model_id = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
pipe = WanImageToVideoPipeline.from_pretrained(
 model_id,
 torch_dtype=torch.bfloat16
)
lightning_hn = hf_hub_download(repo_id="lightx2v/Wan2.2-Distill-Loras", filename="wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_1022.safetensors")
lightning_ln = hf_hub_download(repo_id="lightx2v/Wan2.2-Distill-Loras", filename="wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_1022.safetensors")
pipe.load_lora_weights(
 lightning_hn,
 adapter_name="lightning"
)
pipe.load_lora_weights(
 lightning_ln,
 adapter_name="lightning_2"
)

KeyError Traceback (most recent call last)
Cell In[5], line 31
 28 lightning_hn = hf_hub_download(repo_id="lightx2v/Wan2.2-Distill-Loras", filename="wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_1022.safetensors")
 29 lightning_ln = hf_hub_download(repo_id="lightx2v/Wan2.2-Distill-Loras", filename="wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_1022.safetensors")
---> 31 pipe.load_lora_weights(
 32 lightning_hn,
 33 adapter_name="lightning"
 34 )
 36 pipe.load_lora_weights(
 37 lightning_ln,
 38 adapter_name="lightning_2"
 39 )
 41 pipe.transformer.load_lora_adapter(load_wan_lora(lightning_hn), adapter_name="high_noise")
File /usr/local/lib/python3.11/dist-packages/diffusers/loaders/lora_pipeline.py:4066, in WanLoraLoaderMixin.load_lora_weights(self, pretrained_model_name_or_path_or_dict, adapter_name, hotswap, **kwargs)
 4064 # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
 4065 kwargs["return_lora_metadata"] = True
-> 4066 state_dict, metadata = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
 4067 # convert T2V LoRA to I2V LoRA (when loaded to Wan I2V) by adding zeros for the additional (missing) _img layers
 4068 state_dict = self._maybe_expand_t2v_lora_for_i2v(
 4069 transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
 4070 state_dict=state_dict,
 4071 )
File /usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
 111 if check_use_auth_token:
 112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)
File /usr/local/lib/python3.11/dist-packages/diffusers/loaders/lora_pipeline.py:3980, in WanLoraLoaderMixin.lora_state_dict(cls, pretrained_model_name_or_path_or_dict, **kwargs)
 3965 state_dict, metadata = _fetch_state_dict(
 3966 pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
 3967 weight_name=weight_name,
 (...)
 3977 allow_pickle=allow_pickle,
 3978 )
 3979 if any(k.startswith("diffusion_model.") for k in state_dict):
-> 3980 state_dict = _convert_non_diffusers_wan_lora_to_diffusers(state_dict)
 3981 elif any(k.startswith("lora_unet_") for k in state_dict):
 3982 state_dict = _convert_musubi_wan_lora_to_diffusers(state_dict)
File /usr/local/lib/python3.11/dist-packages/diffusers/loaders/lora_conversion_utils.py:1981, in _convert_non_diffusers_wan_lora_to_diffusers(state_dict)
 1976 converted_state_dict["condition_embedder.time_proj.lora_B.bias"] = original_state_dict.pop(
 1977 "time_projection.1.diff_b"
 1978 )
 1980 if any("head.head" in k for k in state_dict):
-> 1981 converted_state_dict["proj_out.lora_A.weight"] = original_state_dict.pop(
 1982 f"head.head.{lora_down_key}.weight"
 1983 )
 1984 converted_state_dict["proj_out.lora_B.weight"] = original_state_dict.pop(f"head.head.{lora_up_key}.weight")
 1985 if "head.head.diff_b" in original_state_dict:
KeyError: 'head.head.lora_down.weight'