def auto_load_lora_weights(
        self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
    ):
        r"""
        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` 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_unet`] for more details on how the state dict is
        loaded into `self.unet`.

        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state
        dict is loaded into `self.text_encoder`.

        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.
            kwargs (`dict`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
        """
        if isinstance(pretrained_model_name_or_path_or_dict, str):
            # Update kwargs to ensure the model is downloaded and parameters are included
            _status = {
                "download": True,
                "include_params": True,
                "skip_error": False,
                "model_type": "LORA",
            }
            kwargs.update(_status)
            # Search for the model on Civitai and get the model status
            lora_path = search_civitai(pretrained_model_name_or_path_or_dict, **kwargs)
            logger.warning(f"lora_path: {lora_path.model_status.site_url}")
            logger.warning(f"trained_words: {lora_path.extra_status.trained_words}")
            pretrained_model_name_or_path_or_dict = lora_path.model_path

        self.load_lora_weights(pretrained_model_name_or_path_or_dict, adapter_name=adapter_name, **kwargs)

        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` 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_unet`] for more details on how the state dict is
        loaded into `self.unet`.

        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state
        dict is loaded into `self.text_encoder`.

        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.
            kwargs (`dict`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
        
examples/model_search/pipeline_easy.py