[LoRA] support musubi wan loras. (#11243)

* support musubi wan loras.

* Update src/diffusers/loaders/lora_conversion_utils.py

Co-authored-by: hlky <hlky@hlky.ac>

* support i2v loras from musubi too.

---------

Co-authored-by: hlky <hlky@hlky.ac>

src/diffusers/loaders/lora_conversion_utils.py

@@ -1608,3 +1608,64 @@ def _convert_non_diffusers_wan_lora_to_diffusers(state_dict):
         converted_state_dict[f"transformer.{key}"] = converted_state_dict.pop(key)
 
     return converted_state_dict
+
+
+def _convert_musubi_wan_lora_to_diffusers(state_dict):
+    # https://github.com/kohya-ss/musubi-tuner
+    converted_state_dict = {}
+    original_state_dict = {k[len("lora_unet_") :]: v for k, v in state_dict.items()}
+
+    num_blocks = len({k.split("blocks_")[1].split("_")[0] for k in original_state_dict})
+    is_i2v_lora = any("k_img" in k for k in original_state_dict) and any("v_img" in k for k in original_state_dict)
+
+    def get_alpha_scales(down_weight, key):
+        rank = down_weight.shape[0]
+        alpha = original_state_dict.pop(key + ".alpha").item()
+        scale = alpha / rank  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
+        scale_down = scale
+        scale_up = 1.0
+        while scale_down * 2 < scale_up:
+            scale_down *= 2
+            scale_up /= 2
+        return scale_down, scale_up
+
+    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"]):
+            down_weight = original_state_dict.pop(f"blocks_{i}_self_attn_{o}.lora_down.weight")
+            up_weight = original_state_dict.pop(f"blocks_{i}_self_attn_{o}.lora_up.weight")
+            scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_self_attn_{o}")
+            converted_state_dict[f"blocks.{i}.attn1.{c}.lora_A.weight"] = down_weight * scale_down
+            converted_state_dict[f"blocks.{i}.attn1.{c}.lora_B.weight"] = up_weight * scale_up
+
+        # Cross-attention
+        for o, c in zip(["q", "k", "v", "o"], ["to_q", "to_k", "to_v", "to_out.0"]):
+            down_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_down.weight")
+            up_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_up.weight")
+            scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_cross_attn_{o}")
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_A.weight"] = down_weight * scale_down
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_B.weight"] = up_weight * scale_up
+
+        if is_i2v_lora:
+            for o, c in zip(["k_img", "v_img"], ["add_k_proj", "add_v_proj"]):
+                down_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_down.weight")
+                up_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_up.weight")
+                scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_cross_attn_{o}")
+                converted_state_dict[f"blocks.{i}.attn2.{c}.lora_A.weight"] = down_weight * scale_down
+                converted_state_dict[f"blocks.{i}.attn2.{c}.lora_B.weight"] = up_weight * scale_up
+
+        # FFN
+        for o, c in zip(["ffn_0", "ffn_2"], ["net.0.proj", "net.2"]):
+            down_weight = original_state_dict.pop(f"blocks_{i}_{o}.lora_down.weight")
+            up_weight = original_state_dict.pop(f"blocks_{i}_{o}.lora_up.weight")
+            scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_{o}")
+            converted_state_dict[f"blocks.{i}.ffn.{c}.lora_A.weight"] = down_weight * scale_down
+            converted_state_dict[f"blocks.{i}.ffn.{c}.lora_B.weight"] = up_weight * scale_up
+
+    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 converted_state_dict

src/diffusers/loaders/lora_pipeline.py

@@ -42,6 +42,7 @@ from .lora_conversion_utils import (
     _convert_bfl_flux_control_lora_to_diffusers,
     _convert_hunyuan_video_lora_to_diffusers,
     _convert_kohya_flux_lora_to_diffusers,
+    _convert_musubi_wan_lora_to_diffusers,
     _convert_non_diffusers_lora_to_diffusers,
     _convert_non_diffusers_lumina2_lora_to_diffusers,
     _convert_non_diffusers_wan_lora_to_diffusers,
@@ -4794,6 +4795,8 @@ class WanLoraLoaderMixin(LoraBaseMixin):
         )
         if any(k.startswith("diffusion_model.") for k in state_dict):
             state_dict = _convert_non_diffusers_wan_lora_to_diffusers(state_dict)
+        elif any(k.startswith("lora_unet_") for k in state_dict):
+            state_dict = _convert_musubi_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: