revert

src/diffusers/loaders/lora_pipeline.py

@@ -4810,8 +4810,9 @@ class WanLoraLoaderMixin(LoraBaseMixin):
         transformer: torch.nn.Module,
         state_dict,
     ):
-        # if transformer.config.image_dim is None:
-        #     return state_dict
+        print("BEFORE", list(state_dict.keys()))
+        if transformer.config.image_dim is None:
+            return state_dict
 
         target_device = transformer.device
 
@@ -4849,7 +4850,20 @@ class WanLoraLoaderMixin(LoraBaseMixin):
                             ref_lora_B_bias_tensor,
                             device=target_device,
                         )
-        print(state_dict.keys)
+
+        return state_dict
+
+    @classmethod
+    def _maybe_expand_t2v_lora_for_vace(
+            cls,
+            transformer: torch.nn.Module,
+            state_dict,
+    ):
+
+        if not hasattr(transformer, 'vace_blocks'):
+            return state_dict
+
+        target_device = transformer.device
 
         return state_dict
 
@@ -4905,6 +4919,7 @@ class WanLoraLoaderMixin(LoraBaseMixin):
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
+        print("AFTER:", list(state_dict.keys()))
         self.load_lora_into_transformer(
             state_dict,
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,