Update app.py

app.py

@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 # This dictionary will store the manual patches extracted by the converter
 MANUAL_PATCHES_STORE = {}
 
-def _custom_convert_non_diffusers_wan_lora_to_diffusers(state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+def _custom_convert_non_diffusers_wan_lora_to_diffusers(state_dict):
     """
     Custom converter for Wan 2.1 T2V LoRA.
     Separates LoRA A/B weights for PEFT and diff_b/diff for manual patching.
@@ -192,7 +192,7 @@ def _custom_convert_non_diffusers_wan_lora_to_diffusers(state_dict: Dict[str, to
     return final_peft_state_dict
 
 
-def apply_manual_diff_patches(pipe_model: torch.nn.Module, patches: Dict[str, torch.Tensor]):
+def apply_manual_diff_patches(pipe_model, patches):
     """
     Manually applies diff_b/diff patches to the model.
     Assumes PEFT LoRA layers have already been loaded.
@@ -307,20 +307,9 @@ logger.info(f"Downloading LoRA {LORA_FILENAME} from {LORA_REPO_ID}...")
 causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
 
 logger.info("Loading LoRA weights with custom converter...")
-# The load_lora_weights will use the lora_converters mechanism if available.
-# We need to ensure our custom converter is registered or passed correctly.
-# Since WanPipeline inherits from a LoraLoaderMixin that might have its own
-# lora_state_dict, we need to be careful.
-# A robust way is to load the state_dict, convert it, then load the converted dict.
 
 lora_state_dict_raw = WanPipeline.lora_state_dict(causvid_path) # This might already do some conversion
 
-# If WanPipeline.lora_state_dict doesn't directly call our specific wan converter,
-# we might need to load the raw safetensors file and then call our converter.
-# Let's assume for now that lora_state_dict loads it and we then pass it to our converter.
-# If WanPipeline's lora_state_dict already calls a wan-specific converter,
-# then we need to inject our custom one there, which is not possible without modifying the library.
-
 # Alternative: Load raw state_dict and then convert
 from safetensors.torch import load_file as load_safetensors
 raw_lora_state_dict = load_safetensors(causvid_path)