fix sd35-ipadapter

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -4,7 +4,7 @@
 
 - **Features**
   - [Nunchaku](https://github.com/mit-han-lab/nunchaku) inference engine with custom **SVDQuant** 4-bit execution  
-    highly experimental and with limited support, but when it works, its magic: **Flux.1 at 5.90 it/s** *(not sec/it)*!  
+    highly experimental and with limited support, but when it works, its magic: **Flux.1 at 6.0 it/s** *(not sec/it)*!  
     see [Nunchaku Wiki](https://github.com/vladmandic/sdnext/wiki/Nunchaku) for installation guide and list of supported models & features  
   - [CFG-Zero](https://github.com/WeichenFan/CFG-Zero-star) new guidance method optimized for flow-matching models  
     implemented for **FLUX.1, HiDream-I1, SD3.x, CogView4, HunyuanVideo, WanAI**  
@@ -27,10 +27,14 @@
     comma-separate list of regex patterns to skip  
   - ui display reference models with subdued color  
   - xyz grid support bool  
+- **Wiki**  
+  - new Nunchaku page  
+  - updated HiDream, Quantization, NNCF pages  
 - **Fixes**
   - NNCF with TE-only quant  
   - Quanto with TE/LLM quant  
   - HiDream live preview  
+  - SD35 InstantX IP-adapter  
   - **HunyuanVideo-I2V** with latest transformers  
   - trace logging  
 

html/reference.json

@@ -428,13 +428,32 @@
     "preview": "THUDM--CogView3-Plus-3B.jpg",
     "skip": true
   },
+
+  "ShuttleAI Shuttle 3.0 Diffusion": {
+    "path": "shuttleai/shuttle-3-diffusion",
+    "desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
+    "preview": "shuttleai--shuttle-3-diffusion.jpg",
+    "skip": true
+  },
+  "ShuttleAI Shuttle 3.1 Aesthetic": {
+    "path": "shuttleai/shuttle-3.1-aesthetic",
+    "desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
+    "preview": "shuttleai--shuttle-3-diffusion.jpg",
+    "skip": true
+  },
+  "ShuttleAI Shuttle Jaguar": {
+    "path": "shuttleai/shuttle-jaguar",
+    "desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
+    "preview": "shuttleai--shuttle-3-diffusion.jpg",
+    "skip": true
+  },
+
   "Meissonic": {
     "path": "MeissonFlow/Meissonic",
     "desc": "Meissonic is a non-autoregressive mask image modeling text-to-image synthesis model that can generate high-resolution images. It is designed to run on consumer graphics cards.",
     "preview": "MeissonFlow--Meissonic.jpg",
     "skip": true
   },
-
   "aMUSEd 256": {
     "path": "huggingface/amused/amused-256",
     "skip": true,

modules/ipadapter.py

@@ -42,7 +42,7 @@ ADAPTERS_SDXL = {
 }
 ADAPTERS_SD3 = {
     'None': { 'name': 'none', 'repo': 'none', 'subfolder': 'none' },
-    'InstantX Large': { 'name': 'none', 'repo': 'InstantX/SD3.5-Large-IP-Adapter', 'subfolder': 'none', 'revision': 'refs/pr/10' },
+    'InstantX Large': { 'name': 'ip-adapter_diffusers.safetensors', 'repo': 'InstantX/SD3.5-Large-IP-Adapter', 'subfolder': 'none', 'revision': 'refs/pr/10' },
 }
 ADAPTERS_F1 = {
     'None': { 'name': 'none', 'repo': 'none', 'subfolder': 'none' },

modules/model_flux.py

@@ -112,11 +112,20 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant):
         if 'transformer' not in kwargs and model_quant.check_nunchaku('Transformer'):
             import nunchaku
             nunchaku_precision = nunchaku.utils.get_precision()
-            nunchaku_repo = f"mit-han-lab/svdq-{nunchaku_precision}-flux.1-dev" if 'dev' in repo_id else f"mit-han-lab/svdq-{nunchaku_precision}-flux.1-schnell"
-            shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} attention={shared.opts.nunchaku_attention}')
-            kwargs['transformer'] = nunchaku.NunchakuFluxTransformer2dModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
-            if shared.opts.nunchaku_attention:
-                kwargs['transformer'].set_attention_impl("nunchaku-fp16")
+            nunchaku_repo = None
+            if 'dev' in repo_id:
+                nunchaku_repo = f"mit-han-lab/svdq-{nunchaku_precision}-flux.1-dev"
+            elif 'schnell' in repo_id:
+                nunchaku_repo = f"mit-han-lab/svdq-{nunchaku_precision}-flux.1-schnell"
+            elif 'shuttle' in repo_id:
+                nunchaku_repo = 'mit-han-lab/svdq-fp4-shuttle-jaguar'
+            else:
+                shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
+            if nunchaku_repo is not None:
+                shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} attention={shared.opts.nunchaku_attention}')
+                kwargs['transformer'] = nunchaku.NunchakuFluxTransformer2dModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
+                if shared.opts.nunchaku_attention:
+                    kwargs['transformer'].set_attention_impl("nunchaku-fp16")
         elif 'transformer' not in kwargs and model_quant.check_quant('Transformer'):
             quant_args = model_quant.create_config(allow=allow_quant, module='Transformer')
             if quant_args: