 2) ** 2) / (2 * sigma ** 2)),
         (kernel_size, kernel_size)
     )
     kernel = torch.Tensor(kernel / kernel.sum()).to(input_tensor.dtype).to(input_tensor.device)
-    
     # Add batch and channel dimensions to the kernel
     kernel = kernel.unsqueeze(0).unsqueeze(0)
-    
     # Iterate over each 2D slice and apply convolution
     smoothed_slices = []
     for i in range(input_tensor.size(0)):
         slice_tensor = input_tensor[i, :, :]
         slice_tensor = F.conv2d(slice_tensor.unsqueeze(0).unsqueeze(0), kernel, padding=kernel_size // 2)[0, 0]
         smoothed_slices.append(slice_tensor)
-    
     # Stack the smoothed slices to get the final tensor
     smoothed_tensor = torch.stack(smoothed_slices, dim=0)
-
     return smoothed_tensor
 
+
 ## Dense correspondence utils
 
 def cos_dist(a, b):
     a_norm = F.normalize(a, dim=-1)
     b_norm = F.normalize(b, dim=-1)
     res = a_norm @ b_norm.T
-
     return 1 - res
 
+
 def gen_nn_map(src_features, src_mask,  tgt_features, tgt_mask, device, batch_size=100, tgt_size=768):
     resized_src_features = F.interpolate(src_features.unsqueeze(0), size=tgt_size, mode='bilinear', align_corners=False).squeeze(0)
     resized_src_features = resized_src_features.permute(1,2,0).view(tgt_size**2, -1)
     resized_tgt_features = F.interpolate(tgt_features.unsqueeze(0), size=tgt_size, mode='bilinear', align_corners=False).squeeze(0)
     resized_tgt_features = resized_tgt_features.permute(1,2,0).view(tgt_size**2, -1)
-
     nearest_neighbor_indices = torch.zeros(tgt_size**2, dtype=torch.long, device=device)
     nearest_neighbor_distances = torch.zeros(tgt_size**2, dtype=src_features.dtype, device=device)
-
     if not batch_size:
         batch_size = tgt_size**2
-
     for i in range(0, tgt_size**2, batch_size):
         distances = cos_dist(resized_src_features, resized_tgt_features[i:i+batch_size])
         distances[~src_mask] = 2.
         min_distances, min_indices = torch.min(distances, dim=0)
         nearest_neighbor_indices[i:i+batch_size] = min_indices
         nearest_neighbor_distances[i:i+batch_size] = min_distances
-
     return nearest_neighbor_indices, nearest_neighbor_distances
 
+
 def cyclic_nn_map(features, masks, latent_resolutions, device):
     bsz = features.shape[0]
     nn_map_dict = {}
@@ -107,9 +92,10 @@ def cyclic_nn_map(features, masks, latent_resolutions, device):
 
         nn_map_dict[tgt_size] = nn_map
         nn_distances_dict[tgt_size] = nn_distances
-    
+
     return nn_map_dict, nn_distances_dict
 
+
 def anchor_nn_map(features, anchor_features, masks, anchor_masks, latent_resolutions, device):
     bsz = features.shape[0]
     anchor_bsz = anchor_features.shape[0]
@@ -125,9 +111,7 @@ def anchor_nn_map(features, anchor_features, masks, anchor_masks, latent_resolut
                 nearest_neighbor_indices, nearest_neighbor_distances = gen_nn_map(anchor_features[j], anchor_masks[tgt_size][j], features[i], masks[tgt_size][i], device, batch_size=None, tgt_size=tgt_size)
                 nn_map[i,j] = nearest_neighbor_indices
                 nn_distances[i,j] = nearest_neighbor_distances
-        
         nn_map_dict[tgt_size] = nn_map
         nn_distances_dict[tgt_size] = nn_distances
-    
-    return nn_map_dict, nn_distances_dict
 
+    return nn_map_dict, nn_distances_dict
diff --git a/scripts/consistory_ext.py b/scripts/consistory_ext.py
index 316528c18..b7454aa75 100644
--- a/scripts/consistory_ext.py
+++ b/scripts/consistory_ext.py
@@ -1,21 +1,25 @@
 """
 original code from <https://github.com/NVlabs/consistory>
 ported to modules/consistory
-- do not load pipeline and unet, use existing model
-- uses diffusers class definitions from 0.25 needed updates
+- make it non-cuda exclusive
+- separate create anchors and create extra
+- do not force-load pipeline and unet, use existing model
+- uses diffusers==0.25 class definitions, needed quite an update
 - forces uses of xformers, converted attention calls to sdp
 - unsafe tensor to numpy breaks with bfloat16
 - removed debug print statements
 """
+import time
 import gradio as gr
 import diffusers
-from modules import scripts, processing, shared, sd_models, devices
+from modules import scripts, devices, errors, processing, shared, sd_models, sd_samplers
 
 
 class Script(scripts.Script):
     def __init__(self):
         super().__init__()
-        self.orig_pipe = None
+        self.anchor_cache_first_stage = None
+        self.anchor_cache_second_stage = None
 
     def title(self):
         return 'ConsiStory'
@@ -23,83 +27,189 @@ class Script(scripts.Script):
     def show(self, is_img2img):
         return not is_img2img if shared.native and shared.cmd_opts.experimental else False
 
+    def reset(self):
+        self.anchor_cache_first_stage = None
+        self.anchor_cache_second_stage = None
+        shared.log.debug('ConsiStory reset anchors')
+
     def ui(self, _is_img2img): # ui elements
         with gr.Row():
             gr.HTML('<a href="https://github.com/NVlabs/consistory">&nbsp ConsiStory: Consistent Image Generation</a><br>')
         with gr.Row():
-            pass
-        return []
+            gr.HTML('<br> ▪ Anchors are created on first run<br> ▪ Subsequent generate will use anchors and apply to main prompt<br> ▪ Main prompts are separated by newlines')
+        with gr.Row():
+            subject = gr.Textbox(label="Subject", placeholder='short description of a subject', value='')
+        with gr.Row():
+            concepts = gr.Textbox(label="Concept Tokens", placeholder='one or more concepts to extract from subject', value='')
+        with gr.Row():
+            prompts = gr.Textbox(label="Anchor settings", lines=2, placeholder='two scene settings to place subject in', value='')
+        with gr.Row():
+            reset = gr.Button(value="Reset anchors", variant='primary')
+            reset.click(fn=self.reset, inputs=[], outputs=[])
+        with gr.Row():
+            dropout = gr.Slider(label="Mask Dropout", minimum=0.0, maximum=1.0, step=0.1, value=0.5)
+        with gr.Row():
+            sampler = gr.Checkbox(label="Override sampler", value=True)
+            steps = gr.Checkbox(label="Override steps", value=True)
+        with gr.Row():
+            same = gr.Checkbox(label="Same latent", value=False)
+            queries = gr.Checkbox(label="Share queries", value=True)
+        with gr.Row():
+            sdsa = gr.Checkbox(label="Perform SDSA", value=True)
+        with gr.Row():
+            freeu = gr.Checkbox(label="Enable FreeU", value=False)
+            freeu_preset = gr.Textbox(label="FreeU preset", value='0.6, 0.4, 1.1, 1.2')
+        with gr.Row():
+            injection = gr.Checkbox(label="Perform Injection", value=False)
+            alpha = gr.Textbox(label="Alpha preset", value='10, 20, 0.8')
+        return [subject, concepts, prompts, dropout, sampler, steps, same, queries, sdsa, freeu, freeu_preset, alpha, injection]
+
+    def create_model(self):
+        diffusers.models.embeddings.PositionNet = diffusers.models.embeddings.GLIGENTextBoundingboxProjection # patch as renamed in https://github.com/huggingface/diffusers/pull/6244/files
+        import modules.consistory as cs
+        if shared.sd_model.__class__.__name__ != 'ConsistoryExtendAttnSDXLPipeline':
+            shared.log.debug('ConsiStory init')
+            t0 = time.time()
+            state_dict = shared.sd_model.unet.state_dict() # save existing unet
+            shared.sd_model = sd_models.switch_pipe(cs.ConsistoryExtendAttnSDXLPipeline, shared.sd_model)
+            shared.sd_model.unet = cs.ConsistorySDXLUNet2DConditionModel.from_config(shared.sd_model.unet.config)
+            shared.sd_model.unet.load_state_dict(state_dict) # now load it into new class
+            shared.sd_model.unet.to(dtype=devices.dtype)
+            state_dict = None
+            # sd_models.set_diffuser_options(shared.sd_model)
+            sd_models.move_model(shared.sd_model, devices.device)
+            sd_models.move_model(shared.sd_model.unet, devices.device)
+            t1 = time.time()
+            shared.log.debug(f'ConsiStory load: model={shared.sd_model.__class__.__name__} time={t1-t0:.2f}')
+        devices.torch_gc(force=True)
+
+    def set_args(self, p: processing.StableDiffusionProcessing, *args):
+        subject, concepts, prompts, dropout, sampler, steps, same, queries, sdsa, freeu, freeu_preset, alpha, injection = args # pylint: disable=unused-variable
+        processing.fix_seed(p)
+        if sampler:
+            shared.sd_model.scheduler = diffusers.DDIMScheduler.from_config(shared.sd_model.scheduler.config)
+        else:
+            sd_samplers.create_sampler(p.sampler_name, shared.sd_model)
+        if freeu:
+            try:
+                freeu_preset = [float(f.strip()) for f in freeu_preset.split(',')]
+            except Exception:
+                freeu_preset = []
+                shared.log.warning(f'ConsiStory: freeu="{freeu_preset}" invalid')
+            if len(freeu) == 4:
+                shared.sd_model.enable_freeu(s1=freeu[0], s2=freeu[0], b1=freeu[0], b2=freeu[0])
+        steps = 50 if steps else p.steps
+        if injection:
+            try:
+                alpha = [a.strip() for a in alpha.split(',')]
+                if len(alpha) == 3:
+                    alpha = (int(alpha[0]), int(alpha[1]), float(alpha[2]))
+            except Exception:
+                alpha=(10, 20, 0.8)
+                shared.log.warning(f'ConsiStory: alpha="{alpha}" invalid')
+        else:
+            alpha=(10, 20, 0.8)
+        seed = p.seed
+        concepts = [c.strip() for c in concepts.split(',') if c.strip() != '']
+        for c in concepts:
+            if c not in subject:
+                shared.log.warning(f'ConsiStory: concept="{c}" not in subject')
+                subject = f'{subject} {c}'
+        settings = [p.strip() for p in prompts.split('\n') if p.strip() != '']
+        anchors = [f'{subject} {p}' for p in settings]
+        prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)
+        prompts = [p.strip() for p in prompt.split('\n') if p.strip() != '']
+        for i, prompt in enumerate(prompts):
+            if subject not in prompt:
+                prompts[i] = f'{subject} {prompt}'
+        shared.log.debug(f'ConsiStory args: sampler={shared.sd_model.scheduler.__class__.__name__} steps={steps} sdsa={sdsa} queries={queries} same={same} dropout={dropout} freeu={freeu_preset if freeu else None} alpha={alpha if injection else None}')
+        return concepts, anchors, prompts, alpha, steps, seed
+
+    def create_anchors(self, anchors, concepts, seed, steps, dropout, same, queries, sdsa, injection, alpha):
+        import modules.consistory as cs
+        t0 = time.time()
+        if len(anchors) == 0:
+            shared.log.warning('ConsiStory: no anchors')
+            return []
+        shared.log.debug(f'ConsiStory anchors: concepts={concepts} anchors={anchors}')
+        with devices.inference_context():
+            try:
+                images, self.anchor_cache_first_stage, self.anchor_cache_second_stage = cs.run_anchor_generation(
+                    story_pipeline=shared.sd_model,
+                    prompts=anchors,
+                    concept_token=concepts,
+                    seed=seed,
+                    n_steps=steps,
+                    mask_dropout=dropout,
+                    same_latent=same,
+                    share_queries=queries,
+                    perform_sdsa=sdsa,
+                    inject_range_alpha=alpha,
+                    perform_injection=injection,
+                )
+            except Exception as e:
+                shared.log.error(f'ConsiStory: {e}')
+                errors.display(e, 'ConsiStory')
+                images = []
+            devices.torch_gc()
+        t1 = time.time()
+        shared.log.debug(f'ConsiStory anchors: images={len(images)} time={t1-t0:.2f}')
+        return images
+
+    def create_extra(self, prompt, concepts, seed, steps, dropout, same, queries, sdsa, injection, alpha):
+        import modules.consistory as cs
+        t0 = time.time()
+        images = []
+        shared.log.debug(f'ConsiStory extra: concepts={concepts} prompt="{prompt}"')
+        with devices.inference_context():
+            try:
+                images = cs.run_extra_generation(
+                    story_pipeline=shared.sd_model,
+                    prompts=[prompt],
+                    concept_token=concepts,
+                    anchor_cache_first_stage=self.anchor_cache_first_stage,
+                    anchor_cache_second_stage=self.anchor_cache_second_stage,
+                    seed=seed,
+                    n_steps=steps,
+                    mask_dropout=dropout,
+                    same_latent=same,
+                    share_queries=queries,
+                    perform_sdsa=sdsa,
+                    inject_range_alpha=alpha,
+                    perform_injection=injection,
+                )
+            except Exception as e:
+                shared.log.error(f'ConsiStory: {e}')
+                errors.display(e, 'ConsiStory')
+                images = []
+            devices.torch_gc()
+        t1 = time.time()
+        shared.log.debug(f'ConsiStory extra: images={len(images)} time={t1-t0:.2f}')
+        return images
 
     def run(self, p: processing.StableDiffusionProcessing, *args): # pylint: disable=arguments-differ
         supported_model_list = ['sdxl']
-        if shared.sd_model_type not in supported_model_list:
+        if shared.sd_model_type not in supported_model_list and shared.sd_model.__class__.__name__ != 'ConsistoryExtendAttnSDXLPipeline':
             shared.log.warning(f'ConsiStory: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
             return None
-        diffusers.models.embeddings.PositionNet = diffusers.models.embeddings.GLIGENTextBoundingboxProjection # patch as renamed in https://github.com/huggingface/diffusers/pull/6244/files
-        import modules.consistory as cs
-        if shared.sd_model_type == "sdxl":
-            self.orig_pipe = shared.sd_model
-            state_dict = shared.sd_model.unet.state_dict()
-            pipe = sd_models.switch_pipe(cs.ConsistoryExtendAttnSDXLPipeline, shared.sd_model)
-            pipe.unet = cs.ConsistorySDXLUNet2DConditionModel.from_config(pipe.unet.config)
-            pipe.unet.load_state_dict(state_dict)
-            pipe.unet.to(device=devices.device, dtype=devices.dtype)
-            # sd_models.set_diffuser_options(pipe)
-            devices.torch_gc(force=True)
 
-        processing.fix_seed(p)
-        subject="digital image of a cute robot"
-        concept_token=['robot']
-        settings=["sitting in the beach", "standing in the snow", "playing on the beach", "dancing in the meadow"]
-        prompts = [f'{subject} {setting}' for setting in settings]
-        anchor_prompts = prompts[:1]
-        extra_prompts = prompts[1:]
+        subject, concepts, prompts, dropout, sampler, steps, same, queries, sdsa, freeu, freeu_preset, alpha, injection = args # pylint: disable=unused-variable
 
-        p.steps = 50
+        self.create_model() # create model if not already done
+        concepts, anchors, prompts, alpha, steps, seed = self.set_args(p, *args) # set arguments
 
         images = []
-        anchor_out_images, anchor_cache_first_stage, anchor_cache_second_stage = cs.run_anchor_generation(
-            story_pipeline=pipe,
-            prompts=anchor_prompts,
-            concept_token=concept_token,
-            seed=p.seed,
-            n_steps=p.steps,
-            mask_dropout=0.5,
-            same_latent=False,
-            share_queries=True,
-            perform_sdsa=True,
-            perform_injection=True,
-        )
-        devices.torch_gc(force=True)
-        for i, image in enumerate(anchor_out_images):
-            image.save(f'/tmp/anchor_image_{i}.png')
-            images.append(image)
+        if self.anchor_cache_first_stage is None or self.anchor_cache_second_stage is None: # create anchors if not cached
+            images = self.create_anchors(anchors, concepts, seed, steps, dropout, same, queries, sdsa, injection, alpha)
 
-        extra_out_images = cs.run_extra_generation(
-            story_pipeline=pipe,
-            prompts=extra_prompts,
-            concept_token=concept_token,
-            anchor_cache_first_stage=anchor_cache_first_stage,
-            anchor_cache_second_stage=anchor_cache_second_stage,
-            seed=p.seed,
-            n_steps=p.steps,
-            mask_dropout=0.5,
-            same_latent=False,
-            share_queries=True,
-            perform_sdsa=True,
-            perform_injection=True,
-        )
-        for j, image in enumerate(extra_out_images):
-            image.save(f'/tmp/extra_image_{j}.png')
-            images.append(image)
+        for prompt in prompts:
+            extra_out_images = self.create_extra(prompt, concepts, seed, steps, dropout, same, queries, sdsa, injection, alpha)
+            for image in extra_out_images:
+                images.append(image)
 
-        devices.torch_gc(force=True)
-        processed = processing.Processed(p, images_list=images)
+        shared.sd_model.disable_freeu()
+        processed = processing.Processed(p, images)
         return processed
 
-
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=arguments-differ, unused-argument
-        if self.orig_pipe is None:
-            return processed
-        shared.sd_model = self.orig_pipe
         return processed

From e06ee1008a769e2a6ddca39078c28bd055a61a78 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 11:05:00 -0500
Subject: [PATCH 027/119] fix check

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

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 71bdbbd9c..895addec9 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 71bdbbd9c0a55ccea38cbf6fb01483323ac93676
+Subproject commit 895addec9ef65498ed44311d27db0adf699e512d
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 601615238..67066eedc 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -129,7 +129,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if 'prompt' in possible:
         if 'OmniGen' in model.__class__.__name__:
             prompts = [p.replace('|image|', '<|image_1|>') for p in prompts]
-        if hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'prompt_embeds' in possible and len(p.prompt_embeds) > 0 and p.prompt_embeds[0] is not None:
+        if hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'prompt_embeds' in possible and prompt_parser_diffusers.embedder is not None:
             args['prompt_embeds'] = prompt_parser_diffusers.embedder('prompt_embeds')
             if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
                 args['prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('positive_pooleds').unsqueeze(0)

From 6da094165d32141039c335ad2a1e1ffc61a1378d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 21:21:12 -0500
Subject: [PATCH 028/119] pullid offload compatibility and extra samplers

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    |   1 +
 modules/pulid/__init__.py       |   1 +
 modules/pulid/pulid_sampling.py | 571 ++++++++++++++++++++++++++++++++
 modules/pulid/pulid_sdxl.py     |   7 +-
 modules/pulid/pulid_utils.py    | 176 ----------
 modules/sd_models.py            |  15 +-
 scripts/pulid_ext.py            |  12 +-
 7 files changed, 595 insertions(+), 188 deletions(-)
 create mode 100644 modules/pulid/pulid_sampling.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2726a1864..4e08cb147 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -17,6 +17,7 @@ This release can be considered an LTS release before we kick off the next round
     - select in *scripts -> pulid*  
     - compatible with *sdxl*  
     - can be used in xyz grid  
+    - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
     - alternative to traditional `img2img` with more control over restoration process  
     - select in *image -> scripts -> instantir*  
diff --git a/modules/pulid/__init__.py b/modules/pulid/__init__.py
index 000f45293..785b849c2 100644
--- a/modules/pulid/__init__.py
+++ b/modules/pulid/__init__.py
@@ -8,3 +8,4 @@ sys.path.append(os.path.dirname(__file__))
 from pulid_sdxl import StableDiffusionXLPuLIDPipeline
 from pulid_utils import resize_numpy_image_long as resize
 import attention_processor as attention
+import pulid_sampling as sampling
diff --git a/modules/pulid/pulid_sampling.py b/modules/pulid/pulid_sampling.py
new file mode 100644
index 000000000..9996f035a
--- /dev/null
+++ b/modules/pulid/pulid_sampling.py
@@ -0,0 +1,571 @@
+import math
+from scipy import integrate
+import torch
+from torch import nn
+from torchdiffeq import odeint
+import torchsde
+from tqdm.auto import trange
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
+    """Constructs the noise schedule of Karras et al. (2022)."""
+    ramp = torch.linspace(0, 1, n)
+    min_inv_rho = sigma_min ** (1 / rho)
+    max_inv_rho = sigma_max ** (1 / rho)
+    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+    return append_zero(sigmas).to(device)
+
+
+def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
+    """Constructs an exponential noise schedule."""
+    sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
+    return append_zero(sigmas)
+
+
+def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
+    """Constructs an polynomial in log sigma noise schedule."""
+    ramp = torch.linspace(1, 0, n, device=device) ** rho
+    sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
+    return append_zero(sigmas)
+
+
+def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
+    """Constructs a continuous VP noise schedule."""
+    t = torch.linspace(1, eps_s, n, device=device)
+    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
+    return append_zero(sigmas)
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / append_dims(sigma, x.ndim)
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.):
+    """Calculates the noise level (sigma_down) to step down to and the amount
+    of noise to add (sigma_up) when doing an ancestral sampling step."""
+    if not eta:
+        return sigma_to, 0.
+    sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
+    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+    return sigma_down, sigma_up
+
+
+def default_noise_sampler(x):
+    return lambda sigma, sigma_next: torch.randn_like(x)
+
+
+class BatchedBrownianTree:
+    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
+
+    def __init__(self, x, t0, t1, seed=None, **kwargs):
+        t0, t1, self.sign = self.sort(t0, t1)
+        w0 = kwargs.get('w0', torch.zeros_like(x))
+        if seed is None:
+            seed = torch.randint(0, 2 ** 63 - 1, []).item()
+        self.batched = True
+        try:
+            assert len(seed) == x.shape[0]
+            w0 = w0[0]
+        except TypeError:
+            seed = [seed]
+            self.batched = False
+        self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+
+    @staticmethod
+    def sort(a, b):
+        return (a, b, 1) if a < b else (b, a, -1)
+
+    def __call__(self, t0, t1):
+        t0, t1, sign = self.sort(t0, t1)
+        w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+        return w if self.batched else w[0]
+
+
+class BrownianTreeNoiseSampler:
+    """A noise sampler backed by a torchsde.BrownianTree.
+
+    Args:
+        x (Tensor): The tensor whose shape, device and dtype to use to generate
+            random samples.
+        sigma_min (float): The low end of the valid interval.
+        sigma_max (float): The high end of the valid interval.
+        seed (int or List[int]): The random seed. If a list of seeds is
+            supplied instead of a single integer, then the noise sampler will
+            use one BrownianTree per batch item, each with its own seed.
+        transform (callable): A function that maps sigma to the sampler's
+            internal timestep.
+    """
+
+    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
+        self.transform = transform
+        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
+        self.tree = BatchedBrownianTree(x, t0, t1, seed)
+
+    def __call__(self, sigma, sigma_next):
+        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
+        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
+
+
+@torch.no_grad()
+def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        # Euler method
+        x = x + (d * dt).to(x.dtype)
+    return x
+
+
+@torch.no_grad()
+def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with Euler method steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        d = to_d(x, sigmas[i], denoised)
+        # Euler method
+        dt = sigma_down - sigmas[i]
+        x = x + (d * dt).to(x.dtype)
+        if sigmas[i + 1] > 0:
+            x = x + (noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up).to(x.dtype)
+    return x
+
+
+def linear_multistep_coeff(order, t, i, j):
+    if order - 1 > i:
+        raise ValueError(f'Order {order} too high for step {i}')
+    def fn(tau):
+        prod = 1.
+        for k in range(order):
+            if j == k:
+                continue
+            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
+        return prod
+    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
+
+
+@torch.no_grad()
+def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    v = torch.randint_like(x, 2) * 2 - 1
+    fevals = 0
+    def ode_fn(sigma, x):
+        nonlocal fevals
+        with torch.enable_grad():
+            x = x[0].detach().requires_grad_()
+            denoised = model(x, sigma * s_in, **extra_args)
+            d = to_d(x, sigma, denoised)
+            fevals += 1
+            grad = torch.autograd.grad((d * v).sum(), x)[0]
+            d_ll = (v * grad).flatten(1).sum(1)
+        return d.detach(), d_ll
+    x_min = x, x.new_zeros([x.shape[0]])
+    t = x.new_tensor([sigma_min, sigma_max])
+    sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
+    latent, delta_ll = sol[0][-1], sol[1][-1]
+    ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
+    return ll_prior + delta_ll, {'fevals': fevals}
+
+
+class PIDStepSizeController:
+    """A PID controller for ODE adaptive step size control."""
+    def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
+        self.h = h
+        self.b1 = (pcoeff + icoeff + dcoeff) / order
+        self.b2 = -(pcoeff + 2 * dcoeff) / order
+        self.b3 = dcoeff / order
+        self.accept_safety = accept_safety
+        self.eps = eps
+        self.errs = []
+
+    def limiter(self, x):
+        return 1 + math.atan(x - 1)
+
+    def propose_step(self, error):
+        inv_error = 1 / (float(error) + self.eps)
+        if not self.errs:
+            self.errs = [inv_error, inv_error, inv_error]
+        self.errs[0] = inv_error
+        factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
+        factor = self.limiter(factor)
+        accept = factor >= self.accept_safety
+        if accept:
+            self.errs[2] = self.errs[1]
+            self.errs[1] = self.errs[0]
+        self.h *= factor
+        return accept
+
+
+class DPMSolver(nn.Module):
+    """DPM-Solver. See https://arxiv.org/abs/2206.00927."""
+
+    def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
+        super().__init__()
+        self.model = model
+        self.extra_args = {} if extra_args is None else extra_args
+        self.eps_callback = eps_callback
+        self.info_callback = info_callback
+
+    def t(self, sigma):
+        return -sigma.log()
+
+    def sigma(self, t):
+        return t.neg().exp()
+
+    def eps(self, eps_cache, key, x, t, *args, **kwargs):
+        if key in eps_cache:
+            return eps_cache[key], eps_cache
+        sigma = self.sigma(t) * x.new_ones([x.shape[0]])
+        eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
+        if self.eps_callback is not None:
+            self.eps_callback()
+        return eps, {key: eps, **eps_cache}
+
+    def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
+        eps_cache = {} if eps_cache is None else eps_cache
+        h = t_next - t
+        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+        x_1 = x - self.sigma(t_next) * h.expm1() * eps
+        return x_1, eps_cache
+
+    def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
+        eps_cache = {} if eps_cache is None else eps_cache
+        h = t_next - t
+        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+        s1 = t + r1 * h
+        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+        x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
+        return x_2, eps_cache
+
+    def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
+        eps_cache = {} if eps_cache is None else eps_cache
+        h = t_next - t
+        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+        s1 = t + r1 * h
+        s2 = t + r2 * h
+        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+        u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
+        eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
+        x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
+        return x_3, eps_cache
+
+    def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
+        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+        if not t_end > t_start and eta:
+            raise ValueError('eta must be 0 for reverse sampling')
+
+        m = math.floor(nfe / 3) + 1
+        ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
+
+        if nfe % 3 == 0:
+            orders = [3] * (m - 2) + [2, 1]
+        else:
+            orders = [3] * (m - 1) + [nfe % 3]
+
+        for i in range(len(orders)):
+            eps_cache = {}
+            t, t_next = ts[i], ts[i + 1]
+            if eta:
+                sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
+                t_next_ = torch.minimum(t_end, self.t(sd))
+                su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
+            else:
+                t_next_, su = t_next, 0.
+
+            eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+            denoised = x - self.sigma(t) * eps
+            if self.info_callback is not None:
+                self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
+
+            if orders[i] == 1:
+                x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
+            elif orders[i] == 2:
+                x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
+            else:
+                x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
+
+            x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
+
+        return x
+
+    def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
+        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+        if order not in {2, 3}:
+            raise ValueError('order should be 2 or 3')
+        forward = t_end > t_start
+        if not forward and eta:
+            raise ValueError('eta must be 0 for reverse sampling')
+        h_init = abs(h_init) * (1 if forward else -1)
+        atol = torch.tensor(atol)
+        rtol = torch.tensor(rtol)
+        s = t_start
+        x_prev = x
+        accept = True
+        pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
+        info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
+
+        while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
+            eps_cache = {}
+            t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
+            if eta:
+                sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
+                t_ = torch.minimum(t_end, self.t(sd))
+                su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
+            else:
+                t_, su = t, 0.
+
+            eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
+            denoised = x - self.sigma(s) * eps
+
+            if order == 2:
+                x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
+                x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
+            else:
+                x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
+                x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
+            delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
+            error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
+            accept = pid.propose_step(error)
+            if accept:
+                x_prev = x_low
+                x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
+                s = t
+                info['n_accept'] += 1
+            else:
+                info['n_reject'] += 1
+            info['nfe'] += order
+            info['steps'] += 1
+
+            if self.info_callback is not None:
+                self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
+
+        return x, info
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigma_down == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigma_down - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver++(2S)
+            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+            r = 1 / 2
+            h = t_next - t
+            s = t + r * h
+            x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+    """DPM-Solver++ (stochastic)."""
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigmas[i + 1] - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver++
+            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+            h = t_next - t
+            s = t + h * r
+            fac = 1 / (2 * r)
+
+            # Step 1
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
+            s_ = t_fn(sd)
+            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
+            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+
+            # Step 2
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
+            t_next_ = t_fn(sd)
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
+            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+    old_denoised = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_denoised is None or sigmas[i + 1] == 0:
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
+        old_denoised = denoised
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+    """DPM-Solver++(2M) SDE."""
+
+    if solver_type not in {'heun', 'midpoint'}:
+        raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
+
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    old_denoised = None
+    h_last = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            # DPM-Solver++(2M) SDE
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            eta_h = eta * h
+
+            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
+
+            if old_denoised is not None:
+                r = h_last / h
+                if solver_type == 'heun':
+                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
+                elif solver_type == 'midpoint':
+                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
+
+            if eta:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
+
+        old_denoised = denoised
+        h_last = h
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """DPM-Solver++(3M) SDE."""
+
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    denoised_1, denoised_2 = None, None
+    h_1, h_2 = None, None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            h_eta = h * (eta + 1)
+
+            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
+
+            if h_2 is not None:
+                r0 = h_1 / h
+                r1 = h_2 / h
+                d1_0 = (denoised - denoised_1) / r0
+                d1_1 = (denoised_1 - denoised_2) / r1
+                d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
+                d2 = (d1_0 - d1_1) / (r0 + r1)
+                phi_2 = h_eta.neg().expm1() / h_eta + 1
+                phi_3 = phi_2 / h_eta - 0.5
+                x = x + phi_2 * d1 - phi_3 * d2
+            elif h_1 is not None:
+                r = h_1 / h
+                d = (denoised - denoised_1) / r
+                phi_2 = h_eta.neg().expm1() / h_eta + 1
+                x = x + phi_2 * d
+
+            if eta:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+
+        denoised_1, denoised_2 = denoised, denoised_1
+        h_1, h_2 = h, h_1
+    return x
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 0651efab4..de650b839 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -19,13 +19,12 @@ from insightface.app import FaceAnalysis
 from eva_clip import create_model_and_transforms
 from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
 from encoders_transformer import IDFormer
-from pulid_utils import sample_dpmpp_2m, sample_dpmpp_sde
 from attention_processor import AttnProcessor2_0 as AttnProcessor
 from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler='dpmpp_sde', cache_dir=None):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None):
         super().__init__()
         self.device = device
         self.pipe = pipe
@@ -90,12 +89,16 @@ class StableDiffusionXLPuLIDPipeline:
         self.log_sigmas = self.sigmas.log()
         self.sigma_data = 1.0
 
+        if sampler is not None:
+            self.sampler = sampler
+        """
         if sampler == 'dpmpp_sde':
             self.sampler = sample_dpmpp_sde
         elif sampler == 'dpmpp_2m':
             self.sampler = sample_dpmpp_2m
         else:
             raise NotImplementedError(f'sampler {sampler} not implemented')
+        """
 
     @property
     def sigma_min(self):
diff --git a/modules/pulid/pulid_utils.py b/modules/pulid/pulid_utils.py
index 1a8d3ff06..fd7338b5e 100644
--- a/modules/pulid/pulid_utils.py
+++ b/modules/pulid/pulid_utils.py
@@ -6,10 +6,7 @@ import random
 import cv2
 import numpy as np
 import torch
-import torch.nn.functional as F
-import torchsde
 from torchvision.utils import make_grid
-from tqdm.auto import trange
 from transformers import PretrainedConfig
 
 
@@ -21,10 +18,6 @@ def seed_everything(seed):
     torch.cuda.manual_seed_all(seed)
 
 
-def is_torch2_available():
-    return hasattr(F, "scaled_dot_product_attention")
-
-
 def instantiate_from_config(config):
     if "target" not in config:
         if config == '__is_first_stage__' or config == "__is_unconditional__":
@@ -166,172 +159,3 @@ def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
     if len(result) == 1:
         result = result[0]
     return result
-
-
-# We didn't find a correct configuration to make the diffusers scheduler align with dpm++2m (karras) in ComfyUI,
-# so we copied the ComfyUI code directly.
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    expanded = x[(...,) + (None,) * dims_to_append]
-    # MPS will get inf values if it tries to index into the new axes, but detaching fixes this.
-    # https://github.com/pytorch/pytorch/issues/84364
-    return expanded.detach().clone() if expanded.device.type == 'mps' else expanded
-
-
-def to_d(x, sigma, denoised):
-    """Converts a denoiser output to a Karras ODE derivative."""
-    return (x - denoised) / append_dims(sigma, x.ndim)
-
-
-def get_ancestral_step(sigma_from, sigma_to, eta=1.0):
-    """Calculates the noise level (sigma_down) to step down to and the amount
-    of noise to add (sigma_up) when doing an ancestral sampling step."""
-    if not eta:
-        return sigma_to, 0.0
-    sigma_up = min(sigma_to, eta * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
-    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
-    return sigma_down, sigma_up
-
-
-class BatchedBrownianTree:
-    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
-
-    def __init__(self, x, t0, t1, seed=None, **kwargs):
-        self.cpu_tree = True
-        if "cpu" in kwargs:
-            self.cpu_tree = kwargs.pop("cpu")
-        t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.get('w0', torch.zeros_like(x))
-        if seed is None:
-            seed = torch.randint(0, 2**63 - 1, []).item()
-        self.batched = True
-        try:
-            assert len(seed) == x.shape[0]
-            w0 = w0[0]
-        except TypeError:
-            seed = [seed]
-            self.batched = False
-        if self.cpu_tree:
-            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
-        else:
-            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
-
-    @staticmethod
-    def sort(a, b):
-        return (a, b, 1) if a < b else (b, a, -1)
-
-    def __call__(self, t0, t1):
-        t0, t1, sign = self.sort(t0, t1)
-        if self.cpu_tree:
-            w = torch.stack(
-                [tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]
-            ) * (self.sign * sign)
-        else:
-            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
-
-        return w if self.batched else w[0]
-
-
-class BrownianTreeNoiseSampler:
-    """A noise sampler backed by a torchsde.BrownianTree.
-
-    Args:
-        x (Tensor): The tensor whose shape, device and dtype to use to generate
-            random samples.
-        sigma_min (float): The low end of the valid interval.
-        sigma_max (float): The high end of the valid interval.
-        seed (int or List[int]): The random seed. If a list of seeds is
-            supplied instead of a single integer, then the noise sampler will
-            use one BrownianTree per batch item, each with its own seed.
-        transform (callable): A function that maps sigma to the sampler's
-            internal timestep.
-    """
-
-    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
-        self.transform = transform
-        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
-        self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
-
-    def __call__(self, sigma, sigma_next):
-        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
-        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
-
-
-@torch.no_grad()
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    """DPM-Solver++(2M)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp() # pylint: disable=unnecessary-lambda-assignment
-    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=unnecessary-lambda-assignment
-    old_denoised = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-        h = t_next - t
-        if old_denoised is None or sigmas[i + 1] == 0:
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
-        else:
-            h_last = t - t_fn(sigmas[i - 1])
-            r = h_last / h
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
-        old_denoised = denoised
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_sde(
-    model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None, r=1 / 2
-):
-    """DPM-Solver++ (stochastic)."""
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    seed = extra_args.get("seed", None)
-    noise_sampler = (
-        BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
-        if noise_sampler is None
-        else noise_sampler
-    )
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp() # pylint: disable=unnecessary-lambda-assignment
-    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=unnecessary-lambda-assignment
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigmas[i + 1] - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver++
-            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-            h = t_next - t
-            s = t + h * r
-            fac = 1 / (2 * r)
-
-            # Step 1
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
-            s_ = t_fn(sd)
-            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
-            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-
-            # Step 2
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
-            t_next_ = t_fn(sd)
-            denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
-            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
-    return x
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 354ceea7d..0d1e91f91 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -291,10 +291,13 @@ def set_diffuser_options(sd_model, vae = None, op: str = 'model', offload=True):
 
 
 def set_accelerate_to_module(model):
-    for k in model._internal_dict.keys(): # pylint: disable=protected-access
-        component = getattr(model, k, None)
-        if isinstance(component, torch.nn.Module):
-            component.has_accelerate = True
+    if hasattr(model, "pipe"):
+        set_accelerate_to_module(model.pipe)
+    if hasattr(model, "_internal_dict"):
+        for k in model._internal_dict.keys(): # pylint: disable=protected-access
+            component = getattr(model, k, None)
+            if isinstance(component, torch.nn.Module):
+                component.has_accelerate = True
 
 
 def set_accelerate(sd_model):
@@ -397,6 +400,10 @@ def apply_balanced_offload(sd_model):
             return module
 
     def apply_balanced_offload_to_module(pipe):
+        if hasattr(pipe, "pipe"):
+            apply_balanced_offload_to_module(pipe.pipe)
+        if not hasattr(pipe, "_internal_dict"):
+            return
         for module_name in pipe._internal_dict.keys(): # pylint: disable=protected-access
             module = getattr(pipe, module_name, None)
             if isinstance(module, torch.nn.Module):
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index d31c18164..54189cc05 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -30,8 +30,6 @@ class Script(scripts.Script):
             install('insightface', 'insightface', ignore=False)
             install('albumentations==1.4.3', 'albumentations', ignore=False, reinstall=True)
             install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
-        # if not installed('apex', reload=False, quiet=True):
-        #     install('apex', 'apex', ignore=False)
 
     def register(self): # register xyz grid elements
         def apply_field(field):
@@ -74,8 +72,8 @@ class Script(scripts.Script):
             strength = gr.Slider(label = 'Strength', value = 0.8, mininimum = 0, maximum = 1, step = 0.01)
             zero = gr.Slider(label = 'Zero', value = 20, mininimum = 0, maximum = 80, step = 1)
         with gr.Row():
-            sampler = gr.Dropdown(label="Sampler", choices=['dpmpp_sde', 'dpmpp_2m'], value='dpmpp_sde', visible=True)
-            ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2', visible=True)
+            sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
+            ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
         with gr.Row():
             files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
         with gr.Row():
@@ -124,16 +122,17 @@ class Script(scripts.Script):
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
         ortho = getattr(p, 'pulid_ortho', ortho)
+        sampler = getattr(p, 'pulid_sampler', sampler)
+        sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
 
         if shared.sd_model_type == 'sdxl' and not hasattr(shared.sd_model, 'pipe'):
             try:
                 stdout = io.StringIO()
-                ctx = contextlib.nullcontext if debug else contextlib.redirect_stdout(stdout)
+                ctx = contextlib.nullcontext() if debug else contextlib.redirect_stdout(stdout)
                 with ctx:
                     shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
                         pipe =shared.sd_model,
                         device=devices.device,
-                        sampler=sampler,
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -146,6 +145,7 @@ class Script(scripts.Script):
                 errors.display(e, 'PuLID')
                 return None
 
+        shared.sd_model.sampler = sampler_fn
         shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'

From 1c06ec2ca8668e4ee0a7a59e743635de1468b6db Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 21:27:41 -0500
Subject: [PATCH 029/119] package logging

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

diff --git a/modules/loader.py b/modules/loader.py
index 0711c2906..cd51cc8eb 100644
--- a/modules/loader.py
+++ b/modules/loader.py
@@ -126,4 +126,5 @@ except ImportError:
     except ImportError:
         pass  # shrug...
 
-errors.log.info(f'System packages: {get_packages()}')
+errors.log.info(f'Torch: torch=={torch.__version__} torchvision=={torchvision.__version__}')
+errors.log.info(f'Packages: diffusers=={diffusers.__version__} transformers=={transformers.__version__} accelerate=={accelerate.__version__} gradio=={gradio.__version__}')

From 5e044e3f2265659c9b122927e9d18aba4c9f42fc Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 22:14:42 -0500
Subject: [PATCH 030/119] pulid img2img and inpaint placeholders

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_args.py  |  4 ++--
 modules/pulid/__init__.py   |  2 +-
 modules/pulid/pulid_sdxl.py | 19 +++++++++++++++++++
 modules/sd_models.py        |  8 +++++++-
 scripts/pulid_ext.py        | 12 ++++++++----
 5 files changed, 37 insertions(+), 8 deletions(-)

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 002655cd5..0e84f2a4a 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -27,7 +27,7 @@ def task_specific_kwargs(p, model):
                 'height': 8 * math.ceil(p.height / 8),
             }
     elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
-        if shared.sd_model_type == 'sdxl':
+        if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
             model.register_to_config(requires_aesthetics_score = False)
         p.ops.append('img2img')
         task_args = {
@@ -55,7 +55,7 @@ def task_specific_kwargs(p, model):
             'strength': p.denoising_strength,
         }
     elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
-        if shared.sd_model_type == 'sdxl':
+        if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
             model.register_to_config(requires_aesthetics_score = False)
         if p.detailer:
             p.ops.append('detailer')
diff --git a/modules/pulid/__init__.py b/modules/pulid/__init__.py
index 785b849c2..dcee2d7b9 100644
--- a/modules/pulid/__init__.py
+++ b/modules/pulid/__init__.py
@@ -5,7 +5,7 @@ Credit and original implementation: <https://github.com/ToTheBeginning/PuLID>
 import os
 import sys
 sys.path.append(os.path.dirname(__file__))
-from pulid_sdxl import StableDiffusionXLPuLIDPipeline
+from pulid_sdxl import StableDiffusionXLPuLIDPipeline, StableDiffusionXLPuLIDPipelineImage, StableDiffusionXLPuLIDPipelineInpaint
 from pulid_utils import resize_numpy_image_long as resize
 import attention_processor as attention
 import pulid_sampling as sampling
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index de650b839..af7b8e443 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -307,6 +307,7 @@ class StableDiffusionXLPuLIDPipeline:
         num_inference_steps: int=50,
         seed: int=-1,
         image: np.ndarray=None,
+        mask_image: np.ndarray=None,
         strength: float=0.3,
         id_embedding=None,
         uncond_id_embedding=None,
@@ -356,4 +357,22 @@ class StableDiffusionXLPuLIDPipeline:
         images = self.pipe.vae.decode(latents).sample
         images = self.pipe.image_processor.postprocess(images, output_type='pil')
 
+        if mask_image is not None:
+            # TODO: pulid inpaint
+            # easiest inpaint is to use normal img2img and then combine output with input using mask
+            # note that mask can be binary or grayscale (soft mask)
+            raise NotImplementedError('pulid: inpaint')
+
         return images
+
+
+class StableDiffusionXLPuLIDPipelineImage(StableDiffusionXLPuLIDPipeline):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None): # pylint: disable=useless-parent-delegation
+        super().__init__(pipe, device, sampler, cache_dir)
+        # we dont do anything special here, just having different class so task-type can be detected/assigned
+
+
+class StableDiffusionXLPuLIDPipelineInpaint(StableDiffusionXLPuLIDPipeline):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None): # pylint: disable=useless-parent-delegation
+        super().__init__(pipe, device, sampler, cache_dir)
+        # we dont do anything special here, just having different class so task-type can be detected/assigned
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0d1e91f91..279959fde 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1061,7 +1061,6 @@ def set_diffuser_pipe(pipe, new_pipe_type):
         'AnimateDiffSDXLPipeline',
         'OmniGenPipeline',
         'StableDiffusion3ControlNetPipeline',
-        'StableDiffusionXLPuLIDPipeline',
         'InstantIRPipeline',
     ]
 
@@ -1083,6 +1082,13 @@ def set_diffuser_pipe(pipe, new_pipe_type):
             pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
         if n == 'StableDiffusionXLPAGPipeline':
             pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
+        if n == 'StableDiffusionXLPuLIDPipeline':
+            from modules import pulid
+            if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
+                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineImage
+            else:
+                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineInpaint
+            return pipe
 
     sd_checkpoint_info = getattr(pipe, "sd_checkpoint_info", None)
     sd_model_checkpoint = getattr(pipe, "sd_model_checkpoint", None)
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 54189cc05..3a157d0fe 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -106,9 +106,10 @@ class Script(scripts.Script):
             try:
                 from modules import pulid # pylint: disable=redefined-outer-name
                 self.pulid = pulid
-                # from diffusers import pipelines
-                # pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["pilid"] = pulid.StableDiffusionXLPuLIDPipeline
-                # pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["omnigen"] = pulid.StableDiffusionXLPuLIDPipelineImg2Img
+                from diffusers import pipelines
+                pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["pulid"] = pulid.StableDiffusionXLPuLIDPipeline
+                pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["pulid"] = pulid.StableDiffusionXLPuLIDPipelineImage
+                pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["pulid"] = pulid.StableDiffusionXLPuLIDPipelineInpaint
             except Exception as e:
                 shared.log.error(f'PuLID: failed to import library: {e}')
                 return None
@@ -124,6 +125,8 @@ class Script(scripts.Script):
         ortho = getattr(p, 'pulid_ortho', ortho)
         sampler = getattr(p, 'pulid_sampler', sampler)
         sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
+        if sampler_fn is None:
+            sampler_fn = self.pulid.sampling.sample_dpmpp_2m_sde
 
         if shared.sd_model_type == 'sdxl' and not hasattr(shared.sd_model, 'pipe'):
             try:
@@ -146,7 +149,7 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'
@@ -184,6 +187,7 @@ class Script(scripts.Script):
                 p.task_args['image'] = p.init_images[0]
                 p.task_args['strength'] = p.denoising_strength
             p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
+            p.extra_generation_params["Sampler"] = sampler
             if getattr(p, 'xyz', False): # xyz will run its own processing
                 return None
             processed: processing.Processed = processing.process_images(p) # runs processing using main loop

From 7dc9d2b47a6046a26ed89af33730f8b5550d9582 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 22:29:52 -0500
Subject: [PATCH 031/119] pulid optional keep model loaded

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 scripts/pulid_ext.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 3a157d0fe..da195b5e4 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -74,14 +74,16 @@ class Script(scripts.Script):
         with gr.Row():
             sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
             ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
+        with gr.Row():
+            cache = gr.Checkbox(label='Keep model', value=False)
         with gr.Row():
             files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
         with gr.Row():
             gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
-        return [strength, zero, sampler, ortho, gallery]
+        return [strength, zero, sampler, ortho, gallery, cache]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = []): # pylint: disable=arguments-differ
+    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], cache: bool = False): # pylint: disable=arguments-differ, unused-argument
         images = []
         try:
             if len(gallery) == 0:
@@ -197,6 +199,11 @@ class Script(scripts.Script):
         return processed
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
+        _strength, _zero, _sampler, _ortho, _gallery, cache = args
+        cache = getattr(p, 'pulid_cache', cache)
+        if cache:
+            shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
+            return processed
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
             if hasattr(shared.sd_model, 'app'):
                 shared.sd_model.app = None
@@ -204,7 +211,7 @@ class Script(scripts.Script):
                 shared.sd_model.face_helper = None
                 shared.sd_model.clip_vision_model = None
                 shared.sd_model.handler_ante = None
-                devices.torch_gc(force=True)
             shared.sd_model = shared.sd_model.pipe
-            # shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
+            devices.torch_gc(force=True)
+            shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
         return processed

From 53e32ce6336b937ea97539991ac14709e9c4ed24 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 22:40:18 -0500
Subject: [PATCH 032/119] fix xyz duplicate classes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ui_extra_networks.py |  3 ++-
 scripts/apg.py               | 11 ++++++++---
 scripts/pulid_ext.py         | 12 +++++++++---
 3 files changed, 19 insertions(+), 7 deletions(-)

diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 323f4830f..f6e6cee97 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -148,7 +148,8 @@ class ExtraNetworksPage:
             if self.title == 'Model':
                 return
             opt = xyz_grid.AxisOption(f"[Network] {self.title}", str, add_prompt, choices=lambda: [x["name"] for x in self.items])
-            xyz_grid.axis_options.append(opt)
+            if opt not in xyz_grid.axis_options:
+                xyz_grid.axis_options.append(opt)
 
     def link_preview(self, filename):
         quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
diff --git a/scripts/apg.py b/scripts/apg.py
index c7e60c982..6a3020c38 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -32,9 +32,14 @@ class Script(scripts.Script):
 
         import sys
         xyz_classes = [v for k, v in sys.modules.items() if 'xyz_grid_classes' in k][0]
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] ETA", float, apply_field("apg_eta")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] Momentum", float, apply_field("apg_momentum")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] Threshold", float, apply_field("apg_threshold")))
+        options = [
+            xyz_classes.AxisOption("[APG] ETA", float, apply_field("apg_eta")),
+            xyz_classes.AxisOption("[APG] Momentum", float, apply_field("apg_momentum")),
+            xyz_classes.AxisOption("[APG] Threshold", float, apply_field("apg_threshold")),
+        ]
+        for option in options:
+            if option not in xyz_classes.axis_options:
+                xyz_classes.axis_options.append(option)
 
     def run(self, p: processing.StableDiffusionProcessing, eta = 0.0, momentum = 0.0, threshold = 0.0): # pylint: disable=arguments-differ
         supported_model_list = ['sd', 'sdxl', 'sc']
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index da195b5e4..05e83ce1f 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -40,9 +40,15 @@ class Script(scripts.Script):
 
         import sys
         xyz_classes = [v for k, v in sys.modules.items() if 'xyz_grid_classes' in k][0]
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Strength", float, apply_field("pulid_strength")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Zero", int, apply_field("pulid_zero")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Ortho", str, apply_field("pulid_ortho"), choices=lambda: ['off', 'v1', 'v2']))
+        options = [
+            xyz_classes.AxisOption("[PuLID] Strength", float, apply_field("pulid_strength")),
+            xyz_classes.AxisOption("[PuLID] Zero", int, apply_field("pulid_zero")),
+            xyz_classes.AxisOption("[PuLID] Ortho", str, apply_field("pulid_ortho"), choices=lambda: ['off', 'v1', 'v2']),
+        ]
+        for option in options:
+            if option not in xyz_classes.axis_options:
+                xyz_classes.axis_options.append(option)
+
 
     def load_images(self, files):
         self.images = []

From b9c3f0ec85f49490642338193c5389304eb9c4ad Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 08:05:18 -0500
Subject: [PATCH 033/119] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/pulid/pulid_sdxl.py | 2 --
 scripts/pulid_ext.py        | 3 ---
 2 files changed, 5 deletions(-)

diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index af7b8e443..6364309b9 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -155,7 +155,6 @@ class StableDiffusionXLPuLIDPipeline:
             state_dict_dict[module][new_k] = v
 
         for module in state_dict_dict:
-            print(f'loading from {module}')
             getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)
 
     def to_gray(self, img):
@@ -200,7 +199,6 @@ class StableDiffusionXLPuLIDPipeline:
             align_face = self.face_helper.cropped_faces[0]
             # incase insightface didn't detect face
             if id_ante_embedding is None:
-                print('fail to detect face using insightface, extract embedding on align face')
                 id_ante_embedding = self.handler_ante.get_feat(align_face)
 
             id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device)
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 05e83ce1f..22aff993f 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -191,9 +191,6 @@ class Script(scripts.Script):
             p.task_args['id_embedding'] = id_embedding
             p.task_args['uncond_id_embedding'] = uncond_id_embedding
             p.task_args['id_scale'] = strength
-            if len(getattr(p, 'init_images', [])) > 0:
-                p.task_args['image'] = p.init_images[0]
-                p.task_args['strength'] = p.denoising_strength
             p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
             p.extra_generation_params["Sampler"] = sampler
             if getattr(p, 'xyz', False): # xyz will run its own processing

From 6399b2804d61b1a5838011793a8e51773bec2e7d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 10:08:41 -0500
Subject: [PATCH 034/119] improve auto-pipeline switch

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/images_namegen.py   |  8 ++--
 modules/processing_info.py  |  6 ++-
 modules/pulid/pulid_sdxl.py | 16 ++-----
 modules/sd_models.py        | 90 ++++++++++++++++++++-----------------
 modules/sd_vae.py           |  2 +-
 modules/styles.py           |  2 +
 scripts/pulid_ext.py        |  5 ++-
 7 files changed, 68 insertions(+), 61 deletions(-)

diff --git a/modules/images_namegen.py b/modules/images_namegen.py
index d88f85a77..bc58f728a 100644
--- a/modules/images_namegen.py
+++ b/modules/images_namegen.py
@@ -34,10 +34,10 @@ class FilenameGenerator:
         'timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
         '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 else '',
-        'model_shortname': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded else '',
-        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded else '',
-        'model_hash': lambda self: shared.sd_model.sd_checkpoint_info.shorthash if shared.sd_loaded else '',
+        '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 '',
+        'model_shortname': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',
+        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',
+        'model_hash': lambda self: shared.sd_model.sd_checkpoint_info.shorthash if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',
 
         'prompt': lambda self: self.prompt_full(),
         'prompt_no_styles': lambda self: self.prompt_no_style(),
diff --git a/modules/processing_info.py b/modules/processing_info.py
index e798211b1..7d16dd956 100644
--- a/modules/processing_info.py
+++ b/modules/processing_info.py
@@ -47,8 +47,6 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
         "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None,
         "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None,
         "Parser": shared.opts.prompt_attention.split()[0],
-        "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''),
-        "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash),
         "VAE": (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD',
         "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
         "Clip skip": p.clip_skip if p.clip_skip > 1 else None,
@@ -63,6 +61,10 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
         "Comment": comment,
         "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
     }
+    if shared.opts.add_model_name_to_info and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None:
+        args["Model"] = shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
+    if shared.opts.add_model_hash_to_info and getattr(shared.sd_model, 'sd_model_hash', None) is not None:
+        args["Model hash"] = shared.sd_model.sd_model_hash
     # native
     if grid is None and (p.n_iter > 1 or p.batch_size > 1) and index >= 0:
         args['Index'] = f'{p.iteration + 1}x{index + 1}'
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 6364309b9..0ae603a26 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -91,14 +91,6 @@ class StableDiffusionXLPuLIDPipeline:
 
         if sampler is not None:
             self.sampler = sampler
-        """
-        if sampler == 'dpmpp_sde':
-            self.sampler = sample_dpmpp_sde
-        elif sampler == 'dpmpp_2m':
-            self.sampler = sample_dpmpp_2m
-        else:
-            raise NotImplementedError(f'sampler {sampler} not implemented')
-        """
 
     @property
     def sigma_min(self):
@@ -252,8 +244,8 @@ class StableDiffusionXLPuLIDPipeline:
     def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
         import functools
         from tqdm.auto import trange as trange_orig
-        import pulid_utils
-        pulid_utils.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
+        import pulid_sampling
+        pulid_sampling.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
 
     def sample(self, x, sigma, **extra_args):
         x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
@@ -288,7 +280,7 @@ class StableDiffusionXLPuLIDPipeline:
                 add_noise,
             )
             """
-            raise NotImplementedError('pulid: img2img')
+            raise NotImplementedError(f'PuLID: task=img2img class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} image={image} strength={strength}')
         else:
             # standard txt2img will full noise
             latents = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
@@ -359,7 +351,7 @@ class StableDiffusionXLPuLIDPipeline:
             # TODO: pulid inpaint
             # easiest inpaint is to use normal img2img and then combine output with input using mask
             # note that mask can be binary or grayscale (soft mask)
-            raise NotImplementedError('pulid: inpaint')
+            raise NotImplementedError(f'PuLID: task=inpaint class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} mask_image={mask_image}')
 
         return images
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 279959fde..5e89a29ad 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -780,11 +780,11 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
     if shared.opts.data.get('sd_model_checkpoint', '') == 'model.safetensors' or shared.opts.data.get('sd_model_checkpoint', '') == '':
         shared.opts.data['sd_model_checkpoint'] = "stabilityai/stable-diffusion-xl-base-1.0"
 
-    if op == 'model' or op == 'dict':
-        if (model_data.sd_model is not None) and (checkpoint_info is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
+    if (op == 'model' or op == 'dict'):
+        if (model_data.sd_model is not None) and (checkpoint_info is not None) and (getattr(model_data.sd_model, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
             return
     else:
-        if (model_data.sd_refiner is not None) and (checkpoint_info is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
+        if (model_data.sd_refiner is not None) and (checkpoint_info is not None) and (getattr(model_data.sd_refiner, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
             return
 
     sd_model = None
@@ -886,7 +886,8 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
 
         set_diffuser_offload(sd_model, op)
         if op == 'model' and not (os.path.isdir(checkpoint_info.path) or checkpoint_info.type == 'huggingface'):
-            sd_vae.apply_vae_config(shared.sd_model.sd_checkpoint_info.filename, vae_file, sd_model)
+            if getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None:
+                sd_vae.apply_vae_config(shared.sd_model.sd_checkpoint_info.filename, vae_file, sd_model)
         if op == 'refiner' and shared.opts.diffusers_move_refiner:
             shared.log.debug('Moving refiner model to CPU')
             move_model(sd_model, devices.cpu)
@@ -1077,18 +1078,13 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     if 'Onnx' in pipe.__class__.__name__:
         return pipe
 
-    if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE or new_pipe_type == DiffusersTaskType.INPAINTING: # in some cases we want to reset the pipeline as they dont have their own variants
+    new_pipe = None
+    # in some cases we want to reset the pipeline to parent as they dont have their own variants
+    if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE or new_pipe_type == DiffusersTaskType.INPAINTING:
         if n == 'StableDiffusionPAGPipeline':
-            pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
+            new_pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
         if n == 'StableDiffusionXLPAGPipeline':
-            pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
-        if n == 'StableDiffusionXLPuLIDPipeline':
-            from modules import pulid
-            if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
-                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineImage
-            else:
-                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineInpaint
-            return pipe
+            new_pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
 
     sd_checkpoint_info = getattr(pipe, "sd_checkpoint_info", None)
     sd_model_checkpoint = getattr(pipe, "sd_model_checkpoint", None)
@@ -1100,19 +1096,38 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     image_encoder = getattr(pipe, "image_encoder", None)
     feature_extractor = getattr(pipe, "feature_extractor", None)
 
-    try:
-        if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
-            new_pipe = diffusers.AutoPipelineForText2Image.from_pipe(pipe)
-        elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
-            new_pipe = diffusers.AutoPipelineForImage2Image.from_pipe(pipe)
-        elif new_pipe_type == DiffusersTaskType.INPAINTING:
-            new_pipe = diffusers.AutoPipelineForInpainting.from_pipe(pipe)
+    if new_pipe is None:
+        if hasattr(pipe, 'config'): # real pipeline which can be auto-switched
+            try:
+                if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
+                    new_pipe = diffusers.AutoPipelineForText2Image.from_pipe(pipe)
+                elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
+                    new_pipe = diffusers.AutoPipelineForImage2Image.from_pipe(pipe)
+                elif new_pipe_type == DiffusersTaskType.INPAINTING:
+                    new_pipe = diffusers.AutoPipelineForInpainting.from_pipe(pipe)
+                else:
+                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
+                    return pipe
+            except Exception as e: # pylint: disable=unused-variable
+                shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
+                return pipe
         else:
-            shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
-            return pipe
-    except Exception as e: # pylint: disable=unused-variable
-        shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
-        return pipe
+            try: # maybe a wrapper pipeline so just change the class
+                if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    new_pipe = pipe
+                elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    new_pipe = pipe
+                elif new_pipe_type == DiffusersTaskType.INPAINTING:
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    new_pipe = pipe
+                else:
+                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
+                    return pipe
+            except Exception as e: # pylint: disable=unused-variable
+                shared.log.warning(f'Pipeline class set failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
+                return pipe
 
     # if pipe.__class__ == new_pipe.__class__:
     #    return pipe
@@ -1128,8 +1143,12 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     new_pipe.is_sdxl = getattr(pipe, 'is_sdxl', False) # a1111 compatibility item
     new_pipe.is_sd2 = getattr(pipe, 'is_sd2', False)
     new_pipe.is_sd1 = getattr(pipe, 'is_sd1', True)
-    if hasattr(new_pipe, "watermark"):
+    if hasattr(new_pipe, 'watermark'):
         new_pipe.watermark = NoWatermark()
+
+    if hasattr(new_pipe, 'pipe'): # also handle nested pipelines
+        new_pipe.pipe = set_diffuser_pipe(new_pipe.pipe, new_pipe_type)
+
     fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
     shared.log.debug(f"Pipeline class change: original={pipe.__class__.__name__} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
     pipe = new_pipe
@@ -1200,10 +1219,10 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
     if checkpoint_info is None:
         return
     if op == 'model' or op == 'dict':
-        if model_data.sd_model is not None and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
+        if (model_data.sd_model is not None) and (getattr(model_data.sd_model, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
             return
     else:
-        if model_data.sd_refiner is not None and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
+        if (model_data.sd_refiner is not None) and (getattr(model_data.sd_refiner, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
             return
     shared.log.debug(f'Load {op}: name={checkpoint_info.filename} dict={already_loaded_state_dict is not None}')
     if timer is None:
@@ -1212,12 +1231,12 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
     if op == 'model' or op == 'dict':
         if model_data.sd_model is not None:
             sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
-            current_checkpoint_info = model_data.sd_model.sd_checkpoint_info
+            current_checkpoint_info = getattr(model_data.sd_model, 'sd_checkpoint_info', None)
             unload_model_weights(op=op)
     else:
         if model_data.sd_refiner is not None:
             sd_hijack.model_hijack.undo_hijack(model_data.sd_refiner)
-            current_checkpoint_info = model_data.sd_refiner.sd_checkpoint_info
+            current_checkpoint_info = getattr(model_data.sd_refiner, 'sd_checkpoint_info', None)
             unload_model_weights(op=op)
 
     if not shared.native:
@@ -1246,15 +1265,6 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
         sd_model = instantiate_from_config(sd_config.model)
     else:
         with contextlib.redirect_stdout(stdout):
-            """
-            try:
-                clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
-                with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
-                    sd_model = instantiate_from_config(sd_config.model)
-            except Exception as e:
-                shared.log.error(f'LDM: instantiate from config: {e}')
-                sd_model = instantiate_from_config(sd_config.model)
-            """
             sd_model = instantiate_from_config(sd_config.model)
         for line in stdout.getvalue().splitlines():
             if len(line) > 0:
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index f266f8c38..95ac05c93 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -289,7 +289,7 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
         if vae_file is not None:
             shared.log.info(f"VAE weights loaded: {vae_file}")
     else:
-        if hasattr(sd_model, "vae") and hasattr(sd_model, "sd_checkpoint_info"):
+        if hasattr(sd_model, "vae") and getattr(sd_model, "sd_checkpoint_info", None) is not None:
             vae = load_vae_diffusers(sd_model.sd_checkpoint_info.filename, vae_file, vae_source)
             if vae is not None:
                 if not hasattr(sd_model, 'original_vae'):
diff --git a/modules/styles.py b/modules/styles.py
index de9ef43c4..0599bcd86 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -112,6 +112,8 @@ def apply_wildcards_to_prompt(prompt, all_wildcards, seed=-1, silent=False):
 
 
 def get_reference_style():
+    if getattr(shared.sd_model, 'sd_checkpoint_info', None) is None:
+        return None
     name = shared.sd_model.sd_checkpoint_info.name
     name = name.replace('\\', '/').replace('Diffusers/', '')
     for k, v in shared.reference_models.items():
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 22aff993f..5d209c211 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -8,6 +8,7 @@ from modules import shared, devices, errors, scripts, processing, processing_hel
 
 
 debug = os.environ.get('SD_PULID_DEBUG', None) is not None
+direct = False
 
 
 class Script(scripts.Script):
@@ -148,7 +149,7 @@ class Script(scripts.Script):
                     )
                 shared.sd_model.no_recurse = True
                 sd_models.copy_diffuser_options(shared.sd_model, shared.sd_model.pipe)
-                sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
+                # sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
                 sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
                 devices.torch_gc()
             except Exception as e:
@@ -165,7 +166,7 @@ class Script(scripts.Script):
         shared.sd_model.debug_img_list = []
         uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
 
-        if debug: # run pipeline directly
+        if direct: # run pipeline directly
             shared.state.begin('PuLID')
             processing.fix_seed(p)
             p.seed = processing_helpers.get_fixed_seed(p.seed)

From 04c16a574ecb766cc53181c5b47226c9290f1c86 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Thu, 7 Nov 2024 10:11:02 -0600
Subject: [PATCH 035/119] pulid img2img, no inpaint yet

---
 modules/pulid/pulid_sdxl.py | 44 ++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 23 deletions(-)

diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 0ae603a26..2392219c0 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -261,31 +261,25 @@ class StableDiffusionXLPuLIDPipeline:
         return latent
 
     def init_latent(self, seed, size, image, strength): # pylint: disable=unused-argument
+        # standard txt2img will full noise
+        noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
+        noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
         if image is not None and strength > 0:
-            # TODO pulid img2img
-            # input can be PIL.Image or np.ndarray so it needs to be converted to rgb tensor
-            # image must be resized, encoded and noised according to denoising strength
-            # see below for example from StableDiffusionXLImg2ImgPipeline
-            latents = None
-            """
-            image = self.image_processor.preprocess(image)
-            latents = self.prepare_latents(
+            image = self.pipe.image_processor.preprocess(image)
+            latents = self.pipe.prepare_latents(
                 image,
-                latent_timestep,
-                batch_size,
-                num_images_per_prompt,
-                prompt_embeds.dtype,
-                device,
-                generator,
-                add_noise,
+                None,  # timestep (not needed)
+                1,  # batch_size
+                1,  # num_images_per_prompt
+                noise.dtype,
+                noise.device,
+                None,  # generator
+                False,  # add_noise
             )
-            """
-            raise NotImplementedError(f'PuLID: task=img2img class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} image={image} strength={strength}')
         else:
-            # standard txt2img will full noise
-            latents = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
-            latents = latents.to(dtype=self.pipe.unet.dtype, device=self.device)
-        return latents
+            latents = torch.zeros_like(noise)
+
+        return latents, noise
 
     def __call__(
         self,
@@ -309,10 +303,14 @@ class StableDiffusionXLPuLIDPipeline:
         size = (1, height, width)
         # sigmas
         sigmas = self.get_sigmas_karras(num_inference_steps).to(self.device)
+        if image is not None and strength > 0:
+            _, num_inference_steps = self.pipe.get_timesteps(num_inference_steps, strength, self.device, None)  # denoising_start disabled
+            sigmas = sigmas[-(num_inference_steps + 1):].to(self.device) # shorten sigmas in i2i
+
 
         # latents
-        noise = self.init_latent(seed, size, image, strength)
-        latents = noise * sigmas[0].to(noise)
+        latents, noise = self.init_latent(seed, size, image, strength)
+        latents = latents + noise * sigmas[0].to(noise)
 
         (
             prompt_embeds,

From 2b893657f4b2bfdd547872dcc401103e21380f1d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 12:00:15 -0500
Subject: [PATCH 036/119] fix pag switch pipeline

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

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4e08cb147..123cfdbf9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-06
+## Update for 2024-11-07
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -9,7 +9,7 @@ This release can be considered an LTS release before we kick off the next round
   - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to system -> changelog and search/highligh/navigate directly in UI!  
-
+  - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
     - advanced method of face transfer with better quality as well as control over identity and appearance  
@@ -74,6 +74,7 @@ This release can be considered an LTS release before we kick off the next round
     - added `cli/model-keys.py` to quicky display content of any safetensors file
   - Internal:
     - Repo: move screenshots to GH pages  
+    - Auto pipeline switching coveres wrapper classes and nested pipelines  
 
 - Fixes:  
   - custom watermark add alphablending  
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5e89a29ad..4090bef50 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1082,9 +1082,9 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     # in some cases we want to reset the pipeline to parent as they dont have their own variants
     if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE or new_pipe_type == DiffusersTaskType.INPAINTING:
         if n == 'StableDiffusionPAGPipeline':
-            new_pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
+            pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
         if n == 'StableDiffusionXLPAGPipeline':
-            new_pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
+            pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
 
     sd_checkpoint_info = getattr(pipe, "sd_checkpoint_info", None)
     sd_model_checkpoint = getattr(pipe, "sd_model_checkpoint", None)
diff --git a/wiki b/wiki
index 2dba58a69..15dd79d77 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 2dba58a6962b70e92a077dcda8f178f5e811f175
+Subproject commit 15dd79d7749cf2af42371bf3dfdd4c60571a7fbf

From 9b3d6dfdc507c70f14c0c460dd251c872f280dcd Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 12:06:35 -0500
Subject: [PATCH 037/119] 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 123cfdbf9..cdf7163f8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,7 +15,7 @@ This release can be considered an LTS release before we kick off the next round
     - advanced method of face transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
-    - compatible with *sdxl*  
+    - compatible with *sdxl* for text-to-image and image-to-image  
     - can be used in xyz grid  
     - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference

From c790aeeb8700c68ec77ac035f16bde89e1868555 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 12:40:36 -0500
Subject: [PATCH 038/119] update

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

diff --git a/README.md b/README.md
index a2caa5cb7..d39fb7563 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
 - Built-in Control for Text, Image, Batch and video processing!  
   ▹ **ControlNet | ControlNet XS | Control LLLite | T2I Adapters | IP Adapters**  
 - Multiplatform!  
- ▹ **Windows | Linux | MacOS with CPU | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
+ ▹ **Windows | Linux | MacOS | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
 - Platform specific autodetection and tuning performed on install
 - Optimized processing with latest `torch` developments with built-in support for `torch.compile`  
   and multiple compile backends: *Triton, ZLUDA, StableFast, DeepCache, OpenVINO, NNCF, IPEX, OneDiff*  
diff --git a/wiki b/wiki
index 15dd79d77..6efc9b690 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 15dd79d7749cf2af42371bf3dfdd4c60571a7fbf
+Subproject commit 6efc9b6907048762456dbd530ed67e6a48ee4f1b

From 28772ab256c15b1162dad313492ff155fc9548c2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 19:44:53 -0500
Subject: [PATCH 039/119] wiki search

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md            |  4 ++-
 javascript/changelog.js |  7 +++++
 javascript/sdnext.css   |  5 ++++
 modules/ui.py           | 18 ++++-------
 modules/ui_docs.py      | 66 +++++++++++++++++++++++++++++++++++++++++
 wiki                    |  2 +-
 6 files changed, 87 insertions(+), 15 deletions(-)
 create mode 100644 modules/ui_docs.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cdf7163f8..881ba67cf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,9 +6,11 @@ Smaller release just few days after the last one, but with some important fixes
 This release can be considered an LTS release before we kick off the next round of major updates.  
 
 - Docs:  
-  - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
+  - UI built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to system -> changelog and search/highligh/navigate directly in UI!  
+  - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
+    go to system -> wiki and search wiki pages directly in UI!  
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
diff --git a/javascript/changelog.js b/javascript/changelog.js
index 80c9956b8..97dc9daf5 100644
--- a/javascript/changelog.js
+++ b/javascript/changelog.js
@@ -75,3 +75,10 @@ async function initChangelog() {
   };
   search.addEventListener('keyup', searchChangelog);
 }
+
+function wikiSearch(txt) {
+  log('wikiSearch', txt);
+  const url = `https://github.com/search?q=repo%3Avladmandic%2Fautomatic+${encodeURIComponent(txt)}&type=wikis`;
+  // window.open(url, '_blank').focus();
+  return txt;
+}
diff --git a/javascript/sdnext.css b/javascript/sdnext.css
index d412d966f..192d0d487 100644
--- a/javascript/sdnext.css
+++ b/javascript/sdnext.css
@@ -326,6 +326,11 @@ div:has(>#tab-gallery-folders) { flex-grow: 0 !important; background-color: var(
 .changelog_arrow:hover { background-color: var(--button-primary-border-color-hover); }
 .changelog_highlight { background-color: var(--color-warning); }
 
+/* wiki */
+#wiki_result > div > div { padding: 0.5em; margin-right: 2em; }
+#wiki_result li { display: block; }
+#wiki_result h3 { background-color: var(--background-fill-primary); margin: 0; padding: 0.3em; margin-bottom: 0.2em; }
+
 /* loader */
 .splash { position: fixed; top: 0; left: 0; width: 100vw; height: 100vh; z-index: 1000; display: block; text-align: center; }
 .motd { margin-top: 2em; color: var(--body-text-color-subdued); font-family: monospace; font-variant: all-petite-caps; }
diff --git a/modules/ui.py b/modules/ui.py
index 039ef6487..4a9d35728 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -355,20 +355,12 @@ def create_ui(startup_timer = None):
                 ui_onnx.create_ui()
 
             with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
-                def get_changelog():
-                    with open('CHANGELOG.md', 'r', encoding='utf-8') as f:
-                        content = f.read()
-                        content = content.replace('# Change Log for SD.Next', '  ')
-                    return content
+                from modules import ui_docs
+                ui_docs.create_ui_logs()
 
-                with gr.Column():
-                    get_changelog_btn = gr.Button(value='Get changelog', elem_id="get_changelog")
-                with gr.Column():
-                    _changelog_search = gr.Textbox(label="Search", elem_id="changelog_search")
-                    _changelog_result = gr.HTML(elem_id="changelog_result")
-
-                changelog_markdown = gr.Markdown('', elem_id="changelog_markdown")
-                get_changelog_btn.click(fn=get_changelog, outputs=[changelog_markdown], show_progress=True)
+            with gr.TabItem("Wiki", id="wiki", elem_id="system_tab_wiki"):
+                from modules import ui_docs
+                ui_docs.create_ui_wiki()
 
         def unload_sd_weights():
             modules.sd_models.unload_model_weights(op='model')
diff --git a/modules/ui_docs.py b/modules/ui_docs.py
new file mode 100644
index 000000000..7f85aa851
--- /dev/null
+++ b/modules/ui_docs.py
@@ -0,0 +1,66 @@
+import gradio as gr
+from modules import ui_symbols, ui_components
+
+
+def create_ui_logs():
+    def get_changelog():
+        with open('CHANGELOG.md', 'r', encoding='utf-8') as f:
+            content = f.read()
+            content = content.replace('# Change Log for SD.Next', '  ')
+        return content
+
+    with gr.Column():
+        get_changelog_btn = gr.Button(value='Get changelog', elem_id="get_changelog")
+        gr.HTML('<a href="https://github.com/vladmandic/automatic/blob/dev/CHANGELOG.md" style="color: #AAA" target="_blank">&nbsp Open GitHub Changelog</a>')
+    with gr.Column():
+        _changelog_search = gr.Textbox(label="Search Changelog", elem_id="changelog_search")
+        _changelog_result = gr.HTML(elem_id="changelog_result")
+
+    changelog_markdown = gr.Markdown('', elem_id="changelog_markdown")
+    get_changelog_btn.click(fn=get_changelog, outputs=[changelog_markdown], show_progress=True)
+
+
+def create_ui_wiki():
+    def search_github(search_term):
+        import requests
+        from urllib.parse import quote
+        from installer import install
+
+        install('beautifulsoup4')
+        from bs4 import BeautifulSoup
+
+        url = f'https://github.com/search?q=repo%3Avladmandic%2Fautomatic+{quote(search_term)}&type=wikis'
+        res = requests.get(url, timeout=10)
+        if res.status_code == 200:
+            html = res.content
+            soup = BeautifulSoup(html, 'html.parser')
+
+            # remove header links
+            tags = soup.find_all(attrs={"data-hovercard-url": "/vladmandic/automatic/hovercard"})
+            for tag in tags:
+                tag.extract()
+
+            # replace relative links with full links
+            tags = soup.find_all('a')
+            for tag in tags:
+                if tag.has_attr('href') and tag['href'].startswith('/'):
+                    tag['href'] = 'https://github.com' + tag['href']
+
+            # find result only
+            result = soup.find(attrs={"data-testid": "results-list"})
+            if result is None:
+                return 'No results found'
+            html = str(result)
+            return html
+        else:
+            return f'Error: {res.status_code}'
+
+    with gr.Row():
+        gr.HTML('<a href="https://github.com/vladmandic/automatic/wiki" style="color: #AAA" target="_blank">&nbsp Open GitHub Wiki</a>')
+    with gr.Row():
+        wiki_search = gr.Textbox(label="Search Wiki Pages", elem_id="wiki_search")
+        wiki_search_btn = ui_components.ToolButton(value=ui_symbols.search, label="Search", elem_id="wiki_search_btn")
+    with gr.Row():
+        wiki_result = gr.HTML(elem_id="wiki_result", value='test')
+    wiki_search.submit(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])
+    wiki_search_btn.click(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])
diff --git a/wiki b/wiki
index 6efc9b690..371344bcb 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 6efc9b6907048762456dbd530ed67e6a48ee4f1b
+Subproject commit 371344bcbf8da64f7ae373d1d6d1312ef44898c3

From fe07a867321aa52f8d75eb3a4dd57e00341cae95 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 20:45:34 -0500
Subject: [PATCH 040/119] add info tab

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       |  5 +++--
 extensions-builtin/sdnext-modernui |  2 +-
 modules/ui.py                      | 19 +++++++++++--------
 modules/ui_docs.py                 |  2 +-
 4 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 881ba67cf..3626d249f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,11 +6,12 @@ Smaller release just few days after the last one, but with some important fixes
 This release can be considered an LTS release before we kick off the next round of major updates.  
 
 - Docs:  
+  - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)
   - UI built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
-    go to system -> changelog and search/highligh/navigate directly in UI!  
+    go to info -> changelog and search/highligh/navigate directly in UI!  
   - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
-    go to system -> wiki and search wiki pages directly in UI!  
+    go to info -> wiki and search wiki pages directly in UI!  
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 71bdbbd9c..4647bd7f8 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 71bdbbd9c0a55ccea38cbf6fb01483323ac93676
+Subproject commit 4647bd7f86be9d2783a9ba1f38acaa9bcec942d2
diff --git a/modules/ui.py b/modules/ui.py
index 4a9d35728..4490bbf8c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -354,14 +354,6 @@ def create_ui(startup_timer = None):
                 from modules.onnx_impl import ui as ui_onnx
                 ui_onnx.create_ui()
 
-            with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
-                from modules import ui_docs
-                ui_docs.create_ui_logs()
-
-            with gr.TabItem("Wiki", id="wiki", elem_id="system_tab_wiki"):
-                from modules import ui_docs
-                ui_docs.create_ui_wiki()
-
         def unload_sd_weights():
             modules.sd_models.unload_model_weights(op='model')
             modules.sd_models.unload_model_weights(op='refiner')
@@ -382,6 +374,16 @@ def create_ui(startup_timer = None):
 
     timer.startup.record("ui-settings")
 
+    with gr.Blocks(analytics_enabled=False) as info_interface:
+        with gr.Tabs(elem_id="tabs_info"):
+            with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
+                from modules import ui_docs
+                ui_docs.create_ui_logs()
+
+            with gr.TabItem("Wiki", id="wiki", elem_id="system_tab_wiki"):
+                from modules import ui_docs
+                ui_docs.create_ui_wiki()
+
     interfaces = []
     interfaces += [(txt2img_interface, "Text", "txt2img")]
     interfaces += [(img2img_interface, "Image", "img2img")]
@@ -391,6 +393,7 @@ def create_ui(startup_timer = None):
     interfaces += [(models_interface, "Models", "models")]
     interfaces += script_callbacks.ui_tabs_callback()
     interfaces += [(settings_interface, "System", "system")]
+    interfaces += [(info_interface, "Info", "info")]
 
     from modules import ui_extensions
     extensions_interface = ui_extensions.create_ui()
diff --git a/modules/ui_docs.py b/modules/ui_docs.py
index 7f85aa851..08159beb3 100644
--- a/modules/ui_docs.py
+++ b/modules/ui_docs.py
@@ -61,6 +61,6 @@ def create_ui_wiki():
         wiki_search = gr.Textbox(label="Search Wiki Pages", elem_id="wiki_search")
         wiki_search_btn = ui_components.ToolButton(value=ui_symbols.search, label="Search", elem_id="wiki_search_btn")
     with gr.Row():
-        wiki_result = gr.HTML(elem_id="wiki_result", value='test')
+        wiki_result = gr.HTML(elem_id="wiki_result", value='')
     wiki_search.submit(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])
     wiki_search_btn.click(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])

From bab5965e4caccb0d40ed6c1ec95c98d546d3634b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 08:26:11 -0500
Subject: [PATCH 041/119] update docs

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md      |   4 +-
 README.md         | 159 ++++++++++------------------------------------
 cli/README.md     | 116 ---------------------------------
 modules/shared.py |  97 ++++++++++++++--------------
 wiki              |   2 +-
 5 files changed, 85 insertions(+), 293 deletions(-)
 delete mode 100644 cli/README.md

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3626d249f..d78943f42 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-07
+## Update for 2024-11-08
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -12,7 +12,7 @@ This release can be considered an LTS release before we kick off the next round
     go to info -> changelog and search/highligh/navigate directly in UI!  
   - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
     go to info -> wiki and search wiki pages directly in UI!  
-  - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
+  - major [Wiki](https://github.com/vladmandic/automatic/wiki) and [Home](https://github.com/vladmandic/automatic) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
     - advanced method of face transfer with better quality as well as control over identity and appearance  
diff --git a/README.md b/README.md
index d39fb7563..d099496b8 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,12 @@
 <div align="center">
-<img src="https://github.com/vladmandic/automatic/blob/dev/html/favicon.png" width=200 alt="SD.Next">
+<img src="https://github.com/vladmandic/automatic/blob/master/html/logo-transparent.png" width=200 alt="SD.Next">
 
-**Stable Diffusion implementation with advanced features**
+**Image Diffusion implementation with advanced features**
 
-[![Sponsors](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
-![Last Commit](https://img.shields.io/github/last-commit/vladmandic/automatic?svg=true)
+![Last update](https://img.shields.io/github/last-commit/vladmandic/automatic?svg=true)
 ![License](https://img.shields.io/github/license/vladmandic/automatic?svg=true)
 [![Discord](https://img.shields.io/discord/1101998836328697867?logo=Discord&svg=true)](https://discord.gg/VjvR2tabEX)
+[![Sponsors](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 
 [Wiki](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.gg/VjvR2tabEX) | [Changelog](CHANGELOG.md)
 
@@ -18,45 +18,36 @@
 - [SD.Next Features](#sdnext-features)
 - [Model support](#model-support)
 - [Platform support](#platform-support)
-- [Backend support](#backend-support)
-- [Examples](#examples)
-- [Install](#install)
-- [Notes](#notes)
+- [Getting started](#getting-started)
 
 ## SD.Next Features
 
 All individual features are not listed here, instead check [ChangeLog](CHANGELOG.md) for full list of changes
-- Multiple backends!  
-  ▹ **Diffusers | Original**  
 - Multiple UIs!  
   ▹ **Standard | Modern**  
 - Multiple diffusion models!  
-  ▹ **Stable Diffusion 1.5/2.1/XL/3.0/3.5 | LCM | Lightning | Segmind | Kandinsky | Pixart-α | Pixart-Σ | Stable Cascade | FLUX.1 | AuraFlow | Würstchen | Alpha Lumina | Kwai Kolors | aMUSEd | DeepFloyd IF | UniDiffusion | SD-Distilled | BLiP Diffusion | KOALA | SDXS | Hyper-SD | HunyuanDiT | CogView | OmniGen | Meissonic | etc.**
 - Built-in Control for Text, Image, Batch and video processing!  
-  ▹ **ControlNet | ControlNet XS | Control LLLite | T2I Adapters | IP Adapters**  
 - Multiplatform!  
  ▹ **Windows | Linux | MacOS | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
-- Platform specific autodetection and tuning performed on install
+- Multiple backends!  
+  ▹ **Diffusers | Original**  
+- Platform specific autodetection and tuning performed on install  
 - Optimized processing with latest `torch` developments with built-in support for `torch.compile`  
   and multiple compile backends: *Triton, ZLUDA, StableFast, DeepCache, OpenVINO, NNCF, IPEX, OneDiff*  
 - Improved prompt parser  
-- Enhanced *Lora*/*LoCon*/*Lyco* code supporting latest trends in training  
 - Built-in queue management  
 - Enterprise level logging and hardened API  
 - Built in installer with automatic updates and dependency management  
-- Modernized UI with theme support and number of built-in themes *(dark and light)*  
-- Mobile compatible
+- Mobile compatible  
 
 <br>
 
 *Main interface using **StandardUI***:  
-![screenshot-text2image](https://github.com/user-attachments/assets/87ac2813-65c2-45f4-80b8-67b26ccf5cd6)
+![screenshot-standardui](https://github.com/user-attachments/assets/cab47fe3-9adb-4d67-aea9-9ee738df5dcc)
 
 *Main interface using **ModernUI***:  
 
-![screenshot-modernui-f1](https://github.com/user-attachments/assets/b509a280-8d3b-48b5-8525-363bad8c1ed2)
-![screenshot-modernui](https://github.com/user-attachments/assets/fef33127-f733-4e78-b66e-17729539512f)
-![screenshot-modernui-sd3](https://github.com/user-attachments/assets/1ed02ecc-23e4-4fda-8ae5-2d7393dc530c)
+![screenshot-modernui](https://github.com/user-attachments/assets/39e3bc9a-a9f7-4cda-ba33-7da8def08032)
 
 For screenshots and informations on other available themes, see [Themes Wiki](https://github.com/vladmandic/automatic/wiki/Themes)
 
@@ -65,12 +56,10 @@ For screenshots and informations on other available themes, see [Themes Wiki](ht
 ## Model support
 
 Additional models will be added as they become available and there is public interest in them  
-See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for details on each model, including their architecture, complexity and other info  
+See [models overview](wiki/Models) for details on each model, including their architecture, complexity and other info  
 
 - [RunwayML Stable Diffusion](https://github.com/Stability-AI/stablediffusion/) 1.x and 2.x *(all variants)*
-- [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models)
-- [StabilityAI Stable Diffusion](https://stability.ai/news/stable-diffusion-3-medium) 
-- [Stable Diffusion 3.x](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) 3.0 Medium, 3.5 Medium, 3.5 Large, 3.5 Large Turbo
+- [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models), [StabilityAI Stable Diffusion 3.0](https://stability.ai/news/stable-diffusion-3-medium) Medium, [StabilityAI Stable Diffusion 3.5](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) Medium, Large, Large Turbo
 - [StabilityAI Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) Base, XT 1.0, XT 1.1
 - [StabilityAI Stable Cascade](https://github.com/Stability-AI/StableCascade) *Full* and *Lite*
 - [Black Forest Labs FLUX.1](https://blackforestlabs.ai/announcing-black-forest-labs/) Dev, Schnell  
@@ -84,13 +73,9 @@ See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for d
 - [CogView 3+](https://huggingface.co/THUDM/CogView3-Plus-3B)
 - [LCM: Latent Consistency Models](https://github.com/openai/consistency_models)
 - [aMUSEd](https://huggingface.co/amused/amused-256) 256 and 512
-- [Segmind Vega](https://huggingface.co/segmind/Segmind-Vega)
-- [Segmind SSD-1B](https://huggingface.co/segmind/SSD-1B)
-- [Segmind SegMoE](https://github.com/segmind/segmoe) *SD and SD-XL*
-- [Segmind SD Distilled](https://huggingface.co/blog/sd_distillation) *(all variants)*
+- [Segmind Vega](https://huggingface.co/segmind/Segmind-Vega), [Segmind SSD-1B](https://huggingface.co/segmind/SSD-1B), [Segmind SegMoE](https://github.com/segmind/segmoe) *SD and SD-XL*, [Segmind SD Distilled](https://huggingface.co/blog/sd_distillation) *(all variants)*
 - [Kandinsky](https://github.com/ai-forever/Kandinsky-2) *2.1 and 2.2 and latest 3.0*
-- [PixArt-α XL 2](https://github.com/PixArt-alpha/PixArt-alpha) *Medium and Large*
-- [PixArt-Σ](https://github.com/PixArt-alpha/PixArt-sigma)
+- [PixArt-α XL 2](https://github.com/PixArt-alpha/PixArt-alpha) *Medium and Large*, [PixArt-Σ](https://github.com/PixArt-alpha/PixArt-sigma)
 - [Warp Wuerstchen](https://huggingface.co/blog/wuertschen)
 - [Tsinghua UniDiffusion](https://github.com/thu-ml/unidiffuser)
 - [DeepFloyd IF](https://github.com/deep-floyd/IF) *Medium and Large*
@@ -101,15 +86,6 @@ See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for d
 - [SDXS](https://github.com/IDKiro/sdxs)
 - [Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD)
 
-
-Also supported are modifiers such as:
-- **LCM**, **Turbo** and **Lightning** (*adversarial diffusion distillation*) networks
-- All **LoRA** types such as LoCon, LyCORIS, HADA, IA3, Lokr, OFT
-- **IP-Adapters** for SD 1.5 and SD-XL
-- **InstantID**, **FaceSwap**, **FaceID**, **PhotoMerge**  
-- **AnimateDiff** for SD 1.5
-- **MuLAN** multi-language support  
-
 ## Platform support
 
 - *nVidia* GPUs using **CUDA** libraries on both *Windows and Linux*  
@@ -121,6 +97,25 @@ Also supported are modifiers such as:
 - Any GPU or device compatible with **OpenVINO** libraries on both *Windows and Linux*  
 - *Apple M1/M2* on *OSX* using built-in support in Torch with **MPS** optimizations  
 - *ONNX/Olive*  
+- *AMD* GPUs on Windows using **ZLUDA** libraries
+
+## Getting started
+
+- Get started with **SD.Next** by following the [installation instructions](wiki/Installation)  
+- For more details, check out [advanced installation](wiki/Advanced-Install) guide  
+- List and explanation of [command line arguments](wiki/CLI-Arguments)
+- Install walkthrough [video](https://www.youtube.com/watch?v=nWTnTyFTuAs)
+
+> [!TIP]
+> And for platform specific information, check out  
+> [WSL](wiki/WSL) | [Intel Arc](wiki/Intel-ARC) | [DirectML](wiki/DirectML) | [OpenVINO](wiki/OpenVINO) | [ONNX & Olive](wiki/ONNX-Runtime) | [ZLUDA](wiki/ZLUDA) | [AMD ROCm](wiki/AMD-ROCm) | [MacOS](wiki/MacOS-Python.md) | [nVidia](wiki/nVidia)
+
+> [!WARNING]
+> If you run into issues, check out [troubleshooting](wiki/Troubleshooting) and [debugging](wiki/Debug) guides  
+
+> [!TIP]
+> All command line options can also be set via env variable
+> For example `--debug` is same as `set SD_DEBUG=true`  
 
 ## Backend support
 
@@ -129,91 +124,11 @@ Also supported are modifiers such as:
 - **Diffusers**: Based on new [Huggingface Diffusers](https://huggingface.co/docs/diffusers/index) implementation  
   Supports *all* models listed below  
   This backend is set as default for new installations  
-  See [wiki article](https://github.com/vladmandic/automatic/wiki/Diffusers) for more information  
 - **Original**: Based on [LDM](https://github.com/Stability-AI/stablediffusion) reference implementation and significantly expanded on by [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)  
   This backend and is fully compatible with most existing functionality and extensions written for *A1111 SDWebUI*  
   Supports **SD 1.x** and **SD 2.x** models  
   All other model types such as *SD-XL, LCM, Stable Cascade, PixArt, Playground, Segmind, Kandinsky, etc.* require backend **Diffusers**  
 
-## Examples
-
-*IP Adapters*:
-![screenshot-ipadapter](https://github.com/user-attachments/assets/92830894-845c-49ec-92d9-18c8a577d04f)
-
-*Color grading*:  
-![screenshot-control](https://github.com/user-attachments/assets/cdad2722-ae7c-4c9c-94d6-5ea35a4b1356)
-
-*InstantID*:  
-![screenshot-instantid](https://github.com/user-attachments/assets/f38a5660-32b3-4235-9da1-c79eccf5372f)
-
-> [!IMPORTANT]
-> - Loading any model other than standard SD 1.x / SD 2.x requires use of backend **Diffusers**  
-> - Loading any other models using **Original** backend is not supported  
-> - Loading manually download model `.safetensors` files is supported for specified models only (typically SD 1.x / SD 2.x / SD-XL models only)  
-> - For all other model types, use backend **Diffusers** and use built in Model downloader or  
-  select model from Networks -> Models -> Reference list in which case it will be auto-downloaded and loaded  
-
-## Install
-
-- [Step-by-step install guide](https://github.com/vladmandic/automatic/wiki/Installation)
-- [Advanced install notes](https://github.com/vladmandic/automatic/wiki/Advanced-Install)
-- [Video: install and use](https://www.youtube.com/watch?v=nWTnTyFTuAs)
-- [Common installation errors](https://github.com/vladmandic/automatic/discussions/1627)  
-- [FAQ](https://github.com/vladmandic/automatic/discussions/1011)
-
-> [!TIP]
-> - If you can't run SD.Next locally, try cloud deployment using [RunDiffusion](https://rundiffusion.com?utm_source=github&utm_medium=referral&utm_campaign=SDNext)!
-> - Server can run with or without virtual environment,  
-  Recommended to use `VENV` to avoid library version conflicts with other applications  
-> - **nVidia/CUDA** / **AMD/ROCm** / **Intel/OneAPI** are auto-detected if present and available,  
-  For any other use case such as **DirectML**, **ONNX/Olive**, **OpenVINO** specify required parameter explicitly  
-  or wrong packages may be installed as installer will assume CPU-only environment  
-> - Full startup sequence is logged in `sdnext.log`,  
-  so if you encounter any issues, please check it first  
-
-### Run
-
-Once SD.Next is installed, simply run `webui.ps1` or `webui.bat` (*Windows*) or `webui.sh` (*Linux or MacOS*)
-
-For list of available command line options, run `webui --help` for the full & up-to-date list
-
-> [!TIP]
-> All command line options can also be set via env variable
-> For example `--debug` is same as `set SD_DEBUG=true`  
-
-## Notes
-
-> [!TIP]
-> If you don't want to use built-in `venv` support and prefer to run SD.Next in your own environment such as *Docker* container, *Conda* environment or any other virtual environment, you can skip `venv` create/activate and launch SD.Next directly using `python launch.py` (command line flags noted above still apply).
-
-### Quantization
-
-**SD.Next** comes with broad quantization support, including support for BitsAndBytes, Optimum.Quanto, TorchAO, NNCF and GGUF
-See [Quantization Wiki](https://github.com/vladmandic/automatic/wiki/Quantization)
-
-### Control
-
-**SD.Next** comes with built-in control for all types of text2image, image2image, video2video and batch processing
-
-*Control interface*:  
-![screenshot-control](https://github.com/user-attachments/assets/cdad2722-ae7c-4c9c-94d6-5ea35a4b1356)
-
-*Control processors*:  
-![screenshot-processors](https://github.com/user-attachments/assets/7bccb82b-366e-4bdb-ae57-cc53fac95d3c)
-
-*Masking*:
-![screenshot-mask](https://github.com/user-attachments/assets/4b057e65-64f0-44ea-93b4-c3b69bc55532)
-
-### Extensions
-
-SD.Next comes with several extensions pre-installed:
-
-- [System Info](https://github.com/vladmandic/sd-extension-system-info)
-- [chaiNNer](https://github.com/vladmandic/sd-extension-chainner)
-- [RemBg](https://github.com/vladmandic/sd-extension-rembg)
-- [Agent Scheduler](https://github.com/ArtVentureX/sd-webui-agent-scheduler)
-- [Modern UI](https://github.com/BinaryQuantumSoul/sdnext-modernui)
-
 ### Collab
 
 - We'd love to have additional maintainers (with comes with full repo rights). If you're interested, ping us!  
@@ -242,12 +157,6 @@ This should be fully cross-platform, but we'd really love to have additional con
 If you're unsure how to use a feature, best place to start is [Wiki](https://github.com/vladmandic/automatic/wiki) and if its not there,  
 check [ChangeLog](CHANGELOG.md) for when feature was first introduced as it will always have a short note on how to use it  
 
-- [Wiki](https://github.com/vladmandic/automatic/wiki)
-- [ReadMe](README.md)
-- [ToDo](TODO.md)  
-- [ChangeLog](CHANGELOG.md)
-- [CLI Tools](cli/README.md)
-
 ### Sponsors
 
 <div align="center">
diff --git a/cli/README.md b/cli/README.md
deleted file mode 100644
index 838db7a50..000000000
--- a/cli/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# Stable-Diffusion Productivity Scripts
-
-## API Examples
-
-### Run Generate
-
-- `cli/api-txt2img.py`
-- `cli/api-img2img.py`
-- `cli/api-control.py`
-
-### Monitor
-
-- `cli/api-progress.py`
-
-### Generic
-
-- `cli/api-json.py`
-
-### Process
-
-- `cli/api-info.py`
-- `cli/api-upscale.py`
-- `cli/api-vqa.py`
-- `cli/api-preprocess.py`
-
-### Other
-
-- `cli/api-faceid.py`
-- `cli/api-faces.py`
-- `cli/api-mask.py`
-
-### JavaScript
-
-- `cli/api-txt2img.js`
-
-## Generate
-
-Text-to-image with all of the possible parameters  
-Supports upsampling, face restoration and grid creation  
-> python cli/generate.py
-
-By default uses parameters from  `generate.json`
-
-Parameters that are not specified will be randomized:
-
-- Prompt will be dynamically created from template of random samples: `random.json`
-- Sampler/Scheduler will be randomly picked from available ones
-- CFG Scale set to 5-10
-
-<br>
-
-## Auxiliary Scripts
-
-### Benchmark
-
-> python run-benchmark.py
-
-### Create Previews
-
-Create previews for **embeddings**, **lora**, **lycoris**, **dreambooth** and **hypernetwork**
-
-> python create-previews.py
-
-## Image Grid
-
-> python image-grid.py
-
-### Image Watermark
-
-Create invisible image watermark and remove existing EXIF tags  
-
-> python image-watermark.py
-
-### Image Interrogate
-
-Runs CLiP and Booru image interrogation  
-
-> python image-interrogate.py
-
-### Palette Extract
-
-Extract color palette from image(s)  
-
-> python image-palette.py
-
-### Prompt Ideas
-
-Generate complex prompt ideas
-
-> python prompt-ideas.py
-
-### Prompt Promptist
-
-Attempts to beautify the provided prompt  
-
-> python prompt-promptist.py
-
-### Video Extract
-
-Extract frames from video files  
-
-> python video-extract.py
-
-<br>
-
-## Utility Scripts
-
-### SDAPI
-
-Utility module that handles async communication to Automatic API endpoints  
-Note: Requires SD API  
-
-Can be used to manually execute specific commands:
-> python sdapi.py progress  
-> python sdapi.py interrupt
-> python sdapi.py shutdown
diff --git a/modules/shared.py b/modules/shared.py
index c58110214..a6c15d05e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -438,15 +438,11 @@ options_templates.update(options_section(('sd', "Execution & Models"), {
     "sd_checkpoint_autodownload": OptionInfo(True, "Model auto-download on demand"),
     "sd_textencoder_cache": OptionInfo(True, "Cache text encoder results"),
     "stream_load": OptionInfo(False, "Load models using stream loading method", gr.Checkbox, {"visible": not native }),
-    "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
     "prompt_mean_norm": OptionInfo(False, "Prompt attention normalization", gr.Checkbox),
     "comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": not native }),
     "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "xhinker parser", "A1111 parser", "Fixed attention"] }),
     "latent_history": OptionInfo(16, "Latent history size", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": not native }),
-    "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
-    "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format", gr.Checkbox, {"visible": False}),
-    "diffusers_version": OptionInfo("", "Diffusers version", gr.Textbox, {"visible": False}),
 }))
 
 options_templates.update(options_section(('cuda', "Compute Settings"), {
@@ -460,8 +456,7 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
     "upcast_sampling": OptionInfo(False if sys.platform != "darwin" else True, "Upcast sampling"),
     "upcast_attn": OptionInfo(False, "Upcast attention layer"),
     "cuda_cast_unet": OptionInfo(False, "Fixed UNet precision"),
-    "disable_nan_check": OptionInfo(True, "Disable NaN check", gr.Checkbox, {"visible": False}),
-    "nan_skip": OptionInfo(False, "Skip Generation if NaN found in latents", gr.Checkbox, {"visible": True}),
+    "nan_skip": OptionInfo(False, "Skip Generation if NaN found in latents", gr.Checkbox),
     "rollback_vae": OptionInfo(False, "Attempt VAE roll back for NaN values"),
 
     "cross_attention_sep": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
@@ -534,7 +529,6 @@ options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
     "diffusers_eval": OptionInfo(True, "Force model eval"),
     "diffusers_to_gpu": OptionInfo(False, "Load model directly to GPU"),
     "disable_accelerate": OptionInfo(False, "Disable accelerate"),
-    "diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty", gr.Checkbox, {"visible": False}),
     "diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds", gr.Radio, {"choices": ['default', 'weighted']}),
     "diffusers_zeros_prompt_pad": OptionInfo(False, "Use zeros for prompt padding", gr.Checkbox),
     "huggingface_token": OptionInfo('', 'HuggingFace token'),
@@ -615,9 +609,7 @@ options_templates.update(options_section(('system-paths', "System Paths"), {
     "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
     "unet_dir": OptionInfo(os.path.join(paths.models_path, 'UNET'), "Folder with UNET files", folder=True),
     "te_dir": OptionInfo(os.path.join(paths.models_path, 'Text-encoder'), "Folder with Text encoder files", folder=True),
-    "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
     "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
-    "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
     "styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", folder=True),
     "wildcards_dir": OptionInfo(os.path.join(paths.models_path, 'wildcards'), "Folder with user-defined wildcards", folder=True),
     "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Folder with textual inversion embeddings", folder=True),
@@ -693,12 +685,10 @@ options_templates.update(options_section(('saving-paths', "Image Naming & Paths"
     "saving_sep_images": OptionInfo("<h2>Save options</h2>", "", gr.HTML),
     "save_images_add_number": OptionInfo(True, "Numbered filenames", component_args=hide_dirs),
     "use_original_name_batch": OptionInfo(True, "Batch uses original name"),
-    "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
     "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
     "samples_filename_pattern": OptionInfo("[seq]-[model_name]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
     "directories_max_prompt_words": OptionInfo(8, "Max words per pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
 
     "outdir_sep_dirs": OptionInfo("<h2>Folders</h2>", "", gr.HTML),
     "outdir_samples": OptionInfo("", "Images folder", component_args=hide_dirs, folder=True),
@@ -711,8 +701,6 @@ options_templates.update(options_section(('saving-paths', "Image Naming & Paths"
     "outdir_init_images": OptionInfo("outputs/init-images", "Folder for init images", component_args=hide_dirs, folder=True),
 
     "outdir_sep_grids": OptionInfo("<h2>Grids</h2>", "", gr.HTML),
-    "grid_extended_filename": OptionInfo(True, "Add extended info to filename when saving grid", gr.Checkbox, {"visible": False}),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
     "outdir_grids": OptionInfo("", "Grids folder", component_args=hide_dirs, folder=True),
     "outdir_txt2img_grids": OptionInfo("outputs/grids", 'Folder for txt2img grids', component_args=hide_dirs, folder=True),
     "outdir_img2img_grids": OptionInfo("outputs/grids", 'Folder for img2img grids', component_args=hide_dirs, folder=True),
@@ -725,7 +713,6 @@ options_templates.update(options_section(('ui', "User Interface Options"), {
     "gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": theme.list_themes()}, refresh=theme.refresh_themes),
     "autolaunch": OptionInfo(False, "Autolaunch browser upon startup"),
     "font_size": OptionInfo(14, "Font size", gr.Slider, {"minimum": 8, "maximum": 32, "step": 1, "visible": True}),
-    "tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
     "aspect_ratios": OptionInfo("1:1, 4:3, 3:2, 16:9, 16:10, 21:9, 2:3, 3:4, 9:16, 10:16, 9:21", "Allowed aspect ratios"),
     "motd": OptionInfo(True, "Show MOTD"),
     "compact_view": OptionInfo(False, "Compact view"),
@@ -735,22 +722,14 @@ options_templates.update(options_section(('ui', "User Interface Options"), {
     "disable_weights_auto_swap": OptionInfo(True, "Do not change selected model when reading generation parameters"),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
-    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
-    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
-    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }),
     "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", gr.Dropdown, lambda: {"multiselect":True, "choices": list(opts.data_labels.keys())}),
-    "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
 }))
 
 options_templates.update(options_section(('live-preview', "Live Previews"), {
-    "show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
-    "live_previews_enable": OptionInfo(True, "Show live previews", gr.Checkbox, {"visible": False}),
-    "show_progress_grid": OptionInfo(True, "Show previews as a grid", gr.Checkbox, {"visible": False}),
     "notification_audio_enable": OptionInfo(False, "Play a notification upon completion"),
     "notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs, folder=True),
     "show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     "show_progress_type": OptionInfo("Approximate", "Live preview method", gr.Radio, {"choices": ["Simple", "Approximate", "TAESD", "Full VAE"]}),
-    "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
     "live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
     "live_preview_taesd_layers": OptionInfo(3, "TAESD decode layers", gr.Slider, {"minimum": 1, "maximum": 3, "step": 1}),
     "logmonitor_show": OptionInfo(True, "Show log view"),
@@ -797,8 +776,6 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
     'uni_pc_variant': OptionInfo("bh2", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"], "visible": not native}),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"], "visible": not native}),
     "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad'], "visible": not native}),
-    "pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
-    "batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing"), {
@@ -808,12 +785,10 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "postprocessing_sep_img2img": OptionInfo("<h2>Img2Img & Inpainting</h2>", "", gr.HTML),
     "img2img_color_correction": OptionInfo(False, "Apply color correction"),
     "mask_apply_overlay": OptionInfo(True, "Apply mask as overlay"),
-    "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
     "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", gr.ColorPicker, {}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
     "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
 
     # "postprocessing_sep_detailer": OptionInfo("<h2>Detailer</h2>", "", gr.HTML),
     "detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
@@ -835,7 +810,6 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
 
     "postprocessing_sep_upscalers": OptionInfo("<h2>Upscaling</h2>", "", gr.HTML),
     "upscaler_unload": OptionInfo(False, "Unload upscaler after processing"),
-    "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
     "upscaler_tile_size": OptionInfo(192, "Upscaler tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "upscaler_tile_overlap": OptionInfo(8, "Upscaler tile overlap", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))
@@ -846,28 +820,12 @@ options_templates.update(options_section(('control', "Control Options"), {
     "control_unload_processor": OptionInfo(False, "Processor unload after use"),
 }))
 
-options_templates.update(options_section(('interrogate', "Interrogate"), { # "Training" section disabled so just a placeholder
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training", gr.Checkbox, { "visible": False }),
-    "pin_memory": OptionInfo(True, "Pin training dataset to memory", gr.Checkbox, { "visible": False }),
-    "save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training", gr.Checkbox, { "visible": False }),
-    "save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file", gr.Checkbox, { "visible": False }),
-    "dataset_filename_word_regex": OptionInfo("", "Filename word regex", gr.Textbox, { "visible": False }),
-    "dataset_filename_join_string": OptionInfo(" ", "Filename join string", gr.Textbox, { "visible": False }),
-    "embeddings_templates_dir": OptionInfo("", "Embeddings train templates directory", gr.Textbox, { "visible": False }),
-    "training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1, "visible": False }),
-    "training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps", gr.Number, { "visible": False }),
-    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging", gr.Checkbox, { "visible": False }),
-    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard", gr.Checkbox, { "visible": False }),
-    "training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period", gr.Number, { "visible": False }),
-}))
-
 options_templates.update(options_section(('interrogate', "Interrogate"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
     "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "Interrogate: skip categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
@@ -888,7 +846,6 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
     "extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
     "extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
     "extra_networks_fetch": OptionInfo(True, "UI fetch network info on mouse-over"),
-    "extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
     "extra_network_skip_indexing": OptionInfo(False, "Build info on first access", gr.Checkbox),
 
     "extra_networks_model_sep": OptionInfo("<h2>Models</h2>", "", gr.HTML),
@@ -909,17 +866,59 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
     "lora_apply_tags": OptionInfo(0, "LoRA auto-apply tags", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
     "lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
     "lora_quant": OptionInfo("NF4","LoRA precision in quantized models", gr.Radio, {"choices": ["NF4", "FP4"]}),
-    "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
     "lora_load_gpu": OptionInfo(True if not cmd_opts.lowvram else False, "Load LoRA directly to GPU"),
+}))
 
-    "hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support", gr.Checkbox, {"visible": False}),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
+options_templates.update(options_section((None, "Internal options"), {
+    "diffusers_version": OptionInfo("", "Diffusers version", gr.Textbox, {"visible": False}),
+    "disabled_extensions": OptionInfo([], "Disable these extensions"),
+    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+    "tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
 }))
 
 options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable these extensions"),
+    "batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
+    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string", gr.Textbox, { "visible": False }),
+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex", gr.Textbox, { "visible": False }),
+    "diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty", gr.Checkbox, {"visible": False}),
     "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "user", "all"]}),
-    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+    "disable_nan_check": OptionInfo(True, "Disable NaN check", gr.Checkbox, {"visible": False}),
+    "embeddings_templates_dir": OptionInfo("", "Embeddings train templates directory", gr.Textbox, { "visible": False }),
+    "extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
+    "grid_extended_filename": OptionInfo(True, "Add extended info to filename when saving grid", gr.Checkbox, {"visible": False}),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
+    "hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support", gr.Checkbox, {"visible": False}),
+    "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
+    "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
+    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }),
+    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
+    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
+    "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
+    "live_previews_enable": OptionInfo(True, "Show live previews", gr.Checkbox, {"visible": False}),
+    "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
+    "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
+    "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
+    "pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
+    "pin_memory": OptionInfo(True, "Pin training dataset to memory", gr.Checkbox, { "visible": False }),
+    "save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training", gr.Checkbox, { "visible": False }),
+    "save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file", gr.Checkbox, { "visible": False }),
+    "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format", gr.Checkbox, {"visible": False}),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
+    "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
+    "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
+    "show_progress_grid": OptionInfo(True, "Show previews as a grid", gr.Checkbox, {"visible": False}),
+    "show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
+    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging", gr.Checkbox, { "visible": False }),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1, "visible": False }),
+    "training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period", gr.Number, { "visible": False }),
+    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard", gr.Checkbox, { "visible": False }),
+    "training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps", gr.Number, { "visible": False }),
+    "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training", gr.Checkbox, { "visible": False }),
+    "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
+    "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
 }))
 
 options_templates.update()
diff --git a/wiki b/wiki
index 371344bcb..c797e6e2e 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 371344bcbf8da64f7ae373d1d6d1312ef44898c3
+Subproject commit c797e6e2ea491c39e14d78c88dbf4d8a26cd8c70

From 34d6d5f92b96b757d94eb2b89fd76efb4ac568e5 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 08:53:42 -0500
Subject: [PATCH 042/119] update

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/sdnext-modernui | 2 +-
 modules/prompt_parser_diffusers.py | 7 ++++---
 wiki                               | 2 +-
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 895addec9..257be050a 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 895addec9ef65498ed44311d27db0adf699e512d
+Subproject commit 257be050afd46a21e77cc9fe60a04d30ed5ffbe4
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 678649a66..eec6b0d32 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -167,13 +167,14 @@ class PromptEmbedder:
         pipe = prepare_model()
 
     def __call__(self, key, step=0):
-        batch = getattr(self, key)
+        batch = getattr(self, key) # for batch-size=1, len(batch)==1
         res = []
         for i in range(self.batchsize):
-            if len(batch[i]) == 0:
+            if len(batch[i]) == 0: # if not using prompt-scheduling, this will be len(batch[i])==1
                 return None
             else:
-                res.append(batch[i][step])
+                # causes error in callback
+                res.append(batch[i][step]) # and this requests element for specific step when called from callback - but self.scheduled_prompt==False so len(batch[i])==1 and step is list index out-of-bounds!
                 if step != 0:  # For Callback
                     res.append(batch[i][step])  # Diffusers internally doubles batch dimension
         return torch.cat(res)
diff --git a/wiki b/wiki
index 2dba58a69..47ea50e91 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 2dba58a6962b70e92a077dcda8f178f5e811f175
+Subproject commit 47ea50e9152a13325dd1daf92bc50b700783182f

From dab2827dab3dff568d8da3ea4b1e67086d31d52f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 09:49:46 -0500
Subject: [PATCH 043/119] add api override field

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/api-txt2img.js      | 19 +---------
 modules/api/control.py  |  2 +
 modules/api/generate.py |  4 ++
 modules/api/models.py   |  2 +
 modules/images.py       | 84 +++++++++++++++++++++--------------------
 wiki                    |  2 +-
 6 files changed, 54 insertions(+), 59 deletions(-)

diff --git a/cli/api-txt2img.js b/cli/api-txt2img.js
index 46d09b3a2..8d0e9f5d1 100755
--- a/cli/api-txt2img.js
+++ b/cli/api-txt2img.js
@@ -20,23 +20,6 @@ const sd_options = {
   cfg_scale: 6,
   width: 512,
   height: 512,
-  /*
-  // enable second pass
-  enable_hr: true,
-  // second pass: upscale
-  hr_upscaler: 'SCUNet GAN',
-  hr_scale: 2.0,
-  // second pass: hires
-  hr_force: true,
-  hr_second_pass_steps: 20,
-  hr_sampler_name: 'UniPC',
-  denoising_strength: 0.5,
-  // second pass: refiner
-  refiner_steps: 5,
-  refiner_start: 0.8,
-  refiner_prompt: '',
-  refiner_negative: '',
-  */
   // api return options
   save_images: false,
   send_images: true,
@@ -55,7 +38,7 @@ async function main() {
     const json = await res.json();
     console.log('result:', json.info);
     for (const i in json.images) { // eslint-disable-line guard-for-in
-      const f = `/tmp/test-{${i}.jpg`;
+      const f = `/tmp/test-${i}.jpg`;
       fs.writeFileSync(f, atob(json.images[i]), 'binary');
       console.log('image saved:', f);
     }
diff --git a/modules/api/control.py b/modules/api/control.py
index cf8916095..ffb000053 100644
--- a/modules/api/control.py
+++ b/modules/api/control.py
@@ -31,6 +31,7 @@ ReqControl = models.create_model_from_signature(
         {"key": "ip_adapter", "type": Optional[List[models.ItemIPAdapter]], "default": None, "exclude": True},
         {"key": "face", "type": Optional[models.ItemFace], "default": None, "exclude": True},
         {"key": "control", "type": Optional[List[ItemControl]], "default": [], "exclude": True},
+        {"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
     ]
 )
 
@@ -159,6 +160,7 @@ class APIControl():
             output_processed = []
             output_info = ''
             run.control_set({ 'do_not_save_grid': not req.save_images, 'do_not_save_samples': not req.save_images, **self.prepare_ip_adapter(req) })
+            run.control_set(getattr(req, "extra", {}))
             res = run.control_run(**args)
             for item in res:
                 if len(item) > 0 and (isinstance(item[0], list) or item[0] is None): # output_images
diff --git a/modules/api/generate.py b/modules/api/generate.py
index aeafa05a0..e22102057 100644
--- a/modules/api/generate.py
+++ b/modules/api/generate.py
@@ -106,6 +106,8 @@ class APIGenerate():
             p.scripts = script_runner
             p.outpath_grids = shared.opts.outdir_grids or shared.opts.outdir_txt2img_grids
             p.outpath_samples = shared.opts.outdir_samples or shared.opts.outdir_txt2img_samples
+            for key, value in getattr(txt2imgreq, "extra", {}).items():
+                setattr(p, key, value)
             shared.state.begin('API TXT', api=True)
             script_args = script.init_script_args(p, txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
             if selectable_scripts is not None:
@@ -150,6 +152,8 @@ class APIGenerate():
             p.scripts = script_runner
             p.outpath_grids = shared.opts.outdir_img2img_grids
             p.outpath_samples = shared.opts.outdir_img2img_samples
+            for key, value in getattr(img2imgreq, "extra", {}).items():
+                setattr(p, key, value)
             shared.state.begin('API-IMG', api=True)
             script_args = script.init_script_args(p, img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
             if selectable_scripts is not None:
diff --git a/modules/api/models.py b/modules/api/models.py
index 3cf3aade9..740f3c555 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -210,6 +210,7 @@ ReqTxt2Img = PydanticModelGenerator(
         {"key": "alwayson_scripts", "type": dict, "default": {}},
         {"key": "ip_adapter", "type": Optional[List[ItemIPAdapter]], "default": None, "exclude": True},
         {"key": "face", "type": Optional[ItemFace], "default": None, "exclude": True},
+        {"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
     ]
 ).generate_model()
 StableDiffusionTxt2ImgProcessingAPI = ReqTxt2Img
@@ -235,6 +236,7 @@ ReqImg2Img = PydanticModelGenerator(
         {"key": "alwayson_scripts", "type": dict, "default": {}},
         {"key": "ip_adapter", "type": Optional[List[ItemIPAdapter]], "default": None, "exclude": True},
         {"key": "face_id", "type": Optional[ItemFace], "default": None, "exclude": True},
+        {"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
     ]
 ).generate_model()
 StableDiffusionImg2ImgProcessingAPI = ReqImg2Img
diff --git a/modules/images.py b/modules/images.py
index fb9cc9652..910349bef 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -40,8 +40,6 @@ def atomically_save_image():
         except Exception:
             shared.log.warning(f'Save: unknown image format: {extension}')
             image_format = 'JPEG'
-        if shared.opts.image_watermark_enabled or (shared.opts.image_watermark_position != 'none' and shared.opts.image_watermark_image != ''):
-            image = set_watermark(image, shared.opts.image_watermark)
         exifinfo = (exifinfo or "") if shared.opts.image_metadata else ""
         # additional metadata saved in files
         if shared.opts.save_txt and len(exifinfo) > 0:
@@ -153,6 +151,11 @@ def save_image(image,
         info = image.info.get(pnginfo_section_name, '')
     if info is not None:
         pnginfo[pnginfo_section_name] = info
+
+    wm_text = getattr(p, 'watermark_text', shared.opts.image_watermark)
+    wm_image = getattr(p, 'watermark_image', shared.opts.image_watermark_image)
+    image = set_watermark(image, wm_text, wm_image)
+
     params = script_callbacks.ImageSaveParams(image, p, filename, pnginfo)
     params.filename = namegen.sanitize(filename)
     dirname = os.path.dirname(params.filename)
@@ -369,45 +372,46 @@ def draw_overlay(im, text: str = '', y_offset: int = 0):
     return im
 
 
-def set_watermark(image, watermark):
-    if shared.opts.image_watermark_position != 'none': # visible watermark
-        wm_image = None
-        try:
-            wm_image = Image.open(shared.opts.image_watermark_image)
-            if wm_image.mode != 'RGBA':
-                wm_image = wm_image.convert('RGBA')
-        except Exception as e:
-            shared.log.warning(f'Set image watermark: fn="{shared.opts.image_watermark_image}" {e}')
-        if wm_image is not None:
-            if shared.opts.image_watermark_position == 'top/left':
-                position = (0, 0)
-            elif shared.opts.image_watermark_position == 'top/right':
-                position = (image.width - wm_image.width, 0)
-            elif shared.opts.image_watermark_position == 'bottom/left':
-                position = (0, image.height - wm_image.height)
-            elif shared.opts.image_watermark_position == 'bottom/right':
-                position = (image.width - wm_image.width, image.height - wm_image.height)
-            elif shared.opts.image_watermark_position == 'center':
-                position = ((image.width - wm_image.width) // 2, (image.height - wm_image.height) // 2)
-            else:
-                position = (random.randint(0, image.width - wm_image.width), random.randint(0, image.height - wm_image.height))
+def set_watermark(image, wm_text: str = None, wm_image: Image.Image = None):
+    if shared.opts.image_watermark_position != 'none' and wm_image is not None: # visible watermark
+        if isinstance(wm_image, str):
             try:
-                for x in range(wm_image.width):
-                    for y in range(wm_image.height):
-                        rgba = wm_image.getpixel((x, y))
-                        orig = image.getpixel((x+position[0], y+position[1]))
-                        # alpha blend
-                        a = rgba[3] / 255
-                        r = int(rgba[0] * a + orig[0] * (1 - a))
-                        g = int(rgba[1] * a + orig[1] * (1 - a))
-                        b = int(rgba[2] * a + orig[2] * (1 - a))
-                        if not a == 0:
-                            image.putpixel((x+position[0], y+position[1]), (r, g, b))
-                shared.log.debug(f'Set image watermark: fn="{shared.opts.image_watermark_image}" image={wm_image} position={position}')
+                wm_image = Image.open(wm_image)
             except Exception as e:
                 shared.log.warning(f'Set image watermark: image={wm_image} {e}')
+                return image
+        if isinstance(wm_image, Image.Image):
+            if wm_image.mode != 'RGBA':
+                wm_image = wm_image.convert('RGBA')
+        if shared.opts.image_watermark_position == 'top/left':
+            position = (0, 0)
+        elif shared.opts.image_watermark_position == 'top/right':
+            position = (image.width - wm_image.width, 0)
+        elif shared.opts.image_watermark_position == 'bottom/left':
+            position = (0, image.height - wm_image.height)
+        elif shared.opts.image_watermark_position == 'bottom/right':
+            position = (image.width - wm_image.width, image.height - wm_image.height)
+        elif shared.opts.image_watermark_position == 'center':
+            position = ((image.width - wm_image.width) // 2, (image.height - wm_image.height) // 2)
+        else:
+            position = (random.randint(0, image.width - wm_image.width), random.randint(0, image.height - wm_image.height))
+        try:
+            for x in range(wm_image.width):
+                for y in range(wm_image.height):
+                    rgba = wm_image.getpixel((x, y))
+                    orig = image.getpixel((x+position[0], y+position[1]))
+                    # alpha blend
+                    a = rgba[3] / 255
+                    r = int(rgba[0] * a + orig[0] * (1 - a))
+                    g = int(rgba[1] * a + orig[1] * (1 - a))
+                    b = int(rgba[2] * a + orig[2] * (1 - a))
+                    if not a == 0:
+                        image.putpixel((x+position[0], y+position[1]), (r, g, b))
+            shared.log.debug(f'Set image watermark: image={wm_image} position={position}')
+        except Exception as e:
+            shared.log.warning(f'Set image watermark: image={wm_image} {e}')
 
-    if shared.opts.image_watermark_enabled: # invisible watermark
+    if shared.opts.image_watermark_enabled and wm_text is not None: # invisible watermark
         from imwatermark import WatermarkEncoder
         wm_type = 'bytes'
         wm_method = 'dwtDctSvd'
@@ -416,16 +420,16 @@ def set_watermark(image, watermark):
         info = image.info
         data = np.asarray(image)
         encoder = WatermarkEncoder()
-        text = f"{watermark:<{length}}"[:length]
+        text = f"{wm_text:<{length}}"[:length]
         bytearr = text.encode(encoding='ascii', errors='ignore')
         try:
             encoder.set_watermark(wm_type, bytearr)
             encoded = encoder.encode(data, wm_method)
             image = Image.fromarray(encoded)
             image.info = info
-            shared.log.debug(f'Set invisible watermark: {watermark} method={wm_method} bits={wm_length}')
+            shared.log.debug(f'Set invisible watermark: {wm_text} method={wm_method} bits={wm_length}')
         except Exception as e:
-            shared.log.warning(f'Set invisible watermark error: {watermark} method={wm_method} bits={wm_length} {e}')
+            shared.log.warning(f'Set invisible watermark error: {wm_text} method={wm_method} bits={wm_length} {e}')
 
     return image
 
diff --git a/wiki b/wiki
index c797e6e2e..47ea50e91 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit c797e6e2ea491c39e14d78c88dbf4d8a26cd8c70
+Subproject commit 47ea50e9152a13325dd1daf92bc50b700783182f

From 68bc8634a9e8d6308c41ed39c677fa19f16440dd Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Fri, 8 Nov 2024 10:04:02 -0600
Subject: [PATCH 044/119] pulid inpaint, XYZ broken

---
 modules/pulid/pulid_sampling.py | 37 +++++++++++++---
 modules/pulid/pulid_sdxl.py     | 76 ++++++++++++++++++++++++---------
 2 files changed, 86 insertions(+), 27 deletions(-)

diff --git a/modules/pulid/pulid_sampling.py b/modules/pulid/pulid_sampling.py
index 9996f035a..e319c0d27 100644
--- a/modules/pulid/pulid_sampling.py
+++ b/modules/pulid/pulid_sampling.py
@@ -67,6 +67,15 @@ def default_noise_sampler(x):
     return lambda sigma, sigma_next: torch.randn_like(x)
 
 
+def inpaint_mask(x, i, steps, mask_args):
+    noised_original = mask_args["latent"].clone().to(x)
+    latent_mask = mask_args["latent_mask"].to(x)
+    if i < steps:
+        noised_original += mask_args["noise"].to(x) * mask_args["sigmas"][i+1].to(x)
+    x = (latent_mask * x) + ((1 - latent_mask) * noised_original.to(x))
+    return x
+
+
 class BatchedBrownianTree:
     """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
 
@@ -120,7 +129,7 @@ class BrownianTreeNoiseSampler:
 
 
 @torch.no_grad()
-def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., mask_args=None):
     """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
@@ -137,11 +146,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
         dt = sigmas[i + 1] - sigma_hat
         # Euler method
         x = x + (d * dt).to(x.dtype)
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, mask_args=None):
     """Ancestral sampling with Euler method steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -157,6 +168,8 @@ def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
         x = x + (d * dt).to(x.dtype)
         if sigmas[i + 1] > 0:
             x = x + (noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up).to(x.dtype)
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
@@ -375,7 +388,7 @@ class DPMSolver(nn.Module):
 
 
 @torch.no_grad()
-def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, mask_args=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -405,11 +418,13 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
         # Noise addition
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2, mask_args=None):
     """DPM-Solver++ (stochastic)."""
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
@@ -447,11 +462,13 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
             denoised_d = (1 - fac) * denoised + fac * denoised_2
             x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
             x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None, mask_args=None):
     """DPM-Solver++(2M)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
@@ -473,11 +490,13 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
             denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
             x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
         old_denoised = denoised
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint', mask_args=None):
     """DPM-Solver++(2M) SDE."""
 
     if solver_type not in {'heun', 'midpoint'}:
@@ -518,11 +537,13 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 
         old_denoised = denoised
         h_last = h
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, mask_args=None):
     """DPM-Solver++(3M) SDE."""
 
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
@@ -568,4 +589,6 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 
         denoised_1, denoised_2 = denoised, denoised_1
         h_1, h_2 = h, h_1
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 2392219c0..fade7509d 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -260,22 +260,40 @@ class StableDiffusionXLPuLIDPipeline:
             self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
         return latent
 
-    def init_latent(self, seed, size, image, strength): # pylint: disable=unused-argument
+    def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
         # standard txt2img will full noise
         noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
         noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
-        if image is not None and strength > 0:
+        if strength > 0 and image is not None:
             image = self.pipe.image_processor.preprocess(image)
-            latents = self.pipe.prepare_latents(
-                image,
-                None,  # timestep (not needed)
-                1,  # batch_size
-                1,  # num_images_per_prompt
-                noise.dtype,
-                noise.device,
-                None,  # generator
-                False,  # add_noise
-            )
+            if mask_image is not None:  # Inpaint
+                latents = self.pipe.prepare_latents(1,  # batch_size,
+                                                    self.pipe.vae.config.latent_channels,  # num_channels_latents
+                                                    height,
+                                                    width,
+                                                    noise.dtype,
+                                                    noise.device,
+                                                    None,  # generator
+                                                    latents=None,
+                                                    image=image,
+                                                    timestep=1000,
+                                                    is_strength_max=False,
+                                                    add_noise=False,
+                                                    return_noise=False,
+                                                    return_image_latents=False,
+                                                    )
+                latents = latents[0]
+            else:  # img2img
+                latents = self.pipe.prepare_latents(image,
+                                                    None,  # timestep (not needed)
+                                                    1,  # batch_size
+                                                    1,  # num_images_per_prompt
+                                                    noise.dtype,
+                                                    noise.device,
+                                                    None,  # generator
+                                                    False,  # add_noise
+                                                    )
+
         else:
             latents = torch.zeros_like(noise)
 
@@ -309,8 +327,8 @@ class StableDiffusionXLPuLIDPipeline:
 
 
         # latents
-        latents, noise = self.init_latent(seed, size, image, strength)
-        latents = latents + noise * sigmas[0].to(noise)
+        latent, noise = self.init_latent(seed, size, image, mask_image, strength, width, height)
+        noisy_latent = latent + noise * sigmas[0].to(noise)
 
         (
             prompt_embeds,
@@ -339,17 +357,35 @@ class StableDiffusionXLPuLIDPipeline:
                 cross_attention_kwargs={'id_embedding': uncond_id_embedding, 'id_scale': id_scale},
             ),
         )
+        if mask_image is not None:
+            latent_mask = torch.Tensor(np.asarray(mask_image.convert("L").resize((noisy_latent.shape[-1], noisy_latent.shape[-2])))).reshape((noisy_latent.shape[-2], noisy_latent.shape[-1]))
+            latent_mask /= latent_mask.max()
+            mask_args = dict(
+                latent=latent,
+                latent_mask=latent_mask,
+                noise=noise,
+                sigmas=sigmas,
+            )
+        else:
+            mask_args = None
 
-        latents = self.sampler(self.sample, latents, sigmas, extra_args=sampler_kwargs, disable=False)
+        latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
         latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
         images = self.pipe.vae.decode(latents).sample
         images = self.pipe.image_processor.postprocess(images, output_type='pil')
 
-        if mask_image is not None:
-            # TODO: pulid inpaint
-            # easiest inpaint is to use normal img2img and then combine output with input using mask
-            # note that mask can be binary or grayscale (soft mask)
-            raise NotImplementedError(f'PuLID: task=inpaint class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} mask_image={mask_image}')
+        # Pixel space final mask
+        # if mask_image is not None:
+        #     # TODO: Fix XYZ
+        #      from PIL import Image
+        #     mask_image = np.asarray(mask_image.convert("L"))
+        #     mask_image = mask_image / mask_image.max()
+        #     mask_image = mask_image.reshape(1,mask_image.shape[0],mask_image.shape[1],1)
+        #     image = np.asarray(image).astype(mask_image.dtype)
+        #     images = np.asarray(images).astype(mask_image.dtype)
+        #     images = ((1 - mask_image) * image) + (mask_image * images)
+        #     images = images[0].round().astype(np.uint8)
+        #     images = [Image.fromarray(images)]
 
         return images
 

From 0e10feaac347225e02fb5e65f07470386982e217 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 11:32:47 -0500
Subject: [PATCH 045/119] sort and describe all scripts

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                      |  3 +++
 javascript/extraNetworks.js       |  4 +++-
 modules/face/__init__.py          |  4 ++--
 modules/scripts.py                |  4 +++-
 modules/ui_common.py              |  2 +-
 scripts/animatediff.py            |  2 +-
 scripts/apg.py                    |  4 ++--
 scripts/blipdiffusion.py          | 11 ++++-------
 scripts/cogvideo.py               |  2 +-
 scripts/consistory_ext.py         |  2 +-
 scripts/ctrlx.py                  |  4 ++--
 scripts/demofusion.py             |  4 ++--
 scripts/differential_diffusion.py |  4 ++--
 scripts/hdr.py                    |  4 ++--
 scripts/image2video.py            |  3 ++-
 scripts/instantir.py              |  2 +-
 scripts/k_diff.py                 |  4 ++--
 scripts/layerdiffuse.py           |  4 ++--
 scripts/ledits.py                 |  4 ++--
 scripts/lut.py                    |  2 +-
 scripts/mixture_tiling.py         |  4 ++--
 scripts/mulan.py                  |  4 ++--
 scripts/pulid_ext.py              |  2 +-
 scripts/resadapter.py             |  4 ++--
 scripts/sd_upscale.py             |  2 +-
 scripts/stablevideodiffusion.py   |  2 +-
 scripts/t_gate.py                 |  4 ++--
 scripts/text2video.py             |  2 +-
 wiki                              |  2 +-
 29 files changed, 52 insertions(+), 47 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d78943f42..b989132f2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -58,6 +58,7 @@ This release can be considered an LTS release before we kick off the next round
     - create video from generated grid images  
       supports all standard video types and interpolation  
   - UI:  
+    - better gallery and networks sidebar sizing
     - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
     - add show networks on startup setting  
     - better mapping of networks previews  
@@ -68,6 +69,8 @@ This release can be considered an LTS release before we kick off the next round
     - Auto-remove invalid packages from `venv/site-packages`  
       e.g. packages starting with `~` which are left-over due to windows access violation  
     - Requirements: update  
+  - Scripts:
+    - More verbose descriptions for all scripts
   - Model loader:
     - Report modules included in safetensors when attempting to load a model  
   - CLI:  
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 9a33baa86..77fe125f3 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -481,11 +481,13 @@ function setupExtraNetworksForTab(tabname) {
           en.style.position = 'absolute';
           en.style.height = 'auto';
           en.style.width = `${window.opts.extra_networks_sidebar_width}vw`;
+          en.style.maxWidth = '655px';
           en.style.right = '0';
           en.style.top = '13em';
           en.style.transition = 'width 0.3s ease';
           en.style.zIndex = 100;
-          gradioApp().getElementById(`${tabname}_settings`).parentNode.style.width = `${100 - 2 - window.opts.extra_networks_sidebar_width}vw`;
+          // gradioApp().getElementById(`${tabname}_settings`).parentNode.style.width = `${100 - 2 - window.opts.extra_networks_sidebar_width}vw`;
+          gradioApp().getElementById(`${tabname}_settings`).parentNode.style.width = `calc(100vw - 2em - min(${window.opts.extra_networks_sidebar_width}vw, 655px))`;
         } else {
           en.style.position = 'relative';
           en.style.height = 'unset';
diff --git a/modules/face/__init__.py b/modules/face/__init__.py
index aae3cdaa2..19af6d01b 100644
--- a/modules/face/__init__.py
+++ b/modules/face/__init__.py
@@ -9,7 +9,7 @@ debug = shared.log.trace if os.environ.get('SD_FACE_DEBUG', None) is not None el
 
 class Script(scripts.Script):
     def title(self):
-        return 'Face'
+        return 'Face: Multiple ID Transfers'
 
     def show(self, is_img2img):
         return True if shared.native else False
@@ -45,7 +45,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML("<span>&nbsp Face module</span><br>")
+            gr.HTML("<span>&nbsp Face: Multiple ID Transfers</span><br>")
         with gr.Row():
             mode = gr.Dropdown(label='Mode', choices=['None', 'FaceID', 'FaceSwap', 'InstantID', 'PhotoMaker'], value='None')
         with gr.Group(visible=False) as cfg_faceid:
diff --git a/modules/scripts.py b/modules/scripts.py
index 8a67d0a50..cf2cf25b9 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -352,7 +352,9 @@ class ScriptRunner:
         self.selectable_scripts.clear()
         auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
 
-        for script_class, path, _basedir, _script_module in auto_processing_scripts + scripts_data:
+        all_scripts = auto_processing_scripts + scripts_data
+        sorted_scripts = sorted(all_scripts, key=lambda x: x.script_class().title().lower())
+        for script_class, path, _basedir, _script_module in sorted_scripts:
             try:
                 script = script_class()
                 script.filename = path
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 9ad87c17f..9c4bb5cdc 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -246,7 +246,7 @@ def create_output_panel(tabname, preview=True, prompt=None, height=None):
             # columns are for <576px, <768px, <992px, <1200px, <1400px, >1400px
             result_gallery = gr.Gallery(value=[],
                                         label='Output', show_label=False, show_download_button=True, allow_preview=True, container=False, preview=preview,
-                                        columns=5, object_fit='scale-down', height=height,
+                                        columns=4, object_fit='scale-down', height=height,
                                         elem_id=f"{tabname}_gallery",
                                         )
             if prompt is not None:
diff --git a/scripts/animatediff.py b/scripts/animatediff.py
index 09f9e33a9..c4d7ba68f 100644
--- a/scripts/animatediff.py
+++ b/scripts/animatediff.py
@@ -189,7 +189,7 @@ def set_free_noise(frames):
 
 class Script(scripts.Script):
     def title(self):
-        return 'AnimateDiff'
+        return 'Video AnimateDiff'
 
     def show(self, is_img2img):
         # return scripts.AlwaysVisible if shared.native else False
diff --git a/scripts/apg.py b/scripts/apg.py
index 6a3020c38..0476da246 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -9,14 +9,14 @@ class Script(scripts.Script):
         self.register()
 
     def title(self):
-        return 'APG'
+        return 'APG: Adaptive Projected Guidance'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
 
     def ui(self, _is_img2img): # ui elements
         with gr.Row():
-            gr.HTML('<a href="https://arxiv.org/abs/2410.02416">&nbsp APG: Adaptive projected guidance</a><br>')
+            gr.HTML('<a href="https://arxiv.org/abs/2410.02416">&nbsp APG: Adaptive Projected Guidance</a><br>')
         with gr.Row():
             eta = gr.Slider(label="ETA", value=1.0, minimum=0, maximum=2.0, step=0.05)
             momentum = gr.Slider(label="Momentum", value=-0.50, minimum=-1.0, maximum=1.0, step=0.05)
diff --git a/scripts/blipdiffusion.py b/scripts/blipdiffusion.py
index 39d8974e1..0acc80929 100644
--- a/scripts/blipdiffusion.py
+++ b/scripts/blipdiffusion.py
@@ -2,19 +2,16 @@ import gradio as gr
 from modules import scripts, processing, shared, sd_models
 
 
-title = 'BLIP Diffusion'
-
-
 class Script(scripts.Script):
     def title(self):
-        return title
+        return 'BLIP Diffusion: Controllable Generation and Editing'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://huggingface.co/salesforce/blipdiffusion">&nbsp BLIP Diffusion</a><br>')
+            gr.HTML('<a href="https://huggingface.co/salesforce/blipdiffusion">&nbsp BLIP Diffusion: Controllable Generation and Editing</a><br>')
         with gr.Row():
             source_subject = gr.Textbox(value='', label='Source subject')
         with gr.Row():
@@ -26,7 +23,7 @@ class Script(scripts.Script):
     def run(self, p: processing.StableDiffusionProcessing, source_subject, target_subject, prompt_strength): # pylint: disable=arguments-differ, unused-argument
         c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
         if c != 'BlipDiffusionPipeline':
-            shared.log.error(f'{title}: model selected={c} required=BLIPDiffusion')
+            shared.log.error(f'BLIP: model selected={c} required=BLIPDiffusion')
             return None
         if hasattr(p, 'init_images') and len(p.init_images) > 0:
             p.task_args['reference_image'] = p.init_images[0]
@@ -41,5 +38,5 @@ class Script(scripts.Script):
             processed = processing.process_images(p)
             return processed
         else:
-            shared.log.error(f'{title}: no init_images')
+            shared.log.error('BLIP: no init_images')
             return None
diff --git a/scripts/cogvideo.py b/scripts/cogvideo.py
index a4a3141d4..7f2c7225e 100644
--- a/scripts/cogvideo.py
+++ b/scripts/cogvideo.py
@@ -22,7 +22,7 @@ debug = (os.environ.get('SD_LOAD_DEBUG', None) is not None) or (os.environ.get('
 
 class Script(scripts.Script):
     def title(self):
-        return 'CogVideoX'
+        return 'Video CogVideoX'
 
     def show(self, is_img2img):
         return shared.native
diff --git a/scripts/consistory_ext.py b/scripts/consistory_ext.py
index b7454aa75..45de1ea6c 100644
--- a/scripts/consistory_ext.py
+++ b/scripts/consistory_ext.py
@@ -22,7 +22,7 @@ class Script(scripts.Script):
         self.anchor_cache_second_stage = None
 
     def title(self):
-        return 'ConsiStory'
+        return 'ConsiStory: Consistent Image Generation'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native and shared.cmd_opts.experimental else False
diff --git a/scripts/ctrlx.py b/scripts/ctrlx.py
index acfdd5d6e..bba8c846b 100644
--- a/scripts/ctrlx.py
+++ b/scripts/ctrlx.py
@@ -7,14 +7,14 @@ from modules import shared, scripts, processing, processing_helpers, sd_models,
 
 class Script(scripts.Script):
     def title(self):
-        return 'Ctrl-X'
+        return 'Ctrl-X: Controlling Structure and Appearance'
 
     def show(self, is_img2img):
         return shared.native
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/genforce/ctrl-x">&nbsp Ctrl-X</a><br>')
+            gr.HTML('<a href="https://github.com/genforce/ctrl-x">&nbsp Ctrl-X: Controlling Structure and Appearance</a><br>')
         with gr.Accordion(label='Structure', open=True):
             with gr.Row():
                 struct_prompt = gr.Textbox(label='Prompt', value='', rows=1)
diff --git a/scripts/demofusion.py b/scripts/demofusion.py
index f7cdfe543..6625c0c79 100644
--- a/scripts/demofusion.py
+++ b/scripts/demofusion.py
@@ -1221,7 +1221,7 @@ class DemoFusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderM
 
 class Script(scripts.Script):
     def title(self):
-        return 'DemoFusion'
+        return 'DemoFusion: High-Resolution Image Generation'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
@@ -1229,7 +1229,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/PRIS-CV/DemoFusion">&nbsp DemoFusion</a><br>')
+            gr.HTML('<a href="https://github.com/PRIS-CV/DemoFusion">&nbsp DemoFusion: High-Resolution Image Generation</a><br>')
         with gr.Row():
             cosine_scale_1 = gr.Slider(minimum=0, maximum=5, step=0.1, value=3, label="Cosine scale 1")
             cosine_scale_2 = gr.Slider(minimum=0, maximum=5, step=0.1, value=1, label="Cosine scale 2")
diff --git a/scripts/differential_diffusion.py b/scripts/differential_diffusion.py
index 705242987..da4ae0e2e 100644
--- a/scripts/differential_diffusion.py
+++ b/scripts/differential_diffusion.py
@@ -1858,14 +1858,14 @@ MODELS = {
 
 class Script(scripts.Script):
     def title(self):
-        return 'Differential diffusion'
+        return 'Differential diffusion: Individual Pixel Strength'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/exx8/differential-diffusion">&nbsp Differential diffusion</a><br><span>Select a model for auto-preprocess or upload an image map</span><br>')
+            gr.HTML('<a href="https://github.com/exx8/differential-diffusion">&nbsp Differential diffusion: Individual Pixel Strength</a><br><span>Select a model for auto-preprocess or upload an image map</span><br>')
         with gr.Row():
             enabled = gr.Checkbox(label='Enabled', value=True)
             invert = gr.Checkbox(label='Mask invert', value=False)
diff --git a/scripts/hdr.py b/scripts/hdr.py
index 788c0add2..9afc3673b 100644
--- a/scripts/hdr.py
+++ b/scripts/hdr.py
@@ -11,14 +11,14 @@ from modules.shared import opts, state
 
 class Script(scripts.Script):
     def title(self):
-        return "HDR"
+        return "HDR: High Dynamic Range"
 
     def show(self, is_img2img):
         return True
 
     def ui(self, is_img2img):
         with gr.Row():
-            gr.HTML("<span>&nbsp High Dynamic Range</span><br>")
+            gr.HTML("<span>&nbsp HDR: High Dynamic Range</span><br>")
         with gr.Row():
             save_hdr = gr.Checkbox(label="Save HDR image", value=True)
             hdr_range = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.65, label='HDR range')
diff --git a/scripts/image2video.py b/scripts/image2video.py
index 332972a6d..876ed3193 100644
--- a/scripts/image2video.py
+++ b/scripts/image2video.py
@@ -13,7 +13,7 @@ MODELS = [
 
 class Script(scripts.Script):
     def title(self):
-        return 'Image-to-Video'
+        return 'Video VGen Image-to-Video'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
@@ -102,6 +102,7 @@ class Script(scripts.Script):
             processed = processing.process_images(p)
             shared.sd_model.motion_adapter = None
 
+        processed = None
         if model_name == 'VGen':
             if not isinstance(shared.sd_model, diffusers.I2VGenXLPipeline):
                 shared.log.info(f'Image2Video VGen load: model={repo_id}')
diff --git a/scripts/instantir.py b/scripts/instantir.py
index 4c7ce77b7..5eb7d503a 100644
--- a/scripts/instantir.py
+++ b/scripts/instantir.py
@@ -12,7 +12,7 @@ class Script(scripts.Script):
         self.orig_ip_unapply = None
 
     def title(self):
-        return 'InstantIR'
+        return 'InstantIR: Image Restoration'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
diff --git a/scripts/k_diff.py b/scripts/k_diff.py
index 354df5d4b..92b43149d 100644
--- a/scripts/k_diff.py
+++ b/scripts/k_diff.py
@@ -9,14 +9,14 @@ class Script(scripts.Script):
     orig_pipe = None
 
     def title(self):
-        return 'K-Diffusion'
+        return 'K-Diffusion Samplers'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
 
     def ui(self, _is_img2img): # ui elements
         with gr.Row():
-            gr.HTML('<a href="https://github.com/crowsonkb/k-diffusion">&nbsp K-Diffusion samplers</a><br>')
+            gr.HTML('<a href="https://github.com/crowsonkb/k-diffusion">&nbsp K-Diffusion Samplers</a><br>')
         with gr.Row():
             sampler = gr.Dropdown(label="Sampler", choices=self.samplers())
         return [sampler]
diff --git a/scripts/layerdiffuse.py b/scripts/layerdiffuse.py
index a1e15aa8b..ecf7da1d3 100644
--- a/scripts/layerdiffuse.py
+++ b/scripts/layerdiffuse.py
@@ -5,7 +5,7 @@ from modules import shared, scripts, sd_models
 class Script(scripts.Script):
 
     def title(self):
-        return 'LayerDiffuse'
+        return 'LayerDiffuse: Transparent Image'
 
     def show(self, is_img2img):
         return True if shared.native else False
@@ -40,7 +40,7 @@ class Script(scripts.Script):
     def ui(self, _is_img2img):
         with gr.Row():
             gr.HTML("""
-                    <a href="https://github.com/rootonchair/diffuser_layerdiffuse">&nbsp LayerDiffuse</a><br><br>
+                    <a href="https://github.com/rootonchair/diffuser_layerdiffuse">&nbsp LayerDiffuse: Transparent Image</a><br><br>
                     <div>- Click Apply to model to apply LayerDiffuse to current model</div>
                     <div>- Click Reload model to remove LayerDiffuse from current model</div><br>
                     """)
diff --git a/scripts/ledits.py b/scripts/ledits.py
index 1a0e929f0..e860e5a0b 100644
--- a/scripts/ledits.py
+++ b/scripts/ledits.py
@@ -5,7 +5,7 @@ from modules import scripts, processing, shared, devices, sd_models
 
 class Script(scripts.Script):
     def title(self):
-        return 'LEdits++'
+        return 'LEdits: Limitless Image Editing'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
@@ -13,7 +13,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://leditsplusplus-project.static.hf.space/index.html">&nbsp LEdits++</a><br>')
+            gr.HTML('<a href="https://leditsplusplus-project.static.hf.space/index.html">&nbsp LEdits++: Limitless Image Editing</a><br>')
         with gr.Row():
             edit_start = gr.Slider(label='Edit start', minimum=0.0, maximum=1.0, step=0.01, value=0.1)
             edit_stop = gr.Slider(label='Edit stop', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
diff --git a/scripts/lut.py b/scripts/lut.py
index 3d240f291..573222161 100644
--- a/scripts/lut.py
+++ b/scripts/lut.py
@@ -17,7 +17,7 @@ class Script(scripts.Script):
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML("<span>&nbsp Color grading</span><br>")
+            gr.HTML("<span>&nbsp LUT Color grading</span><br>")
         with gr.Row():
             original = gr.Checkbox(label='Include original image', value=True)
         with gr.Row():
diff --git a/scripts/mixture_tiling.py b/scripts/mixture_tiling.py
index 5dcaf0156..29182d0a9 100644
--- a/scripts/mixture_tiling.py
+++ b/scripts/mixture_tiling.py
@@ -26,14 +26,14 @@ def check_dependencies():
 
 class Script(scripts.Script):
     def title(self):
-        return 'Mixture tiling'
+        return 'Mixture Tiling: Scene Composition'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://arxiv.org/abs/2302.02412">&nbsp Mixture tiling</a><br>')
+            gr.HTML('<a href="https://arxiv.org/abs/2302.02412">&nbsp Mixture Tiling: Scene Composition</a><br>')
         with gr.Row():
             gr.HTML('<span>&nbsp Separated prompts using new lines<br>&nbsp Number of prompts must matcxh X*Y</span>')
         with gr.Row():
diff --git a/scripts/mulan.py b/scripts/mulan.py
index 4b80a7c87..260212721 100644
--- a/scripts/mulan.py
+++ b/scripts/mulan.py
@@ -46,14 +46,14 @@ text_encoder_path = None
 
 class Script(scripts.Script):
     def title(self):
-        return 'MuLan'
+        return 'MuLan: Multi Language Prompts'
 
     def show(self, is_img2img):
         return True if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/mulanai/MuLan">&nbsp MuLan</a><br>')
+            gr.HTML('<a href="https://github.com/mulanai/MuLan">&nbsp MuLan: Multi Language Prompts</a><br>')
         with gr.Row():
             selected_encoder = gr.Dropdown(label='Encoder', choices=ENCODERS, value=ENCODERS[0])
         return [selected_encoder]
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 5d209c211..b7fad31bc 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -20,7 +20,7 @@ class Script(scripts.Script):
         self.register() # pulid is script with processing override so xyz doesnt execute
 
     def title(self):
-        return 'PuLID'
+        return 'PuLID: ID Customization'
 
     def show(self, _is_img2img):
         return shared.native
diff --git a/scripts/resadapter.py b/scripts/resadapter.py
index cbd0bf671..58162f9ab 100644
--- a/scripts/resadapter.py
+++ b/scripts/resadapter.py
@@ -19,7 +19,7 @@ models = {
 
 class Script(scripts.Script):
     def title(self):
-        return 'ResAdapter'
+        return 'ResAdapter: Domain Consistent Resolution'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
@@ -27,7 +27,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/bytedance/res-adapter">&nbsp ResAdapter</a><br>')
+            gr.HTML('<a href="https://github.com/bytedance/res-adapter">&nbsp ResAdapter: Domain Consistent Resolution</a><br>')
         with gr.Row():
             model = gr.Dropdown(label="Model", choices=list(models), value="None")
             weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Weight", value=1.0)
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 9f21c5645..9c5a72204 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -9,7 +9,7 @@ from modules.shared import opts, state, log
 
 class Script(scripts.Script):
     def title(self):
-        return "SD upscale"
+        return "SD Upscale"
 
     def show(self, is_img2img):
         return is_img2img
diff --git a/scripts/stablevideodiffusion.py b/scripts/stablevideodiffusion.py
index 585871edc..cbf2ce003 100644
--- a/scripts/stablevideodiffusion.py
+++ b/scripts/stablevideodiffusion.py
@@ -16,7 +16,7 @@ models = {
 
 class Script(scripts.Script):
     def title(self):
-        return 'Stable Video Diffusion'
+        return 'Video: SVD'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
diff --git a/scripts/t_gate.py b/scripts/t_gate.py
index 3bd51445d..3808a796d 100644
--- a/scripts/t_gate.py
+++ b/scripts/t_gate.py
@@ -5,7 +5,7 @@ from installer import install
 
 class Script(scripts.Script):
     def title(self):
-        return 'T-Gate'
+        return 'T-Gate: Accelerate via Gating Attention'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
@@ -13,7 +13,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/HaozheLiu-ST/T-GATE">&nbsp T-Gate</a><br>')
+            gr.HTML('<a href="https://github.com/HaozheLiu-ST/T-GATE">&nbsp T-Gate: Accelerate via Gating Attention</a><br>')
         with gr.Row():
             enabled = gr.Checkbox(label="Enabled", value=True)
         with gr.Row():
diff --git a/scripts/text2video.py b/scripts/text2video.py
index 8dec9bd0e..dc4c44cac 100644
--- a/scripts/text2video.py
+++ b/scripts/text2video.py
@@ -23,7 +23,7 @@ MODELS = [
 
 class Script(scripts.Script):
     def title(self):
-        return 'Text-to-Video'
+        return 'Video: ModelScope'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
diff --git a/wiki b/wiki
index 47ea50e91..352fc655b 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 47ea50e9152a13325dd1daf92bc50b700783182f
+Subproject commit 352fc655b0dc9edb22aac093186da087ba18b474

From 576d2240765aae6589c2447541f99bd321353afd Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 14:00:50 -0500
Subject: [PATCH 046/119] update pulid

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_class.py     |  2 +-
 modules/pulid/pulid_sampling.py | 12 ++++++------
 modules/sd_models.py            |  9 +++++++--
 scripts/apg.py                  |  7 +++++++
 scripts/pulid_ext.py            | 29 +++++++++++++++++++----------
 scripts/xyz_grid_classes.py     |  1 +
 6 files changed, 41 insertions(+), 19 deletions(-)

diff --git a/modules/processing_class.py b/modules/processing_class.py
index 9265ea3cf..f4300a156 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -485,7 +485,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = images.resize_image(self.resize_mode, image, self.width, self.height, upscaler_name=self.resize_name, context=self.resize_context)
                 self.width = image.width
                 self.height = image.height
-            if self.image_mask is not None and shared.opts.mask_apply_overlay:
+            if self.image_mask is not None and shared.opts.mask_apply_overlay and not hasattr(self, 'xyz'):
                 image_masked = Image.new('RGBa', (image.width, image.height))
                 image_to_paste = image.convert("RGBA").convert("RGBa")
                 image_to_mask = ImageOps.invert(self.mask_for_overlay.convert('L')) if self.mask_for_overlay is not None else None
diff --git a/modules/pulid/pulid_sampling.py b/modules/pulid/pulid_sampling.py
index e319c0d27..6a2ef31f3 100644
--- a/modules/pulid/pulid_sampling.py
+++ b/modules/pulid/pulid_sampling.py
@@ -393,8 +393,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=C3001
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=C3001
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -430,8 +430,8 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=C3001
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=C3001
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -472,8 +472,8 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
     """DPM-Solver++(2M)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=C3001
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=C3001
     old_denoised = None
 
     for i in trange(len(sigmas) - 1, disable=disable):
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4090bef50..4d3b2ad60 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -449,6 +449,9 @@ def move_model(model, device=None, force=False):
         devices.torch_gc()
         return
 
+    if hasattr(model, 'pipe'):
+        move_model(model.pipe, device, force)
+
     fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
     if getattr(model, 'vae', None) is not None and get_diffusers_task(model) != DiffusersTaskType.TEXT_2_IMAGE:
         if device == devices.device and model.vae.device.type != "meta": # force vae back to gpu if not in txt2img mode
@@ -476,7 +479,8 @@ def move_model(model, device=None, force=False):
     try:
         t0 = time.time()
         try:
-            model.to(device)
+            if hasattr(model, 'to'):
+                model.to(device)
             if hasattr(model, "prior_pipe"):
                 model.prior_pipe.to(device)
         except Exception as e0:
@@ -486,7 +490,8 @@ def move_model(model, device=None, force=False):
                         if hasattr(component, 'modules'):
                             for module in component.modules():
                                 try:
-                                    module.to(device)
+                                    if hasattr(module, 'to'):
+                                        module.to(device)
                                 except Exception as e2:
                                     if 'Cannot copy out of meta tensor' in str(e2):
                                         if os.environ.get('SD_MOVE_DEBUG', None):
diff --git a/scripts/apg.py b/scripts/apg.py
index 0476da246..6d0ec107e 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -2,6 +2,9 @@ import gradio as gr
 from modules import scripts, processing, shared, sd_models
 
 
+registered = False
+
+
 class Script(scripts.Script):
     def __init__(self):
         super().__init__()
@@ -24,6 +27,10 @@ class Script(scripts.Script):
         return [eta, momentum, threshold]
 
     def register(self): # register xyz grid elements
+        global registered # pylint: disable=global-statement
+        if registered:
+            return
+        registered = True
         def apply_field(field):
             def fun(p, x, xs): # pylint: disable=unused-argument
                 setattr(p, field, x)
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index b7fad31bc..7ec32e904 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -9,13 +9,15 @@ from modules import shared, devices, errors, scripts, processing, processing_hel
 
 debug = os.environ.get('SD_PULID_DEBUG', None) is not None
 direct = False
+registered = False
+uploaded_images = []
 
 
 class Script(scripts.Script):
     def __init__(self):
-        self.images = []
         self.pulid = None
         self.cache = None
+        self.mask_apply_overlay = shared.opts.mask_apply_overlay
         super().__init__()
         self.register() # pulid is script with processing override so xyz doesnt execute
 
@@ -33,6 +35,10 @@ class Script(scripts.Script):
             install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
 
     def register(self): # register xyz grid elements
+        global registered # pylint: disable=global-statement
+        if registered:
+            return
+        registered = True
         def apply_field(field):
             def fun(p, x, xs): # pylint: disable=unused-argument
                 setattr(p, field, x)
@@ -52,7 +58,7 @@ class Script(scripts.Script):
 
 
     def load_images(self, files):
-        self.images = []
+        uploaded_images.clear()
         for file in files or []:
             try:
                 if isinstance(file, str):
@@ -66,10 +72,10 @@ class Script(scripts.Script):
                     image = Image.open(file.name) # _TemporaryFileWrapper from gr.Files
                 else:
                     raise ValueError(f'IP adapter unknown input: {file}')
-                self.images.append(image)
+                uploaded_images.append(image)
             except Exception as e:
                 shared.log.warning(f'IP adapter failed to load image: {e}')
-        return gr.update(value=self.images, visible=len(self.images) > 0)
+        return gr.update(value=uploaded_images, visible=len(uploaded_images) > 0)
 
     # return signature is array of gradio components
     def ui(self, _is_img2img):
@@ -95,7 +101,7 @@ class Script(scripts.Script):
         try:
             if len(gallery) == 0:
                 from modules.api.api import decode_base64_to_image
-                images = getattr(p, 'pulid_images', self.images)
+                images = getattr(p, 'pulid_images', uploaded_images)
                 images = [decode_base64_to_image(image) if isinstance(image, str) else image for image in images]
             else:
                 images = [Image.open(f['name']) if isinstance(f, dict) else f for f in gallery]
@@ -134,6 +140,8 @@ class Script(scripts.Script):
         ortho = getattr(p, 'pulid_ortho', ortho)
         sampler = getattr(p, 'pulid_sampler', sampler)
         sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
+        self.mask_apply_overlay = shared.opts.mask_apply_overlay
+        shared.opts.data['mask_apply_overlay'] = False
         if sampler_fn is None:
             sampler_fn = self.pulid.sampling.sample_dpmpp_2m_sde
 
@@ -149,7 +157,7 @@ class Script(scripts.Script):
                     )
                 shared.sd_model.no_recurse = True
                 sd_models.copy_diffuser_options(shared.sd_model, shared.sd_model.pipe)
-                # sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
+                sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
                 sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
                 devices.torch_gc()
             except Exception as e:
@@ -204,11 +212,12 @@ class Script(scripts.Script):
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
         _strength, _zero, _sampler, _ortho, _gallery, cache = args
-        cache = getattr(p, 'pulid_cache', cache)
-        if cache:
-            shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
-            return processed
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
+            shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
+            cache = getattr(p, 'pulid_cache', cache)
+            if cache:
+                shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
+                return processed
             if hasattr(shared.sd_model, 'app'):
                 shared.sd_model.app = None
                 shared.sd_model.ip_adapter = None
diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py
index 4898c6b73..b292ae767 100644
--- a/scripts/xyz_grid_classes.py
+++ b/scripts/xyz_grid_classes.py
@@ -132,6 +132,7 @@ axis_options = [
     AxisOption("[Postprocess] Upscaler", str, apply_upscaler, cost=0.4, choices=lambda: [x.name for x in shared.sd_upscalers][1:]),
     AxisOption("[Postprocess] Context", str, apply_context, choices=lambda: ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]),
     AxisOption("[Postprocess] Detailer", str, apply_detailer, fmt=format_value_add_label),
+    AxisOption("[Postprocess] Detailer strength", str, apply_field("detailer_strength")),
     AxisOption("[HDR] Mode", int, apply_field("hdr_mode")),
     AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")),
     AxisOption("[HDR] Color", float, apply_field("hdr_color")),

From 882a5800c14bc3d649b5a74224c0a87220c4df13 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 14:06:28 -0500
Subject: [PATCH 047/119] 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 b989132f2..417165276 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,7 +18,7 @@ This release can be considered an LTS release before we kick off the next round
     - advanced method of face transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
-    - compatible with *sdxl* for text-to-image and image-to-image  
+    - compatible with *sdxl* for text-to-image, image-to-image, inpaint and detailer workflows  
     - can be used in xyz grid  
     - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference

From 79f96fb5096ce0551cbe10f8c11e416f64761e60 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 18:07:59 -0500
Subject: [PATCH 048/119] pulid fix seed

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

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 0e84f2a4a..cc8d39c11 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -181,6 +181,8 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
         model.scheduler.noise_sampler = None # noise needs to be reset instead of using cached values
         model.scheduler.noise_sampler_seed = p.seeds # some schedulers have internal noise generator and do not use pipeline generator
+    if 'seed' in possible:
+        args['seed'] = p.seed
     if 'noise_sampler_seed' in possible:
         args['noise_sampler_seed'] = p.seeds
     if 'guidance_scale' in possible:
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 7ec32e904..d0d738b6a 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -174,10 +174,10 @@ class Script(scripts.Script):
         shared.sd_model.debug_img_list = []
         uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
 
+        p.seed = processing_helpers.get_fixed_seed(p.seed)
         if direct: # run pipeline directly
             shared.state.begin('PuLID')
             processing.fix_seed(p)
-            p.seed = processing_helpers.get_fixed_seed(p.seed)
             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)
             with devices.inference_context():

From fb4638288b33f7aa47fd1f57ee60c15e3d24e038 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Fri, 8 Nov 2024 23:31:52 -0600
Subject: [PATCH 049/119] fix IndexError, change callback type

---
 modules/processing_callbacks.py    | 2 +-
 modules/prompt_parser_diffusers.py | 9 +++++----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 59887c5c4..9e3c0cd31 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -73,7 +73,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = {}
             if 'negative_prompt_embeds' in kwargs:
                 kwargs["negative_prompt_embeds"] = prompt_parser_diffusers.embedder("negative_prompt_embeds", step + 1)
         except Exception as e:
-            shared.log.debug(f"Callback: {e}")
+            debug_callback(f"Callback: {e}")
     if step == int(getattr(pipe, 'num_timesteps', 100) * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
         if "PAG" in shared.sd_model.__class__.__name__:
             pipe._guidance_scale = 1.001 if pipe._guidance_scale > 1 else pipe._guidance_scale  # pylint: disable=protected-access
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index eec6b0d32..907cc7208 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -172,11 +172,12 @@ class PromptEmbedder:
         for i in range(self.batchsize):
             if len(batch[i]) == 0: # if not using prompt-scheduling, this will be len(batch[i])==1
                 return None
-            else:
-                # causes error in callback
+            try:
                 res.append(batch[i][step]) # and this requests element for specific step when called from callback - but self.scheduled_prompt==False so len(batch[i])==1 and step is list index out-of-bounds!
-                if step != 0:  # For Callback
-                    res.append(batch[i][step])  # Diffusers internally doubles batch dimension
+            except IndexError:
+                res.append(batch[i][0])
+            if step != 0:  # For Callback
+                res.append(res[-1])  # Diffusers internally doubles batch dimension
         return torch.cat(res)
 
 

From 6510a139045ce0aff18d7bb0fe698a5c44176e53 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 9 Nov 2024 20:03:41 -0500
Subject: [PATCH 050/119] pulid optimizations: dtype, vae, offload

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_diffusers.py      | 14 +++--
 modules/processing_vae.py            | 11 +++-
 modules/pulid/attention_processor.py | 15 ++---
 modules/pulid/eva_clip/pretrained.py |  3 +-
 modules/pulid/pulid_sdxl.py          | 93 +++++++++++++---------------
 modules/sd_models.py                 |  8 ++-
 scripts/pulid_ext.py                 | 51 +++++++++------
 7 files changed, 103 insertions(+), 92 deletions(-)

diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 7ec0dd08a..7537d0209 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -71,7 +71,7 @@ def process_base(p: processing.StableDiffusionProcessing):
         guidance_rescale=p.diffusers_guidance_rescale,
         denoising_start=0 if use_refiner_start else p.refiner_start if use_denoise_start else None,
         denoising_end=p.refiner_start if use_refiner_start else 1 if use_denoise_start else None,
-        output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
+        output_type='latent',
         clip_skip=p.clip_skip,
         desc='Base',
     )
@@ -217,7 +217,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 eta=shared.opts.scheduler_eta,
                 guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
                 guidance_rescale=p.diffusers_guidance_rescale,
-                output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
+                output_type='latent',
                 clip_skip=p.clip_skip,
                 image=output.images,
                 strength=p.denoising_strength,
@@ -278,7 +278,7 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
         for i in range(len(output.images)):
             image = output.images[i]
             noise_level = round(350 * p.denoising_strength)
-            output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np'
+            output_type='latent'
             if 'Upscale' in shared.sd_refiner.__class__.__name__ or 'Flux' in shared.sd_refiner.__class__.__name__:
                 image = processing_vae.vae_decode(latents=image, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
                 p.extra_generation_params['Noise level'] = noise_level
@@ -346,7 +346,11 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
         if not hasattr(output, 'images') and hasattr(output, 'frames'):
             shared.log.debug(f'Generated: frames={len(output.frames[0])}')
             output.images = output.frames[0]
-        if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
+        model = shared.sd_model if not is_refiner_enabled(p) else shared.sd_refiner
+        if not hasattr(model, 'vae'):
+            if hasattr(model, 'pipe') and hasattr(model.pipe, 'vae'):
+                model = model.pipe
+        if hasattr(model, "vae") and output.images is not None and len(output.images) > 0:
             if p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
                 width = max(getattr(p, 'width', 0), getattr(p, 'hr_upscale_to_x', 0))
                 height = max(getattr(p, 'height', 0), getattr(p, 'hr_upscale_to_y', 0))
@@ -355,7 +359,7 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
                 height = getattr(p, 'height', 0)
             results = processing_vae.vae_decode(
                 latents = output.images,
-                model = shared.sd_model if not is_refiner_enabled(p) else shared.sd_refiner,
+                model = model,
                 full_quality = p.full_quality,
                 width = width,
                 height = height,
diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index 75347f416..5e6fa68f4 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -35,7 +35,7 @@ def create_latents(image, p, dtype=None, device=None):
 
 def full_vae_decode(latents, model):
     t0 = time.time()
-    if not hasattr(model, 'vae'):
+    if model is None or not hasattr(model, 'vae'):
         shared.log.error('VAE not found in model')
         return []
     if debug:
@@ -170,7 +170,14 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
     if latents.shape[-1] <= 4: # not a latent, likely an image
         decoded = latents.float().cpu().numpy()
     elif full_quality and hasattr(shared.sd_model, "vae"):
-        decoded = full_vae_decode(latents=latents, model=shared.sd_model)
+        parent = shared.sd_model if hasattr(shared.sd_model, 'vae') else None
+        if hasattr(shared.sd_model, 'vae'):
+            parent = shared.sd_model
+        elif hasattr(shared.sd_model, 'pipe') and hasattr(shared.sd_model.pipe, 'vae'):
+            parent = shared.sd_model.pipe
+        else:
+            parent = None
+        decoded = full_vae_decode(latents=latents, model=parent)
     else:
         decoded = taesd_vae_decode(latents=latents)
 
diff --git a/modules/pulid/attention_processor.py b/modules/pulid/attention_processor.py
index 9756decc1..fa9e4ff82 100644
--- a/modules/pulid/attention_processor.py
+++ b/modules/pulid/attention_processor.py
@@ -345,10 +345,7 @@ class IDAttnProcessor2_0(torch.nn.Module):
         value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
+        hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
         hidden_states = hidden_states.to(query.dtype)
 
@@ -363,17 +360,15 @@ class IDAttnProcessor2_0(torch.nn.Module):
                     dtype=id_embedding.dtype,
                     device=id_embedding.device,
                 )
-                id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
-                id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
+                id_cat = torch.cat((id_embedding, zero_tensor), dim=1)
+                id_key = self.id_to_k(id_cat).to(query.dtype)
+                id_value = self.id_to_v(id_cat).to(query.dtype)
 
             id_key = id_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
             id_value = id_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
             # the output of sdp = (batch, num_heads, seq_len, head_dim)
-            id_hidden_states = F.scaled_dot_product_attention(
-                query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False
-            )
-
+            id_hidden_states = F.scaled_dot_product_attention(query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False)
             id_hidden_states = id_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
             id_hidden_states = id_hidden_states.to(query.dtype)
 
diff --git a/modules/pulid/eva_clip/pretrained.py b/modules/pulid/eva_clip/pretrained.py
index a1e55dcf3..bb87c540c 100644
--- a/modules/pulid/eva_clip/pretrained.py
+++ b/modules/pulid/eva_clip/pretrained.py
@@ -2,7 +2,6 @@ import hashlib
 import os
 import urllib
 import warnings
-from functools import partial
 from typing import Dict, Union
 
 from tqdm import tqdm
@@ -277,7 +276,7 @@ def download_pretrained_from_url(
                 loop.update(len(buffer))
 
     if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
-        raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
 
     return download_target
 
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index fade7509d..8bd28dbe2 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -5,6 +5,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
 from huggingface_hub import hf_hub_download, snapshot_download
 from safetensors.torch import load_file
@@ -24,14 +25,17 @@ from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None):
         super().__init__()
         self.device = device
+        self.dtype = dtype or torch.float16
         self.pipe = pipe
         self.cache_dir = cache_dir
+        self.offload = offload
         self.hack_unet_attn_layers(self.pipe.unet)
         self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
-        self.id_adapter = IDFormer().to(self.device)
+        self.id_adapter = IDFormer().to(self.device, self.dtype)
+        self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
 
         # preprocessors
         # face align and parsing
@@ -43,13 +47,12 @@ class StableDiffusionXLPuLIDPipeline:
             save_ext='png',
             device=self.device,
         )
-        self.face_helper.face_parse = None
         self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
 
         # clip-vit backbone
-        model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True)
-        model = model.visual
-        self.clip_vision_model = model.to(self.device)
+        eva_precision = 'fp16' if self.dtype == torch.float16 or self.dtype == torch.bfloat16 else 'fp32'
+        eva_model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, precision=eva_precision, device=self.device)
+        self.clip_vision_model = eva_model.visual.to(dtype=self.dtype)
         eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
         eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
         if not isinstance(eva_transform_mean, (list, tuple)):
@@ -60,13 +63,12 @@ class StableDiffusionXLPuLIDPipeline:
         self.eva_transform_std = eva_transform_std
 
         # antelopev2
-        # snapshot_download('DIAMONIK7777/antelopev2', local_dir='models/antelopev2')
         local_dir = os.path.join(self.cache_dir, 'pulid', 'models', 'antelopev2')
         _loc = snapshot_download('DIAMONIK7777/antelopev2', local_dir=local_dir)
         self.app = FaceAnalysis(
             name='antelopev2',
             root=os.path.join(self.cache_dir, 'pulid'),
-            providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
+            providers=self.providers,
         )
         self.app.prepare(ctx_id=0, det_size=(640, 640))
         self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
@@ -89,8 +91,12 @@ class StableDiffusionXLPuLIDPipeline:
         self.log_sigmas = self.sigmas.log()
         self.sigma_data = 1.0
 
+        # default scheduler
         if sampler is not None:
             self.sampler = sampler
+        else:
+            from modules.pulid import sampling
+            self.sampler = sampling.sample_dpmpp_sde
 
     @property
     def sigma_min(self):
@@ -130,7 +136,7 @@ class StableDiffusionXLPuLIDPipeline:
                 id_adapter_attn_procs[name] = IDAttnProcessor(
                     hidden_size=hidden_size,
                     cross_attention_dim=cross_attention_dim,
-                ).to(unet.device)
+                ).to(unet.device, unet.dtype)
             else:
                 id_adapter_attn_procs[name] = AttnProcessor()
         unet.set_attn_processor(id_adapter_attn_procs)
@@ -144,7 +150,7 @@ class StableDiffusionXLPuLIDPipeline:
             module = k.split('.')[0]
             state_dict_dict.setdefault(module, {})
             new_k = k[len(module) + 1 :]
-            state_dict_dict[module][new_k] = v
+            state_dict_dict[module][new_k] = v.to(self.dtype)
 
         for module in state_dict_dict:
             getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)
@@ -161,24 +167,17 @@ class StableDiffusionXLPuLIDPipeline:
         """
         id_cond_list = []
         id_vit_hidden_list = []
+        self.face_helper.face_det.to(self.device)
+        self.clip_vision_model.to(self.device)
         for _ii, image in enumerate(image_list):
             self.face_helper.clean_all()
             image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
             # get antelopev2 embedding
             face_info = self.app.get(image_bgr)
             if len(face_info) > 0:
-                face_info = sorted(
-                    face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1])
-                )[
-                    -1
-                ]  # only use the maximum face
+                face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[-1] # only use the maximum face
                 id_ante_embedding = face_info['embedding']
-                self.debug_img_list.append(
-                    image[
-                        int(face_info['bbox'][1]) : int(face_info['bbox'][3]),
-                        int(face_info['bbox'][0]) : int(face_info['bbox'][2]),
-                    ]
-                )
+                self.debug_img_list.append(image[int(face_info['bbox'][1]) : int(face_info['bbox'][3]), int(face_info['bbox'][0]) : int(face_info['bbox'][2])])
             else:
                 id_ante_embedding = None
 
@@ -210,13 +209,9 @@ class StableDiffusionXLPuLIDPipeline:
             self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False))
 
             # transform img before sending to eva-clip-vit
-            face_features_image = resize(
-                face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC
-            )
-            face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std)
-            id_cond_vit, id_vit_hidden = self.clip_vision_model(
-                face_features_image, return_all_features=False, return_hidden=True, shuffle=False
-            )
+            face_features_image = resize(face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC)
+            face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std).to(self.dtype)
+            id_cond_vit, id_vit_hidden = self.clip_vision_model(face_features_image, return_all_features=False, return_hidden=True, shuffle=False)
             id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
             id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)
 
@@ -225,19 +220,25 @@ class StableDiffusionXLPuLIDPipeline:
             id_cond_list.append(id_cond)
             id_vit_hidden_list.append(id_vit_hidden)
 
-        id_uncond = torch.zeros_like(id_cond_list[0])
+        self.id_adapter.to(self.device)
+        id_uncond = torch.zeros_like(id_cond_list[0]).to(self.dtype)
         id_vit_hidden_uncond = []
         for layer_idx in range(0, len(id_vit_hidden_list[0])):
-            id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden_list[0][layer_idx]))
+            id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden_list[0][layer_idx]).to(self.dtype))
 
-        id_cond = torch.stack(id_cond_list, dim=1)
+        id_cond = torch.stack(id_cond_list, dim=1).to(self.dtype)
         id_vit_hidden = id_vit_hidden_list[0]
         for i in range(1, len(image_list)):
             for j, x in enumerate(id_vit_hidden_list[i]):
-                id_vit_hidden[j] = torch.cat([id_vit_hidden[j], x], dim=1)
+                id_vit_hidden[j] = torch.cat([id_vit_hidden[j], x], dim=1).to(self.dtype)
         id_embedding = self.id_adapter(id_cond, id_vit_hidden)
         uncond_id_embedding = self.id_adapter(id_uncond, id_vit_hidden_uncond)
 
+        if self.offload:
+            self.face_helper.face_det.to('cpu')
+            self.id_adapter.to('cpu')
+            self.clip_vision_model.to('cpu')
+
         # return id_embedding
         return uncond_id_embedding, id_embedding
 
@@ -314,6 +315,7 @@ class StableDiffusionXLPuLIDPipeline:
         id_embedding=None,
         uncond_id_embedding=None,
         id_scale: float=1.0,
+        output_type: str='pil',
         callback_on_step_end=None,
     ):
         self.step = 0 # pylint: disable=attribute-defined-outside-init
@@ -370,24 +372,15 @@ class StableDiffusionXLPuLIDPipeline:
             mask_args = None
 
         latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
-        latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
-        images = self.pipe.vae.decode(latents).sample
-        images = self.pipe.image_processor.postprocess(images, output_type='pil')
-
-        # Pixel space final mask
-        # if mask_image is not None:
-        #     # TODO: Fix XYZ
-        #      from PIL import Image
-        #     mask_image = np.asarray(mask_image.convert("L"))
-        #     mask_image = mask_image / mask_image.max()
-        #     mask_image = mask_image.reshape(1,mask_image.shape[0],mask_image.shape[1],1)
-        #     image = np.asarray(image).astype(mask_image.dtype)
-        #     images = np.asarray(images).astype(mask_image.dtype)
-        #     images = ((1 - mask_image) * image) + (mask_image * images)
-        #     images = images[0].round().astype(np.uint8)
-        #     images = [Image.fromarray(images)]
-
-        return images
+        if output_type == 'latent':
+            images = self.pipe.image_processor.postprocess(latents, output_type='latent')
+        elif output_type == 'np':
+            images = self.pipe.image_processor.postprocess(latents, output_type='np')
+        else:
+            latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
+            images = self.pipe.vae.decode(latents).sample
+            images = self.pipe.image_processor.postprocess(images, output_type='pil')
+        return StableDiffusionXLPipelineOutput(images)
 
 
 class StableDiffusionXLPuLIDPipelineImage(StableDiffusionXLPuLIDPipeline):
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4d3b2ad60..801c3705d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -402,9 +402,11 @@ def apply_balanced_offload(sd_model):
     def apply_balanced_offload_to_module(pipe):
         if hasattr(pipe, "pipe"):
             apply_balanced_offload_to_module(pipe.pipe)
-        if not hasattr(pipe, "_internal_dict"):
-            return
-        for module_name in pipe._internal_dict.keys(): # pylint: disable=protected-access
+        if hasattr(pipe, "_internal_dict"):
+            keys = pipe._internal_dict.keys() # pylint: disable=protected-access
+        else:
+            keys = get_signature(shared.sd_model).keys()
+        for module_name in keys: # pylint: disable=protected-access
             module = getattr(pipe, module_name, None)
             if isinstance(module, torch.nn.Module):
                 checkpoint_name = pipe.sd_checkpoint_info.name if getattr(pipe, "sd_checkpoint_info", None) is not None else None
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index d0d738b6a..6599fa2e8 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -1,5 +1,6 @@
 import io
 import os
+import time
 import contextlib
 import gradio as gr
 import numpy as np
@@ -18,6 +19,7 @@ class Script(scripts.Script):
         self.pulid = None
         self.cache = None
         self.mask_apply_overlay = shared.opts.mask_apply_overlay
+        self.preprocess = 0
         super().__init__()
         self.register() # pulid is script with processing override so xyz doesnt execute
 
@@ -88,15 +90,16 @@ class Script(scripts.Script):
             sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
             ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
         with gr.Row():
-            cache = gr.Checkbox(label='Keep model', value=False)
+            restore = gr.Checkbox(label='Restore pipe on end', value=False)
+            offload = gr.Checkbox(label='Offload face module', value=True)
         with gr.Row():
             files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
         with gr.Row():
             gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
-        return [strength, zero, sampler, ortho, gallery, cache]
+        return [strength, zero, sampler, ortho, gallery, restore, offload]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], cache: bool = False): # pylint: disable=arguments-differ, unused-argument
+    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], restore: bool = False, offload: bool = True): # pylint: disable=arguments-differ, unused-argument
         images = []
         try:
             if len(gallery) == 0:
@@ -135,13 +138,13 @@ class Script(scripts.Script):
             shared.log.warning('PuLID: batch size not supported')
             p.batch_size = 1
 
+        self.mask_apply_overlay = shared.opts.mask_apply_overlay
+        shared.opts.data['mask_apply_overlay'] = False
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
         ortho = getattr(p, 'pulid_ortho', ortho)
         sampler = getattr(p, 'pulid_sampler', sampler)
         sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
-        self.mask_apply_overlay = shared.opts.mask_apply_overlay
-        shared.opts.data['mask_apply_overlay'] = False
         if sampler_fn is None:
             sampler_fn = self.pulid.sampling.sample_dpmpp_2m_sde
 
@@ -153,6 +156,9 @@ class Script(scripts.Script):
                     shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
                         pipe =shared.sd_model,
                         device=devices.device,
+                        dtype=devices.dtype,
+                        providers=devices.onnx,
+                        offload=offload,
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -166,13 +172,20 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'
         images = [self.pulid.resize(image, 1024) for image in images]
         shared.sd_model.debug_img_list = []
+
+        # get id embedding used for attention
+        t0 = time.time()
         uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
+        if offload:
+            devices.torch_gc()
+        t1 = time.time()
+        self.preprocess = t1-t0
 
         p.seed = processing_helpers.get_fixed_seed(p.seed)
         if direct: # run pipeline directly
@@ -211,20 +224,18 @@ class Script(scripts.Script):
         return processed
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
-        _strength, _zero, _sampler, _ortho, _gallery, cache = args
+        _strength, _zero, _sampler, _ortho, _gallery, restore, _offload = args
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
             shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
-            cache = getattr(p, 'pulid_cache', cache)
-            if cache:
-                shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
-                return processed
-            if hasattr(shared.sd_model, 'app'):
-                shared.sd_model.app = None
-                shared.sd_model.ip_adapter = None
-                shared.sd_model.face_helper = None
-                shared.sd_model.clip_vision_model = None
-                shared.sd_model.handler_ante = None
-            shared.sd_model = shared.sd_model.pipe
-            devices.torch_gc(force=True)
-            shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
+            restore = getattr(p, 'pulid_restore', restore)
+            if restore:
+                if hasattr(shared.sd_model, 'app'):
+                    shared.sd_model.app = None
+                    shared.sd_model.ip_adapter = None
+                    shared.sd_model.face_helper = None
+                    shared.sd_model.clip_vision_model = None
+                    shared.sd_model.handler_ante = None
+                shared.sd_model = shared.sd_model.pipe
+                devices.torch_gc(force=True)
+            shared.log.debug(f'PuLID complete: class={shared.sd_model.__class__.__name__} preprocess={self.preprocess:.2f} pipe={"restore" if restore else "cache"}')
         return processed

From b75567eefecf9f31416fd7d9fc4727fdc0967bd5 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 10 Nov 2024 09:42:05 -0500
Subject: [PATCH 051/119] pullid support sdpa add both v1.0 and v1.1 models

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                          |  2 +-
 modules/errors.py                     |  4 +-
 modules/pulid/encoders_transformer.py | 68 ++++++++++++++++++++++++---
 modules/pulid/pulid_sdxl.py           | 38 +++++++++++----
 scripts/pulid_ext.py                  | 25 ++++++++--
 5 files changed, 113 insertions(+), 24 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 417165276..9affd53db 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-08
+## Update for 2024-11-10
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
diff --git a/modules/errors.py b/modules/errors.py
index c4d66c351..527884cf1 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -59,7 +59,7 @@ def exception(suppress=[]):
     console.print_exception(show_locals=False, max_frames=16, extra_lines=2, suppress=suppress, theme="ansi_dark", word_wrap=False, width=min([console.width, 200]))
 
 
-def profile(profiler, msg: str):
+def profile(profiler, msg: str, n: int = 5):
     profiler.disable()
     import io
     import pstats
@@ -83,7 +83,7 @@ def profile(profiler, msg: str):
              and 'rich' not in x
              and x.strip() != ''
             ]
-    txt = '\n'.join(lines[:min(5, len(lines))])
+    txt = '\n'.join(lines[:min(n, len(lines))])
     log.debug(f'Profile {msg}: {txt}')
 
 
diff --git a/modules/pulid/encoders_transformer.py b/modules/pulid/encoders_transformer.py
index d1ecef2c6..ae245044b 100644
--- a/modules/pulid/encoders_transformer.py
+++ b/modules/pulid/encoders_transformer.py
@@ -186,23 +186,79 @@ class IDFormer(nn.Module):
         )
 
     def forward(self, x, y):
-
         latents = self.latents.repeat(x.size(0), 1, 1)
-
         num_duotu = x.shape[1] if x.ndim == 3 else 1
-
         x = self.id_embedding_mapping(x)
         x = x.reshape(-1, self.num_id_token * num_duotu, self.dim)
-
         latents = torch.cat((latents, x), dim=1)
-
         for i in range(5):
             vit_feature = getattr(self, f'mapping_{i}')(y[i])
             ctx_feature = torch.cat((x, vit_feature), dim=1)
             for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]:
                 latents = attn(ctx_feature, latents) + latents
                 latents = ff(latents) + latents
-
         latents = latents[:, :self.num_queries]
         latents = latents @ self.proj_out
         return latents
+
+
+class IDEncoder(nn.Module):
+    def __init__(self, width=1280, context_dim=2048, num_token=5):
+        super().__init__()
+        self.num_token = num_token
+        self.context_dim = context_dim
+        h1 = min((context_dim * num_token) // 4, 1024)
+        h2 = min((context_dim * num_token) // 2, 1024)
+        self.body = nn.Sequential(
+            nn.Linear(width, h1),
+            nn.LayerNorm(h1),
+            nn.LeakyReLU(),
+            nn.Linear(h1, h2),
+            nn.LayerNorm(h2),
+            nn.LeakyReLU(),
+            nn.Linear(h2, context_dim * num_token),
+        )
+
+        for i in range(5):
+            setattr(
+                self,
+                f'mapping_{i}',
+                nn.Sequential(
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, context_dim),
+                ),
+            )
+
+            setattr(
+                self,
+                f'mapping_patch_{i}',
+                nn.Sequential(
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, context_dim),
+                ),
+            )
+
+    def forward(self, x, y):
+        # x shape [N, C]
+        x = self.body(x)
+        x = x.reshape(-1, self.num_token, self.context_dim)
+
+        hidden_states = ()
+        for i, emb in enumerate(y):
+            hidden_state = getattr(self, f'mapping_{i}')(emb[:, :1]) + getattr(self, f'mapping_patch_{i}')(
+                emb[:, 1:]
+            ).mean(dim=1, keepdim=True)
+            hidden_states += (hidden_state,)
+        hidden_states = torch.cat(hidden_states, dim=1)
+
+        return torch.cat([x, hidden_states], dim=1)
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 8bd28dbe2..d2a761654 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -19,23 +19,28 @@ from insightface.app import FaceAnalysis
 
 from eva_clip import create_model_and_transforms
 from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-from encoders_transformer import IDFormer
-from attention_processor import AttnProcessor2_0 as AttnProcessor
-from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
+from encoders_transformer import IDFormer, IDEncoder
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None, sdp: bool=True, version: str='v1.1'):
         super().__init__()
         self.device = device
         self.dtype = dtype or torch.float16
         self.pipe = pipe
         self.cache_dir = cache_dir
         self.offload = offload
-        self.hack_unet_attn_layers(self.pipe.unet)
+        self.sdp = sdp
+        self.version = version
+        self.folder = 'models--ToTheBeginning--PuLID'
+
         self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
-        self.id_adapter = IDFormer().to(self.device, self.dtype)
+        if self.version == 'v1.1':
+            self.id_adapter = IDFormer().to(self.device, self.dtype)
+        else:
+            self.id_adapter = IDEncoder().to(self.device, self.dtype)
         self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
+        self.hack_unet_attn_layers(self.pipe.unet)
 
         # preprocessors
         # face align and parsing
@@ -63,11 +68,11 @@ class StableDiffusionXLPuLIDPipeline:
         self.eva_transform_std = eva_transform_std
 
         # antelopev2
-        local_dir = os.path.join(self.cache_dir, 'pulid', 'models', 'antelopev2')
+        local_dir = os.path.join(self.cache_dir, self.folder, 'models', 'antelopev2')
         _loc = snapshot_download('DIAMONIK7777/antelopev2', local_dir=local_dir)
         self.app = FaceAnalysis(
             name='antelopev2',
-            root=os.path.join(self.cache_dir, 'pulid'),
+            root=os.path.join(self.cache_dir, self.folder),
             providers=self.providers,
         )
         self.app.prepare(ctx_id=0, det_size=(640, 640))
@@ -119,6 +124,12 @@ class StableDiffusionXLPuLIDPipeline:
         return torch.cat([sigmas, sigmas.new_zeros([1])])
 
     def hack_unet_attn_layers(self, unet):
+        if self.sdp:
+            from attention_processor import AttnProcessor2_0 as AttnProcessor
+            from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
+        else:
+            from attention_processor import AttnProcessor
+            from attention_processor import IDAttnProcessor
         id_adapter_attn_procs = {}
         for name, _ in unet.attn_processors.items():
             cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
@@ -143,8 +154,12 @@ class StableDiffusionXLPuLIDPipeline:
         self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
 
     def load_pretrain(self):
-        ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.1.safetensors', local_dir=os.path.join(self.cache_dir, 'pulid'))
-        state_dict = load_file(ckpt_path)
+        if self.version == 'v1.1':
+            ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.1.safetensors', local_dir=os.path.join(self.cache_dir, self.folder))
+            state_dict = load_file(ckpt_path)
+        else:
+            ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir=os.path.join(self.cache_dir, self.folder))
+            state_dict = torch.load(ckpt_path, map_location="cpu")
         state_dict_dict = {}
         for k, v in state_dict.items():
             module = k.split('.')[0]
@@ -371,7 +386,10 @@ class StableDiffusionXLPuLIDPipeline:
         else:
             mask_args = None
 
+        # actual sampling loop
         latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
+
+        # process output
         if output_type == 'latent':
             images = self.pipe.image_processor.postprocess(latents, output_type='latent')
         elif output_type == 'np':
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 6599fa2e8..181e954db 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -89,6 +89,8 @@ class Script(scripts.Script):
         with gr.Row():
             sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
             ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
+        with gr.Row():
+            version = gr.Dropdown(label="Version", value='v1.1', choices=['v1.0', 'v1.1'])
         with gr.Row():
             restore = gr.Checkbox(label='Restore pipe on end', value=False)
             offload = gr.Checkbox(label='Offload face module', value=True)
@@ -97,9 +99,20 @@ class Script(scripts.Script):
         with gr.Row():
             gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
-        return [strength, zero, sampler, ortho, gallery, restore, offload]
+        return [strength, zero, sampler, ortho, gallery, restore, offload, version]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], restore: bool = False, offload: bool = True): # pylint: disable=arguments-differ, unused-argument
+    def run(
+            self,
+            p: processing.StableDiffusionProcessing,
+            strength: float = 0.8,
+            zero: int = 20,
+            sampler: str = 'dpmpp_sde',
+            ortho: str = 'v2',
+            gallery: list = [],
+            restore: bool = False,
+            offload: bool = True,
+            version: str = 'v1.1'
+        ): # pylint: disable=arguments-differ, unused-argument
         images = []
         try:
             if len(gallery) == 0:
@@ -154,11 +167,13 @@ class Script(scripts.Script):
                 ctx = contextlib.nullcontext() if debug else contextlib.redirect_stdout(stdout)
                 with ctx:
                     shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
-                        pipe =shared.sd_model,
+                        pipe=shared.sd_model,
                         device=devices.device,
                         dtype=devices.dtype,
                         providers=devices.onnx,
                         offload=offload,
+                        version=version,
+                        sdp=shared.opts.cross_attention_optimization == "Scaled-Dot-Product",
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -172,7 +187,7 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} version="{version}" strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'
@@ -224,7 +239,7 @@ class Script(scripts.Script):
         return processed
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
-        _strength, _zero, _sampler, _ortho, _gallery, restore, _offload = args
+        _strength, _zero, _sampler, _ortho, _gallery, restore, _offload, _version = args
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
             shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
             restore = getattr(p, 'pulid_restore', restore)

From 0f49987f119034d299cb1c1ed9a185d8242a6fda Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 10 Nov 2024 16:36:08 -0500
Subject: [PATCH 052/119] css fix margin

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                          |  5 +++--
 javascript/sdnext.css                 |  3 ++-
 modules/pulid/encoders_transformer.py | 14 --------------
 scripts/pulid_ext.py                  |  5 +++--
 4 files changed, 8 insertions(+), 19 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9affd53db..5db77983c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,7 +15,7 @@ This release can be considered an LTS release before we kick off the next round
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) and [Home](https://github.com/vladmandic/automatic) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
-    - advanced method of face transfer with better quality as well as control over identity and appearance  
+    - advanced method of face id transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
     - compatible with *sdxl* for text-to-image, image-to-image, inpaint and detailer workflows  
@@ -98,7 +98,8 @@ This release can be considered an LTS release before we kick off the next round
   - fix network height in standard vs modern ui  
   - fix k-diff enum on startup  
   - fix text2video scripts  
-  - dont uninstall flash-attn
+  - dont uninstall flash-attn  
+  - ui css fixes  
   - move downloads of some auxillary models to hfcache instead of models folder  
 
 ## Update for 2024-10-29
diff --git a/javascript/sdnext.css b/javascript/sdnext.css
index 192d0d487..08fae2eb8 100644
--- a/javascript/sdnext.css
+++ b/javascript/sdnext.css
@@ -13,11 +13,12 @@ footer { display: none; margin-top: 0 !important;}
 table { overflow-x: auto !important; overflow-y: auto !important; }
 td { border-bottom: none !important; padding: 0 0.5em !important; }
 tr { border-bottom: none !important; padding: 0 0.5em !important; }
+td > div > span { overflow-y: auto; max-height: 3em; overflow-x: hidden; }
 textarea { overflow-y: auto !important; }
 span { font-size: var(--text-md) !important; }
 button { font-size: var(--text-lg) !important; }
 input[type='color'] { width: 64px; height: 32px; }
-td > div > span { overflow-y: auto; max-height: 3em; overflow-x: hidden; }
+input::-webkit-outer-spin-button, input::-webkit-inner-spin-button { margin-left: 4px; }
 
 /* gradio elements */
 .block .padded:not(.gradio-accordion) { padding: 4px 0 0 0 !important; margin-right: 0; min-width: 90px !important; }
diff --git a/modules/pulid/encoders_transformer.py b/modules/pulid/encoders_transformer.py
index ae245044b..834d5aa94 100644
--- a/modules/pulid/encoders_transformer.py
+++ b/modules/pulid/encoders_transformer.py
@@ -32,10 +32,8 @@ class PerceiverAttentionCA(nn.Module):
         self.dim_head = dim_head
         self.heads = heads
         inner_dim = dim_head * heads
-
         self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
         self.norm2 = nn.LayerNorm(dim)
-
         self.to_q = nn.Linear(dim, inner_dim, bias=False)
         self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
         self.to_out = nn.Linear(inner_dim, dim, bias=False)
@@ -50,12 +48,9 @@ class PerceiverAttentionCA(nn.Module):
         """
         x = self.norm1(x)
         latents = self.norm2(latents)
-
         b, seq_len, _ = latents.shape
-
         q = self.to_q(latents)
         k, v = self.to_kv(x).chunk(2, dim=-1)
-
         q = reshape_tensor(q, self.heads)
         k = reshape_tensor(k, self.heads)
         v = reshape_tensor(v, self.heads)
@@ -65,7 +60,6 @@ class PerceiverAttentionCA(nn.Module):
         weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         out = weight @ v
-
         out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
 
         return self.to_out(out)
@@ -78,10 +72,8 @@ class PerceiverAttention(nn.Module):
         self.dim_head = dim_head
         self.heads = heads
         inner_dim = dim_head * heads
-
         self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
         self.norm2 = nn.LayerNorm(dim)
-
         self.to_q = nn.Linear(dim, inner_dim, bias=False)
         self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
         self.to_out = nn.Linear(inner_dim, dim, bias=False)
@@ -96,13 +88,10 @@ class PerceiverAttention(nn.Module):
         """
         x = self.norm1(x)
         latents = self.norm2(latents)
-
         b, seq_len, _ = latents.shape
-
         q = self.to_q(latents)
         kv_input = torch.cat((x, latents), dim=-2)
         k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
         q = reshape_tensor(q, self.heads)
         k = reshape_tensor(k, self.heads)
         v = reshape_tensor(v, self.heads)
@@ -112,7 +101,6 @@ class PerceiverAttention(nn.Module):
         weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         out = weight @ v
-
         out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
 
         return self.to_out(out)
@@ -145,7 +133,6 @@ class IDFormer(nn.Module):
         assert depth % 5 == 0
         self.depth = depth // 5
         scale = dim ** -0.5
-
         self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale)
         self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim))
 
@@ -233,7 +220,6 @@ class IDEncoder(nn.Module):
                     nn.Linear(1024, context_dim),
                 ),
             )
-
             setattr(
                 self,
                 f'mapping_patch_{i}',
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 181e954db..43039d73a 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -153,6 +153,7 @@ class Script(scripts.Script):
 
         self.mask_apply_overlay = shared.opts.mask_apply_overlay
         shared.opts.data['mask_apply_overlay'] = False
+        sdp = shared.opts.cross_attention_optimization == "Scaled-Dot-Product"
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
         ortho = getattr(p, 'pulid_ortho', ortho)
@@ -173,7 +174,7 @@ class Script(scripts.Script):
                         providers=devices.onnx,
                         offload=offload,
                         version=version,
-                        sdp=shared.opts.cross_attention_optimization == "Scaled-Dot-Product",
+                        sdp=sdp,
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -187,7 +188,7 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} version="{version}" strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} version="{version}" sdp={sdp} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'

From c5eb80accb4ed6512639d6e18d2fcc6111a14f94 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 09:53:05 -0500
Subject: [PATCH 053/119] add pulid debug

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py                |  3 +++
 modules/pulid/pulid_sdxl.py | 38 +++++++++++++++++++++++++++++--------
 2 files changed, 33 insertions(+), 8 deletions(-)

diff --git a/installer.py b/installer.py
index b19eae87e..e50271280 100644
--- a/installer.py
+++ b/installer.py
@@ -110,6 +110,9 @@ def setup_logging():
         "traceback.border": "black",
         "traceback.border.syntax_error": "black",
         "inspect.value.border": "black",
+        "logging.level.info": "blue_violet",
+        "logging.level.debug": "purple4",
+        "logging.level.trace": "dark_blue",
     }))
     logging.basicConfig(level=logging.ERROR, format='%(asctime)s | %(name)s | %(levelname)s | %(module)s | %(message)s', handlers=[logging.NullHandler()]) # redirect default logger to null
     pretty_install(console=console)
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index d2a761654..3053d759d 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -4,7 +4,7 @@ import insightface
 import numpy as np
 import torch
 import torch.nn as nn
-from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
+from diffusers import StableDiffusionXLPipeline
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
 from huggingface_hub import hf_hub_download, snapshot_download
@@ -20,6 +20,10 @@ from insightface.app import FaceAnalysis
 from eva_clip import create_model_and_transforms
 from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
 from encoders_transformer import IDFormer, IDEncoder
+from modules.errors import log
+
+
+debug = log.trace if os.environ.get('SD_PULID_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
 class StableDiffusionXLPuLIDPipeline:
@@ -33,14 +37,17 @@ class StableDiffusionXLPuLIDPipeline:
         self.sdp = sdp
         self.version = version
         self.folder = 'models--ToTheBeginning--PuLID'
+        debug(f'PulID init: device={self.device} dtype={self.dtype} dir={self.cache_dir} offload={self.offload} sdp={self.sdp} version={self.version}')
 
-        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        # self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        self.hack_unet_attn_layers(self.pipe.unet)
         if self.version == 'v1.1':
             self.id_adapter = IDFormer().to(self.device, self.dtype)
         else:
             self.id_adapter = IDEncoder().to(self.device, self.dtype)
+        debug(f'PulID load: adapter={self.id_adapter.__class__.__name__}')
         self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
-        self.hack_unet_attn_layers(self.pipe.unet)
+        debug(f'PulID load: providers={self.providers}')
 
         # preprocessors
         # face align and parsing
@@ -53,11 +60,13 @@ class StableDiffusionXLPuLIDPipeline:
             device=self.device,
         )
         self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
+        debug(f'PulID load: facehelper={self.face_helper.__class__.__name__}')
 
         # clip-vit backbone
         eva_precision = 'fp16' if self.dtype == torch.float16 or self.dtype == torch.bfloat16 else 'fp32'
         eva_model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, precision=eva_precision, device=self.device)
         self.clip_vision_model = eva_model.visual.to(dtype=self.dtype)
+        debug(f'PulID load: evaclip={self.clip_vision_model.__class__.__name__} precision={eva_precision}')
         eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
         eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
         if not isinstance(eva_transform_mean, (list, tuple)):
@@ -75,9 +84,11 @@ class StableDiffusionXLPuLIDPipeline:
             root=os.path.join(self.cache_dir, self.folder),
             providers=self.providers,
         )
+        debug(f'PulID load: faceanalysis={_loc}')
         self.app.prepare(ctx_id=0, det_size=(640, 640))
         self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
         self.handler_ante.prepare(ctx_id=0)
+        debug(f'PulID load: handler={self.handler_ante.__class__.__name__}')
 
         self.load_pretrain()
 
@@ -150,6 +161,7 @@ class StableDiffusionXLPuLIDPipeline:
                 ).to(unet.device, unet.dtype)
             else:
                 id_adapter_attn_procs[name] = AttnProcessor()
+        debug(f'PulID attention: cls={IDAttnProcessor} std={AttnProcessor} len={len(id_adapter_attn_procs.keys())}')
         unet.set_attn_processor(id_adapter_attn_procs)
         self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
 
@@ -160,6 +172,7 @@ class StableDiffusionXLPuLIDPipeline:
         else:
             ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir=os.path.join(self.cache_dir, self.folder))
             state_dict = torch.load(ckpt_path, map_location="cpu")
+        debug(f'PulID load: fn="{ckpt_path}"')
         state_dict_dict = {}
         for k, v in state_dict.items():
             module = k.split('.')[0]
@@ -255,6 +268,7 @@ class StableDiffusionXLPuLIDPipeline:
             self.clip_vision_model.to('cpu')
 
         # return id_embedding
+        debug(f'PulID embedding: cond={id_embedding.shape} uncond={uncond_id_embedding.shape}')
         return uncond_id_embedding, id_embedding
 
     def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
@@ -264,9 +278,10 @@ class StableDiffusionXLPuLIDPipeline:
         pulid_sampling.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
 
     def sample(self, x, sigma, **extra_args):
-        x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
         t = self.timestep(sigma)
+        x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
         cfg_scale = extra_args['cfg_scale']
+        debug(f'PulID sample start: step={self.step+1} x={x.shape} dtype={x.dtype} timestep={t.item()} sigma={sigma.shape} cfg={cfg_scale} args={extra_args.keys()}')
         eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
         eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
         noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
@@ -274,6 +289,7 @@ class StableDiffusionXLPuLIDPipeline:
         if self.callback_on_step_end is not None:
             self.step += 1
             self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
+        debug(f'PulID sample end:   step={self.step} x={latent.shape} dtype={x.dtype} min={torch.amin(latent)} max={torch.amax(latent)}')
         return latent
 
     def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
@@ -299,6 +315,7 @@ class StableDiffusionXLPuLIDPipeline:
                                                     return_image_latents=False,
                                                     )
                 latents = latents[0]
+                debug(f'PulID noise: op=inpaint latent={latents.shape} image={image} mask={mask_image} dtype={latents.dtype}')
             else:  # img2img
                 latents = self.pipe.prepare_latents(image,
                                                     None,  # timestep (not needed)
@@ -309,10 +326,10 @@ class StableDiffusionXLPuLIDPipeline:
                                                     None,  # generator
                                                     False,  # add_noise
                                                     )
-
+                debug(f'PulID noise: op=img2img latent={latents.shape} image={image} dtype={latents.dtype}')
         else:
             latents = torch.zeros_like(noise)
-
+            debug(f'PulID noise: op=txt2img latent={latents.shape} dtype={latents.dtype}')
         return latents, noise
 
     def __call__(
@@ -333,6 +350,7 @@ class StableDiffusionXLPuLIDPipeline:
         output_type: str='pil',
         callback_on_step_end=None,
     ):
+        debug(f'PulID call: width={width} height={height} cfg={guidance_scale} steps={num_inference_steps} seed={seed} strength={strength} id_scale={id_scale} output={output_type}')
         self.step = 0 # pylint: disable=attribute-defined-outside-init
         self.callback_on_step_end = callback_on_step_end # pylint: disable=attribute-defined-outside-init
         size = (1, height, width)
@@ -341,11 +359,12 @@ class StableDiffusionXLPuLIDPipeline:
         if image is not None and strength > 0:
             _, num_inference_steps = self.pipe.get_timesteps(num_inference_steps, strength, self.device, None)  # denoising_start disabled
             sigmas = sigmas[-(num_inference_steps + 1):].to(self.device) # shorten sigmas in i2i
-
+        debug(f'PulID sigmas: sigmas={sigmas.shape} dtype={sigmas.dtype}')
 
         # latents
         latent, noise = self.init_latent(seed, size, image, mask_image, strength, width, height)
         noisy_latent = latent + noise * sigmas[0].to(noise)
+        debug(f'PulID noisy: latent={noisy_latent.shape} dtype={noisy_latent.dtype}')
 
         (
             prompt_embeds,
@@ -390,14 +409,17 @@ class StableDiffusionXLPuLIDPipeline:
         latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
 
         # process output
+        latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device)
+        debug(f'PulID output: latent={latents.shape} dtype={latents.dtype}')
         if output_type == 'latent':
             images = self.pipe.image_processor.postprocess(latents, output_type='latent')
         elif output_type == 'np':
             images = self.pipe.image_processor.postprocess(latents, output_type='np')
         else:
-            latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
+            latents = latents / self.pipe.vae.config.scaling_factor
             images = self.pipe.vae.decode(latents).sample
             images = self.pipe.image_processor.postprocess(images, output_type='pil')
+        debug(f'PulID output: type={type(images)} images={images.shape if hasattr(images, "shape") else images}')
         return StableDiffusionXLPipelineOutput(images)
 
 

From e7f74c176803c77ffd6a0de344253db82031399d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 10:46:33 -0500
Subject: [PATCH 054/119] fix pulid vae

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_vae.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index 5e6fa68f4..0473beeb1 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -147,6 +147,7 @@ def taesd_vae_encode(image):
 
 def vae_decode(latents, model, output_type='np', full_quality=True, width=None, height=None):
     t0 = time.time()
+    model = model or shared.sd_model
     if latents is None or not torch.is_tensor(latents): # already decoded
         return latents
     prev_job = shared.state.job
@@ -169,15 +170,8 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
 
     if latents.shape[-1] <= 4: # not a latent, likely an image
         decoded = latents.float().cpu().numpy()
-    elif full_quality and hasattr(shared.sd_model, "vae"):
-        parent = shared.sd_model if hasattr(shared.sd_model, 'vae') else None
-        if hasattr(shared.sd_model, 'vae'):
-            parent = shared.sd_model
-        elif hasattr(shared.sd_model, 'pipe') and hasattr(shared.sd_model.pipe, 'vae'):
-            parent = shared.sd_model.pipe
-        else:
-            parent = None
-        decoded = full_vae_decode(latents=latents, model=parent)
+    elif full_quality and hasattr(model, "vae"):
+        decoded = full_vae_decode(latents=latents, model=model)
     else:
         decoded = taesd_vae_decode(latents=latents)
 

From 172693eb992f7cb396e59bae7b15b59600085d79 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 15:23:11 -0500
Subject: [PATCH 055/119] refactor processing class

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .../Lora/extra_networks_lora.py               |   1 -
 modules/api/models.py                         |   8 -
 modules/img2img.py                            |  19 +-
 modules/processing_class.py                   | 478 ++++++++----------
 modules/shared.py                             |   6 +-
 modules/txt2img.py                            |   1 -
 modules/ui_img2img.py                         |   2 +
 modules/unipc/sampler.py                      |   2 +-
 8 files changed, 238 insertions(+), 279 deletions(-)

diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py
index 9172d7336..69b234df7 100644
--- a/extensions-builtin/Lora/extra_networks_lora.py
+++ b/extensions-builtin/Lora/extra_networks_lora.py
@@ -145,7 +145,6 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
         if self.active and networks.debug:
             shared.log.debug(f"Network end: type=LoRA load={networks.timer['load']:.2f} apply={networks.timer['apply']:.2f} restore={networks.timer['restore']:.2f}")
         if self.errors:
-            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
             for k, v in self.errors.items():
                 shared.log.error(f'LoRA: name="{k}" errors={v}')
             self.errors.clear()
diff --git a/modules/api/models.py b/modules/api/models.py
index 740f3c555..f5b89c2a9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -11,14 +11,6 @@ API_NOT_ALLOWED = [
     "sd_model",
     "outpath_samples",
     "outpath_grids",
-    "sampler_index",
-    "extra_generation_params",
-    "overlay_images",
-    "do_not_reload_embeddings",
-    "seed_enable_extras",
-    "prompt_for_display",
-    "sampler_noise_scheduler_override",
-    "ddim_discretize"
 ]
 
 class ModelDef(BaseModel):
diff --git a/modules/img2img.py b/modules/img2img.py
index f3bec5b02..8274386cc 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -137,6 +137,7 @@ def img2img(id_task: str, state: str, mode: int,
             inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
             img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
             hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio,
+            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,
             override_settings_texts,
             *args): # pylint: disable=unused-argument
 
@@ -214,7 +215,6 @@ def img2img(id_task: str, state: str, mode: int,
         subseed_strength=subseed_strength,
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=True,
         sampler_name = processing.get_sampler_name(sampler_index, img=True),
         batch_size=batch_size,
         n_iter=n_iter,
@@ -247,6 +247,23 @@ def img2img(id_task: str, state: str, mode: int,
         inpainting_mask_invert=inpainting_mask_invert,
         hdr_mode=hdr_mode, hdr_brightness=hdr_brightness, hdr_color=hdr_color, hdr_sharpen=hdr_sharpen, hdr_clamp=hdr_clamp,
         hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold, hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry, hdr_color_picker=hdr_color_picker, hdr_tint_ratio=hdr_tint_ratio,
+        # refiner
+        enable_hr=enable_hr,
+        hr_denoising_strength=hr_denoising_strength,
+        hr_scale=hr_scale,
+        hr_resize_mode=hr_resize_mode,
+        hr_resize_context=hr_resize_context,
+        hr_upscaler=hr_upscaler,
+        hr_force=hr_force,
+        hr_second_pass_steps=hr_second_pass_steps,
+        hr_resize_x=hr_resize_x,
+        hr_resize_y=hr_resize_y,
+        hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
+        refiner_steps=refiner_steps,
+        hr_refiner_start=hr_refiner_start,
+        refiner_prompt=refiner_prompt,
+        refiner_negative=refiner_negative,
+        # override
         override_settings=override_settings,
     )
     p.scripts = modules.scripts.scripts_img2img
diff --git a/modules/processing_class.py b/modules/processing_class.py
index f4300a156..1cf469e28 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -17,53 +17,44 @@ debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None
 
 @dataclass(repr=False)
 class StableDiffusionProcessing:
-    """
-    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    """
     def __init__(self,
-                 sd_model=None,
-                 outpath_samples=None,
-                 outpath_grids=None,
+                 sd_model=None, # pylint: disable=unused-argument # local instance of sd_model
+                 # base params
                  prompt: str = "",
-                 styles: List[str] = None,
+                 negative_prompt: str = None,
                  seed: int = -1,
                  subseed: int = -1,
                  subseed_strength: float = 0,
                  seed_resize_from_h: int = -1,
                  seed_resize_from_w: int = -1,
-                 seed_enable_extras: bool = True,
-                 sampler_name: str = None,
-                 hr_sampler_name: str = None,
                  batch_size: int = 1,
                  n_iter: int = 1,
                  steps: int = 50,
-                 cfg_scale: float = 7.0,
-                 image_cfg_scale: float = None,
                  clip_skip: int = 1,
                  width: int = 512,
                  height: int = 512,
-                 full_quality: bool = True,
-                 detailer: bool = False,
-                 restore_faces: bool = False,
-                 tiling: bool = False,
-                 hidiffusion: bool = False,
-                 do_not_save_samples: bool = False,
-                 do_not_save_grid: bool = False,
-                 extra_generation_params: Dict[Any, Any] = None,
-                 overlay_images: Any = None,
-                 negative_prompt: str = None,
+                 # samplers
+                 sampler_index: int = None, # pylint: disable=unused-argument # used only to set sampler_name
+                 sampler_name: str = None,
+                 hr_sampler_name: str = None,
                  eta: float = None,
-                 do_not_reload_embeddings: bool = False,
-                 denoising_strength: float = 0,
+                 # guidance
+                 cfg_scale: float = 7.0,
+                 cfg_end: float = 1,
                  diffusers_guidance_rescale: float = 0.7,
                  pag_scale: float = 0.0,
                  pag_adaptive: float = 0.5,
-                 cfg_end: float = 1,
-                 resize_mode: int = 0,
-                 resize_name: str = 'None',
-                 resize_context: str = 'None',
-                 scale_by: float = 0,
-                 selected_scale_tab: int = 0,
+                 # styles
+                 styles: List[str] = None,
+                 # vae
+                 tiling: bool = False,
+                 full_quality: bool = True,
+                 # other
+                 hidiffusion: bool = False,
+                 do_not_reload_embeddings: bool = False,
+                 detailer: bool = False,
+                 restore_faces: bool = False,
+                 # hdr corrections
                  hdr_mode: int = 0,
                  hdr_brightness: float = 0,
                  hdr_color: float = 0,
@@ -76,92 +67,209 @@ class StableDiffusionProcessing:
                  hdr_max_boundry: float = 1.0,
                  hdr_color_picker: str = None,
                  hdr_tint_ratio: float = 0,
-                 override_settings: Dict[str, Any] = None,
+                 # img2img
+                 init_images: list = None,
+                 init_latent: Any = None,
+                 resize_mode: int = 0,
+                 resize_name: str = 'None',
+                 resize_context: str = 'None',
+                 denoising_strength: float = 0.3,
+                 image_cfg_scale: float = None,
+                 initial_noise_multiplier: float = None, # pylint: disable=unused-argument # a1111 compatibility
+                 scale_by: float = 1,
+                 selected_scale_tab: int = 0, # pylint: disable=unused-argument # a1111 compatibility
+                 # inpaint
+                 mask: Any = None,
+                 image_mask: Any = None,
+                 latent_mask: Any = None,
+                 mask_for_overlay: Any = None,
+                 mask_blur: int = 4,
+                 paste_to: Any = None,
+                 inpainting_fill: int = 0,
+                 inpaint_full_res: bool = False,
+                 inpaint_full_res_padding: int = 0,
+                 inpainting_mask_invert: int = 0,
+                 overlay_images: Any = None,
+                 # refiner
+                 enable_hr: bool = False,
+                 firstphase_width: int = 0,
+                 firstphase_height: int = 0,
+                 hr_scale: float = 2.0,
+                 hr_force: bool = False,
+                 hr_resize_mode: int = 0,
+                 hr_resize_context: str = 'None',
+                 hr_upscaler: str = None,
+                 hr_second_pass_steps: int = 0,
+                 hr_resize_x: int = 0,
+                 hr_resize_y: int = 0,
+                 hr_denoising_strength: float = 0.50,
+                 refiner_steps: int = 5,
+                 refiner_start: float = 0,
+                 refiner_prompt: str = '',
+                 refiner_negative: str = '',
+                 hr_refiner_start: float = 0,
+                 # save options
+                 outpath_samples=None,
+                 outpath_grids=None,
+                 do_not_save_samples: bool = False,
+                 do_not_save_grid: bool = False,
+                 # scripts
+                 script_args: list = [],
+                 # overrides
+                 override_settings: Dict[str, Any] = {},
                  override_settings_restore_afterwards: bool = True,
-                 sampler_index: int = None,
-                 script_args: list = None
-                ): # pylint: disable=unused-argument
-
+                 # metadata
+                 extra_generation_params: Dict[Any, Any] = {},
+                ):
+        self.task_args = {}
+        # state items
         self.state: str = ''
+        self.ops = []
         self.skip = []
-        self.outpath_samples: str = outpath_samples
-        self.outpath_grids: str = outpath_grids
-        self.prompt: str = prompt
-        self.prompt_for_display: str = None
-        self.negative_prompt: str = (negative_prompt or "")
-        self.styles: list = styles or []
-        self.seed: int = seed
-        self.subseed: int = subseed
-        self.subseed_strength: float = subseed_strength
-        self.seed_resize_from_h: int = seed_resize_from_h
-        self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_name: str = sampler_name
-        self.hr_sampler_name: str = hr_sampler_name if hr_sampler_name != 'Same as primary' else sampler_name
-        self.batch_size: int = batch_size
-        self.n_iter: int = n_iter
-        self.steps: int = steps
-        self.hr_second_pass_steps = 0
-        self.cfg_scale: float = cfg_scale
-        self.scale_by: float = scale_by
+        self.color_corrections = []
+        self.is_control = False
+        self.is_hr_pass = False
+        self.is_refiner_pass = False
+        self.is_api = False
+        self.scheduled_prompt = False
+        self.prompt_embeds = []
+        self.positive_pooleds = []
+        self.negative_embeds = []
+        self.negative_pooleds = []
+        self.disable_extra_networks = False
+        self.iteration = 0
+        # initializers
+        self.prompt = prompt
+        self.seed = seed
+        self.subseed = subseed
+        self.subseed_strength = subseed_strength
+        self.seed_resize_from_h = seed_resize_from_h
+        self.seed_resize_from_w = seed_resize_from_w
+        self.batch_size = batch_size
+        self.n_iter = n_iter
+        self.steps = steps
+        self.clip_skip = clip_skip
+        self.width = width
+        self.height = height
+        self.negative_prompt = negative_prompt
+        self.styles = styles
+        self.tiling = tiling
+        self.full_quality = full_quality
+        self.hidiffusion = hidiffusion
+        self.do_not_reload_embeddings = do_not_reload_embeddings
+        self.detailer = detailer
+        self.restore_faces = restore_faces
+        self.hdr_mode = hdr_mode
+        self.hdr_brightness = hdr_brightness
+        self.hdr_color = hdr_color
+        self.hdr_sharpen = hdr_sharpen
+        self.hdr_clamp = hdr_clamp
+        self.hdr_boundary = hdr_boundary
+        self.hdr_threshold = hdr_threshold
+        self.hdr_maximize = hdr_maximize
+        self.hdr_max_center = hdr_max_center
+        self.hdr_max_boundry = hdr_max_boundry
+        self.hdr_color_picker = hdr_color_picker
+        self.hdr_tint_ratio = hdr_tint_ratio
+        self.init_images = init_images
+        self.resize_mode = resize_mode
+        self.resize_name = resize_name
+        self.resize_context = resize_context
+        self.denoising_strength = denoising_strength
         self.image_cfg_scale = image_cfg_scale
+        self.scale_by = scale_by
+        self.mask = mask
+        self.image_mask = mask
+        self.latent_mask = latent_mask
+        self.mask_blur = mask_blur
+        self.inpainting_fill = inpainting_fill
+        self.inpaint_full_res_padding = inpaint_full_res_padding
+        self.inpainting_mask_invert = inpainting_mask_invert
+        self.overlay_images = overlay_images
+        self.enable_hr = enable_hr
+        self.firstphase_width = firstphase_width
+        self.firstphase_height = firstphase_height
+        self.hr_scale = hr_scale
+        self.hr_force = hr_force
+        self.hr_resize_mode = hr_resize_mode
+        self.hr_resize_context = hr_resize_context
+        self.hr_upscaler = hr_upscaler
+        self.hr_second_pass_steps = hr_second_pass_steps
+        self.hr_resize_x = hr_resize_x
+        self.hr_resize_y = hr_resize_y
+        self.hr_upscale_to_x = hr_resize_x
+        self.hr_upscale_to_y = hr_resize_y
+        self.hr_denoising_strength = hr_denoising_strength
+        self.refiner_steps = refiner_steps
+        self.refiner_start = refiner_start
+        self.refiner_prompt = refiner_prompt
+        self.refiner_negative = refiner_negative
+        self.hr_refiner_start = hr_refiner_start
+        self.outpath_samples = outpath_samples
+        self.outpath_grids = outpath_grids
+        self.do_not_save_samples = do_not_save_samples
+        self.do_not_save_grid = do_not_save_grid
+        self.override_settings_restore_afterwards = override_settings_restore_afterwards
+        self.extra_generation_params = extra_generation_params
+        self.eta = eta
+        self.cfg_scale = cfg_scale
+        self.cfg_end = cfg_end
         self.diffusers_guidance_rescale = diffusers_guidance_rescale
         self.pag_scale = pag_scale
         self.pag_adaptive = pag_adaptive
-        self.cfg_end = cfg_end
-        self.width: int = width
-        self.height: int = height
-        self.full_quality: bool = full_quality
-        self.detailer: bool = detailer
-        self.restore_faces: bool = restore_faces
-        self.tiling: bool = tiling
-        self.hidiffusion: bool = hidiffusion
-        self.do_not_save_samples: bool = do_not_save_samples
-        self.do_not_save_grid: bool = do_not_save_grid
-        self.extra_generation_params: dict = extra_generation_params or {}
-        self.overlay_images = overlay_images
-        self.eta = eta
-        self.do_not_reload_embeddings = do_not_reload_embeddings
-        self.paste_to = None
-        self.color_corrections = None
-        self.denoising_strength: float = denoising_strength
+        self.selected_scale_tab = selected_scale_tab
+        self.mask_for_overlay = mask_for_overlay
+        self.paste_to = paste_to
+        self.init_latent = None
+        # special handled items
+        if firstphase_width != 0 or firstphase_height != 0:
+            self.hr_upscale_to_x = self.width
+            self.hr_upscale_to_y = self.height
+            self.width = firstphase_width
+            self.height = firstphase_height
+        self.sampler_name = sampler_name or processing_helpers.get_sampler_name(sampler_index, img=True)
+        self.hr_sampler_name: str = hr_sampler_name if hr_sampler_name != 'Same as primary' else self.sampler_name
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
-        self.override_settings_restore_afterwards = override_settings_restore_afterwards
-        self.is_using_inpainting_conditioning = False # a111 compatibility
-        self.disable_extra_networks = False
-        # self.scripts = scripts.ScriptRunner() # set via property
-        # self.script_args = script_args or [] # set via property
-        self.per_script_args = {}
+        self.inpaint_full_res = inpaint_full_res if isinstance(inpaint_full_res, bool) else self.inpaint_full_res
+        self.inpaint_full_res = inpaint_full_res != 0 if isinstance(inpaint_full_res, int) else self.inpaint_full_res
+
+        # null items initialized later
         self.all_prompts = None
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
-        self.clip_skip = clip_skip
+        # ip adapter
+        self.ip_adapter_names = []
+        self.ip_adapter_scales = [0.0]
+        self.ip_adapter_images = []
+        self.ip_adapter_starts = [0.0]
+        self.ip_adapter_ends = [1.0]
+        self.ip_adapter_crops = []
+        # a1111 compatibility items
         shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
-        self.iteration = 0
-        self.is_control = False
-        self.is_hr_pass = False
-        self.is_refiner_pass = False
-        self.hr_force = False
-        self.enable_hr = None
-        self.hr_scale = None
-        self.hr_upscaler = None
-        self.hr_resize_mode = 0
-        self.hr_resize_context = 'None'
-        self.hr_resize_x = 0
-        self.hr_resize_y = 0
-        self.hr_upscale_to_x = 0
-        self.hr_upscale_to_y = 0
+        self.seed_enable_extras: bool = True,
+        self.is_using_inpainting_conditioning = False # a111 compatibility
+        self.batch_index = 0
+        self.refiner_switch_at = 0
+        self.hr_prompt = ''
+        self.all_hr_prompts = []
+        self.hr_negative_prompt = ''
+        self.all_hr_negative_prompts = []
         self.truncate_x = 0
         self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-        self.refiner_steps = 5
-        self.refiner_start = 0
-        self.refiner_prompt = ''
-        self.refiner_negative = ''
-        self.ops = []
-        self.resize_mode: int = resize_mode
-        self.resize_name: str = resize_name
-        self.resize_context: str = resize_context
+        self.comments = {}
+        self.sampler = None
+        self.nmask = None
+        self.initial_noise_multiplier = initial_noise_multiplier or shared.opts.initial_noise_multiplier
+        self.image_conditioning = None
+        self.prompt_for_display: str = None
+        # scripts
+        self.scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+        self.script_args_value: list = field(default=None, init=False)
+        self.scripts_setup_complete: bool = field(default=False, init=False)
+        self.script_args = script_args
+        self.per_script_args = {}
+        # settings to processing
         self.ddim_discretize = shared.opts.ddim_discretize
         self.s_min_uncond = shared.opts.s_min_uncond
         self.s_churn = shared.opts.s_churn
@@ -170,45 +278,6 @@ class StableDiffusionProcessing:
         self.s_max = shared.opts.s_max
         self.s_tmin = shared.opts.s_tmin
         self.s_tmax = float('inf')  # not representable as a standard ui option
-        self.task_args = {}
-        # a1111 compatibility items
-        self.batch_index = 0
-        self.refiner_switch_at = 0
-        self.hr_prompt = ''
-        self.all_hr_prompts = []
-        self.hr_negative_prompt = ''
-        self.all_hr_negative_prompts = []
-        self.comments = {}
-        self.is_api = False
-        self.scripts_value: scripts.ScriptRunner = field(default=None, init=False)
-        self.script_args_value: list = field(default=None, init=False)
-        self.scripts_setup_complete: bool = field(default=False, init=False)
-        # ip adapter
-        self.ip_adapter_names = []
-        self.ip_adapter_scales = [0.0]
-        self.ip_adapter_images = []
-        self.ip_adapter_starts = [0.0]
-        self.ip_adapter_ends = [1.0]
-        self.ip_adapter_crops = []
-        # hdr
-        self.hdr_mode=hdr_mode
-        self.hdr_brightness=hdr_brightness
-        self.hdr_color=hdr_color
-        self.hdr_sharpen=hdr_sharpen
-        self.hdr_clamp=hdr_clamp
-        self.hdr_boundary=hdr_boundary
-        self.hdr_threshold=hdr_threshold
-        self.hdr_maximize=hdr_maximize
-        self.hdr_max_center=hdr_max_center
-        self.hdr_max_boundry=hdr_max_boundry
-        self.hdr_color_picker=hdr_color_picker
-        self.hdr_tint_ratio=hdr_tint_ratio
-        # globals
-        self.scheduled_prompt: bool = False
-        self.prompt_embeds = []
-        self.positive_pooleds = []
-        self.negative_embeds = []
-        self.negative_pooleds = []
 
     @property
     def sd_model(self):
@@ -252,57 +321,9 @@ class StableDiffusionProcessing:
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-
-    def __init__(self,
-                 enable_hr: bool = False,
-                 denoising_strength: float = 0.75,
-                 firstphase_width: int = 0,
-                 firstphase_height: int = 0,
-                 hr_scale: float = 2.0,
-                 hr_force: bool = False,
-                 hr_resize_mode: int = 0,
-                 hr_resize_context: str = 'None',
-                 hr_upscaler: str = None,
-                 hr_second_pass_steps: int = 0,
-                 hr_resize_x: int = 0,
-                 hr_resize_y: int = 0,
-                 refiner_steps: int = 5,
-                 refiner_start: float = 0,
-                 refiner_prompt: str = '',
-                 refiner_negative: str = '',
-                 **kwargs
-                ):
-
+    def __init__(self, **kwargs):
+        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
         super().__init__(**kwargs)
-        self.reprocess = {}
-        self.enable_hr = enable_hr
-        self.denoising_strength = denoising_strength
-        self.hr_scale = hr_scale
-        self.hr_upscaler = hr_upscaler
-        self.hr_resize_mode = hr_resize_mode
-        self.hr_resize_context = hr_resize_context
-        self.hr_force = hr_force
-        self.hr_second_pass_steps = hr_second_pass_steps
-        self.hr_resize_x = hr_resize_x
-        self.hr_resize_y = hr_resize_y
-        self.hr_upscale_to_x = hr_resize_x
-        self.hr_upscale_to_y = hr_resize_y
-        if firstphase_width != 0 or firstphase_height != 0:
-            self.hr_upscale_to_x = self.width
-            self.hr_upscale_to_y = self.height
-            self.width = firstphase_width
-            self.height = firstphase_height
-        self.truncate_x = 0
-        self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-        self.refiner_steps = refiner_steps
-        self.refiner_start = refiner_start
-        self.refiner_prompt = refiner_prompt
-        self.refiner_negative = refiner_negative
-        self.sampler = None
-        self.scripts = None
-        self.script_args = []
-
 
     def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
         if shared.native:
@@ -360,41 +381,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-
-    def __init__(self, init_images: list = None, resize_mode: int = 0, resize_name: str = 'None', resize_context: str = 'None', denoising_strength: float = 0.3, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = False, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, scale_by: float = 1, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
+    def __init__(self, **kwargs):
+        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
         super().__init__(**kwargs)
-        self.init_images = init_images
-        self.resize_mode: int = resize_mode
-        self.resize_name: str = resize_name
-        self.resize_context: str = resize_context
-        self.denoising_strength: float = denoising_strength
-        self.hr_denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale
-        self.init_latent = None
-        self.image_mask = mask
-        self.latent_mask = None
-        self.mask_for_overlay = None
-        self.mask_blur_x = mask_blur # a1111 compatibility item
-        self.mask_blur_y = mask_blur # a1111 compatibility item
-        self.mask_blur = mask_blur
-        self.inpainting_fill = inpainting_fill
-        self.inpaint_full_res = inpaint_full_res
-        self.inpaint_full_res_padding = inpaint_full_res_padding
-        self.inpainting_mask_invert = inpainting_mask_invert
-        self.initial_noise_multiplier = shared.opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
-        self.mask = None
-        self.nmask = None
-        self.image_conditioning = None
-        self.refiner_steps = refiner_steps
-        self.refiner_start = refiner_start
-        self.refiner_prompt = refiner_prompt
-        self.refiner_negative = refiner_negative
-        self.enable_hr = None
-        self.is_batch = False
-        self.scale_by = scale_by
-        self.sampler = None
-        self.scripts = None
-        self.script_args = []
 
     def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
         if hasattr(self, 'init_images') and self.init_images is not None and len(self.init_images) > 0:
@@ -544,47 +533,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
 class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
     def __init__(self, **kwargs):
+        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
         super().__init__(**kwargs)
-        self.strength = None
-        self.adapter_conditioning_scale = None
-        self.adapter_conditioning_factor = None
-        self.guess_mode = None
-        self.controlnet_conditioning_scale = None
-        self.control_guidance_start = None
-        self.control_guidance_end = None
-        self.control_mode = None
-        self.reference_attn = None
-        self.reference_adain = None
-        self.attention_auto_machine_weight = None
-        self.gn_auto_machine_weight = None
-        self.style_fidelity = None
-        self.ref_image = None
-        self.image = None
-        self.query_weight = None
-        self.adain_weight = None
-        self.adapter_conditioning_factor = 1.0
-        self.attention = 'Attention'
-        self.fidelity = 0.5
-        self.mask_image = None
-        self.override = None
-        self.resize_mode_before = None
-        self.resize_name_before = None
-        self.width_before = None
-        self.height_before = None
-        self.scale_by_before = None
-        self.selected_scale_tab_before = None
-        self.resize_mode_after = None
-        self.resize_name_after = None
-        self.width_after = None
-        self.height_after = None
-        self.scale_by_after = None
-        self.selected_scale_tab_after = None
-        self.resize_mode_mask = None
-        self.resize_name_mask = None
-        self.width_mask = None
-        self.height_mask = None
-        self.scale_by_mask = None
-        self.selected_scale_tab_mask = None
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): # abstract
         pass
diff --git a/modules/shared.py b/modules/shared.py
index a6c15d05e..52f40f313 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -787,8 +787,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "mask_apply_overlay": OptionInfo(True, "Apply mask as overlay"),
     "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", gr.ColorPicker, {}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
-    "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01, "visible": not native}),
+    "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
 
     # "postprocessing_sep_detailer": OptionInfo("<h2>Detailer</h2>", "", gr.HTML),
     "detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
@@ -1110,7 +1110,7 @@ cmd_opts = cmd_args.settings_args(opts, cmd_opts)
 if cmd_opts.use_xformers:
     opts.data['cross_attention_optimization'] = 'xFormers'
 opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
-opts.data['uni_pc_order'] = opts.schedulers_solver_order # compatibility
+opts.data['uni_pc_order'] = max(2, opts.schedulers_solver_order) # compatibility
 log.info(f'Engine: backend={backend} compute={devices.backend} device={devices.get_optimal_device_name()} attention="{opts.cross_attention_optimization}" mode={devices.inference_context.__name__}')
 if not native:
     log.warning('Backend=original is in maintainance-only mode')
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 38cde0aca..2f0e2f4b3 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -49,7 +49,6 @@ def txt2img(id_task, state,
         subseed_strength=subseed_strength,
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=True,
         sampler_name = processing.get_sampler_name(sampler_index),
         hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
         batch_size=batch_size,
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index 4cb8e4c18..22c89dac8 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -131,6 +131,7 @@ def create_ui():
 
                     full_quality, tiling, hidiffusion, cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = 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_boundry, 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')
                     detailer = shared.yolo.ui('img2img')
 
                     # with gr.Group(elem_id="inpaint_controls", visible=False) as inpaint_controls:
@@ -192,6 +193,7 @@ def create_ui():
                 inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
                 img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
                 hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio,
+                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,
                 override_settings,
             ]
             img2img_dict = dict(
diff --git a/modules/unipc/sampler.py b/modules/unipc/sampler.py
index bcc4eed76..b5e116d61 100644
--- a/modules/unipc/sampler.py
+++ b/modules/unipc/sampler.py
@@ -186,6 +186,6 @@ class UniPCSampler(object):
         )
 
         uni_pc = UniPC(model_fn, self.noise_schedule, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
-        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.schedulers_solver_order, lower_order_final=shared.opts.schedulers_use_loworder)
+        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
 
         return x.to(device), None

From d50f150976fb92a0f56259501e81d42ff612f189 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 15:51:30 -0500
Subject: [PATCH 056/119] add refine/hires to img2img

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                | 2 ++
 modules/control/run.py      | 2 +-
 modules/processing_class.py | 9 ++++++---
 3 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5db77983c..f4178db22 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -63,6 +63,8 @@ This release can be considered an LTS release before we kick off the next round
     - add show networks on startup setting  
     - better mapping of networks previews  
     - optimize networks display load  
+  - Image2image:
+    - integrated refine/upscale/hires workflow
 - Other:  
   - Installer:
     - Log `venv` and package search paths
diff --git a/modules/control/run.py b/modules/control/run.py
index 74bab35c4..d2e1b36ef 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -70,7 +70,7 @@ def control_run(state: str = '',
                 enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.3, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
                 hr_scale: float = 1.0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0.0, refiner_prompt: str = '', refiner_negative: str = '',
                 video_skip_frames: int = 0, video_type: str = 'None', video_duration: float = 2.0, video_loop: bool = False, video_pad: int = 0, video_interpolate: int = 0,
-                *input_script_args
+                *input_script_args,
         ):
     # handle optional initialization via ui
     for u in units:
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 1cf469e28..37cb3d72a 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -69,7 +69,6 @@ class StableDiffusionProcessing:
                  hdr_tint_ratio: float = 0,
                  # img2img
                  init_images: list = None,
-                 init_latent: Any = None,
                  resize_mode: int = 0,
                  resize_name: str = 'None',
                  resize_context: str = 'None',
@@ -80,7 +79,6 @@ class StableDiffusionProcessing:
                  selected_scale_tab: int = 0, # pylint: disable=unused-argument # a1111 compatibility
                  # inpaint
                  mask: Any = None,
-                 image_mask: Any = None,
                  latent_mask: Any = None,
                  mask_for_overlay: Any = None,
                  mask_blur: int = 4,
@@ -179,7 +177,7 @@ class StableDiffusionProcessing:
         self.image_cfg_scale = image_cfg_scale
         self.scale_by = scale_by
         self.mask = mask
-        self.image_mask = mask
+        self.image_mask = mask # TODO duplciate mask params
         self.latent_mask = latent_mask
         self.mask_blur = mask_blur
         self.inpainting_fill = inpainting_fill
@@ -221,6 +219,7 @@ class StableDiffusionProcessing:
         self.mask_for_overlay = mask_for_overlay
         self.paste_to = paste_to
         self.init_latent = None
+
         # special handled items
         if firstphase_width != 0 or firstphase_height != 0:
             self.hr_upscale_to_x = self.width
@@ -238,6 +237,7 @@ class StableDiffusionProcessing:
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
+
         # ip adapter
         self.ip_adapter_names = []
         self.ip_adapter_scales = [0.0]
@@ -245,6 +245,7 @@ class StableDiffusionProcessing:
         self.ip_adapter_starts = [0.0]
         self.ip_adapter_ends = [1.0]
         self.ip_adapter_crops = []
+
         # a1111 compatibility items
         shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
         self.seed_enable_extras: bool = True,
@@ -263,12 +264,14 @@ class StableDiffusionProcessing:
         self.initial_noise_multiplier = initial_noise_multiplier or shared.opts.initial_noise_multiplier
         self.image_conditioning = None
         self.prompt_for_display: str = None
+
         # scripts
         self.scripts_value: scripts.ScriptRunner = field(default=None, init=False)
         self.script_args_value: list = field(default=None, init=False)
         self.scripts_setup_complete: bool = field(default=False, init=False)
         self.script_args = script_args
         self.per_script_args = {}
+
         # settings to processing
         self.ddim_discretize = shared.opts.ddim_discretize
         self.s_min_uncond = shared.opts.s_min_uncond

From 168e9445d1e0d1d36ea60ad662c8c89ea81b37a6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 16:16:22 -0500
Subject: [PATCH 057/119] add bnb and quanto version info

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

diff --git a/modules/model_quant.py b/modules/model_quant.py
index 547a3d7ae..68bdfa7b2 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -32,14 +32,14 @@ def load_bnb(msg='', silent=False):
     global bnb # pylint: disable=global-statement
     if bnb is not None:
         return bnb
-    fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
-    log.debug(f'Quantization: type=bitsandbytes fn={fn}') # pylint: disable=protected-access
     install('bitsandbytes', quiet=True)
     try:
         import bitsandbytes
         bnb = bitsandbytes
         diffusers.utils.import_utils._bitsandbytes_available = True # pylint: disable=protected-access
         diffusers.utils.import_utils._bitsandbytes_version = '0.43.3' # pylint: disable=protected-access
+        fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
+        log.debug(f'Quantization: type=bitsandbytes version={bnb.__version__} fn={fn}') # pylint: disable=protected-access
         return bnb
     except Exception as e:
         if len(msg) > 0:
@@ -54,12 +54,12 @@ def load_quanto(msg='', silent=False):
     global quanto # pylint: disable=global-statement
     if quanto is not None:
         return quanto
-    fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
-    log.debug(f'Quantization: type=quanto fn={fn}') # pylint: disable=protected-access
     install('optimum-quanto', quiet=True)
     try:
         from optimum import quanto as optimum_quanto # pylint: disable=no-name-in-module
         quanto = optimum_quanto
+        fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
+        log.debug(f'Quantization: type=quanto version={quanto.__version__} fn={fn}') # pylint: disable=protected-access
         return quanto
     except Exception as e:
         if len(msg) > 0:

From d495a4bb7db7a7b8586649842b46560b7c50d231 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 16:38:37 -0500
Subject: [PATCH 058/119] img2img refine upscale

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_diffusers.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 7537d0209..76009d2e1 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -152,13 +152,14 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
         p.is_hr_pass = True
         if hasattr(p, 'init_hr'):
             p.init_hr(p.hr_scale, p.hr_upscaler, force=p.hr_force)
-        else: # fake hires for img2img
-            p.hr_scale = p.scale_by
-            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_upscale_to_y = p.height
+        else:
+            if not p.is_hr_pass: # fake hires for img2img if not actual hr pass
+                p.hr_scale = p.scale_by
+                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
         prev_job = shared.state.job
 
         # hires runs on original pipeline

From 26359067421c117cbeb1ba28204128ce1db64df7 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Mon, 11 Nov 2024 18:37:08 -0600
Subject: [PATCH 059/119] fix scheduled prompt because diffusers callback is
 broken

---
 modules/processing_callbacks.py    | 22 ++++++++++++++--------
 modules/prompt_parser_diffusers.py | 10 ++++------
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 9e3c0cd31..52ea3e575 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -13,6 +13,19 @@ def set_callbacks_p(processing):
     global p # pylint: disable=global-statement
     p = processing
 
+def prompt_callback(step, kwargs):
+    if prompt_parser_diffusers.embedder is None or 'prompt_embeds' not in kwargs:
+        return kwargs
+    try:
+        prompt_embeds = prompt_parser_diffusers.embedder('prompt_embeds', step + 1)
+        negative_prompt_embeds = prompt_parser_diffusers.embedder('negative_prompt_embeds', step + 1)
+        if p.cfg_scale > 1:  # Perform guidance
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)  # Combined embeds
+        assert prompt_embeds.shape == kwargs['prompt_embeds'].shape, f"prompt_embed shape mismatch {kwargs['prompt_embeds'].shape} {prompt_embeds.shape}"
+        kwargs['prompt_embeds'] = prompt_embeds
+    except Exception as e:
+        debug_callback(f"Callback: {e}")
+    return kwargs
 
 def diffusers_callback_legacy(step: int, timestep: int, latents: typing.Union[torch.FloatTensor, np.ndarray]):
     if p is None:
@@ -66,14 +79,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = {}
             pipe.set_ip_adapter_scale(ip_adapter_scales)
     if step != getattr(pipe, 'num_timesteps', 0):
         kwargs = processing_correction.correction_callback(p, timestep, kwargs)
-    if prompt_parser_diffusers.embedder is not None:
-        try:
-            if 'prompt_embeds' in kwargs:
-                kwargs["prompt_embeds"] = prompt_parser_diffusers.embedder("prompt_embeds", step + 1)
-            if 'negative_prompt_embeds' in kwargs:
-                kwargs["negative_prompt_embeds"] = prompt_parser_diffusers.embedder("negative_prompt_embeds", step + 1)
-        except Exception as e:
-            debug_callback(f"Callback: {e}")
+    kwargs = prompt_callback(step, kwargs)  # monkey patch for diffusers callback issues
     if step == int(getattr(pipe, 'num_timesteps', 100) * p.cfg_end) and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
         if "PAG" in shared.sd_model.__class__.__name__:
             pipe._guidance_scale = 1.001 if pipe._guidance_scale > 1 else pipe._guidance_scale  # pylint: disable=protected-access
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 907cc7208..0e10e5fe1 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -167,17 +167,15 @@ class PromptEmbedder:
         pipe = prepare_model()
 
     def __call__(self, key, step=0):
-        batch = getattr(self, key) # for batch-size=1, len(batch)==1
+        batch = getattr(self, key)
         res = []
         for i in range(self.batchsize):
-            if len(batch[i]) == 0: # if not using prompt-scheduling, this will be len(batch[i])==1
+            if len(batch[i]) == 0:  # if asking for a null key, ie pooled on SD1.5
                 return None
             try:
-                res.append(batch[i][step]) # and this requests element for specific step when called from callback - but self.scheduled_prompt==False so len(batch[i])==1 and step is list index out-of-bounds!
+                res.append(batch[i][step])
             except IndexError:
-                res.append(batch[i][0])
-            if step != 0:  # For Callback
-                res.append(res[-1])  # Diffusers internally doubles batch dimension
+                res.append(batch[i][0])  # if not scheduled, return default
         return torch.cat(res)
 
 

From e2124bc6f651bde53199a403d701ee1fb6897682 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Mon, 11 Nov 2024 19:18:38 -0600
Subject: [PATCH 060/119] lora loading for wrapped models (pulid)

---
 extensions-builtin/Lora/networks.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 160487e88..b227f82f2 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -50,44 +50,45 @@ convert_diffusers_name_to_compvis = lora_convert.convert_diffusers_name_to_compv
 def assign_network_names_to_compvis_modules(sd_model):
     if sd_model is None:
         return
+    sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)  # wrapped model compatiblility
     network_layer_mapping = {}
     if shared.native:
-        if hasattr(shared.sd_model, 'text_encoder') and shared.sd_model.text_encoder is not None:
-            for name, module in shared.sd_model.text_encoder.named_modules():
-                prefix = "lora_te1_" if hasattr(shared.sd_model, 'text_encoder_2') else "lora_te_"
+        if hasattr(sd_model, 'text_encoder') and sd_model.text_encoder is not None:
+            for name, module in sd_model.text_encoder.named_modules():
+                prefix = "lora_te1_" if hasattr(sd_model, 'text_encoder_2') else "lora_te_"
                 network_name = prefix + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 module.network_layer_name = network_name
-        if hasattr(shared.sd_model, 'text_encoder_2'):
-            for name, module in shared.sd_model.text_encoder_2.named_modules():
+        if hasattr(sd_model, 'text_encoder_2'):
+            for name, module in sd_model.text_encoder_2.named_modules():
                 network_name = "lora_te2_" + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 module.network_layer_name = network_name
-        if hasattr(shared.sd_model, 'unet'):
-            for name, module in shared.sd_model.unet.named_modules():
+        if hasattr(sd_model, 'unet'):
+            for name, module in sd_model.unet.named_modules():
                 network_name = "lora_unet_" + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 module.network_layer_name = network_name
-        if hasattr(shared.sd_model, 'transformer'):
-            for name, module in shared.sd_model.transformer.named_modules():
+        if hasattr(sd_model, 'transformer'):
+            for name, module in sd_model.transformer.named_modules():
                 network_name = "lora_transformer_" + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 if "norm" in network_name and "linear" not in network_name:
                     continue
                 module.network_layer_name = network_name
     else:
-        if not hasattr(shared.sd_model, 'cond_stage_model'):
+        if not hasattr(sd_model, 'cond_stage_model'):
             sd_model.network_layer_mapping = {}
             return
-        for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
+        for name, module in sd_model.cond_stage_model.wrapped.named_modules():
             network_name = name.replace(".", "_")
             network_layer_mapping[network_name] = module
             module.network_layer_name = network_name
-        for name, module in shared.sd_model.model.named_modules():
+        for name, module in sd_model.model.named_modules():
             network_name = name.replace(".", "_")
             network_layer_mapping[network_name] = module
             module.network_layer_name = network_name
-    sd_model.network_layer_mapping = network_layer_mapping
+    shared.sd_model.network_layer_mapping = network_layer_mapping
 
 
 def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> network.Network:

From 66820edb63858885561aa7d1d2d24a92f3e76615 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 12 Nov 2024 11:02:34 -0500
Subject: [PATCH 061/119] update

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/face/instantid.py          |  2 +-
 modules/face/photomaker.py         |  2 +-
 modules/processing_args.py         |  9 +++--
 modules/processing_info.py         | 17 +++++----
 modules/prompt_parser.py           | 16 ++++----
 modules/prompt_parser_diffusers.py | 41 ++++++++++++++-------
 modules/prompt_parser_xhinker.py   |  2 +-
 modules/shared.py                  | 59 ++++++++++++++++++++++++------
 scripts/animatediff.py             |  2 +-
 scripts/ctrlx.py                   |  2 +-
 scripts/ledits.py                  |  2 +-
 scripts/mixture_tiling.py          |  2 +-
 scripts/mulan.py                   |  2 +-
 scripts/regional_prompting.py      |  2 +-
 scripts/x_adapter.py               |  2 +-
 scripts/xyz_grid_classes.py        |  4 ++
 wiki                               |  2 +-
 17 files changed, 113 insertions(+), 55 deletions(-)

diff --git a/modules/face/instantid.py b/modules/face/instantid.py
index 9c7c16f61..662d17c7d 100644
--- a/modules/face/instantid.py
+++ b/modules/face/instantid.py
@@ -68,7 +68,7 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_images, stre
         processing.process_init(p)
         p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
     orig_prompt_attention = shared.opts.prompt_attention
-    shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask
+    shared.opts.data['prompt_attention'] = 'fixed' # otherwise need to deal with class_tokens_mask
     p.task_args['image_embeds'] = face_embeds[0].shape # placeholder
     p.task_args['image'] = face_images[0]
     p.task_args['controlnet_conditioning_scale'] = float(conditioning)
diff --git a/modules/face/photomaker.py b/modules/face/photomaker.py
index c8f58b42a..b89f28a10 100644
--- a/modules/face/photomaker.py
+++ b/modules/face/photomaker.py
@@ -49,7 +49,7 @@ def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger,
     shared.sd_model.to(dtype=devices.dtype)
 
     orig_prompt_attention = shared.opts.prompt_attention
-    shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask
+    shared.opts.data['prompt_attention'] = 'fixed' # otherwise need to deal with class_tokens_mask
     p.task_args['input_id_images'] = input_images
     p.task_args['start_merge_step'] = int(start * p.steps)
     p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts is not None else p.prompt
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 67066eedc..4cd12a04d 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -107,12 +107,11 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
 
     debug(f'Diffusers pipeline possible: {possible}')
     prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
-    parser = 'Fixed attention'
     steps = kwargs.get("num_inference_steps", None) or len(getattr(p, 'timesteps', ['1']))
     clip_skip = kwargs.pop("clip_skip", 1)
 
-    # prompt_parser_diffusers.fix_position_ids(model)
-    if shared.opts.prompt_attention != 'Fixed attention' and 'Onnx' not in model.__class__.__name__ and (
+    parser = 'fixed'
+    if shared.opts.prompt_attention != 'fixed' and 'Onnx' not in model.__class__.__name__ and (
         'StableDiffusion' in model.__class__.__name__ or
         'StableCascade' in model.__class__.__name__ or
         'Flux' in model.__class__.__name__
@@ -125,6 +124,8 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
             if os.environ.get('SD_PROMPT_DEBUG', None) is not None:
                 errors.display(e, 'Prompt parser encode')
         timer.process.record('encode', reset=False)
+    else:
+        prompt_parser_diffusers.embedder = None
 
     if 'prompt' in possible:
         if 'OmniGen' in model.__class__.__name__:
@@ -156,7 +157,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
             else:
                 args['negative_prompt'] = negative_prompts
 
-    if 'clip_skip' in possible and parser == 'Fixed attention':
+    if 'clip_skip' in possible and parser == 'fixed':
         if clip_skip == 1:
             pass # clip_skip = None
         else:
diff --git a/modules/processing_info.py b/modules/processing_info.py
index e798211b1..f721b3717 100644
--- a/modules/processing_info.py
+++ b/modules/processing_info.py
@@ -4,6 +4,7 @@ from modules import shared, sd_samplers_common, sd_vae, generation_parameters_co
 from modules.processing_class import StableDiffusionProcessing
 
 
+debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
 if not shared.native:
     from modules import sd_hijack
 else:
@@ -39,27 +40,27 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
     ops.reverse()
     args = {
         # basic
+        "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None,
+        "Sampler": p.sampler_name if p.sampler_name != 'Default' else None,
         "Steps": p.steps,
         "Seed": all_seeds[index],
-        "Sampler": p.sampler_name if p.sampler_name != 'Default' else None,
+        "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
         "CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else None,
         "CFG end": p.cfg_end if p.cfg_end < 1.0 else None,
-        "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') 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,
-        "Parser": shared.opts.prompt_attention.split()[0],
         "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''),
         "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash),
         "VAE": (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD',
-        "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
-        "Clip skip": p.clip_skip if p.clip_skip > 1 else None,
         "Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
         "Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None,
         "Styles": "; ".join(p.styles) if p.styles is not None and len(p.styles) > 0 else None,
-        "Tiling": p.tiling if p.tiling else None,
         # sdnext
-        "Backend": 'Diffusers' if shared.native else 'Original',
         "App": 'SD.Next',
         "Version": git_commit,
+        "Backend": 'Diffusers' if shared.native else 'Original',
+        "Pipeline": 'LDM',
+        "Parser": shared.opts.prompt_attention.split()[0],
         "Comment": comment,
         "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
     }
@@ -165,7 +166,9 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
         if isinstance(v, str):
             if len(v) == 0 or v == '0x0':
                 del args[k]
+    debug(f'Infotext: args={args}')
     params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in args.items()])
     negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
     infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip()
+    debug(f'Infotext: "{infotext}"')
     return infotext
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 2a71d5053..3a1288097 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -308,11 +308,11 @@ def parse_prompt_attention(text):
     res = []
     round_brackets = []
     square_brackets = []
-    if opts.prompt_attention == 'Fixed attention':
+    if opts.prompt_attention == 'fixed':
         res = [[text, 1.0]]
         debug(f'Prompt: parser="{opts.prompt_attention}" {res}')
         return res
-    elif opts.prompt_attention == 'Compel parser':
+    elif opts.prompt_attention == 'compel':
         conjunction = Compel.parse_prompt_string(text)
         if conjunction is None or conjunction.prompts is None or conjunction.prompts is None or len(conjunction.prompts[0].children) == 0:
             return [["", 1.0]]
@@ -321,7 +321,7 @@ def parse_prompt_attention(text):
             res.append([frag.text, frag.weight])
         debug(f'Prompt: parser="{opts.prompt_attention}" {res}')
         return res
-    elif opts.prompt_attention == 'A1111 parser':
+    elif opts.prompt_attention == 'a1111':
         re_attention = re_attention_v1
         whitespace = ''
     else:
@@ -360,7 +360,7 @@ def parse_prompt_attention(text):
                 for i, part in enumerate(parts):
                     if i > 0:
                         res.append(["BREAK", -1])
-                    if opts.prompt_attention == 'Full parser':
+                    if opts.prompt_attention == 'native':
                         part = re_clean.sub("", part)
                         part = re_whitespace.sub(" ", part).strip()
                         if len(part) == 0:
@@ -392,15 +392,15 @@ if __name__ == "__main__":
     log.info(f'Schedules: {all_schedules}')
     for schedule in all_schedules:
         log.info(f'Schedule: {schedule[0]}')
-        opts.data['prompt_attention'] = 'Fixed attention'
+        opts.data['prompt_attention'] = 'fixed'
         output_list = parse_prompt_attention(schedule[1])
         log.info(f'  Fixed: {output_list}')
-        opts.data['prompt_attention'] = 'Compel parser'
+        opts.data['prompt_attention'] = 'compel'
         output_list = parse_prompt_attention(schedule[1])
         log.info(f'  Compel: {output_list}')
-        opts.data['prompt_attention'] = 'A1111 parser'
+        opts.data['prompt_attention'] = 'a1111'
         output_list = parse_prompt_attention(schedule[1])
         log.info(f'  A1111: {output_list}')
-        opts.data['prompt_attention'] = 'Full parser'
+        opts.data['prompt_attention'] = 'native'
         log.info = parse_prompt_attention(schedule[1])
         log.info(f'  Full:  {output_list}')
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 0e10e5fe1..e53af7957 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -47,6 +47,7 @@ class PromptEmbedder:
         self.prompts = prompts
         self.negative_prompts = negative_prompts
         self.batchsize = len(self.prompts)
+        self.attention = None
         self.allsame = self.compare_prompts()  # collapses batched prompts to single prompt if possible
         self.steps = steps
         self.clip_skip = clip_skip
@@ -75,6 +76,10 @@ class PromptEmbedder:
     def checkcache(self, p):
         if shared.opts.sd_textencoder_cache_size == 0:
             return False
+        if self.attention != shared.opts.prompt_attention:
+            debug(f"Prompt change: parser={shared.opts.prompt_attention}")
+            cache.clear()
+            return False
 
         def flatten(xss):
             return [x for xs in xss for x in xs]
@@ -97,23 +102,22 @@ class PromptEmbedder:
                               'positive_pooleds': self.positive_pooleds,
                               'negative_pooleds': self.negative_pooleds,
                               }
-                debug(f"Prompt cache: Adding {key}")
+                debug(f"Prompt cache: add={key}")
                 while len(cache) > int(shared.opts.sd_textencoder_cache_size):
                     cache.popitem(last=False)
         if item:
             self.__dict__.update(cache[key])
             cache.move_to_end(key)
-            if self.allsame and len(self.prompt_embeds) < self.batchsize:  # If current batch larger than cached
+            if self.allsame and len(self.prompt_embeds) < self.batchsize:
                 self.prompt_embeds = [self.prompt_embeds[0]] * self.batchsize
                 self.positive_pooleds = [self.positive_pooleds[0]] * self.batchsize
                 self.negative_prompt_embeds = [self.negative_prompt_embeds[0]] * self.batchsize
                 self.negative_pooleds = [self.negative_pooleds[0]] * self.batchsize
-            debug(f"Prompt cache: Retrieving {key}")
+            debug(f"Prompt cache: get={key}")
             return True
 
     def compare_prompts(self):
-        same = (self.prompts == [self.prompts[0]] * len(self.prompts) and
-                self.negative_prompts == [self.negative_prompts[0]] * len(self.negative_prompts))
+        same = (self.prompts == [self.prompts[0]] * len(self.prompts) and self.negative_prompts == [self.negative_prompts[0]] * len(self.negative_prompts))
         if same:
             self.prompts = [self.prompts[0]]
             self.negative_prompts = [self.negative_prompts[0]]
@@ -123,6 +127,7 @@ class PromptEmbedder:
         self.positive_schedule, scheduled = get_prompt_schedule(prompt, self.steps)
         self.negative_schedule, neg_scheduled = get_prompt_schedule(negative_prompt, self.steps)
         self.scheduled_prompt = scheduled or neg_scheduled
+        debug(f"Prompt schedule: positive={self.positive_schedule} negative={self.negative_schedule} scheduled={scheduled}")
 
     def scheduled_encode(self, pipe, batchidx):
         prompt_dict = {}  # index cache
@@ -138,20 +143,21 @@ class PromptEmbedder:
             prompt_dict[positive_prompt+negative_prompt] = i
 
     def extend_embeds(self, batchidx, idx):  # Extends scheduled prompt via index
-        self.prompt_embeds[batchidx].append(self.prompt_embeds[batchidx][idx])
-        self.negative_prompt_embeds[batchidx].append(self.negative_prompt_embeds[batchidx][idx])
+        if len(self.prompt_embeds[batchidx]) > 0:
+            self.prompt_embeds[batchidx].append(self.prompt_embeds[batchidx][idx])
+        if len(self.negative_prompt_embeds[batchidx]) > 0:
+            self.negative_prompt_embeds[batchidx].append(self.negative_prompt_embeds[batchidx][idx])
         if len(self.positive_pooleds[batchidx]) > 0:
             self.positive_pooleds[batchidx].append(self.positive_pooleds[batchidx][idx])
         if len(self.negative_pooleds[batchidx]) > 0:
             self.negative_pooleds[batchidx].append(self.negative_pooleds[batchidx][idx])
 
     def encode(self, pipe, positive_prompt, negative_prompt, batchidx):
-        if shared.opts.prompt_attention == "xhinker parser" or 'Flux' in pipe.__class__.__name__:
-            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(
-                pipe, positive_prompt, negative_prompt, self.clip_skip)
+        self.attention = shared.opts.prompt_attention
+        if self.attention == "xhinker" or 'Flux' in pipe.__class__.__name__:
+            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(pipe, positive_prompt, negative_prompt, self.clip_skip)
         else:
-            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(
-                pipe, positive_prompt, negative_prompt, self.clip_skip)
+            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, self.clip_skip)
         if prompt_embed is not None:
             self.prompt_embeds[batchidx].append(prompt_embed)
         if negative_embed is not None:
@@ -311,6 +317,7 @@ def get_tokens(msg, prompt):
                 tokens.append(f'UNK_{i}')
         token_count = len(ids) - int(has_bos_token) - int(has_eos_token)
         debug(f'Prompt tokenizer: type={msg} tokens={token_count} {tokens}')
+    return token_count
 
 
 def normalize_prompt(pairs: list):
@@ -338,6 +345,12 @@ def get_prompts_with_weights(prompt: str):
     if shared.opts.prompt_mean_norm:
         texts_and_weights = normalize_prompt(texts_and_weights)
     texts, text_weights = zip(*texts_and_weights)
+    if debug_enabled:
+        all_tokens = 0
+        for text in texts:
+            tokens = get_tokens('section', text)
+            all_tokens += tokens
+        debug(f'Prompt tokenizer: parser={shared.opts.prompt_attention} tokens={all_tokens}')
     debug(f'Prompt: weights={texts_and_weights} time={(time.time() - t0):.3f}')
     return texts, text_weights
 
@@ -479,7 +492,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
         # negative prompt has no keywords
         embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]], fragment_weights_batch=[negative_weights[i]], device=device, should_return_tokens=True)
         negative_prompt_embeds.append(embed)
-        debug(f'Prompt: unpadded shape={prompt_embeds[0].shape} TE{i+1} ptokens={torch.count_nonzero(ptokens)} ntokens={torch.count_nonzero(ntokens)} time={(time.time() - t0):.3f}')
+        debug(f'Prompt: unpadded={prompt_embeds[0].shape} TE{i+1} ptokens={torch.count_nonzero(ptokens)} ntokens={torch.count_nonzero(ntokens)} time={(time.time() - t0):.3f}')
     if SD3:
         t0 = time.time()
         pooled_prompt_embeds.append(embedding_providers[0].get_pooled_embeddings(texts=positives[0] if len(positives[0]) == 1 else [" ".join(positives[0])], device=device))
@@ -488,7 +501,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
         negative_pooled_prompt_embeds.append(embedding_providers[1].get_pooled_embeddings(texts=negatives[-1] if len(negatives[-1]) == 1 else [" ".join(negatives[-1])], device=device))
         pooled_prompt_embeds = torch.cat(pooled_prompt_embeds, dim=-1)
         negative_pooled_prompt_embeds = torch.cat(negative_pooled_prompt_embeds, dim=-1)
-        debug(f'Prompt: pooled shape={pooled_prompt_embeds[0].shape} time={(time.time() - t0):.3f}')
+        debug(f'Prompt: pooled={pooled_prompt_embeds[0].shape} time={(time.time() - t0):.3f}')
     elif prompt_embeds[-1].shape[-1] > 768:
         t0 = time.time()
         if shared.opts.diffusers_pooled == "weighted":
diff --git a/modules/prompt_parser_xhinker.py b/modules/prompt_parser_xhinker.py
index 6a8acf8c6..c0ddc9bc7 100644
--- a/modules/prompt_parser_xhinker.py
+++ b/modules/prompt_parser_xhinker.py
@@ -1305,7 +1305,7 @@ def get_weighted_text_embeddings_sd3(
         # ---------------------- get neg t5 embeddings -------------------------
         neg_prompt_tokens_3 = torch.tensor([neg_prompt_tokens_3], dtype=torch.long)
 
-        t5_neg_prompt_embeds = pipe.text_encoder_3(neg_prompt_tokens_3.to(pipe.pipe.text_encoder_3.device))[0].squeeze(0)
+        t5_neg_prompt_embeds = pipe.text_encoder_3(neg_prompt_tokens_3.to(pipe.text_encoder_3.device))[0].squeeze(0)
         t5_neg_prompt_embeds = t5_neg_prompt_embeds.to(device=pipe.text_encoder_3.device)
 
         # add weight to neg t5 embeddings
diff --git a/modules/shared.py b/modules/shared.py
index 867651e7a..171a777e9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -273,6 +273,40 @@ class OptionInfo:
         self.comment_after += " <span class='info'>(requires restart)</span>"
         return self
 
+    def validate(self, opt, value):
+        args = self.component_args if self.component_args is not None else {}
+        if callable(args):
+            try:
+                args = args()
+            except Exception:
+                args = {}
+        choices = args.get("choices", [])
+        if callable(choices):
+            try:
+                choices = choices()
+            except Exception:
+                choices = []
+        if len(choices) > 0:
+            if not isinstance(value, list):
+                value = [value]
+            for v in value:
+                if v not in choices:
+                    log.warning(f'Setting validation: "{opt}"="{v}" default="{self.default}" choices={choices}')
+                    return False
+        minimum = args.get("minimum", None)
+        maximum = args.get("maximum", None)
+        if (minimum is not None and value < minimum) or (maximum is not None and value > maximum):
+            log.error(f'Setting validation: "{opt}"={value} default={self.default} minimum={minimum} maximum={maximum}')
+            return False
+        return True
+
+    def __str__(self) -> str:
+        args = self.component_args if self.component_args is not None else {}
+        if callable(args):
+            args = args()
+        choices = args.get("choices", [])
+        return f'OptionInfo: label="{self.label}" section="{self.section}" component="{self.component}" default="{self.default}" refresh="{self.refresh is not None}" change="{self.onchange is not None}" args={args} choices={choices}'
+
 
 def options_section(section_identifier, options_dict):
     for v in options_dict.values():
@@ -442,7 +476,7 @@ options_templates.update(options_section(('sd', "Execution & Models"), {
     "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
     "prompt_mean_norm": OptionInfo(False, "Prompt attention normalization", gr.Checkbox),
     "comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": not native }),
-    "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "xhinker parser", "A1111 parser", "Fixed attention"] }),
+    "prompt_attention": OptionInfo("native", "Prompt attention parser", gr.Radio, {"choices": ["native", "compel", "xhinker", "a1111", "fixed"] }),
     "latent_history": OptionInfo(16, "Latent history size", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": not native }),
     "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
@@ -994,7 +1028,7 @@ class Options:
         if filename is None:
             filename = self.filename
         if cmd_opts.freeze:
-            log.warning(f'Settings saving is disabled: {filename}')
+            log.warning(f'Setting: fn="{filename}" save disabled')
             return
         try:
             # output = json.dumps(self.data, indent=2)
@@ -1002,12 +1036,12 @@ class Options:
             unused_settings = []
 
             if os.environ.get('SD_CONFIG_DEBUG', None) is not None:
-                log.debug('Config: user settings')
+                log.debug('Settings: user')
                 for k, v in self.data.items():
                     log.trace(f'  Config: item={k} value={v} default={self.data_labels[k].default if k in self.data_labels else None}')
-                log.debug('Config: default settings')
+                log.debug('Settings: defaults')
                 for k in self.data_labels.keys():
-                    log.trace(f'  Config: item={k} default={self.data_labels[k].default}')
+                    log.trace(f'  Setting: item={k} default={self.data_labels[k].default}')
 
             for k, v in self.data.items():
                 if k in self.data_labels:
@@ -1022,9 +1056,9 @@ class Options:
                             unused_settings.append(k)
             writefile(diff, filename, silent=silent)
             if len(unused_settings) > 0:
-                log.debug(f"Unused settings: {unused_settings}")
+                log.debug(f"Settings: unused={unused_settings}")
         except Exception as err:
-            log.error(f'Save settings failed: {filename} {err}')
+            log.error(f'Settings: fn="{filename}" {err}')
 
     def save(self, filename=None, silent=False):
         threading.Thread(target=self.save_atomic, args=(filename, silent)).start()
@@ -1040,7 +1074,7 @@ class Options:
         if filename is None:
             filename = self.filename
         if not os.path.isfile(filename):
-            log.debug(f'Created default config: {filename}')
+            log.debug(f'Settings: fn="{filename}" created')
             self.save(filename)
             return
         self.data = readfile(filename, lock=True)
@@ -1048,13 +1082,16 @@ class Options:
             self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
         unknown_settings = []
         for k, v in self.data.items():
-            info = self.data_labels.get(k, None)
+            info: OptionInfo = self.data_labels.get(k, None)
+            if not info.validate(k, v):
+                self.data[k] = info.default
             if info is not None and not self.same_type(info.default, v):
-                log.error(f"Error: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
+                log.warning(f"Setting validation: {k}={v} ({type(v).__name__} expected={type(info.default).__name__})")
+                self.data[k] = info.default
             if info is None and k not in compatibility_opts and not k.startswith('uiux_'):
                 unknown_settings.append(k)
         if len(unknown_settings) > 0:
-            log.debug(f"Unknown settings: {unknown_settings}")
+            log.warning(f"Setting validation: unknown={unknown_settings}")
 
     def onchange(self, key, func, call=True):
         item = self.data_labels.get(key)
diff --git a/scripts/animatediff.py b/scripts/animatediff.py
index 09f9e33a9..fca09424b 100644
--- a/scripts/animatediff.py
+++ b/scripts/animatediff.py
@@ -258,7 +258,7 @@ class Script(scripts.Script):
         shared.log.debug(f'AnimateDiff args: {p.task_args}')
         set_prompt(p)
         orig_prompt_attention = shared.opts.prompt_attention
-        shared.opts.data['prompt_attention'] = 'Fixed attention'
+        shared.opts.data['prompt_attention'] = 'fixed'
         processed: processing.Processed = processing.process_images(p) # runs processing using main loop
         shared.opts.data['prompt_attention'] = orig_prompt_attention
         devices.torch_gc()
diff --git a/scripts/ctrlx.py b/scripts/ctrlx.py
index acfdd5d6e..69d5994df 100644
--- a/scripts/ctrlx.py
+++ b/scripts/ctrlx.py
@@ -49,7 +49,7 @@ class Script(scripts.Script):
         from modules.ctrlx.utils import get_self_recurrence_schedule
 
         orig_prompt_attention = shared.opts.prompt_attention
-        shared.opts.data['prompt_attention'] = 'Fixed attention'
+        shared.opts.data['prompt_attention'] = 'fixed'
         shared.sd_model = sd_models.switch_pipe(CtrlXStableDiffusionXLPipeline, shared.sd_model)
         shared.sd_model.restore_pipeline = self.restore
 
diff --git a/scripts/ledits.py b/scripts/ledits.py
index 1a0e929f0..ba9d49f89 100644
--- a/scripts/ledits.py
+++ b/scripts/ledits.py
@@ -44,7 +44,7 @@ class Script(scripts.Script):
         orig_offload = shared.opts.diffusers_model_cpu_offload
         orig_prompt_attention = shared.opts.prompt_attention
         shared.opts.data['diffusers_model_cpu_offload'] = False
-        shared.opts.data['prompt_attention'] = 'Fixed attention'
+        shared.opts.data['prompt_attention'] = 'fixed'
         # shared.sd_model.maybe_free_model_hooks() # ledits is not compatible with offloading
         # shared.sd_model.has_accelerate = False
         sd_models.move_model(shared.sd_model, devices.device, force=True)
diff --git a/scripts/mixture_tiling.py b/scripts/mixture_tiling.py
index 5dcaf0156..13e48ce11 100644
--- a/scripts/mixture_tiling.py
+++ b/scripts/mixture_tiling.py
@@ -66,7 +66,7 @@ class Script(scripts.Script):
             shared.sd_model = orig_pipeline
             return
         sd_models.set_diffuser_options(shared.sd_model)
-        shared.opts.data['prompt_attention'] = 'Fixed attention' # this pipeline is not compatible with embeds
+        shared.opts.data['prompt_attention'] = 'fixed' # this pipeline is not compatible with embeds
         shared.sd_model.to(torch.float32) # this pipeline unet is not compatible with fp16
         processing.fix_seed(p)
         # set pipeline specific params, note that standard params are applied when applicable
diff --git a/scripts/mulan.py b/scripts/mulan.py
index 4b80a7c87..c2ad10d2e 100644
--- a/scripts/mulan.py
+++ b/scripts/mulan.py
@@ -87,7 +87,7 @@ class Script(scripts.Script):
         # mulan only works with single image, single prompt and in fixed attention
         p.batch_size = 1
         p.n_iter = 1
-        shared.opts.prompt_attention = 'Fixed attention'
+        shared.opts.prompt_attention = 'fixed'
         if isinstance(p.prompt, list):
             p.prompt = p.prompt[0]
         p.task_args['prompt'] = p.prompt
diff --git a/scripts/regional_prompting.py b/scripts/regional_prompting.py
index cecef747d..08b84dd94 100644
--- a/scripts/regional_prompting.py
+++ b/scripts/regional_prompting.py
@@ -64,7 +64,7 @@ class Script(scripts.Script):
             shared.sd_model = orig_pipeline
             return
         sd_models.set_diffuser_options(shared.sd_model)
-        shared.opts.data['prompt_attention'] = 'Fixed attention' # this pipeline is not compatible with embeds
+        shared.opts.data['prompt_attention'] = 'fixed' # this pipeline is not compatible with embeds
         processing.fix_seed(p)
         # set pipeline specific params, note that standard params are applied when applicable
         rp_args = {
diff --git a/scripts/x_adapter.py b/scripts/x_adapter.py
index 553a20d30..c67eca18b 100644
--- a/scripts/x_adapter.py
+++ b/scripts/x_adapter.py
@@ -107,7 +107,7 @@ class Script(scripts.Script):
                 pipe.to(device=devices.device, dtype=devices.dtype)
             except Exception:
                 pass
-            shared.opts.data['prompt_attention'] = 'Fixed attention'
+            shared.opts.data['prompt_attention'] = 'fixed'
             prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)
             negative = shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
             p.task_args['prompt'] = prompt
diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py
index 4898c6b73..08ea279f4 100644
--- a/scripts/xyz_grid_classes.py
+++ b/scripts/xyz_grid_classes.py
@@ -37,6 +37,7 @@ class SharedSettingsStackHelper(object):
     sd_text_encoder = None
     extra_networks_default_multiplier = None
     disable_weights_auto_swap = None
+    prompt_attention = None
 
     def __enter__(self):
         #Save overridden settings so they can be restored later.
@@ -52,6 +53,7 @@ class SharedSettingsStackHelper(object):
         self.sd_text_encoder = shared.opts.sd_text_encoder
         self.extra_networks_default_multiplier = shared.opts.extra_networks_default_multiplier
         self.disable_weights_auto_swap = shared.opts.disable_weights_auto_swap
+        self.prompt_attention = shared.opts.prompt_attention
         shared.opts.data["disable_weights_auto_swap"] = False
 
     def __exit__(self, exc_type, exc_value, tb):
@@ -62,6 +64,7 @@ class SharedSettingsStackHelper(object):
         shared.opts.data["tome_ratio"] = self.tome_ratio
         shared.opts.data["todo_ratio"] = self.todo_ratio
         shared.opts.data["extra_networks_default_multiplier"] = self.extra_networks_default_multiplier
+        shared.opts.data["prompt_attention"] = self.prompt_attention
         if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint:
             shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint
             sd_models.reload_model_weights(op='model')
@@ -92,6 +95,7 @@ axis_options = [
     AxisOption("[Model] Dictionary", str, apply_dict, fmt=format_value_add_label, cost=0.9, choices=lambda: ['None'] + list(sd_models.checkpoints_list)),
     AxisOption("[Prompt] Search & replace", str, apply_prompt, fmt=format_value_add_label),
     AxisOption("[Prompt] Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
+    AxisOption("[Prompt] Prompt parser", str, apply_setting("prompt_attention"), choices=lambda: ["native", "compel", "xhinker", "a1111", "fixed"]),
     AxisOption("[Network] LoRA", str, apply_lora, cost=0.5, choices=list_lora),
     AxisOption("[Network] LoRA strength", float, apply_setting('extra_networks_default_multiplier')),
     AxisOption("[Network] Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]),
diff --git a/wiki b/wiki
index 47ea50e91..352fc655b 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 47ea50e9152a13325dd1daf92bc50b700783182f
+Subproject commit 352fc655b0dc9edb22aac093186da087ba18b474

From b42e9253e34dd1e267193eb8a6d0ea177295e02a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 21:21:12 -0500
Subject: [PATCH 062/119] pullid offload compatibility and extra samplers

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                    |   1 +
 modules/pulid/__init__.py       |   1 +
 modules/pulid/pulid_sampling.py | 571 ++++++++++++++++++++++++++++++++
 modules/pulid/pulid_sdxl.py     |   7 +-
 modules/pulid/pulid_utils.py    | 176 ----------
 modules/sd_models.py            |  15 +-
 scripts/pulid_ext.py            |  12 +-
 7 files changed, 595 insertions(+), 188 deletions(-)
 create mode 100644 modules/pulid/pulid_sampling.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2726a1864..4e08cb147 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -17,6 +17,7 @@ This release can be considered an LTS release before we kick off the next round
     - select in *scripts -> pulid*  
     - compatible with *sdxl*  
     - can be used in xyz grid  
+    - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
     - alternative to traditional `img2img` with more control over restoration process  
     - select in *image -> scripts -> instantir*  
diff --git a/modules/pulid/__init__.py b/modules/pulid/__init__.py
index 000f45293..785b849c2 100644
--- a/modules/pulid/__init__.py
+++ b/modules/pulid/__init__.py
@@ -8,3 +8,4 @@ sys.path.append(os.path.dirname(__file__))
 from pulid_sdxl import StableDiffusionXLPuLIDPipeline
 from pulid_utils import resize_numpy_image_long as resize
 import attention_processor as attention
+import pulid_sampling as sampling
diff --git a/modules/pulid/pulid_sampling.py b/modules/pulid/pulid_sampling.py
new file mode 100644
index 000000000..9996f035a
--- /dev/null
+++ b/modules/pulid/pulid_sampling.py
@@ -0,0 +1,571 @@
+import math
+from scipy import integrate
+import torch
+from torch import nn
+from torchdiffeq import odeint
+import torchsde
+from tqdm.auto import trange
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
+    """Constructs the noise schedule of Karras et al. (2022)."""
+    ramp = torch.linspace(0, 1, n)
+    min_inv_rho = sigma_min ** (1 / rho)
+    max_inv_rho = sigma_max ** (1 / rho)
+    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+    return append_zero(sigmas).to(device)
+
+
+def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
+    """Constructs an exponential noise schedule."""
+    sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
+    return append_zero(sigmas)
+
+
+def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
+    """Constructs an polynomial in log sigma noise schedule."""
+    ramp = torch.linspace(1, 0, n, device=device) ** rho
+    sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
+    return append_zero(sigmas)
+
+
+def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
+    """Constructs a continuous VP noise schedule."""
+    t = torch.linspace(1, eps_s, n, device=device)
+    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
+    return append_zero(sigmas)
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / append_dims(sigma, x.ndim)
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.):
+    """Calculates the noise level (sigma_down) to step down to and the amount
+    of noise to add (sigma_up) when doing an ancestral sampling step."""
+    if not eta:
+        return sigma_to, 0.
+    sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
+    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+    return sigma_down, sigma_up
+
+
+def default_noise_sampler(x):
+    return lambda sigma, sigma_next: torch.randn_like(x)
+
+
+class BatchedBrownianTree:
+    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
+
+    def __init__(self, x, t0, t1, seed=None, **kwargs):
+        t0, t1, self.sign = self.sort(t0, t1)
+        w0 = kwargs.get('w0', torch.zeros_like(x))
+        if seed is None:
+            seed = torch.randint(0, 2 ** 63 - 1, []).item()
+        self.batched = True
+        try:
+            assert len(seed) == x.shape[0]
+            w0 = w0[0]
+        except TypeError:
+            seed = [seed]
+            self.batched = False
+        self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+
+    @staticmethod
+    def sort(a, b):
+        return (a, b, 1) if a < b else (b, a, -1)
+
+    def __call__(self, t0, t1):
+        t0, t1, sign = self.sort(t0, t1)
+        w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+        return w if self.batched else w[0]
+
+
+class BrownianTreeNoiseSampler:
+    """A noise sampler backed by a torchsde.BrownianTree.
+
+    Args:
+        x (Tensor): The tensor whose shape, device and dtype to use to generate
+            random samples.
+        sigma_min (float): The low end of the valid interval.
+        sigma_max (float): The high end of the valid interval.
+        seed (int or List[int]): The random seed. If a list of seeds is
+            supplied instead of a single integer, then the noise sampler will
+            use one BrownianTree per batch item, each with its own seed.
+        transform (callable): A function that maps sigma to the sampler's
+            internal timestep.
+    """
+
+    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
+        self.transform = transform
+        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
+        self.tree = BatchedBrownianTree(x, t0, t1, seed)
+
+    def __call__(self, sigma, sigma_next):
+        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
+        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
+
+
+@torch.no_grad()
+def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        # Euler method
+        x = x + (d * dt).to(x.dtype)
+    return x
+
+
+@torch.no_grad()
+def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with Euler method steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        d = to_d(x, sigmas[i], denoised)
+        # Euler method
+        dt = sigma_down - sigmas[i]
+        x = x + (d * dt).to(x.dtype)
+        if sigmas[i + 1] > 0:
+            x = x + (noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up).to(x.dtype)
+    return x
+
+
+def linear_multistep_coeff(order, t, i, j):
+    if order - 1 > i:
+        raise ValueError(f'Order {order} too high for step {i}')
+    def fn(tau):
+        prod = 1.
+        for k in range(order):
+            if j == k:
+                continue
+            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
+        return prod
+    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
+
+
+@torch.no_grad()
+def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    v = torch.randint_like(x, 2) * 2 - 1
+    fevals = 0
+    def ode_fn(sigma, x):
+        nonlocal fevals
+        with torch.enable_grad():
+            x = x[0].detach().requires_grad_()
+            denoised = model(x, sigma * s_in, **extra_args)
+            d = to_d(x, sigma, denoised)
+            fevals += 1
+            grad = torch.autograd.grad((d * v).sum(), x)[0]
+            d_ll = (v * grad).flatten(1).sum(1)
+        return d.detach(), d_ll
+    x_min = x, x.new_zeros([x.shape[0]])
+    t = x.new_tensor([sigma_min, sigma_max])
+    sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
+    latent, delta_ll = sol[0][-1], sol[1][-1]
+    ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
+    return ll_prior + delta_ll, {'fevals': fevals}
+
+
+class PIDStepSizeController:
+    """A PID controller for ODE adaptive step size control."""
+    def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
+        self.h = h
+        self.b1 = (pcoeff + icoeff + dcoeff) / order
+        self.b2 = -(pcoeff + 2 * dcoeff) / order
+        self.b3 = dcoeff / order
+        self.accept_safety = accept_safety
+        self.eps = eps
+        self.errs = []
+
+    def limiter(self, x):
+        return 1 + math.atan(x - 1)
+
+    def propose_step(self, error):
+        inv_error = 1 / (float(error) + self.eps)
+        if not self.errs:
+            self.errs = [inv_error, inv_error, inv_error]
+        self.errs[0] = inv_error
+        factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
+        factor = self.limiter(factor)
+        accept = factor >= self.accept_safety
+        if accept:
+            self.errs[2] = self.errs[1]
+            self.errs[1] = self.errs[0]
+        self.h *= factor
+        return accept
+
+
+class DPMSolver(nn.Module):
+    """DPM-Solver. See https://arxiv.org/abs/2206.00927."""
+
+    def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
+        super().__init__()
+        self.model = model
+        self.extra_args = {} if extra_args is None else extra_args
+        self.eps_callback = eps_callback
+        self.info_callback = info_callback
+
+    def t(self, sigma):
+        return -sigma.log()
+
+    def sigma(self, t):
+        return t.neg().exp()
+
+    def eps(self, eps_cache, key, x, t, *args, **kwargs):
+        if key in eps_cache:
+            return eps_cache[key], eps_cache
+        sigma = self.sigma(t) * x.new_ones([x.shape[0]])
+        eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
+        if self.eps_callback is not None:
+            self.eps_callback()
+        return eps, {key: eps, **eps_cache}
+
+    def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
+        eps_cache = {} if eps_cache is None else eps_cache
+        h = t_next - t
+        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+        x_1 = x - self.sigma(t_next) * h.expm1() * eps
+        return x_1, eps_cache
+
+    def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
+        eps_cache = {} if eps_cache is None else eps_cache
+        h = t_next - t
+        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+        s1 = t + r1 * h
+        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+        x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
+        return x_2, eps_cache
+
+    def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
+        eps_cache = {} if eps_cache is None else eps_cache
+        h = t_next - t
+        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+        s1 = t + r1 * h
+        s2 = t + r2 * h
+        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+        u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
+        eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
+        x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
+        return x_3, eps_cache
+
+    def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
+        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+        if not t_end > t_start and eta:
+            raise ValueError('eta must be 0 for reverse sampling')
+
+        m = math.floor(nfe / 3) + 1
+        ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
+
+        if nfe % 3 == 0:
+            orders = [3] * (m - 2) + [2, 1]
+        else:
+            orders = [3] * (m - 1) + [nfe % 3]
+
+        for i in range(len(orders)):
+            eps_cache = {}
+            t, t_next = ts[i], ts[i + 1]
+            if eta:
+                sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
+                t_next_ = torch.minimum(t_end, self.t(sd))
+                su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
+            else:
+                t_next_, su = t_next, 0.
+
+            eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+            denoised = x - self.sigma(t) * eps
+            if self.info_callback is not None:
+                self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
+
+            if orders[i] == 1:
+                x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
+            elif orders[i] == 2:
+                x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
+            else:
+                x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
+
+            x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
+
+        return x
+
+    def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
+        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+        if order not in {2, 3}:
+            raise ValueError('order should be 2 or 3')
+        forward = t_end > t_start
+        if not forward and eta:
+            raise ValueError('eta must be 0 for reverse sampling')
+        h_init = abs(h_init) * (1 if forward else -1)
+        atol = torch.tensor(atol)
+        rtol = torch.tensor(rtol)
+        s = t_start
+        x_prev = x
+        accept = True
+        pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
+        info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
+
+        while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
+            eps_cache = {}
+            t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
+            if eta:
+                sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
+                t_ = torch.minimum(t_end, self.t(sd))
+                su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
+            else:
+                t_, su = t, 0.
+
+            eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
+            denoised = x - self.sigma(s) * eps
+
+            if order == 2:
+                x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
+                x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
+            else:
+                x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
+                x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
+            delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
+            error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
+            accept = pid.propose_step(error)
+            if accept:
+                x_prev = x_low
+                x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
+                s = t
+                info['n_accept'] += 1
+            else:
+                info['n_reject'] += 1
+            info['nfe'] += order
+            info['steps'] += 1
+
+            if self.info_callback is not None:
+                self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
+
+        return x, info
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigma_down == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigma_down - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver++(2S)
+            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+            r = 1 / 2
+            h = t_next - t
+            s = t + r * h
+            x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+    """DPM-Solver++ (stochastic)."""
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigmas[i + 1] - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver++
+            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+            h = t_next - t
+            s = t + h * r
+            fac = 1 / (2 * r)
+
+            # Step 1
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
+            s_ = t_fn(sd)
+            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
+            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+
+            # Step 2
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
+            t_next_ = t_fn(sd)
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
+            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+    old_denoised = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_denoised is None or sigmas[i + 1] == 0:
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
+        old_denoised = denoised
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+    """DPM-Solver++(2M) SDE."""
+
+    if solver_type not in {'heun', 'midpoint'}:
+        raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
+
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    old_denoised = None
+    h_last = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            # DPM-Solver++(2M) SDE
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            eta_h = eta * h
+
+            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
+
+            if old_denoised is not None:
+                r = h_last / h
+                if solver_type == 'heun':
+                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
+                elif solver_type == 'midpoint':
+                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
+
+            if eta:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
+
+        old_denoised = denoised
+        h_last = h
+    return x
+
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """DPM-Solver++(3M) SDE."""
+
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    denoised_1, denoised_2 = None, None
+    h_1, h_2 = None, None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            h_eta = h * (eta + 1)
+
+            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
+
+            if h_2 is not None:
+                r0 = h_1 / h
+                r1 = h_2 / h
+                d1_0 = (denoised - denoised_1) / r0
+                d1_1 = (denoised_1 - denoised_2) / r1
+                d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
+                d2 = (d1_0 - d1_1) / (r0 + r1)
+                phi_2 = h_eta.neg().expm1() / h_eta + 1
+                phi_3 = phi_2 / h_eta - 0.5
+                x = x + phi_2 * d1 - phi_3 * d2
+            elif h_1 is not None:
+                r = h_1 / h
+                d = (denoised - denoised_1) / r
+                phi_2 = h_eta.neg().expm1() / h_eta + 1
+                x = x + phi_2 * d
+
+            if eta:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+
+        denoised_1, denoised_2 = denoised, denoised_1
+        h_1, h_2 = h, h_1
+    return x
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 0651efab4..de650b839 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -19,13 +19,12 @@ from insightface.app import FaceAnalysis
 from eva_clip import create_model_and_transforms
 from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
 from encoders_transformer import IDFormer
-from pulid_utils import sample_dpmpp_2m, sample_dpmpp_sde
 from attention_processor import AttnProcessor2_0 as AttnProcessor
 from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler='dpmpp_sde', cache_dir=None):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None):
         super().__init__()
         self.device = device
         self.pipe = pipe
@@ -90,12 +89,16 @@ class StableDiffusionXLPuLIDPipeline:
         self.log_sigmas = self.sigmas.log()
         self.sigma_data = 1.0
 
+        if sampler is not None:
+            self.sampler = sampler
+        """
         if sampler == 'dpmpp_sde':
             self.sampler = sample_dpmpp_sde
         elif sampler == 'dpmpp_2m':
             self.sampler = sample_dpmpp_2m
         else:
             raise NotImplementedError(f'sampler {sampler} not implemented')
+        """
 
     @property
     def sigma_min(self):
diff --git a/modules/pulid/pulid_utils.py b/modules/pulid/pulid_utils.py
index 1a8d3ff06..fd7338b5e 100644
--- a/modules/pulid/pulid_utils.py
+++ b/modules/pulid/pulid_utils.py
@@ -6,10 +6,7 @@ import random
 import cv2
 import numpy as np
 import torch
-import torch.nn.functional as F
-import torchsde
 from torchvision.utils import make_grid
-from tqdm.auto import trange
 from transformers import PretrainedConfig
 
 
@@ -21,10 +18,6 @@ def seed_everything(seed):
     torch.cuda.manual_seed_all(seed)
 
 
-def is_torch2_available():
-    return hasattr(F, "scaled_dot_product_attention")
-
-
 def instantiate_from_config(config):
     if "target" not in config:
         if config == '__is_first_stage__' or config == "__is_unconditional__":
@@ -166,172 +159,3 @@ def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
     if len(result) == 1:
         result = result[0]
     return result
-
-
-# We didn't find a correct configuration to make the diffusers scheduler align with dpm++2m (karras) in ComfyUI,
-# so we copied the ComfyUI code directly.
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    expanded = x[(...,) + (None,) * dims_to_append]
-    # MPS will get inf values if it tries to index into the new axes, but detaching fixes this.
-    # https://github.com/pytorch/pytorch/issues/84364
-    return expanded.detach().clone() if expanded.device.type == 'mps' else expanded
-
-
-def to_d(x, sigma, denoised):
-    """Converts a denoiser output to a Karras ODE derivative."""
-    return (x - denoised) / append_dims(sigma, x.ndim)
-
-
-def get_ancestral_step(sigma_from, sigma_to, eta=1.0):
-    """Calculates the noise level (sigma_down) to step down to and the amount
-    of noise to add (sigma_up) when doing an ancestral sampling step."""
-    if not eta:
-        return sigma_to, 0.0
-    sigma_up = min(sigma_to, eta * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
-    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
-    return sigma_down, sigma_up
-
-
-class BatchedBrownianTree:
-    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
-
-    def __init__(self, x, t0, t1, seed=None, **kwargs):
-        self.cpu_tree = True
-        if "cpu" in kwargs:
-            self.cpu_tree = kwargs.pop("cpu")
-        t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.get('w0', torch.zeros_like(x))
-        if seed is None:
-            seed = torch.randint(0, 2**63 - 1, []).item()
-        self.batched = True
-        try:
-            assert len(seed) == x.shape[0]
-            w0 = w0[0]
-        except TypeError:
-            seed = [seed]
-            self.batched = False
-        if self.cpu_tree:
-            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
-        else:
-            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
-
-    @staticmethod
-    def sort(a, b):
-        return (a, b, 1) if a < b else (b, a, -1)
-
-    def __call__(self, t0, t1):
-        t0, t1, sign = self.sort(t0, t1)
-        if self.cpu_tree:
-            w = torch.stack(
-                [tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]
-            ) * (self.sign * sign)
-        else:
-            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
-
-        return w if self.batched else w[0]
-
-
-class BrownianTreeNoiseSampler:
-    """A noise sampler backed by a torchsde.BrownianTree.
-
-    Args:
-        x (Tensor): The tensor whose shape, device and dtype to use to generate
-            random samples.
-        sigma_min (float): The low end of the valid interval.
-        sigma_max (float): The high end of the valid interval.
-        seed (int or List[int]): The random seed. If a list of seeds is
-            supplied instead of a single integer, then the noise sampler will
-            use one BrownianTree per batch item, each with its own seed.
-        transform (callable): A function that maps sigma to the sampler's
-            internal timestep.
-    """
-
-    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
-        self.transform = transform
-        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
-        self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
-
-    def __call__(self, sigma, sigma_next):
-        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
-        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
-
-
-@torch.no_grad()
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    """DPM-Solver++(2M)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp() # pylint: disable=unnecessary-lambda-assignment
-    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=unnecessary-lambda-assignment
-    old_denoised = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-        h = t_next - t
-        if old_denoised is None or sigmas[i + 1] == 0:
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
-        else:
-            h_last = t - t_fn(sigmas[i - 1])
-            r = h_last / h
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
-        old_denoised = denoised
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_sde(
-    model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None, r=1 / 2
-):
-    """DPM-Solver++ (stochastic)."""
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    seed = extra_args.get("seed", None)
-    noise_sampler = (
-        BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
-        if noise_sampler is None
-        else noise_sampler
-    )
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp() # pylint: disable=unnecessary-lambda-assignment
-    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=unnecessary-lambda-assignment
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigmas[i + 1] - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver++
-            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-            h = t_next - t
-            s = t + h * r
-            fac = 1 / (2 * r)
-
-            # Step 1
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
-            s_ = t_fn(sd)
-            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
-            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-
-            # Step 2
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
-            t_next_ = t_fn(sd)
-            denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
-            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
-    return x
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3ca081d6c..9dd87cec1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -291,10 +291,13 @@ def set_diffuser_options(sd_model, vae = None, op: str = 'model', offload=True):
 
 
 def set_accelerate_to_module(model):
-    for k in model._internal_dict.keys(): # pylint: disable=protected-access
-        component = getattr(model, k, None)
-        if isinstance(component, torch.nn.Module):
-            component.has_accelerate = True
+    if hasattr(model, "pipe"):
+        set_accelerate_to_module(model.pipe)
+    if hasattr(model, "_internal_dict"):
+        for k in model._internal_dict.keys(): # pylint: disable=protected-access
+            component = getattr(model, k, None)
+            if isinstance(component, torch.nn.Module):
+                component.has_accelerate = True
 
 
 def set_accelerate(sd_model):
@@ -397,6 +400,10 @@ def apply_balanced_offload(sd_model):
             return module
 
     def apply_balanced_offload_to_module(pipe):
+        if hasattr(pipe, "pipe"):
+            apply_balanced_offload_to_module(pipe.pipe)
+        if not hasattr(pipe, "_internal_dict"):
+            return
         for module_name in pipe._internal_dict.keys(): # pylint: disable=protected-access
             module = getattr(pipe, module_name, None)
             if isinstance(module, torch.nn.Module):
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index d31c18164..54189cc05 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -30,8 +30,6 @@ class Script(scripts.Script):
             install('insightface', 'insightface', ignore=False)
             install('albumentations==1.4.3', 'albumentations', ignore=False, reinstall=True)
             install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
-        # if not installed('apex', reload=False, quiet=True):
-        #     install('apex', 'apex', ignore=False)
 
     def register(self): # register xyz grid elements
         def apply_field(field):
@@ -74,8 +72,8 @@ class Script(scripts.Script):
             strength = gr.Slider(label = 'Strength', value = 0.8, mininimum = 0, maximum = 1, step = 0.01)
             zero = gr.Slider(label = 'Zero', value = 20, mininimum = 0, maximum = 80, step = 1)
         with gr.Row():
-            sampler = gr.Dropdown(label="Sampler", choices=['dpmpp_sde', 'dpmpp_2m'], value='dpmpp_sde', visible=True)
-            ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2', visible=True)
+            sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
+            ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
         with gr.Row():
             files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
         with gr.Row():
@@ -124,16 +122,17 @@ class Script(scripts.Script):
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
         ortho = getattr(p, 'pulid_ortho', ortho)
+        sampler = getattr(p, 'pulid_sampler', sampler)
+        sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
 
         if shared.sd_model_type == 'sdxl' and not hasattr(shared.sd_model, 'pipe'):
             try:
                 stdout = io.StringIO()
-                ctx = contextlib.nullcontext if debug else contextlib.redirect_stdout(stdout)
+                ctx = contextlib.nullcontext() if debug else contextlib.redirect_stdout(stdout)
                 with ctx:
                     shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
                         pipe =shared.sd_model,
                         device=devices.device,
-                        sampler=sampler,
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -146,6 +145,7 @@ class Script(scripts.Script):
                 errors.display(e, 'PuLID')
                 return None
 
+        shared.sd_model.sampler = sampler_fn
         shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'

From 3046417584b785b29e3606bd751dd2a8ead69374 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 21:27:41 -0500
Subject: [PATCH 063/119] package logging

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

diff --git a/modules/loader.py b/modules/loader.py
index 0711c2906..cd51cc8eb 100644
--- a/modules/loader.py
+++ b/modules/loader.py
@@ -126,4 +126,5 @@ except ImportError:
     except ImportError:
         pass  # shrug...
 
-errors.log.info(f'System packages: {get_packages()}')
+errors.log.info(f'Torch: torch=={torch.__version__} torchvision=={torchvision.__version__}')
+errors.log.info(f'Packages: diffusers=={diffusers.__version__} transformers=={transformers.__version__} accelerate=={accelerate.__version__} gradio=={gradio.__version__}')

From 13cb5704b99551d8cbea8c1fece4a242e1f0e966 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 22:14:42 -0500
Subject: [PATCH 064/119] pulid img2img and inpaint placeholders

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_args.py  |  4 ++--
 modules/pulid/__init__.py   |  2 +-
 modules/pulid/pulid_sdxl.py | 19 +++++++++++++++++++
 modules/sd_models.py        |  8 +++++++-
 scripts/pulid_ext.py        | 12 ++++++++----
 5 files changed, 37 insertions(+), 8 deletions(-)

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 4cd12a04d..1cb9457c5 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -27,7 +27,7 @@ def task_specific_kwargs(p, model):
                 'height': 8 * math.ceil(p.height / 8),
             }
     elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
-        if shared.sd_model_type == 'sdxl':
+        if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
             model.register_to_config(requires_aesthetics_score = False)
         p.ops.append('img2img')
         task_args = {
@@ -55,7 +55,7 @@ def task_specific_kwargs(p, model):
             'strength': p.denoising_strength,
         }
     elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
-        if shared.sd_model_type == 'sdxl':
+        if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
             model.register_to_config(requires_aesthetics_score = False)
         if p.detailer:
             p.ops.append('detailer')
diff --git a/modules/pulid/__init__.py b/modules/pulid/__init__.py
index 785b849c2..dcee2d7b9 100644
--- a/modules/pulid/__init__.py
+++ b/modules/pulid/__init__.py
@@ -5,7 +5,7 @@ Credit and original implementation: <https://github.com/ToTheBeginning/PuLID>
 import os
 import sys
 sys.path.append(os.path.dirname(__file__))
-from pulid_sdxl import StableDiffusionXLPuLIDPipeline
+from pulid_sdxl import StableDiffusionXLPuLIDPipeline, StableDiffusionXLPuLIDPipelineImage, StableDiffusionXLPuLIDPipelineInpaint
 from pulid_utils import resize_numpy_image_long as resize
 import attention_processor as attention
 import pulid_sampling as sampling
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index de650b839..af7b8e443 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -307,6 +307,7 @@ class StableDiffusionXLPuLIDPipeline:
         num_inference_steps: int=50,
         seed: int=-1,
         image: np.ndarray=None,
+        mask_image: np.ndarray=None,
         strength: float=0.3,
         id_embedding=None,
         uncond_id_embedding=None,
@@ -356,4 +357,22 @@ class StableDiffusionXLPuLIDPipeline:
         images = self.pipe.vae.decode(latents).sample
         images = self.pipe.image_processor.postprocess(images, output_type='pil')
 
+        if mask_image is not None:
+            # TODO: pulid inpaint
+            # easiest inpaint is to use normal img2img and then combine output with input using mask
+            # note that mask can be binary or grayscale (soft mask)
+            raise NotImplementedError('pulid: inpaint')
+
         return images
+
+
+class StableDiffusionXLPuLIDPipelineImage(StableDiffusionXLPuLIDPipeline):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None): # pylint: disable=useless-parent-delegation
+        super().__init__(pipe, device, sampler, cache_dir)
+        # we dont do anything special here, just having different class so task-type can be detected/assigned
+
+
+class StableDiffusionXLPuLIDPipelineInpaint(StableDiffusionXLPuLIDPipeline):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None): # pylint: disable=useless-parent-delegation
+        super().__init__(pipe, device, sampler, cache_dir)
+        # we dont do anything special here, just having different class so task-type can be detected/assigned
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9dd87cec1..5ae14a64a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1062,7 +1062,6 @@ def set_diffuser_pipe(pipe, new_pipe_type):
         'AnimateDiffSDXLPipeline',
         'OmniGenPipeline',
         'StableDiffusion3ControlNetPipeline',
-        'StableDiffusionXLPuLIDPipeline',
         'InstantIRPipeline',
     ]
 
@@ -1084,6 +1083,13 @@ def set_diffuser_pipe(pipe, new_pipe_type):
             pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
         if n == 'StableDiffusionXLPAGPipeline':
             pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
+        if n == 'StableDiffusionXLPuLIDPipeline':
+            from modules import pulid
+            if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
+                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineImage
+            else:
+                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineInpaint
+            return pipe
 
     sd_checkpoint_info = getattr(pipe, "sd_checkpoint_info", None)
     sd_model_checkpoint = getattr(pipe, "sd_model_checkpoint", None)
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 54189cc05..3a157d0fe 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -106,9 +106,10 @@ class Script(scripts.Script):
             try:
                 from modules import pulid # pylint: disable=redefined-outer-name
                 self.pulid = pulid
-                # from diffusers import pipelines
-                # pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["pilid"] = pulid.StableDiffusionXLPuLIDPipeline
-                # pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["omnigen"] = pulid.StableDiffusionXLPuLIDPipelineImg2Img
+                from diffusers import pipelines
+                pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["pulid"] = pulid.StableDiffusionXLPuLIDPipeline
+                pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["pulid"] = pulid.StableDiffusionXLPuLIDPipelineImage
+                pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["pulid"] = pulid.StableDiffusionXLPuLIDPipelineInpaint
             except Exception as e:
                 shared.log.error(f'PuLID: failed to import library: {e}')
                 return None
@@ -124,6 +125,8 @@ class Script(scripts.Script):
         ortho = getattr(p, 'pulid_ortho', ortho)
         sampler = getattr(p, 'pulid_sampler', sampler)
         sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
+        if sampler_fn is None:
+            sampler_fn = self.pulid.sampling.sample_dpmpp_2m_sde
 
         if shared.sd_model_type == 'sdxl' and not hasattr(shared.sd_model, 'pipe'):
             try:
@@ -146,7 +149,7 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler} images={[i.shape for i in images]}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'
@@ -184,6 +187,7 @@ class Script(scripts.Script):
                 p.task_args['image'] = p.init_images[0]
                 p.task_args['strength'] = p.denoising_strength
             p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
+            p.extra_generation_params["Sampler"] = sampler
             if getattr(p, 'xyz', False): # xyz will run its own processing
                 return None
             processed: processing.Processed = processing.process_images(p) # runs processing using main loop

From ce49460b191fc6f817b10ad3ef38cd7e72906156 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 22:29:52 -0500
Subject: [PATCH 065/119] pulid optional keep model loaded

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 scripts/pulid_ext.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 3a157d0fe..da195b5e4 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -74,14 +74,16 @@ class Script(scripts.Script):
         with gr.Row():
             sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
             ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
+        with gr.Row():
+            cache = gr.Checkbox(label='Keep model', value=False)
         with gr.Row():
             files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
         with gr.Row():
             gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
-        return [strength, zero, sampler, ortho, gallery]
+        return [strength, zero, sampler, ortho, gallery, cache]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = []): # pylint: disable=arguments-differ
+    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], cache: bool = False): # pylint: disable=arguments-differ, unused-argument
         images = []
         try:
             if len(gallery) == 0:
@@ -197,6 +199,11 @@ class Script(scripts.Script):
         return processed
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
+        _strength, _zero, _sampler, _ortho, _gallery, cache = args
+        cache = getattr(p, 'pulid_cache', cache)
+        if cache:
+            shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
+            return processed
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
             if hasattr(shared.sd_model, 'app'):
                 shared.sd_model.app = None
@@ -204,7 +211,7 @@ class Script(scripts.Script):
                 shared.sd_model.face_helper = None
                 shared.sd_model.clip_vision_model = None
                 shared.sd_model.handler_ante = None
-                devices.torch_gc(force=True)
             shared.sd_model = shared.sd_model.pipe
-            # shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
+            devices.torch_gc(force=True)
+            shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
         return processed

From 0160b703f30b31222c66c632d17a8fe2896ed165 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 6 Nov 2024 22:40:18 -0500
Subject: [PATCH 066/119] fix xyz duplicate classes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/ui_extra_networks.py |  3 ++-
 scripts/apg.py               | 11 ++++++++---
 scripts/pulid_ext.py         | 12 +++++++++---
 3 files changed, 19 insertions(+), 7 deletions(-)

diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 323f4830f..f6e6cee97 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -148,7 +148,8 @@ class ExtraNetworksPage:
             if self.title == 'Model':
                 return
             opt = xyz_grid.AxisOption(f"[Network] {self.title}", str, add_prompt, choices=lambda: [x["name"] for x in self.items])
-            xyz_grid.axis_options.append(opt)
+            if opt not in xyz_grid.axis_options:
+                xyz_grid.axis_options.append(opt)
 
     def link_preview(self, filename):
         quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
diff --git a/scripts/apg.py b/scripts/apg.py
index c7e60c982..6a3020c38 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -32,9 +32,14 @@ class Script(scripts.Script):
 
         import sys
         xyz_classes = [v for k, v in sys.modules.items() if 'xyz_grid_classes' in k][0]
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] ETA", float, apply_field("apg_eta")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] Momentum", float, apply_field("apg_momentum")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] Threshold", float, apply_field("apg_threshold")))
+        options = [
+            xyz_classes.AxisOption("[APG] ETA", float, apply_field("apg_eta")),
+            xyz_classes.AxisOption("[APG] Momentum", float, apply_field("apg_momentum")),
+            xyz_classes.AxisOption("[APG] Threshold", float, apply_field("apg_threshold")),
+        ]
+        for option in options:
+            if option not in xyz_classes.axis_options:
+                xyz_classes.axis_options.append(option)
 
     def run(self, p: processing.StableDiffusionProcessing, eta = 0.0, momentum = 0.0, threshold = 0.0): # pylint: disable=arguments-differ
         supported_model_list = ['sd', 'sdxl', 'sc']
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index da195b5e4..05e83ce1f 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -40,9 +40,15 @@ class Script(scripts.Script):
 
         import sys
         xyz_classes = [v for k, v in sys.modules.items() if 'xyz_grid_classes' in k][0]
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Strength", float, apply_field("pulid_strength")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Zero", int, apply_field("pulid_zero")))
-        xyz_classes.axis_options.append(xyz_classes.AxisOption("[PuLID] Ortho", str, apply_field("pulid_ortho"), choices=lambda: ['off', 'v1', 'v2']))
+        options = [
+            xyz_classes.AxisOption("[PuLID] Strength", float, apply_field("pulid_strength")),
+            xyz_classes.AxisOption("[PuLID] Zero", int, apply_field("pulid_zero")),
+            xyz_classes.AxisOption("[PuLID] Ortho", str, apply_field("pulid_ortho"), choices=lambda: ['off', 'v1', 'v2']),
+        ]
+        for option in options:
+            if option not in xyz_classes.axis_options:
+                xyz_classes.axis_options.append(option)
+
 
     def load_images(self, files):
         self.images = []

From 49f1ca2880677e2eb2b705611b7cd90020dbcefe Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 08:05:18 -0500
Subject: [PATCH 067/119] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/pulid/pulid_sdxl.py | 2 --
 scripts/pulid_ext.py        | 3 ---
 2 files changed, 5 deletions(-)

diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index af7b8e443..6364309b9 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -155,7 +155,6 @@ class StableDiffusionXLPuLIDPipeline:
             state_dict_dict[module][new_k] = v
 
         for module in state_dict_dict:
-            print(f'loading from {module}')
             getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)
 
     def to_gray(self, img):
@@ -200,7 +199,6 @@ class StableDiffusionXLPuLIDPipeline:
             align_face = self.face_helper.cropped_faces[0]
             # incase insightface didn't detect face
             if id_ante_embedding is None:
-                print('fail to detect face using insightface, extract embedding on align face')
                 id_ante_embedding = self.handler_ante.get_feat(align_face)
 
             id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device)
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 05e83ce1f..22aff993f 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -191,9 +191,6 @@ class Script(scripts.Script):
             p.task_args['id_embedding'] = id_embedding
             p.task_args['uncond_id_embedding'] = uncond_id_embedding
             p.task_args['id_scale'] = strength
-            if len(getattr(p, 'init_images', [])) > 0:
-                p.task_args['image'] = p.init_images[0]
-                p.task_args['strength'] = p.denoising_strength
             p.extra_generation_params["PuLID"] = f'Strength={strength} Zero={zero} Ortho={ortho}'
             p.extra_generation_params["Sampler"] = sampler
             if getattr(p, 'xyz', False): # xyz will run its own processing

From 94922281751eb094c9b54399a965789885f983c3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 10:08:41 -0500
Subject: [PATCH 068/119] improve auto-pipeline switch

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/images_namegen.py   |  8 ++--
 modules/processing_info.py  |  4 ++
 modules/pulid/pulid_sdxl.py | 16 ++-----
 modules/sd_models.py        | 90 ++++++++++++++++++++-----------------
 modules/sd_vae.py           |  2 +-
 modules/styles.py           |  2 +
 scripts/pulid_ext.py        |  5 ++-
 7 files changed, 68 insertions(+), 59 deletions(-)

diff --git a/modules/images_namegen.py b/modules/images_namegen.py
index d88f85a77..bc58f728a 100644
--- a/modules/images_namegen.py
+++ b/modules/images_namegen.py
@@ -34,10 +34,10 @@ class FilenameGenerator:
         'timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
         '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 else '',
-        'model_shortname': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded else '',
-        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded else '',
-        'model_hash': lambda self: shared.sd_model.sd_checkpoint_info.shorthash if shared.sd_loaded else '',
+        '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 '',
+        'model_shortname': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',
+        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',
+        'model_hash': lambda self: shared.sd_model.sd_checkpoint_info.shorthash if shared.sd_loaded and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None else '',
 
         'prompt': lambda self: self.prompt_full(),
         'prompt_no_styles': lambda self: self.prompt_no_style(),
diff --git a/modules/processing_info.py b/modules/processing_info.py
index f721b3717..714ebf35f 100644
--- a/modules/processing_info.py
+++ b/modules/processing_info.py
@@ -64,6 +64,10 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
         "Comment": comment,
         "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
     }
+    if shared.opts.add_model_name_to_info and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None:
+        args["Model"] = shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
+    if shared.opts.add_model_hash_to_info and getattr(shared.sd_model, 'sd_model_hash', None) is not None:
+        args["Model hash"] = shared.sd_model.sd_model_hash
     # native
     if grid is None and (p.n_iter > 1 or p.batch_size > 1) and index >= 0:
         args['Index'] = f'{p.iteration + 1}x{index + 1}'
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 6364309b9..0ae603a26 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -91,14 +91,6 @@ class StableDiffusionXLPuLIDPipeline:
 
         if sampler is not None:
             self.sampler = sampler
-        """
-        if sampler == 'dpmpp_sde':
-            self.sampler = sample_dpmpp_sde
-        elif sampler == 'dpmpp_2m':
-            self.sampler = sample_dpmpp_2m
-        else:
-            raise NotImplementedError(f'sampler {sampler} not implemented')
-        """
 
     @property
     def sigma_min(self):
@@ -252,8 +244,8 @@ class StableDiffusionXLPuLIDPipeline:
     def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
         import functools
         from tqdm.auto import trange as trange_orig
-        import pulid_utils
-        pulid_utils.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
+        import pulid_sampling
+        pulid_sampling.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
 
     def sample(self, x, sigma, **extra_args):
         x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
@@ -288,7 +280,7 @@ class StableDiffusionXLPuLIDPipeline:
                 add_noise,
             )
             """
-            raise NotImplementedError('pulid: img2img')
+            raise NotImplementedError(f'PuLID: task=img2img class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} image={image} strength={strength}')
         else:
             # standard txt2img will full noise
             latents = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
@@ -359,7 +351,7 @@ class StableDiffusionXLPuLIDPipeline:
             # TODO: pulid inpaint
             # easiest inpaint is to use normal img2img and then combine output with input using mask
             # note that mask can be binary or grayscale (soft mask)
-            raise NotImplementedError('pulid: inpaint')
+            raise NotImplementedError(f'PuLID: task=inpaint class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} mask_image={mask_image}')
 
         return images
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5ae14a64a..34ffb7feb 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -780,11 +780,11 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
     if shared.opts.data.get('sd_model_checkpoint', '') == 'model.safetensors' or shared.opts.data.get('sd_model_checkpoint', '') == '':
         shared.opts.data['sd_model_checkpoint'] = "stabilityai/stable-diffusion-xl-base-1.0"
 
-    if op == 'model' or op == 'dict':
-        if (model_data.sd_model is not None) and (checkpoint_info is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
+    if (op == 'model' or op == 'dict'):
+        if (model_data.sd_model is not None) and (checkpoint_info is not None) and (getattr(model_data.sd_model, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
             return
     else:
-        if (model_data.sd_refiner is not None) and (checkpoint_info is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
+        if (model_data.sd_refiner is not None) and (checkpoint_info is not None) and (getattr(model_data.sd_refiner, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
             return
 
     sd_model = None
@@ -887,7 +887,8 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
 
         set_diffuser_offload(sd_model, op)
         if op == 'model' and not (os.path.isdir(checkpoint_info.path) or checkpoint_info.type == 'huggingface'):
-            sd_vae.apply_vae_config(shared.sd_model.sd_checkpoint_info.filename, vae_file, sd_model)
+            if getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None:
+                sd_vae.apply_vae_config(shared.sd_model.sd_checkpoint_info.filename, vae_file, sd_model)
         if op == 'refiner' and shared.opts.diffusers_move_refiner:
             shared.log.debug('Moving refiner model to CPU')
             move_model(sd_model, devices.cpu)
@@ -1078,18 +1079,13 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     if 'Onnx' in pipe.__class__.__name__:
         return pipe
 
-    if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE or new_pipe_type == DiffusersTaskType.INPAINTING: # in some cases we want to reset the pipeline as they dont have their own variants
+    new_pipe = None
+    # in some cases we want to reset the pipeline to parent as they dont have their own variants
+    if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE or new_pipe_type == DiffusersTaskType.INPAINTING:
         if n == 'StableDiffusionPAGPipeline':
-            pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
+            new_pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
         if n == 'StableDiffusionXLPAGPipeline':
-            pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
-        if n == 'StableDiffusionXLPuLIDPipeline':
-            from modules import pulid
-            if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
-                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineImage
-            else:
-                pipe.__class__ = pulid.StableDiffusionXLPuLIDPipelineInpaint
-            return pipe
+            new_pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
 
     sd_checkpoint_info = getattr(pipe, "sd_checkpoint_info", None)
     sd_model_checkpoint = getattr(pipe, "sd_model_checkpoint", None)
@@ -1101,19 +1097,38 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     image_encoder = getattr(pipe, "image_encoder", None)
     feature_extractor = getattr(pipe, "feature_extractor", None)
 
-    try:
-        if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
-            new_pipe = diffusers.AutoPipelineForText2Image.from_pipe(pipe)
-        elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
-            new_pipe = diffusers.AutoPipelineForImage2Image.from_pipe(pipe)
-        elif new_pipe_type == DiffusersTaskType.INPAINTING:
-            new_pipe = diffusers.AutoPipelineForInpainting.from_pipe(pipe)
+    if new_pipe is None:
+        if hasattr(pipe, 'config'): # real pipeline which can be auto-switched
+            try:
+                if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
+                    new_pipe = diffusers.AutoPipelineForText2Image.from_pipe(pipe)
+                elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
+                    new_pipe = diffusers.AutoPipelineForImage2Image.from_pipe(pipe)
+                elif new_pipe_type == DiffusersTaskType.INPAINTING:
+                    new_pipe = diffusers.AutoPipelineForInpainting.from_pipe(pipe)
+                else:
+                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
+                    return pipe
+            except Exception as e: # pylint: disable=unused-variable
+                shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
+                return pipe
         else:
-            shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
-            return pipe
-    except Exception as e: # pylint: disable=unused-variable
-        shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
-        return pipe
+            try: # maybe a wrapper pipeline so just change the class
+                if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    new_pipe = pipe
+                elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    new_pipe = pipe
+                elif new_pipe_type == DiffusersTaskType.INPAINTING:
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    new_pipe = pipe
+                else:
+                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
+                    return pipe
+            except Exception as e: # pylint: disable=unused-variable
+                shared.log.warning(f'Pipeline class set failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
+                return pipe
 
     # if pipe.__class__ == new_pipe.__class__:
     #    return pipe
@@ -1129,8 +1144,12 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     new_pipe.is_sdxl = getattr(pipe, 'is_sdxl', False) # a1111 compatibility item
     new_pipe.is_sd2 = getattr(pipe, 'is_sd2', False)
     new_pipe.is_sd1 = getattr(pipe, 'is_sd1', True)
-    if hasattr(new_pipe, "watermark"):
+    if hasattr(new_pipe, 'watermark'):
         new_pipe.watermark = NoWatermark()
+
+    if hasattr(new_pipe, 'pipe'): # also handle nested pipelines
+        new_pipe.pipe = set_diffuser_pipe(new_pipe.pipe, new_pipe_type)
+
     fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
     shared.log.debug(f"Pipeline class change: original={pipe.__class__.__name__} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
     pipe = new_pipe
@@ -1201,10 +1220,10 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
     if checkpoint_info is None:
         return
     if op == 'model' or op == 'dict':
-        if model_data.sd_model is not None and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
+        if (model_data.sd_model is not None) and (getattr(model_data.sd_model, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
             return
     else:
-        if model_data.sd_refiner is not None and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
+        if (model_data.sd_refiner is not None) and (getattr(model_data.sd_refiner, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
             return
     shared.log.debug(f'Load {op}: name={checkpoint_info.filename} dict={already_loaded_state_dict is not None}')
     if timer is None:
@@ -1213,12 +1232,12 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
     if op == 'model' or op == 'dict':
         if model_data.sd_model is not None:
             sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
-            current_checkpoint_info = model_data.sd_model.sd_checkpoint_info
+            current_checkpoint_info = getattr(model_data.sd_model, 'sd_checkpoint_info', None)
             unload_model_weights(op=op)
     else:
         if model_data.sd_refiner is not None:
             sd_hijack.model_hijack.undo_hijack(model_data.sd_refiner)
-            current_checkpoint_info = model_data.sd_refiner.sd_checkpoint_info
+            current_checkpoint_info = getattr(model_data.sd_refiner, 'sd_checkpoint_info', None)
             unload_model_weights(op=op)
 
     if not shared.native:
@@ -1247,15 +1266,6 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
         sd_model = instantiate_from_config(sd_config.model)
     else:
         with contextlib.redirect_stdout(stdout):
-            """
-            try:
-                clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
-                with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
-                    sd_model = instantiate_from_config(sd_config.model)
-            except Exception as e:
-                shared.log.error(f'LDM: instantiate from config: {e}')
-                sd_model = instantiate_from_config(sd_config.model)
-            """
             sd_model = instantiate_from_config(sd_config.model)
         for line in stdout.getvalue().splitlines():
             if len(line) > 0:
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index f266f8c38..95ac05c93 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -289,7 +289,7 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
         if vae_file is not None:
             shared.log.info(f"VAE weights loaded: {vae_file}")
     else:
-        if hasattr(sd_model, "vae") and hasattr(sd_model, "sd_checkpoint_info"):
+        if hasattr(sd_model, "vae") and getattr(sd_model, "sd_checkpoint_info", None) is not None:
             vae = load_vae_diffusers(sd_model.sd_checkpoint_info.filename, vae_file, vae_source)
             if vae is not None:
                 if not hasattr(sd_model, 'original_vae'):
diff --git a/modules/styles.py b/modules/styles.py
index de9ef43c4..0599bcd86 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -112,6 +112,8 @@ def apply_wildcards_to_prompt(prompt, all_wildcards, seed=-1, silent=False):
 
 
 def get_reference_style():
+    if getattr(shared.sd_model, 'sd_checkpoint_info', None) is None:
+        return None
     name = shared.sd_model.sd_checkpoint_info.name
     name = name.replace('\\', '/').replace('Diffusers/', '')
     for k, v in shared.reference_models.items():
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 22aff993f..5d209c211 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -8,6 +8,7 @@ from modules import shared, devices, errors, scripts, processing, processing_hel
 
 
 debug = os.environ.get('SD_PULID_DEBUG', None) is not None
+direct = False
 
 
 class Script(scripts.Script):
@@ -148,7 +149,7 @@ class Script(scripts.Script):
                     )
                 shared.sd_model.no_recurse = True
                 sd_models.copy_diffuser_options(shared.sd_model, shared.sd_model.pipe)
-                sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
+                # sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
                 sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
                 devices.torch_gc()
             except Exception as e:
@@ -165,7 +166,7 @@ class Script(scripts.Script):
         shared.sd_model.debug_img_list = []
         uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
 
-        if debug: # run pipeline directly
+        if direct: # run pipeline directly
             shared.state.begin('PuLID')
             processing.fix_seed(p)
             p.seed = processing_helpers.get_fixed_seed(p.seed)

From 6fa55332b8ed48c44c6a821708fd9dc687154318 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Thu, 7 Nov 2024 10:11:02 -0600
Subject: [PATCH 069/119] pulid img2img, no inpaint yet

---
 modules/pulid/pulid_sdxl.py | 44 ++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 23 deletions(-)

diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 0ae603a26..2392219c0 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -261,31 +261,25 @@ class StableDiffusionXLPuLIDPipeline:
         return latent
 
     def init_latent(self, seed, size, image, strength): # pylint: disable=unused-argument
+        # standard txt2img will full noise
+        noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
+        noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
         if image is not None and strength > 0:
-            # TODO pulid img2img
-            # input can be PIL.Image or np.ndarray so it needs to be converted to rgb tensor
-            # image must be resized, encoded and noised according to denoising strength
-            # see below for example from StableDiffusionXLImg2ImgPipeline
-            latents = None
-            """
-            image = self.image_processor.preprocess(image)
-            latents = self.prepare_latents(
+            image = self.pipe.image_processor.preprocess(image)
+            latents = self.pipe.prepare_latents(
                 image,
-                latent_timestep,
-                batch_size,
-                num_images_per_prompt,
-                prompt_embeds.dtype,
-                device,
-                generator,
-                add_noise,
+                None,  # timestep (not needed)
+                1,  # batch_size
+                1,  # num_images_per_prompt
+                noise.dtype,
+                noise.device,
+                None,  # generator
+                False,  # add_noise
             )
-            """
-            raise NotImplementedError(f'PuLID: task=img2img class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} image={image} strength={strength}')
         else:
-            # standard txt2img will full noise
-            latents = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
-            latents = latents.to(dtype=self.pipe.unet.dtype, device=self.device)
-        return latents
+            latents = torch.zeros_like(noise)
+
+        return latents, noise
 
     def __call__(
         self,
@@ -309,10 +303,14 @@ class StableDiffusionXLPuLIDPipeline:
         size = (1, height, width)
         # sigmas
         sigmas = self.get_sigmas_karras(num_inference_steps).to(self.device)
+        if image is not None and strength > 0:
+            _, num_inference_steps = self.pipe.get_timesteps(num_inference_steps, strength, self.device, None)  # denoising_start disabled
+            sigmas = sigmas[-(num_inference_steps + 1):].to(self.device) # shorten sigmas in i2i
+
 
         # latents
-        noise = self.init_latent(seed, size, image, strength)
-        latents = noise * sigmas[0].to(noise)
+        latents, noise = self.init_latent(seed, size, image, strength)
+        latents = latents + noise * sigmas[0].to(noise)
 
         (
             prompt_embeds,

From 8249865f41b22481be782af34fcaf808685d0fa3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 12:00:15 -0500
Subject: [PATCH 070/119] fix pag switch pipeline

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

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4e08cb147..123cfdbf9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-06
+## Update for 2024-11-07
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -9,7 +9,7 @@ This release can be considered an LTS release before we kick off the next round
   - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to system -> changelog and search/highligh/navigate directly in UI!  
-
+  - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
     - advanced method of face transfer with better quality as well as control over identity and appearance  
@@ -74,6 +74,7 @@ This release can be considered an LTS release before we kick off the next round
     - added `cli/model-keys.py` to quicky display content of any safetensors file
   - Internal:
     - Repo: move screenshots to GH pages  
+    - Auto pipeline switching coveres wrapper classes and nested pipelines  
 
 - Fixes:  
   - custom watermark add alphablending  
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 34ffb7feb..1f932bd30 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1083,9 +1083,9 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     # in some cases we want to reset the pipeline to parent as they dont have their own variants
     if new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE or new_pipe_type == DiffusersTaskType.INPAINTING:
         if n == 'StableDiffusionPAGPipeline':
-            new_pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
+            pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
         if n == 'StableDiffusionXLPAGPipeline':
-            new_pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
+            pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
 
     sd_checkpoint_info = getattr(pipe, "sd_checkpoint_info", None)
     sd_model_checkpoint = getattr(pipe, "sd_model_checkpoint", None)

From 77db0c9768060d11c1283e62b4c2c712f7446796 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 12:06:35 -0500
Subject: [PATCH 071/119] 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 123cfdbf9..cdf7163f8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,7 +15,7 @@ This release can be considered an LTS release before we kick off the next round
     - advanced method of face transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
-    - compatible with *sdxl*  
+    - compatible with *sdxl* for text-to-image and image-to-image  
     - can be used in xyz grid  
     - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference

From 9b5c0c738d553cb7390388bf497f5dc04bad0f47 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 12:40:36 -0500
Subject: [PATCH 072/119] update

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

diff --git a/README.md b/README.md
index a2caa5cb7..d39fb7563 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
 - Built-in Control for Text, Image, Batch and video processing!  
   ▹ **ControlNet | ControlNet XS | Control LLLite | T2I Adapters | IP Adapters**  
 - Multiplatform!  
- ▹ **Windows | Linux | MacOS with CPU | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
+ ▹ **Windows | Linux | MacOS | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
 - Platform specific autodetection and tuning performed on install
 - Optimized processing with latest `torch` developments with built-in support for `torch.compile`  
   and multiple compile backends: *Triton, ZLUDA, StableFast, DeepCache, OpenVINO, NNCF, IPEX, OneDiff*  

From 9fef22735fd758d92758ab0ebb0e9ddb26cc2b4b Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Fri, 8 Nov 2024 10:04:02 -0600
Subject: [PATCH 073/119] pulid inpaint, XYZ broken

---
 modules/pulid/pulid_sampling.py | 37 +++++++++++++---
 modules/pulid/pulid_sdxl.py     | 76 ++++++++++++++++++++++++---------
 2 files changed, 86 insertions(+), 27 deletions(-)

diff --git a/modules/pulid/pulid_sampling.py b/modules/pulid/pulid_sampling.py
index 9996f035a..e319c0d27 100644
--- a/modules/pulid/pulid_sampling.py
+++ b/modules/pulid/pulid_sampling.py
@@ -67,6 +67,15 @@ def default_noise_sampler(x):
     return lambda sigma, sigma_next: torch.randn_like(x)
 
 
+def inpaint_mask(x, i, steps, mask_args):
+    noised_original = mask_args["latent"].clone().to(x)
+    latent_mask = mask_args["latent_mask"].to(x)
+    if i < steps:
+        noised_original += mask_args["noise"].to(x) * mask_args["sigmas"][i+1].to(x)
+    x = (latent_mask * x) + ((1 - latent_mask) * noised_original.to(x))
+    return x
+
+
 class BatchedBrownianTree:
     """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
 
@@ -120,7 +129,7 @@ class BrownianTreeNoiseSampler:
 
 
 @torch.no_grad()
-def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., mask_args=None):
     """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
@@ -137,11 +146,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
         dt = sigmas[i + 1] - sigma_hat
         # Euler method
         x = x + (d * dt).to(x.dtype)
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, mask_args=None):
     """Ancestral sampling with Euler method steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -157,6 +168,8 @@ def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
         x = x + (d * dt).to(x.dtype)
         if sigmas[i + 1] > 0:
             x = x + (noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up).to(x.dtype)
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
@@ -375,7 +388,7 @@ class DPMSolver(nn.Module):
 
 
 @torch.no_grad()
-def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, mask_args=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -405,11 +418,13 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
         # Noise addition
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2, mask_args=None):
     """DPM-Solver++ (stochastic)."""
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
@@ -447,11 +462,13 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
             denoised_d = (1 - fac) * denoised + fac * denoised_2
             x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
             x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None, mask_args=None):
     """DPM-Solver++(2M)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
@@ -473,11 +490,13 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
             denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
             x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
         old_denoised = denoised
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint', mask_args=None):
     """DPM-Solver++(2M) SDE."""
 
     if solver_type not in {'heun', 'midpoint'}:
@@ -518,11 +537,13 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 
         old_denoised = denoised
         h_last = h
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
 
 
 @torch.no_grad()
-def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, mask_args=None):
     """DPM-Solver++(3M) SDE."""
 
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
@@ -568,4 +589,6 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 
         denoised_1, denoised_2 = denoised, denoised_1
         h_1, h_2 = h, h_1
+        if mask_args is not None:
+            x = inpaint_mask(x, i, len(sigmas) - 2, mask_args)
     return x
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 2392219c0..fade7509d 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -260,22 +260,40 @@ class StableDiffusionXLPuLIDPipeline:
             self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
         return latent
 
-    def init_latent(self, seed, size, image, strength): # pylint: disable=unused-argument
+    def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
         # standard txt2img will full noise
         noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
         noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
-        if image is not None and strength > 0:
+        if strength > 0 and image is not None:
             image = self.pipe.image_processor.preprocess(image)
-            latents = self.pipe.prepare_latents(
-                image,
-                None,  # timestep (not needed)
-                1,  # batch_size
-                1,  # num_images_per_prompt
-                noise.dtype,
-                noise.device,
-                None,  # generator
-                False,  # add_noise
-            )
+            if mask_image is not None:  # Inpaint
+                latents = self.pipe.prepare_latents(1,  # batch_size,
+                                                    self.pipe.vae.config.latent_channels,  # num_channels_latents
+                                                    height,
+                                                    width,
+                                                    noise.dtype,
+                                                    noise.device,
+                                                    None,  # generator
+                                                    latents=None,
+                                                    image=image,
+                                                    timestep=1000,
+                                                    is_strength_max=False,
+                                                    add_noise=False,
+                                                    return_noise=False,
+                                                    return_image_latents=False,
+                                                    )
+                latents = latents[0]
+            else:  # img2img
+                latents = self.pipe.prepare_latents(image,
+                                                    None,  # timestep (not needed)
+                                                    1,  # batch_size
+                                                    1,  # num_images_per_prompt
+                                                    noise.dtype,
+                                                    noise.device,
+                                                    None,  # generator
+                                                    False,  # add_noise
+                                                    )
+
         else:
             latents = torch.zeros_like(noise)
 
@@ -309,8 +327,8 @@ class StableDiffusionXLPuLIDPipeline:
 
 
         # latents
-        latents, noise = self.init_latent(seed, size, image, strength)
-        latents = latents + noise * sigmas[0].to(noise)
+        latent, noise = self.init_latent(seed, size, image, mask_image, strength, width, height)
+        noisy_latent = latent + noise * sigmas[0].to(noise)
 
         (
             prompt_embeds,
@@ -339,17 +357,35 @@ class StableDiffusionXLPuLIDPipeline:
                 cross_attention_kwargs={'id_embedding': uncond_id_embedding, 'id_scale': id_scale},
             ),
         )
+        if mask_image is not None:
+            latent_mask = torch.Tensor(np.asarray(mask_image.convert("L").resize((noisy_latent.shape[-1], noisy_latent.shape[-2])))).reshape((noisy_latent.shape[-2], noisy_latent.shape[-1]))
+            latent_mask /= latent_mask.max()
+            mask_args = dict(
+                latent=latent,
+                latent_mask=latent_mask,
+                noise=noise,
+                sigmas=sigmas,
+            )
+        else:
+            mask_args = None
 
-        latents = self.sampler(self.sample, latents, sigmas, extra_args=sampler_kwargs, disable=False)
+        latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
         latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
         images = self.pipe.vae.decode(latents).sample
         images = self.pipe.image_processor.postprocess(images, output_type='pil')
 
-        if mask_image is not None:
-            # TODO: pulid inpaint
-            # easiest inpaint is to use normal img2img and then combine output with input using mask
-            # note that mask can be binary or grayscale (soft mask)
-            raise NotImplementedError(f'PuLID: task=inpaint class={self.__class__.__name__} pipe={self.pipe.__class__.__name__} mask_image={mask_image}')
+        # Pixel space final mask
+        # if mask_image is not None:
+        #     # TODO: Fix XYZ
+        #      from PIL import Image
+        #     mask_image = np.asarray(mask_image.convert("L"))
+        #     mask_image = mask_image / mask_image.max()
+        #     mask_image = mask_image.reshape(1,mask_image.shape[0],mask_image.shape[1],1)
+        #     image = np.asarray(image).astype(mask_image.dtype)
+        #     images = np.asarray(images).astype(mask_image.dtype)
+        #     images = ((1 - mask_image) * image) + (mask_image * images)
+        #     images = images[0].round().astype(np.uint8)
+        #     images = [Image.fromarray(images)]
 
         return images
 

From 3f2d3d208d9133193abf9b0c46ec1bca0b9577a8 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 19:44:53 -0500
Subject: [PATCH 074/119] wiki search

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md            |  4 ++-
 javascript/changelog.js |  7 +++++
 javascript/sdnext.css   |  5 ++++
 modules/ui.py           | 18 ++++-------
 modules/ui_docs.py      | 66 +++++++++++++++++++++++++++++++++++++++++
 5 files changed, 86 insertions(+), 14 deletions(-)
 create mode 100644 modules/ui_docs.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cdf7163f8..881ba67cf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,9 +6,11 @@ Smaller release just few days after the last one, but with some important fixes
 This release can be considered an LTS release before we kick off the next round of major updates.  
 
 - Docs:  
-  - add built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
+  - UI built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to system -> changelog and search/highligh/navigate directly in UI!  
+  - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
+    go to system -> wiki and search wiki pages directly in UI!  
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
diff --git a/javascript/changelog.js b/javascript/changelog.js
index 80c9956b8..97dc9daf5 100644
--- a/javascript/changelog.js
+++ b/javascript/changelog.js
@@ -75,3 +75,10 @@ async function initChangelog() {
   };
   search.addEventListener('keyup', searchChangelog);
 }
+
+function wikiSearch(txt) {
+  log('wikiSearch', txt);
+  const url = `https://github.com/search?q=repo%3Avladmandic%2Fautomatic+${encodeURIComponent(txt)}&type=wikis`;
+  // window.open(url, '_blank').focus();
+  return txt;
+}
diff --git a/javascript/sdnext.css b/javascript/sdnext.css
index d412d966f..192d0d487 100644
--- a/javascript/sdnext.css
+++ b/javascript/sdnext.css
@@ -326,6 +326,11 @@ div:has(>#tab-gallery-folders) { flex-grow: 0 !important; background-color: var(
 .changelog_arrow:hover { background-color: var(--button-primary-border-color-hover); }
 .changelog_highlight { background-color: var(--color-warning); }
 
+/* wiki */
+#wiki_result > div > div { padding: 0.5em; margin-right: 2em; }
+#wiki_result li { display: block; }
+#wiki_result h3 { background-color: var(--background-fill-primary); margin: 0; padding: 0.3em; margin-bottom: 0.2em; }
+
 /* loader */
 .splash { position: fixed; top: 0; left: 0; width: 100vw; height: 100vh; z-index: 1000; display: block; text-align: center; }
 .motd { margin-top: 2em; color: var(--body-text-color-subdued); font-family: monospace; font-variant: all-petite-caps; }
diff --git a/modules/ui.py b/modules/ui.py
index 039ef6487..4a9d35728 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -355,20 +355,12 @@ def create_ui(startup_timer = None):
                 ui_onnx.create_ui()
 
             with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
-                def get_changelog():
-                    with open('CHANGELOG.md', 'r', encoding='utf-8') as f:
-                        content = f.read()
-                        content = content.replace('# Change Log for SD.Next', '  ')
-                    return content
+                from modules import ui_docs
+                ui_docs.create_ui_logs()
 
-                with gr.Column():
-                    get_changelog_btn = gr.Button(value='Get changelog', elem_id="get_changelog")
-                with gr.Column():
-                    _changelog_search = gr.Textbox(label="Search", elem_id="changelog_search")
-                    _changelog_result = gr.HTML(elem_id="changelog_result")
-
-                changelog_markdown = gr.Markdown('', elem_id="changelog_markdown")
-                get_changelog_btn.click(fn=get_changelog, outputs=[changelog_markdown], show_progress=True)
+            with gr.TabItem("Wiki", id="wiki", elem_id="system_tab_wiki"):
+                from modules import ui_docs
+                ui_docs.create_ui_wiki()
 
         def unload_sd_weights():
             modules.sd_models.unload_model_weights(op='model')
diff --git a/modules/ui_docs.py b/modules/ui_docs.py
new file mode 100644
index 000000000..7f85aa851
--- /dev/null
+++ b/modules/ui_docs.py
@@ -0,0 +1,66 @@
+import gradio as gr
+from modules import ui_symbols, ui_components
+
+
+def create_ui_logs():
+    def get_changelog():
+        with open('CHANGELOG.md', 'r', encoding='utf-8') as f:
+            content = f.read()
+            content = content.replace('# Change Log for SD.Next', '  ')
+        return content
+
+    with gr.Column():
+        get_changelog_btn = gr.Button(value='Get changelog', elem_id="get_changelog")
+        gr.HTML('<a href="https://github.com/vladmandic/automatic/blob/dev/CHANGELOG.md" style="color: #AAA" target="_blank">&nbsp Open GitHub Changelog</a>')
+    with gr.Column():
+        _changelog_search = gr.Textbox(label="Search Changelog", elem_id="changelog_search")
+        _changelog_result = gr.HTML(elem_id="changelog_result")
+
+    changelog_markdown = gr.Markdown('', elem_id="changelog_markdown")
+    get_changelog_btn.click(fn=get_changelog, outputs=[changelog_markdown], show_progress=True)
+
+
+def create_ui_wiki():
+    def search_github(search_term):
+        import requests
+        from urllib.parse import quote
+        from installer import install
+
+        install('beautifulsoup4')
+        from bs4 import BeautifulSoup
+
+        url = f'https://github.com/search?q=repo%3Avladmandic%2Fautomatic+{quote(search_term)}&type=wikis'
+        res = requests.get(url, timeout=10)
+        if res.status_code == 200:
+            html = res.content
+            soup = BeautifulSoup(html, 'html.parser')
+
+            # remove header links
+            tags = soup.find_all(attrs={"data-hovercard-url": "/vladmandic/automatic/hovercard"})
+            for tag in tags:
+                tag.extract()
+
+            # replace relative links with full links
+            tags = soup.find_all('a')
+            for tag in tags:
+                if tag.has_attr('href') and tag['href'].startswith('/'):
+                    tag['href'] = 'https://github.com' + tag['href']
+
+            # find result only
+            result = soup.find(attrs={"data-testid": "results-list"})
+            if result is None:
+                return 'No results found'
+            html = str(result)
+            return html
+        else:
+            return f'Error: {res.status_code}'
+
+    with gr.Row():
+        gr.HTML('<a href="https://github.com/vladmandic/automatic/wiki" style="color: #AAA" target="_blank">&nbsp Open GitHub Wiki</a>')
+    with gr.Row():
+        wiki_search = gr.Textbox(label="Search Wiki Pages", elem_id="wiki_search")
+        wiki_search_btn = ui_components.ToolButton(value=ui_symbols.search, label="Search", elem_id="wiki_search_btn")
+    with gr.Row():
+        wiki_result = gr.HTML(elem_id="wiki_result", value='test')
+    wiki_search.submit(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])
+    wiki_search_btn.click(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])

From 3077aaf4c0972adab1bb4c402afef427e2322ede Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 7 Nov 2024 20:45:34 -0500
Subject: [PATCH 075/119] add info tab

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md       |  5 +++--
 modules/ui.py      | 19 +++++++++++--------
 modules/ui_docs.py |  2 +-
 3 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 881ba67cf..3626d249f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,11 +6,12 @@ Smaller release just few days after the last one, but with some important fixes
 This release can be considered an LTS release before we kick off the next round of major updates.  
 
 - Docs:  
+  - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)
   - UI built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
-    go to system -> changelog and search/highligh/navigate directly in UI!  
+    go to info -> changelog and search/highligh/navigate directly in UI!  
   - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
-    go to system -> wiki and search wiki pages directly in UI!  
+    go to info -> wiki and search wiki pages directly in UI!  
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
diff --git a/modules/ui.py b/modules/ui.py
index 4a9d35728..4490bbf8c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -354,14 +354,6 @@ def create_ui(startup_timer = None):
                 from modules.onnx_impl import ui as ui_onnx
                 ui_onnx.create_ui()
 
-            with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
-                from modules import ui_docs
-                ui_docs.create_ui_logs()
-
-            with gr.TabItem("Wiki", id="wiki", elem_id="system_tab_wiki"):
-                from modules import ui_docs
-                ui_docs.create_ui_wiki()
-
         def unload_sd_weights():
             modules.sd_models.unload_model_weights(op='model')
             modules.sd_models.unload_model_weights(op='refiner')
@@ -382,6 +374,16 @@ def create_ui(startup_timer = None):
 
     timer.startup.record("ui-settings")
 
+    with gr.Blocks(analytics_enabled=False) as info_interface:
+        with gr.Tabs(elem_id="tabs_info"):
+            with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
+                from modules import ui_docs
+                ui_docs.create_ui_logs()
+
+            with gr.TabItem("Wiki", id="wiki", elem_id="system_tab_wiki"):
+                from modules import ui_docs
+                ui_docs.create_ui_wiki()
+
     interfaces = []
     interfaces += [(txt2img_interface, "Text", "txt2img")]
     interfaces += [(img2img_interface, "Image", "img2img")]
@@ -391,6 +393,7 @@ def create_ui(startup_timer = None):
     interfaces += [(models_interface, "Models", "models")]
     interfaces += script_callbacks.ui_tabs_callback()
     interfaces += [(settings_interface, "System", "system")]
+    interfaces += [(info_interface, "Info", "info")]
 
     from modules import ui_extensions
     extensions_interface = ui_extensions.create_ui()
diff --git a/modules/ui_docs.py b/modules/ui_docs.py
index 7f85aa851..08159beb3 100644
--- a/modules/ui_docs.py
+++ b/modules/ui_docs.py
@@ -61,6 +61,6 @@ def create_ui_wiki():
         wiki_search = gr.Textbox(label="Search Wiki Pages", elem_id="wiki_search")
         wiki_search_btn = ui_components.ToolButton(value=ui_symbols.search, label="Search", elem_id="wiki_search_btn")
     with gr.Row():
-        wiki_result = gr.HTML(elem_id="wiki_result", value='test')
+        wiki_result = gr.HTML(elem_id="wiki_result", value='')
     wiki_search.submit(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])
     wiki_search_btn.click(_js="wikiSearch", fn=search_github, inputs=[wiki_search], outputs=[wiki_result])

From 0cf3283ac81b85d8493ac54e4821562661ad2d4d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 08:26:11 -0500
Subject: [PATCH 076/119] update docs

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md      |   4 +-
 README.md         | 159 ++++++++++------------------------------------
 cli/README.md     | 116 ---------------------------------
 modules/shared.py |  97 ++++++++++++++--------------
 4 files changed, 84 insertions(+), 292 deletions(-)
 delete mode 100644 cli/README.md

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3626d249f..d78943f42 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-07
+## Update for 2024-11-08
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
@@ -12,7 +12,7 @@ This release can be considered an LTS release before we kick off the next round
     go to info -> changelog and search/highligh/navigate directly in UI!  
   - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
     go to info -> wiki and search wiki pages directly in UI!  
-  - major [Wiki](https://github.com/vladmandic/automatic/wiki) updates  
+  - major [Wiki](https://github.com/vladmandic/automatic/wiki) and [Home](https://github.com/vladmandic/automatic) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
     - advanced method of face transfer with better quality as well as control over identity and appearance  
diff --git a/README.md b/README.md
index d39fb7563..d099496b8 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,12 @@
 <div align="center">
-<img src="https://github.com/vladmandic/automatic/blob/dev/html/favicon.png" width=200 alt="SD.Next">
+<img src="https://github.com/vladmandic/automatic/blob/master/html/logo-transparent.png" width=200 alt="SD.Next">
 
-**Stable Diffusion implementation with advanced features**
+**Image Diffusion implementation with advanced features**
 
-[![Sponsors](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
-![Last Commit](https://img.shields.io/github/last-commit/vladmandic/automatic?svg=true)
+![Last update](https://img.shields.io/github/last-commit/vladmandic/automatic?svg=true)
 ![License](https://img.shields.io/github/license/vladmandic/automatic?svg=true)
 [![Discord](https://img.shields.io/discord/1101998836328697867?logo=Discord&svg=true)](https://discord.gg/VjvR2tabEX)
+[![Sponsors](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 
 [Wiki](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.gg/VjvR2tabEX) | [Changelog](CHANGELOG.md)
 
@@ -18,45 +18,36 @@
 - [SD.Next Features](#sdnext-features)
 - [Model support](#model-support)
 - [Platform support](#platform-support)
-- [Backend support](#backend-support)
-- [Examples](#examples)
-- [Install](#install)
-- [Notes](#notes)
+- [Getting started](#getting-started)
 
 ## SD.Next Features
 
 All individual features are not listed here, instead check [ChangeLog](CHANGELOG.md) for full list of changes
-- Multiple backends!  
-  ▹ **Diffusers | Original**  
 - Multiple UIs!  
   ▹ **Standard | Modern**  
 - Multiple diffusion models!  
-  ▹ **Stable Diffusion 1.5/2.1/XL/3.0/3.5 | LCM | Lightning | Segmind | Kandinsky | Pixart-α | Pixart-Σ | Stable Cascade | FLUX.1 | AuraFlow | Würstchen | Alpha Lumina | Kwai Kolors | aMUSEd | DeepFloyd IF | UniDiffusion | SD-Distilled | BLiP Diffusion | KOALA | SDXS | Hyper-SD | HunyuanDiT | CogView | OmniGen | Meissonic | etc.**
 - Built-in Control for Text, Image, Batch and video processing!  
-  ▹ **ControlNet | ControlNet XS | Control LLLite | T2I Adapters | IP Adapters**  
 - Multiplatform!  
  ▹ **Windows | Linux | MacOS | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
-- Platform specific autodetection and tuning performed on install
+- Multiple backends!  
+  ▹ **Diffusers | Original**  
+- Platform specific autodetection and tuning performed on install  
 - Optimized processing with latest `torch` developments with built-in support for `torch.compile`  
   and multiple compile backends: *Triton, ZLUDA, StableFast, DeepCache, OpenVINO, NNCF, IPEX, OneDiff*  
 - Improved prompt parser  
-- Enhanced *Lora*/*LoCon*/*Lyco* code supporting latest trends in training  
 - Built-in queue management  
 - Enterprise level logging and hardened API  
 - Built in installer with automatic updates and dependency management  
-- Modernized UI with theme support and number of built-in themes *(dark and light)*  
-- Mobile compatible
+- Mobile compatible  
 
 <br>
 
 *Main interface using **StandardUI***:  
-![screenshot-text2image](https://github.com/user-attachments/assets/87ac2813-65c2-45f4-80b8-67b26ccf5cd6)
+![screenshot-standardui](https://github.com/user-attachments/assets/cab47fe3-9adb-4d67-aea9-9ee738df5dcc)
 
 *Main interface using **ModernUI***:  
 
-![screenshot-modernui-f1](https://github.com/user-attachments/assets/b509a280-8d3b-48b5-8525-363bad8c1ed2)
-![screenshot-modernui](https://github.com/user-attachments/assets/fef33127-f733-4e78-b66e-17729539512f)
-![screenshot-modernui-sd3](https://github.com/user-attachments/assets/1ed02ecc-23e4-4fda-8ae5-2d7393dc530c)
+![screenshot-modernui](https://github.com/user-attachments/assets/39e3bc9a-a9f7-4cda-ba33-7da8def08032)
 
 For screenshots and informations on other available themes, see [Themes Wiki](https://github.com/vladmandic/automatic/wiki/Themes)
 
@@ -65,12 +56,10 @@ For screenshots and informations on other available themes, see [Themes Wiki](ht
 ## Model support
 
 Additional models will be added as they become available and there is public interest in them  
-See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for details on each model, including their architecture, complexity and other info  
+See [models overview](wiki/Models) for details on each model, including their architecture, complexity and other info  
 
 - [RunwayML Stable Diffusion](https://github.com/Stability-AI/stablediffusion/) 1.x and 2.x *(all variants)*
-- [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models)
-- [StabilityAI Stable Diffusion](https://stability.ai/news/stable-diffusion-3-medium) 
-- [Stable Diffusion 3.x](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) 3.0 Medium, 3.5 Medium, 3.5 Large, 3.5 Large Turbo
+- [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models), [StabilityAI Stable Diffusion 3.0](https://stability.ai/news/stable-diffusion-3-medium) Medium, [StabilityAI Stable Diffusion 3.5](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) Medium, Large, Large Turbo
 - [StabilityAI Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) Base, XT 1.0, XT 1.1
 - [StabilityAI Stable Cascade](https://github.com/Stability-AI/StableCascade) *Full* and *Lite*
 - [Black Forest Labs FLUX.1](https://blackforestlabs.ai/announcing-black-forest-labs/) Dev, Schnell  
@@ -84,13 +73,9 @@ See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for d
 - [CogView 3+](https://huggingface.co/THUDM/CogView3-Plus-3B)
 - [LCM: Latent Consistency Models](https://github.com/openai/consistency_models)
 - [aMUSEd](https://huggingface.co/amused/amused-256) 256 and 512
-- [Segmind Vega](https://huggingface.co/segmind/Segmind-Vega)
-- [Segmind SSD-1B](https://huggingface.co/segmind/SSD-1B)
-- [Segmind SegMoE](https://github.com/segmind/segmoe) *SD and SD-XL*
-- [Segmind SD Distilled](https://huggingface.co/blog/sd_distillation) *(all variants)*
+- [Segmind Vega](https://huggingface.co/segmind/Segmind-Vega), [Segmind SSD-1B](https://huggingface.co/segmind/SSD-1B), [Segmind SegMoE](https://github.com/segmind/segmoe) *SD and SD-XL*, [Segmind SD Distilled](https://huggingface.co/blog/sd_distillation) *(all variants)*
 - [Kandinsky](https://github.com/ai-forever/Kandinsky-2) *2.1 and 2.2 and latest 3.0*
-- [PixArt-α XL 2](https://github.com/PixArt-alpha/PixArt-alpha) *Medium and Large*
-- [PixArt-Σ](https://github.com/PixArt-alpha/PixArt-sigma)
+- [PixArt-α XL 2](https://github.com/PixArt-alpha/PixArt-alpha) *Medium and Large*, [PixArt-Σ](https://github.com/PixArt-alpha/PixArt-sigma)
 - [Warp Wuerstchen](https://huggingface.co/blog/wuertschen)
 - [Tsinghua UniDiffusion](https://github.com/thu-ml/unidiffuser)
 - [DeepFloyd IF](https://github.com/deep-floyd/IF) *Medium and Large*
@@ -101,15 +86,6 @@ See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for d
 - [SDXS](https://github.com/IDKiro/sdxs)
 - [Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD)
 
-
-Also supported are modifiers such as:
-- **LCM**, **Turbo** and **Lightning** (*adversarial diffusion distillation*) networks
-- All **LoRA** types such as LoCon, LyCORIS, HADA, IA3, Lokr, OFT
-- **IP-Adapters** for SD 1.5 and SD-XL
-- **InstantID**, **FaceSwap**, **FaceID**, **PhotoMerge**  
-- **AnimateDiff** for SD 1.5
-- **MuLAN** multi-language support  
-
 ## Platform support
 
 - *nVidia* GPUs using **CUDA** libraries on both *Windows and Linux*  
@@ -121,6 +97,25 @@ Also supported are modifiers such as:
 - Any GPU or device compatible with **OpenVINO** libraries on both *Windows and Linux*  
 - *Apple M1/M2* on *OSX* using built-in support in Torch with **MPS** optimizations  
 - *ONNX/Olive*  
+- *AMD* GPUs on Windows using **ZLUDA** libraries
+
+## Getting started
+
+- Get started with **SD.Next** by following the [installation instructions](wiki/Installation)  
+- For more details, check out [advanced installation](wiki/Advanced-Install) guide  
+- List and explanation of [command line arguments](wiki/CLI-Arguments)
+- Install walkthrough [video](https://www.youtube.com/watch?v=nWTnTyFTuAs)
+
+> [!TIP]
+> And for platform specific information, check out  
+> [WSL](wiki/WSL) | [Intel Arc](wiki/Intel-ARC) | [DirectML](wiki/DirectML) | [OpenVINO](wiki/OpenVINO) | [ONNX & Olive](wiki/ONNX-Runtime) | [ZLUDA](wiki/ZLUDA) | [AMD ROCm](wiki/AMD-ROCm) | [MacOS](wiki/MacOS-Python.md) | [nVidia](wiki/nVidia)
+
+> [!WARNING]
+> If you run into issues, check out [troubleshooting](wiki/Troubleshooting) and [debugging](wiki/Debug) guides  
+
+> [!TIP]
+> All command line options can also be set via env variable
+> For example `--debug` is same as `set SD_DEBUG=true`  
 
 ## Backend support
 
@@ -129,91 +124,11 @@ Also supported are modifiers such as:
 - **Diffusers**: Based on new [Huggingface Diffusers](https://huggingface.co/docs/diffusers/index) implementation  
   Supports *all* models listed below  
   This backend is set as default for new installations  
-  See [wiki article](https://github.com/vladmandic/automatic/wiki/Diffusers) for more information  
 - **Original**: Based on [LDM](https://github.com/Stability-AI/stablediffusion) reference implementation and significantly expanded on by [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)  
   This backend and is fully compatible with most existing functionality and extensions written for *A1111 SDWebUI*  
   Supports **SD 1.x** and **SD 2.x** models  
   All other model types such as *SD-XL, LCM, Stable Cascade, PixArt, Playground, Segmind, Kandinsky, etc.* require backend **Diffusers**  
 
-## Examples
-
-*IP Adapters*:
-![screenshot-ipadapter](https://github.com/user-attachments/assets/92830894-845c-49ec-92d9-18c8a577d04f)
-
-*Color grading*:  
-![screenshot-control](https://github.com/user-attachments/assets/cdad2722-ae7c-4c9c-94d6-5ea35a4b1356)
-
-*InstantID*:  
-![screenshot-instantid](https://github.com/user-attachments/assets/f38a5660-32b3-4235-9da1-c79eccf5372f)
-
-> [!IMPORTANT]
-> - Loading any model other than standard SD 1.x / SD 2.x requires use of backend **Diffusers**  
-> - Loading any other models using **Original** backend is not supported  
-> - Loading manually download model `.safetensors` files is supported for specified models only (typically SD 1.x / SD 2.x / SD-XL models only)  
-> - For all other model types, use backend **Diffusers** and use built in Model downloader or  
-  select model from Networks -> Models -> Reference list in which case it will be auto-downloaded and loaded  
-
-## Install
-
-- [Step-by-step install guide](https://github.com/vladmandic/automatic/wiki/Installation)
-- [Advanced install notes](https://github.com/vladmandic/automatic/wiki/Advanced-Install)
-- [Video: install and use](https://www.youtube.com/watch?v=nWTnTyFTuAs)
-- [Common installation errors](https://github.com/vladmandic/automatic/discussions/1627)  
-- [FAQ](https://github.com/vladmandic/automatic/discussions/1011)
-
-> [!TIP]
-> - If you can't run SD.Next locally, try cloud deployment using [RunDiffusion](https://rundiffusion.com?utm_source=github&utm_medium=referral&utm_campaign=SDNext)!
-> - Server can run with or without virtual environment,  
-  Recommended to use `VENV` to avoid library version conflicts with other applications  
-> - **nVidia/CUDA** / **AMD/ROCm** / **Intel/OneAPI** are auto-detected if present and available,  
-  For any other use case such as **DirectML**, **ONNX/Olive**, **OpenVINO** specify required parameter explicitly  
-  or wrong packages may be installed as installer will assume CPU-only environment  
-> - Full startup sequence is logged in `sdnext.log`,  
-  so if you encounter any issues, please check it first  
-
-### Run
-
-Once SD.Next is installed, simply run `webui.ps1` or `webui.bat` (*Windows*) or `webui.sh` (*Linux or MacOS*)
-
-For list of available command line options, run `webui --help` for the full & up-to-date list
-
-> [!TIP]
-> All command line options can also be set via env variable
-> For example `--debug` is same as `set SD_DEBUG=true`  
-
-## Notes
-
-> [!TIP]
-> If you don't want to use built-in `venv` support and prefer to run SD.Next in your own environment such as *Docker* container, *Conda* environment or any other virtual environment, you can skip `venv` create/activate and launch SD.Next directly using `python launch.py` (command line flags noted above still apply).
-
-### Quantization
-
-**SD.Next** comes with broad quantization support, including support for BitsAndBytes, Optimum.Quanto, TorchAO, NNCF and GGUF
-See [Quantization Wiki](https://github.com/vladmandic/automatic/wiki/Quantization)
-
-### Control
-
-**SD.Next** comes with built-in control for all types of text2image, image2image, video2video and batch processing
-
-*Control interface*:  
-![screenshot-control](https://github.com/user-attachments/assets/cdad2722-ae7c-4c9c-94d6-5ea35a4b1356)
-
-*Control processors*:  
-![screenshot-processors](https://github.com/user-attachments/assets/7bccb82b-366e-4bdb-ae57-cc53fac95d3c)
-
-*Masking*:
-![screenshot-mask](https://github.com/user-attachments/assets/4b057e65-64f0-44ea-93b4-c3b69bc55532)
-
-### Extensions
-
-SD.Next comes with several extensions pre-installed:
-
-- [System Info](https://github.com/vladmandic/sd-extension-system-info)
-- [chaiNNer](https://github.com/vladmandic/sd-extension-chainner)
-- [RemBg](https://github.com/vladmandic/sd-extension-rembg)
-- [Agent Scheduler](https://github.com/ArtVentureX/sd-webui-agent-scheduler)
-- [Modern UI](https://github.com/BinaryQuantumSoul/sdnext-modernui)
-
 ### Collab
 
 - We'd love to have additional maintainers (with comes with full repo rights). If you're interested, ping us!  
@@ -242,12 +157,6 @@ This should be fully cross-platform, but we'd really love to have additional con
 If you're unsure how to use a feature, best place to start is [Wiki](https://github.com/vladmandic/automatic/wiki) and if its not there,  
 check [ChangeLog](CHANGELOG.md) for when feature was first introduced as it will always have a short note on how to use it  
 
-- [Wiki](https://github.com/vladmandic/automatic/wiki)
-- [ReadMe](README.md)
-- [ToDo](TODO.md)  
-- [ChangeLog](CHANGELOG.md)
-- [CLI Tools](cli/README.md)
-
 ### Sponsors
 
 <div align="center">
diff --git a/cli/README.md b/cli/README.md
deleted file mode 100644
index 838db7a50..000000000
--- a/cli/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# Stable-Diffusion Productivity Scripts
-
-## API Examples
-
-### Run Generate
-
-- `cli/api-txt2img.py`
-- `cli/api-img2img.py`
-- `cli/api-control.py`
-
-### Monitor
-
-- `cli/api-progress.py`
-
-### Generic
-
-- `cli/api-json.py`
-
-### Process
-
-- `cli/api-info.py`
-- `cli/api-upscale.py`
-- `cli/api-vqa.py`
-- `cli/api-preprocess.py`
-
-### Other
-
-- `cli/api-faceid.py`
-- `cli/api-faces.py`
-- `cli/api-mask.py`
-
-### JavaScript
-
-- `cli/api-txt2img.js`
-
-## Generate
-
-Text-to-image with all of the possible parameters  
-Supports upsampling, face restoration and grid creation  
-> python cli/generate.py
-
-By default uses parameters from  `generate.json`
-
-Parameters that are not specified will be randomized:
-
-- Prompt will be dynamically created from template of random samples: `random.json`
-- Sampler/Scheduler will be randomly picked from available ones
-- CFG Scale set to 5-10
-
-<br>
-
-## Auxiliary Scripts
-
-### Benchmark
-
-> python run-benchmark.py
-
-### Create Previews
-
-Create previews for **embeddings**, **lora**, **lycoris**, **dreambooth** and **hypernetwork**
-
-> python create-previews.py
-
-## Image Grid
-
-> python image-grid.py
-
-### Image Watermark
-
-Create invisible image watermark and remove existing EXIF tags  
-
-> python image-watermark.py
-
-### Image Interrogate
-
-Runs CLiP and Booru image interrogation  
-
-> python image-interrogate.py
-
-### Palette Extract
-
-Extract color palette from image(s)  
-
-> python image-palette.py
-
-### Prompt Ideas
-
-Generate complex prompt ideas
-
-> python prompt-ideas.py
-
-### Prompt Promptist
-
-Attempts to beautify the provided prompt  
-
-> python prompt-promptist.py
-
-### Video Extract
-
-Extract frames from video files  
-
-> python video-extract.py
-
-<br>
-
-## Utility Scripts
-
-### SDAPI
-
-Utility module that handles async communication to Automatic API endpoints  
-Note: Requires SD API  
-
-Can be used to manually execute specific commands:
-> python sdapi.py progress  
-> python sdapi.py interrupt
-> python sdapi.py shutdown
diff --git a/modules/shared.py b/modules/shared.py
index 171a777e9..48984c8be 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -473,15 +473,11 @@ options_templates.update(options_section(('sd', "Execution & Models"), {
     "sd_textencoder_cache": OptionInfo(True, "Cache text encoder results", gr.Checkbox, {"visible": False}),
     "sd_textencoder_cache_size": OptionInfo(4, "Text encoder results LRU cache size", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "stream_load": OptionInfo(False, "Load models using stream loading method", gr.Checkbox, {"visible": not native }),
-    "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
     "prompt_mean_norm": OptionInfo(False, "Prompt attention normalization", gr.Checkbox),
     "comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": not native }),
     "prompt_attention": OptionInfo("native", "Prompt attention parser", gr.Radio, {"choices": ["native", "compel", "xhinker", "a1111", "fixed"] }),
     "latent_history": OptionInfo(16, "Latent history size", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": not native }),
-    "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
-    "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format", gr.Checkbox, {"visible": False}),
-    "diffusers_version": OptionInfo("", "Diffusers version", gr.Textbox, {"visible": False}),
 }))
 
 options_templates.update(options_section(('cuda', "Compute Settings"), {
@@ -495,8 +491,7 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
     "upcast_sampling": OptionInfo(False if sys.platform != "darwin" else True, "Upcast sampling"),
     "upcast_attn": OptionInfo(False, "Upcast attention layer"),
     "cuda_cast_unet": OptionInfo(False, "Fixed UNet precision"),
-    "disable_nan_check": OptionInfo(True, "Disable NaN check", gr.Checkbox, {"visible": False}),
-    "nan_skip": OptionInfo(False, "Skip Generation if NaN found in latents", gr.Checkbox, {"visible": True}),
+    "nan_skip": OptionInfo(False, "Skip Generation if NaN found in latents", gr.Checkbox),
     "rollback_vae": OptionInfo(False, "Attempt VAE roll back for NaN values"),
 
     "cross_attention_sep": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
@@ -569,7 +564,6 @@ options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
     "diffusers_eval": OptionInfo(True, "Force model eval"),
     "diffusers_to_gpu": OptionInfo(False, "Load model directly to GPU"),
     "disable_accelerate": OptionInfo(False, "Disable accelerate"),
-    "diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty", gr.Checkbox, {"visible": False}),
     "diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds", gr.Radio, {"choices": ['default', 'weighted']}),
     "diffusers_zeros_prompt_pad": OptionInfo(False, "Use zeros for prompt padding", gr.Checkbox),
     "huggingface_token": OptionInfo('', 'HuggingFace token'),
@@ -650,9 +644,7 @@ options_templates.update(options_section(('system-paths', "System Paths"), {
     "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
     "unet_dir": OptionInfo(os.path.join(paths.models_path, 'UNET'), "Folder with UNET files", folder=True),
     "te_dir": OptionInfo(os.path.join(paths.models_path, 'Text-encoder'), "Folder with Text encoder files", folder=True),
-    "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
     "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
-    "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
     "styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", folder=True),
     "wildcards_dir": OptionInfo(os.path.join(paths.models_path, 'wildcards'), "Folder with user-defined wildcards", folder=True),
     "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Folder with textual inversion embeddings", folder=True),
@@ -728,12 +720,10 @@ options_templates.update(options_section(('saving-paths', "Image Naming & Paths"
     "saving_sep_images": OptionInfo("<h2>Save options</h2>", "", gr.HTML),
     "save_images_add_number": OptionInfo(True, "Numbered filenames", component_args=hide_dirs),
     "use_original_name_batch": OptionInfo(True, "Batch uses original name"),
-    "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
     "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
     "samples_filename_pattern": OptionInfo("[seq]-[model_name]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
     "directories_max_prompt_words": OptionInfo(8, "Max words per pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
 
     "outdir_sep_dirs": OptionInfo("<h2>Folders</h2>", "", gr.HTML),
     "outdir_samples": OptionInfo("", "Images folder", component_args=hide_dirs, folder=True),
@@ -746,8 +736,6 @@ options_templates.update(options_section(('saving-paths', "Image Naming & Paths"
     "outdir_init_images": OptionInfo("outputs/init-images", "Folder for init images", component_args=hide_dirs, folder=True),
 
     "outdir_sep_grids": OptionInfo("<h2>Grids</h2>", "", gr.HTML),
-    "grid_extended_filename": OptionInfo(True, "Add extended info to filename when saving grid", gr.Checkbox, {"visible": False}),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
     "outdir_grids": OptionInfo("", "Grids folder", component_args=hide_dirs, folder=True),
     "outdir_txt2img_grids": OptionInfo("outputs/grids", 'Folder for txt2img grids', component_args=hide_dirs, folder=True),
     "outdir_img2img_grids": OptionInfo("outputs/grids", 'Folder for img2img grids', component_args=hide_dirs, folder=True),
@@ -760,7 +748,6 @@ options_templates.update(options_section(('ui', "User Interface Options"), {
     "gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": theme.list_themes()}, refresh=theme.refresh_themes),
     "autolaunch": OptionInfo(False, "Autolaunch browser upon startup"),
     "font_size": OptionInfo(14, "Font size", gr.Slider, {"minimum": 8, "maximum": 32, "step": 1, "visible": True}),
-    "tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
     "aspect_ratios": OptionInfo("1:1, 4:3, 3:2, 16:9, 16:10, 21:9, 2:3, 3:4, 9:16, 10:16, 9:21", "Allowed aspect ratios"),
     "motd": OptionInfo(True, "Show MOTD"),
     "compact_view": OptionInfo(False, "Compact view"),
@@ -770,22 +757,14 @@ options_templates.update(options_section(('ui', "User Interface Options"), {
     "disable_weights_auto_swap": OptionInfo(True, "Do not change selected model when reading generation parameters"),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
-    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
-    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
-    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }),
     "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", gr.Dropdown, lambda: {"multiselect":True, "choices": list(opts.data_labels.keys())}),
-    "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
 }))
 
 options_templates.update(options_section(('live-preview', "Live Previews"), {
-    "show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
-    "live_previews_enable": OptionInfo(True, "Show live previews", gr.Checkbox, {"visible": False}),
-    "show_progress_grid": OptionInfo(True, "Show previews as a grid", gr.Checkbox, {"visible": False}),
     "notification_audio_enable": OptionInfo(False, "Play a notification upon completion"),
     "notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs, folder=True),
     "show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     "show_progress_type": OptionInfo("Approximate", "Live preview method", gr.Radio, {"choices": ["Simple", "Approximate", "TAESD", "Full VAE"]}),
-    "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
     "live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
     "live_preview_taesd_layers": OptionInfo(3, "TAESD decode layers", gr.Slider, {"minimum": 1, "maximum": 3, "step": 1}),
     "logmonitor_show": OptionInfo(True, "Show log view"),
@@ -832,8 +811,6 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
     'uni_pc_variant': OptionInfo("bh2", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"], "visible": not native}),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"], "visible": not native}),
     "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad'], "visible": not native}),
-    "pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
-    "batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing"), {
@@ -843,12 +820,10 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "postprocessing_sep_img2img": OptionInfo("<h2>Img2Img & Inpainting</h2>", "", gr.HTML),
     "img2img_color_correction": OptionInfo(False, "Apply color correction"),
     "mask_apply_overlay": OptionInfo(True, "Apply mask as overlay"),
-    "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
     "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", gr.ColorPicker, {}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
     "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
 
     # "postprocessing_sep_detailer": OptionInfo("<h2>Detailer</h2>", "", gr.HTML),
     "detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
@@ -870,7 +845,6 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
 
     "postprocessing_sep_upscalers": OptionInfo("<h2>Upscaling</h2>", "", gr.HTML),
     "upscaler_unload": OptionInfo(False, "Unload upscaler after processing"),
-    "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
     "upscaler_tile_size": OptionInfo(192, "Upscaler tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "upscaler_tile_overlap": OptionInfo(8, "Upscaler tile overlap", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))
@@ -881,28 +855,12 @@ options_templates.update(options_section(('control', "Control Options"), {
     "control_unload_processor": OptionInfo(False, "Processor unload after use"),
 }))
 
-options_templates.update(options_section(('interrogate', "Interrogate"), { # "Training" section disabled so just a placeholder
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training", gr.Checkbox, { "visible": False }),
-    "pin_memory": OptionInfo(True, "Pin training dataset to memory", gr.Checkbox, { "visible": False }),
-    "save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training", gr.Checkbox, { "visible": False }),
-    "save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file", gr.Checkbox, { "visible": False }),
-    "dataset_filename_word_regex": OptionInfo("", "Filename word regex", gr.Textbox, { "visible": False }),
-    "dataset_filename_join_string": OptionInfo(" ", "Filename join string", gr.Textbox, { "visible": False }),
-    "embeddings_templates_dir": OptionInfo("", "Embeddings train templates directory", gr.Textbox, { "visible": False }),
-    "training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1, "visible": False }),
-    "training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps", gr.Number, { "visible": False }),
-    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging", gr.Checkbox, { "visible": False }),
-    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard", gr.Checkbox, { "visible": False }),
-    "training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period", gr.Number, { "visible": False }),
-}))
-
 options_templates.update(options_section(('interrogate', "Interrogate"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
     "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "Interrogate: skip categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
@@ -923,7 +881,6 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
     "extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
     "extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
     "extra_networks_fetch": OptionInfo(True, "UI fetch network info on mouse-over"),
-    "extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
     "extra_network_skip_indexing": OptionInfo(False, "Build info on first access", gr.Checkbox),
 
     "extra_networks_model_sep": OptionInfo("<h2>Models</h2>", "", gr.HTML),
@@ -944,17 +901,59 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
     "lora_apply_tags": OptionInfo(0, "LoRA auto-apply tags", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
     "lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
     "lora_quant": OptionInfo("NF4","LoRA precision in quantized models", gr.Radio, {"choices": ["NF4", "FP4"]}),
-    "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
     "lora_load_gpu": OptionInfo(True if not cmd_opts.lowvram else False, "Load LoRA directly to GPU"),
+}))
 
-    "hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support", gr.Checkbox, {"visible": False}),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
+options_templates.update(options_section((None, "Internal options"), {
+    "diffusers_version": OptionInfo("", "Diffusers version", gr.Textbox, {"visible": False}),
+    "disabled_extensions": OptionInfo([], "Disable these extensions"),
+    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+    "tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
 }))
 
 options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable these extensions"),
+    "batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
+    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string", gr.Textbox, { "visible": False }),
+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex", gr.Textbox, { "visible": False }),
+    "diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty", gr.Checkbox, {"visible": False}),
     "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "user", "all"]}),
-    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+    "disable_nan_check": OptionInfo(True, "Disable NaN check", gr.Checkbox, {"visible": False}),
+    "embeddings_templates_dir": OptionInfo("", "Embeddings train templates directory", gr.Textbox, { "visible": False }),
+    "extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
+    "grid_extended_filename": OptionInfo(True, "Add extended info to filename when saving grid", gr.Checkbox, {"visible": False}),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
+    "hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support", gr.Checkbox, {"visible": False}),
+    "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
+    "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
+    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }),
+    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
+    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
+    "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
+    "live_previews_enable": OptionInfo(True, "Show live previews", gr.Checkbox, {"visible": False}),
+    "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
+    "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
+    "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
+    "pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
+    "pin_memory": OptionInfo(True, "Pin training dataset to memory", gr.Checkbox, { "visible": False }),
+    "save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training", gr.Checkbox, { "visible": False }),
+    "save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file", gr.Checkbox, { "visible": False }),
+    "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format", gr.Checkbox, {"visible": False}),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
+    "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
+    "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
+    "show_progress_grid": OptionInfo(True, "Show previews as a grid", gr.Checkbox, {"visible": False}),
+    "show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
+    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging", gr.Checkbox, { "visible": False }),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1, "visible": False }),
+    "training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period", gr.Number, { "visible": False }),
+    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard", gr.Checkbox, { "visible": False }),
+    "training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps", gr.Number, { "visible": False }),
+    "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training", gr.Checkbox, { "visible": False }),
+    "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
+    "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
 }))
 
 options_templates.update()

From ba3a32ae470a371fdc59341abddc5304f22841c2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 09:49:46 -0500
Subject: [PATCH 077/119] add api override field

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/api-txt2img.js      | 19 +---------
 modules/api/control.py  |  2 +
 modules/api/generate.py |  4 ++
 modules/api/models.py   |  2 +
 modules/images.py       | 84 +++++++++++++++++++++--------------------
 5 files changed, 53 insertions(+), 58 deletions(-)

diff --git a/cli/api-txt2img.js b/cli/api-txt2img.js
index 46d09b3a2..8d0e9f5d1 100755
--- a/cli/api-txt2img.js
+++ b/cli/api-txt2img.js
@@ -20,23 +20,6 @@ const sd_options = {
   cfg_scale: 6,
   width: 512,
   height: 512,
-  /*
-  // enable second pass
-  enable_hr: true,
-  // second pass: upscale
-  hr_upscaler: 'SCUNet GAN',
-  hr_scale: 2.0,
-  // second pass: hires
-  hr_force: true,
-  hr_second_pass_steps: 20,
-  hr_sampler_name: 'UniPC',
-  denoising_strength: 0.5,
-  // second pass: refiner
-  refiner_steps: 5,
-  refiner_start: 0.8,
-  refiner_prompt: '',
-  refiner_negative: '',
-  */
   // api return options
   save_images: false,
   send_images: true,
@@ -55,7 +38,7 @@ async function main() {
     const json = await res.json();
     console.log('result:', json.info);
     for (const i in json.images) { // eslint-disable-line guard-for-in
-      const f = `/tmp/test-{${i}.jpg`;
+      const f = `/tmp/test-${i}.jpg`;
       fs.writeFileSync(f, atob(json.images[i]), 'binary');
       console.log('image saved:', f);
     }
diff --git a/modules/api/control.py b/modules/api/control.py
index cf8916095..ffb000053 100644
--- a/modules/api/control.py
+++ b/modules/api/control.py
@@ -31,6 +31,7 @@ ReqControl = models.create_model_from_signature(
         {"key": "ip_adapter", "type": Optional[List[models.ItemIPAdapter]], "default": None, "exclude": True},
         {"key": "face", "type": Optional[models.ItemFace], "default": None, "exclude": True},
         {"key": "control", "type": Optional[List[ItemControl]], "default": [], "exclude": True},
+        {"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
     ]
 )
 
@@ -159,6 +160,7 @@ class APIControl():
             output_processed = []
             output_info = ''
             run.control_set({ 'do_not_save_grid': not req.save_images, 'do_not_save_samples': not req.save_images, **self.prepare_ip_adapter(req) })
+            run.control_set(getattr(req, "extra", {}))
             res = run.control_run(**args)
             for item in res:
                 if len(item) > 0 and (isinstance(item[0], list) or item[0] is None): # output_images
diff --git a/modules/api/generate.py b/modules/api/generate.py
index aeafa05a0..e22102057 100644
--- a/modules/api/generate.py
+++ b/modules/api/generate.py
@@ -106,6 +106,8 @@ class APIGenerate():
             p.scripts = script_runner
             p.outpath_grids = shared.opts.outdir_grids or shared.opts.outdir_txt2img_grids
             p.outpath_samples = shared.opts.outdir_samples or shared.opts.outdir_txt2img_samples
+            for key, value in getattr(txt2imgreq, "extra", {}).items():
+                setattr(p, key, value)
             shared.state.begin('API TXT', api=True)
             script_args = script.init_script_args(p, txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
             if selectable_scripts is not None:
@@ -150,6 +152,8 @@ class APIGenerate():
             p.scripts = script_runner
             p.outpath_grids = shared.opts.outdir_img2img_grids
             p.outpath_samples = shared.opts.outdir_img2img_samples
+            for key, value in getattr(img2imgreq, "extra", {}).items():
+                setattr(p, key, value)
             shared.state.begin('API-IMG', api=True)
             script_args = script.init_script_args(p, img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
             if selectable_scripts is not None:
diff --git a/modules/api/models.py b/modules/api/models.py
index 3cf3aade9..740f3c555 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -210,6 +210,7 @@ ReqTxt2Img = PydanticModelGenerator(
         {"key": "alwayson_scripts", "type": dict, "default": {}},
         {"key": "ip_adapter", "type": Optional[List[ItemIPAdapter]], "default": None, "exclude": True},
         {"key": "face", "type": Optional[ItemFace], "default": None, "exclude": True},
+        {"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
     ]
 ).generate_model()
 StableDiffusionTxt2ImgProcessingAPI = ReqTxt2Img
@@ -235,6 +236,7 @@ ReqImg2Img = PydanticModelGenerator(
         {"key": "alwayson_scripts", "type": dict, "default": {}},
         {"key": "ip_adapter", "type": Optional[List[ItemIPAdapter]], "default": None, "exclude": True},
         {"key": "face_id", "type": Optional[ItemFace], "default": None, "exclude": True},
+        {"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
     ]
 ).generate_model()
 StableDiffusionImg2ImgProcessingAPI = ReqImg2Img
diff --git a/modules/images.py b/modules/images.py
index fb9cc9652..910349bef 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -40,8 +40,6 @@ def atomically_save_image():
         except Exception:
             shared.log.warning(f'Save: unknown image format: {extension}')
             image_format = 'JPEG'
-        if shared.opts.image_watermark_enabled or (shared.opts.image_watermark_position != 'none' and shared.opts.image_watermark_image != ''):
-            image = set_watermark(image, shared.opts.image_watermark)
         exifinfo = (exifinfo or "") if shared.opts.image_metadata else ""
         # additional metadata saved in files
         if shared.opts.save_txt and len(exifinfo) > 0:
@@ -153,6 +151,11 @@ def save_image(image,
         info = image.info.get(pnginfo_section_name, '')
     if info is not None:
         pnginfo[pnginfo_section_name] = info
+
+    wm_text = getattr(p, 'watermark_text', shared.opts.image_watermark)
+    wm_image = getattr(p, 'watermark_image', shared.opts.image_watermark_image)
+    image = set_watermark(image, wm_text, wm_image)
+
     params = script_callbacks.ImageSaveParams(image, p, filename, pnginfo)
     params.filename = namegen.sanitize(filename)
     dirname = os.path.dirname(params.filename)
@@ -369,45 +372,46 @@ def draw_overlay(im, text: str = '', y_offset: int = 0):
     return im
 
 
-def set_watermark(image, watermark):
-    if shared.opts.image_watermark_position != 'none': # visible watermark
-        wm_image = None
-        try:
-            wm_image = Image.open(shared.opts.image_watermark_image)
-            if wm_image.mode != 'RGBA':
-                wm_image = wm_image.convert('RGBA')
-        except Exception as e:
-            shared.log.warning(f'Set image watermark: fn="{shared.opts.image_watermark_image}" {e}')
-        if wm_image is not None:
-            if shared.opts.image_watermark_position == 'top/left':
-                position = (0, 0)
-            elif shared.opts.image_watermark_position == 'top/right':
-                position = (image.width - wm_image.width, 0)
-            elif shared.opts.image_watermark_position == 'bottom/left':
-                position = (0, image.height - wm_image.height)
-            elif shared.opts.image_watermark_position == 'bottom/right':
-                position = (image.width - wm_image.width, image.height - wm_image.height)
-            elif shared.opts.image_watermark_position == 'center':
-                position = ((image.width - wm_image.width) // 2, (image.height - wm_image.height) // 2)
-            else:
-                position = (random.randint(0, image.width - wm_image.width), random.randint(0, image.height - wm_image.height))
+def set_watermark(image, wm_text: str = None, wm_image: Image.Image = None):
+    if shared.opts.image_watermark_position != 'none' and wm_image is not None: # visible watermark
+        if isinstance(wm_image, str):
             try:
-                for x in range(wm_image.width):
-                    for y in range(wm_image.height):
-                        rgba = wm_image.getpixel((x, y))
-                        orig = image.getpixel((x+position[0], y+position[1]))
-                        # alpha blend
-                        a = rgba[3] / 255
-                        r = int(rgba[0] * a + orig[0] * (1 - a))
-                        g = int(rgba[1] * a + orig[1] * (1 - a))
-                        b = int(rgba[2] * a + orig[2] * (1 - a))
-                        if not a == 0:
-                            image.putpixel((x+position[0], y+position[1]), (r, g, b))
-                shared.log.debug(f'Set image watermark: fn="{shared.opts.image_watermark_image}" image={wm_image} position={position}')
+                wm_image = Image.open(wm_image)
             except Exception as e:
                 shared.log.warning(f'Set image watermark: image={wm_image} {e}')
+                return image
+        if isinstance(wm_image, Image.Image):
+            if wm_image.mode != 'RGBA':
+                wm_image = wm_image.convert('RGBA')
+        if shared.opts.image_watermark_position == 'top/left':
+            position = (0, 0)
+        elif shared.opts.image_watermark_position == 'top/right':
+            position = (image.width - wm_image.width, 0)
+        elif shared.opts.image_watermark_position == 'bottom/left':
+            position = (0, image.height - wm_image.height)
+        elif shared.opts.image_watermark_position == 'bottom/right':
+            position = (image.width - wm_image.width, image.height - wm_image.height)
+        elif shared.opts.image_watermark_position == 'center':
+            position = ((image.width - wm_image.width) // 2, (image.height - wm_image.height) // 2)
+        else:
+            position = (random.randint(0, image.width - wm_image.width), random.randint(0, image.height - wm_image.height))
+        try:
+            for x in range(wm_image.width):
+                for y in range(wm_image.height):
+                    rgba = wm_image.getpixel((x, y))
+                    orig = image.getpixel((x+position[0], y+position[1]))
+                    # alpha blend
+                    a = rgba[3] / 255
+                    r = int(rgba[0] * a + orig[0] * (1 - a))
+                    g = int(rgba[1] * a + orig[1] * (1 - a))
+                    b = int(rgba[2] * a + orig[2] * (1 - a))
+                    if not a == 0:
+                        image.putpixel((x+position[0], y+position[1]), (r, g, b))
+            shared.log.debug(f'Set image watermark: image={wm_image} position={position}')
+        except Exception as e:
+            shared.log.warning(f'Set image watermark: image={wm_image} {e}')
 
-    if shared.opts.image_watermark_enabled: # invisible watermark
+    if shared.opts.image_watermark_enabled and wm_text is not None: # invisible watermark
         from imwatermark import WatermarkEncoder
         wm_type = 'bytes'
         wm_method = 'dwtDctSvd'
@@ -416,16 +420,16 @@ def set_watermark(image, watermark):
         info = image.info
         data = np.asarray(image)
         encoder = WatermarkEncoder()
-        text = f"{watermark:<{length}}"[:length]
+        text = f"{wm_text:<{length}}"[:length]
         bytearr = text.encode(encoding='ascii', errors='ignore')
         try:
             encoder.set_watermark(wm_type, bytearr)
             encoded = encoder.encode(data, wm_method)
             image = Image.fromarray(encoded)
             image.info = info
-            shared.log.debug(f'Set invisible watermark: {watermark} method={wm_method} bits={wm_length}')
+            shared.log.debug(f'Set invisible watermark: {wm_text} method={wm_method} bits={wm_length}')
         except Exception as e:
-            shared.log.warning(f'Set invisible watermark error: {watermark} method={wm_method} bits={wm_length} {e}')
+            shared.log.warning(f'Set invisible watermark error: {wm_text} method={wm_method} bits={wm_length} {e}')
 
     return image
 

From 5de457bb18dbf2d4564e50840b44c2ca576066a3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 11:32:47 -0500
Subject: [PATCH 078/119] sort and describe all scripts

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                      |  3 +++
 javascript/extraNetworks.js       |  4 +++-
 modules/face/__init__.py          |  4 ++--
 modules/scripts.py                |  4 +++-
 modules/ui_common.py              |  2 +-
 scripts/animatediff.py            |  2 +-
 scripts/apg.py                    |  4 ++--
 scripts/blipdiffusion.py          | 11 ++++-------
 scripts/cogvideo.py               |  2 +-
 scripts/consistory_ext.py         |  2 +-
 scripts/ctrlx.py                  |  4 ++--
 scripts/demofusion.py             |  4 ++--
 scripts/differential_diffusion.py |  4 ++--
 scripts/hdr.py                    |  4 ++--
 scripts/image2video.py            |  3 ++-
 scripts/instantir.py              |  2 +-
 scripts/k_diff.py                 |  4 ++--
 scripts/layerdiffuse.py           |  4 ++--
 scripts/ledits.py                 |  4 ++--
 scripts/lut.py                    |  2 +-
 scripts/mixture_tiling.py         |  4 ++--
 scripts/mulan.py                  |  4 ++--
 scripts/pulid_ext.py              |  2 +-
 scripts/resadapter.py             |  4 ++--
 scripts/sd_upscale.py             |  2 +-
 scripts/stablevideodiffusion.py   |  2 +-
 scripts/t_gate.py                 |  4 ++--
 scripts/text2video.py             |  2 +-
 28 files changed, 51 insertions(+), 46 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d78943f42..b989132f2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -58,6 +58,7 @@ This release can be considered an LTS release before we kick off the next round
     - create video from generated grid images  
       supports all standard video types and interpolation  
   - UI:  
+    - better gallery and networks sidebar sizing
     - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
     - add show networks on startup setting  
     - better mapping of networks previews  
@@ -68,6 +69,8 @@ This release can be considered an LTS release before we kick off the next round
     - Auto-remove invalid packages from `venv/site-packages`  
       e.g. packages starting with `~` which are left-over due to windows access violation  
     - Requirements: update  
+  - Scripts:
+    - More verbose descriptions for all scripts
   - Model loader:
     - Report modules included in safetensors when attempting to load a model  
   - CLI:  
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 9a33baa86..77fe125f3 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -481,11 +481,13 @@ function setupExtraNetworksForTab(tabname) {
           en.style.position = 'absolute';
           en.style.height = 'auto';
           en.style.width = `${window.opts.extra_networks_sidebar_width}vw`;
+          en.style.maxWidth = '655px';
           en.style.right = '0';
           en.style.top = '13em';
           en.style.transition = 'width 0.3s ease';
           en.style.zIndex = 100;
-          gradioApp().getElementById(`${tabname}_settings`).parentNode.style.width = `${100 - 2 - window.opts.extra_networks_sidebar_width}vw`;
+          // gradioApp().getElementById(`${tabname}_settings`).parentNode.style.width = `${100 - 2 - window.opts.extra_networks_sidebar_width}vw`;
+          gradioApp().getElementById(`${tabname}_settings`).parentNode.style.width = `calc(100vw - 2em - min(${window.opts.extra_networks_sidebar_width}vw, 655px))`;
         } else {
           en.style.position = 'relative';
           en.style.height = 'unset';
diff --git a/modules/face/__init__.py b/modules/face/__init__.py
index aae3cdaa2..19af6d01b 100644
--- a/modules/face/__init__.py
+++ b/modules/face/__init__.py
@@ -9,7 +9,7 @@ debug = shared.log.trace if os.environ.get('SD_FACE_DEBUG', None) is not None el
 
 class Script(scripts.Script):
     def title(self):
-        return 'Face'
+        return 'Face: Multiple ID Transfers'
 
     def show(self, is_img2img):
         return True if shared.native else False
@@ -45,7 +45,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML("<span>&nbsp Face module</span><br>")
+            gr.HTML("<span>&nbsp Face: Multiple ID Transfers</span><br>")
         with gr.Row():
             mode = gr.Dropdown(label='Mode', choices=['None', 'FaceID', 'FaceSwap', 'InstantID', 'PhotoMaker'], value='None')
         with gr.Group(visible=False) as cfg_faceid:
diff --git a/modules/scripts.py b/modules/scripts.py
index 8a67d0a50..cf2cf25b9 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -352,7 +352,9 @@ class ScriptRunner:
         self.selectable_scripts.clear()
         auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
 
-        for script_class, path, _basedir, _script_module in auto_processing_scripts + scripts_data:
+        all_scripts = auto_processing_scripts + scripts_data
+        sorted_scripts = sorted(all_scripts, key=lambda x: x.script_class().title().lower())
+        for script_class, path, _basedir, _script_module in sorted_scripts:
             try:
                 script = script_class()
                 script.filename = path
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 9ad87c17f..9c4bb5cdc 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -246,7 +246,7 @@ def create_output_panel(tabname, preview=True, prompt=None, height=None):
             # columns are for <576px, <768px, <992px, <1200px, <1400px, >1400px
             result_gallery = gr.Gallery(value=[],
                                         label='Output', show_label=False, show_download_button=True, allow_preview=True, container=False, preview=preview,
-                                        columns=5, object_fit='scale-down', height=height,
+                                        columns=4, object_fit='scale-down', height=height,
                                         elem_id=f"{tabname}_gallery",
                                         )
             if prompt is not None:
diff --git a/scripts/animatediff.py b/scripts/animatediff.py
index fca09424b..4c50f9cf6 100644
--- a/scripts/animatediff.py
+++ b/scripts/animatediff.py
@@ -189,7 +189,7 @@ def set_free_noise(frames):
 
 class Script(scripts.Script):
     def title(self):
-        return 'AnimateDiff'
+        return 'Video AnimateDiff'
 
     def show(self, is_img2img):
         # return scripts.AlwaysVisible if shared.native else False
diff --git a/scripts/apg.py b/scripts/apg.py
index 6a3020c38..0476da246 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -9,14 +9,14 @@ class Script(scripts.Script):
         self.register()
 
     def title(self):
-        return 'APG'
+        return 'APG: Adaptive Projected Guidance'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
 
     def ui(self, _is_img2img): # ui elements
         with gr.Row():
-            gr.HTML('<a href="https://arxiv.org/abs/2410.02416">&nbsp APG: Adaptive projected guidance</a><br>')
+            gr.HTML('<a href="https://arxiv.org/abs/2410.02416">&nbsp APG: Adaptive Projected Guidance</a><br>')
         with gr.Row():
             eta = gr.Slider(label="ETA", value=1.0, minimum=0, maximum=2.0, step=0.05)
             momentum = gr.Slider(label="Momentum", value=-0.50, minimum=-1.0, maximum=1.0, step=0.05)
diff --git a/scripts/blipdiffusion.py b/scripts/blipdiffusion.py
index 39d8974e1..0acc80929 100644
--- a/scripts/blipdiffusion.py
+++ b/scripts/blipdiffusion.py
@@ -2,19 +2,16 @@ import gradio as gr
 from modules import scripts, processing, shared, sd_models
 
 
-title = 'BLIP Diffusion'
-
-
 class Script(scripts.Script):
     def title(self):
-        return title
+        return 'BLIP Diffusion: Controllable Generation and Editing'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://huggingface.co/salesforce/blipdiffusion">&nbsp BLIP Diffusion</a><br>')
+            gr.HTML('<a href="https://huggingface.co/salesforce/blipdiffusion">&nbsp BLIP Diffusion: Controllable Generation and Editing</a><br>')
         with gr.Row():
             source_subject = gr.Textbox(value='', label='Source subject')
         with gr.Row():
@@ -26,7 +23,7 @@ class Script(scripts.Script):
     def run(self, p: processing.StableDiffusionProcessing, source_subject, target_subject, prompt_strength): # pylint: disable=arguments-differ, unused-argument
         c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
         if c != 'BlipDiffusionPipeline':
-            shared.log.error(f'{title}: model selected={c} required=BLIPDiffusion')
+            shared.log.error(f'BLIP: model selected={c} required=BLIPDiffusion')
             return None
         if hasattr(p, 'init_images') and len(p.init_images) > 0:
             p.task_args['reference_image'] = p.init_images[0]
@@ -41,5 +38,5 @@ class Script(scripts.Script):
             processed = processing.process_images(p)
             return processed
         else:
-            shared.log.error(f'{title}: no init_images')
+            shared.log.error('BLIP: no init_images')
             return None
diff --git a/scripts/cogvideo.py b/scripts/cogvideo.py
index a4a3141d4..7f2c7225e 100644
--- a/scripts/cogvideo.py
+++ b/scripts/cogvideo.py
@@ -22,7 +22,7 @@ debug = (os.environ.get('SD_LOAD_DEBUG', None) is not None) or (os.environ.get('
 
 class Script(scripts.Script):
     def title(self):
-        return 'CogVideoX'
+        return 'Video CogVideoX'
 
     def show(self, is_img2img):
         return shared.native
diff --git a/scripts/consistory_ext.py b/scripts/consistory_ext.py
index b7454aa75..45de1ea6c 100644
--- a/scripts/consistory_ext.py
+++ b/scripts/consistory_ext.py
@@ -22,7 +22,7 @@ class Script(scripts.Script):
         self.anchor_cache_second_stage = None
 
     def title(self):
-        return 'ConsiStory'
+        return 'ConsiStory: Consistent Image Generation'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native and shared.cmd_opts.experimental else False
diff --git a/scripts/ctrlx.py b/scripts/ctrlx.py
index 69d5994df..f372e2a94 100644
--- a/scripts/ctrlx.py
+++ b/scripts/ctrlx.py
@@ -7,14 +7,14 @@ from modules import shared, scripts, processing, processing_helpers, sd_models,
 
 class Script(scripts.Script):
     def title(self):
-        return 'Ctrl-X'
+        return 'Ctrl-X: Controlling Structure and Appearance'
 
     def show(self, is_img2img):
         return shared.native
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/genforce/ctrl-x">&nbsp Ctrl-X</a><br>')
+            gr.HTML('<a href="https://github.com/genforce/ctrl-x">&nbsp Ctrl-X: Controlling Structure and Appearance</a><br>')
         with gr.Accordion(label='Structure', open=True):
             with gr.Row():
                 struct_prompt = gr.Textbox(label='Prompt', value='', rows=1)
diff --git a/scripts/demofusion.py b/scripts/demofusion.py
index f7cdfe543..6625c0c79 100644
--- a/scripts/demofusion.py
+++ b/scripts/demofusion.py
@@ -1221,7 +1221,7 @@ class DemoFusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderM
 
 class Script(scripts.Script):
     def title(self):
-        return 'DemoFusion'
+        return 'DemoFusion: High-Resolution Image Generation'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
@@ -1229,7 +1229,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/PRIS-CV/DemoFusion">&nbsp DemoFusion</a><br>')
+            gr.HTML('<a href="https://github.com/PRIS-CV/DemoFusion">&nbsp DemoFusion: High-Resolution Image Generation</a><br>')
         with gr.Row():
             cosine_scale_1 = gr.Slider(minimum=0, maximum=5, step=0.1, value=3, label="Cosine scale 1")
             cosine_scale_2 = gr.Slider(minimum=0, maximum=5, step=0.1, value=1, label="Cosine scale 2")
diff --git a/scripts/differential_diffusion.py b/scripts/differential_diffusion.py
index 705242987..da4ae0e2e 100644
--- a/scripts/differential_diffusion.py
+++ b/scripts/differential_diffusion.py
@@ -1858,14 +1858,14 @@ MODELS = {
 
 class Script(scripts.Script):
     def title(self):
-        return 'Differential diffusion'
+        return 'Differential diffusion: Individual Pixel Strength'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/exx8/differential-diffusion">&nbsp Differential diffusion</a><br><span>Select a model for auto-preprocess or upload an image map</span><br>')
+            gr.HTML('<a href="https://github.com/exx8/differential-diffusion">&nbsp Differential diffusion: Individual Pixel Strength</a><br><span>Select a model for auto-preprocess or upload an image map</span><br>')
         with gr.Row():
             enabled = gr.Checkbox(label='Enabled', value=True)
             invert = gr.Checkbox(label='Mask invert', value=False)
diff --git a/scripts/hdr.py b/scripts/hdr.py
index 788c0add2..9afc3673b 100644
--- a/scripts/hdr.py
+++ b/scripts/hdr.py
@@ -11,14 +11,14 @@ from modules.shared import opts, state
 
 class Script(scripts.Script):
     def title(self):
-        return "HDR"
+        return "HDR: High Dynamic Range"
 
     def show(self, is_img2img):
         return True
 
     def ui(self, is_img2img):
         with gr.Row():
-            gr.HTML("<span>&nbsp High Dynamic Range</span><br>")
+            gr.HTML("<span>&nbsp HDR: High Dynamic Range</span><br>")
         with gr.Row():
             save_hdr = gr.Checkbox(label="Save HDR image", value=True)
             hdr_range = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.65, label='HDR range')
diff --git a/scripts/image2video.py b/scripts/image2video.py
index 332972a6d..876ed3193 100644
--- a/scripts/image2video.py
+++ b/scripts/image2video.py
@@ -13,7 +13,7 @@ MODELS = [
 
 class Script(scripts.Script):
     def title(self):
-        return 'Image-to-Video'
+        return 'Video VGen Image-to-Video'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
@@ -102,6 +102,7 @@ class Script(scripts.Script):
             processed = processing.process_images(p)
             shared.sd_model.motion_adapter = None
 
+        processed = None
         if model_name == 'VGen':
             if not isinstance(shared.sd_model, diffusers.I2VGenXLPipeline):
                 shared.log.info(f'Image2Video VGen load: model={repo_id}')
diff --git a/scripts/instantir.py b/scripts/instantir.py
index 4c7ce77b7..5eb7d503a 100644
--- a/scripts/instantir.py
+++ b/scripts/instantir.py
@@ -12,7 +12,7 @@ class Script(scripts.Script):
         self.orig_ip_unapply = None
 
     def title(self):
-        return 'InstantIR'
+        return 'InstantIR: Image Restoration'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
diff --git a/scripts/k_diff.py b/scripts/k_diff.py
index 354df5d4b..92b43149d 100644
--- a/scripts/k_diff.py
+++ b/scripts/k_diff.py
@@ -9,14 +9,14 @@ class Script(scripts.Script):
     orig_pipe = None
 
     def title(self):
-        return 'K-Diffusion'
+        return 'K-Diffusion Samplers'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
 
     def ui(self, _is_img2img): # ui elements
         with gr.Row():
-            gr.HTML('<a href="https://github.com/crowsonkb/k-diffusion">&nbsp K-Diffusion samplers</a><br>')
+            gr.HTML('<a href="https://github.com/crowsonkb/k-diffusion">&nbsp K-Diffusion Samplers</a><br>')
         with gr.Row():
             sampler = gr.Dropdown(label="Sampler", choices=self.samplers())
         return [sampler]
diff --git a/scripts/layerdiffuse.py b/scripts/layerdiffuse.py
index a1e15aa8b..ecf7da1d3 100644
--- a/scripts/layerdiffuse.py
+++ b/scripts/layerdiffuse.py
@@ -5,7 +5,7 @@ from modules import shared, scripts, sd_models
 class Script(scripts.Script):
 
     def title(self):
-        return 'LayerDiffuse'
+        return 'LayerDiffuse: Transparent Image'
 
     def show(self, is_img2img):
         return True if shared.native else False
@@ -40,7 +40,7 @@ class Script(scripts.Script):
     def ui(self, _is_img2img):
         with gr.Row():
             gr.HTML("""
-                    <a href="https://github.com/rootonchair/diffuser_layerdiffuse">&nbsp LayerDiffuse</a><br><br>
+                    <a href="https://github.com/rootonchair/diffuser_layerdiffuse">&nbsp LayerDiffuse: Transparent Image</a><br><br>
                     <div>- Click Apply to model to apply LayerDiffuse to current model</div>
                     <div>- Click Reload model to remove LayerDiffuse from current model</div><br>
                     """)
diff --git a/scripts/ledits.py b/scripts/ledits.py
index ba9d49f89..b75c6ff6f 100644
--- a/scripts/ledits.py
+++ b/scripts/ledits.py
@@ -5,7 +5,7 @@ from modules import scripts, processing, shared, devices, sd_models
 
 class Script(scripts.Script):
     def title(self):
-        return 'LEdits++'
+        return 'LEdits: Limitless Image Editing'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
@@ -13,7 +13,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://leditsplusplus-project.static.hf.space/index.html">&nbsp LEdits++</a><br>')
+            gr.HTML('<a href="https://leditsplusplus-project.static.hf.space/index.html">&nbsp LEdits++: Limitless Image Editing</a><br>')
         with gr.Row():
             edit_start = gr.Slider(label='Edit start', minimum=0.0, maximum=1.0, step=0.01, value=0.1)
             edit_stop = gr.Slider(label='Edit stop', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
diff --git a/scripts/lut.py b/scripts/lut.py
index 3d240f291..573222161 100644
--- a/scripts/lut.py
+++ b/scripts/lut.py
@@ -17,7 +17,7 @@ class Script(scripts.Script):
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML("<span>&nbsp Color grading</span><br>")
+            gr.HTML("<span>&nbsp LUT Color grading</span><br>")
         with gr.Row():
             original = gr.Checkbox(label='Include original image', value=True)
         with gr.Row():
diff --git a/scripts/mixture_tiling.py b/scripts/mixture_tiling.py
index 13e48ce11..5b5aab9db 100644
--- a/scripts/mixture_tiling.py
+++ b/scripts/mixture_tiling.py
@@ -26,14 +26,14 @@ def check_dependencies():
 
 class Script(scripts.Script):
     def title(self):
-        return 'Mixture tiling'
+        return 'Mixture Tiling: Scene Composition'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://arxiv.org/abs/2302.02412">&nbsp Mixture tiling</a><br>')
+            gr.HTML('<a href="https://arxiv.org/abs/2302.02412">&nbsp Mixture Tiling: Scene Composition</a><br>')
         with gr.Row():
             gr.HTML('<span>&nbsp Separated prompts using new lines<br>&nbsp Number of prompts must matcxh X*Y</span>')
         with gr.Row():
diff --git a/scripts/mulan.py b/scripts/mulan.py
index c2ad10d2e..829ce1463 100644
--- a/scripts/mulan.py
+++ b/scripts/mulan.py
@@ -46,14 +46,14 @@ text_encoder_path = None
 
 class Script(scripts.Script):
     def title(self):
-        return 'MuLan'
+        return 'MuLan: Multi Language Prompts'
 
     def show(self, is_img2img):
         return True if shared.native else False
 
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/mulanai/MuLan">&nbsp MuLan</a><br>')
+            gr.HTML('<a href="https://github.com/mulanai/MuLan">&nbsp MuLan: Multi Language Prompts</a><br>')
         with gr.Row():
             selected_encoder = gr.Dropdown(label='Encoder', choices=ENCODERS, value=ENCODERS[0])
         return [selected_encoder]
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 5d209c211..b7fad31bc 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -20,7 +20,7 @@ class Script(scripts.Script):
         self.register() # pulid is script with processing override so xyz doesnt execute
 
     def title(self):
-        return 'PuLID'
+        return 'PuLID: ID Customization'
 
     def show(self, _is_img2img):
         return shared.native
diff --git a/scripts/resadapter.py b/scripts/resadapter.py
index cbd0bf671..58162f9ab 100644
--- a/scripts/resadapter.py
+++ b/scripts/resadapter.py
@@ -19,7 +19,7 @@ models = {
 
 class Script(scripts.Script):
     def title(self):
-        return 'ResAdapter'
+        return 'ResAdapter: Domain Consistent Resolution'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
@@ -27,7 +27,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/bytedance/res-adapter">&nbsp ResAdapter</a><br>')
+            gr.HTML('<a href="https://github.com/bytedance/res-adapter">&nbsp ResAdapter: Domain Consistent Resolution</a><br>')
         with gr.Row():
             model = gr.Dropdown(label="Model", choices=list(models), value="None")
             weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Weight", value=1.0)
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 9f21c5645..9c5a72204 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -9,7 +9,7 @@ from modules.shared import opts, state, log
 
 class Script(scripts.Script):
     def title(self):
-        return "SD upscale"
+        return "SD Upscale"
 
     def show(self, is_img2img):
         return is_img2img
diff --git a/scripts/stablevideodiffusion.py b/scripts/stablevideodiffusion.py
index 585871edc..cbf2ce003 100644
--- a/scripts/stablevideodiffusion.py
+++ b/scripts/stablevideodiffusion.py
@@ -16,7 +16,7 @@ models = {
 
 class Script(scripts.Script):
     def title(self):
-        return 'Stable Video Diffusion'
+        return 'Video: SVD'
 
     def show(self, is_img2img):
         return is_img2img if shared.native else False
diff --git a/scripts/t_gate.py b/scripts/t_gate.py
index 3bd51445d..3808a796d 100644
--- a/scripts/t_gate.py
+++ b/scripts/t_gate.py
@@ -5,7 +5,7 @@ from installer import install
 
 class Script(scripts.Script):
     def title(self):
-        return 'T-Gate'
+        return 'T-Gate: Accelerate via Gating Attention'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False
@@ -13,7 +13,7 @@ class Script(scripts.Script):
     # return signature is array of gradio components
     def ui(self, _is_img2img):
         with gr.Row():
-            gr.HTML('<a href="https://github.com/HaozheLiu-ST/T-GATE">&nbsp T-Gate</a><br>')
+            gr.HTML('<a href="https://github.com/HaozheLiu-ST/T-GATE">&nbsp T-Gate: Accelerate via Gating Attention</a><br>')
         with gr.Row():
             enabled = gr.Checkbox(label="Enabled", value=True)
         with gr.Row():
diff --git a/scripts/text2video.py b/scripts/text2video.py
index 8dec9bd0e..dc4c44cac 100644
--- a/scripts/text2video.py
+++ b/scripts/text2video.py
@@ -23,7 +23,7 @@ MODELS = [
 
 class Script(scripts.Script):
     def title(self):
-        return 'Text-to-Video'
+        return 'Video: ModelScope'
 
     def show(self, is_img2img):
         return not is_img2img if shared.native else False

From 34bc7377d86def0a635265c662a4f0d8f622bd2f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 14:00:50 -0500
Subject: [PATCH 079/119] update pulid

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_class.py     |  2 +-
 modules/pulid/pulid_sampling.py | 12 ++++++------
 modules/sd_models.py            |  9 +++++++--
 scripts/apg.py                  |  7 +++++++
 scripts/pulid_ext.py            | 29 +++++++++++++++++++----------
 scripts/xyz_grid_classes.py     |  1 +
 6 files changed, 41 insertions(+), 19 deletions(-)

diff --git a/modules/processing_class.py b/modules/processing_class.py
index d38aae790..2f11db375 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -486,7 +486,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = images.resize_image(self.resize_mode, image, self.width, self.height, upscaler_name=self.resize_name, context=self.resize_context)
                 self.width = image.width
                 self.height = image.height
-            if self.image_mask is not None and shared.opts.mask_apply_overlay:
+            if self.image_mask is not None and shared.opts.mask_apply_overlay and not hasattr(self, 'xyz'):
                 image_masked = Image.new('RGBa', (image.width, image.height))
                 image_to_paste = image.convert("RGBA").convert("RGBa")
                 image_to_mask = ImageOps.invert(self.mask_for_overlay.convert('L')) if self.mask_for_overlay is not None else None
diff --git a/modules/pulid/pulid_sampling.py b/modules/pulid/pulid_sampling.py
index e319c0d27..6a2ef31f3 100644
--- a/modules/pulid/pulid_sampling.py
+++ b/modules/pulid/pulid_sampling.py
@@ -393,8 +393,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=C3001
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=C3001
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -430,8 +430,8 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=C3001
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=C3001
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -472,8 +472,8 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
     """DPM-Solver++(2M)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+    sigma_fn = lambda t: t.neg().exp() # pylint: disable=C3001
+    t_fn = lambda sigma: sigma.log().neg() # pylint: disable=C3001
     old_denoised = None
 
     for i in trange(len(sigmas) - 1, disable=disable):
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1f932bd30..8320ac293 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -449,6 +449,9 @@ def move_model(model, device=None, force=False):
         devices.torch_gc()
         return
 
+    if hasattr(model, 'pipe'):
+        move_model(model.pipe, device, force)
+
     fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
     if getattr(model, 'vae', None) is not None and get_diffusers_task(model) != DiffusersTaskType.TEXT_2_IMAGE:
         if device == devices.device and model.vae.device.type != "meta": # force vae back to gpu if not in txt2img mode
@@ -476,7 +479,8 @@ def move_model(model, device=None, force=False):
     try:
         t0 = time.time()
         try:
-            model.to(device)
+            if hasattr(model, 'to'):
+                model.to(device)
             if hasattr(model, "prior_pipe"):
                 model.prior_pipe.to(device)
         except Exception as e0:
@@ -486,7 +490,8 @@ def move_model(model, device=None, force=False):
                         if hasattr(component, 'modules'):
                             for module in component.modules():
                                 try:
-                                    module.to(device)
+                                    if hasattr(module, 'to'):
+                                        module.to(device)
                                 except Exception as e2:
                                     if 'Cannot copy out of meta tensor' in str(e2):
                                         if os.environ.get('SD_MOVE_DEBUG', None):
diff --git a/scripts/apg.py b/scripts/apg.py
index 0476da246..6d0ec107e 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -2,6 +2,9 @@ import gradio as gr
 from modules import scripts, processing, shared, sd_models
 
 
+registered = False
+
+
 class Script(scripts.Script):
     def __init__(self):
         super().__init__()
@@ -24,6 +27,10 @@ class Script(scripts.Script):
         return [eta, momentum, threshold]
 
     def register(self): # register xyz grid elements
+        global registered # pylint: disable=global-statement
+        if registered:
+            return
+        registered = True
         def apply_field(field):
             def fun(p, x, xs): # pylint: disable=unused-argument
                 setattr(p, field, x)
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index b7fad31bc..7ec32e904 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -9,13 +9,15 @@ from modules import shared, devices, errors, scripts, processing, processing_hel
 
 debug = os.environ.get('SD_PULID_DEBUG', None) is not None
 direct = False
+registered = False
+uploaded_images = []
 
 
 class Script(scripts.Script):
     def __init__(self):
-        self.images = []
         self.pulid = None
         self.cache = None
+        self.mask_apply_overlay = shared.opts.mask_apply_overlay
         super().__init__()
         self.register() # pulid is script with processing override so xyz doesnt execute
 
@@ -33,6 +35,10 @@ class Script(scripts.Script):
             install('pydantic==1.10.15', 'pydantic', ignore=False, reinstall=True)
 
     def register(self): # register xyz grid elements
+        global registered # pylint: disable=global-statement
+        if registered:
+            return
+        registered = True
         def apply_field(field):
             def fun(p, x, xs): # pylint: disable=unused-argument
                 setattr(p, field, x)
@@ -52,7 +58,7 @@ class Script(scripts.Script):
 
 
     def load_images(self, files):
-        self.images = []
+        uploaded_images.clear()
         for file in files or []:
             try:
                 if isinstance(file, str):
@@ -66,10 +72,10 @@ class Script(scripts.Script):
                     image = Image.open(file.name) # _TemporaryFileWrapper from gr.Files
                 else:
                     raise ValueError(f'IP adapter unknown input: {file}')
-                self.images.append(image)
+                uploaded_images.append(image)
             except Exception as e:
                 shared.log.warning(f'IP adapter failed to load image: {e}')
-        return gr.update(value=self.images, visible=len(self.images) > 0)
+        return gr.update(value=uploaded_images, visible=len(uploaded_images) > 0)
 
     # return signature is array of gradio components
     def ui(self, _is_img2img):
@@ -95,7 +101,7 @@ class Script(scripts.Script):
         try:
             if len(gallery) == 0:
                 from modules.api.api import decode_base64_to_image
-                images = getattr(p, 'pulid_images', self.images)
+                images = getattr(p, 'pulid_images', uploaded_images)
                 images = [decode_base64_to_image(image) if isinstance(image, str) else image for image in images]
             else:
                 images = [Image.open(f['name']) if isinstance(f, dict) else f for f in gallery]
@@ -134,6 +140,8 @@ class Script(scripts.Script):
         ortho = getattr(p, 'pulid_ortho', ortho)
         sampler = getattr(p, 'pulid_sampler', sampler)
         sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
+        self.mask_apply_overlay = shared.opts.mask_apply_overlay
+        shared.opts.data['mask_apply_overlay'] = False
         if sampler_fn is None:
             sampler_fn = self.pulid.sampling.sample_dpmpp_2m_sde
 
@@ -149,7 +157,7 @@ class Script(scripts.Script):
                     )
                 shared.sd_model.no_recurse = True
                 sd_models.copy_diffuser_options(shared.sd_model, shared.sd_model.pipe)
-                # sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
+                sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
                 sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
                 devices.torch_gc()
             except Exception as e:
@@ -204,11 +212,12 @@ class Script(scripts.Script):
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
         _strength, _zero, _sampler, _ortho, _gallery, cache = args
-        cache = getattr(p, 'pulid_cache', cache)
-        if cache:
-            shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
-            return processed
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
+            shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
+            cache = getattr(p, 'pulid_cache', cache)
+            if cache:
+                shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
+                return processed
             if hasattr(shared.sd_model, 'app'):
                 shared.sd_model.app = None
                 shared.sd_model.ip_adapter = None
diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py
index 08ea279f4..c3d3554e2 100644
--- a/scripts/xyz_grid_classes.py
+++ b/scripts/xyz_grid_classes.py
@@ -136,6 +136,7 @@ axis_options = [
     AxisOption("[Postprocess] Upscaler", str, apply_upscaler, cost=0.4, choices=lambda: [x.name for x in shared.sd_upscalers][1:]),
     AxisOption("[Postprocess] Context", str, apply_context, choices=lambda: ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]),
     AxisOption("[Postprocess] Detailer", str, apply_detailer, fmt=format_value_add_label),
+    AxisOption("[Postprocess] Detailer strength", str, apply_field("detailer_strength")),
     AxisOption("[HDR] Mode", int, apply_field("hdr_mode")),
     AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")),
     AxisOption("[HDR] Color", float, apply_field("hdr_color")),

From 9afc6b186f4e71813be01f6df16419c40f6e68a6 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 14:06:28 -0500
Subject: [PATCH 080/119] 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 b989132f2..417165276 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,7 +18,7 @@ This release can be considered an LTS release before we kick off the next round
     - advanced method of face transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
-    - compatible with *sdxl* for text-to-image and image-to-image  
+    - compatible with *sdxl* for text-to-image, image-to-image, inpaint and detailer workflows  
     - can be used in xyz grid  
     - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference

From 94e188eab3762bfc64a371e9f6c57a6c27ffb158 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 8 Nov 2024 18:07:59 -0500
Subject: [PATCH 081/119] pulid fix seed

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

diff --git a/modules/processing_args.py b/modules/processing_args.py
index 1cb9457c5..5b320f9ea 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -182,6 +182,8 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
     if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
         model.scheduler.noise_sampler = None # noise needs to be reset instead of using cached values
         model.scheduler.noise_sampler_seed = p.seeds # some schedulers have internal noise generator and do not use pipeline generator
+    if 'seed' in possible:
+        args['seed'] = p.seed
     if 'noise_sampler_seed' in possible:
         args['noise_sampler_seed'] = p.seeds
     if 'guidance_scale' in possible:
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 7ec32e904..d0d738b6a 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -174,10 +174,10 @@ class Script(scripts.Script):
         shared.sd_model.debug_img_list = []
         uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
 
+        p.seed = processing_helpers.get_fixed_seed(p.seed)
         if direct: # run pipeline directly
             shared.state.begin('PuLID')
             processing.fix_seed(p)
-            p.seed = processing_helpers.get_fixed_seed(p.seed)
             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)
             with devices.inference_context():

From c199e3e66883f3db1221a54490ea910157c1d4c0 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 9 Nov 2024 20:03:41 -0500
Subject: [PATCH 082/119] pulid optimizations: dtype, vae, offload

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_diffusers.py      | 14 +++--
 modules/processing_vae.py            | 11 +++-
 modules/pulid/attention_processor.py | 15 ++---
 modules/pulid/eva_clip/pretrained.py |  3 +-
 modules/pulid/pulid_sdxl.py          | 93 +++++++++++++---------------
 modules/sd_models.py                 |  8 ++-
 scripts/pulid_ext.py                 | 51 +++++++++------
 7 files changed, 103 insertions(+), 92 deletions(-)

diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 7ec0dd08a..7537d0209 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -71,7 +71,7 @@ def process_base(p: processing.StableDiffusionProcessing):
         guidance_rescale=p.diffusers_guidance_rescale,
         denoising_start=0 if use_refiner_start else p.refiner_start if use_denoise_start else None,
         denoising_end=p.refiner_start if use_refiner_start else 1 if use_denoise_start else None,
-        output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
+        output_type='latent',
         clip_skip=p.clip_skip,
         desc='Base',
     )
@@ -217,7 +217,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 eta=shared.opts.scheduler_eta,
                 guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
                 guidance_rescale=p.diffusers_guidance_rescale,
-                output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
+                output_type='latent',
                 clip_skip=p.clip_skip,
                 image=output.images,
                 strength=p.denoising_strength,
@@ -278,7 +278,7 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
         for i in range(len(output.images)):
             image = output.images[i]
             noise_level = round(350 * p.denoising_strength)
-            output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np'
+            output_type='latent'
             if 'Upscale' in shared.sd_refiner.__class__.__name__ or 'Flux' in shared.sd_refiner.__class__.__name__:
                 image = processing_vae.vae_decode(latents=image, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
                 p.extra_generation_params['Noise level'] = noise_level
@@ -346,7 +346,11 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
         if not hasattr(output, 'images') and hasattr(output, 'frames'):
             shared.log.debug(f'Generated: frames={len(output.frames[0])}')
             output.images = output.frames[0]
-        if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
+        model = shared.sd_model if not is_refiner_enabled(p) else shared.sd_refiner
+        if not hasattr(model, 'vae'):
+            if hasattr(model, 'pipe') and hasattr(model.pipe, 'vae'):
+                model = model.pipe
+        if hasattr(model, "vae") and output.images is not None and len(output.images) > 0:
             if p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
                 width = max(getattr(p, 'width', 0), getattr(p, 'hr_upscale_to_x', 0))
                 height = max(getattr(p, 'height', 0), getattr(p, 'hr_upscale_to_y', 0))
@@ -355,7 +359,7 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
                 height = getattr(p, 'height', 0)
             results = processing_vae.vae_decode(
                 latents = output.images,
-                model = shared.sd_model if not is_refiner_enabled(p) else shared.sd_refiner,
+                model = model,
                 full_quality = p.full_quality,
                 width = width,
                 height = height,
diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index 75347f416..5e6fa68f4 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -35,7 +35,7 @@ def create_latents(image, p, dtype=None, device=None):
 
 def full_vae_decode(latents, model):
     t0 = time.time()
-    if not hasattr(model, 'vae'):
+    if model is None or not hasattr(model, 'vae'):
         shared.log.error('VAE not found in model')
         return []
     if debug:
@@ -170,7 +170,14 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
     if latents.shape[-1] <= 4: # not a latent, likely an image
         decoded = latents.float().cpu().numpy()
     elif full_quality and hasattr(shared.sd_model, "vae"):
-        decoded = full_vae_decode(latents=latents, model=shared.sd_model)
+        parent = shared.sd_model if hasattr(shared.sd_model, 'vae') else None
+        if hasattr(shared.sd_model, 'vae'):
+            parent = shared.sd_model
+        elif hasattr(shared.sd_model, 'pipe') and hasattr(shared.sd_model.pipe, 'vae'):
+            parent = shared.sd_model.pipe
+        else:
+            parent = None
+        decoded = full_vae_decode(latents=latents, model=parent)
     else:
         decoded = taesd_vae_decode(latents=latents)
 
diff --git a/modules/pulid/attention_processor.py b/modules/pulid/attention_processor.py
index 9756decc1..fa9e4ff82 100644
--- a/modules/pulid/attention_processor.py
+++ b/modules/pulid/attention_processor.py
@@ -345,10 +345,7 @@ class IDAttnProcessor2_0(torch.nn.Module):
         value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
+        hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
         hidden_states = hidden_states.to(query.dtype)
 
@@ -363,17 +360,15 @@ class IDAttnProcessor2_0(torch.nn.Module):
                     dtype=id_embedding.dtype,
                     device=id_embedding.device,
                 )
-                id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
-                id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
+                id_cat = torch.cat((id_embedding, zero_tensor), dim=1)
+                id_key = self.id_to_k(id_cat).to(query.dtype)
+                id_value = self.id_to_v(id_cat).to(query.dtype)
 
             id_key = id_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
             id_value = id_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
             # the output of sdp = (batch, num_heads, seq_len, head_dim)
-            id_hidden_states = F.scaled_dot_product_attention(
-                query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False
-            )
-
+            id_hidden_states = F.scaled_dot_product_attention(query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False)
             id_hidden_states = id_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
             id_hidden_states = id_hidden_states.to(query.dtype)
 
diff --git a/modules/pulid/eva_clip/pretrained.py b/modules/pulid/eva_clip/pretrained.py
index a1e55dcf3..bb87c540c 100644
--- a/modules/pulid/eva_clip/pretrained.py
+++ b/modules/pulid/eva_clip/pretrained.py
@@ -2,7 +2,6 @@ import hashlib
 import os
 import urllib
 import warnings
-from functools import partial
 from typing import Dict, Union
 
 from tqdm import tqdm
@@ -277,7 +276,7 @@ def download_pretrained_from_url(
                 loop.update(len(buffer))
 
     if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
-        raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
 
     return download_target
 
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index fade7509d..8bd28dbe2 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -5,6 +5,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
 from huggingface_hub import hf_hub_download, snapshot_download
 from safetensors.torch import load_file
@@ -24,14 +25,17 @@ from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None):
         super().__init__()
         self.device = device
+        self.dtype = dtype or torch.float16
         self.pipe = pipe
         self.cache_dir = cache_dir
+        self.offload = offload
         self.hack_unet_attn_layers(self.pipe.unet)
         self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
-        self.id_adapter = IDFormer().to(self.device)
+        self.id_adapter = IDFormer().to(self.device, self.dtype)
+        self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
 
         # preprocessors
         # face align and parsing
@@ -43,13 +47,12 @@ class StableDiffusionXLPuLIDPipeline:
             save_ext='png',
             device=self.device,
         )
-        self.face_helper.face_parse = None
         self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
 
         # clip-vit backbone
-        model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True)
-        model = model.visual
-        self.clip_vision_model = model.to(self.device)
+        eva_precision = 'fp16' if self.dtype == torch.float16 or self.dtype == torch.bfloat16 else 'fp32'
+        eva_model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, precision=eva_precision, device=self.device)
+        self.clip_vision_model = eva_model.visual.to(dtype=self.dtype)
         eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
         eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
         if not isinstance(eva_transform_mean, (list, tuple)):
@@ -60,13 +63,12 @@ class StableDiffusionXLPuLIDPipeline:
         self.eva_transform_std = eva_transform_std
 
         # antelopev2
-        # snapshot_download('DIAMONIK7777/antelopev2', local_dir='models/antelopev2')
         local_dir = os.path.join(self.cache_dir, 'pulid', 'models', 'antelopev2')
         _loc = snapshot_download('DIAMONIK7777/antelopev2', local_dir=local_dir)
         self.app = FaceAnalysis(
             name='antelopev2',
             root=os.path.join(self.cache_dir, 'pulid'),
-            providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
+            providers=self.providers,
         )
         self.app.prepare(ctx_id=0, det_size=(640, 640))
         self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
@@ -89,8 +91,12 @@ class StableDiffusionXLPuLIDPipeline:
         self.log_sigmas = self.sigmas.log()
         self.sigma_data = 1.0
 
+        # default scheduler
         if sampler is not None:
             self.sampler = sampler
+        else:
+            from modules.pulid import sampling
+            self.sampler = sampling.sample_dpmpp_sde
 
     @property
     def sigma_min(self):
@@ -130,7 +136,7 @@ class StableDiffusionXLPuLIDPipeline:
                 id_adapter_attn_procs[name] = IDAttnProcessor(
                     hidden_size=hidden_size,
                     cross_attention_dim=cross_attention_dim,
-                ).to(unet.device)
+                ).to(unet.device, unet.dtype)
             else:
                 id_adapter_attn_procs[name] = AttnProcessor()
         unet.set_attn_processor(id_adapter_attn_procs)
@@ -144,7 +150,7 @@ class StableDiffusionXLPuLIDPipeline:
             module = k.split('.')[0]
             state_dict_dict.setdefault(module, {})
             new_k = k[len(module) + 1 :]
-            state_dict_dict[module][new_k] = v
+            state_dict_dict[module][new_k] = v.to(self.dtype)
 
         for module in state_dict_dict:
             getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)
@@ -161,24 +167,17 @@ class StableDiffusionXLPuLIDPipeline:
         """
         id_cond_list = []
         id_vit_hidden_list = []
+        self.face_helper.face_det.to(self.device)
+        self.clip_vision_model.to(self.device)
         for _ii, image in enumerate(image_list):
             self.face_helper.clean_all()
             image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
             # get antelopev2 embedding
             face_info = self.app.get(image_bgr)
             if len(face_info) > 0:
-                face_info = sorted(
-                    face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1])
-                )[
-                    -1
-                ]  # only use the maximum face
+                face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[-1] # only use the maximum face
                 id_ante_embedding = face_info['embedding']
-                self.debug_img_list.append(
-                    image[
-                        int(face_info['bbox'][1]) : int(face_info['bbox'][3]),
-                        int(face_info['bbox'][0]) : int(face_info['bbox'][2]),
-                    ]
-                )
+                self.debug_img_list.append(image[int(face_info['bbox'][1]) : int(face_info['bbox'][3]), int(face_info['bbox'][0]) : int(face_info['bbox'][2])])
             else:
                 id_ante_embedding = None
 
@@ -210,13 +209,9 @@ class StableDiffusionXLPuLIDPipeline:
             self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False))
 
             # transform img before sending to eva-clip-vit
-            face_features_image = resize(
-                face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC
-            )
-            face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std)
-            id_cond_vit, id_vit_hidden = self.clip_vision_model(
-                face_features_image, return_all_features=False, return_hidden=True, shuffle=False
-            )
+            face_features_image = resize(face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC)
+            face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std).to(self.dtype)
+            id_cond_vit, id_vit_hidden = self.clip_vision_model(face_features_image, return_all_features=False, return_hidden=True, shuffle=False)
             id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
             id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)
 
@@ -225,19 +220,25 @@ class StableDiffusionXLPuLIDPipeline:
             id_cond_list.append(id_cond)
             id_vit_hidden_list.append(id_vit_hidden)
 
-        id_uncond = torch.zeros_like(id_cond_list[0])
+        self.id_adapter.to(self.device)
+        id_uncond = torch.zeros_like(id_cond_list[0]).to(self.dtype)
         id_vit_hidden_uncond = []
         for layer_idx in range(0, len(id_vit_hidden_list[0])):
-            id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden_list[0][layer_idx]))
+            id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden_list[0][layer_idx]).to(self.dtype))
 
-        id_cond = torch.stack(id_cond_list, dim=1)
+        id_cond = torch.stack(id_cond_list, dim=1).to(self.dtype)
         id_vit_hidden = id_vit_hidden_list[0]
         for i in range(1, len(image_list)):
             for j, x in enumerate(id_vit_hidden_list[i]):
-                id_vit_hidden[j] = torch.cat([id_vit_hidden[j], x], dim=1)
+                id_vit_hidden[j] = torch.cat([id_vit_hidden[j], x], dim=1).to(self.dtype)
         id_embedding = self.id_adapter(id_cond, id_vit_hidden)
         uncond_id_embedding = self.id_adapter(id_uncond, id_vit_hidden_uncond)
 
+        if self.offload:
+            self.face_helper.face_det.to('cpu')
+            self.id_adapter.to('cpu')
+            self.clip_vision_model.to('cpu')
+
         # return id_embedding
         return uncond_id_embedding, id_embedding
 
@@ -314,6 +315,7 @@ class StableDiffusionXLPuLIDPipeline:
         id_embedding=None,
         uncond_id_embedding=None,
         id_scale: float=1.0,
+        output_type: str='pil',
         callback_on_step_end=None,
     ):
         self.step = 0 # pylint: disable=attribute-defined-outside-init
@@ -370,24 +372,15 @@ class StableDiffusionXLPuLIDPipeline:
             mask_args = None
 
         latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
-        latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
-        images = self.pipe.vae.decode(latents).sample
-        images = self.pipe.image_processor.postprocess(images, output_type='pil')
-
-        # Pixel space final mask
-        # if mask_image is not None:
-        #     # TODO: Fix XYZ
-        #      from PIL import Image
-        #     mask_image = np.asarray(mask_image.convert("L"))
-        #     mask_image = mask_image / mask_image.max()
-        #     mask_image = mask_image.reshape(1,mask_image.shape[0],mask_image.shape[1],1)
-        #     image = np.asarray(image).astype(mask_image.dtype)
-        #     images = np.asarray(images).astype(mask_image.dtype)
-        #     images = ((1 - mask_image) * image) + (mask_image * images)
-        #     images = images[0].round().astype(np.uint8)
-        #     images = [Image.fromarray(images)]
-
-        return images
+        if output_type == 'latent':
+            images = self.pipe.image_processor.postprocess(latents, output_type='latent')
+        elif output_type == 'np':
+            images = self.pipe.image_processor.postprocess(latents, output_type='np')
+        else:
+            latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
+            images = self.pipe.vae.decode(latents).sample
+            images = self.pipe.image_processor.postprocess(images, output_type='pil')
+        return StableDiffusionXLPipelineOutput(images)
 
 
 class StableDiffusionXLPuLIDPipelineImage(StableDiffusionXLPuLIDPipeline):
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8320ac293..e139895ea 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -402,9 +402,11 @@ def apply_balanced_offload(sd_model):
     def apply_balanced_offload_to_module(pipe):
         if hasattr(pipe, "pipe"):
             apply_balanced_offload_to_module(pipe.pipe)
-        if not hasattr(pipe, "_internal_dict"):
-            return
-        for module_name in pipe._internal_dict.keys(): # pylint: disable=protected-access
+        if hasattr(pipe, "_internal_dict"):
+            keys = pipe._internal_dict.keys() # pylint: disable=protected-access
+        else:
+            keys = get_signature(shared.sd_model).keys()
+        for module_name in keys: # pylint: disable=protected-access
             module = getattr(pipe, module_name, None)
             if isinstance(module, torch.nn.Module):
                 checkpoint_name = pipe.sd_checkpoint_info.name if getattr(pipe, "sd_checkpoint_info", None) is not None else None
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index d0d738b6a..6599fa2e8 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -1,5 +1,6 @@
 import io
 import os
+import time
 import contextlib
 import gradio as gr
 import numpy as np
@@ -18,6 +19,7 @@ class Script(scripts.Script):
         self.pulid = None
         self.cache = None
         self.mask_apply_overlay = shared.opts.mask_apply_overlay
+        self.preprocess = 0
         super().__init__()
         self.register() # pulid is script with processing override so xyz doesnt execute
 
@@ -88,15 +90,16 @@ class Script(scripts.Script):
             sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
             ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
         with gr.Row():
-            cache = gr.Checkbox(label='Keep model', value=False)
+            restore = gr.Checkbox(label='Restore pipe on end', value=False)
+            offload = gr.Checkbox(label='Offload face module', value=True)
         with gr.Row():
             files = gr.File(label='Input images', file_count='multiple', file_types=['image'], type='file', interactive=True, height=100)
         with gr.Row():
             gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
-        return [strength, zero, sampler, ortho, gallery, cache]
+        return [strength, zero, sampler, ortho, gallery, restore, offload]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], cache: bool = False): # pylint: disable=arguments-differ, unused-argument
+    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], restore: bool = False, offload: bool = True): # pylint: disable=arguments-differ, unused-argument
         images = []
         try:
             if len(gallery) == 0:
@@ -135,13 +138,13 @@ class Script(scripts.Script):
             shared.log.warning('PuLID: batch size not supported')
             p.batch_size = 1
 
+        self.mask_apply_overlay = shared.opts.mask_apply_overlay
+        shared.opts.data['mask_apply_overlay'] = False
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
         ortho = getattr(p, 'pulid_ortho', ortho)
         sampler = getattr(p, 'pulid_sampler', sampler)
         sampler_fn = getattr(self.pulid.sampling, f'sample_{sampler}', None)
-        self.mask_apply_overlay = shared.opts.mask_apply_overlay
-        shared.opts.data['mask_apply_overlay'] = False
         if sampler_fn is None:
             sampler_fn = self.pulid.sampling.sample_dpmpp_2m_sde
 
@@ -153,6 +156,9 @@ class Script(scripts.Script):
                     shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
                         pipe =shared.sd_model,
                         device=devices.device,
+                        dtype=devices.dtype,
+                        providers=devices.onnx,
+                        offload=offload,
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -166,13 +172,20 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'
         images = [self.pulid.resize(image, 1024) for image in images]
         shared.sd_model.debug_img_list = []
+
+        # get id embedding used for attention
+        t0 = time.time()
         uncond_id_embedding, id_embedding = shared.sd_model.get_id_embedding(images)
+        if offload:
+            devices.torch_gc()
+        t1 = time.time()
+        self.preprocess = t1-t0
 
         p.seed = processing_helpers.get_fixed_seed(p.seed)
         if direct: # run pipeline directly
@@ -211,20 +224,18 @@ class Script(scripts.Script):
         return processed
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
-        _strength, _zero, _sampler, _ortho, _gallery, cache = args
+        _strength, _zero, _sampler, _ortho, _gallery, restore, _offload = args
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
             shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
-            cache = getattr(p, 'pulid_cache', cache)
-            if cache:
-                shared.log.debug(f'PuLID cache: class={shared.sd_model.__class__.__name__}')
-                return processed
-            if hasattr(shared.sd_model, 'app'):
-                shared.sd_model.app = None
-                shared.sd_model.ip_adapter = None
-                shared.sd_model.face_helper = None
-                shared.sd_model.clip_vision_model = None
-                shared.sd_model.handler_ante = None
-            shared.sd_model = shared.sd_model.pipe
-            devices.torch_gc(force=True)
-            shared.log.debug(f'PuLID restore: class={shared.sd_model.__class__.__name__}')
+            restore = getattr(p, 'pulid_restore', restore)
+            if restore:
+                if hasattr(shared.sd_model, 'app'):
+                    shared.sd_model.app = None
+                    shared.sd_model.ip_adapter = None
+                    shared.sd_model.face_helper = None
+                    shared.sd_model.clip_vision_model = None
+                    shared.sd_model.handler_ante = None
+                shared.sd_model = shared.sd_model.pipe
+                devices.torch_gc(force=True)
+            shared.log.debug(f'PuLID complete: class={shared.sd_model.__class__.__name__} preprocess={self.preprocess:.2f} pipe={"restore" if restore else "cache"}')
         return processed

From 1c74d36e29f033117f1b5d867c9ff0a07232ffa2 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 10 Nov 2024 09:42:05 -0500
Subject: [PATCH 083/119] pullid support sdpa add both v1.0 and v1.1 models

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                          |  2 +-
 modules/errors.py                     |  4 +-
 modules/pulid/encoders_transformer.py | 68 ++++++++++++++++++++++++---
 modules/pulid/pulid_sdxl.py           | 38 +++++++++++----
 scripts/pulid_ext.py                  | 25 ++++++++--
 5 files changed, 113 insertions(+), 24 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 417165276..9affd53db 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-08
+## Update for 2024-11-10
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
diff --git a/modules/errors.py b/modules/errors.py
index c4d66c351..527884cf1 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -59,7 +59,7 @@ def exception(suppress=[]):
     console.print_exception(show_locals=False, max_frames=16, extra_lines=2, suppress=suppress, theme="ansi_dark", word_wrap=False, width=min([console.width, 200]))
 
 
-def profile(profiler, msg: str):
+def profile(profiler, msg: str, n: int = 5):
     profiler.disable()
     import io
     import pstats
@@ -83,7 +83,7 @@ def profile(profiler, msg: str):
              and 'rich' not in x
              and x.strip() != ''
             ]
-    txt = '\n'.join(lines[:min(5, len(lines))])
+    txt = '\n'.join(lines[:min(n, len(lines))])
     log.debug(f'Profile {msg}: {txt}')
 
 
diff --git a/modules/pulid/encoders_transformer.py b/modules/pulid/encoders_transformer.py
index d1ecef2c6..ae245044b 100644
--- a/modules/pulid/encoders_transformer.py
+++ b/modules/pulid/encoders_transformer.py
@@ -186,23 +186,79 @@ class IDFormer(nn.Module):
         )
 
     def forward(self, x, y):
-
         latents = self.latents.repeat(x.size(0), 1, 1)
-
         num_duotu = x.shape[1] if x.ndim == 3 else 1
-
         x = self.id_embedding_mapping(x)
         x = x.reshape(-1, self.num_id_token * num_duotu, self.dim)
-
         latents = torch.cat((latents, x), dim=1)
-
         for i in range(5):
             vit_feature = getattr(self, f'mapping_{i}')(y[i])
             ctx_feature = torch.cat((x, vit_feature), dim=1)
             for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]:
                 latents = attn(ctx_feature, latents) + latents
                 latents = ff(latents) + latents
-
         latents = latents[:, :self.num_queries]
         latents = latents @ self.proj_out
         return latents
+
+
+class IDEncoder(nn.Module):
+    def __init__(self, width=1280, context_dim=2048, num_token=5):
+        super().__init__()
+        self.num_token = num_token
+        self.context_dim = context_dim
+        h1 = min((context_dim * num_token) // 4, 1024)
+        h2 = min((context_dim * num_token) // 2, 1024)
+        self.body = nn.Sequential(
+            nn.Linear(width, h1),
+            nn.LayerNorm(h1),
+            nn.LeakyReLU(),
+            nn.Linear(h1, h2),
+            nn.LayerNorm(h2),
+            nn.LeakyReLU(),
+            nn.Linear(h2, context_dim * num_token),
+        )
+
+        for i in range(5):
+            setattr(
+                self,
+                f'mapping_{i}',
+                nn.Sequential(
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, context_dim),
+                ),
+            )
+
+            setattr(
+                self,
+                f'mapping_patch_{i}',
+                nn.Sequential(
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, 1024),
+                    nn.LayerNorm(1024),
+                    nn.LeakyReLU(),
+                    nn.Linear(1024, context_dim),
+                ),
+            )
+
+    def forward(self, x, y):
+        # x shape [N, C]
+        x = self.body(x)
+        x = x.reshape(-1, self.num_token, self.context_dim)
+
+        hidden_states = ()
+        for i, emb in enumerate(y):
+            hidden_state = getattr(self, f'mapping_{i}')(emb[:, :1]) + getattr(self, f'mapping_patch_{i}')(
+                emb[:, 1:]
+            ).mean(dim=1, keepdim=True)
+            hidden_states += (hidden_state,)
+        hidden_states = torch.cat(hidden_states, dim=1)
+
+        return torch.cat([x, hidden_states], dim=1)
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 8bd28dbe2..d2a761654 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -19,23 +19,28 @@ from insightface.app import FaceAnalysis
 
 from eva_clip import create_model_and_transforms
 from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-from encoders_transformer import IDFormer
-from attention_processor import AttnProcessor2_0 as AttnProcessor
-from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
+from encoders_transformer import IDFormer, IDEncoder
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None):
+    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None, sdp: bool=True, version: str='v1.1'):
         super().__init__()
         self.device = device
         self.dtype = dtype or torch.float16
         self.pipe = pipe
         self.cache_dir = cache_dir
         self.offload = offload
-        self.hack_unet_attn_layers(self.pipe.unet)
+        self.sdp = sdp
+        self.version = version
+        self.folder = 'models--ToTheBeginning--PuLID'
+
         self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
-        self.id_adapter = IDFormer().to(self.device, self.dtype)
+        if self.version == 'v1.1':
+            self.id_adapter = IDFormer().to(self.device, self.dtype)
+        else:
+            self.id_adapter = IDEncoder().to(self.device, self.dtype)
         self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
+        self.hack_unet_attn_layers(self.pipe.unet)
 
         # preprocessors
         # face align and parsing
@@ -63,11 +68,11 @@ class StableDiffusionXLPuLIDPipeline:
         self.eva_transform_std = eva_transform_std
 
         # antelopev2
-        local_dir = os.path.join(self.cache_dir, 'pulid', 'models', 'antelopev2')
+        local_dir = os.path.join(self.cache_dir, self.folder, 'models', 'antelopev2')
         _loc = snapshot_download('DIAMONIK7777/antelopev2', local_dir=local_dir)
         self.app = FaceAnalysis(
             name='antelopev2',
-            root=os.path.join(self.cache_dir, 'pulid'),
+            root=os.path.join(self.cache_dir, self.folder),
             providers=self.providers,
         )
         self.app.prepare(ctx_id=0, det_size=(640, 640))
@@ -119,6 +124,12 @@ class StableDiffusionXLPuLIDPipeline:
         return torch.cat([sigmas, sigmas.new_zeros([1])])
 
     def hack_unet_attn_layers(self, unet):
+        if self.sdp:
+            from attention_processor import AttnProcessor2_0 as AttnProcessor
+            from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
+        else:
+            from attention_processor import AttnProcessor
+            from attention_processor import IDAttnProcessor
         id_adapter_attn_procs = {}
         for name, _ in unet.attn_processors.items():
             cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
@@ -143,8 +154,12 @@ class StableDiffusionXLPuLIDPipeline:
         self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
 
     def load_pretrain(self):
-        ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.1.safetensors', local_dir=os.path.join(self.cache_dir, 'pulid'))
-        state_dict = load_file(ckpt_path)
+        if self.version == 'v1.1':
+            ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.1.safetensors', local_dir=os.path.join(self.cache_dir, self.folder))
+            state_dict = load_file(ckpt_path)
+        else:
+            ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir=os.path.join(self.cache_dir, self.folder))
+            state_dict = torch.load(ckpt_path, map_location="cpu")
         state_dict_dict = {}
         for k, v in state_dict.items():
             module = k.split('.')[0]
@@ -371,7 +386,10 @@ class StableDiffusionXLPuLIDPipeline:
         else:
             mask_args = None
 
+        # actual sampling loop
         latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
+
+        # process output
         if output_type == 'latent':
             images = self.pipe.image_processor.postprocess(latents, output_type='latent')
         elif output_type == 'np':
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 6599fa2e8..181e954db 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -89,6 +89,8 @@ class Script(scripts.Script):
         with gr.Row():
             sampler = gr.Dropdown(label="Sampler", value='dpmpp_sde', choices=['dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2s_ancestral', 'dpmpp_3m_sde', 'dpmpp_sde', 'euler', 'euler_ancestral'])
             ortho = gr.Dropdown(label="Ortho", choices=['off', 'v1', 'v2'], value='v2')
+        with gr.Row():
+            version = gr.Dropdown(label="Version", value='v1.1', choices=['v1.0', 'v1.1'])
         with gr.Row():
             restore = gr.Checkbox(label='Restore pipe on end', value=False)
             offload = gr.Checkbox(label='Offload face module', value=True)
@@ -97,9 +99,20 @@ class Script(scripts.Script):
         with gr.Row():
             gallery = gr.Gallery(show_label=False, value=[], visible=False, container=False, rows=1)
         files.change(fn=self.load_images, inputs=[files], outputs=[gallery])
-        return [strength, zero, sampler, ortho, gallery, restore, offload]
+        return [strength, zero, sampler, ortho, gallery, restore, offload, version]
 
-    def run(self, p: processing.StableDiffusionProcessing, strength: float = 0.8, zero: int = 20, sampler: str = 'dpmpp_sde', ortho: str = 'v2', gallery: list = [], restore: bool = False, offload: bool = True): # pylint: disable=arguments-differ, unused-argument
+    def run(
+            self,
+            p: processing.StableDiffusionProcessing,
+            strength: float = 0.8,
+            zero: int = 20,
+            sampler: str = 'dpmpp_sde',
+            ortho: str = 'v2',
+            gallery: list = [],
+            restore: bool = False,
+            offload: bool = True,
+            version: str = 'v1.1'
+        ): # pylint: disable=arguments-differ, unused-argument
         images = []
         try:
             if len(gallery) == 0:
@@ -154,11 +167,13 @@ class Script(scripts.Script):
                 ctx = contextlib.nullcontext() if debug else contextlib.redirect_stdout(stdout)
                 with ctx:
                     shared.sd_model = self.pulid.StableDiffusionXLPuLIDPipeline(
-                        pipe =shared.sd_model,
+                        pipe=shared.sd_model,
                         device=devices.device,
                         dtype=devices.dtype,
                         providers=devices.onnx,
                         offload=offload,
+                        version=version,
+                        sdp=shared.opts.cross_attention_optimization == "Scaled-Dot-Product",
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -172,7 +187,7 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} version="{version}" strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'
@@ -224,7 +239,7 @@ class Script(scripts.Script):
         return processed
 
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
-        _strength, _zero, _sampler, _ortho, _gallery, restore, _offload = args
+        _strength, _zero, _sampler, _ortho, _gallery, restore, _offload, _version = args
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
             shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
             restore = getattr(p, 'pulid_restore', restore)

From 3cd21d6b74f83304fce67b2a8704eaf69b7b4acd Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 10 Nov 2024 16:36:08 -0500
Subject: [PATCH 084/119] css fix margin

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                          |  5 +++--
 javascript/sdnext.css                 |  3 ++-
 modules/pulid/encoders_transformer.py | 14 --------------
 scripts/pulid_ext.py                  |  5 +++--
 4 files changed, 8 insertions(+), 19 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9affd53db..5db77983c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,7 +15,7 @@ This release can be considered an LTS release before we kick off the next round
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) and [Home](https://github.com/vladmandic/automatic) updates  
 - Integrations:
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
-    - advanced method of face transfer with better quality as well as control over identity and appearance  
+    - advanced method of face id transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
     - compatible with *sdxl* for text-to-image, image-to-image, inpaint and detailer workflows  
@@ -98,7 +98,8 @@ This release can be considered an LTS release before we kick off the next round
   - fix network height in standard vs modern ui  
   - fix k-diff enum on startup  
   - fix text2video scripts  
-  - dont uninstall flash-attn
+  - dont uninstall flash-attn  
+  - ui css fixes  
   - move downloads of some auxillary models to hfcache instead of models folder  
 
 ## Update for 2024-10-29
diff --git a/javascript/sdnext.css b/javascript/sdnext.css
index 192d0d487..08fae2eb8 100644
--- a/javascript/sdnext.css
+++ b/javascript/sdnext.css
@@ -13,11 +13,12 @@ footer { display: none; margin-top: 0 !important;}
 table { overflow-x: auto !important; overflow-y: auto !important; }
 td { border-bottom: none !important; padding: 0 0.5em !important; }
 tr { border-bottom: none !important; padding: 0 0.5em !important; }
+td > div > span { overflow-y: auto; max-height: 3em; overflow-x: hidden; }
 textarea { overflow-y: auto !important; }
 span { font-size: var(--text-md) !important; }
 button { font-size: var(--text-lg) !important; }
 input[type='color'] { width: 64px; height: 32px; }
-td > div > span { overflow-y: auto; max-height: 3em; overflow-x: hidden; }
+input::-webkit-outer-spin-button, input::-webkit-inner-spin-button { margin-left: 4px; }
 
 /* gradio elements */
 .block .padded:not(.gradio-accordion) { padding: 4px 0 0 0 !important; margin-right: 0; min-width: 90px !important; }
diff --git a/modules/pulid/encoders_transformer.py b/modules/pulid/encoders_transformer.py
index ae245044b..834d5aa94 100644
--- a/modules/pulid/encoders_transformer.py
+++ b/modules/pulid/encoders_transformer.py
@@ -32,10 +32,8 @@ class PerceiverAttentionCA(nn.Module):
         self.dim_head = dim_head
         self.heads = heads
         inner_dim = dim_head * heads
-
         self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
         self.norm2 = nn.LayerNorm(dim)
-
         self.to_q = nn.Linear(dim, inner_dim, bias=False)
         self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
         self.to_out = nn.Linear(inner_dim, dim, bias=False)
@@ -50,12 +48,9 @@ class PerceiverAttentionCA(nn.Module):
         """
         x = self.norm1(x)
         latents = self.norm2(latents)
-
         b, seq_len, _ = latents.shape
-
         q = self.to_q(latents)
         k, v = self.to_kv(x).chunk(2, dim=-1)
-
         q = reshape_tensor(q, self.heads)
         k = reshape_tensor(k, self.heads)
         v = reshape_tensor(v, self.heads)
@@ -65,7 +60,6 @@ class PerceiverAttentionCA(nn.Module):
         weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         out = weight @ v
-
         out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
 
         return self.to_out(out)
@@ -78,10 +72,8 @@ class PerceiverAttention(nn.Module):
         self.dim_head = dim_head
         self.heads = heads
         inner_dim = dim_head * heads
-
         self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
         self.norm2 = nn.LayerNorm(dim)
-
         self.to_q = nn.Linear(dim, inner_dim, bias=False)
         self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
         self.to_out = nn.Linear(inner_dim, dim, bias=False)
@@ -96,13 +88,10 @@ class PerceiverAttention(nn.Module):
         """
         x = self.norm1(x)
         latents = self.norm2(latents)
-
         b, seq_len, _ = latents.shape
-
         q = self.to_q(latents)
         kv_input = torch.cat((x, latents), dim=-2)
         k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
         q = reshape_tensor(q, self.heads)
         k = reshape_tensor(k, self.heads)
         v = reshape_tensor(v, self.heads)
@@ -112,7 +101,6 @@ class PerceiverAttention(nn.Module):
         weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         out = weight @ v
-
         out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1)
 
         return self.to_out(out)
@@ -145,7 +133,6 @@ class IDFormer(nn.Module):
         assert depth % 5 == 0
         self.depth = depth // 5
         scale = dim ** -0.5
-
         self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale)
         self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim))
 
@@ -233,7 +220,6 @@ class IDEncoder(nn.Module):
                     nn.Linear(1024, context_dim),
                 ),
             )
-
             setattr(
                 self,
                 f'mapping_patch_{i}',
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 181e954db..43039d73a 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -153,6 +153,7 @@ class Script(scripts.Script):
 
         self.mask_apply_overlay = shared.opts.mask_apply_overlay
         shared.opts.data['mask_apply_overlay'] = False
+        sdp = shared.opts.cross_attention_optimization == "Scaled-Dot-Product"
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
         ortho = getattr(p, 'pulid_ortho', ortho)
@@ -173,7 +174,7 @@ class Script(scripts.Script):
                         providers=devices.onnx,
                         offload=offload,
                         version=version,
-                        sdp=shared.opts.cross_attention_optimization == "Scaled-Dot-Product",
+                        sdp=sdp,
                         cache_dir=shared.opts.hfcache_dir,
                     )
                 shared.sd_model.no_recurse = True
@@ -187,7 +188,7 @@ class Script(scripts.Script):
                 return None
 
         shared.sd_model.sampler = sampler_fn
-        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} version="{version}" strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
+        shared.log.info(f'PuLID: class={shared.sd_model.__class__.__name__} version="{version}" sdp={sdp} strength={strength} zero={zero} ortho={ortho} sampler={sampler_fn} images={[i.shape for i in images]} offload={offload}')
         self.pulid.attention.NUM_ZERO = zero
         self.pulid.attention.ORTHO = ortho == 'v1'
         self.pulid.attention.ORTHO_v2 = ortho == 'v2'

From 15381eb1038b010fe32b3acfc756a76ff317c49f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 09:53:05 -0500
Subject: [PATCH 085/119] add pulid debug

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 installer.py                |  3 +++
 modules/pulid/pulid_sdxl.py | 38 +++++++++++++++++++++++++++++--------
 2 files changed, 33 insertions(+), 8 deletions(-)

diff --git a/installer.py b/installer.py
index b19eae87e..e50271280 100644
--- a/installer.py
+++ b/installer.py
@@ -110,6 +110,9 @@ def setup_logging():
         "traceback.border": "black",
         "traceback.border.syntax_error": "black",
         "inspect.value.border": "black",
+        "logging.level.info": "blue_violet",
+        "logging.level.debug": "purple4",
+        "logging.level.trace": "dark_blue",
     }))
     logging.basicConfig(level=logging.ERROR, format='%(asctime)s | %(name)s | %(levelname)s | %(module)s | %(message)s', handlers=[logging.NullHandler()]) # redirect default logger to null
     pretty_install(console=console)
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index d2a761654..3053d759d 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -4,7 +4,7 @@ import insightface
 import numpy as np
 import torch
 import torch.nn as nn
-from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
+from diffusers import StableDiffusionXLPipeline
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
 from huggingface_hub import hf_hub_download, snapshot_download
@@ -20,6 +20,10 @@ from insightface.app import FaceAnalysis
 from eva_clip import create_model_and_transforms
 from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
 from encoders_transformer import IDFormer, IDEncoder
+from modules.errors import log
+
+
+debug = log.trace if os.environ.get('SD_PULID_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
 class StableDiffusionXLPuLIDPipeline:
@@ -33,14 +37,17 @@ class StableDiffusionXLPuLIDPipeline:
         self.sdp = sdp
         self.version = version
         self.folder = 'models--ToTheBeginning--PuLID'
+        debug(f'PulID init: device={self.device} dtype={self.dtype} dir={self.cache_dir} offload={self.offload} sdp={self.sdp} version={self.version}')
 
-        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        # self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        self.hack_unet_attn_layers(self.pipe.unet)
         if self.version == 'v1.1':
             self.id_adapter = IDFormer().to(self.device, self.dtype)
         else:
             self.id_adapter = IDEncoder().to(self.device, self.dtype)
+        debug(f'PulID load: adapter={self.id_adapter.__class__.__name__}')
         self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
-        self.hack_unet_attn_layers(self.pipe.unet)
+        debug(f'PulID load: providers={self.providers}')
 
         # preprocessors
         # face align and parsing
@@ -53,11 +60,13 @@ class StableDiffusionXLPuLIDPipeline:
             device=self.device,
         )
         self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
+        debug(f'PulID load: facehelper={self.face_helper.__class__.__name__}')
 
         # clip-vit backbone
         eva_precision = 'fp16' if self.dtype == torch.float16 or self.dtype == torch.bfloat16 else 'fp32'
         eva_model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, precision=eva_precision, device=self.device)
         self.clip_vision_model = eva_model.visual.to(dtype=self.dtype)
+        debug(f'PulID load: evaclip={self.clip_vision_model.__class__.__name__} precision={eva_precision}')
         eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
         eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
         if not isinstance(eva_transform_mean, (list, tuple)):
@@ -75,9 +84,11 @@ class StableDiffusionXLPuLIDPipeline:
             root=os.path.join(self.cache_dir, self.folder),
             providers=self.providers,
         )
+        debug(f'PulID load: faceanalysis={_loc}')
         self.app.prepare(ctx_id=0, det_size=(640, 640))
         self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
         self.handler_ante.prepare(ctx_id=0)
+        debug(f'PulID load: handler={self.handler_ante.__class__.__name__}')
 
         self.load_pretrain()
 
@@ -150,6 +161,7 @@ class StableDiffusionXLPuLIDPipeline:
                 ).to(unet.device, unet.dtype)
             else:
                 id_adapter_attn_procs[name] = AttnProcessor()
+        debug(f'PulID attention: cls={IDAttnProcessor} std={AttnProcessor} len={len(id_adapter_attn_procs.keys())}')
         unet.set_attn_processor(id_adapter_attn_procs)
         self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
 
@@ -160,6 +172,7 @@ class StableDiffusionXLPuLIDPipeline:
         else:
             ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir=os.path.join(self.cache_dir, self.folder))
             state_dict = torch.load(ckpt_path, map_location="cpu")
+        debug(f'PulID load: fn="{ckpt_path}"')
         state_dict_dict = {}
         for k, v in state_dict.items():
             module = k.split('.')[0]
@@ -255,6 +268,7 @@ class StableDiffusionXLPuLIDPipeline:
             self.clip_vision_model.to('cpu')
 
         # return id_embedding
+        debug(f'PulID embedding: cond={id_embedding.shape} uncond={uncond_id_embedding.shape}')
         return uncond_id_embedding, id_embedding
 
     def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
@@ -264,9 +278,10 @@ class StableDiffusionXLPuLIDPipeline:
         pulid_sampling.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
 
     def sample(self, x, sigma, **extra_args):
-        x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
         t = self.timestep(sigma)
+        x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
         cfg_scale = extra_args['cfg_scale']
+        debug(f'PulID sample start: step={self.step+1} x={x.shape} dtype={x.dtype} timestep={t.item()} sigma={sigma.shape} cfg={cfg_scale} args={extra_args.keys()}')
         eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
         eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
         noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
@@ -274,6 +289,7 @@ class StableDiffusionXLPuLIDPipeline:
         if self.callback_on_step_end is not None:
             self.step += 1
             self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
+        debug(f'PulID sample end:   step={self.step} x={latent.shape} dtype={x.dtype} min={torch.amin(latent)} max={torch.amax(latent)}')
         return latent
 
     def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
@@ -299,6 +315,7 @@ class StableDiffusionXLPuLIDPipeline:
                                                     return_image_latents=False,
                                                     )
                 latents = latents[0]
+                debug(f'PulID noise: op=inpaint latent={latents.shape} image={image} mask={mask_image} dtype={latents.dtype}')
             else:  # img2img
                 latents = self.pipe.prepare_latents(image,
                                                     None,  # timestep (not needed)
@@ -309,10 +326,10 @@ class StableDiffusionXLPuLIDPipeline:
                                                     None,  # generator
                                                     False,  # add_noise
                                                     )
-
+                debug(f'PulID noise: op=img2img latent={latents.shape} image={image} dtype={latents.dtype}')
         else:
             latents = torch.zeros_like(noise)
-
+            debug(f'PulID noise: op=txt2img latent={latents.shape} dtype={latents.dtype}')
         return latents, noise
 
     def __call__(
@@ -333,6 +350,7 @@ class StableDiffusionXLPuLIDPipeline:
         output_type: str='pil',
         callback_on_step_end=None,
     ):
+        debug(f'PulID call: width={width} height={height} cfg={guidance_scale} steps={num_inference_steps} seed={seed} strength={strength} id_scale={id_scale} output={output_type}')
         self.step = 0 # pylint: disable=attribute-defined-outside-init
         self.callback_on_step_end = callback_on_step_end # pylint: disable=attribute-defined-outside-init
         size = (1, height, width)
@@ -341,11 +359,12 @@ class StableDiffusionXLPuLIDPipeline:
         if image is not None and strength > 0:
             _, num_inference_steps = self.pipe.get_timesteps(num_inference_steps, strength, self.device, None)  # denoising_start disabled
             sigmas = sigmas[-(num_inference_steps + 1):].to(self.device) # shorten sigmas in i2i
-
+        debug(f'PulID sigmas: sigmas={sigmas.shape} dtype={sigmas.dtype}')
 
         # latents
         latent, noise = self.init_latent(seed, size, image, mask_image, strength, width, height)
         noisy_latent = latent + noise * sigmas[0].to(noise)
+        debug(f'PulID noisy: latent={noisy_latent.shape} dtype={noisy_latent.dtype}')
 
         (
             prompt_embeds,
@@ -390,14 +409,17 @@ class StableDiffusionXLPuLIDPipeline:
         latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
 
         # process output
+        latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device)
+        debug(f'PulID output: latent={latents.shape} dtype={latents.dtype}')
         if output_type == 'latent':
             images = self.pipe.image_processor.postprocess(latents, output_type='latent')
         elif output_type == 'np':
             images = self.pipe.image_processor.postprocess(latents, output_type='np')
         else:
-            latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
+            latents = latents / self.pipe.vae.config.scaling_factor
             images = self.pipe.vae.decode(latents).sample
             images = self.pipe.image_processor.postprocess(images, output_type='pil')
+        debug(f'PulID output: type={type(images)} images={images.shape if hasattr(images, "shape") else images}')
         return StableDiffusionXLPipelineOutput(images)
 
 

From 70eec4349ecc9dc38c60a22d55dd2a0952406414 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 10:46:33 -0500
Subject: [PATCH 086/119] fix pulid vae

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_vae.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index 5e6fa68f4..0473beeb1 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -147,6 +147,7 @@ def taesd_vae_encode(image):
 
 def vae_decode(latents, model, output_type='np', full_quality=True, width=None, height=None):
     t0 = time.time()
+    model = model or shared.sd_model
     if latents is None or not torch.is_tensor(latents): # already decoded
         return latents
     prev_job = shared.state.job
@@ -169,15 +170,8 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
 
     if latents.shape[-1] <= 4: # not a latent, likely an image
         decoded = latents.float().cpu().numpy()
-    elif full_quality and hasattr(shared.sd_model, "vae"):
-        parent = shared.sd_model if hasattr(shared.sd_model, 'vae') else None
-        if hasattr(shared.sd_model, 'vae'):
-            parent = shared.sd_model
-        elif hasattr(shared.sd_model, 'pipe') and hasattr(shared.sd_model.pipe, 'vae'):
-            parent = shared.sd_model.pipe
-        else:
-            parent = None
-        decoded = full_vae_decode(latents=latents, model=parent)
+    elif full_quality and hasattr(model, "vae"):
+        decoded = full_vae_decode(latents=latents, model=model)
     else:
         decoded = taesd_vae_decode(latents=latents)
 

From e2c7c8cf2e2c4996b04685c03f036b4dc7a963b3 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 15:23:11 -0500
Subject: [PATCH 087/119] refactor processing class

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .../Lora/extra_networks_lora.py               |   1 -
 modules/api/models.py                         |   8 -
 modules/img2img.py                            |  19 +-
 modules/processing_class.py                   | 449 +++++++++---------
 modules/shared.py                             |   6 +-
 modules/txt2img.py                            |   1 -
 modules/ui_img2img.py                         |   2 +
 modules/unipc/sampler.py                      |   2 +-
 8 files changed, 243 insertions(+), 245 deletions(-)

diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py
index 9172d7336..69b234df7 100644
--- a/extensions-builtin/Lora/extra_networks_lora.py
+++ b/extensions-builtin/Lora/extra_networks_lora.py
@@ -145,7 +145,6 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
         if self.active and networks.debug:
             shared.log.debug(f"Network end: type=LoRA load={networks.timer['load']:.2f} apply={networks.timer['apply']:.2f} restore={networks.timer['restore']:.2f}")
         if self.errors:
-            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
             for k, v in self.errors.items():
                 shared.log.error(f'LoRA: name="{k}" errors={v}')
             self.errors.clear()
diff --git a/modules/api/models.py b/modules/api/models.py
index 740f3c555..f5b89c2a9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -11,14 +11,6 @@ API_NOT_ALLOWED = [
     "sd_model",
     "outpath_samples",
     "outpath_grids",
-    "sampler_index",
-    "extra_generation_params",
-    "overlay_images",
-    "do_not_reload_embeddings",
-    "seed_enable_extras",
-    "prompt_for_display",
-    "sampler_noise_scheduler_override",
-    "ddim_discretize"
 ]
 
 class ModelDef(BaseModel):
diff --git a/modules/img2img.py b/modules/img2img.py
index f3bec5b02..8274386cc 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -137,6 +137,7 @@ def img2img(id_task: str, state: str, mode: int,
             inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
             img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
             hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio,
+            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,
             override_settings_texts,
             *args): # pylint: disable=unused-argument
 
@@ -214,7 +215,6 @@ def img2img(id_task: str, state: str, mode: int,
         subseed_strength=subseed_strength,
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=True,
         sampler_name = processing.get_sampler_name(sampler_index, img=True),
         batch_size=batch_size,
         n_iter=n_iter,
@@ -247,6 +247,23 @@ def img2img(id_task: str, state: str, mode: int,
         inpainting_mask_invert=inpainting_mask_invert,
         hdr_mode=hdr_mode, hdr_brightness=hdr_brightness, hdr_color=hdr_color, hdr_sharpen=hdr_sharpen, hdr_clamp=hdr_clamp,
         hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold, hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry, hdr_color_picker=hdr_color_picker, hdr_tint_ratio=hdr_tint_ratio,
+        # refiner
+        enable_hr=enable_hr,
+        hr_denoising_strength=hr_denoising_strength,
+        hr_scale=hr_scale,
+        hr_resize_mode=hr_resize_mode,
+        hr_resize_context=hr_resize_context,
+        hr_upscaler=hr_upscaler,
+        hr_force=hr_force,
+        hr_second_pass_steps=hr_second_pass_steps,
+        hr_resize_x=hr_resize_x,
+        hr_resize_y=hr_resize_y,
+        hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
+        refiner_steps=refiner_steps,
+        hr_refiner_start=hr_refiner_start,
+        refiner_prompt=refiner_prompt,
+        refiner_negative=refiner_negative,
+        # override
         override_settings=override_settings,
     )
     p.scripts = modules.scripts.scripts_img2img
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 2f11db375..215507b4a 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -17,53 +17,44 @@ debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None
 
 @dataclass(repr=False)
 class StableDiffusionProcessing:
-    """
-    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    """
     def __init__(self,
-                 sd_model=None,
-                 outpath_samples=None,
-                 outpath_grids=None,
+                 sd_model=None, # pylint: disable=unused-argument # local instance of sd_model
+                 # base params
                  prompt: str = "",
-                 styles: List[str] = None,
+                 negative_prompt: str = None,
                  seed: int = -1,
                  subseed: int = -1,
                  subseed_strength: float = 0,
                  seed_resize_from_h: int = -1,
                  seed_resize_from_w: int = -1,
-                 seed_enable_extras: bool = True,
-                 sampler_name: str = None,
-                 hr_sampler_name: str = None,
                  batch_size: int = 1,
                  n_iter: int = 1,
                  steps: int = 50,
-                 cfg_scale: float = 7.0,
-                 image_cfg_scale: float = None,
                  clip_skip: int = 1,
                  width: int = 512,
                  height: int = 512,
-                 full_quality: bool = True,
-                 detailer: bool = False,
-                 restore_faces: bool = False,
-                 tiling: bool = False,
-                 hidiffusion: bool = False,
-                 do_not_save_samples: bool = False,
-                 do_not_save_grid: bool = False,
-                 extra_generation_params: Dict[Any, Any] = None,
-                 overlay_images: Any = None,
-                 negative_prompt: str = None,
+                 # samplers
+                 sampler_index: int = None, # pylint: disable=unused-argument # used only to set sampler_name
+                 sampler_name: str = None,
+                 hr_sampler_name: str = None,
                  eta: float = None,
-                 do_not_reload_embeddings: bool = False,
-                 denoising_strength: float = 0,
+                 # guidance
+                 cfg_scale: float = 7.0,
+                 cfg_end: float = 1,
                  diffusers_guidance_rescale: float = 0.7,
                  pag_scale: float = 0.0,
                  pag_adaptive: float = 0.5,
-                 cfg_end: float = 1,
-                 resize_mode: int = 0,
-                 resize_name: str = 'None',
-                 resize_context: str = 'None',
-                 scale_by: float = 0,
-                 selected_scale_tab: int = 0,
+                 # styles
+                 styles: List[str] = None,
+                 # vae
+                 tiling: bool = False,
+                 full_quality: bool = True,
+                 # other
+                 hidiffusion: bool = False,
+                 do_not_reload_embeddings: bool = False,
+                 detailer: bool = False,
+                 restore_faces: bool = False,
+                 # hdr corrections
                  hdr_mode: int = 0,
                  hdr_brightness: float = 0,
                  hdr_color: float = 0,
@@ -76,92 +67,209 @@ class StableDiffusionProcessing:
                  hdr_max_boundry: float = 1.0,
                  hdr_color_picker: str = None,
                  hdr_tint_ratio: float = 0,
-                 override_settings: Dict[str, Any] = None,
+                 # img2img
+                 init_images: list = None,
+                 init_latent: Any = None,
+                 resize_mode: int = 0,
+                 resize_name: str = 'None',
+                 resize_context: str = 'None',
+                 denoising_strength: float = 0.3,
+                 image_cfg_scale: float = None,
+                 initial_noise_multiplier: float = None, # pylint: disable=unused-argument # a1111 compatibility
+                 scale_by: float = 1,
+                 selected_scale_tab: int = 0, # pylint: disable=unused-argument # a1111 compatibility
+                 # inpaint
+                 mask: Any = None,
+                 image_mask: Any = None,
+                 latent_mask: Any = None,
+                 mask_for_overlay: Any = None,
+                 mask_blur: int = 4,
+                 paste_to: Any = None,
+                 inpainting_fill: int = 0,
+                 inpaint_full_res: bool = False,
+                 inpaint_full_res_padding: int = 0,
+                 inpainting_mask_invert: int = 0,
+                 overlay_images: Any = None,
+                 # refiner
+                 enable_hr: bool = False,
+                 firstphase_width: int = 0,
+                 firstphase_height: int = 0,
+                 hr_scale: float = 2.0,
+                 hr_force: bool = False,
+                 hr_resize_mode: int = 0,
+                 hr_resize_context: str = 'None',
+                 hr_upscaler: str = None,
+                 hr_second_pass_steps: int = 0,
+                 hr_resize_x: int = 0,
+                 hr_resize_y: int = 0,
+                 hr_denoising_strength: float = 0.50,
+                 refiner_steps: int = 5,
+                 refiner_start: float = 0,
+                 refiner_prompt: str = '',
+                 refiner_negative: str = '',
+                 hr_refiner_start: float = 0,
+                 # save options
+                 outpath_samples=None,
+                 outpath_grids=None,
+                 do_not_save_samples: bool = False,
+                 do_not_save_grid: bool = False,
+                 # scripts
+                 script_args: list = [],
+                 # overrides
+                 override_settings: Dict[str, Any] = {},
                  override_settings_restore_afterwards: bool = True,
-                 sampler_index: int = None,
-                 script_args: list = None
-                ): # pylint: disable=unused-argument
-
+                 # metadata
+                 extra_generation_params: Dict[Any, Any] = {},
+                ):
+        self.task_args = {}
+        # state items
         self.state: str = ''
+        self.ops = []
         self.skip = []
-        self.outpath_samples: str = outpath_samples
-        self.outpath_grids: str = outpath_grids
-        self.prompt: str = prompt
-        self.prompt_for_display: str = None
-        self.negative_prompt: str = (negative_prompt or "")
-        self.styles: list = styles or []
-        self.seed: int = seed
-        self.subseed: int = subseed
-        self.subseed_strength: float = subseed_strength
-        self.seed_resize_from_h: int = seed_resize_from_h
-        self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_name: str = sampler_name
-        self.hr_sampler_name: str = hr_sampler_name if hr_sampler_name != 'Same as primary' else sampler_name
-        self.batch_size: int = batch_size
-        self.n_iter: int = n_iter
-        self.steps: int = steps
-        self.hr_second_pass_steps = 0
-        self.cfg_scale: float = cfg_scale
-        self.scale_by: float = scale_by
+        self.color_corrections = []
+        self.is_control = False
+        self.is_hr_pass = False
+        self.is_refiner_pass = False
+        self.is_api = False
+        self.scheduled_prompt = False
+        self.prompt_embeds = []
+        self.positive_pooleds = []
+        self.negative_embeds = []
+        self.negative_pooleds = []
+        self.disable_extra_networks = False
+        self.iteration = 0
+        # initializers
+        self.prompt = prompt
+        self.seed = seed
+        self.subseed = subseed
+        self.subseed_strength = subseed_strength
+        self.seed_resize_from_h = seed_resize_from_h
+        self.seed_resize_from_w = seed_resize_from_w
+        self.batch_size = batch_size
+        self.n_iter = n_iter
+        self.steps = steps
+        self.clip_skip = clip_skip
+        self.width = width
+        self.height = height
+        self.negative_prompt = negative_prompt
+        self.styles = styles
+        self.tiling = tiling
+        self.full_quality = full_quality
+        self.hidiffusion = hidiffusion
+        self.do_not_reload_embeddings = do_not_reload_embeddings
+        self.detailer = detailer
+        self.restore_faces = restore_faces
+        self.hdr_mode = hdr_mode
+        self.hdr_brightness = hdr_brightness
+        self.hdr_color = hdr_color
+        self.hdr_sharpen = hdr_sharpen
+        self.hdr_clamp = hdr_clamp
+        self.hdr_boundary = hdr_boundary
+        self.hdr_threshold = hdr_threshold
+        self.hdr_maximize = hdr_maximize
+        self.hdr_max_center = hdr_max_center
+        self.hdr_max_boundry = hdr_max_boundry
+        self.hdr_color_picker = hdr_color_picker
+        self.hdr_tint_ratio = hdr_tint_ratio
+        self.init_images = init_images
+        self.resize_mode = resize_mode
+        self.resize_name = resize_name
+        self.resize_context = resize_context
+        self.denoising_strength = denoising_strength
         self.image_cfg_scale = image_cfg_scale
+        self.scale_by = scale_by
+        self.mask = mask
+        self.image_mask = mask
+        self.latent_mask = latent_mask
+        self.mask_blur = mask_blur
+        self.inpainting_fill = inpainting_fill
+        self.inpaint_full_res_padding = inpaint_full_res_padding
+        self.inpainting_mask_invert = inpainting_mask_invert
+        self.overlay_images = overlay_images
+        self.enable_hr = enable_hr
+        self.firstphase_width = firstphase_width
+        self.firstphase_height = firstphase_height
+        self.hr_scale = hr_scale
+        self.hr_force = hr_force
+        self.hr_resize_mode = hr_resize_mode
+        self.hr_resize_context = hr_resize_context
+        self.hr_upscaler = hr_upscaler
+        self.hr_second_pass_steps = hr_second_pass_steps
+        self.hr_resize_x = hr_resize_x
+        self.hr_resize_y = hr_resize_y
+        self.hr_upscale_to_x = hr_resize_x
+        self.hr_upscale_to_y = hr_resize_y
+        self.hr_denoising_strength = hr_denoising_strength
+        self.refiner_steps = refiner_steps
+        self.refiner_start = refiner_start
+        self.refiner_prompt = refiner_prompt
+        self.refiner_negative = refiner_negative
+        self.hr_refiner_start = hr_refiner_start
+        self.outpath_samples = outpath_samples
+        self.outpath_grids = outpath_grids
+        self.do_not_save_samples = do_not_save_samples
+        self.do_not_save_grid = do_not_save_grid
+        self.override_settings_restore_afterwards = override_settings_restore_afterwards
+        self.extra_generation_params = extra_generation_params
+        self.eta = eta
+        self.cfg_scale = cfg_scale
+        self.cfg_end = cfg_end
         self.diffusers_guidance_rescale = diffusers_guidance_rescale
         self.pag_scale = pag_scale
         self.pag_adaptive = pag_adaptive
-        self.cfg_end = cfg_end
-        self.width: int = width
-        self.height: int = height
-        self.full_quality: bool = full_quality
-        self.detailer: bool = detailer
-        self.restore_faces: bool = restore_faces
-        self.tiling: bool = tiling
-        self.hidiffusion: bool = hidiffusion
-        self.do_not_save_samples: bool = do_not_save_samples
-        self.do_not_save_grid: bool = do_not_save_grid
-        self.extra_generation_params: dict = extra_generation_params or {}
-        self.overlay_images = overlay_images
-        self.eta = eta
-        self.do_not_reload_embeddings = do_not_reload_embeddings
-        self.paste_to = None
-        self.color_corrections = None
-        self.denoising_strength: float = denoising_strength
+        self.selected_scale_tab = selected_scale_tab
+        self.mask_for_overlay = mask_for_overlay
+        self.paste_to = paste_to
+        self.init_latent = None
+        # special handled items
+        if firstphase_width != 0 or firstphase_height != 0:
+            self.hr_upscale_to_x = self.width
+            self.hr_upscale_to_y = self.height
+            self.width = firstphase_width
+            self.height = firstphase_height
+        self.sampler_name = sampler_name or processing_helpers.get_sampler_name(sampler_index, img=True)
+        self.hr_sampler_name: str = hr_sampler_name if hr_sampler_name != 'Same as primary' else self.sampler_name
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
-        self.override_settings_restore_afterwards = override_settings_restore_afterwards
-        self.is_using_inpainting_conditioning = False # a111 compatibility
-        self.disable_extra_networks = False
-        # self.scripts = scripts.ScriptRunner() # set via property
-        # self.script_args = script_args or [] # set via property
-        self.per_script_args = {}
+        self.inpaint_full_res = inpaint_full_res if isinstance(inpaint_full_res, bool) else self.inpaint_full_res
+        self.inpaint_full_res = inpaint_full_res != 0 if isinstance(inpaint_full_res, int) else self.inpaint_full_res
+
+        # null items initialized later
         self.all_prompts = None
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
-        self.clip_skip = clip_skip
+        # ip adapter
+        self.ip_adapter_names = []
+        self.ip_adapter_scales = [0.0]
+        self.ip_adapter_images = []
+        self.ip_adapter_starts = [0.0]
+        self.ip_adapter_ends = [1.0]
+        self.ip_adapter_crops = []
+        # a1111 compatibility items
         shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
-        self.iteration = 0
-        self.is_control = False
-        self.is_hr_pass = False
-        self.is_refiner_pass = False
-        self.hr_force = False
-        self.enable_hr = None
-        self.hr_scale = None
-        self.hr_upscaler = None
-        self.hr_resize_mode = 0
-        self.hr_resize_context = 'None'
-        self.hr_resize_x = 0
-        self.hr_resize_y = 0
-        self.hr_upscale_to_x = 0
-        self.hr_upscale_to_y = 0
+        self.seed_enable_extras: bool = True,
+        self.is_using_inpainting_conditioning = False # a111 compatibility
+        self.batch_index = 0
+        self.refiner_switch_at = 0
+        self.hr_prompt = ''
+        self.all_hr_prompts = []
+        self.hr_negative_prompt = ''
+        self.all_hr_negative_prompts = []
         self.truncate_x = 0
         self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-        self.refiner_steps = 5
-        self.refiner_start = 0
-        self.refiner_prompt = ''
-        self.refiner_negative = ''
-        self.ops = []
-        self.resize_mode: int = resize_mode
-        self.resize_name: str = resize_name
-        self.resize_context: str = resize_context
+        self.comments = {}
+        self.sampler = None
+        self.nmask = None
+        self.initial_noise_multiplier = initial_noise_multiplier or shared.opts.initial_noise_multiplier
+        self.image_conditioning = None
+        self.prompt_for_display: str = None
+        # scripts
+        self.scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+        self.script_args_value: list = field(default=None, init=False)
+        self.scripts_setup_complete: bool = field(default=False, init=False)
+        self.script_args = script_args
+        self.per_script_args = {}
+        # settings to processing
         self.ddim_discretize = shared.opts.ddim_discretize
         self.s_min_uncond = shared.opts.s_min_uncond
         self.s_churn = shared.opts.s_churn
@@ -205,11 +313,11 @@ class StableDiffusionProcessing:
         self.hdr_tint_ratio=hdr_tint_ratio
         # globals
         self.embedder = None
-        # self.scheduled_prompt: bool = False
-        # self.prompt_embeds = []
-        # self.positive_pooleds = []
-        # self.negative_embeds = []
-        # self.negative_pooleds = []
+        self.scheduled_prompt: bool = False
+        self.prompt_embeds = []
+        self.positive_pooleds = []
+        self.negative_embeds = []
+        self.negative_pooleds = []
 
     @property
     def sd_model(self):
@@ -253,57 +361,9 @@ class StableDiffusionProcessing:
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-
-    def __init__(self,
-                 enable_hr: bool = False,
-                 denoising_strength: float = 0.75,
-                 firstphase_width: int = 0,
-                 firstphase_height: int = 0,
-                 hr_scale: float = 2.0,
-                 hr_force: bool = False,
-                 hr_resize_mode: int = 0,
-                 hr_resize_context: str = 'None',
-                 hr_upscaler: str = None,
-                 hr_second_pass_steps: int = 0,
-                 hr_resize_x: int = 0,
-                 hr_resize_y: int = 0,
-                 refiner_steps: int = 5,
-                 refiner_start: float = 0,
-                 refiner_prompt: str = '',
-                 refiner_negative: str = '',
-                 **kwargs
-                ):
-
+    def __init__(self, **kwargs):
+        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
         super().__init__(**kwargs)
-        self.reprocess = {}
-        self.enable_hr = enable_hr
-        self.denoising_strength = denoising_strength
-        self.hr_scale = hr_scale
-        self.hr_upscaler = hr_upscaler
-        self.hr_resize_mode = hr_resize_mode
-        self.hr_resize_context = hr_resize_context
-        self.hr_force = hr_force
-        self.hr_second_pass_steps = hr_second_pass_steps
-        self.hr_resize_x = hr_resize_x
-        self.hr_resize_y = hr_resize_y
-        self.hr_upscale_to_x = hr_resize_x
-        self.hr_upscale_to_y = hr_resize_y
-        if firstphase_width != 0 or firstphase_height != 0:
-            self.hr_upscale_to_x = self.width
-            self.hr_upscale_to_y = self.height
-            self.width = firstphase_width
-            self.height = firstphase_height
-        self.truncate_x = 0
-        self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-        self.refiner_steps = refiner_steps
-        self.refiner_start = refiner_start
-        self.refiner_prompt = refiner_prompt
-        self.refiner_negative = refiner_negative
-        self.sampler = None
-        self.scripts = None
-        self.script_args = []
-
 
     def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
         if shared.native:
@@ -361,41 +421,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-
-    def __init__(self, init_images: list = None, resize_mode: int = 0, resize_name: str = 'None', resize_context: str = 'None', denoising_strength: float = 0.3, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = False, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, scale_by: float = 1, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
+    def __init__(self, **kwargs):
+        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
         super().__init__(**kwargs)
-        self.init_images = init_images
-        self.resize_mode: int = resize_mode
-        self.resize_name: str = resize_name
-        self.resize_context: str = resize_context
-        self.denoising_strength: float = denoising_strength
-        self.hr_denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale
-        self.init_latent = None
-        self.image_mask = mask
-        self.latent_mask = None
-        self.mask_for_overlay = None
-        self.mask_blur_x = mask_blur # a1111 compatibility item
-        self.mask_blur_y = mask_blur # a1111 compatibility item
-        self.mask_blur = mask_blur
-        self.inpainting_fill = inpainting_fill
-        self.inpaint_full_res = inpaint_full_res
-        self.inpaint_full_res_padding = inpaint_full_res_padding
-        self.inpainting_mask_invert = inpainting_mask_invert
-        self.initial_noise_multiplier = shared.opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
-        self.mask = None
-        self.nmask = None
-        self.image_conditioning = None
-        self.refiner_steps = refiner_steps
-        self.refiner_start = refiner_start
-        self.refiner_prompt = refiner_prompt
-        self.refiner_negative = refiner_negative
-        self.enable_hr = None
-        self.is_batch = False
-        self.scale_by = scale_by
-        self.sampler = None
-        self.scripts = None
-        self.script_args = []
 
     def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
         if hasattr(self, 'init_images') and self.init_images is not None and len(self.init_images) > 0:
@@ -545,47 +573,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
 class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
     def __init__(self, **kwargs):
+        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
         super().__init__(**kwargs)
-        self.strength = None
-        self.adapter_conditioning_scale = None
-        self.adapter_conditioning_factor = None
-        self.guess_mode = None
-        self.controlnet_conditioning_scale = None
-        self.control_guidance_start = None
-        self.control_guidance_end = None
-        self.control_mode = None
-        self.reference_attn = None
-        self.reference_adain = None
-        self.attention_auto_machine_weight = None
-        self.gn_auto_machine_weight = None
-        self.style_fidelity = None
-        self.ref_image = None
-        self.image = None
-        self.query_weight = None
-        self.adain_weight = None
-        self.adapter_conditioning_factor = 1.0
-        self.attention = 'Attention'
-        self.fidelity = 0.5
-        self.mask_image = None
-        self.override = None
-        self.resize_mode_before = None
-        self.resize_name_before = None
-        self.width_before = None
-        self.height_before = None
-        self.scale_by_before = None
-        self.selected_scale_tab_before = None
-        self.resize_mode_after = None
-        self.resize_name_after = None
-        self.width_after = None
-        self.height_after = None
-        self.scale_by_after = None
-        self.selected_scale_tab_after = None
-        self.resize_mode_mask = None
-        self.resize_name_mask = None
-        self.width_mask = None
-        self.height_mask = None
-        self.scale_by_mask = None
-        self.selected_scale_tab_mask = None
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): # abstract
         pass
diff --git a/modules/shared.py b/modules/shared.py
index 48984c8be..7d19c721e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -822,8 +822,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "mask_apply_overlay": OptionInfo(True, "Apply mask as overlay"),
     "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", gr.ColorPicker, {}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
-    "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01, "visible": not native}),
+    "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
 
     # "postprocessing_sep_detailer": OptionInfo("<h2>Detailer</h2>", "", gr.HTML),
     "detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
@@ -1148,7 +1148,7 @@ cmd_opts = cmd_args.settings_args(opts, cmd_opts)
 if cmd_opts.use_xformers:
     opts.data['cross_attention_optimization'] = 'xFormers'
 opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
-opts.data['uni_pc_order'] = opts.schedulers_solver_order # compatibility
+opts.data['uni_pc_order'] = max(2, opts.schedulers_solver_order) # compatibility
 log.info(f'Engine: backend={backend} compute={devices.backend} device={devices.get_optimal_device_name()} attention="{opts.cross_attention_optimization}" mode={devices.inference_context.__name__}')
 if not native:
     log.warning('Backend=original is in maintainance-only mode')
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 38cde0aca..2f0e2f4b3 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -49,7 +49,6 @@ def txt2img(id_task, state,
         subseed_strength=subseed_strength,
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=True,
         sampler_name = processing.get_sampler_name(sampler_index),
         hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
         batch_size=batch_size,
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index 4cb8e4c18..22c89dac8 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -131,6 +131,7 @@ def create_ui():
 
                     full_quality, tiling, hidiffusion, cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = 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_boundry, 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')
                     detailer = shared.yolo.ui('img2img')
 
                     # with gr.Group(elem_id="inpaint_controls", visible=False) as inpaint_controls:
@@ -192,6 +193,7 @@ def create_ui():
                 inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
                 img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
                 hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio,
+                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,
                 override_settings,
             ]
             img2img_dict = dict(
diff --git a/modules/unipc/sampler.py b/modules/unipc/sampler.py
index bcc4eed76..b5e116d61 100644
--- a/modules/unipc/sampler.py
+++ b/modules/unipc/sampler.py
@@ -186,6 +186,6 @@ class UniPCSampler(object):
         )
 
         uni_pc = UniPC(model_fn, self.noise_schedule, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
-        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.schedulers_solver_order, lower_order_final=shared.opts.schedulers_use_loworder)
+        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
 
         return x.to(device), None

From 910b88e632f00e9ffcfc3f37858ffb4ec5dca86e Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 15:51:30 -0500
Subject: [PATCH 088/119] add refine/hires to img2img

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                | 2 ++
 modules/control/run.py      | 2 +-
 modules/processing_class.py | 9 ++++++---
 3 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5db77983c..f4178db22 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -63,6 +63,8 @@ This release can be considered an LTS release before we kick off the next round
     - add show networks on startup setting  
     - better mapping of networks previews  
     - optimize networks display load  
+  - Image2image:
+    - integrated refine/upscale/hires workflow
 - Other:  
   - Installer:
     - Log `venv` and package search paths
diff --git a/modules/control/run.py b/modules/control/run.py
index 74bab35c4..d2e1b36ef 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -70,7 +70,7 @@ def control_run(state: str = '',
                 enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.3, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
                 hr_scale: float = 1.0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0.0, refiner_prompt: str = '', refiner_negative: str = '',
                 video_skip_frames: int = 0, video_type: str = 'None', video_duration: float = 2.0, video_loop: bool = False, video_pad: int = 0, video_interpolate: int = 0,
-                *input_script_args
+                *input_script_args,
         ):
     # handle optional initialization via ui
     for u in units:
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 215507b4a..333e11925 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -69,7 +69,6 @@ class StableDiffusionProcessing:
                  hdr_tint_ratio: float = 0,
                  # img2img
                  init_images: list = None,
-                 init_latent: Any = None,
                  resize_mode: int = 0,
                  resize_name: str = 'None',
                  resize_context: str = 'None',
@@ -80,7 +79,6 @@ class StableDiffusionProcessing:
                  selected_scale_tab: int = 0, # pylint: disable=unused-argument # a1111 compatibility
                  # inpaint
                  mask: Any = None,
-                 image_mask: Any = None,
                  latent_mask: Any = None,
                  mask_for_overlay: Any = None,
                  mask_blur: int = 4,
@@ -179,7 +177,7 @@ class StableDiffusionProcessing:
         self.image_cfg_scale = image_cfg_scale
         self.scale_by = scale_by
         self.mask = mask
-        self.image_mask = mask
+        self.image_mask = mask # TODO duplciate mask params
         self.latent_mask = latent_mask
         self.mask_blur = mask_blur
         self.inpainting_fill = inpainting_fill
@@ -221,6 +219,7 @@ class StableDiffusionProcessing:
         self.mask_for_overlay = mask_for_overlay
         self.paste_to = paste_to
         self.init_latent = None
+
         # special handled items
         if firstphase_width != 0 or firstphase_height != 0:
             self.hr_upscale_to_x = self.width
@@ -238,6 +237,7 @@ class StableDiffusionProcessing:
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
+
         # ip adapter
         self.ip_adapter_names = []
         self.ip_adapter_scales = [0.0]
@@ -245,6 +245,7 @@ class StableDiffusionProcessing:
         self.ip_adapter_starts = [0.0]
         self.ip_adapter_ends = [1.0]
         self.ip_adapter_crops = []
+
         # a1111 compatibility items
         shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
         self.seed_enable_extras: bool = True,
@@ -263,12 +264,14 @@ class StableDiffusionProcessing:
         self.initial_noise_multiplier = initial_noise_multiplier or shared.opts.initial_noise_multiplier
         self.image_conditioning = None
         self.prompt_for_display: str = None
+
         # scripts
         self.scripts_value: scripts.ScriptRunner = field(default=None, init=False)
         self.script_args_value: list = field(default=None, init=False)
         self.scripts_setup_complete: bool = field(default=False, init=False)
         self.script_args = script_args
         self.per_script_args = {}
+
         # settings to processing
         self.ddim_discretize = shared.opts.ddim_discretize
         self.s_min_uncond = shared.opts.s_min_uncond

From b8cbe10c836995719582827acc0b6dde8728ce09 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 16:16:22 -0500
Subject: [PATCH 089/119] add bnb and quanto version info

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

diff --git a/modules/model_quant.py b/modules/model_quant.py
index 547a3d7ae..68bdfa7b2 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -32,14 +32,14 @@ def load_bnb(msg='', silent=False):
     global bnb # pylint: disable=global-statement
     if bnb is not None:
         return bnb
-    fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
-    log.debug(f'Quantization: type=bitsandbytes fn={fn}') # pylint: disable=protected-access
     install('bitsandbytes', quiet=True)
     try:
         import bitsandbytes
         bnb = bitsandbytes
         diffusers.utils.import_utils._bitsandbytes_available = True # pylint: disable=protected-access
         diffusers.utils.import_utils._bitsandbytes_version = '0.43.3' # pylint: disable=protected-access
+        fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
+        log.debug(f'Quantization: type=bitsandbytes version={bnb.__version__} fn={fn}') # pylint: disable=protected-access
         return bnb
     except Exception as e:
         if len(msg) > 0:
@@ -54,12 +54,12 @@ def load_quanto(msg='', silent=False):
     global quanto # pylint: disable=global-statement
     if quanto is not None:
         return quanto
-    fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
-    log.debug(f'Quantization: type=quanto fn={fn}') # pylint: disable=protected-access
     install('optimum-quanto', quiet=True)
     try:
         from optimum import quanto as optimum_quanto # pylint: disable=no-name-in-module
         quanto = optimum_quanto
+        fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
+        log.debug(f'Quantization: type=quanto version={quanto.__version__} fn={fn}') # pylint: disable=protected-access
         return quanto
     except Exception as e:
         if len(msg) > 0:

From f590445cd665ca62772148515e0cafb661ea0e8b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 11 Nov 2024 16:38:37 -0500
Subject: [PATCH 090/119] img2img refine upscale

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_diffusers.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 7537d0209..76009d2e1 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -152,13 +152,14 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
         p.is_hr_pass = True
         if hasattr(p, 'init_hr'):
             p.init_hr(p.hr_scale, p.hr_upscaler, force=p.hr_force)
-        else: # fake hires for img2img
-            p.hr_scale = p.scale_by
-            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_upscale_to_y = p.height
+        else:
+            if not p.is_hr_pass: # fake hires for img2img if not actual hr pass
+                p.hr_scale = p.scale_by
+                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
         prev_job = shared.state.job
 
         # hires runs on original pipeline

From 8b3d99535db450fbaea119d64a9273c9d87687d4 Mon Sep 17 00:00:00 2001
From: AI-Casanova <54461896+AI-Casanova@users.noreply.github.com>
Date: Mon, 11 Nov 2024 19:18:38 -0600
Subject: [PATCH 091/119] lora loading for wrapped models (pulid)

---
 extensions-builtin/Lora/networks.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 160487e88..b227f82f2 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -50,44 +50,45 @@ convert_diffusers_name_to_compvis = lora_convert.convert_diffusers_name_to_compv
 def assign_network_names_to_compvis_modules(sd_model):
     if sd_model is None:
         return
+    sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)  # wrapped model compatiblility
     network_layer_mapping = {}
     if shared.native:
-        if hasattr(shared.sd_model, 'text_encoder') and shared.sd_model.text_encoder is not None:
-            for name, module in shared.sd_model.text_encoder.named_modules():
-                prefix = "lora_te1_" if hasattr(shared.sd_model, 'text_encoder_2') else "lora_te_"
+        if hasattr(sd_model, 'text_encoder') and sd_model.text_encoder is not None:
+            for name, module in sd_model.text_encoder.named_modules():
+                prefix = "lora_te1_" if hasattr(sd_model, 'text_encoder_2') else "lora_te_"
                 network_name = prefix + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 module.network_layer_name = network_name
-        if hasattr(shared.sd_model, 'text_encoder_2'):
-            for name, module in shared.sd_model.text_encoder_2.named_modules():
+        if hasattr(sd_model, 'text_encoder_2'):
+            for name, module in sd_model.text_encoder_2.named_modules():
                 network_name = "lora_te2_" + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 module.network_layer_name = network_name
-        if hasattr(shared.sd_model, 'unet'):
-            for name, module in shared.sd_model.unet.named_modules():
+        if hasattr(sd_model, 'unet'):
+            for name, module in sd_model.unet.named_modules():
                 network_name = "lora_unet_" + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 module.network_layer_name = network_name
-        if hasattr(shared.sd_model, 'transformer'):
-            for name, module in shared.sd_model.transformer.named_modules():
+        if hasattr(sd_model, 'transformer'):
+            for name, module in sd_model.transformer.named_modules():
                 network_name = "lora_transformer_" + name.replace(".", "_")
                 network_layer_mapping[network_name] = module
                 if "norm" in network_name and "linear" not in network_name:
                     continue
                 module.network_layer_name = network_name
     else:
-        if not hasattr(shared.sd_model, 'cond_stage_model'):
+        if not hasattr(sd_model, 'cond_stage_model'):
             sd_model.network_layer_mapping = {}
             return
-        for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
+        for name, module in sd_model.cond_stage_model.wrapped.named_modules():
             network_name = name.replace(".", "_")
             network_layer_mapping[network_name] = module
             module.network_layer_name = network_name
-        for name, module in shared.sd_model.model.named_modules():
+        for name, module in sd_model.model.named_modules():
             network_name = name.replace(".", "_")
             network_layer_mapping[network_name] = module
             module.network_layer_name = network_name
-    sd_model.network_layer_mapping = network_layer_mapping
+    shared.sd_model.network_layer_mapping = network_layer_mapping
 
 
 def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> network.Network:

From 17a5f34cceba0fd69702cfd6e6e84faca4ee15e8 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 12 Nov 2024 11:42:02 -0500
Subject: [PATCH 092/119] update dependencies and changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                       | 48 +++++++++++++++++-------------
 extensions-builtin/sdnext-modernui |  2 +-
 installer.py                       |  4 +--
 modules/api/middleware.py          |  2 +-
 modules/prompt_parser_diffusers.py |  2 +-
 requirements.txt                   | 15 +++++++---
 webui.py                           |  6 ++--
 7 files changed, 46 insertions(+), 33 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f4178db22..bfed5ac4f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,19 +1,19 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-10
+## Update for 2024-11-12
 
 Smaller release just few days after the last one, but with some important fixes and improvements.  
 This release can be considered an LTS release before we kick off the next round of major updates.  
 
 - Docs:  
-  - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)
+  - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)  
   - UI built-in [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) search  
     since changelog is the best up-to-date source of info  
     go to info -> changelog and search/highligh/navigate directly in UI!  
   - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
     go to info -> wiki and search wiki pages directly in UI!  
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) and [Home](https://github.com/vladmandic/automatic) updates  
-- Integrations:
+- Integrations:  
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
     - advanced method of face id transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
@@ -21,7 +21,7 @@ This release can be considered an LTS release before we kick off the next round
     - compatible with *sdxl* for text-to-image, image-to-image, inpaint and detailer workflows  
     - can be used in xyz grid  
     - *note*: this module contains several advanced features on top of original implementation  
-  - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
+  - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference  
     - alternative to traditional `img2img` with more control over restoration process  
     - select in *image -> scripts -> instantir*  
     - compatible with *sdxl*  
@@ -29,19 +29,19 @@ This release can be considered an LTS release before we kick off the next round
   - [ConsiStory](https://github.com/NVlabs/consistory): Consistent Image Generation  
     - create consistent anchor image and then generate images that are consistent with anchor  
     - select in *scripts -> consistory*  
-    - compatible with *sdxl*
+    - compatible with *sdxl*  
     - *note*: very resource intensive and not compatible with model offloading  
     - *note*: changing default parameters can lead to unexpected results and/or failures  
     - *note*: after used once it cannot be unloaded without reloading base model  
-  - [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) base and large:
-    - *in process -> visual query*
-    - caption modes:
+  - [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) base and large:  
+    - *in process -> visual query*  
+    - caption modes:  
       `<GENERATE_TAGS>` generate tags  
       `<CAPTION>`, `<DETAILED_CAPTION>`, `<MORE_DETAILED_CAPTION>` caption image  
       `<ANALYZE>` image composition  
       `<MIXED_CAPTION>`, `<MIXED_CAPTION_PLUS>` detailed caption and tags with optional analyze  
 
-- Model improvements:
+- Model improvements:  
   - SD3: ControlNets:  
     - *InstantX Canny, Pose, Depth, Tile*  
     - *Alimama Inpainting, SoftEdge*  
@@ -51,38 +51,44 @@ This release can be considered an LTS release before we kick off the next round
     - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
     - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
 
-- Workflow improvements:
+- Workflow improvements:  
   - XYZ grid:  
     - optional time benchmark info to individual images  
     - optional add params to individual images  
     - create video from generated grid images  
       supports all standard video types and interpolation  
+  - Prompt parser:  
+    - support for prompt scheduling  
+    - renamed parser options: `native`, `xhinker`, `compel`, `a1111`, `fixed`  
+    - improved caching  
   - UI:  
-    - better gallery and networks sidebar sizing
+    - better gallery and networks sidebar sizing  
     - add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys)  
     - add show networks on startup setting  
     - better mapping of networks previews  
     - optimize networks display load  
-  - Image2image:
-    - integrated refine/upscale/hires workflow
+  - Image2image:  
+    - integrated refine/upscale/hires workflow  
 - Other:  
-  - Installer:
-    - Log `venv` and package search paths
+  - Installer:  
+    - Log `venv` and package search paths  
     - Auto-remove invalid packages from `venv/site-packages`  
       e.g. packages starting with `~` which are left-over due to windows access violation  
     - Requirements: update  
-  - Scripts:
-    - More verbose descriptions for all scripts
-  - Model loader:
+  - Scripts:  
+    - More verbose descriptions for all scripts  
+  - Model loader:  
     - Report modules included in safetensors when attempting to load a model  
   - CLI:  
     - refactor command line params  
       run `webui.sh`/`webui.bat` with `--help` to see all options  
-    - added `cli/model-metadata.py` to display metadata in any safetensors file
-    - added `cli/model-keys.py` to quicky display content of any safetensors file
-  - Internal:
+    - added `cli/model-metadata.py` to display metadata in any safetensors file  
+    - added `cli/model-keys.py` to quicky display content of any safetensors file  
+  - Internal:  
     - Repo: move screenshots to GH pages  
     - Auto pipeline switching coveres wrapper classes and nested pipelines  
+    - Full settings validation on load of `config.json`  
+    - Refactor of all params in main processing classes  
 
 - Fixes:  
   - custom watermark add alphablending  
diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui
index 257be050a..4647bd7f8 160000
--- a/extensions-builtin/sdnext-modernui
+++ b/extensions-builtin/sdnext-modernui
@@ -1 +1 @@
-Subproject commit 257be050afd46a21e77cc9fe60a04d30ed5ffbe4
+Subproject commit 4647bd7f86be9d2783a9ba1f38acaa9bcec942d2
diff --git a/installer.py b/installer.py
index e50271280..6700f4f19 100644
--- a/installer.py
+++ b/installer.py
@@ -459,12 +459,12 @@ def check_python(supported_minors=[9, 10, 11, 12], reason=None):
 
 # check diffusers version
 def check_diffusers():
-    sha = '0d1d267b12e47b40b0e8f265339c76e0f45f8c49'
+    sha = 'dac623b59f52c58383a39207d5147aa34e0047cd'
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else 0)
     cur = opts.get('diffusers_version', '') if minor > 0 else ''
     if (minor == 0) or (cur != sha):
-        log.debug(f'Diffusers {"install" if minor == 0 else "upgrade"}: package={pkg} current={cur} target={sha}')
+        log.info(f'Diffusers {"install" if minor == 0 else "upgrade"}: package={pkg} current={cur} target={sha}')
         if minor > 0:
             pip('uninstall --yes diffusers', ignore=True, quiet=True, uv=False)
         pip(f'install --upgrade git+https://github.com/huggingface/diffusers@{sha}', ignore=False, quiet=True, uv=False)
diff --git a/modules/api/middleware.py b/modules/api/middleware.py
index 095c5b23d..7eb2c40e8 100644
--- a/modules/api/middleware.py
+++ b/modules/api/middleware.py
@@ -90,4 +90,4 @@ def setup_middleware(app: FastAPI, cmd_opts):
             return handle_exception(req, e)
 
     app.build_middleware_stack() # rebuild middleware stack on-the-fly
-    log.debug(f'FastAPI middleware: {[m.__class__.__name__ for m in app.user_middleware]}')
+    log.debug(f'API middleware: {[m.cls for m in app.user_middleware]}')
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index e53af7957..97696a5e1 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -291,7 +291,7 @@ def get_prompt_schedule(prompt, steps):
 def get_tokens(msg, prompt):
     global token_dict, token_type # pylint: disable=global-statement
     if not shared.native:
-        return
+        return 0
     if shared.sd_loaded and hasattr(shared.sd_model, 'tokenizer') and shared.sd_model.tokenizer is not None:
         if token_dict is None or token_type != shared.sd_model_type:
             token_type = shared.sd_model_type
diff --git a/requirements.txt b/requirements.txt
index de0223bd5..12a9f85cb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,7 @@
+# required for python 3.12
 setuptools==69.5.1
+
+# standard
 patch-ng
 anyio
 addict
@@ -27,6 +30,8 @@ ruff
 pylint
 invisible-watermark
 pi-heif
+
+# versioned
 safetensors==0.4.5
 tensordict==0.1.2
 peft==0.13.1
@@ -36,7 +41,7 @@ torchsde==0.2.6
 antlr4-python3-runtime==4.9.3
 requests==2.32.3
 tqdm==4.66.5
-accelerate==1.0.1
+accelerate==1.1.1
 opencv-contrib-python-headless==4.9.0.80
 einops==0.4.1
 gradio==3.43.2
@@ -44,9 +49,6 @@ huggingface_hub==0.26.2
 numexpr==2.8.8
 numpy==1.26.4
 numba==0.59.1
-blendmodes
-scipy
-pandas
 protobuf==4.25.3
 pytorch_lightning==1.9.4
 tokenizers==0.20.3
@@ -57,6 +59,11 @@ timm==0.9.16
 pydantic==1.10.15
 pyparsing==3.1.4
 typing-extensions==4.12.2
+
+# additional
+blendmodes
+scipy
+pandas
 torchdiffeq
 dctorch
 scikit-image
diff --git a/webui.py b/webui.py
index 32e7ed75b..82c41ecab 100644
--- a/webui.py
+++ b/webui.py
@@ -110,7 +110,7 @@ def initialize():
     yolo.initialize()
     timer.startup.record("detailer")
 
-    log.debug('Load extensions')
+    log.info('Load extensions')
     t_timer, t_total = modules.scripts.load_scripts()
     timer.startup.record("extensions")
     timer.startup.records["extensions"] = t_total # scripts can reset the time
@@ -179,7 +179,7 @@ def load_model():
 
 
 def create_api(app):
-    log.debug('Creating API')
+    log.debug('API initialize')
     from modules.api.api import Api
     api = Api(app, queue_lock)
     return api
@@ -231,7 +231,7 @@ def start_common():
 
 
 def start_ui():
-    log.debug('Creating UI')
+    log.info('UI start')
     modules.script_callbacks.before_ui_callback()
     timer.startup.record("before-ui")
     shared.demo = modules.ui.create_ui(timer.startup)

From aabe523a5f4ac20f1c4449883e6441bf6046b01d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 12 Nov 2024 12:12:46 -0500
Subject: [PATCH 093/119] update changelog

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md             | 56 +++++++++++++++++++++++++++++++---------
 modules/face/__init__.py |  4 +--
 2 files changed, 46 insertions(+), 14 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bfed5ac4f..381fe370f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,8 +2,30 @@
 
 ## Update for 2024-11-12
 
-Smaller release just few days after the last one, but with some important fixes and improvements.  
-This release can be considered an LTS release before we kick off the next round of major updates.  
+### Highlights for 2024-11-12
+
+*What's New?*
+
+First, a massive update to docs including new UI top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki), many updates and new articles AND full **built-in search** capabilities
+
+**New integrations**:
+- [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment
+- [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference
+- [ConsiStory](https://github.com/NVlabs/consistory): Consistent Image Generation
+- [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) VQA captioning
+
+**Workflow Improvements**:
+- SD3x: ControlNets and all-in-one-safetensors
+- XYZ grid: benchmarking, video creation, etc.
+- Enhanced prompt parsing
+- UI improvements
+- Installer self-healing `venv`
+
+And quite a few more improvements and fixes since the last update - for full details see changelog...
+
+[README](https://github.com/vladmandic/automatic/blob/master/README.md) | [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
+
+### Details for 2024-11-12
 
 - Docs:  
   - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)  
@@ -60,6 +82,7 @@ This release can be considered an LTS release before we kick off the next round
   - Prompt parser:  
     - support for prompt scheduling  
     - renamed parser options: `native`, `xhinker`, `compel`, `a1111`, `fixed`  
+    - parser options are available in xyz grid  
     - improved caching  
   - UI:  
     - better gallery and networks sidebar sizing  
@@ -85,21 +108,12 @@ This release can be considered an LTS release before we kick off the next round
     - added `cli/model-metadata.py` to display metadata in any safetensors file  
     - added `cli/model-keys.py` to quicky display content of any safetensors file  
   - Internal:  
-    - Repo: move screenshots to GH pages  
     - Auto pipeline switching coveres wrapper classes and nested pipelines  
     - Full settings validation on load of `config.json`  
     - Refactor of all params in main processing classes  
 
 - Fixes:  
   - custom watermark add alphablending  
-  - detailer min/max size as fractions of image size  
-  - ipadapter load on-demand  
-  - ipadapter face use correct yolo model  
-  - list diffusers remove duplicates  
-  - fix legacy extensions access to shared objects  
-  - fix diffusers load from folder  
-  - fix lora enum logging on windows  
-  - fix xyz grid with batch count  
   - fix xyz grid include images  
   - fix xyz skip on interrupted  
   - fix vqa models ignoring hfcache folder setting  
@@ -108,7 +122,25 @@ This release can be considered an LTS release before we kick off the next round
   - fix text2video scripts  
   - dont uninstall flash-attn  
   - ui css fixes  
-  - move downloads of some auxillary models to hfcache instead of models folder  
+
+## Update for 2024-11-01
+
+Smaller release just 3 days after the last one, but with some important fixes and improvements.  
+This release can be considered an LTS release before we kick off the next round of major updates.  
+
+- Other:
+  - Repo: move screenshots to GH pages
+  - Update requirements
+- Fixes:
+  - detailer min/max size as fractions of image size  
+  - ipadapter load on-demand  
+  - ipadapter face use correct yolo model  
+  - list diffusers remove duplicates  
+  - fix legacy extensions access to shared objects  
+  - fix diffusers load from folder  
+  - fix lora enum logging on windows  
+  - fix xyz grid with batch count  
+  - move dowwloads of some auxillary models to hfcache instead of models folder  
 
 ## Update for 2024-10-29
 
diff --git a/modules/face/__init__.py b/modules/face/__init__.py
index 19af6d01b..c18da6e2e 100644
--- a/modules/face/__init__.py
+++ b/modules/face/__init__.py
@@ -28,10 +28,10 @@ class Script(scripts.Script):
                 elif hasattr(file, 'name'):
                     image = Image.open(file.name) # _TemporaryFileWrapper from gr.Files
                 else:
-                    raise ValueError(f'PhotoMaker unknown input: {file}')
+                    raise ValueError(f'Face: unknown input: {file}')
                 init_images.append(image)
             except Exception as e:
-                shared.log.warning(f'PhotoMaker failed to load image: {e}')
+                shared.log.warning(f'Face: failed to load image: {e}')
         return init_images
 
     def mode_change(self, mode):

From adec80861b5b89a917aede3749ebc0051f320f92 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 12 Nov 2024 13:20:01 -0500
Subject: [PATCH 094/119] fix validation

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/shared.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 7d19c721e..9d56c25d5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1082,8 +1082,9 @@ class Options:
         unknown_settings = []
         for k, v in self.data.items():
             info: OptionInfo = self.data_labels.get(k, None)
-            if not info.validate(k, v):
-                self.data[k] = info.default
+            if info is not None:
+                if not info.validate(k, v):
+                    self.data[k] = info.default
             if info is not None and not self.same_type(info.default, v):
                 log.warning(f"Setting validation: {k}={v} ({type(v).__name__} expected={type(info.default).__name__})")
                 self.data[k] = info.default

From bea12ee6f253c43b46a175e217033c24585fe0b7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 12 Nov 2024 13:33:50 -0500
Subject: [PATCH 095/119] fix api

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/styles.py | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/modules/styles.py b/modules/styles.py
index 0599bcd86..a5bfbecfd 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -253,27 +253,33 @@ class StyleDatabase:
         return found[0] if len(found) > 0 else self.no_style
 
     def get_style_prompts(self, styles):
-        if styles is None or not isinstance(styles, list):
+        if styles is None:
+            return []
+        if styles is not isinstance(styles, list):
             shared.log.error(f'Styles invalid: {styles}')
             return []
         return [self.find_style(x).prompt for x in styles]
 
     def get_negative_style_prompts(self, styles):
-        if styles is None or not isinstance(styles, list):
+        if styles is None:
+            return []
+        if styles is not isinstance(styles, list):
             shared.log.error(f'Styles invalid: {styles}')
             return []
         return [self.find_style(x).negative_prompt for x in styles]
 
     def apply_styles_to_prompts(self, prompts, negatives, styles, seeds):
-        if styles is None or not isinstance(styles, list):
+        if styles is None:
+            return prompts, negatives
+        if styles is not isinstance(styles, list):
             shared.log.error(f'Styles invalid styles: {styles}')
-            return prompts
+            return prompts, negatives
         if prompts is None or not isinstance(prompts, list):
             shared.log.error(f'Styles invalid prompts: {prompts}')
-            return prompts
+            return prompts, negatives
         if seeds is None or not isinstance(prompts, list):
             shared.log.error(f'Styles invalid seeds: {seeds}')
-            return prompts
+            return prompts, negatives
         parsed_positive = []
         parsed_negative = []
         for i in range(len(prompts)):
@@ -304,6 +310,8 @@ class StyleDatabase:
         return prompt
 
     def apply_styles_to_extra(self, p):
+        if p.styles is None:
+            return
         if p.styles is None or not isinstance(p.styles, list):
             shared.log.error(f'Styles invalid: {p.styles}')
             return

From 8654e3e1bd1ee3c2d6cd333279ee9012bc048189 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 13 Nov 2024 08:07:40 -0500
Subject: [PATCH 096/119] update

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_class.py |  6 +++++-
 modules/styles.py           | 14 +++++++++-----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/modules/processing_class.py b/modules/processing_class.py
index 333e11925..24ec0d8d9 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -45,7 +45,7 @@ class StableDiffusionProcessing:
                  pag_scale: float = 0.0,
                  pag_adaptive: float = 0.5,
                  # styles
-                 styles: List[str] = None,
+                 styles: List[str] = [],
                  # vae
                  tiling: bool = False,
                  full_quality: bool = True,
@@ -119,7 +119,10 @@ class StableDiffusionProcessing:
                  # metadata
                  extra_generation_params: Dict[Any, Any] = {},
                 ):
+
+        # extra args set by processing loop
         self.task_args = {}
+
         # state items
         self.state: str = ''
         self.ops = []
@@ -136,6 +139,7 @@ class StableDiffusionProcessing:
         self.negative_pooleds = []
         self.disable_extra_networks = False
         self.iteration = 0
+
         # initializers
         self.prompt = prompt
         self.seed = seed
diff --git a/modules/styles.py b/modules/styles.py
index a5bfbecfd..0dd48eb7f 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -255,7 +255,7 @@ class StyleDatabase:
     def get_style_prompts(self, styles):
         if styles is None:
             return []
-        if styles is not isinstance(styles, list):
+        if not isinstance(styles, list):
             shared.log.error(f'Styles invalid: {styles}')
             return []
         return [self.find_style(x).prompt for x in styles]
@@ -263,7 +263,7 @@ class StyleDatabase:
     def get_negative_style_prompts(self, styles):
         if styles is None:
             return []
-        if styles is not isinstance(styles, list):
+        if not isinstance(styles, list):
             shared.log.error(f'Styles invalid: {styles}')
             return []
         return [self.find_style(x).negative_prompt for x in styles]
@@ -271,7 +271,7 @@ class StyleDatabase:
     def apply_styles_to_prompts(self, prompts, negatives, styles, seeds):
         if styles is None:
             return prompts, negatives
-        if styles is not isinstance(styles, list):
+        if not isinstance(styles, list):
             shared.log.error(f'Styles invalid styles: {styles}')
             return prompts, negatives
         if prompts is None or not isinstance(prompts, list):
@@ -294,7 +294,9 @@ class StyleDatabase:
         return parsed_positive, parsed_negative
 
     def apply_styles_to_prompt(self, prompt, styles):
-        if styles is None or not isinstance(styles, list):
+        if styles is None:
+            return prompt
+        if not isinstance(styles, list):
             shared.log.error(f'Styles invalid: {styles}')
             return prompt
         prompt = apply_styles_to_prompt(prompt, [self.find_style(x).prompt for x in styles])
@@ -302,7 +304,9 @@ class StyleDatabase:
         return prompt
 
     def apply_negative_styles_to_prompt(self, prompt, styles):
-        if styles is None or not isinstance(styles, list):
+        if styles is None:
+            return prompt
+        if not isinstance(styles, list):
             shared.log.error(f'Styles invalid: {styles}')
             return prompt
         prompt = apply_styles_to_prompt(prompt, [self.find_style(x).negative_prompt for x in styles])

From 880f6f6c4b37f01772d2537fdb9e7b306d997b43 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 13 Nov 2024 13:23:24 -0500
Subject: [PATCH 097/119] fix denoising strength

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 TODO.md                         |  3 +++
 modules/control/run.py          |  4 ++--
 modules/processing_class.py     |  4 ++--
 modules/processing_diffusers.py | 12 ++++++------
 4 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/TODO.md b/TODO.md
index 2f3f90852..ed3ebfbdb 100644
--- a/TODO.md
+++ b/TODO.md
@@ -4,6 +4,9 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ## Future Candidates
 
+- sd35 ip-adapter
+- flux.1 ip-adapter
+- flow-match scheudlers: <https://github.com/huggingface/diffusers/issues/9607>
 - async lowvram: <https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14855>
 - fp8: <https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031>
 - ipadapter-negative: <https://github.com/huggingface/diffusers/discussions/7167>
diff --git a/modules/control/run.py b/modules/control/run.py
index d2e1b36ef..c141c8443 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -66,8 +66,8 @@ def control_run(state: str = '',
                 resize_mode_before: int = 0, resize_name_before: str = 'None', resize_context_before: str = 'None', width_before: int = 512, height_before: int = 512, scale_by_before: float = 1.0, selected_scale_tab_before: int = 0,
                 resize_mode_after: int = 0, resize_name_after: str = 'None', resize_context_after: str = 'None', width_after: int = 0, height_after: int = 0, scale_by_after: float = 1.0, selected_scale_tab_after: int = 0,
                 resize_mode_mask: int = 0, resize_name_mask: str = 'None', resize_context_mask: str = 'None', width_mask: int = 0, height_mask: int = 0, scale_by_mask: float = 1.0, selected_scale_tab_mask: int = 0,
-                denoising_strength: float = 0, batch_count: int = 1, batch_size: int = 1,
-                enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.3, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
+                denoising_strength: float = 0.0, batch_count: int = 1, batch_size: int = 1,
+                enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.0, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
                 hr_scale: float = 1.0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0.0, refiner_prompt: str = '', refiner_negative: str = '',
                 video_skip_frames: int = 0, video_type: str = 'None', video_duration: float = 2.0, video_loop: bool = False, video_pad: int = 0, video_interpolate: int = 0,
                 *input_script_args,
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 24ec0d8d9..9a5bc088c 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -72,7 +72,7 @@ class StableDiffusionProcessing:
                  resize_mode: int = 0,
                  resize_name: str = 'None',
                  resize_context: str = 'None',
-                 denoising_strength: float = 0.3,
+                 denoising_strength: float = 0.0,
                  image_cfg_scale: float = None,
                  initial_noise_multiplier: float = None, # pylint: disable=unused-argument # a1111 compatibility
                  scale_by: float = 1,
@@ -100,7 +100,7 @@ class StableDiffusionProcessing:
                  hr_second_pass_steps: int = 0,
                  hr_resize_x: int = 0,
                  hr_resize_y: int = 0,
-                 hr_denoising_strength: float = 0.50,
+                 hr_denoising_strength: float = 0.0,
                  refiner_steps: int = 5,
                  refiner_start: float = 0,
                  refiner_prompt: str = '',
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 76009d2e1..2164134b1 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -177,7 +177,8 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
             sd_hijack_hypertile.hypertile_set(p, hr=True)
 
         latent_upscale = shared.latent_upscale_modes.get(p.hr_upscaler, None)
-        if (latent_upscale is not None or p.hr_force) and getattr(p, 'hr_denoising_strength', p.denoising_strength) > 0:
+        strength = p.hr_denoising_strength if p.hr_denoising_strength > 0 else p.denoising_strength
+        if (latent_upscale is not None or p.hr_force) and strength > 0:
             p.ops.append('hires')
             sd_models_compile.openvino_recompile_model(p, hires=True, refiner=False)
             if shared.sd_model.__class__.__name__ == "OnnxRawPipeline":
@@ -185,8 +186,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
             p.hr_force = True
 
         # hires
-        p.denoising_strength = getattr(p, 'hr_denoising_strength', p.denoising_strength)
-        if p.hr_force and p.denoising_strength == 0:
+        if p.hr_force and strength == 0:
             shared.log.warning('HiRes skip: denoising=0')
             p.hr_force = False
         if p.hr_force:
@@ -204,9 +204,9 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 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)
-            orig_denoise = p.denoising_strength
-            p.denoising_strength = getattr(p, 'hr_denoising_strength', p.denoising_strength)
             update_sampler(p, shared.sd_model, second_pass=True)
+            orig_denoise = p.denoising_strength
+            p.denoising_strength = strength
             hires_args = set_pipeline_args(
                 p=p,
                 model=shared.sd_model,
@@ -221,7 +221,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 output_type='latent',
                 clip_skip=p.clip_skip,
                 image=output.images,
-                strength=p.denoising_strength,
+                strength=strength,
                 desc='Hires',
             )
             shared.state.job = 'HiRes'

From 8d506612914df71e7e8443a4df8e5a338aa068ed Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 13 Nov 2024 17:28:04 -0500
Subject: [PATCH 098/119] add docker support

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .dockerignore      | 26 ++++++++++++++++++++++++++
 .gitignore         |  1 -
 CHANGELOG.md       |  2 ++
 Dockerfile         | 28 ++++++++++++++++++++++++++++
 cli/api-txt2img.js |  2 +-
 installer.py       | 12 ++++++++++--
 launch.py          |  4 ++--
 modules/shared.py  |  2 ++
 8 files changed, 71 insertions(+), 6 deletions(-)
 create mode 100644 .dockerignore
 create mode 100644 Dockerfile

diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 000000000..21e56ed85
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,26 @@
+# defaults
+.history
+.vscode/
+/__pycache__
+/.ruff_cache
+/cache
+/cache.json
+/config.json
+/extensions/*
+/html/extensions.json
+/html/themes.json
+/metadata.json
+/node_modules
+/outputs/*
+/package-lock.json
+/params.txt
+/pnpm-lock.yaml
+/styles.csv
+/tmp
+/ui-config.json
+/user.css
+/venv
+/webui-user.bat
+/webui-user.sh
+/*.log.*
+/*.log
diff --git a/.gitignore b/.gitignore
index dca4e17ad..9e72426c7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -74,4 +74,3 @@ dist/
 !/models/VAE-approx/model.pt
 !/models/Reference
 !/models/Reference/**/*
-
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 381fe370f..5274d2153 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,7 @@ First, a massive update to docs including new UI top-level **info** tab with acc
 - [MiaoshouAI PromptGen v2.0](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v2.0) VQA captioning
 
 **Workflow Improvements**:
+- Native Docker support
 - SD3x: ControlNets and all-in-one-safetensors
 - XYZ grid: benchmarking, video creation, etc.
 - Enhanced prompt parsing
@@ -74,6 +75,7 @@ And quite a few more improvements and fixes since the last update - for full det
     - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
 
 - Workflow improvements:  
+  - Native Docker support with pre-defined [Dockerfile](https://github.com/vladmandic/automatic/blob/dev/Dockerfile)
   - XYZ grid:  
     - optional time benchmark info to individual images  
     - optional add params to individual images  
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 000000000..e2c689478
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,28 @@
+# SD.Next Dockerfile
+FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
+# TBD add org info
+LABEL org.opencontainers.image.authors="vladmandic"
+WORKDIR /
+COPY . .
+# stop pip and uv from caching
+ENV PIP_NO_CACHE_DIR=true
+ENV UV_NO_CACHE=true
+# disable model hashing for faster startup
+ENV SD_NOHASHING=true
+# set data directories
+ENV SD_DATADIR="/mnt/data"
+ENV SD_MODELSDIR="/mnt/models"
+# install dependencies
+RUN ["apt-get", "-y", "update"]
+RUN ["apt-get", "-y", "install", "git"]
+# sdnext will run all necessary pip install ops and then exit
+RUN ["python", "launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test"]
+# preinstall additional packages to avoid installation during runtime
+RUN ["uv", "pip", "install", "-r", "requirements-extra.txt"]
+# actually run sdnext
+CMD ["python", "launch.py", "--debug", "--skip-all", "--listen", "--quick", "--api-log", "--log", "sdnext.log"]
+# expose port
+EXPOSE 7860
+# TBD add healthcheck function
+HEALTHCHECK NONE
+STOPSIGNAL SIGINT
diff --git a/cli/api-txt2img.js b/cli/api-txt2img.js
index 8d0e9f5d1..43e09449b 100755
--- a/cli/api-txt2img.js
+++ b/cli/api-txt2img.js
@@ -5,7 +5,7 @@
 const fs = require('fs'); // eslint-disable-line no-undef
 const process = require('process'); // eslint-disable-line no-undef
 
-const sd_url = process.env.SDAPI_URL || 'http://127.0.0.1:7860';
+const sd_url = process.env.SDAPI_URL || 'http://127.0.0.1:32769';
 const sd_username = process.env.SDAPI_USR;
 const sd_password = process.env.SDAPI_PWD;
 const sd_options = {
diff --git a/installer.py b/installer.py
index 6700f4f19..80bca224c 100644
--- a/installer.py
+++ b/installer.py
@@ -459,6 +459,8 @@ def check_python(supported_minors=[9, 10, 11, 12], reason=None):
 
 # check diffusers version
 def check_diffusers():
+    if args.skip_all or args.skip_requirements:
+        return
     sha = 'dac623b59f52c58383a39207d5147aa34e0047cd'
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else 0)
@@ -474,6 +476,8 @@ def check_diffusers():
 
 # check onnx version
 def check_onnx():
+    if args.skip_all or args.skip_requirements:
+        return
     if not installed('onnx', quiet=True):
         install('onnx', 'onnx', ignore=True)
     if not installed('onnxruntime', quiet=True) and not (installed('onnxruntime-gpu', quiet=True) or installed('onnxruntime-openvino', quiet=True) or installed('onnxruntime-training', quiet=True)): # allow either
@@ -481,6 +485,8 @@ def check_onnx():
 
 
 def check_torchao():
+    if args.skip_all or args.skip_requirements:
+        return
     if installed('torchao', quiet=True):
         ver = package_version('torchao')
         if ver != '0.5.0':
@@ -492,14 +498,16 @@ def check_torchao():
 
 def install_cuda():
     log.info('CUDA: nVidia toolkit detected')
-    install('onnxruntime-gpu', 'onnxruntime-gpu', ignore=True, quiet=True)
+    if not (args.skip_all or args.skip_requirements):
+        install('onnxruntime-gpu', 'onnxruntime-gpu', ignore=True, quiet=True)
     # return os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/cu124')
     return os.environ.get('TORCH_COMMAND', 'torch==2.5.1+cu124 torchvision==0.20.1+cu124 --index-url https://download.pytorch.org/whl/cu124')
 
 
 def install_rocm_zluda():
+    if args.skip_all or args.skip_requirements:
+        return
     from modules import rocm
-
     if not rocm.is_installed:
         log.warning('ROCm: could not find ROCm toolkit installed')
         log.info('Using CPU-only torch')
diff --git a/launch.py b/launch.py
index 903234490..af3db2c0f 100755
--- a/launch.py
+++ b/launch.py
@@ -204,13 +204,13 @@ def main():
     installer.check_python()
     if args.reset:
         installer.git_reset()
-    if args.skip_git:
+    if args.skip_git or args.skip_all:
         installer.log.info('Skipping GIT operations')
     installer.check_version()
     installer.log.info(f'Platform: {installer.print_dict(installer.get_platform())}')
     installer.check_venv()
     installer.log.info(f'Args: {sys.argv[1:]}')
-    if not args.skip_env:
+    if not args.skip_env or args.skip_all:
         installer.set_environment()
     if args.uv:
         installer.install("uv", "uv")
diff --git a/modules/shared.py b/modules/shared.py
index 9d56c25d5..c32aa80c0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -83,6 +83,8 @@ console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
 dir_timestamps = {}
 dir_cache = {}
 max_workers = 8
+if os.environ.get("SD_HFCACHEDIR", None) is not None:
+    hfcache_dir = os.environ.get("SD_HFCACHEDIR")
 if os.environ.get("HF_HUB_CACHE", None) is not None:
     hfcache_dir = os.environ.get("HF_HUB_CACHE")
 elif os.environ.get("HF_HUB", None) is not None:

From c77370ef26be2fdea7f9e532103855a7e34c572f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 13 Nov 2024 17:30:52 -0500
Subject: [PATCH 099/119] filter sd_metadata_file

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_checkpoint.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py
index e1787246f..afc5842e4 100644
--- a/modules/sd_checkpoint.py
+++ b/modules/sd_checkpoint.py
@@ -295,13 +295,7 @@ def read_metadata_from_safetensors(filename):
                 if k == 'format' and v == 'pt':
                     continue
                 large = True if len(v) > 2048 else False
-                if large and k == 'ss_datasets':
-                    continue
-                if large and k == 'workflow':
-                    continue
-                if large and k == 'prompt':
-                    continue
-                if large and k == 'ss_bucket_info':
+                if large and k in ['ss_datasets', 'workflow', 'prompt', 'ss_bucket_info', 'sd_metadata_file']:
                     continue
                 if v[0:1] == '{':
                     try:

From a0d55a5956e23f12638b6bb1b666169adc2ebb9e Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 13 Nov 2024 18:22:20 -0500
Subject: [PATCH 100/119] pulid with refine pass

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/processing_vae.py          |  6 +++
 modules/prompt_parser_diffusers.py | 76 +++++++++++++++++-------------
 modules/pulid/pulid_sdxl.py        |  4 ++
 wiki                               |  2 +-
 4 files changed, 53 insertions(+), 35 deletions(-)

diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index 0473beeb1..3c0357c81 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -35,6 +35,8 @@ def create_latents(image, p, dtype=None, device=None):
 
 def full_vae_decode(latents, model):
     t0 = time.time()
+    if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
+        model = model.pipe
     if model is None or not hasattr(model, 'vae'):
         shared.log.error('VAE not found in model')
         return []
@@ -148,6 +150,8 @@ def taesd_vae_encode(image):
 def vae_decode(latents, model, output_type='np', full_quality=True, width=None, height=None):
     t0 = time.time()
     model = model or shared.sd_model
+    if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
+        model = model.pipe
     if latents is None or not torch.is_tensor(latents): # already decoded
         return latents
     prev_job = shared.state.job
@@ -196,6 +200,8 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
 def vae_encode(image, model, full_quality=True): # pylint: disable=unused-variable
     if shared.state.interrupted or shared.state.skipped:
         return []
+    if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
+        model = model.pipe
     if not hasattr(model, 'vae'):
         shared.log.error('VAE not found in model')
         return []
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 97696a5e1..234272907 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -19,20 +19,25 @@ cache = OrderedDict()
 embedder = None
 
 
-def prompt_compatible():
+def prompt_compatible(pipe = None):
+    pipe = pipe or shared.sd_model
     if (
-            'StableDiffusion' not in shared.sd_model.__class__.__name__ and
-            'DemoFusion' not in shared.sd_model.__class__.__name__ and
-            'StableCascade' not in shared.sd_model.__class__.__name__ and
-            'Flux' not in shared.sd_model.__class__.__name__
+        'StableDiffusion' not in pipe.__class__.__name__ and
+        'DemoFusion' not in pipe.__class__.__name__ and
+        'StableCascade' not in pipe.__class__.__name__ and
+        'Flux' not in pipe.__class__.__name__
     ):
-        shared.log.warning(f"Prompt parser not supported: {shared.sd_model.__class__.__name__}")
+        shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
         return False
     return True
 
 
-def prepare_model():
-    pipe = shared.sd_model
+def prepare_model(pipe = None):
+    pipe = pipe or shared.sd_model
+    if not hasattr(pipe, "text_encoder") and hasattr(shared.sd_model, "pipe"):
+        pipe = pipe.pipe
+    if not hasattr(pipe, "text_encoder"):
+        return None
     if shared.opts.diffusers_offload_mode == "balanced":
         pipe = sd_models.apply_balanced_offload(pipe)
     elif hasattr(pipe, "maybe_free_model_hooks"):
@@ -62,7 +67,10 @@ class PromptEmbedder:
         earlyout = self.checkcache(p)
         if earlyout:
             return
-        pipe = prepare_model()
+        pipe = prepare_model(p.sd_model)
+        if pipe is None:
+            shared.log.error("Prompt encode: cannot find text encoder in model")
+            return
         # per prompt in batch
         for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts)):
             self.prepare_schedule(prompt, negative_prompt)
@@ -168,8 +176,8 @@ class PromptEmbedder:
             self.negative_pooleds[batchidx].append(negative_pooled)
 
         if debug_enabled:
-            get_tokens('positive', positive_prompt)
-            get_tokens('negative', negative_prompt)
+            get_tokens(pipe, 'positive', positive_prompt)
+            get_tokens(pipe, 'negative', negative_prompt)
         pipe = prepare_model()
 
     def __call__(self, key, step=0):
@@ -288,25 +296,25 @@ def get_prompt_schedule(prompt, steps):
     return temp, len(schedule) > 1
 
 
-def get_tokens(msg, prompt):
+def get_tokens(pipe, msg, prompt):
     global token_dict, token_type # pylint: disable=global-statement
     if not shared.native:
         return 0
-    if shared.sd_loaded and hasattr(shared.sd_model, 'tokenizer') and shared.sd_model.tokenizer is not None:
+    if shared.sd_loaded and hasattr(pipe, 'tokenizer') and pipe.tokenizer is not None:
         if token_dict is None or token_type != shared.sd_model_type:
             token_type = shared.sd_model_type
-            fn = shared.sd_model.tokenizer.name_or_path
+            fn = pipe.tokenizer.name_or_path
             if fn.endswith('tokenizer'):
-                fn = os.path.join(shared.sd_model.tokenizer.name_or_path, 'vocab.json')
+                fn = os.path.join(pipe.tokenizer.name_or_path, 'vocab.json')
             else:
-                fn = os.path.join(shared.sd_model.tokenizer.name_or_path, 'tokenizer', 'vocab.json')
+                fn = os.path.join(pipe.tokenizer.name_or_path, 'tokenizer', 'vocab.json')
             token_dict = shared.readfile(fn, silent=True)
-            for k, v in shared.sd_model.tokenizer.added_tokens_decoder.items():
+            for k, v in pipe.tokenizer.added_tokens_decoder.items():
                 token_dict[str(v)] = k
             shared.log.debug(f'Tokenizer: words={len(token_dict)} file="{fn}"')
-        has_bos_token = shared.sd_model.tokenizer.bos_token_id is not None
-        has_eos_token = shared.sd_model.tokenizer.eos_token_id is not None
-        ids = shared.sd_model.tokenizer(prompt)
+        has_bos_token = pipe.tokenizer.bos_token_id is not None
+        has_eos_token = pipe.tokenizer.eos_token_id is not None
+        ids = pipe.tokenizer(prompt)
         ids = getattr(ids, 'input_ids', [])
         tokens = []
         for i in ids:
@@ -337,10 +345,10 @@ def normalize_prompt(pairs: list):
     return pairs
 
 
-def get_prompts_with_weights(prompt: str):
+def get_prompts_with_weights(pipe, prompt: str):
     t0 = time.time()
-    manager = DiffusersTextualInversionManager(shared.sd_model, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
-    prompt = manager.maybe_convert_prompt(prompt, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
+    manager = DiffusersTextualInversionManager(pipe, pipe.tokenizer or pipe.tokenizer_2)
+    prompt = manager.maybe_convert_prompt(prompt, pipe.tokenizer or pipe.tokenizer_2)
     texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
     if shared.opts.prompt_mean_norm:
         texts_and_weights = normalize_prompt(texts_and_weights)
@@ -348,7 +356,7 @@ def get_prompts_with_weights(prompt: str):
     if debug_enabled:
         all_tokens = 0
         for text in texts:
-            tokens = get_tokens('section', text)
+            tokens = get_tokens(pipe, 'section', text)
             all_tokens += tokens
         debug(f'Prompt tokenizer: parser={shared.opts.prompt_attention} tokens={all_tokens}')
     debug(f'Prompt: weights={texts_and_weights} time={(time.time() - t0):.3f}')
@@ -412,7 +420,7 @@ def pad_to_same_length(pipe, embeds, empty_embedding_providers=None):
     return embeds
 
 
-def split_prompts(prompt, SD3 = False):
+def split_prompts(pipe, prompt, SD3 = False):
     if prompt.find("TE2:") != -1:
         prompt, prompt2 = prompt.split("TE2:")
     else:
@@ -430,7 +438,7 @@ def split_prompts(prompt, SD3 = False):
     prompt3 = " " if prompt3.strip() == "" else prompt3.strip()
 
     if SD3 and prompt3 != " ":
-        ps, _ws = get_prompts_with_weights(prompt3)
+        ps, _ws = get_prompts_with_weights(pipe, prompt3)
         prompt3 = " ".join(ps)
     return prompt, prompt2, prompt3
 
@@ -438,15 +446,15 @@ def split_prompts(prompt, SD3 = False):
 def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
     device = devices.device
     SD3 = hasattr(pipe, 'text_encoder_3')
-    prompt, prompt_2, prompt_3 = split_prompts(prompt, SD3)
-    neg_prompt, neg_prompt_2, neg_prompt_3 = split_prompts(neg_prompt, SD3)
+    prompt, prompt_2, prompt_3 = split_prompts(pipe, prompt, SD3)
+    neg_prompt, neg_prompt_2, neg_prompt_3 = split_prompts(pipe, neg_prompt, SD3)
 
     if prompt != prompt_2:
-        ps = [get_prompts_with_weights(p) for p in [prompt, prompt_2]]
-        ns = [get_prompts_with_weights(p) for p in [neg_prompt, neg_prompt_2]]
+        ps = [get_prompts_with_weights(pipe, p) for p in [prompt, prompt_2]]
+        ns = [get_prompts_with_weights(pipe, p) for p in [neg_prompt, neg_prompt_2]]
     else:
-        ps = 2 * [get_prompts_with_weights(prompt)]
-        ns = 2 * [get_prompts_with_weights(neg_prompt)]
+        ps = 2 * [get_prompts_with_weights(pipe, prompt)]
+        ns = 2 * [get_prompts_with_weights(pipe, neg_prompt)]
 
     positives, positive_weights = zip(*ps)
     negatives, negative_weights = zip(*ns)
@@ -561,8 +569,8 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
 
 def get_xhinker_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
     is_sd3 = hasattr(pipe, 'text_encoder_3')
-    prompt, prompt_2, _prompt_3 = split_prompts(prompt, is_sd3)
-    neg_prompt, neg_prompt_2, _neg_prompt_3 = split_prompts(neg_prompt, is_sd3)
+    prompt, prompt_2, _prompt_3 = split_prompts(pipe, prompt, is_sd3)
+    neg_prompt, neg_prompt_2, _neg_prompt_3 = split_prompts(pipe, neg_prompt, is_sd3)
     try:
         prompt = pipe.maybe_convert_prompt(prompt, pipe.tokenizer)
         neg_prompt = pipe.maybe_convert_prompt(neg_prompt, pipe.tokenizer)
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 3053d759d..7ee9a138e 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -4,6 +4,7 @@ import insightface
 import numpy as np
 import torch
 import torch.nn as nn
+from PIL import Image
 from diffusers import StableDiffusionXLPipeline
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
@@ -353,6 +354,9 @@ class StableDiffusionXLPuLIDPipeline:
         debug(f'PulID call: width={width} height={height} cfg={guidance_scale} steps={num_inference_steps} seed={seed} strength={strength} id_scale={id_scale} output={output_type}')
         self.step = 0 # pylint: disable=attribute-defined-outside-init
         self.callback_on_step_end = callback_on_step_end # pylint: disable=attribute-defined-outside-init
+        if isinstance(image, list) and len(image) > 0 and isinstance(image[0], Image.Image):
+            if image[0].width != width or image[0].height != height: # override width/height if different
+                width, height = image[0].width, image[0].height
         size = (1, height, width)
         # sigmas
         sigmas = self.get_sigmas_karras(num_inference_steps).to(self.device)
diff --git a/wiki b/wiki
index 352fc655b..96f28bb7c 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 352fc655b0dc9edb22aac093186da087ba18b474
+Subproject commit 96f28bb7cec5a4e198a3244a88309f1957f75d03

From c4f90328fb81331ce430fb8aa3cf49418591e187 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 13 Nov 2024 18:28:28 -0500
Subject: [PATCH 101/119] add extra requirements

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .gitignore             |  2 ++
 CHANGELOG.md           |  2 +-
 requirements-extra.txt | 14 ++++++++++++++
 3 files changed, 17 insertions(+), 1 deletion(-)
 create mode 100644 requirements-extra.txt

diff --git a/.gitignore b/.gitignore
index 9e72426c7..9fac9b310 100644
--- a/.gitignore
+++ b/.gitignore
@@ -43,6 +43,8 @@ tunableop_results*.csv
 !webui.bat
 !webui.sh
 !package.json
+!requirements.txt
+!requirements-extra.txt
 
 # pyinstaller
 *.spec
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5274d2153..dd463f727 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,7 +2,7 @@
 
 ## Update for 2024-11-12
 
-### Highlights for 2024-11-12
+### Highlights for 2024-11-13
 
 *What's New?*
 
diff --git a/requirements-extra.txt b/requirements-extra.txt
new file mode 100644
index 000000000..d4c18f1f0
--- /dev/null
+++ b/requirements-extra.txt
@@ -0,0 +1,14 @@
+# additional requirements that are not required for sdnext base operations
+basicsr
+gfpgan
+clean-fid
+insightface
+pydantic==1.10.15
+albumentations==1.4.3
+optimum-quanto
+nncf==2.7.0
+clip_interrogator==0.6.0
+bitsandbytes
+gguf
+pynvml
+ultralytics

From b59a21f9240fcc00d793cb8456d5f2d957c702e0 Mon Sep 17 00:00:00 2001
From: Seunghoon Lee <lshqqytiger@naver.com>
Date: Thu, 14 Nov 2024 12:48:29 +0900
Subject: [PATCH 102/119] zluda hijack rfftn

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

diff --git a/modules/zluda_hijacks.py b/modules/zluda_hijacks.py
index ea906e7c0..0f42a5448 100644
--- a/modules/zluda_hijacks.py
+++ b/modules/zluda_hijacks.py
@@ -19,6 +19,11 @@ def fft_ifftn(input: torch.Tensor, *args, **kwargs) -> torch.Tensor: # pylint: d
     return _fft_ifftn(input.cpu(), *args, **kwargs).to(input.device)
 
 
+_fft_rfftn = torch.fft.rfftn
+def fft_rfftn(input: torch.Tensor, *args, **kwargs) -> torch.Tensor: # pylint: disable=redefined-builtin
+    return _fft_rfftn(input.cpu(), *args, **kwargs).to(input.device)
+
+
 def jit_script(f, *_, **__): # experiment / provide dummy graph
     f.graph = torch._C.Graph() # pylint: disable=protected-access
     return f
@@ -29,4 +34,5 @@ def do_hijack():
     torch.topk = topk
     torch.fft.fftn = fft_fftn
     torch.fft.ifftn = fft_ifftn
+    torch.fft.rfftn = fft_rfftn
     torch.jit.script = jit_script

From 4033f2b63f58bc17d5d8eae035c2768213512962 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 08:16:09 -0500
Subject: [PATCH 103/119] pulid enable inpaint mask only

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 TODO.md                     |  5 -----
 modules/pulid/pulid_sdxl.py | 19 +++++++++++++++----
 modules/sd_models.py        | 22 +++++++++++++---------
 scripts/pulid_ext.py        |  4 ----
 4 files changed, 28 insertions(+), 22 deletions(-)

diff --git a/TODO.md b/TODO.md
index ed3ebfbdb..d5cc19cf7 100644
--- a/TODO.md
+++ b/TODO.md
@@ -7,11 +7,6 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 - sd35 ip-adapter
 - flux.1 ip-adapter
 - flow-match scheudlers: <https://github.com/huggingface/diffusers/issues/9607>
-- async lowvram: <https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14855>
-- fp8: <https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031>
 - ipadapter-negative: <https://github.com/huggingface/diffusers/discussions/7167>
 - include reference styles
-
-### Missing
-
 - control api scripts compatibility
diff --git a/modules/pulid/pulid_sdxl.py b/modules/pulid/pulid_sdxl.py
index 7ee9a138e..01ca660a7 100644
--- a/modules/pulid/pulid_sdxl.py
+++ b/modules/pulid/pulid_sdxl.py
@@ -1,3 +1,4 @@
+from typing import Union
 import os
 import cv2
 import insightface
@@ -5,7 +6,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from PIL import Image
-from diffusers import StableDiffusionXLPipeline
+from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
 from huggingface_hub import hf_hub_download, snapshot_download
@@ -28,7 +29,17 @@ debug = log.trace if os.environ.get('SD_PULID_DEBUG', None) is not None else lam
 
 
 class StableDiffusionXLPuLIDPipeline:
-    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, dtype: torch.dtype=None, providers: list=None, offload: bool=True, sampler=None, cache_dir=None, sdp: bool=True, version: str='v1.1'):
+    def __init__(self,
+                 pipe: Union[StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline],
+                 device: torch.device,
+                 dtype: torch.dtype=None,
+                 providers: list=None,
+                 offload: bool=True,
+                 sampler=None,
+                 cache_dir=None,
+                 sdp: bool=True,
+                 version: str='v1.1',
+                ):
         super().__init__()
         self.device = device
         self.dtype = dtype or torch.float16
@@ -282,7 +293,7 @@ class StableDiffusionXLPuLIDPipeline:
         t = self.timestep(sigma)
         x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
         cfg_scale = extra_args['cfg_scale']
-        debug(f'PulID sample start: step={self.step+1} x={x.shape} dtype={x.dtype} timestep={t.item()} sigma={sigma.shape} cfg={cfg_scale} args={extra_args.keys()}')
+        # debug(f'PulID sample start: step={self.step+1} x={x.shape} dtype={x.dtype} timestep={t.item()} sigma={sigma.shape} cfg={cfg_scale} args={extra_args.keys()}')
         eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
         eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
         noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
@@ -290,7 +301,7 @@ class StableDiffusionXLPuLIDPipeline:
         if self.callback_on_step_end is not None:
             self.step += 1
             self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
-        debug(f'PulID sample end:   step={self.step} x={latent.shape} dtype={x.dtype} min={torch.amin(latent)} max={torch.amax(latent)}')
+        # debug(f'PulID sample end:   step={self.step} x={latent.shape} dtype={x.dtype} min={torch.amin(latent)} max={torch.amax(latent)}')
         return latent
 
     def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
diff --git a/modules/sd_models.py b/modules/sd_models.py
index e139895ea..be543cb49 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1081,9 +1081,10 @@ def set_diffuser_pipe(pipe, new_pipe_type):
         return pipe
 
     # skip specific pipelines
+    cls = pipe.__class__.__name__
     if n in exclude:
         return pipe
-    if 'Onnx' in pipe.__class__.__name__:
+    if 'Onnx' in cls:
         return pipe
 
     new_pipe = None
@@ -1114,27 +1115,27 @@ def set_diffuser_pipe(pipe, new_pipe_type):
                 elif new_pipe_type == DiffusersTaskType.INPAINTING:
                     new_pipe = diffusers.AutoPipelineForInpainting.from_pipe(pipe)
                 else:
-                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
+                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls}')
                     return pipe
             except Exception as e: # pylint: disable=unused-variable
-                shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
+                shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls} {e}')
                 return pipe
         else:
             try: # maybe a wrapper pipeline so just change the class
                 if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
-                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING, cls) # pylint: disable=protected-access
                     new_pipe = pipe
                 elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
-                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, cls) # pylint: disable=protected-access
                     new_pipe = pipe
                 elif new_pipe_type == DiffusersTaskType.INPAINTING:
-                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING, pipe.__class__.__name__) # pylint: disable=protected-access
+                    pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING, cls) # pylint: disable=protected-access
                     new_pipe = pipe
                 else:
-                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={pipe.__class__.__name__}')
+                    shared.log.error(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls}')
                     return pipe
             except Exception as e: # pylint: disable=unused-variable
-                shared.log.warning(f'Pipeline class set failed: type={new_pipe_type} pipeline={pipe.__class__.__name__} {e}')
+                shared.log.warning(f'Pipeline class set failed: type={new_pipe_type} pipeline={cls} {e}')
                 return pipe
 
     # if pipe.__class__ == new_pipe.__class__:
@@ -1158,7 +1159,7 @@ def set_diffuser_pipe(pipe, new_pipe_type):
         new_pipe.pipe = set_diffuser_pipe(new_pipe.pipe, new_pipe_type)
 
     fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
-    shared.log.debug(f"Pipeline class change: original={pipe.__class__.__name__} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
+    shared.log.debug(f"Pipeline class change: original={cls} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
     pipe = new_pipe
     return pipe
 
@@ -1187,6 +1188,9 @@ def set_diffusers_attention(pipe):
             else:
                 module.set_attn_processor(attention)
 
+    if hasattr(pipe, 'pipe'):
+        set_diffusers_attention(pipe.pipe)
+
     if 'ControlNet' in pipe.__class__.__name__: # do not replace attention in ControlNet pipelines
         return
     shared.log.debug(f'Setting model: attention="{shared.opts.cross_attention_optimization}"')
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index 43039d73a..d01ca2847 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -18,7 +18,6 @@ class Script(scripts.Script):
     def __init__(self):
         self.pulid = None
         self.cache = None
-        self.mask_apply_overlay = shared.opts.mask_apply_overlay
         self.preprocess = 0
         super().__init__()
         self.register() # pulid is script with processing override so xyz doesnt execute
@@ -151,8 +150,6 @@ class Script(scripts.Script):
             shared.log.warning('PuLID: batch size not supported')
             p.batch_size = 1
 
-        self.mask_apply_overlay = shared.opts.mask_apply_overlay
-        shared.opts.data['mask_apply_overlay'] = False
         sdp = shared.opts.cross_attention_optimization == "Scaled-Dot-Product"
         strength = getattr(p, 'pulid_strength', strength)
         zero = getattr(p, 'pulid_zero', zero)
@@ -242,7 +239,6 @@ class Script(scripts.Script):
     def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=unused-argument
         _strength, _zero, _sampler, _ortho, _gallery, restore, _offload, _version = args
         if hasattr(shared.sd_model, 'pipe') and shared.sd_model_type == "sdxl":
-            shared.opts.data['mask_apply_overlay'] = self.mask_apply_overlay
             restore = getattr(p, 'pulid_restore', restore)
             if restore:
                 if hasattr(shared.sd_model, 'app'):

From 94b71003e01363708df4ef2eb0ffaea1aad620ee Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 10:11:20 -0500
Subject: [PATCH 104/119] custom swagger docs

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md        |   3 +-
 html/swagger.css    | 868 ++++++++++++++++++++++++++++++++++++++++++++
 modules/api/api.py  |   6 +-
 modules/api/docs.py |  96 +++++
 webui.py            |  12 +-
 5 files changed, 974 insertions(+), 11 deletions(-)
 create mode 100644 html/swagger.css
 create mode 100644 modules/api/docs.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index dd463f727..bac0f3756 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -36,12 +36,13 @@ And quite a few more improvements and fixes since the last update - for full det
   - UI built-in [wiki](https://github.com/vladmandic/automatic/wiki)  
     go to info -> wiki and search wiki pages directly in UI!  
   - major [Wiki](https://github.com/vladmandic/automatic/wiki) and [Home](https://github.com/vladmandic/automatic) updates  
+  - updated API swagger docs for at `/docs`  
 - Integrations:  
   - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment  
     - advanced method of face id transfer with better quality as well as control over identity and appearance  
       try it out, likely the best quality available for sdxl models  
     - select in *scripts -> pulid*  
-    - compatible with *sdxl* for text-to-image, image-to-image, inpaint and detailer workflows  
+    - compatible with *sdxl* for text-to-image, image-to-image, inpaint, refine, detailer workflows  
     - can be used in xyz grid  
     - *note*: this module contains several advanced features on top of original implementation  
   - [InstantIR](https://github.com/instantX-research/InstantIR): Blind Image Restoration with Instant Generative Reference  
diff --git a/html/swagger.css b/html/swagger.css
new file mode 100644
index 000000000..3c24a39ab
--- /dev/null
+++ b/html/swagger.css
@@ -0,0 +1,868 @@
+.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;
+}
+
+.swagger-ui .json-schema-2020-12-keyword__name--primary {
+  color: aqua;
+}
+
+.swagger-ui .json-schema-2020-12-property .json-schema-2020-12__title {
+  color: aqua;
+}
+
+@media only screen and (prefers-color-scheme: dark) {
+
+  a { color: #8c8cfa; }
+
+  ::-webkit-scrollbar-track-piece { background-color: rgba(255, 255, 255, .2) !important; }
+
+  ::-webkit-scrollbar-track { background-color: rgba(255, 255, 255, .3) !important; }
+
+  ::-webkit-scrollbar-thumb { background-color: rgba(255, 255, 255, .5) !important; }
+
+  embed[type="application/pdf"] { filter: invert(90%); }
+
+  html {
+      background: #1f1f1f !important;
+      box-sizing: border-box;
+      filter: contrast(100%) brightness(100%) saturate(100%);
+      overflow-y: scroll;
+  }
+
+  body {
+      background: #1f1f1f;
+      background-color: #1f1f1f;
+      background-image: none !important;
+  }
+
+  button, input, select, textarea {
+      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; }
+
+  .swagger-ui .debug * { outline: #e6da99 solid 1px; }
+
+  .swagger-ui .debug-white * { outline: #fff solid 1px; }
+
+  .swagger-ui .debug-black * { outline: #bfbfbf solid 1px; }
+
+  .swagger-ui .debug-grid { background: url(data:image/png;base64,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) 0 0; }
+
+  .swagger-ui .debug-grid-16 { background: url(data:image/png;base64,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) 0 0; }
+
+  .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; }
+
+  .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; }
+
+  .swagger-ui .b--washed-blue { border-color: #0c312d; }
+
+  .swagger-ui .b--washed-green { border-color: #0f3d2c; }
+
+  .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; }
+
+  .swagger-ui .shadow-3 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
+
+  .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; }
+
+      .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,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiIHN0YW5kYWxvbmU9Im5vIj8+CjxzdmcKICAgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIgogICB4bWxuczpjYz0iaHR0cDovL2NyZWF0aXZlY29tbW9ucy5vcmcvbnMjIgogICB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiCiAgIHhtbG5zOnN2Zz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciCiAgIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIKICAgeG1sbnM6c29kaXBvZGk9Imh0dHA6Ly9zb2RpcG9kaS5zb3VyY2Vmb3JnZS5uZXQvRFREL3NvZGlwb2RpLTAuZHRkIgogICB4bWxuczppbmtzY2FwZT0iaHR0cDovL3d3dy5pbmtzY2FwZS5vcmcvbmFtZXNwYWNlcy9pbmtzY2FwZSIKICAgaW5rc2NhcGU6dmVyc2lvbj0iMS4wICg0MDM1YTRmYjQ5LCAyMDIwLTA1LTAxKSIKICAgc29kaXBvZGk6ZG9jbmFtZT0iZG93bmxvYWQuc3ZnIgogICBpZD0ic3ZnNCIKICAgdmVyc2lvbj0iMS4xIgogICB2aWV3Qm94PSIwIDAgMjAgMjAiPgogIDxtZXRhZGF0YQogICAgIGlkPSJtZXRhZGF0YTEwIj4KICAgIDxyZGY6UkRGPgogICAgICA8Y2M6V29yawogICAgICAgICByZGY6YWJvdXQ9IiI+CiAgICAgICAgPGRjOmZvcm1hdD5pbWFnZS9zdmcreG1sPC9kYzpmb3JtYXQ+CiAgICAgICAgPGRjOnR5cGUKICAgICAgICAgICByZGY6cmVzb3VyY2U9Imh0dHA6Ly9wdXJsLm9yZy9kYy9kY21pdHlwZS9TdGlsbEltYWdlIiAvPgogICAgICA8L2NjOldvcms+CiAgICA8L3JkZjpSREY+CiAgPC9tZXRhZGF0YT4KICA8ZGVmcwogICAgIGlkPSJkZWZzOCIgLz4KICA8c29kaXBvZGk6bmFtZWR2aWV3CiAgICAgaW5rc2NhcGU6Y3VycmVudC1sYXllcj0ic3ZnNCIKICAgICBpbmtzY2FwZTp3aW5kb3ctbWF4aW1pemVkPSIxIgogICAgIGlua3NjYXBlOndpbmRvdy15PSItOSIKICAgICBpbmtzY2FwZTp3aW5kb3cteD0iLTkiCiAgICAgaW5rc2NhcGU6Y3k9IjEwIgogICAgIGlua3NjYXBlOmN4PSIxMCIKICAgICBpbmtzY2FwZTp6b29tPSI0MS41IgogICAgIHNob3dncmlkPSJmYWxzZSIKICAgICBpZD0ibmFtZWR2aWV3NiIKICAgICBpbmtzY2FwZTp3aW5kb3ctaGVpZ2h0PSIxMDAxIgogICAgIGlua3NjYXBlOndpbmRvdy13aWR0aD0iMTkyMCIKICAgICBpbmtzY2FwZTpwYWdlc2hhZG93PSIyIgogICAgIGlua3NjYXBlOnBhZ2VvcGFjaXR5PSIwIgogICAgIGd1aWRldG9sZXJhbmNlPSIxMCIKICAgICBncmlkdG9sZXJhbmNlPSIxMCIKICAgICBvYmplY3R0b2xlcmFuY2U9IjEwIgogICAgIGJvcmRlcm9wYWNpdHk9IjEiCiAgICAgYm9yZGVyY29sb3I9IiM2NjY2NjYiCiAgICAgcGFnZWNvbG9yPSIjZmZmZmZmIiAvPgogIDxwYXRoCiAgICAgc3R5bGU9ImZpbGw6I2ZmZmZmZiIKICAgICBpZD0icGF0aDIiCiAgICAgZD0iTTEzLjQxOCA3Ljg1OWEuNjk1LjY5NSAwIDAxLjk3OCAwIC42OC42OCAwIDAxMCAuOTY5bC0zLjkwOCAzLjgzYS42OTcuNjk3IDAgMDEtLjk3OSAwbC0zLjkwOC0zLjgzYS42OC42OCAwIDAxMC0uOTY5LjY5NS42OTUgMCAwMS45NzggMEwxMCAxMWwzLjQxOC0zLjE0MXoiIC8+Cjwvc3ZnPgo=) 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;
+  }
+}
\ No newline at end of file
diff --git a/modules/api/api.py b/modules/api/api.py
index 23c0a77f1..47612559b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -5,7 +5,7 @@ from fastapi import FastAPI, APIRouter, Depends, Request
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from fastapi.exceptions import HTTPException
 from modules import errors, shared, postprocessing
-from modules.api import models, endpoints, script, helpers, server, nvml, generate, process, control, gallery
+from modules.api import models, endpoints, script, helpers, server, nvml, generate, process, control, gallery, docs
 
 
 errors.install()
@@ -23,8 +23,10 @@ class Api:
                 for line in file.readlines():
                     user, password = line.split(":")
                     self.credentials[user.replace('"', '').strip()] = password.replace('"', '').strip()
-
         self.router = APIRouter()
+        if shared.cmd_opts.docs:
+            docs.create_docs(app)
+            docs.create_redocs(app)
         self.app = app
         self.queue_lock = queue_lock
         self.generate = generate.APIGenerate(queue_lock)
diff --git a/modules/api/docs.py b/modules/api/docs.py
new file mode 100644
index 000000000..f61042e26
--- /dev/null
+++ b/modules/api/docs.py
@@ -0,0 +1,96 @@
+import json
+from starlette.responses import HTMLResponse
+from fastapi import FastAPI
+from fastapi.openapi.docs import get_redoc_html, swagger_ui_default_parameters
+from fastapi.encoders import jsonable_encoder
+
+
+def get_swagger_ui_html(*,
+                        openapi_url: str,
+                        title: str,
+                        swagger_js_url: str = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui-bundle.js",
+                        swagger_css_url: str = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui.css",
+                        swagger_extra_css_url: str = None,
+                        swagger_favicon_url: str = "https://fastapi.tiangolo.com/img/favicon.png",
+                        oauth2_redirect_url: str = None,
+                        init_oauth: dict = None,
+                        swagger_ui_parameters: dict = None,
+                       ) -> HTMLResponse:
+    current_swagger_ui_parameters = swagger_ui_default_parameters.copy()
+    if swagger_ui_parameters:
+        current_swagger_ui_parameters.update(swagger_ui_parameters)
+    html = f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <link type="text/css" rel="stylesheet" href="{swagger_css_url}">
+            <link rel="shortcut icon" href="{swagger_favicon_url}">
+            <title>{title}</title>
+        </head>
+        <body>
+            <div id="swagger-ui"></div>
+            <script src="{swagger_js_url}"></script>
+            <script>
+                const ui = SwaggerUIBundle({{
+                    url: '{openapi_url}',
+    """
+    if swagger_extra_css_url is not None:
+        html = html.replace('</head>', f'<link type="text/css" rel="stylesheet" href="{swagger_extra_css_url}"></head>')
+    for key, value in current_swagger_ui_parameters.items():
+        html += f"{json.dumps(key)}: {json.dumps(jsonable_encoder(value))},\n"
+    if oauth2_redirect_url:
+        html += f"oauth2RedirectUrl: window.location.origin + '{oauth2_redirect_url}',"
+    html += """
+    presets: [
+        SwaggerUIBundle.presets.apis,
+        SwaggerUIBundle.SwaggerUIStandalonePreset
+        ],
+    })"""
+    if init_oauth:
+        html += f"""
+        ui.initOAuth({json.dumps(jsonable_encoder(init_oauth))})
+        """
+    html += """
+    </script>
+    </body>
+    </html>
+    """
+    return HTMLResponse(html)
+
+
+def create_docs(app: FastAPI):
+    swagger_ui_parameters = {
+        "displayOperationId": True,
+        "layout": "BaseLayout",
+        "showExtensions": True,
+        "showCommonExtensions": True,
+        "deepLinking": False,
+        "dom_id": "#swagger-ui",
+    }
+
+    @app.get("/docs", include_in_schema=True)
+    async def custom_swagger_html():
+        res = get_swagger_ui_html(
+            title=f'{app.title}: Swagger UI',
+            openapi_url=app.openapi_url,
+            swagger_favicon_url='/file=html/favicon.svg',
+            swagger_ui_parameters=swagger_ui_parameters,
+            swagger_extra_css_url='file=html/swagger.css',
+        )
+        # res = inject_css(html.content, 'html/swagger.css')
+        return res
+
+
+def create_redocs(app: FastAPI):
+    @app.get("/redocs", include_in_schema=True)
+    async def custom_redoc_html():
+        res = get_redoc_html(
+            title=f'{app.title}: ReDoc',
+            openapi_url=app.openapi_url,
+            redoc_favicon_url='/file=html/favicon.svg',
+        )
+        return res
+
+"""
+https://github.com/Amoenus/SwaggerDark/blob/master/SwaggerDark.css
+"""
\ No newline at end of file
diff --git a/webui.py b/webui.py
index 82c41ecab..9684ef7c8 100644
--- a/webui.py
+++ b/webui.py
@@ -51,13 +51,10 @@ fastapi_args = {
     "version": f'0.0.{git_commit}',
     "title": "SD.Next",
     "description": "SD.Next",
-    "docs_url": "/docs" if cmd_opts.docs else None,
-    "redoc_url": "/redocs" if cmd_opts.docs else None,
-    "swagger_ui_parameters": {
-        "displayOperationId": True,
-        "showCommonExtensions": True,
-        "deepLinking": False,
-    }
+    "docs_url": None,
+    "redoc_url": None,
+    # "docs_url": "/docs" if cmd_opts.docs else None, # custom handler in api.py
+    # "redoc_url": "/redocs" if cmd_opts.docs else None,
 }
 
 import modules.sd_hijack
@@ -277,7 +274,6 @@ def start_ui():
             max_threads=64,
             show_api=False,
             quiet=True,
-            # favicon_path='html/logo.ico',
             favicon_path='html/favicon.svg',
             allowed_paths=allowed_paths,
             app_kwargs=fastapi_args,

From 4f1c9cf5c73340ff668e7f513b74e7c74381e428 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 11:18:24 -0500
Subject: [PATCH 105/119] api validation

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/api/docs.py           |  4 ----
 modules/api/helpers.py        |  7 +++++--
 modules/api/models.py         | 12 +++++++++---
 modules/api/script.py         | 13 +++++++++++--
 modules/processing_args.py    |  5 ++++-
 modules/processing_class.py   |  2 +-
 modules/processing_helpers.py | 27 +++++++++++++++++----------
 7 files changed, 47 insertions(+), 23 deletions(-)

diff --git a/modules/api/docs.py b/modules/api/docs.py
index f61042e26..f384a1328 100644
--- a/modules/api/docs.py
+++ b/modules/api/docs.py
@@ -90,7 +90,3 @@ def create_redocs(app: FastAPI):
             redoc_favicon_url='/file=html/favicon.svg',
         )
         return res
-
-"""
-https://github.com/Amoenus/SwaggerDark/blob/master/SwaggerDark.css
-"""
\ No newline at end of file
diff --git a/modules/api/helpers.py b/modules/api/helpers.py
index 1678a851e..f13047b9b 100644
--- a/modules/api/helpers.py
+++ b/modules/api/helpers.py
@@ -14,7 +14,7 @@ def validate_sampler_name(name):
     return name
 
 
-def decode_base64_to_image(encoding):
+def decode_base64_to_image(encoding, quiet=False):
     if encoding.startswith("data:image/"):
         encoding = encoding.split(";")[1].split(",")[1]
     try:
@@ -22,7 +22,10 @@ def decode_base64_to_image(encoding):
         return image
     except Exception as e:
         shared.log.warning(f'API cannot decode image: {e}')
-        raise HTTPException(status_code=500, detail="Invalid encoded image") from e
+        if not quiet:
+            raise HTTPException(status_code=500, detail="Invalid encoded image") from e
+        else:
+            return None
 
 
 def encode_pil_to_base64(image):
diff --git a/modules/api/models.py b/modules/api/models.py
index f5b89c2a9..4f01f47d5 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -194,8 +194,10 @@ ReqTxt2Img = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img",
     StableDiffusionProcessingTxt2Img,
     [
-        {"key": "sampler_index", "type": str, "default": "UniPC"},
-        {"key": "script_name", "type": str, "default": None},
+        {"key": "sampler_index", "type": int, "default": 0},
+        {"key": "sampler_name", "type": str, "default": "UniPC"},
+        {"key": "hr_sampler_name", "type": str, "default": "Same as primary"},
+        {"key": "script_name", "type": str, "default": "none"},
         {"key": "script_args", "type": list, "default": []},
         {"key": "send_images", "type": bool, "default": True},
         {"key": "save_images", "type": bool, "default": False},
@@ -216,7 +218,11 @@ ReqImg2Img = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img",
     StableDiffusionProcessingImg2Img,
     [
-        {"key": "sampler_index", "type": str, "default": "UniPC"},
+        {"key": "sampler_index", "type": int, "default": 0},
+        {"key": "sampler_name", "type": str, "default": "UniPC"},
+        {"key": "hr_sampler_name", "type": str, "default": "Same as primary"},
+        {"key": "script_name", "type": str, "default": "none"},
+        {"key": "script_args", "type": list, "default": []},
         {"key": "init_images", "type": list, "default": None},
         {"key": "denoising_strength", "type": float, "default": 0.5},
         {"key": "mask", "type": str, "default": None},
diff --git a/modules/api/script.py b/modules/api/script.py
index cae59791e..6ccaa161e 100644
--- a/modules/api/script.py
+++ b/modules/api/script.py
@@ -3,18 +3,25 @@ from fastapi.exceptions import HTTPException
 import gradio as gr
 from modules.api import models
 from modules import scripts
+from modules.errors import log
 
 
 def script_name_to_index(name, scripts_list):
+    if name is None or len(name) == 0:
+        return None
     try:
         return [script.title().lower() for script in scripts_list].index(name.lower())
-    except Exception as e:
-        raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
+    except Exception:
+        log.error(f'API: script={name} not found')
+        return None
+        # raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
 
 def get_selectable_script(script_name, script_runner):
     if script_name is None or script_name == "":
         return None, None
     script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
+    if script_idx is None:
+        return None, None
     script = script_runner.selectable_scripts[script_idx]
     return script, script_idx
 
@@ -36,6 +43,8 @@ def get_script(script_name, script_runner):
     if script_name is None or script_name == "":
         return None, None
     script_idx = script_name_to_index(script_name, script_runner.scripts)
+    if script_idx is None:
+        return None
     return script_runner.scripts[script_idx]
 
 def init_default_script_args(script_runner):
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 5b320f9ea..0a066ec6e 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -6,9 +6,11 @@ import time
 import inspect
 import torch
 import numpy as np
+from PIL import Image
 from modules import shared, errors, sd_models, processing, processing_vae, processing_helpers, sd_hijack_hypertile, prompt_parser_diffusers, timer
 from modules.processing_callbacks import diffusers_callback_legacy, diffusers_callback, set_callbacks_p
 from modules.processing_helpers import resize_hires, fix_prompts, calculate_base_steps, calculate_hires_steps, calculate_refiner_steps, get_generator, set_latents, apply_circular # pylint: disable=unused-import
+from modules.api import helpers
 
 
 debug = shared.log.trace if os.environ.get('SD_DIFFUSERS_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -18,7 +20,8 @@ def task_specific_kwargs(p, model):
     task_args = {}
     is_img2img_model = bool('Zero123' in shared.sd_model.__class__.__name__)
     if len(getattr(p, 'init_images', [])) > 0:
-        p.init_images = [p.convert('RGB') for p in p.init_images]
+        p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images if isinstance(i, str)]
+        p.init_images = [i.convert('RGB') for i in p.init_images if isinstance(i, Image.Image)]
     if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE or len(getattr(p, 'init_images', [])) == 0 and not is_img2img_model:
         p.ops.append('txt2img')
         if hasattr(p, 'width') and hasattr(p, 'height'):
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 9a5bc088c..c61fd7e8e 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -21,7 +21,7 @@ class StableDiffusionProcessing:
                  sd_model=None, # pylint: disable=unused-argument # local instance of sd_model
                  # base params
                  prompt: str = "",
-                 negative_prompt: str = None,
+                 negative_prompt: str = "",
                  seed: int = -1,
                  subseed: int = -1,
                  subseed_strength: float = 0,
diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py
index ef83834e5..ec7fbf048 100644
--- a/modules/processing_helpers.py
+++ b/modules/processing_helpers.py
@@ -47,16 +47,23 @@ def apply_overlay(image: Image, paste_loc, index, overlays):
         return image
     debug(f'Apply overlay: image={image} loc={paste_loc} index={index} overlays={overlays}')
     overlay = overlays[index]
-    if paste_loc is not None:
-        x, y, w, h = paste_loc
-        if image.width != w or image.height != h or x != 0 or y != 0:
-            base_image = Image.new('RGBA', (overlay.width, overlay.height))
-            image = images.resize_image(2, image, w, h)
-            base_image.paste(image, (x, y))
-            image = base_image
-    image = image.convert('RGBA')
-    image.alpha_composite(overlay)
-    image = image.convert('RGB')
+    if not isinstance(image, Image.Image) or not isinstance(overlay, Image.Image):
+        return image
+    try:
+        if paste_loc is not None and (isinstance(paste_loc, tuple) or isinstance(paste_loc, list)):
+            x, y, w, h = paste_loc
+            if x is None or y is None or w is None or h is None:
+                return image
+            if image.width != w or image.height != h or x != 0 or y != 0:
+                base_image = Image.new('RGBA', (overlay.width, overlay.height))
+                image = images.resize_image(2, image, w, h)
+                base_image.paste(image, (x, y))
+                image = base_image
+        image = image.convert('RGBA')
+        image.alpha_composite(overlay)
+        image = image.convert('RGB')
+    except Exception as e:
+        shared.log.error(f'Apply overlay: {e}')
     return image
 
 

From f36c4d8a2b2c0336e710453fef2295ca48f725a0 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 11:22:20 -0500
Subject: [PATCH 106/119] cleanup

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/api-txt2img.js    | 2 +-
 modules/api/script.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/cli/api-txt2img.js b/cli/api-txt2img.js
index 43e09449b..8d0e9f5d1 100755
--- a/cli/api-txt2img.js
+++ b/cli/api-txt2img.js
@@ -5,7 +5,7 @@
 const fs = require('fs'); // eslint-disable-line no-undef
 const process = require('process'); // eslint-disable-line no-undef
 
-const sd_url = process.env.SDAPI_URL || 'http://127.0.0.1:32769';
+const sd_url = process.env.SDAPI_URL || 'http://127.0.0.1:7860';
 const sd_username = process.env.SDAPI_USR;
 const sd_password = process.env.SDAPI_PWD;
 const sd_options = {
diff --git a/modules/api/script.py b/modules/api/script.py
index 6ccaa161e..e7d6b1f1a 100644
--- a/modules/api/script.py
+++ b/modules/api/script.py
@@ -7,7 +7,7 @@ from modules.errors import log
 
 
 def script_name_to_index(name, scripts_list):
-    if name is None or len(name) == 0:
+    if name is None or len(name) == 0 or name == 'none':
         return None
     try:
         return [script.title().lower() for script in scripts_list].index(name.lower())
@@ -17,7 +17,7 @@ def script_name_to_index(name, scripts_list):
         # raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
 
 def get_selectable_script(script_name, script_runner):
-    if script_name is None or script_name == "":
+    if script_name is None or script_name == "" or script_name == 'none':
         return None, None
     script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
     if script_idx is None:
@@ -40,7 +40,7 @@ def get_script_info(script_name: Optional[str] = None):
     return res
 
 def get_script(script_name, script_runner):
-    if script_name is None or script_name == "":
+    if script_name is None or script_name == "" or script_name == 'none':
         return None, None
     script_idx = script_name_to_index(script_name, script_runner.scripts)
     if script_idx is None:

From bdd885eab4b6391d6a981c6704613ba7736963e4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 12:37:38 -0500
Subject: [PATCH 107/119] multiple param validation fixes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/{api-faces.py => api-detect.py} | 11 +++++------
 cli/api-faceid.py                   | 21 ++-------------------
 cli/api-json.py                     |  2 +-
 cli/api-progress.py                 |  6 +++++-
 cli/api-txt2img.py                  |  2 +-
 cli/api-upscale.py                  |  5 +++--
 modules/api/api.py                  |  2 +-
 modules/api/generate.py             | 12 +++++++++---
 modules/api/process.py              | 24 +++++++++++++++++-------
 modules/api/script.py               | 20 ++++++++++++++------
 modules/postprocess/yolo.py         | 14 +++++++++++---
 modules/processing_args.py          |  5 +++--
 modules/vqa.py                      |  4 ++--
 13 files changed, 74 insertions(+), 54 deletions(-)
 rename cli/{api-faces.py => api-detect.py} (80%)

diff --git a/cli/api-faces.py b/cli/api-detect.py
similarity index 80%
rename from cli/api-faces.py
rename to cli/api-detect.py
index 0a98843c0..ca121b220 100755
--- a/cli/api-faces.py
+++ b/cli/api-detect.py
@@ -44,16 +44,15 @@ def encode(f):
 
 
 def detect(args): # pylint: disable=redefined-outer-name
-    data = post('/sdapi/v1/faces', { 'image': encode(args.image) })
-    for face in zip(data['images'], data['scores']):
-        log.info(f'Face: score={face[1]}')
-        image = Image.open(io.BytesIO(base64.b64decode(face[0])))
-        image.save(f'/tmp/face_{face[1]}.jpg')
+    data = post('/sdapi/v1/detect', { 'image': encode(args.image), 'model': args.model })
+    for i in range(len(data['images'])):
+        log.info(f"Item {i}: score={data['scores'][i]} cls={data['classes'][i]} box={data['boxes'][i]} label={data['labels'][i]}")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description = 'api-faces')
     parser.add_argument('--image', required=True, help='input image')
+    parser.add_argument('--model', required=False, default='', help='model')
     args = parser.parse_args()
-    log.info(f'api-faces: {args}')
+    log.info(f'api-detect: {args}')
     detect(args)
diff --git a/cli/api-faceid.py b/cli/api-faceid.py
index e656a4a47..18f1d3503 100755
--- a/cli/api-faceid.py
+++ b/cli/api-faceid.py
@@ -63,7 +63,7 @@ def generate(args): # pylint: disable=redefined-outer-name
     options['height'] = args.height
     options['face'] = {
         'mode': 'FaceID',
-        'ip_model': 'FaceID Base',
+        'ip_model': 'FaceID XL',
         'source_images': [encode(args.face)],
     }
     data = post('/sdapi/v1/txt2img', options)
@@ -86,7 +86,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description = 'api-faceid')
     parser.add_argument('--width', required=False, default=512, help='image width')
     parser.add_argument('--height', required=False, default=512, help='image height')
-    parser.add_argument('--face', required=False, help='face image')
+    parser.add_argument('--face', required=True, help='face image')
     parser.add_argument('--prompt', required=False, default='', help='prompt text')
     parser.add_argument('--negative', required=False, default='', help='negative prompt text')
     parser.add_argument('--steps', required=False, default=20, help='number of steps')
@@ -97,20 +97,3 @@ if __name__ == "__main__":
     args = parser.parse_args()
     log.info(f'api-faceid: {args}')
     generate(args)
-
-"""
-request.face.mode,
-request.face.source_images,
-request.face.ip_model,
-request.face.ip_override_sampler,
-request.face.ip_cache_model,
-request.face.ip_strength,
-request.face.ip_structure,
-request.face.id_strength,
-request.face.id_conditioning,
-request.face.id_cache,
-request.face.pm_trigger,
-request.face.pm_strength,
-request.face.pm_start,
-request.face.fs_cache
-"""
diff --git a/cli/api-json.py b/cli/api-json.py
index 79c0ebc3b..61e5ec3ce 100755
--- a/cli/api-json.py
+++ b/cli/api-json.py
@@ -45,7 +45,7 @@ if __name__ == "__main__":
     log.info(f'api-json: {args}')
     if os.path.isfile(args.json[0]):
         with open(args.json[0], 'r', encoding='ascii') as f:
-            dct = json.load(f) # TODO fails with b64 encoded images inside json due to string encoding
+            dct = json.load(f)
     else:
         dct = json.loads(args.json[0])
     res = post(endpoint=args.endpoint[0], payload=dct)
diff --git a/cli/api-progress.py b/cli/api-progress.py
index 2c90fe95f..00ed618d2 100755
--- a/cli/api-progress.py
+++ b/cli/api-progress.py
@@ -1,5 +1,9 @@
 #!/usr/bin/env python
 
+"""
+check progress of last job and shutdown system if timeout reached
+"""
+
 import os
 import time
 import datetime
@@ -16,7 +20,7 @@ opts = Dot({
     "timeout": 3600,
     "frequency": 60,
     "action": "sudo shutdown now",
-    "url": "https://127.0.0.1:7860",
+    "url": "http://127.0.0.1:7860",
     "user": "",
     "password": "",
 })
diff --git a/cli/api-txt2img.py b/cli/api-txt2img.py
index 89d84be80..868b13eee 100755
--- a/cli/api-txt2img.py
+++ b/cli/api-txt2img.py
@@ -48,7 +48,7 @@ def generate(args): # pylint: disable=redefined-outer-name
     options['sampler_name'] = args.sampler
     options['width'] = int(args.width)
     options['height'] = int(args.height)
-    if args.faces:
+    if args.detailer:
         options['detailer'] = args.detailer
         options['denoising_strength'] = 0.5
         options['hr_sampler_name'] = args.sampler
diff --git a/cli/api-upscale.py b/cli/api-upscale.py
index 7f188650f..488f2db45 100755
--- a/cli/api-upscale.py
+++ b/cli/api-upscale.py
@@ -73,7 +73,8 @@ def upscale(args): # pylint: disable=redefined-outer-name
     if 'image' in data:
         b64 = data['image'].split(',',1)[0]
         image = Image.open(io.BytesIO(base64.b64decode(b64)))
-        image.save(args.output)
+        if args.output:
+            image.save(args.output)
         log.info(f'received: image={image} file={args.output} time={t1-t0:.2f}')
     else:
         log.warning(f'no images received: {data}')
@@ -82,7 +83,7 @@ def upscale(args): # pylint: disable=redefined-outer-name
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description = 'api-upscale')
     parser.add_argument('--input', required=True, help='input image')
-    parser.add_argument('--output', required=True, help='output image')
+    parser.add_argument('--output', required=False, help='output image')
     parser.add_argument('--upscaler', required=False, default='Nearest', help='upscaler name')
     parser.add_argument('--scale', required=False, default=2, help='upscaler scale')
     args = parser.parse_args()
diff --git a/modules/api/api.py b/modules/api/api.py
index 47612559b..0ba388855 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -57,7 +57,7 @@ class Api:
         self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ResProcessBatch)
         self.add_api_route("/sdapi/v1/preprocess", self.process.post_preprocess, methods=["POST"])
         self.add_api_route("/sdapi/v1/mask", self.process.post_mask, methods=["POST"])
-        self.add_api_route("/sdapi/v1/faces", self.process.post_face, methods=["POST"])
+        self.add_api_route("/sdapi/v1/detect", self.process.post_detect, methods=["POST"])
 
         # api dealing with optional scripts
         self.add_api_route("/sdapi/v1/scripts", script.get_scripts_list, methods=["GET"], response_model=models.ResScripts)
diff --git a/modules/api/generate.py b/modules/api/generate.py
index e22102057..d940036fc 100644
--- a/modules/api/generate.py
+++ b/modules/api/generate.py
@@ -40,7 +40,7 @@ class APIGenerate():
             sanitize_str(request.script_args)
 
     def prepare_face_module(self, request):
-        if hasattr(request, "face") and request.face and not request.script_name and (not request.alwayson_scripts or "face" not in request.alwayson_scripts.keys()):
+        if getattr(request, "face", None) is not None and (not request.alwayson_scripts or "face" not in request.alwayson_scripts.keys()):
             request.script_name = "face"
             request.script_args = [
                 request.face.mode,
@@ -116,7 +116,10 @@ class APIGenerate():
                 p.script_args = tuple(script_args) # Need to pass args as tuple here
                 processed = process_images(p)
             shared.state.end(api=False)
-        b64images = list(map(helpers.encode_pil_to_base64, processed.images)) if send_images else []
+        if processed.images is None or len(processed.images) == 0:
+            b64images = []
+        else:
+            b64images = list(map(helpers.encode_pil_to_base64, processed.images)) if send_images else []
         self.sanitize_b64(txt2imgreq)
         return models.ResTxt2Img(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
@@ -162,7 +165,10 @@ class APIGenerate():
                 p.script_args = tuple(script_args) # Need to pass args as tuple here
                 processed = process_images(p)
             shared.state.end(api=False)
-        b64images = list(map(helpers.encode_pil_to_base64, processed.images)) if send_images else []
+        if processed.images is None or len(processed.images) == 0:
+            b64images = []
+        else:
+            b64images = list(map(helpers.encode_pil_to_base64, processed.images)) if send_images else []
         if not img2imgreq.include_init_images:
             img2imgreq.init_images = None
             img2imgreq.mask = None
diff --git a/modules/api/process.py b/modules/api/process.py
index f50d58381..80b19c52e 100644
--- a/modules/api/process.py
+++ b/modules/api/process.py
@@ -28,8 +28,12 @@ class ReqMask(BaseModel):
 
 class ReqFace(BaseModel):
     image: str = Field(title="Image", description="The base64 encoded image")
+    model: Optional[str] = Field(title="Model", description="The model to use for detection")
 
 class ResFace(BaseModel):
+    classes: List[int] = Field(title="Class", description="The class of detected item")
+    labels: List[str] = Field(title="Label", description="The label of detected item")
+    boxes: List[List[int]] = Field(title="Box", description="The bounding box of detected item")
     images: List[str] = Field(title="Image", description="The base64 encoded images of detected faces")
     scores: List[float] = Field(title="Scores", description="The scores of the detected faces")
 
@@ -106,16 +110,22 @@ class APIProcess():
         image = encode_pil_to_base64(processed)
         return ResMask(mask=image)
 
-    def post_face(self, req: ReqFace):
-        from shared import yolo # pylint: disable=no-name-in-module
+    def post_detect(self, req: ReqFace):
+        from modules.shared import yolo # pylint: disable=no-name-in-module
         image = decode_base64_to_image(req.image)
         shared.state.begin('API-FACE', api=True)
         images = []
         scores = []
+        classes = []
+        boxes = []
+        labels = []
         with self.queue_lock:
-            faces = yolo.predict('face-yolo8n', image)
-            for face in faces:
-                images.append(encode_pil_to_base64(face.item))
-                scores.append(face.score)
+            items = yolo.predict(req.model, image)
+            for item in items:
+                images.append(encode_pil_to_base64(item.item))
+                scores.append(item.score)
+                classes.append(item.cls)
+                labels.append(item.label)
+                boxes.append(item.box)
         shared.state.end(api=False)
-        return ResFace(images=images, scores=scores)
+        return ResFace(classes=classes, labels=labels, scores=scores, boxes=boxes, images=images)
diff --git a/modules/api/script.py b/modules/api/script.py
index e7d6b1f1a..2c0814ef0 100644
--- a/modules/api/script.py
+++ b/modules/api/script.py
@@ -9,12 +9,15 @@ from modules.errors import log
 def script_name_to_index(name, scripts_list):
     if name is None or len(name) == 0 or name == 'none':
         return None
-    try:
-        return [script.title().lower() for script in scripts_list].index(name.lower())
-    except Exception:
-        log.error(f'API: script={name} not found')
-        return None
-        # raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
+    available = [script.title().lower() for script in scripts_list]
+    if name.lower() in available:
+        return available.index(name.lower())
+    short = [available.split(':')[0] for available in available]
+    if name.lower() in short:
+        return short.index(name.lower())
+    log.error(f'API: script={name} available={available} not found')
+    return None
+
 
 def get_selectable_script(script_name, script_runner):
     if script_name is None or script_name == "" or script_name == 'none':
@@ -25,12 +28,14 @@ def get_selectable_script(script_name, script_runner):
     script = script_runner.selectable_scripts[script_idx]
     return script, script_idx
 
+
 def get_scripts_list():
     t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None]
     i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None]
     control = [script.name for script in scripts.scripts_control.scripts if script.name is not None]
     return models.ResScripts(txt2img = t2ilist, img2img = i2ilist, control = control)
 
+
 def get_script_info(script_name: Optional[str] = None):
     res = []
     for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts, scripts.scripts_control.scripts]:
@@ -39,6 +44,7 @@ def get_script_info(script_name: Optional[str] = None):
                 res.append(script.api_info)
     return res
 
+
 def get_script(script_name, script_runner):
     if script_name is None or script_name == "" or script_name == 'none':
         return None, None
@@ -47,6 +53,7 @@ def get_script(script_name, script_runner):
         return None
     return script_runner.scripts[script_idx]
 
+
 def init_default_script_args(script_runner):
     # find max idx from the scripts in runner and generate a none array to init script_args
     last_arg_index = 1
@@ -69,6 +76,7 @@ def init_default_script_args(script_runner):
                 script_args[script.args_from:script.args_to] = ui_default_values
     return script_args
 
+
 def init_script_args(p, request, default_script_args, selectable_scripts, selectable_script_idx, script_runner):
     script_args = default_script_args.copy()
     # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
diff --git a/modules/postprocess/yolo.py b/modules/postprocess/yolo.py
index 1a52e1334..920e126e2 100644
--- a/modules/postprocess/yolo.py
+++ b/modules/postprocess/yolo.py
@@ -29,6 +29,9 @@ class YoloResult:
         self.height = height
         self.args = args
 
+    def __str__(self):
+        return f'cls={self.cls} label={self.label} score={self.score} box={self.box} mask={self.mask} item={self.item} size={self.width}x{self.height} args={self.args}'
+
 
 class YoloRestorer(Detailer):
     def __init__(self):
@@ -76,11 +79,15 @@ class YoloRestorer(Detailer):
             offload: bool = shared.opts.detailer_unload,
         ) -> list[YoloResult]:
 
+        if model is None or (isinstance(model, str) and len(model) == 0):
+            model = 'yolo11m'
         result = []
         if isinstance(model, str):
-            model = self.models.get(model, None)
-            if model is None:
+            cached = self.models.get(model, None)
+            if cached is None:
                 _, model = self.load(model)
+            else:
+                model = cached
         if model is None:
             return result
         args = {
@@ -136,7 +143,8 @@ class YoloRestorer(Detailer):
                         draw = ImageDraw.Draw(mask_image)
                         draw.rectangle(box, fill="white", outline=None, width=0)
                         cropped = image.crop(box)
-                        result.append(YoloResult(cls=cls, label=label, score=round(score, 2), box=box, mask=mask_image, item=cropped, width=w, height=h, args=args))
+                        res = YoloResult(cls=cls, label=label, score=round(score, 2), box=box, mask=mask_image, item=cropped, width=w, height=h, args=args)
+                        result.append(res)
                 if len(result) >= shared.opts.detailer_max:
                     break
         return result
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 0a066ec6e..6c9a98369 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -20,8 +20,9 @@ def task_specific_kwargs(p, model):
     task_args = {}
     is_img2img_model = bool('Zero123' in shared.sd_model.__class__.__name__)
     if len(getattr(p, 'init_images', [])) > 0:
-        p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images if isinstance(i, str)]
-        p.init_images = [i.convert('RGB') for i in p.init_images if isinstance(i, Image.Image)]
+        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 = [i.convert('RGB') if i.mode != 'RGB' else i for i in p.init_images]
     if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE or len(getattr(p, 'init_images', [])) == 0 and not is_img2img_model:
         p.ops.append('txt2img')
         if hasattr(p, 'width') and hasattr(p, 'height'):
diff --git a/modules/vqa.py b/modules/vqa.py
index a0a5d147c..ee4197a5e 100644
--- a/modules/vqa.py
+++ b/modules/vqa.py
@@ -156,8 +156,8 @@ def florence(question: str, image: Image.Image, repo: str = None, revision: str
         task = question.split('>', 1)[0] + '>'
     else:
         task = '<MORE_DETAILED_CAPTION>'
-        question = task + question
-    inputs = processor(text=question, images=image, return_tensors="pt")
+        # question = task + question
+    inputs = processor(text=task, images=image, return_tensors="pt")
     input_ids = inputs['input_ids'].to(devices.device)
     pixel_values = inputs['pixel_values'].to(devices.device, devices.dtype)
     with devices.inference_context():

From 4110bc746bd6eb5590e975938d8127f97af6a7da Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 13:19:34 -0500
Subject: [PATCH 108/119] param validation

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 cli/full-test.sh            | 29 ++++++++++++++++++++++++++++
 modules/api/control.py      |  2 +-
 modules/control/run.py      |  2 +-
 modules/processing_class.py | 38 +++++--------------------------------
 modules/shared.py           |  3 +++
 package.json                |  4 +++-
 6 files changed, 42 insertions(+), 36 deletions(-)
 create mode 100755 cli/full-test.sh

diff --git a/cli/full-test.sh b/cli/full-test.sh
new file mode 100755
index 000000000..e410528ad
--- /dev/null
+++ b/cli/full-test.sh
@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+
+source venv/bin/activate
+echo image-exif
+python cli/api-info.py --input html/logo-bg-0.jpg
+echo txt2img
+python cli/api-txt2img.py --detailer --prompt "girl on a mountain" --seed 42 --sampler DEIS --width 1280 --height 800 --steps 10
+echo img2img
+python cli/api-img2img.py --init html/logo-bg-0.jpg --steps 10
+echo inpaint
+python cli/api-img2img.py --init html/logo-bg-0.jpg --mask html/logo-dark.png --steps 10
+echo upscale
+python cli/api-upscale.py --input html/logo-bg-0.jpg --upscaler "ESRGAN 4x Valar" --scale 4
+echo vqa
+python cli/api-vqa.py --input html/logo-bg-0.jpg
+echo detailer
+python cli/api-detect.py --image html/invoked.jpg
+echo faceid
+python cli/api-faceid.py --face html/simple-dark.jpg
+echo control-txt2img
+python cli/api-control.py --prompt "cute robot"
+echo control-img2img
+python cli/api-control.py --prompt "cute robot" --input html/logo-bg-0.jpg
+echo control-ipsadapter
+python cli/api-control.py --prompt "cute robot" --ipadapter "Base SDXL:html/logo-bg-0.jpg:0.8"
+echo control-preprocess
+python cli/api-preprocess.py --input html/logo-bg-0.jpg --model "Zoe Depth"
+echo control-controlnet
+python cli/api-control.py --prompt "cute robot" --input html/logo-bg-0.jpg --type controlnet --control "Zoe Depth:Xinsir Union XL:0.5"
diff --git a/modules/api/control.py b/modules/api/control.py
index ffb000053..29c5a77f1 100644
--- a/modules/api/control.py
+++ b/modules/api/control.py
@@ -104,7 +104,7 @@ class APIControl():
                 args['ip_adapter_scales'].append(ipadapter.scale)
                 args['ip_adapter_starts'].append(ipadapter.start)
                 args['ip_adapter_ends'].append(ipadapter.end)
-                args['ip_adapter_crops'].append(ipadapter.end)
+                args['ip_adapter_crops'].append(ipadapter.crop)
                 args['ip_adapter_images'].append([helpers.decode_base64_to_image(x) for x in ipadapter.images])
                 if ipadapter.masks:
                     args['ip_adapter_masks'].append([helpers.decode_base64_to_image(x) for x in ipadapter.masks])
diff --git a/modules/control/run.py b/modules/control/run.py
index c141c8443..5d6343c98 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -66,7 +66,7 @@ def control_run(state: str = '',
                 resize_mode_before: int = 0, resize_name_before: str = 'None', resize_context_before: str = 'None', width_before: int = 512, height_before: int = 512, scale_by_before: float = 1.0, selected_scale_tab_before: int = 0,
                 resize_mode_after: int = 0, resize_name_after: str = 'None', resize_context_after: str = 'None', width_after: int = 0, height_after: int = 0, scale_by_after: float = 1.0, selected_scale_tab_after: int = 0,
                 resize_mode_mask: int = 0, resize_name_mask: str = 'None', resize_context_mask: str = 'None', width_mask: int = 0, height_mask: int = 0, scale_by_mask: float = 1.0, selected_scale_tab_mask: int = 0,
-                denoising_strength: float = 0.0, batch_count: int = 1, batch_size: int = 1,
+                denoising_strength: float = 0.3, batch_count: int = 1, batch_size: int = 1,
                 enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.0, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
                 hr_scale: float = 1.0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0.0, refiner_prompt: str = '', refiner_negative: str = '',
                 video_skip_frames: int = 0, video_type: str = 'None', video_duration: float = 2.0, video_loop: bool = False, video_pad: int = 0, video_interpolate: int = 0,
diff --git a/modules/processing_class.py b/modules/processing_class.py
index c61fd7e8e..b0b9e1f1b 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -72,7 +72,7 @@ class StableDiffusionProcessing:
                  resize_mode: int = 0,
                  resize_name: str = 'None',
                  resize_context: str = 'None',
-                 denoising_strength: float = 0.0,
+                 denoising_strength: float = 0.3,
                  image_cfg_scale: float = None,
                  initial_noise_multiplier: float = None, # pylint: disable=unused-argument # a1111 compatibility
                  scale_by: float = 1,
@@ -161,18 +161,6 @@ class StableDiffusionProcessing:
         self.do_not_reload_embeddings = do_not_reload_embeddings
         self.detailer = detailer
         self.restore_faces = restore_faces
-        self.hdr_mode = hdr_mode
-        self.hdr_brightness = hdr_brightness
-        self.hdr_color = hdr_color
-        self.hdr_sharpen = hdr_sharpen
-        self.hdr_clamp = hdr_clamp
-        self.hdr_boundary = hdr_boundary
-        self.hdr_threshold = hdr_threshold
-        self.hdr_maximize = hdr_maximize
-        self.hdr_max_center = hdr_max_center
-        self.hdr_max_boundry = hdr_max_boundry
-        self.hdr_color_picker = hdr_color_picker
-        self.hdr_tint_ratio = hdr_tint_ratio
         self.init_images = init_images
         self.resize_mode = resize_mode
         self.resize_name = resize_name
@@ -242,14 +230,6 @@ class StableDiffusionProcessing:
         self.all_seeds = None
         self.all_subseeds = None
 
-        # ip adapter
-        self.ip_adapter_names = []
-        self.ip_adapter_scales = [0.0]
-        self.ip_adapter_images = []
-        self.ip_adapter_starts = [0.0]
-        self.ip_adapter_ends = [1.0]
-        self.ip_adapter_crops = []
-
         # a1111 compatibility items
         shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
         self.seed_enable_extras: bool = True,
@@ -286,18 +266,7 @@ class StableDiffusionProcessing:
         self.s_tmin = shared.opts.s_tmin
         self.s_tmax = float('inf')  # not representable as a standard ui option
         self.task_args = {}
-        # a1111 compatibility items
-        self.batch_index = 0
-        self.refiner_switch_at = 0
-        self.hr_prompt = ''
-        self.all_hr_prompts = []
-        self.hr_negative_prompt = ''
-        self.all_hr_negative_prompts = []
-        self.comments = {}
-        self.is_api = False
-        self.scripts_value: scripts.ScriptRunner = field(default=None, init=False)
-        self.script_args_value: list = field(default=None, init=False)
-        self.scripts_setup_complete: bool = field(default=False, init=False)
+
         # ip adapter
         self.ip_adapter_names = []
         self.ip_adapter_scales = [0.0]
@@ -305,6 +274,7 @@ class StableDiffusionProcessing:
         self.ip_adapter_starts = [0.0]
         self.ip_adapter_ends = [1.0]
         self.ip_adapter_crops = []
+
         # hdr
         self.hdr_mode=hdr_mode
         self.hdr_brightness=hdr_brightness
@@ -318,8 +288,10 @@ class StableDiffusionProcessing:
         self.hdr_max_boundry=hdr_max_boundry
         self.hdr_color_picker=hdr_color_picker
         self.hdr_tint_ratio=hdr_tint_ratio
+
         # globals
         self.embedder = None
+        self.override = None
         self.scheduled_prompt: bool = False
         self.prompt_embeds = []
         self.positive_pooleds = []
diff --git a/modules/shared.py b/modules/shared.py
index c32aa80c0..7b273b0f9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -251,6 +251,7 @@ class OptionInfo:
         self.comment_before = comment_before # HTML text that will be added after label in UI
         self.comment_after = comment_after # HTML text that will be added before label in UI
         self.submit = submit
+        self.exclude = ['sd_model_checkpoint', 'sd_model_refiner', 'sd_vae', 'sd_unet', 'sd_text_encoder', 'sd_model_dict']
 
     def needs_reload_ui(self):
         return self
@@ -276,6 +277,8 @@ class OptionInfo:
         return self
 
     def validate(self, opt, value):
+        if opt in self.exclude:
+            return True
         args = self.component_args if self.component_args is not None else {}
         if callable(args):
             try:
diff --git a/package.json b/package.json
index bf5a366ea..b30d3f87d 100644
--- a/package.json
+++ b/package.json
@@ -16,11 +16,13 @@
     "url": "git+https://github.com/vladmandic/automatic.git"
   },
   "scripts": {
+    "venv": "source venv/bin/activate",
     "start": "python launch.py --debug --experimental",
     "ruff": "ruff check",
     "eslint": "eslint javascript/ extensions-builtin/sdnext-modernui/javascript/",
     "pylint": "pylint *.py modules/ extensions-builtin/",
-    "lint": "npm run eslint; npm run ruff; npm run pylint"
+    "lint": "npm run eslint; npm run ruff; npm run pylint",
+    "test": "cli/test.sh"
   },
   "devDependencies": {
     "esbuild": "^0.18.15"

From 9c381deaa727186eb3c05850499bf5c3d26092dc Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 13:26:12 -0500
Subject: [PATCH 109/119] update dockerfile

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 Dockerfile             |  2 +-
 requirements-extra.txt | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index e2c689478..28af761f7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -18,7 +18,7 @@ RUN ["apt-get", "-y", "install", "git"]
 # sdnext will run all necessary pip install ops and then exit
 RUN ["python", "launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test"]
 # preinstall additional packages to avoid installation during runtime
-RUN ["uv", "pip", "install", "-r", "requirements-extra.txt"]
+RUN ["uv", "pip", "install", "-r", "requirements-extra.txt", "--system"]
 # actually run sdnext
 CMD ["python", "launch.py", "--debug", "--skip-all", "--listen", "--quick", "--api-log", "--log", "sdnext.log"]
 # expose port
diff --git a/requirements-extra.txt b/requirements-extra.txt
index d4c18f1f0..0e3e8db2c 100644
--- a/requirements-extra.txt
+++ b/requirements-extra.txt
@@ -2,13 +2,13 @@
 basicsr
 gfpgan
 clean-fid
-insightface
-pydantic==1.10.15
-albumentations==1.4.3
 optimum-quanto
-nncf==2.7.0
-clip_interrogator==0.6.0
 bitsandbytes
 gguf
 pynvml
 ultralytics
+pydantic==1.10.15
+albumentations==1.4.3
+clip_interrogator==0.6.0
+# insightface # problematic build
+# nncf==2.7.0 # requires older pandas

From c8b08f49cbc326587ba2f8e30d3d20fdd9be413d Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 14:36:13 -0500
Subject: [PATCH 110/119] cleanup

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

diff --git a/requirements-extra.txt b/requirements-extra.txt
index 0e3e8db2c..5be6fd24c 100644
--- a/requirements-extra.txt
+++ b/requirements-extra.txt
@@ -4,7 +4,6 @@ gfpgan
 clean-fid
 optimum-quanto
 bitsandbytes
-gguf
 pynvml
 ultralytics
 pydantic==1.10.15
@@ -12,3 +11,4 @@ albumentations==1.4.3
 clip_interrogator==0.6.0
 # insightface # problematic build
 # nncf==2.7.0 # requires older pandas
+# gguf # installs scripts

From 7344a8c3d8d78cc9bfed7733fa9b65475dde8391 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 17:08:11 -0500
Subject: [PATCH 111/119] fix pulid attention

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_models.py | 4 ++--
 scripts/pulid_ext.py | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index be543cb49..e34f7c6f9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1188,8 +1188,8 @@ def set_diffusers_attention(pipe):
             else:
                 module.set_attn_processor(attention)
 
-    if hasattr(pipe, 'pipe'):
-        set_diffusers_attention(pipe.pipe)
+    # if hasattr(pipe, 'pipe'):
+    #    set_diffusers_attention(pipe.pipe)
 
     if 'ControlNet' in pipe.__class__.__name__: # do not replace attention in ControlNet pipelines
         return
diff --git a/scripts/pulid_ext.py b/scripts/pulid_ext.py
index d01ca2847..40a21e3f7 100644
--- a/scripts/pulid_ext.py
+++ b/scripts/pulid_ext.py
@@ -178,6 +178,7 @@ class Script(scripts.Script):
                 sd_models.copy_diffuser_options(shared.sd_model, shared.sd_model.pipe)
                 sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
                 sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
+                # shared.sd_model.hack_unet_attn_layers(shared.sd_model.pipe.unet) # reapply attention layers
                 devices.torch_gc()
             except Exception as e:
                 shared.log.error(f'PuLID: failed to create pipeline: {e}')

From 6be52643b57b626b9ae3bbc9497314ee6297167a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Thu, 14 Nov 2024 18:09:53 -0500
Subject: [PATCH 112/119] update docker build

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .gitignore             |  1 -
 Dockerfile             |  7 ++++---
 installer.py           | 44 ++++++++++++++++++++++++++++++++----------
 launch.py              |  2 +-
 requirements-extra.txt | 14 --------------
 wiki                   |  2 +-
 6 files changed, 40 insertions(+), 30 deletions(-)
 delete mode 100644 requirements-extra.txt

diff --git a/.gitignore b/.gitignore
index 9fac9b310..6df029445 100644
--- a/.gitignore
+++ b/.gitignore
@@ -44,7 +44,6 @@ tunableop_results*.csv
 !webui.sh
 !package.json
 !requirements.txt
-!requirements-extra.txt
 
 # pyinstaller
 *.spec
diff --git a/Dockerfile b/Dockerfile
index 28af761f7..8e19d145c 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -6,7 +6,9 @@ WORKDIR /
 COPY . .
 # stop pip and uv from caching
 ENV PIP_NO_CACHE_DIR=true
+ENV PIP_ROOT_USER_ACTION=ignore
 ENV UV_NO_CACHE=true
+ENV SD_INSTALL_DEBUG=true
 # disable model hashing for faster startup
 ENV SD_NOHASHING=true
 # set data directories
@@ -14,11 +16,10 @@ ENV SD_DATADIR="/mnt/data"
 ENV SD_MODELSDIR="/mnt/models"
 # install dependencies
 RUN ["apt-get", "-y", "update"]
-RUN ["apt-get", "-y", "install", "git"]
+RUN ["apt-get", "-y", "install", "git", "build-essential"]
 # sdnext will run all necessary pip install ops and then exit
-RUN ["python", "launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test"]
+RUN ["python", "launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test", "--optional"]
 # preinstall additional packages to avoid installation during runtime
-RUN ["uv", "pip", "install", "-r", "requirements-extra.txt", "--system"]
 # actually run sdnext
 CMD ["python", "launch.py", "--debug", "--skip-all", "--listen", "--quick", "--api-log", "--log", "sdnext.log"]
 # expose port
diff --git a/installer.py b/installer.py
index 80bca224c..7a5b9d756 100644
--- a/installer.py
+++ b/installer.py
@@ -27,7 +27,7 @@ log_file = os.path.join(os.path.dirname(__file__), 'sdnext.log')
 log_rolled = False
 first_call = True
 quick_allowed = True
-errors = 0
+errors = []
 opts = {}
 args = Dot({
     'debug': False,
@@ -280,8 +280,7 @@ def pip(arg: str, ignore: bool = False, quiet: bool = False, uv = True):
     txt = txt.strip()
     debug(f'Install {pipCmd}: {txt}')
     if result.returncode != 0 and not ignore:
-        global errors # pylint: disable=global-statement
-        errors += 1
+        errors.append(f'pip: {package}')
         log.error(f'Install: {pipCmd}: {arg}')
         log.debug(f'Install: pip output {txt}')
     return txt
@@ -324,8 +323,7 @@ def git(arg: str, folder: str = None, ignore: bool = False, optional: bool = Fal
     if result.returncode != 0 and not ignore:
         if "couldn't find remote ref" in txt: # not a git repo
             return txt
-        global errors # pylint: disable=global-statement
-        errors += 1
+        errors.append(f'git: {folder}')
         log.error(f'Git: {folder} / {arg}')
         if 'or stash them' in txt:
             log.error(f'Git local changes detected: check details log="{log_file}"')
@@ -668,7 +666,7 @@ def install_torch_addons():
     triton_command = os.environ.get('TRITON_COMMAND', 'triton') if sys.platform == 'linux' else None
     if 'xformers' in xformers_package:
         try:
-            install(f'--no-deps {xformers_package}', ignore=True)
+            install(xformers_package, ignore=True, no_deps=True)
             import torch # pylint: disable=unused-import
             import xformers # pylint: disable=unused-import
         except Exception as e:
@@ -855,8 +853,7 @@ def run_extension_installer(folder):
         txt = result.stdout.decode(encoding="utf8", errors="ignore")
         debug(f'Extension installer: file="{path_installer}" {txt}')
         if result.returncode != 0:
-            global errors # pylint: disable=global-statement
-            errors += 1
+            errors.append(f'ext: {os.path.basename(folder)}')
             if len(result.stderr) > 0:
                 txt = txt + '\n' + result.stderr.decode(encoding="utf8", errors="ignore")
             log.error(f'Extension installer error: {path_installer}')
@@ -997,6 +994,29 @@ def ensure_base_requirements():
     install('requests', 'requests', quiet=True)
 
 
+def install_optional():
+    log.info('Installing optional requirements...')
+    install('basicsr')
+    install('gfpgan')
+    install('clean-fid')
+    install('optimum-quanto', ignore=True)
+    install('bitsandbytes', ignore=True)
+    install('pynvml', ignore=True)
+    install('ultralytics', ignore=True)
+    install('Cython', ignore=True)
+    install('insightface', ignore=True) # problematic build
+    install('nncf==2.7.0', ignore=True, no_deps=True) # requires older pandas
+    # install('flash-attn', ignore=True) # requires cuda and nvcc to be installed
+    install('gguf', ignore=True)
+    try:
+        import gguf
+        scripts_dir = os.path.join(os.path.dirname(gguf.__file__), '..', 'scripts')
+        if os.path.exists(scripts_dir):
+            os.rename(scripts_dir, scripts_dir + '_gguf')
+    except Exception:
+        pass
+
+
 def install_requirements():
     if args.profile:
         pr = cProfile.Profile()
@@ -1006,10 +1026,13 @@ def install_requirements():
     if not installed('diffusers', quiet=True): # diffusers are not installed, so run initial installation
         global quick_allowed # pylint: disable=global-statement
         quick_allowed = False
-        log.info('Installing requirements: this may take a while...')
+        log.info('Install requirements: this may take a while...')
         pip('install -r requirements.txt')
+    if args.optional:
+        quick_allowed = False
+        install_optional()
     installed('torch', reload=True) # reload packages cache
-    log.info('Verifying requirements')
+    log.info('Install: verifying requirements')
     with open('requirements.txt', 'r', encoding='utf8') as f:
         lines = [line.strip() for line in f.readlines() if line.strip() != '' and not line.startswith('#') and line is not None]
         for line in lines:
@@ -1272,6 +1295,7 @@ def add_args(parser):
     group_setup.add_argument('--upgrade', '--update', default = os.environ.get("SD_UPGRADE",False), action='store_true', help = "Upgrade main repository to latest version, default: %(default)s")
     group_setup.add_argument('--requirements', default = os.environ.get("SD_REQUIREMENTS",False), action='store_true', help = "Force re-check of requirements, default: %(default)s")
     group_setup.add_argument('--reinstall', default = os.environ.get("SD_REINSTALL",False), action='store_true', help = "Force reinstallation of all requirements, default: %(default)s")
+    group_setup.add_argument('--optional', default = os.environ.get("SD_OPTIONAL",False), action='store_true', help = "Force installation of optional requirements, default: %(default)s")
     group_setup.add_argument('--uv', default = os.environ.get("SD_UV",False), action='store_true', help = "Use uv instead of pip to install the packages")
 
     group_startup = parser.add_argument_group('Startup')
diff --git a/launch.py b/launch.py
index af3db2c0f..9075c358b 100755
--- a/launch.py
+++ b/launch.py
@@ -240,7 +240,7 @@ def main():
             installer.install_extensions()
             installer.install_requirements() # redo requirements since extensions may change them
             installer.update_wiki()
-            if installer.errors == 0:
+            if len(installer.errors) == 0:
                 installer.log.debug(f'Setup complete without errors: {round(time.time())}')
             else:
                 installer.log.warning(f'Setup complete with errors: {installer.errors}')
diff --git a/requirements-extra.txt b/requirements-extra.txt
deleted file mode 100644
index 5be6fd24c..000000000
--- a/requirements-extra.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# additional requirements that are not required for sdnext base operations
-basicsr
-gfpgan
-clean-fid
-optimum-quanto
-bitsandbytes
-pynvml
-ultralytics
-pydantic==1.10.15
-albumentations==1.4.3
-clip_interrogator==0.6.0
-# insightface # problematic build
-# nncf==2.7.0 # requires older pandas
-# gguf # installs scripts
diff --git a/wiki b/wiki
index 96f28bb7c..4ceff5627 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 96f28bb7cec5a4e198a3244a88309f1957f75d03
+Subproject commit 4ceff56271ad78faa6ea2678494187d45f4e0fa6

From 4e9e04b24cdd2e7271ab988bebca221411304f0b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 15 Nov 2024 17:35:13 -0500
Subject: [PATCH 113/119] update docker config and notes

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 CHANGELOG.md                                   |  8 ++++----
 Dockerfile                                     | 16 +++++++++++++---
 extensions-builtin/Lora/extra_networks_lora.py |  4 +++-
 wiki                                           |  2 +-
 4 files changed, 21 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bac0f3756..6c25b49e6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,12 +1,12 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-12
+## Update for 2024-11-15
 
-### Highlights for 2024-11-13
+### Highlights for 2024-11-15
 
 *What's New?*
 
-First, a massive update to docs including new UI top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki), many updates and new articles AND full **built-in search** capabilities
+First, a massive update to docs including new UI top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki), many updates and new articles AND full **built-in documentation search** capabilities
 
 **New integrations**:
 - [PuLID](https://github.com/ToTheBeginning/PuLID): Pure and Lightning ID Customization via Contrastive Alignment
@@ -26,7 +26,7 @@ And quite a few more improvements and fixes since the last update - for full det
 
 [README](https://github.com/vladmandic/automatic/blob/master/README.md) | [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
 
-### Details for 2024-11-12
+### Details for 2024-11-15
 
 - Docs:  
   - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)  
diff --git a/Dockerfile b/Dockerfile
index 8e19d145c..fbfc1eb9d 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,7 +1,14 @@
 # SD.Next Dockerfile
 FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
-# TBD add org info
+LABEL org.opencontainers.image.vendor="SD.Next"
 LABEL org.opencontainers.image.authors="vladmandic"
+LABEL org.opencontainers.image.url="https://github.com/vladmandic/automatic/"
+LABEL org.opencontainers.image.documentation="https://github.com/vladmandic/automatic/wiki/Docker"
+LABEL org.opencontainers.image.source="https://github.com/vladmandic/automatic/"
+LABEL org.opencontainers.image.licenses="AGPL-3.0"
+LABEL org.opencontainers.image.title="SD.Next"
+LABEL org.opencontainers.image.description="SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models"
+LABEL org.opencontainers.image.base.name="https://hub.docker.com/pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime"
 WORKDIR /
 COPY . .
 # stop pip and uv from caching
@@ -14,9 +21,12 @@ ENV SD_NOHASHING=true
 # set data directories
 ENV SD_DATADIR="/mnt/data"
 ENV SD_MODELSDIR="/mnt/models"
-# install dependencies
+# minimum install
 RUN ["apt-get", "-y", "update"]
-RUN ["apt-get", "-y", "install", "git", "build-essential"]
+RUN ["apt-get", "-y", "install", "git", "build-essential", "google-perftools"]
+RUN ["ldconfig"]
+# 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", "launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test", "--optional"]
 # preinstall additional packages to avoid installation during runtime
diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py
index 69b234df7..307d8cc13 100644
--- a/extensions-builtin/Lora/extra_networks_lora.py
+++ b/extensions-builtin/Lora/extra_networks_lora.py
@@ -26,7 +26,9 @@ def get_stepwise(param, step, steps):
             return v
         else:
             return m
-    return calculate_weight(sorted_positions(param), step, steps)
+
+    stepwise = calculate_weight(sorted_positions(param), step, steps)
+    return stepwise
 
 
 class ExtraNetworkLora(extra_networks.ExtraNetwork):
diff --git a/wiki b/wiki
index 4ceff5627..82ca84bc9 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 4ceff56271ad78faa6ea2678494187d45f4e0fa6
+Subproject commit 82ca84bc91dd1c915393a96207cd3e5d848a388f

From 59cd08f5daa6e289825102ba558939252018b360 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 16 Nov 2024 10:49:08 -0500
Subject: [PATCH 114/119] update docker and progress monitoring

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 Dockerfile              | 38 ++++++++++++++++++++++++++++----------
 installer.py            |  6 +++---
 launch.py               |  4 +---
 modules/api/api.py      |  1 +
 modules/api/models.py   | 17 +++++++++++++++++
 modules/api/server.py   |  5 ++++-
 modules/progress.py     |  1 -
 modules/shared.py       |  2 ++
 modules/shared_state.py | 32 ++++++++++++++++++++++++++++++++
 9 files changed, 88 insertions(+), 18 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index fbfc1eb9d..5f38d6caa 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,10 @@
 # SD.Next Dockerfile
+# docs: <https://github.com/vladmandic/automatic/wiki/Docker>
+
+# base image
 FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
+
+# metadata
 LABEL org.opencontainers.image.vendor="SD.Next"
 LABEL org.opencontainers.image.authors="vladmandic"
 LABEL org.opencontainers.image.url="https://github.com/vladmandic/automatic/"
@@ -9,31 +14,44 @@ LABEL org.opencontainers.image.licenses="AGPL-3.0"
 LABEL org.opencontainers.image.title="SD.Next"
 LABEL org.opencontainers.image.description="SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models"
 LABEL org.opencontainers.image.base.name="https://hub.docker.com/pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime"
-WORKDIR /
-COPY . .
+LABEL org.opencontainers.image.version="latest"
+
+# minimum install
+RUN ["apt-get", "-y", "update"]
+RUN ["apt-get", "-y", "install", "git", "build-essential", "google-perftools", "curl"]
+# optional if full cuda-dev is required by some downstream library
+# RUN ["apt-get", "-y", "nvidia-cuda-toolkit"]
+RUN ["/usr/sbin/ldconfig"]
+
+# copy sdnext
+COPY . /app
+WORKDIR /app
+
 # stop pip and uv from caching
 ENV PIP_NO_CACHE_DIR=true
 ENV PIP_ROOT_USER_ACTION=ignore
 ENV UV_NO_CACHE=true
-ENV SD_INSTALL_DEBUG=true
 # disable model hashing for faster startup
 ENV SD_NOHASHING=true
 # set data directories
 ENV SD_DATADIR="/mnt/data"
 ENV SD_MODELSDIR="/mnt/models"
-# minimum install
-RUN ["apt-get", "-y", "update"]
-RUN ["apt-get", "-y", "install", "git", "build-essential", "google-perftools"]
-RUN ["ldconfig"]
+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", "launch.py", "--debug", "--uv", "--use-cuda", "--log", "sdnext.log", "--test", "--optional"]
+RUN ["python", "/app/launch.py", "--debug", "--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"]
+
 # expose port
 EXPOSE 7860
-# TBD add healthcheck function
-HEALTHCHECK NONE
+
+# healthcheck function
+# HEALTHCHECK --interval=60s --timeout=10s --start-period=60s --retries=3 CMD curl --fail http://localhost:7860/sdapi/v1/status || exit 1
+
+# stop signal
 STOPSIGNAL SIGINT
diff --git a/installer.py b/installer.py
index 7a5b9d756..ba744b65f 100644
--- a/installer.py
+++ b/installer.py
@@ -412,14 +412,14 @@ def get_platform():
         else:
             release = platform.release()
         return {
-            # 'host': platform.node(),
             'arch': platform.machine(),
             'cpu': platform.processor(),
             'system': platform.system(),
             'release': release,
-            # 'platform': platform.platform(aliased = True, terse = False),
-            # 'version': platform.version(),
             'python': platform.python_version(),
+            'docker': os.environ.get('SD_INSTALL_DEBUG', None) is not None,
+            # 'host': platform.node(),
+            # 'version': platform.version(),
         }
     except Exception as e:
         return { 'error': e }
diff --git a/launch.py b/launch.py
index 9075c358b..f944a7e54 100755
--- a/launch.py
+++ b/launch.py
@@ -258,9 +258,7 @@ def main():
             alive = False
             requests = 0
         if round(time.time()) % 120 == 0:
-            state = f'job="{instance.state.job}" {instance.state.job_no}/{instance.state.job_count}' if instance.state.job != '' or instance.state.job_no != 0 or instance.state.job_count != 0 else 'idle'
-            uptime = round(time.time() - instance.state.server_start)
-            installer.log.debug(f'Server: alive={alive} jobs={instance.state.total_jobs} requests={requests} uptime={uptime} memory={get_memory_stats()} backend={instance.backend} state={state}')
+            installer.log.debug(f'Server: alive={alive} requests={requests} memory={get_memory_stats()} {instance.state.status()}')
         if not alive:
             if uv is not None and uv.wants_restart:
                 installer.log.info('Server restarting...')
diff --git a/modules/api/api.py b/modules/api/api.py
index 0ba388855..f8346995d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -38,6 +38,7 @@ class Api:
         self.add_api_route("/sdapi/v1/log", server.get_log_buffer, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/start", self.get_session_start, methods=["GET"])
         self.add_api_route("/sdapi/v1/version", server.get_version, methods=["GET"])
+        self.add_api_route("/sdapi/v1/status", server.get_status, methods=["GET"], response_model=models.ResStatus)
         self.add_api_route("/sdapi/v1/platform", server.get_platform, methods=["GET"])
         self.add_api_route("/sdapi/v1/progress", server.get_progress, methods=["GET"], response_model=models.ResProgress)
         self.add_api_route("/sdapi/v1/interrupt", server.post_interrupt, methods=["POST"])
diff --git a/modules/api/models.py b/modules/api/models.py
index 4f01f47d5..e68ebf081 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -300,6 +300,23 @@ class ResProgress(BaseModel):
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
     textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
 
+class ResStatus(BaseModel):
+    status: str = Field(title="Status", description="Current status")
+    task: str = Field(title="Task", description="Current task")
+    timestamp: Optional[str] = Field(title="Timestamp", description="Timestamp of the current job")
+    id: str = Field(title="ID", description="ID of the current task")
+    job: int = Field(title="Job", description="Current job")
+    jobs: int = Field(title="Jobs", description="Total jobs")
+    total: int = Field(title="Total Jobs", description="Total jobs")
+    step: int = Field(title="Step", description="Current step")
+    steps: int = Field(title="Steps", description="Total steps")
+    queued: int = Field(title="Queued", description="Number of queued tasks")
+    uptime: int = Field(title="Uptime", description="Uptime of the server")
+    elapsed: Optional[float] = Field(title="Elapsed time")
+    eta: Optional[float] = Field(title="ETA in secs")
+    progress: Optional[float] = Field(title="Progress", description="The progress with a range of 0 to 1")
+
+
 class ReqInterrogate(BaseModel):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
     clip_model: str = Field(default="", title="CLiP Model", description="The interrogate model used.")
diff --git a/modules/api/server.py b/modules/api/server.py
index 95233dbcd..939e19c86 100644
--- a/modules/api/server.py
+++ b/modules/api/server.py
@@ -1,3 +1,4 @@
+import time
 from typing import Any, Dict
 from fastapi import Depends
 from modules import shared
@@ -66,7 +67,6 @@ def get_cmd_flags():
     return vars(shared.cmd_opts)
 
 def get_progress(req: models.ReqProgress = Depends()):
-    import time
     if shared.state.job_count == 0:
         return models.ResProgress(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
     shared.state.do_set_current_image()
@@ -85,6 +85,9 @@ def get_progress(req: models.ReqProgress = Depends()):
     res = models.ResProgress(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
     return res
 
+def get_status():
+    return shared.state.status()
+
 def post_interrupt():
     shared.state.interrupt()
     return {}
diff --git a/modules/progress.py b/modules/progress.py
index abd6d906d..d18d1ee9f 100644
--- a/modules/progress.py
+++ b/modules/progress.py
@@ -73,7 +73,6 @@ def progressapi(req: ProgressRequest):
     elapsed = time.time() - shared.state.time_start if shared.state.time_start is not None else 0
     predicted = elapsed / progress if progress > 0 else None
     eta = predicted - elapsed if predicted is not None else None
-    # shared.log.debug(f'Progress: step={step_x}:{step_y} batch={batch_x}:{batch_y} current={current} total={total} progress={progress} elapsed={elapsed} eta={eta}')
     id_live_preview = req.id_live_preview
     live_preview = None
     shared.state.set_current_image()
diff --git a/modules/shared.py b/modules/shared.py
index 7b273b0f9..46d47b16d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1,3 +1,4 @@
+from functools import lru_cache
 import io
 import os
 import sys
@@ -1271,6 +1272,7 @@ def html(filename):
     return ""
 
 
+@lru_cache(maxsize=1)
 def get_version():
     version = None
     if version is None:
diff --git a/modules/shared_state.py b/modules/shared_state.py
index 067fb21eb..9947dcb70 100644
--- a/modules/shared_state.py
+++ b/modules/shared_state.py
@@ -62,6 +62,38 @@ class State:
         }
         return obj
 
+    def status(self):
+        from modules import progress
+        from modules.api import models
+        res = models.ResStatus(
+            task=self.job,
+            id=progress.current_task or '',
+            job=max(self.job_no, 0),
+            jobs=max(self.frame_count, self.job_count, self.job_no),
+            total=self.total_jobs,
+            timestamp=self.job_timestamp if self.job != '' else None,
+            step=self.sampling_step,
+            steps=self.sampling_steps,
+            queued=len(progress.pending_tasks),
+            status='unknown',
+            uptime = round(time.time() - self.server_start)
+        )
+        res.step = res.steps * res.job + res.step
+        res.steps = res.steps * res.jobs
+        res.progress = round(min(1, abs(res.step / res.steps) if res.steps > 0 else 0), 2)
+        res.elapsed = round(time.time() - self.time_start, 2) if self.time_start is not None else None
+        predicted = round(res.elapsed / res.progress, 2) if res.progress > 0 and res.elapsed is not None else None
+        res.eta = round(predicted - res.elapsed, 2) if predicted is not None else None
+        if self.paused:
+            res.status = 'paused'
+        elif self.interrupted:
+            res.status = 'interrupted'
+        elif self.skipped:
+            res.status = 'skipped'
+        else:
+            res.status = 'running' if self.job != '' else 'idle'
+        return res
+
     def begin(self, title="", api=None):
         import modules.devices
         self.total_jobs += 1

From bbd224c9f096b4a01a0daecfdf8b80c853d00f7c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 17 Nov 2024 07:58:21 -0500
Subject: [PATCH 115/119] fix xyz grid with scripts/detailer

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/postprocess/yolo.py |  1 -
 scripts/xyz_grid_on.py      | 14 +++++++++++---
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/modules/postprocess/yolo.py b/modules/postprocess/yolo.py
index 920e126e2..f42b6bb9f 100644
--- a/modules/postprocess/yolo.py
+++ b/modules/postprocess/yolo.py
@@ -202,7 +202,6 @@ class YoloRestorer(Detailer):
                 shared.log.info(f'Detailer: model="{name}" no items detected')
                 continue
 
-            pp = None
             shared.opts.data['mask_apply_overlay'] = True
             resolution = 512 if shared.sd_model_type in ['none', 'sd', 'lcm', 'unknown'] else 1024
             orig_prompt: str = orig_p.get('all_prompts', [''])[0]
diff --git a/scripts/xyz_grid_on.py b/scripts/xyz_grid_on.py
index f0455f0f1..202a2cfc4 100644
--- a/scripts/xyz_grid_on.py
+++ b/scripts/xyz_grid_on.py
@@ -419,6 +419,14 @@ class Script(scripts.Script):
 
 
     def process_images(self, p, *args): # pylint: disable=W0221, W0613
-        if p.iteration > 0 and cache is not None and len(cache.images) > 0:
-            cache.images = [] # avoid returning same images multiple items
-        return cache
+        if hasattr(cache, 'used'):
+            cache.images.clear()
+            cache.used = False
+        elif cache is not None and len(cache.images) > 0:
+            cache.used = True
+            p.restore_faces = False
+            p.detailer = False
+            p.color_corrections = None
+            p.scripts = None
+            return cache
+        return None

From aa52feb757e8a88165ba19eda49f25bf31d3b241 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sun, 17 Nov 2024 12:43:46 -0500
Subject: [PATCH 116/119] dpm flowmatch

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                          |   1 +
 .ruff.toml                         |   1 +
 CHANGELOG.md                       |  11 +-
 installer.py                       |   2 +-
 modules/api/helpers.py             |   3 +-
 modules/flowmatch/__init__.py      |   1 +
 modules/flowmatch/flowmatch_dpm.py | 897 +++++++++++++++++++++++++++++
 modules/processing_args.py         |   1 -
 modules/processing_class.py        |   2 +-
 modules/sd_samplers.py             |  33 +-
 modules/sd_samplers_diffusers.py   |  32 +-
 wiki                               |   2 +-
 12 files changed, 957 insertions(+), 29 deletions(-)
 create mode 100644 modules/flowmatch/__init__.py
 create mode 100644 modules/flowmatch/flowmatch_dpm.py

diff --git a/.pylintrc b/.pylintrc
index 2d8e4869b..534e7fdce 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -33,6 +33,7 @@ ignore-paths=/usr/lib/.*$,
              modules/omnigen,
              modules/instantir,
              modules/consistory,
+             modules/flowmatch,
              modules/pulid/eva_clip,
              repositories,
              extensions-builtin/sd-webui-agent-scheduler,
diff --git a/.ruff.toml b/.ruff.toml
index 2a29fb089..8852729f1 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -28,6 +28,7 @@ exclude = [
     "modules/omnigen",
     "modules/instantir",
     "modules/consistory",
+    "modules/flowmatch",
     "modules/pulid/eva_clip",
     "repositories",
     "extensions-builtin/sd-extension-chainner/nodes",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6c25b49e6..dadb63d81 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,8 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-15
+## Update for 2024-11-17
 
-### Highlights for 2024-11-15
+### Highlights for 2024-11-17
 
 *What's New?*
 
@@ -16,7 +16,7 @@ First, a massive update to docs including new UI top-level **info** tab with acc
 
 **Workflow Improvements**:
 - Native Docker support
-- SD3x: ControlNets and all-in-one-safetensors
+- SD3x & Flux.1: more ControlNets, all-in-one-safetensors, DPM samplers, etc.
 - XYZ grid: benchmarking, video creation, etc.
 - Enhanced prompt parsing
 - UI improvements
@@ -26,7 +26,7 @@ And quite a few more improvements and fixes since the last update - for full det
 
 [README](https://github.com/vladmandic/automatic/blob/master/README.md) | [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
 
-### Details for 2024-11-15
+### Details for 2024-11-17
 
 - Docs:  
   - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)  
@@ -74,6 +74,9 @@ And quite a few more improvements and fixes since the last update - for full det
   - SD3: all-in-one safetensors  
     - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327)  
     - *note*: enable *bnb* on-the-fly quantization for even bigger gains  
+  - FlowMatch samplers:
+    - Applicable to SD 3.x and Flux.1 models
+    - Complete family: 
 
 - Workflow improvements:  
   - Native Docker support with pre-defined [Dockerfile](https://github.com/vladmandic/automatic/blob/dev/Dockerfile)
diff --git a/installer.py b/installer.py
index ba744b65f..e338a8aa7 100644
--- a/installer.py
+++ b/installer.py
@@ -504,7 +504,7 @@ def install_cuda():
 
 def install_rocm_zluda():
     if args.skip_all or args.skip_requirements:
-        return
+        return None
     from modules import rocm
     if not rocm.is_installed:
         log.warning('ROCm: could not find ROCm toolkit installed')
diff --git a/modules/api/helpers.py b/modules/api/helpers.py
index f13047b9b..d9a87537e 100644
--- a/modules/api/helpers.py
+++ b/modules/api/helpers.py
@@ -24,8 +24,7 @@ def decode_base64_to_image(encoding, quiet=False):
         shared.log.warning(f'API cannot decode image: {e}')
         if not quiet:
             raise HTTPException(status_code=500, detail="Invalid encoded image") from e
-        else:
-            return None
+        return None
 
 
 def encode_pil_to_base64(image):
diff --git a/modules/flowmatch/__init__.py b/modules/flowmatch/__init__.py
new file mode 100644
index 000000000..5a577cce8
--- /dev/null
+++ b/modules/flowmatch/__init__.py
@@ -0,0 +1 @@
+from .flowmatch_dpm import FlowMatchDPMSolverMultistepScheduler, FlowMatchDPMSolverMultistepSchedulerOutput
diff --git a/modules/flowmatch/flowmatch_dpm.py b/modules/flowmatch/flowmatch_dpm.py
new file mode 100644
index 000000000..83573105e
--- /dev/null
+++ b/modules/flowmatch/flowmatch_dpm.py
@@ -0,0 +1,897 @@
+# Credits: @ukaprch <https://github.com/huggingface/diffusers/issues/9924>
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torchsde
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import BaseOutput, logging
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+class BatchedBrownianTree:
+    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
+
+    def __init__(self, x, t0, t1, seed=None, **kwargs):
+        t0, t1, self.sign = self.sort(t0, t1)
+        w0 = kwargs.get("w0", torch.zeros_like(x))
+        if seed is None:
+            seed = torch.randint(0, 2**63 - 1, []).item()
+        self.batched = True
+        try:
+            assert len(seed) == x.shape[0]
+            w0 = w0[0]
+        except TypeError:
+            seed = [seed]
+            self.batched = False
+        self.trees = [
+            torchsde.BrownianInterval(
+                t0=t0,
+                t1=t1,
+                size=w0.shape,
+                dtype=w0.dtype,
+                device=w0.device,
+                entropy=s,
+                tol=1e-6,
+                pool_size=24,
+                halfway_tree=True,
+            )
+            for s in seed
+        ]
+
+    @staticmethod
+    def sort(a, b):
+        return (a, b, 1) if a < b else (b, a, -1)
+
+    def __call__(self, t0, t1):
+        t0, t1, sign = self.sort(t0, t1)
+        w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+        return w if self.batched else w[0]
+
+class BrownianTreeNoiseSampler:
+    """A noise sampler backed by a torchsde.BrownianTree.
+
+    Args:
+        x (Tensor): The tensor whose shape, device and dtype to use to generate
+            random samples.
+        sigma_min (float): The low end of the valid interval.
+        sigma_max (float): The high end of the valid interval.
+        seed (int or List[int]): The random seed. If a list of seeds is
+            supplied instead of a single integer, then the noise sampler will use one BrownianTree per batch item, each
+            with its own seed.
+        transform (callable): A function that maps sigma to the sampler's
+            internal timestep.
+    """
+
+    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
+        self.transform = transform
+        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
+        self.tree = BatchedBrownianTree(x, t0, t1, seed)
+
+    def __call__(self, sigma, sigma_next):
+        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
+        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
+
+@dataclass
+class FlowMatchDPMSolverMultistepSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+    """
+
+    prev_sample: torch.FloatTensor
+
+class FlowMatchDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
+    """
+    `DPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
+
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        solver_order (`int`, defaults to 2):
+            The DPMSolver order which can be `2` or `3`. It is recommended to use `solver_order=2` for guided
+            sampling, and `solver_order=3` for unconditional sampling.
+        thresholding (`bool`, defaults to `False`):
+            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
+            as Stable Diffusion.
+        dynamic_thresholding_ratio (`float`, defaults to 0.995):
+            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
+        sample_max_value (`float`, defaults to 1.0):
+            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
+        algorithm_type (`str`, defaults to `dpmsolver++2M`):
+            Algorithm type for the solver; can be `dpmsolver2`, `dpmsolver2A`, `dpmsolver++2M`, `dpmsolver++2S`, `dpmsolver++sde`, `dpmsolver++2Msde`, 
+            or `dpmsolver++3Msde`.
+        solver_type (`str`, defaults to `midpoint`):
+            Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
+            sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.
+        sigma_schedule (`str`, *optional*, defaults to None): Sigma schedule to compute the `sigmas`. Optionally, we use 
+            the schedule "karras" introduced in the EDM paper (https://arxiv.org/abs/2206.00364). Other acceptable values are 
+            "exponential". The exponential schedule was incorporated in this model: https://huggingface.co/stabilityai/cosxl. 
+            Other acceptable values are "lambdas". The uniform-logSNR for step sizes proposed by Lu's DPM-Solver in the 
+            noise schedule during the sampling process. The sigmas and time steps are determined according to a sequence of `lambda(t)`.
+        use_noise_sampler for BrownianTreeNoiseSampler (only valid for `dpmsolver++2S`, `dpmsolver++sde`, `dpmsolver++2Msde`, 
+            or `dpmsolver++3Msde`): A noise sampler backed by a torchsde increasing the stability of convergence. Default strategy 
+            (random noise) has it jumping all over the place, but Brownian sampling is more stable. Utilizes the model generation seed provided.
+        midpoint_ratio (`float`, *optional*, range: 0.4 to 0.6, default=0.5): Only valid for (`dpmsolver++sde`, `dpmsolver++2S`).
+            Higher values may result in smoothing, more vivid colors and less noise at the expense of more detail and effect.
+        s_noise (`float`, *optional*, defaults to 1.0): Sigma noise strength: range 0 - 1.1 (only valid for `dpmsolver++2S`, `dpmsolver++sde`, 
+            `dpmsolver++2Msde`, or `dpmsolver++3Msde`). The amount of additional noise to counteract loss of detail during sampling. A 
+            reasonable range is [1.000, 1.011]. Defaults to 1.0 from the original implementation.
+        use_SD35_sigmas: (`bool` defaults to False for FLUX and True for SD3). Based on original interpretation of using beta values for determining sigmas.
+        use_dynamic_shifting (`bool` defaults to False for SD3 and True for FLUX). When `True`, shift is ignored.
+        shift (`float`, defaults to 3.0): The shift value for the timestep schedule for SD3 when not using dynamic shifting
+        The remaining args are specific to Flux's dynamic shifting based on resolution
+    """
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        solver_order: int = 2,
+        thresholding: Optional[bool] = False,
+        dynamic_thresholding_ratio: float = 0.995,
+        sample_max_value: Optional[float] = 1.0,
+        algorithm_type: str = "dpmsolver++2M",
+        solver_type: str = "midpoint",
+        sigma_schedule: Optional[str] = None,
+        shift: float = 3.0,
+        midpoint_ratio: Optional[float] = 0.5,
+        s_noise: Optional[float] = 1.0,
+        use_noise_sampler: Optional[bool] = True,
+        use_SD35_sigmas: Optional[bool] = False,
+        use_dynamic_shifting=False,
+        base_shift: Optional[float] = 0.5,
+        max_shift: Optional[float] = 1.15,
+        base_image_seq_len: Optional[int] = 256,
+        max_image_seq_len: Optional[int] = 4096,
+    ):
+        # settings for DPM-Solver
+        if algorithm_type not in ["dpmsolver2", "dpmsolver2A", "dpmsolver++2M", "dpmsolver++2S", "dpmsolver++sde", "dpmsolver++2Msde", "dpmsolver++3Msde"]:
+            raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}")
+
+        if solver_type not in ["midpoint", "heun"]:
+            raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
+
+        # setable values
+        timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
+        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+
+        sigmas = timesteps / num_train_timesteps
+        if not use_dynamic_shifting:
+            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+
+        self.timesteps = sigmas * num_train_timesteps
+        self.h_last = None
+        self.h_1 = None
+        self.h_2 = None
+        self.noise_sampler = None
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+        self.model_outputs = [None] * solver_order
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    def set_timesteps(self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`")
+
+        if sigmas is None:
+            self.use_SD35_sigmas = True
+            self.num_inference_steps = num_inference_steps
+            sigmas1 = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps, dtype=np.float64)
+            beta_start = 0.00085
+            beta_end = 0.012
+            betas = torch.linspace(beta_start**0.5, beta_end**0.5, self.config.num_train_timesteps, dtype=torch.float64) ** 2
+            alphas = 1.0 - betas
+            alphas_cumprod = torch.cumprod(alphas, dim=0)
+            sigmas = np.array(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5)
+            del alphas_cumprod
+            del alphas
+            del betas
+        elif self.use_SD35_sigmas:
+            num_inference_steps = len(sigmas)
+            self.num_inference_steps = num_inference_steps
+            sigmas1 = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps, dtype=np.float64)
+            beta_start = 0.00085
+            beta_end = 0.012
+            betas = torch.linspace(beta_start**0.5, beta_end**0.5, self.config.num_train_timesteps, dtype=torch.float64) ** 2
+            alphas = 1.0 - betas
+            alphas_cumprod = torch.cumprod(alphas, dim=0)
+            sigmas = np.array(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5)
+            del alphas_cumprod
+            del alphas
+            del betas
+        else:
+            num_inference_steps = len(sigmas)
+            self.num_inference_steps = num_inference_steps
+
+        if self.config.sigma_schedule == "exponential":
+            if self.use_SD35_sigmas:
+                sigmas = np.flip(sigmas).copy()
+                sigma_min = sigmas[-1]
+                sigma_max = sigmas[0]
+                sigmas = self._convert_to_exponential(sigma_min, sigma_max, num_inference_steps=num_inference_steps)
+                OldRange = sigma_max - sigma_min
+                NewRange = 1.0 - sigma_min
+                sigmas = (((sigmas - sigma_min) * NewRange) / OldRange) + sigma_min
+                del sigmas1
+            else:
+                sigma_min = sigmas[-1]
+                sigma_max = sigmas[0]
+                sigmas = self._convert_to_exponential(sigma_min, sigma_max, num_inference_steps=num_inference_steps)
+        elif self.config.sigma_schedule == "karras":
+            if self.use_SD35_sigmas:
+                sigmas = np.flip(sigmas).copy()
+                sigma_min = sigmas[-1]
+                sigma_max = sigmas[0]
+                sigmas = self._convert_to_karras(sigma_min, sigma_max, num_inference_steps=num_inference_steps)
+                OldRange = sigma_max - sigma_min
+                NewRange = 1.0 - sigma_min
+                sigmas = (((sigmas - sigma_min) * NewRange) / OldRange) + sigma_min
+                del sigmas1
+            else:
+                sigma_min = sigmas[-1]
+                sigma_max = sigmas[0]
+                sigmas = self._convert_to_karras(sigma_min, sigma_max, num_inference_steps=num_inference_steps)
+            sigmas = torch.from_numpy(sigmas).to(dtype=torch.float64, device=device)
+        elif self.config.sigma_schedule == "lambdas":
+            if self.use_SD35_sigmas:
+                log_sigmas = np.log(sigmas)
+                lambdas = np.flip(log_sigmas.copy())
+                lambdas = self._convert_to_lu(in_lambdas=lambdas, num_inference_steps=num_inference_steps)
+                sigmas = np.exp(lambdas)
+                sigma_min = sigmas[-1]
+                sigma_max = sigmas[0]
+                OldRange = sigma_max - sigma_min
+                NewRange = 1.0 - sigma_min
+                sigmas = (((sigmas - sigma_min) * NewRange) / OldRange) + sigma_min
+                del sigmas1
+                del lambdas
+                del log_sigmas
+            else:
+                log_sigmas = np.log(sigmas)
+                lambdas = log_sigmas.copy()
+                lambdas = self._convert_to_lu(in_lambdas=lambdas, num_inference_steps=num_inference_steps)
+                sigmas = np.exp(lambdas)
+                del lambdas
+                del log_sigmas
+            sigmas = torch.from_numpy(sigmas).to(dtype=torch.float64, device=device)
+        else:
+            if self.use_SD35_sigmas:
+                sigmas = np.flip(sigmas).copy()
+                sigma_min = sigmas[-1]
+                sigmas = np.linspace(1.0, sigma_min, num_inference_steps)
+                del sigmas1
+            sigmas = torch.from_numpy(sigmas).to(dtype=torch.float64, device=device)
+        
+        if self.config.use_dynamic_shifting:
+            sigmas = self.time_shift(mu, 1.0, sigmas)
+        else:
+            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
+        
+        timesteps = sigmas * self.config.num_train_timesteps
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+        self.h_last = None
+        self.h_1 = None
+        self.h_2 = None
+        self.noise_sampler = None
+        self.model_outputs = [None] * self.config.solver_order
+        self._step_index = None
+        self._begin_index = None
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
+    def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
+        """
+        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
+        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
+        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
+        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
+        photorealism as well as better image-text alignment, especially when using very large guidance weights."
+
+        https://arxiv.org/abs/2205.11487
+        """
+        dtype = sample.dtype
+        batch_size, channels, *remaining_dims = sample.shape
+
+        if dtype not in (torch.float32, torch.float64):
+            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
+
+        # Flatten sample for doing quantile calculation along each image
+        sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
+
+        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
+
+        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
+        s = torch.clamp(
+            s, min=1, max=self.config.sample_max_value
+        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
+        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
+        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
+
+        sample = sample.reshape(batch_size, channels, *remaining_dims)
+        sample = sample.to(dtype)
+
+        return sample
+
+    def _convert_to_lu(self, in_lambdas: torch.Tensor, num_inference_steps) -> torch.Tensor:
+        """Constructs the noise schedule of Lu et al. (2022)."""
+
+        lambda_min: float = in_lambdas[-1].item()
+        lambda_max: float = in_lambdas[0].item()
+
+        rho = 1.0  # 1.0 is the value used in the paper
+        ramp = np.linspace(0, 1, num_inference_steps)
+        min_inv_rho = lambda_min ** (1 / rho)
+        max_inv_rho = lambda_max ** (1 / rho)
+        lambdas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        return lambdas
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
+    def _convert_to_karras(self, sigma_min, sigma_max, num_inference_steps) -> torch.Tensor:
+        rho = 7.0  # 7.0 is the value used in the paper
+        ramp = np.linspace(0, 1, num_inference_steps)
+        min_inv_rho = sigma_min ** (1 / rho)
+        max_inv_rho = sigma_max ** (1 / rho)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        return sigmas
+
+    def _convert_to_exponential(self, sigma_min, sigma_max, num_inference_steps) -> torch.Tensor:
+        sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps).exp()
+        return sigmas
+
+    def convert_model_output(
+        self,
+        model_output: torch.Tensor,
+        sample: torch.Tensor = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
+        designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
+        integral of the data prediction model.
+
+        <Tip>
+
+        The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
+        prediction and data prediction models.
+
+        </Tip>
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from the learned diffusion model.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+
+        Returns:
+            `torch.Tensor`:
+                The converted model output.
+        """
+        timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
+        if sample is None:
+            if len(args) > 1:
+                sample = args[1]
+            else:
+                raise ValueError("missing `sample` as a required keyward argument")
+
+        # Flow Match needs to solve an integral of the data prediction model.
+        sigma = self.sigmas[self.step_index]
+        x0_pred = sample - sigma * model_output
+
+        if self.config.thresholding:
+            x0_pred = self._threshold_sample(x0_pred)
+
+        return x0_pred
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        generator: Optional[torch.Generator] = None,
+        variance_noise: Optional[torch.FloatTensor] = None,
+        return_dict: bool = True,
+    ) -> Union[FlowMatchDPMSolverMultistepSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
+        the multistep DPMSolver.
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from learned diffusion model.
+            timestep (`int`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            variance_noise (`torch.Tensor`):
+                Alternative to generating noise with `generator` by directly providing the noise for the variance
+                itself. Useful for methods such as [`LEdits++`].
+            return_dict (`bool`):
+                Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
+
+        Returns:
+            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
+                tuple is returned where the first element is the sample tensor.
+
+        """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        if self.config.algorithm_type in ["dpmsolver2", "dpmsolver2A"]:
+            pass
+        else:
+            model_output = self.convert_model_output(model_output, sample=sample)
+            for i in range(self.config.solver_order - 1):
+                self.model_outputs[i] = self.model_outputs[i + 1]
+            self.model_outputs[-1] = model_output
+
+        # Upcast to avoid precision issues when computing prev_sample
+        if sample.dtype != model_output.dtype:
+            sample = sample.to(model_output.dtype)
+
+        if self.config.algorithm_type in ["dpmsolver2A", "dpmsolver++2S", "dpmsolver++sde", "dpmsolver++2Msde", "dpmsolver++3Msde"] and variance_noise is None:
+            # Create a noise sampler if it hasn't been created yet
+            if self.config.use_noise_sampler:
+                if self.noise_sampler is None:
+                    min_sigma, max_sigma = self.sigmas[self.sigmas > 0].min(), self.sigmas.max()
+                    self.noise_sampler = BrownianTreeNoiseSampler(sample, min_sigma, max_sigma, generator)
+            else:
+                noise = randn_tensor(model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype)
+        elif self.config.algorithm_type in ["dpmsolver2A", "dpmsolver++2S", "dpmsolver++sde", "dpmsolver++2Msde", "dpmsolver++3Msde"]:
+            noise = variance_noise.to(device=model_output.device, dtype=model_output.dtype)
+        else:
+            noise = None
+
+        def sigma_fn(_t: torch.Tensor) -> torch.Tensor:
+            return _t.neg().exp()
+        def t_fn(_sigma: torch.Tensor) -> torch.Tensor:
+            return _sigma.log().neg()
+        sigma = self.sigmas[self.step_index]
+        sigma_next = self.sigmas[self.step_index + 1]
+        sigma_prev = self.sigmas[self.step_index - 1]
+        if self.config.algorithm_type == "dpmsolver2":
+            if self.config.solver_order == 2:
+                if sigma_next == 0:
+                    # Euler method
+                    model_output = sample - sigma * model_output
+                    d = (sample - model_output) / sigma
+                    dt = sigma_next - sigma
+                    sample = sample + d * dt                
+                else:
+                    # DPM-Solver2
+                    sigma_mid = sigma.log().lerp(sigma_next.log(), 0.5).exp()
+
+                    #using epsilon for new model output:
+                    pred_original_sample = sample - sigma * model_output
+                    # 2. Convert to an ODE derivative for 1st order
+                    d = (sample - pred_original_sample) / sigma
+                    # 3. delta timestep
+                    dt = sigma_mid - sigma
+                    x_2 = sample + d * dt
+
+                    #using epsilon for new model output:
+                    denoised_2 = x_2 - sigma_mid * model_output
+                    # 2. Convert to an ODE derivative for 2nd order
+                    d = (x_2 - denoised_2) / sigma_mid
+
+                    # 3. delta timestep
+                    dt = sigma_next - sigma
+                    sample = sample + d * dt
+
+                    del pred_original_sample
+                    del denoised_2
+                    del x_2
+                    del d
+        elif self.config.algorithm_type == "dpmsolver2A":
+            if self.config.solver_order == 2:
+                # get ancestral step
+                sigma_from = sigma
+                sigma_to = sigma_next
+                su = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
+                sd = (sigma_to**2 - su**2) ** 0.5
+                if sd == 0:
+                    # Euler method
+                    model_output = sample - sigma * model_output
+                    d = (sample - model_output) / sigma
+                    dt = sd - sigma
+                    sample = sample + d * dt
+                else:
+                    # DPM-Solver2A
+                    sigma_mid = sigma.log().lerp(sd.log(), 0.5).exp()
+
+                    #using epsilon for new model output:
+                    model_output = sample - sigma * model_output
+                    # 2. Convert to an ODE derivative for 1st order
+                    d = (sample - model_output) / sigma
+                    dt = sd - sigma
+                    sample = sample + d * dt
+
+                    #using epsilon for new model output:
+                    pred_original_sample = sample - sigma * model_output
+                    # 2. Convert to an ODE derivative for 1st order
+                    d = (sample - pred_original_sample) / sigma
+                    # 3. delta timestep
+                    dt_1 = sigma_mid - sigma
+                    x_2 = sample + d * dt_1
+
+                    #using epsilon for new model output:
+                    denoised_2 = x_2 - sigma_mid * model_output
+                    # 2. Convert to an ODE derivative for 2nd order
+                    d_2 = (x_2 - denoised_2) / sigma_mid
+
+                    # 3. delta timestep
+                    dt_2 = sd - sigma_mid
+                    sample = sample + d_2 * dt_2
+
+                    if self.config.use_noise_sampler:
+                        sample = sample + self.noise_sampler(sigma, sigma_next) * self.config.s_noise * su
+                    else:
+                        sample = sample + noise * self.config.s_noise * su
+
+                    del pred_original_sample
+                    del denoised_2
+                    del x_2
+                    del d
+        elif self.config.algorithm_type == "dpmsolver++2M":
+            if self.config.solver_order == 2:
+                t, t_next = t_fn(sigma), t_fn(sigma_next)
+                h = t_next - t 
+                if self.model_outputs[-2] is None or sigma_next == 0:
+                    sample = (sigma_fn(t_next) / sigma_fn(t)) * sample - (-h).expm1() * model_output
+                else:
+                    # DPM-Solver++(2M)
+                    h_last = t - t_fn(sigma_prev)
+                    r = h_last / h
+                    denoised_d = (1 + 1 / (2 * r)) * model_output - (1 / (2 * r)) * self.model_outputs[-2]
+                    sample = (sigma_fn(t_next) / sigma_fn(t)) * sample - (-h).expm1() * denoised_d
+                    del denoised_d
+        elif self.config.algorithm_type == "dpmsolver++2S":
+            if self.config.solver_order == 2:
+                # get ancestral step
+                sigma_from = sigma
+                sigma_to = sigma_next
+                su = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
+                sd = (sigma_to**2 - su**2) ** 0.5
+                if sd == 0:
+                    # Euler method
+                    d = (sample - model_output) / sigma
+                    dt = sd - sigma
+                    sample = sample + d * dt
+                else:
+                    # DPM-Solver++(2S)
+                    t, t_next = t_fn(sigma), t_fn(sd)
+                    r = self.config.midpoint_ratio
+                    h = t_next - t
+                    s = t + r * h
+
+                    # Euler method
+                    d = (sample - model_output) / sigma
+                    dt = sd - sigma
+                    sample = sample + d * dt
+
+                    x_2 = (sigma_fn(s) / sigma_fn(t)) * sample - (-h * r).expm1() * model_output
+
+                    #using epsilon for new model output:
+                    denoised_2 = x_2 - sigma_fn(s) * model_output
+                    # 2. Convert to an ODE derivative for 2nd order
+                    d = (x_2 - denoised_2) / sigma_fn(s)
+                    dt = sd - sigma_next
+                    sample = sample + d * dt
+
+                    del x_2
+                    del denoised_2
+                    del d
+                # Noise addition
+                if sigma_next > 0:
+                    if self.config.use_noise_sampler:
+                        sample = sample + self.noise_sampler(sigma, sigma_next) * self.config.s_noise * su
+                    else:
+                        sample = sample + noise * self.config.s_noise * su
+        elif self.config.algorithm_type == "dpmsolver++sde":
+            if self.config.solver_order == 2:
+                if sigma_next == 0:
+                    # Euler method
+                    d = (sample - model_output) / sigma
+                    dt = sigma_next - sigma
+                    sample = sample + d * dt
+                else:
+                    # DPM-Solver++(SDE)
+                    t, t_next = t_fn(sigma), t_fn(sigma_next)
+                    r = self.config.midpoint_ratio
+                    h = t_next - t
+                    s = t + r * h
+
+                    # Euler method
+                    d = (sample - model_output) / sigma
+                    dt = sigma_next - sigma
+                    sample = sample + d * dt
+
+                    # Step 1
+                    # get ancestral step
+                    sigma_from = sigma_fn(t)
+                    sigma_to = sigma_fn(s)
+                    su = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
+                    sd = (sigma_to**2 - su**2) ** 0.5
+
+                    # Euler method
+                    d = (sample - model_output) / sigma
+                    dt = sd - sigma
+                    sample = sample + d * dt
+
+                    s_ = t_fn(sd)
+                    x_2 = (sigma_fn(s_) / sigma_fn(t)) * sample - (t - s_).expm1() * model_output
+                    if self.config.use_noise_sampler:
+                        x_2 = x_2 + self.noise_sampler(sigma_fn(t), sigma_fn(s)) * self.config.s_noise * su
+                    else:
+                        x_2 = x_2 + noise * self.config.s_noise * su
+
+                    # Step 2
+                    # get ancestral step
+                    sigma_from = sigma_fn(t)
+                    sigma_to = sigma_fn(t_next)
+                    su = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
+                    sd = (sigma_to**2 - su**2) ** 0.5
+
+                    #using epsilon for new model output:
+                    denoised_2 = x_2 - sigma_fn(s) * model_output
+                    # 2. Convert to an ODE derivative for 2nd order
+                    d = (x_2 - denoised_2) / sigma_fn(s)
+                    dt = sd - sigma_next
+                    sample = sample + d * dt
+
+                    if self.config.use_noise_sampler:
+                        sample = sample + self.noise_sampler(sigma_fn(t), sigma_fn(t_next)) * self.config.s_noise * su
+                    else:
+                        sample = sample + noise * self.config.s_noise * su                    
+                    del x_2
+                    del denoised_2
+                    del d
+        elif self.config.algorithm_type == "dpmsolver++2Msde":
+            if self.config.solver_order == 2:
+                if sigma_next == 0:
+                    sample = model_output
+                else:
+                    # DPM-Solver++(2M) SDE
+                    t, s = -sigma.log(), -sigma_next.log()
+                    h = s - t
+                    eta_h = h * 1
+
+                    # 3. Delta timestep
+                    dt = sigma_next - sigma
+                    sample = sample + model_output * dt
+
+                    sample = sigma_next / sigma * (-eta_h).exp() * sample + (-h - eta_h).expm1().neg() * model_output
+
+                    if self.model_outputs[-2] is not None:
+                        r = self.h_last / h
+                        if self.solver_type == 'heun':
+                            sample = sample + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (model_output - self.model_outputs[-2])
+                        elif self.solver_type == 'midpoint':
+                            sample = sample + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (model_output - self.model_outputs[-2])
+
+                    if self.config.use_noise_sampler:
+                        sample = sample + self.noise_sampler(sigma, sigma_next) * sigma_next * (-2 * eta_h).expm1().neg().sqrt() * self.config.s_noise
+                    else:
+                        sample = sample + noise * sigma_next * (-2 * eta_h).expm1().neg().sqrt() * self.config.s_noise
+
+                    self.h_last = h
+        elif self.config.algorithm_type == "dpmsolver++3Msde":
+            if self.config.solver_order == 3:
+                if sigma_next == 0:
+                    sample = model_output
+                else:
+                    # DPM-Solver++(3M) SDE
+                    t, s = -sigma.log(), -sigma_next.log()
+                    h = s - t
+                    h_eta = h * 2
+                     
+                    # 3. Delta timestep
+                    dt = sigma_next - sigma
+                    sample = sample + model_output * dt
+
+                    sample = torch.exp(-h_eta) * sample + (-h_eta).expm1().neg() * model_output
+                
+                    if self.h_2 is not None:
+                        r0 = self.h_1 / h
+                        r1 = self.h_2 / h
+                        d1_0 = (model_output - self.model_outputs[-2]) / r0
+                        d1_1 = (self.model_outputs[-2] - self.model_outputs[-3]) / r1
+                        d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
+                        d2 = (d1_0 - d1_1) / (r0 + r1)
+                        phi_2 = h_eta.neg().expm1() / h_eta + 1
+                        phi_3 = phi_2 / h_eta - 0.5
+                        sample = sample + phi_2 * d1 - phi_3 * d2
+                        del d1_0
+                        del d1_1
+                        del d1
+                        del d2
+                        del phi_2
+                        del phi_3
+                    elif self.h_1 is not None:
+                        r = self.h_1 / h
+                        d = (model_output - self.model_outputs[-2]) / r
+                        phi_2 = h_eta.neg().expm1() / h_eta + 1
+                        sample = sample + phi_2 * d
+                        del d
+                        del phi_2
+
+                    if self.config.use_noise_sampler:
+                        sample = sample + self.noise_sampler(sigma, sigma_next) * sigma_next * (-2 * h).expm1().neg().sqrt() * self.config.s_noise
+                    else:
+                        sample = sample + noise * sigma_next * (-2 * h).expm1().neg().sqrt() * self.config.s_noise
+            
+                    self.h_2 = self.h_1
+                    self.h_1 = h
+            if not self.config.use_noise_sampler and noise is not None:
+                del noise
+        prev_sample = sample
+     
+        # Cast sample back to expected dtype
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        torch.cuda.empty_cache()
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return FlowMatchDPMSolverMultistepSchedulerOutput(prev_sample=prev_sample)
+
+    def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+
+        Args:
+            sample (`torch.Tensor`):
+                The input sample.
+
+        Returns:
+            `torch.Tensor`:
+                A scaled input sample.
+        """
+        return sample
+
+    def scale_noise(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        noise: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        """
+        Forward process in flow-matching
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The input sample.
+            timestep (`int`, *optional*):
+                The current timestep in the diffusion chain.
+
+        Returns:
+            `torch.FloatTensor`:
+                A scaled input sample.
+        """
+        # Make sure sigmas and timesteps have the same device and dtype as original_samples
+        sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)
+
+        if sample.device.type == "mps" and torch.is_floating_point(timestep):
+            # mps does not support float64
+            schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
+            timestep = timestep.to(sample.device, dtype=torch.float32)
+        else:
+            schedule_timesteps = self.timesteps.to(sample.device)
+            timestep = timestep.to(sample.device)
+
+        # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
+        if self.begin_index is None:
+            step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep]
+        elif self.step_index is not None:
+            # add_noise is called after first denoising step (for inpainting)
+            step_indices = [self.step_index] * timestep.shape[0]
+        else:
+            # add noise is called before first denoising step to create initial latent(img2img)
+            step_indices = [self.begin_index] * timestep.shape[0]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < len(sample.shape):
+            sigma = sigma.unsqueeze(-1)
+
+        sample = sigma * noise + (1.0 - sigma) * sample
+
+        return sample
+
+    def __len__(self):
+        return self.config.num_train_timesteps
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 6c9a98369..ff766ec04 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -6,7 +6,6 @@ import time
 import inspect
 import torch
 import numpy as np
-from PIL import Image
 from modules import shared, errors, sd_models, processing, processing_vae, processing_helpers, sd_hijack_hypertile, prompt_parser_diffusers, timer
 from modules.processing_callbacks import diffusers_callback_legacy, diffusers_callback, set_callbacks_p
 from modules.processing_helpers import resize_hires, fix_prompts, calculate_base_steps, calculate_hires_steps, calculate_refiner_steps, get_generator, set_latents, apply_circular # pylint: disable=unused-import
diff --git a/modules/processing_class.py b/modules/processing_class.py
index b0b9e1f1b..79f51576f 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -232,7 +232,7 @@ class StableDiffusionProcessing:
 
         # a1111 compatibility items
         shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
-        self.seed_enable_extras: bool = True,
+        self.seed_enable_extras: bool = True
         self.is_using_inpainting_conditioning = False # a111 compatibility
         self.batch_index = 0
         self.refiner_switch_at = 0
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index bbc2f360b..e560744dd 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -47,6 +47,10 @@ def visible_sampler_names():
 
 
 def create_sampler(name, model):
+    try:
+        current = model.scheduler.__class__.__name__
+    except Exception:
+        current = None
     if name == 'Default' and hasattr(model, 'scheduler'):
         if getattr(model, "default_scheduler", None) is not None:
             model.scheduler = copy.deepcopy(model.default_scheduler)
@@ -54,12 +58,13 @@ def create_sampler(name, model):
                 model.prior_pipe.scheduler = copy.deepcopy(model.default_scheduler)
                 model.prior_pipe.scheduler.config.clip_sample = False
         config = {k: v for k, v in model.scheduler.config.items() if not k.startswith('_')}
-        shared.log.debug(f'Sampler: sampler=default class={model.scheduler.__class__.__name__}: {config}')
+        shared.log.debug(f'Sampler: sampler=default class={current}: {config}')
         return model.scheduler
     config = find_sampler_config(name)
     if config is None or config.constructor is None:
         # shared.log.warning(f'Sampler: sampler="{name}" not found')
         return None
+    sampler = None
     if not shared.native:
         sampler = config.constructor(model)
         sampler.config = config
@@ -68,24 +73,18 @@ def create_sampler(name, model):
         shared.log.debug(f'Sampler: sampler="{name}" config={config.options}')
         return sampler
     elif shared.native:
-        sampler = config.constructor(model)
-        if 'Flux' in model.__class__.__name__:
-            if 'base_image_seq_len' not in sampler.sampler.config or 'max_image_seq_len' not in sampler.sampler.config or 'base_shift' not in sampler.sampler.config or 'max_shift' not in sampler.sampler.config:
-                shared.log.warning(f'FLUX: sampler="{name}" unsupported')
-                # sampler.sampler.register_to_config(base_image_seq_len=256, max_image_seq_len=4096, base_shift=0.5, max_shift=1.15)
-                return None
-        if 'Lumina' in model.__class__.__name__:
-            shared.log.warning(f'AlphaVLLM-Lumina: sampler="{name}" unsupported')
-            return None
-        if 'StableDiffusion3' in model.__class__.__name__:
-            if sampler.name != 'Heun FlowMatch':
-                return None
-            return None
-        if 'AuraFlow' in model.__class__.__name__:
-            shared.log.warning(f'AuraFlow: sampler="{name}" unsupported')
-            return None
+        FlowModels = ['Flux', 'StableDiffusion3', 'Lumina', 'AuraFlow']
         if 'KDiffusion' in model.__class__.__name__:
             return None
+        if any(x in model.__class__.__name__ for x in FlowModels) and 'FlowMatch' not in name:
+            shared.log.warning(f'Sampler: default={current} target="{name}" class={model.__class__.__name__} linear scheduler unsupported')
+            return None
+        if not any(x in model.__class__.__name__ for x in FlowModels) and 'FlowMatch' in name:
+            shared.log.warning(f'Sampler: default={current} target="{name}" class={model.__class__.__name__} flow-match scheduler unsupported')
+            return None
+        sampler = config.constructor(model)
+        if sampler is None:
+            sampler = config.constructor(model)
         if not hasattr(model, 'scheduler_config'):
             model.scheduler_config = sampler.sampler.config.copy() if hasattr(sampler.sampler, 'config') else {}
         model.scheduler = sampler.sampler
diff --git a/modules/sd_samplers_diffusers.py b/modules/sd_samplers_diffusers.py
index c7eedf59b..22ec2be46 100644
--- a/modules/sd_samplers_diffusers.py
+++ b/modules/sd_samplers_diffusers.py
@@ -43,6 +43,9 @@ try:
         KDPM2DiscreteScheduler,
         KDPM2AncestralDiscreteScheduler,
     )
+
+    from modules.flowmatch import FlowMatchDPMSolverMultistepScheduler # pylint: disable=ungrouped-imports
+
 except Exception as e:
     import diffusers
     shared.log.error(f'Diffusers import error: version={diffusers.__version__} error: {e}')
@@ -70,7 +73,15 @@ config = {
     'DPM++ 2M SDE': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "sde-dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 },
     'DPM++ 2M EDM': { 'solver_order': 2, 'solver_type': 'midpoint', 'final_sigmas_type': 'zero', 'algorithm_type': 'dpmsolver++' },
     'DPM++ Cosine': { 'solver_order': 2, 'sigma_schedule': "exponential", 'prediction_type': "v-prediction" },
-    'DPM SDE': { 'use_karras_sigmas': False, 'noise_sampler_seed': None, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
+    'DPM SDE': { 'use_karras_sigmas': False, 'noise_sampler_seed': None, 'timestep_spacing': 'linspace', 'steps_offset': 0,  },
+
+    'DPM2 FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver2', 'use_noise_sampler': True },
+    'DPM2a FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver2A', 'use_noise_sampler': True },
+    'DPM2++ 2M FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++2M', 'use_noise_sampler': True },
+    'DPM2++ 2S FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++2S', 'use_noise_sampler': True },
+    'DPM2++ SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++sde', 'use_noise_sampler': True },
+    'DPM2++ 2M SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++2Msde', 'use_noise_sampler': True },
+    'DPM2++ 3M SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 3, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++3Msde', 'use_noise_sampler': True },
 
     'Heun': { 'use_beta_sigmas': False, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace' },
     'Heun FlowMatch': { 'timestep_spacing': "linspace", 'shift': 1 },
@@ -112,6 +123,14 @@ samplers_data_diffusers = [
     sd_samplers_common.SamplerData('DPM++ Cosine', lambda model: DiffusionSampler('DPM++ 2M EDM', CosineDPMSolverMultistepScheduler, model), [], {}),
     sd_samplers_common.SamplerData('DPM SDE', lambda model: DiffusionSampler('DPM SDE', DPMSolverSDEScheduler, model), [], {}),
 
+    sd_samplers_common.SamplerData('DPM2 FlowMatch', lambda model: DiffusionSampler('DPM2 FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('DPM2a FlowMatch', lambda model: DiffusionSampler('DPM2a FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('DPM2++ 2M FlowMatch', lambda model: DiffusionSampler('DPM2++ 2M FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('DPM2++ 2S FlowMatch', lambda model: DiffusionSampler('DPM2++ 2S FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('DPM2++ SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('DPM2++ 2M SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ 2M SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('DPM2++ 3M SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ 3M SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
+
     sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
     sd_samplers_common.SamplerData('Heun FlowMatch', lambda model: DiffusionSampler('Heun FlowMatch', FlowMatchHeunDiscreteScheduler, model), [], {}),
 
@@ -183,6 +202,12 @@ class DiffusionSampler:
                 self.config['use_karras_sigmas'] = shared.opts.schedulers_sigma == 'karras'
             if 'use_exponential_sigmas' in self.config:
                 self.config['use_exponential_sigmas'] = shared.opts.schedulers_sigma == 'exponential'
+            if 'sigma_schedule' in self.config and shared.opts.schedulers_sigma == 'beta':
+                self.config['sigma_schedule'] = 'beta'
+            if 'sigma_schedule' in self.config and shared.opts.schedulers_sigma == 'karras':
+                self.config['sigma_schedule'] = 'karras'
+            if 'sigma_schedule' in self.config and shared.opts.schedulers_sigma == 'exponential':
+                self.config['sigma_schedule'] = 'exponential'
         else:
             pass # timesteps are set using set_timesteps in set_pipeline_args
 
@@ -201,7 +226,10 @@ class DiffusionSampler:
         if 'shift' in self.config:
             self.config['shift'] = shared.opts.schedulers_shift
         if 'use_dynamic_shifting' in self.config:
-            self.config['use_dynamic_shifting'] = shared.opts.schedulers_dynamic_shift
+            if 'Flux' in model.__class__.__name__:
+                self.config['use_dynamic_shifting'] = shared.opts.schedulers_dynamic_shift
+        if 'use_SD35_sigmas' in self.config:
+            self.config['use_SD35_sigmas'] = 'StableDiffusion3' in model.__class__.__name__
         if 'rescale_betas_zero_snr' in self.config:
             self.config['rescale_betas_zero_snr'] = shared.opts.schedulers_rescale_betas
         if 'timestep_spacing' in self.config and shared.opts.schedulers_timestep_spacing != 'default' and shared.opts.schedulers_timestep_spacing is not None:
diff --git a/wiki b/wiki
index 82ca84bc9..713906e92 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 82ca84bc91dd1c915393a96207cd3e5d848a388f
+Subproject commit 713906e920e02607ea04951858aabeff7ce641f2

From 013efaf14cf07f21b6e39c77c07042eaec3b7518 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 18 Nov 2024 11:55:07 -0500
Subject: [PATCH 117/119] add lambdas sigma method

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 extensions-builtin/Lora/networks.py | 19 +++++++++++----
 installer.py                        |  2 +-
 modules/sd_samplers_diffusers.py    | 37 +++++++++++++++--------------
 modules/ui_models.py                |  2 +-
 modules/ui_sections.py              |  2 +-
 scripts/xyz_grid_classes.py         |  2 +-
 6 files changed, 37 insertions(+), 27 deletions(-)

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index b227f82f2..db617ee5b 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -227,6 +227,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
     loaded_networks.clear()
     diffuser_loaded.clear()
     diffuser_scales.clear()
+
     for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
         net = None
         if network_on_disk is not None:
@@ -262,14 +263,22 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
     while len(lora_cache) > shared.opts.lora_in_memory_limit:
         name = next(iter(lora_cache))
         lora_cache.pop(name, None)
+
     if len(diffuser_loaded) > 0:
-        shared.log.debug(f'Load network: type=LoRA loaded={diffuser_loaded} scales={diffuser_scales}')
-        shared.sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
-        if shared.opts.lora_fuse_diffusers:
-            shared.sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # fuse uses fixed scale since later apply does the scaling
-            shared.sd_model.unload_lora_weights()
+        shared.log.debug(f'Load network: type=LoRA loaded={diffuser_loaded} available={shared.sd_model.get_list_adapters()} active={shared.sd_model.get_active_adapters()} scales={diffuser_scales}')
+        try:
+            shared.sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
+            if shared.opts.lora_fuse_diffusers:
+                shared.sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # fuse uses fixed scale since later apply does the scaling
+                shared.sd_model.unload_lora_weights()
+        except Exception as e:
+            shared.log.error(f'Load network: type=LoRA {e}')
+            if debug:
+                errors.display(e, 'LoRA')
+
     if len(loaded_networks) > 0 and debug:
         shared.log.debug(f'Load network: type=LoRA loaded={len(loaded_networks)} cache={list(lora_cache)}')
+
     devices.torch_gc()
 
     if recompile_model:
diff --git a/installer.py b/installer.py
index e338a8aa7..6f48e5790 100644
--- a/installer.py
+++ b/installer.py
@@ -459,7 +459,7 @@ def check_python(supported_minors=[9, 10, 11, 12], reason=None):
 def check_diffusers():
     if args.skip_all or args.skip_requirements:
         return
-    sha = 'dac623b59f52c58383a39207d5147aa34e0047cd'
+    sha = '345907f32de71c8ca67f3d9d00e37127192da543'
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else 0)
     cur = opts.get('diffusers_version', '') if minor > 0 else ''
diff --git a/modules/sd_samplers_diffusers.py b/modules/sd_samplers_diffusers.py
index 22ec2be46..afd3f4588 100644
--- a/modules/sd_samplers_diffusers.py
+++ b/modules/sd_samplers_diffusers.py
@@ -52,6 +52,9 @@ except Exception as e:
     if os.environ.get('SD_SAMPLER_DEBUG', None) is not None:
         errors.display(e, 'Samplers')
 
+"""
+"""
+
 config = {
     # beta_start, beta_end are typically per-scheduler, but we don't want them as they should be taken from the model itself as those are values model was trained on
     # prediction_type is ideally set in model as well, but it maybe needed that we do auto-detect of model type in the future
@@ -66,14 +69,14 @@ config = {
     'Euler EDM': { 'sigma_schedule': "karras" },
     'Euler FlowMatch': { 'timestep_spacing': "linspace", 'shift': 1, 'use_dynamic_shifting': False },
 
-    'DPM++': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'sigma_min' },
-    'DPM++ 1S': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 1 },
-    'DPM++ 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 },
-    'DPM++ 3M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 3 },
-    'DPM++ 2M SDE': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "sde-dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 },
+    'DPM++': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'final_sigmas_type': 'sigma_min' },
+    'DPM++ 1S': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 1 },
+    'DPM++ 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 },
+    'DPM++ 3M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 3 },
+    'DPM++ 2M SDE': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "sde-dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 },
     'DPM++ 2M EDM': { 'solver_order': 2, 'solver_type': 'midpoint', 'final_sigmas_type': 'zero', 'algorithm_type': 'dpmsolver++' },
     'DPM++ Cosine': { 'solver_order': 2, 'sigma_schedule': "exponential", 'prediction_type': "v-prediction" },
-    'DPM SDE': { 'use_karras_sigmas': False, 'noise_sampler_seed': None, 'timestep_spacing': 'linspace', 'steps_offset': 0,  },
+    'DPM SDE': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'noise_sampler_seed': None, 'timestep_spacing': 'linspace', 'steps_offset': 0,  },
 
     'DPM2 FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver2', 'use_noise_sampler': True },
     'DPM2a FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver2A', 'use_noise_sampler': True },
@@ -83,11 +86,11 @@ config = {
     'DPM2++ 2M SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++2Msde', 'use_noise_sampler': True },
     'DPM2++ 3M SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 3, 'sigma_schedule': None, 'use_SD35_sigmas': False, 'algorithm_type': 'dpmsolver++3Msde', 'use_noise_sampler': True },
 
-    'Heun': { 'use_beta_sigmas': False, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace' },
+    'Heun': { 'use_beta_sigmas': False, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'timestep_spacing': 'linspace' },
     'Heun FlowMatch': { 'timestep_spacing': "linspace", 'shift': 1 },
 
-    'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True, 'timestep_spacing': 'linspace' },
-    'SA Solver': {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace'},
+    'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True, 'timestep_spacing': 'linspace', 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False },
+    'SA Solver': {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'timestep_spacing': 'linspace'},
     'DC Solver': { 'beta_start': 0.0001, 'beta_end': 0.02, 'solver_order': 2, 'prediction_type': "epsilon", 'thresholding': False, 'solver_type': 'bh2', 'lower_order_final': True, 'dc_order': 2, 'disable_corrector': [0] },
     'VDM Solver': { 'clip_sample_range': 2.0, },
 
@@ -97,9 +100,9 @@ config = {
     'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' },
     'IPNDM': { },
     'DDPM': { 'variance_type': "fixed_small", 'clip_sample': False, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace', 'rescale_betas_zero_snr': False },
-    'LMSD': { 'use_karras_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
-    'KDPM2': { 'steps_offset': 0, 'timestep_spacing': 'linspace' },
-    'KDPM2 a': { 'steps_offset': 0, 'timestep_spacing': 'linspace' },
+    'LMSD': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
+    'KDPM2': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' },
+    'KDPM2 a': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' },
     'CMSI': { }, #{ 'sigma_min':  0.002, 'sigma_max': 80.0, 'sigma_data': 0.5, 's_noise': 1.0, 'rho': 7.0, 'clip_denoised': True },
 }
 
@@ -202,12 +205,10 @@ class DiffusionSampler:
                 self.config['use_karras_sigmas'] = shared.opts.schedulers_sigma == 'karras'
             if 'use_exponential_sigmas' in self.config:
                 self.config['use_exponential_sigmas'] = shared.opts.schedulers_sigma == 'exponential'
-            if 'sigma_schedule' in self.config and shared.opts.schedulers_sigma == 'beta':
-                self.config['sigma_schedule'] = 'beta'
-            if 'sigma_schedule' in self.config and shared.opts.schedulers_sigma == 'karras':
-                self.config['sigma_schedule'] = 'karras'
-            if 'sigma_schedule' in self.config and shared.opts.schedulers_sigma == 'exponential':
-                self.config['sigma_schedule'] = 'exponential'
+            if 'use_lu_lambdas' in self.config:
+                self.config['use_lu_lambdas'] = shared.opts.schedulers_sigma == 'lambdas'
+            if 'sigma_schedule' in self.config:
+                self.config['sigma_schedule'] = shared.opts.schedulers_sigma if shared.opts.schedulers_sigma != 'default' else None
         else:
             pass # timesteps are set using set_timesteps in set_pipeline_args
 
diff --git a/modules/ui_models.py b/modules/ui_models.py
index e9be428b4..624c3849d 100644
--- a/modules/ui_models.py
+++ b/modules/ui_models.py
@@ -453,7 +453,7 @@ def create_ui():
                     if tag is not None and len(tag) > 0:
                         url += f'&tag={tag}'
                     r = req(url)
-                    log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} url="{url}" status={r.status_code}')
+                    log.debug(f'CivitAI search: type={model_type} name="{name}" tag={tag or "none"} url="{url}" status={r.status_code}')
                     if r.status_code != 200:
                         log.warning(f'CivitAI search: name="{name}" tag={tag} status={r.status_code}')
                         return [], gr.update(visible=False, value=[]), gr.update(visible=False, value=None), gr.update(visible=False, value=None)
diff --git a/modules/ui_sections.py b/modules/ui_sections.py
index 391b5c609..7951a9227 100644
--- a/modules/ui_sections.py
+++ b/modules/ui_sections.py
@@ -276,7 +276,7 @@ def create_sampler_options(tabname):
 
     else: # shared.native
         with gr.Row(elem_classes=['flex-break']):
-            sampler_sigma = gr.Dropdown(label='Sigma method', elem_id=f"{tabname}_sampler_sigma", choices=['default', 'karras', 'beta', 'exponential'], value=shared.opts.schedulers_sigma, type='value')
+            sampler_sigma = gr.Dropdown(label='Sigma method', elem_id=f"{tabname}_sampler_sigma", choices=['default', 'karras', 'beta', 'exponential', 'lambdas'], value=shared.opts.schedulers_sigma, type='value')
             sampler_spacing = gr.Dropdown(label='Timestep spacing', elem_id=f"{tabname}_sampler_spacing", choices=['default', 'linspace', 'leading', 'trailing'], value=shared.opts.schedulers_timestep_spacing, type='value')
         with gr.Row(elem_classes=['flex-break']):
             sampler_beta = gr.Dropdown(label='Beta schedule', elem_id=f"{tabname}_sampler_beta", choices=['default', 'linear', 'scaled', 'cosine'], value=shared.opts.schedulers_beta_schedule, type='value')
diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py
index c3d3554e2..84a11daff 100644
--- a/scripts/xyz_grid_classes.py
+++ b/scripts/xyz_grid_classes.py
@@ -115,7 +115,7 @@ axis_options = [
     AxisOption("[Process] Server options", str, apply_options),
     AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
     AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
-    AxisOption("[Sampler] Sigma method", str, apply_setting("schedulers_sigma"), choices=lambda: ['default', 'karras', 'beta', 'exponential']),
+    AxisOption("[Sampler] Sigma method", str, apply_setting("schedulers_sigma"), choices=lambda: ['default', 'karras', 'beta', 'exponential', 'lambdas']),
     AxisOption("[Sampler] Timestep spacing", str, apply_setting("schedulers_timestep_spacing"), choices=lambda: ['default', 'linspace', 'leading', 'trailing']),
     AxisOption("[Sampler] Timestep range", int, apply_setting("schedulers_timesteps_range")),
     AxisOption("[Sampler] Solver order", int, apply_setting("schedulers_solver_order")),

From 96ae1ec0191ce91a1bb9c8c9b98f851f98b69d08 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 18 Nov 2024 14:26:08 -0500
Subject: [PATCH 118/119] set default flowmatch shift value

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 modules/sd_samplers_diffusers.py | 10 ++++++++--
 modules/shared.py                |  2 +-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/modules/sd_samplers_diffusers.py b/modules/sd_samplers_diffusers.py
index afd3f4588..3cf153bf1 100644
--- a/modules/sd_samplers_diffusers.py
+++ b/modules/sd_samplers_diffusers.py
@@ -103,7 +103,7 @@ config = {
     'LMSD': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
     'KDPM2': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' },
     'KDPM2 a': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' },
-    'CMSI': { }, #{ 'sigma_min':  0.002, 'sigma_max': 80.0, 'sigma_data': 0.5, 's_noise': 1.0, 'rho': 7.0, 'clip_denoised': True },
+    'CMSI': { },
 }
 
 samplers_data_diffusers = [
@@ -225,7 +225,13 @@ class DiffusionSampler:
         if 'beta_end' in self.config and shared.opts.schedulers_beta_end > 0:
             self.config['beta_end'] = shared.opts.schedulers_beta_end
         if 'shift' in self.config:
-            self.config['shift'] = shared.opts.schedulers_shift
+            if shared.opts.schedulers_shift == 0:
+                if 'StableDiffusion3' in model.__class__.__name__:
+                    self.config['shift'] = 3
+                if 'Flux' in model.__class__.__name__:
+                    self.config['shift'] = 1
+            else:
+                self.config['shift'] = shared.opts.schedulers_shift
         if 'use_dynamic_shifting' in self.config:
             if 'Flux' in model.__class__.__name__:
                 self.config['use_dynamic_shifting'] = shared.opts.schedulers_dynamic_shift
diff --git a/modules/shared.py b/modules/shared.py
index 46d47b16d..0bd893c9e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -801,7 +801,7 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
     'schedulers_beta_start': OptionInfo(0, "Beta start", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001, "visible": native}),
     'schedulers_beta_end': OptionInfo(0, "Beta end", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001, "visible": native}),
     'schedulers_timesteps_range': OptionInfo(1000, "Timesteps range", gr.Slider, {"minimum": 250, "maximum": 4000, "step": 1, "visible": native}),
-    'schedulers_shift': OptionInfo(1, "Sampler shift", gr.Slider, {"minimum": 0.1, "maximum": 10, "step": 0.1, "visible": native}),
+    'schedulers_shift': OptionInfo(0, "Sampler shift", gr.Slider, {"minimum": 0.1, "maximum": 10, "step": 0.1, "visible": native}),
     'schedulers_dynamic_shift': OptionInfo(True, "Sampler dynamic shift", gr.Checkbox, {"visible": native}),
 
     # managed from ui.py for backend original k-diffusion

From 31f219e3f3c26f56281ae5798972f1d810be4e90 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 19 Nov 2024 07:43:30 -0500
Subject: [PATCH 119/119] cleanup custom schedulers

Signed-off-by: Vladimir Mandic <mandic00@live.com>
---
 .pylintrc                                     | 14 +++++------
 .ruff.toml                                    | 14 +++++------
 CHANGELOG.md                                  |  6 ++---
 modules/flowmatch/__init__.py                 |  1 -
 .../scheduler_dc.py}                          |  0
 .../scheduler_dpm_flowmatch.py}               |  0
 .../scheduler_tcd.py}                         |  0
 .../scheduler_vdm.py}                         |  0
 modules/sd_samplers_diffusers.py              | 25 ++++++++++---------
 9 files changed, 28 insertions(+), 32 deletions(-)
 delete mode 100644 modules/flowmatch/__init__.py
 rename modules/{dcsolver/__init__.py => schedulers/scheduler_dc.py} (100%)
 rename modules/{flowmatch/flowmatch_dpm.py => schedulers/scheduler_dpm_flowmatch.py} (100%)
 rename modules/{tcd/__init__.py => schedulers/scheduler_tcd.py} (100%)
 rename modules/{vdm/__init__.py => schedulers/scheduler_vdm.py} (100%)

diff --git a/.pylintrc b/.pylintrc
index 534e7fdce..59f1cb127 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -8,33 +8,31 @@ fail-under=10
 ignore=CVS
 ignore-paths=/usr/lib/.*$,
              modules/apg,
+             modules/consistory,
              modules/control/proc,
              modules/control/units,
              modules/ctrlx,
-             modules/dcsolver,
              modules/dml,
              modules/ggml,
              modules/hidiffusion,
              modules/hijack,
+             modules/instantir,
              modules/intel/ipex,
              modules/intel/openvino,
              modules/k-diffusion,
              modules/ldsr,
+             modules/meissonic,
+             modules/omnigen,
              modules/onnx_impl,
              modules/pag,
              modules/prompt_parser_xhinker.py,
+             modules/pulid/eva_clip,
              modules/rife,
+             modules/schedulers,
              modules/taesd,
              modules/todo,
              modules/unipc,
-             modules/vdm,
              modules/xadapter,
-             modules/meissonic,
-             modules/omnigen,
-             modules/instantir,
-             modules/consistory,
-             modules/flowmatch,
-             modules/pulid/eva_clip,
              repositories,
              extensions-builtin/sd-webui-agent-scheduler,
              extensions-builtin/sd-extension-chainner/nodes,
diff --git a/.ruff.toml b/.ruff.toml
index 8852729f1..c2d4a6f9a 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -4,32 +4,30 @@ exclude = [
     ".ruff_cache",
     ".vscode",
     "modules/apg",
+    "modules/consistory",
     "modules/control/proc",
     "modules/control/units",
-    "modules/dcsolver",
     "modules/ggml",
     "modules/hidiffusion",
     "modules/hijack",
+    "modules/instantir",
     "modules/intel/ipex",
     "modules/intel/openvino",
     "modules/k-diffusion",
     "modules/ldsr",
+    "modules/meissonic",
+    "modules/omnigen",
     "modules/pag",
     "modules/postprocess/aurasr_arch.py",
     "modules/prompt_parser_xhinker.py",
+    "modules/pulid/eva_clip",
     "modules/rife",
+    "modules/schedulers",
     "modules/segmoe",
     "modules/taesd",
     "modules/todo",
     "modules/unipc",
-    "modules/vdm",
     "modules/xadapter",
-    "modules/meissonic",
-    "modules/omnigen",
-    "modules/instantir",
-    "modules/consistory",
-    "modules/flowmatch",
-    "modules/pulid/eva_clip",
     "repositories",
     "extensions-builtin/sd-extension-chainner/nodes",
     "extensions-builtin/sd-webui-agent-scheduler",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index dadb63d81..795647a7c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,8 @@
 # Change Log for SD.Next
 
-## Update for 2024-11-17
+## Update for 2024-11-19
 
-### Highlights for 2024-11-17
+### Highlights for 2024-11-19
 
 *What's New?*
 
@@ -26,7 +26,7 @@ And quite a few more improvements and fixes since the last update - for full det
 
 [README](https://github.com/vladmandic/automatic/blob/master/README.md) | [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
 
-### Details for 2024-11-17
+### Details for 2024-11-19
 
 - Docs:  
   - new top-level **info** tab with access to [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) and [wiki](https://github.com/vladmandic/automatic/wiki)  
diff --git a/modules/flowmatch/__init__.py b/modules/flowmatch/__init__.py
deleted file mode 100644
index 5a577cce8..000000000
--- a/modules/flowmatch/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .flowmatch_dpm import FlowMatchDPMSolverMultistepScheduler, FlowMatchDPMSolverMultistepSchedulerOutput
diff --git a/modules/dcsolver/__init__.py b/modules/schedulers/scheduler_dc.py
similarity index 100%
rename from modules/dcsolver/__init__.py
rename to modules/schedulers/scheduler_dc.py
diff --git a/modules/flowmatch/flowmatch_dpm.py b/modules/schedulers/scheduler_dpm_flowmatch.py
similarity index 100%
rename from modules/flowmatch/flowmatch_dpm.py
rename to modules/schedulers/scheduler_dpm_flowmatch.py
diff --git a/modules/tcd/__init__.py b/modules/schedulers/scheduler_tcd.py
similarity index 100%
rename from modules/tcd/__init__.py
rename to modules/schedulers/scheduler_tcd.py
diff --git a/modules/vdm/__init__.py b/modules/schedulers/scheduler_vdm.py
similarity index 100%
rename from modules/vdm/__init__.py
rename to modules/schedulers/scheduler_vdm.py
diff --git a/modules/sd_samplers_diffusers.py b/modules/sd_samplers_diffusers.py
index 3cf153bf1..370cb767b 100644
--- a/modules/sd_samplers_diffusers.py
+++ b/modules/sd_samplers_diffusers.py
@@ -1,16 +1,16 @@
 import os
-import copy
 import re
+import copy
 import inspect
+import diffusers
 from modules import shared, errors
 from modules import sd_samplers_common
-from modules.tcd import TCDScheduler
-from modules.dcsolver import DCSolverMultistepScheduler
-from modules.vdm import VDMScheduler
+
 
 debug = shared.log.trace if os.environ.get('SD_SAMPLER_DEBUG', None) is not None else lambda *args, **kwargs: None
 debug('Trace: SAMPLER')
 
+
 try:
     from diffusers import (
         CMStochasticIterativeScheduler,
@@ -43,17 +43,19 @@ try:
         KDPM2DiscreteScheduler,
         KDPM2AncestralDiscreteScheduler,
     )
-
-    from modules.flowmatch import FlowMatchDPMSolverMultistepScheduler # pylint: disable=ungrouped-imports
-
 except Exception as e:
-    import diffusers
     shared.log.error(f'Diffusers import error: version={diffusers.__version__} error: {e}')
     if os.environ.get('SD_SAMPLER_DEBUG', None) is not None:
         errors.display(e, 'Samplers')
-
-"""
-"""
+try:
+    from modules.schedulers.scheduler_tcd import TCDScheduler # pylint: disable=ungrouped-imports
+    from modules.schedulers.scheduler_dc import DCSolverMultistepScheduler # pylint: disable=ungrouped-imports
+    from modules.schedulers.scheduler_vdm import VDMScheduler # pylint: disable=ungrouped-imports
+    from modules.schedulers.scheduler_dpm_flowmatch import FlowMatchDPMSolverMultistepScheduler # pylint: disable=ungrouped-imports
+except Exception as e:
+    shared.log.error(f'Diffusers import error: version={diffusers.__version__} error: {e}')
+    if os.environ.get('SD_SAMPLER_DEBUG', None) is not None:
+        errors.display(e, 'Samplers')
 
 config = {
     # beta_start, beta_end are typically per-scheduler, but we don't want them as they should be taken from the model itself as those are values model was trained on
@@ -93,7 +95,6 @@ config = {
     'SA Solver': {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'timestep_spacing': 'linspace'},
     'DC Solver': { 'beta_start': 0.0001, 'beta_end': 0.02, 'solver_order': 2, 'prediction_type': "epsilon", 'thresholding': False, 'solver_type': 'bh2', 'lower_order_final': True, 'dc_order': 2, 'disable_corrector': [0] },
     'VDM Solver': { 'clip_sample_range': 2.0, },
-
     'LCM': { 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': "scaled_linear", 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'thresholding': False, 'timestep_spacing': 'linspace' },
     'TCD': { 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'beta_schedule': 'scaled_linear' },