fix wan22-i2v

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

CHANGELOG.md

@@ -1,8 +1,8 @@
 # Change Log for SD.Next
 
-## Update for 2025-09-13
+## Update for 2025-09-14
 
-### Highlights for 2025-09-13
+### Highlights for 2025-09-14
 
 *What's new*? Big one is that we're (finally) switching the default UI to **ModernUI**!  
 StandardUI is still available and can be selected in settings, but ModernUI is now the default for new installs  
@@ -13,7 +13,7 @@ And check out new **history** tab in the right panel, it now shows visualization
 
 [ReadMe](https://github.com/vladmandic/automatic/blob/master/README.md) | [ChangeLog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [Docs](https://vladmandic.github.io/sdnext-docs/) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867) | [Sponsor](https://github.com/sponsors/vladmandic)  
 
-### Details for 2025-09-13
+### Details for 2025-09-14
 
 - **Models**
   - **Chroma** final versions: [Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD), [Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) and [Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash)  
@@ -1261,6 +1261,7 @@ Models...And support for new models: **CogView-4**, **SANA 1.5**,
   - fix paste incorrect float to int cast  
   - fix server restart from ui  
   - fix style apply params  
+  - fix `wan22-i2v`  
   - do not allow edit of built-in styles  
   - improve lora compatibility with balanced offload  
 

html/reference.json

@@ -254,13 +254,20 @@
     "skip": true,
     "extras": "sampler: Default"
   },
-  "Wan-AI Wan2.2 A14B": {
+  "Wan-AI Wan2.2 A14B T2I": {
     "path": "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
     "preview": "Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
     "desc": "Wan2.2, offering more powerful capabilities, better performance, and superior visual quality. With Wan2.2, we have focused on incorporating the following technical innovations: MoE Architecture, Data Scalling, Cinematic Aesthetics, Efficient High-Definition Hybrid",
     "skip": true,
     "extras": "sampler: Default"
   },
+  "Wan-AI Wan2.2 A14B I2I": {
+    "path": "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
+    "preview": "Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
+    "desc": "Wan2.2, offering more powerful capabilities, better performance, and superior visual quality. With Wan2.2, we have focused on incorporating the following technical innovations: MoE Architecture, Data Scalling, Cinematic Aesthetics, Efficient High-Definition Hybrid",
+    "skip": true,
+    "extras": "sampler: Default"
+  },
 
   "Freepik F-Lite": {
     "path": "Freepik/F-Lite",

modules/video_models/models_def.py

@@ -164,7 +164,7 @@ models = {
               dit_folder=("transformer", "transformer_2")),
         Model(name='WAN 2.2 A14B I2V',
               url='https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers',
-              repo='Wan-AI/Wan2.2-T2V-A14B-Diffusers',
+              repo='Wan-AI/Wan2.2-I2V-A14B-Diffusers',
               repo_cls=diffusers.WanImageToVideoPipeline,
               te_cls=transformers.T5EncoderModel,
               dit_cls=diffusers.WanTransformer3DModel,

pipelines/model_wanai.py

@@ -81,9 +81,14 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
 
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model')
     boundary_ratio = shared.opts.model_wan_boundary if transformer_2 is not None else None
-    shared.log.debug(f'Load model: type=WanAI model="{checkpoint_info.name}" repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args} stage="{shared.opts.model_wan_stage}" boundary={boundary_ratio}')
 
-    cls = diffusers.WanPipeline
+    if 'Wan2.2-I2V' in repo_id:
+        cls = diffusers.WanImageToVideoPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanImageToVideoPipeline
+    else:
+        cls = diffusers.WanPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanPipeline
+    shared.log.debug(f'Load model: type=WanAI model="{checkpoint_info.name}" repo="{repo_id}" cls={cls.__name__} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args} stage="{shared.opts.model_wan_stage}" boundary={boundary_ratio}')
     pipe = cls.from_pretrained(
         repo_id,
         transformer=transformer,
@@ -102,9 +107,6 @@ def load_wan(checkpoint_info, diffusers_load_config={}):
     del transformer
     del transformer_2
 
-    diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanPipeline
-    # diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["wanai"] = diffusers.WanImageToVideoPipeline
-
     sd_hijack_te.init_hijack(pipe)
     sd_hijack_vae.init_hijack(pipe)