support wan i2v loras from the world.

Author: sayakpaul

src/diffusers/loaders/lora_conversion_utils.py

@@ -1348,3 +1348,53 @@ def process_block(prefix, index, convert_norm):
         converted_state_dict[f"transformer.{key}"] = converted_state_dict.pop(key)
 
     return converted_state_dict
+
+
+def _convert_non_diffusers_wan_lora_to_diffusers(state_dict):
+    converted_state_dict = {}
+    original_state_dict = {k[len("diffusion_model.") :]: v for k, v in state_dict.items()}
+
+    num_blocks = len({k.split("blocks.")[1].split(".")[0] for k in original_state_dict})
+
+    for i in range(num_blocks):
+        # Self-attention
+        for o, c in zip(["q", "k", "v", "o"], ["to_q", "to_k", "to_v", "to_out.0"]):
+            converted_state_dict[f"blocks.{i}.attn1.{c}.lora_A.weight"] = original_state_dict.pop(
+                f"blocks.{i}.self_attn.{o}.lora_A.weight"
+            )
+            converted_state_dict[f"blocks.{i}.attn1.{c}.lora_B.weight"] = original_state_dict.pop(
+                f"blocks.{i}.self_attn.{o}.lora_B.weight"
+            )
+
+        # Cross-attention
+        for o, c in zip(["q", "k", "v", "o"], ["to_q", "to_k", "to_v", "to_out.0"]):
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_A.weight"] = original_state_dict.pop(
+                f"blocks.{i}.cross_attn.{o}.lora_A.weight"
+            )
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_B.weight"] = original_state_dict.pop(
+                f"blocks.{i}.cross_attn.{o}.lora_B.weight"
+            )
+        for o, c in zip(["k_img", "v_img"], ["add_k_proj", "add_v_proj"]):
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_A.weight"] = original_state_dict.pop(
+                f"blocks.{i}.cross_attn.{o}.lora_A.weight"
+            )
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_B.weight"] = original_state_dict.pop(
+                f"blocks.{i}.cross_attn.{o}.lora_B.weight"
+            )
+
+        # FFN
+        for o, c in zip(["ffn.0", "ffn.2"], ["net.0.proj", "net.2"]):
+            converted_state_dict[f"blocks.{i}.ffn.{c}.lora_A.weight"] = original_state_dict.pop(
+                f"blocks.{i}.{o}.lora_A.weight"
+            )
+            converted_state_dict[f"blocks.{i}.ffn.{c}.lora_B.weight"] = original_state_dict.pop(
+                f"blocks.{i}.{o}.lora_B.weight"
+            )
+
+    if len(original_state_dict) > 0:
+        raise ValueError(f"`state_dict` should be empty at this point but has {original_state_dict.keys()=}")
+
+    for key in list(converted_state_dict.keys()):
+        converted_state_dict[f"transformer.{key}"] = converted_state_dict.pop(key)
+
+    return state_dict

src/diffusers/loaders/lora_pipeline.py

@@ -42,6 +42,7 @@
     _convert_kohya_flux_lora_to_diffusers,
     _convert_non_diffusers_lora_to_diffusers,
     _convert_non_diffusers_lumina2_lora_to_diffusers,
+    _convert_non_diffusers_wan_lora_to_diffusers,
     _convert_xlabs_flux_lora_to_diffusers,
     _maybe_map_sgm_blocks_to_diffusers,
 )
@@ -4198,6 +4199,8 @@ def lora_state_dict(
             user_agent=user_agent,
             allow_pickle=allow_pickle,
         )
+        if any(k.startswith("diffusion_model.") for k in state_dict):
+            state_dict = _convert_non_diffusers_wan_lora_to_diffusers(state_dict)
 
         is_dora_scale_present = any("dora_scale" in k for k in state_dict)
         if is_dora_scale_present: