fix(lora): patch non-diffusers Wan LoRA converter for ComfyUI and Civitai key compatibility

app.py

@@ -24,6 +24,53 @@ from PIL import Image, ImageOps
 from diffusers.utils import export_to_video
 from safetensors import safe_open
 
+# =========================================================================
+# Monkey-patch Diffusers Wan LoRA converter for broad community compatibility
+# =========================================================================
+try:
+    import diffusers.loaders.lora_conversion_utils as conv_utils
+    import diffusers.loaders.lora_pipeline as lora_pipe
+
+    _orig_wan_converter = getattr(conv_utils, "_convert_non_diffusers_wan_lora_to_diffusers", None)
+
+    def patched_convert_non_diffusers_wan_lora_to_diffusers(original_state_dict):
+        # 1. Normalize prefixes: community LoRAs often start directly with "blocks."
+        # while diffusers internal converter expects "diffusion_model.blocks."
+        prefixed_dict = {}
+        for k, v in original_state_dict.items():
+            if k.startswith("blocks."):
+                prefixed_dict[f"diffusion_model.{k}"] = v
+            elif k.startswith("transformer.blocks."):
+                prefixed_dict[f"diffusion_model.{k[12:]}"] = v
+            else:
+                prefixed_dict[k] = v
+
+        if _orig_wan_converter is not None:
+            try:
+                # Attempt standard conversion with normalized keys
+                return _orig_wan_converter(prefixed_dict.copy())
+            except Exception as err:
+                print(f"[wan] standard converter note: {err}; applying robust fallback mapping", flush=True)
+
+        # 2. Robust fallback mapping for any non-standard Wan LoRA format
+        converted_dict = {}
+        for k, v in prefixed_dict.items():
+            new_k = k
+            if new_k.startswith("diffusion_model."):
+                new_k = new_k[len("diffusion_model."):]
+            if not new_k.startswith("transformer."):
+                new_k = f"transformer.{new_k}"
+            converted_dict[new_k] = v
+        return converted_dict
+
+    if _orig_wan_converter is not None:
+        conv_utils._convert_non_diffusers_wan_lora_to_diffusers = patched_convert_non_diffusers_wan_lora_to_diffusers
+    if hasattr(lora_pipe, "_convert_non_diffusers_wan_lora_to_diffusers"):
+        lora_pipe._convert_non_diffusers_wan_lora_to_diffusers = patched_convert_non_diffusers_wan_lora_to_diffusers
+    print("[wan] LoRA converter patch successfully applied.", flush=True)
+except Exception as patch_err:
+    print(f"[wan] could not apply LoRA converter patch: {patch_err}", flush=True)
+
 DEFAULT_CIVITAI_KEY = "50a9e1bd474c03b856070a7272d8015c"
 DEFAULT_MODEL_REPO = os.environ.get("WAN_MODEL_REPO", "Wan-AI/Wan2.2-I2V-A14B-Diffusers")
 GPU_SIZE = os.environ.get("WAN_GPU_SIZE", "xlarge")
@@ -534,11 +581,14 @@ def _generate_video_gpu(
         for lora_path, lora_scale in lora_configs:
             if lora_path and lora_scale > 0:
                 adapter_name = f"lora_{len(active_lora_names)}"
-                pipe.load_lora_weights(lora_path, adapter_name=adapter_name)
-                active_lora_names.append(adapter_name)
+                try:
+                    pipe.load_lora_weights(lora_path, adapter_name=adapter_name)
+                    active_lora_names.append(adapter_name)
+                except Exception as lora_err:
+                    print(f"[wan] warning: skipping incompatible LoRA `{lora_path}`: {lora_err}", flush=True)
 
         if active_lora_names:
-            scales = [scale for _, scale in lora_configs if scale > 0]
+            scales = [scale for _, scale in lora_configs if scale > 0][:len(active_lora_names)]
             pipe.set_adapters(active_lora_names, adapter_weights=scales)
 
     generator = torch.Generator("cuda").manual_seed(int(seed))