def load_lora_weights(
        self,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
        adapter_name: Optional[str] = None,
        hotswap: bool = False,
        **kwargs,
    ):
        """
        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
        `self.text_encoder`. All kwargs are forwarded to `self.lora_state_dict`. See
        [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer`] for more details on how the state
        dict is loaded into `self.transformer`.

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
            low_cpu_mem_usage (`bool`, *optional*):
                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                weights.
            hotswap (`bool`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
            kwargs (`dict`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
        """
        if not USE_PEFT_BACKEND:
            raise ValueError("PEFT backend is required for this method.")

        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
            raise ValueError(
                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
            )

        # if a dict is passed, copy it instead of modifying it inplace
        if isinstance(pretrained_model_name_or_path_or_dict, dict):
            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()

        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
        # convert T2V LoRA to I2V LoRA (when loaded to Wan I2V) by adding zeros for the additional (missing) _img layers
        state_dict = self._maybe_expand_t2v_lora_for_i2v(
            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
            state_dict=state_dict,
        )
        is_correct_format = all("lora" in key for key in state_dict.keys())
        if not is_correct_format:
            raise ValueError("Invalid LoRA checkpoint.")

        self.load_lora_into_transformer(
            state_dict,
            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
            adapter_name=adapter_name,
            _pipeline=self,
            low_cpu_mem_usage=low_cpu_mem_usage,
            hotswap=hotswap,
        )

        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
        `self.text_encoder`. All kwargs are forwarded to `self.lora_state_dict`. See
        [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer`] for more details on how the state
        dict is loaded into `self.transformer`.

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
            low_cpu_mem_usage (`bool`, *optional*):
                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                weights.
            hotswap (`bool`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
            kwargs (`dict`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
        
src/diffusers/loaders/lora_pipeline.py