class FluxLoraLoaderMixin(LoraBaseMixin):
    r"""
    Load LoRA layers into [`FluxTransformer2DModel`],
    [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).

    Specific to [`StableDiffusion3Pipeline`].
    """

    _lora_loadable_modules = ["transformer", "text_encoder"]
    transformer_name = TRANSFORMER_NAME
    text_encoder_name = TEXT_ENCODER_NAME
    _control_lora_supported_norm_keys = ["norm_q", "norm_k", "norm_added_q", "norm_added_k"]

    @classmethod
    @validate_hf_hub_args
    def lora_state_dict(
        cls,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
        return_alphas: bool = False,
        **kwargs,
    ):
        r"""
        Return state dict for lora weights and the network alphas.

        <Tip warning={true}>

        We support loading A1111 formatted LoRA checkpoints in a limited capacity.

        This function is experimental and might change in the future.

        </Tip>

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                Can be either:

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      with [`ModelMixin.save_pretrained`].
                    - A [torch state
                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.

            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only (`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            subfolder (`str`, *optional*, defaults to `""`):
                The subfolder location of a model file within a larger model repository on the Hub or locally.

        """
        # Load the main state dict first which has the LoRA layers for either of
        # transformer and text encoder or both.
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)
        weight_name = kwargs.pop("weight_name", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = True
            allow_pickle = True

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }

        state_dict = _fetch_state_dict(
            pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
            weight_name=weight_name,
            use_safetensors=use_safetensors,
            local_files_only=local_files_only,
            cache_dir=cache_dir,
            force_download=force_download,
            proxies=proxies,
            token=token,
            revision=revision,
            subfolder=subfolder,
            user_agent=user_agent,
            allow_pickle=allow_pickle,
        )
        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
        if is_dora_scale_present:
            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
            logger.warning(warn_msg)
            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}

        # TODO (sayakpaul): to a follow-up to clean and try to unify the conditions.
        is_kohya = any(".lora_down.weight" in k for k in state_dict)
        if is_kohya:
            state_dict = _convert_kohya_flux_lora_to_diffusers(state_dict)
            # Kohya already takes care of scaling the LoRA parameters with alpha.
            return (state_dict, None) if return_alphas else state_dict

        is_xlabs = any("processor" in k for k in state_dict)
        if is_xlabs:
            state_dict = _convert_xlabs_flux_lora_to_diffusers(state_dict)
            # xlabs doesn't use `alpha`.
            return (state_dict, None) if return_alphas else state_dict

        is_bfl_control = any("query_norm.scale" in k for k in state_dict)
        if is_bfl_control:
            state_dict = _convert_bfl_flux_control_lora_to_diffusers(state_dict)
            return (state_dict, None) if return_alphas else state_dict

        # For state dicts like
        # https://huggingface.co/TheLastBen/Jon_Snow_Flux_LoRA
        keys = list(state_dict.keys())
        network_alphas = {}
        for k in keys:
            if "alpha" in k:
                alpha_value = state_dict.get(k)
                if (torch.is_tensor(alpha_value) and torch.is_floating_point(alpha_value)) or isinstance(
                    alpha_value, float
                ):
                    network_alphas[k] = state_dict.pop(k)
                else:
                    raise ValueError(
                        f"The alpha key ({k}) seems to be incorrect. If you think this error is unexpected, please open as issue."
                    )

        if return_alphas:
            return state_dict, network_alphas
        else:
            return state_dict

    def load_lora_weights(
        self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **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`].
            kwargs (`dict`, *optional*):
                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.
        """
        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 not is_peft_version(">=", "0.13.1"):
            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, network_alphas = self.lora_state_dict(
            pretrained_model_name_or_path_or_dict, return_alphas=True, **kwargs
        )

        has_lora_keys = any("lora" in key for key in state_dict.keys())

        # Flux Control LoRAs also have norm keys
        has_norm_keys = any(
            norm_key in key for key in state_dict.keys() for norm_key in self._control_lora_supported_norm_keys
        )

        if not (has_lora_keys or has_norm_keys):
            raise ValueError("Invalid LoRA checkpoint.")

        transformer_lora_state_dict = {
            k: state_dict.pop(k) for k in list(state_dict.keys()) if "transformer." in k and "lora" in k
        }
        transformer_norm_state_dict = {
            k: state_dict.pop(k)
            for k in list(state_dict.keys())
            if "transformer." in k and any(norm_key in k for norm_key in self._control_lora_supported_norm_keys)
        }

        transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
        has_param_with_expanded_shape = self._maybe_expand_transformer_param_shape_or_error_(
            transformer, transformer_lora_state_dict, transformer_norm_state_dict
        )

        if has_param_with_expanded_shape:
            logger.info(
                "The LoRA weights contain parameters that have different shapes that expected by the transformer. "
                "As a result, the state_dict of the transformer has been expanded to match the LoRA parameter shapes. "
                "To get a comprehensive list of parameter names that were modified, enable debug logging."
            )
        transformer_lora_state_dict = self._maybe_expand_lora_state_dict(
            transformer=transformer, lora_state_dict=transformer_lora_state_dict
        )

        if len(transformer_lora_state_dict) > 0:
            self.load_lora_into_transformer(
                transformer_lora_state_dict,
                network_alphas=network_alphas,
                transformer=transformer,
                adapter_name=adapter_name,
                _pipeline=self,
                low_cpu_mem_usage=low_cpu_mem_usage,
            )

        if len(transformer_norm_state_dict) > 0:
            transformer._transformer_norm_layers = self._load_norm_into_transformer(
                transformer_norm_state_dict,
                transformer=transformer,
                discard_original_layers=False,
            )

        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
        if len(text_encoder_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder,
                prefix="text_encoder",
                lora_scale=self.lora_scale,
                adapter_name=adapter_name,
                _pipeline=self,
                low_cpu_mem_usage=low_cpu_mem_usage,
            )

    @classmethod
    def load_lora_into_transformer(
        cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False
    ):
        """
        This will load the LoRA layers specified in `state_dict` into `transformer`.

        Parameters:
            state_dict (`dict`):
                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
                encoder lora layers.
            network_alphas (`Dict[str, float]`):
                The value of the network alpha used for stable learning and preventing underflow. This value has the
                same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
                link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
            transformer (`FluxTransformer2DModel`):
                The Transformer model to load the LoRA layers into.
            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.
        """
        if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
            raise ValueError(
                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
            )

        # Load the layers corresponding to transformer.
        keys = list(state_dict.keys())
        transformer_present = any(key.startswith(cls.transformer_name) for key in keys)
        if transformer_present:
            logger.info(f"Loading {cls.transformer_name}.")
            transformer.load_lora_adapter(
                state_dict,
                network_alphas=network_alphas,
                adapter_name=adapter_name,
                _pipeline=_pipeline,
                low_cpu_mem_usage=low_cpu_mem_usage,
            )

    @classmethod
    def _load_norm_into_transformer(
        cls,
        state_dict,
        transformer,
        prefix=None,
        discard_original_layers=False,
    ) -> Dict[str, torch.Tensor]:
        # Remove prefix if present
        prefix = prefix or cls.transformer_name
        for key in list(state_dict.keys()):
            if key.split(".")[0] == prefix:
                state_dict[key[len(f"{prefix}.") :]] = state_dict.pop(key)

        # Find invalid keys
        transformer_state_dict = transformer.state_dict()
        transformer_keys = set(transformer_state_dict.keys())
        state_dict_keys = set(state_dict.keys())
        extra_keys = list(state_dict_keys - transformer_keys)

        if extra_keys:
            logger.warning(
                f"Unsupported keys found in state dict when trying to load normalization layers into the transformer. The following keys will be ignored:\n{extra_keys}."
            )

        for key in extra_keys:
            state_dict.pop(key)

        # Save the layers that are going to be overwritten so that unload_lora_weights can work as expected
        overwritten_layers_state_dict = {}
        if not discard_original_layers:
            for key in state_dict.keys():
                overwritten_layers_state_dict[key] = transformer_state_dict[key].clone()

        logger.info(
            "The provided state dict contains normalization layers in addition to LoRA layers. The normalization layers will directly update the state_dict of the transformer "
            'as opposed to the LoRA layers that will co-exist separately until the "fuse_lora()" method is called. That is to say, the normalization layers will always be directly '
            "fused into the transformer and can only be unfused if `discard_original_layers=True` is passed. This might also have implications when dealing with multiple LoRAs. "
            "If you notice something unexpected, please open an issue: https://github.com/huggingface/diffusers/issues."
        )

        # We can't load with strict=True because the current state_dict does not contain all the transformer keys
        incompatible_keys = transformer.load_state_dict(state_dict, strict=False)
        unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)

        # We shouldn't expect to see the supported norm keys here being present in the unexpected keys.
        if unexpected_keys:
            if any(norm_key in k for k in unexpected_keys for norm_key in cls._control_lora_supported_norm_keys):
                raise ValueError(
                    f"Found {unexpected_keys} as unexpected keys while trying to load norm layers into the transformer."
                )

        return overwritten_layers_state_dict

    @classmethod
    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder
    def load_lora_into_text_encoder(
        cls,
        state_dict,
        network_alphas,
        text_encoder,
        prefix=None,
        lora_scale=1.0,
        adapter_name=None,
        _pipeline=None,
        low_cpu_mem_usage=False,
    ):
        """
        This will load the LoRA layers specified in `state_dict` into `text_encoder`

        Parameters:
            state_dict (`dict`):
                A standard state dict containing the lora layer parameters. The key should be prefixed with an
                additional `text_encoder` to distinguish between unet lora layers.
            network_alphas (`Dict[str, float]`):
                The value of the network alpha used for stable learning and preventing underflow. This value has the
                same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
                link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
            text_encoder (`CLIPTextModel`):
                The text encoder model to load the LoRA layers into.
            prefix (`str`):
                Expected prefix of the `text_encoder` in the `state_dict`.
            lora_scale (`float`):
                How much to scale the output of the lora linear layer before it is added with the output of the regular
                lora layer.
            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.
        """
        _load_lora_into_text_encoder(
            state_dict=state_dict,
            network_alphas=network_alphas,
            lora_scale=lora_scale,
            text_encoder=text_encoder,
            prefix=prefix,
            text_encoder_name=cls.text_encoder_name,
            adapter_name=adapter_name,
            _pipeline=_pipeline,
            low_cpu_mem_usage=low_cpu_mem_usage,
        )

    @classmethod
    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.save_lora_weights with unet->transformer
    def save_lora_weights(
        cls,
        save_directory: Union[str, os.PathLike],
        transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
        text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
        is_main_process: bool = True,
        weight_name: str = None,
        save_function: Callable = None,
        safe_serialization: bool = True,
    ):
        r"""
        Save the LoRA parameters corresponding to the UNet and text encoder.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to save LoRA parameters to. Will be created if it doesn't exist.
            transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `transformer`.
            text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
                encoder LoRA state dict because it comes from 🤗 Transformers.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful during distributed training and you
                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful during distributed training when you need to
                replace `torch.save` with another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `True`):
                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
        """
        state_dict = {}

        if not (transformer_lora_layers or text_encoder_lora_layers):
            raise ValueError("You must pass at least one of `transformer_lora_layers` and `text_encoder_lora_layers`.")

        if transformer_lora_layers:
            state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))

        if text_encoder_lora_layers:
            state_dict.update(cls.pack_weights(text_encoder_lora_layers, cls.text_encoder_name))

        # Save the model
        cls.write_lora_layers(
            state_dict=state_dict,
            save_directory=save_directory,
            is_main_process=is_main_process,
            weight_name=weight_name,
            save_function=save_function,
            safe_serialization=safe_serialization,
        )

    def fuse_lora(
        self,
        components: List[str] = ["transformer"],
        lora_scale: float = 1.0,
        safe_fusing: bool = False,
        adapter_names: Optional[List[str]] = None,
        **kwargs,
    ):
        r"""
        Fuses the LoRA parameters into the original parameters of the corresponding blocks.

        <Tip warning={true}>

        This is an experimental API.

        </Tip>

        Args:
            components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
            lora_scale (`float`, defaults to 1.0):
                Controls how much to influence the outputs with the LoRA parameters.
            safe_fusing (`bool`, defaults to `False`):
                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
            adapter_names (`List[str]`, *optional*):
                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.

        Example:

        ```py
        from diffusers import DiffusionPipeline
        import torch

        pipeline = DiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
        ).to("cuda")
        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
        pipeline.fuse_lora(lora_scale=0.7)
        ```
        """

        transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
        if (
            hasattr(transformer, "_transformer_norm_layers")
            and isinstance(transformer._transformer_norm_layers, dict)
            and len(transformer._transformer_norm_layers.keys()) > 0
        ):
            logger.info(
                "The provided state dict contains normalization layers in addition to LoRA layers. The normalization layers will be directly updated the state_dict of the transformer "
                "as opposed to the LoRA layers that will co-exist separately until the 'fuse_lora()' method is called. That is to say, the normalization layers will always be directly "
                "fused into the transformer and can only be unfused if `discard_original_layers=True` is passed."
            )

        super().fuse_lora(
            components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
        )

    def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
        r"""
        Reverses the effect of
        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).

        <Tip warning={true}>

        This is an experimental API.

        </Tip>

        Args:
            components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
        """
        transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
        if hasattr(transformer, "_transformer_norm_layers") and transformer._transformer_norm_layers:
            transformer.load_state_dict(transformer._transformer_norm_layers, strict=False)

        super().unfuse_lora(components=components)

    # We override this here account for `_transformer_norm_layers` and `_overwritten_params`.
    def unload_lora_weights(self, reset_to_overwritten_params=False):
        """
        Unloads the LoRA parameters.

        Args:
            reset_to_overwritten_params (`bool`, defaults to `False`): Whether to reset the LoRA-loaded modules
                to their original params. Refer to the [Flux
                documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) to learn more.

        Examples:

        ```python
        >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
        >>> pipeline.unload_lora_weights()
        >>> ...
        ```
        """
        super().unload_lora_weights()

        transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
        if hasattr(transformer, "_transformer_norm_layers") and transformer._transformer_norm_layers:
            transformer.load_state_dict(transformer._transformer_norm_layers, strict=False)
            transformer._transformer_norm_layers = None

        if reset_to_overwritten_params and getattr(transformer, "_overwritten_params", None) is not None:
            overwritten_params = transformer._overwritten_params
            module_names = set()

            for param_name in overwritten_params:
                if param_name.endswith(".weight"):
                    module_names.add(param_name.replace(".weight", ""))

            for name, module in transformer.named_modules():
                if isinstance(module, torch.nn.Linear) and name in module_names:
                    module_weight = module.weight.data
                    module_bias = module.bias.data if module.bias is not None else None
                    bias = module_bias is not None

                    parent_module_name, _, current_module_name = name.rpartition(".")
                    parent_module = transformer.get_submodule(parent_module_name)

                    current_param_weight = overwritten_params[f"{name}.weight"]
                    in_features, out_features = current_param_weight.shape[1], current_param_weight.shape[0]
                    with torch.device("meta"):
                        original_module = torch.nn.Linear(
                            in_features,
                            out_features,
                            bias=bias,
                            dtype=module_weight.dtype,
                        )

                    tmp_state_dict = {"weight": current_param_weight}
                    if module_bias is not None:
                        tmp_state_dict.update({"bias": overwritten_params[f"{name}.bias"]})
                    original_module.load_state_dict(tmp_state_dict, assign=True, strict=True)
                    setattr(parent_module, current_module_name, original_module)

                    del tmp_state_dict

                    if current_module_name in _MODULE_NAME_TO_ATTRIBUTE_MAP_FLUX:
                        attribute_name = _MODULE_NAME_TO_ATTRIBUTE_MAP_FLUX[current_module_name]
                        new_value = int(current_param_weight.shape[1])
                        old_value = getattr(transformer.config, attribute_name)
                        setattr(transformer.config, attribute_name, new_value)
                        logger.info(
                            f"Set the {attribute_name} attribute of the model to {new_value} from {old_value}."
                        )

    @classmethod
    def _maybe_expand_transformer_param_shape_or_error_(
        cls,
        transformer: torch.nn.Module,
        lora_state_dict=None,
        norm_state_dict=None,
        prefix=None,
    ) -> bool:
        """
        Control LoRA expands the shape of the input layer from (3072, 64) to (3072, 128). This method handles that and
        generalizes things a bit so that any parameter that needs expansion receives appropriate treatement.
        """
        state_dict = {}
        if lora_state_dict is not None:
            state_dict.update(lora_state_dict)
        if norm_state_dict is not None:
            state_dict.update(norm_state_dict)

        # Remove prefix if present
        prefix = prefix or cls.transformer_name
        for key in list(state_dict.keys()):
            if key.split(".")[0] == prefix:
                state_dict[key[len(f"{prefix}.") :]] = state_dict.pop(key)

        # Expand transformer parameter shapes if they don't match lora
        has_param_with_shape_update = False
        overwritten_params = {}

        is_peft_loaded = getattr(transformer, "peft_config", None) is not None
        for name, module in transformer.named_modules():
            if isinstance(module, torch.nn.Linear):
                module_weight = module.weight.data
                module_bias = module.bias.data if module.bias is not None else None
                bias = module_bias is not None

                lora_base_name = name.replace(".base_layer", "") if is_peft_loaded else name
                lora_A_weight_name = f"{lora_base_name}.lora_A.weight"
                lora_B_weight_name = f"{lora_base_name}.lora_B.weight"
                if lora_A_weight_name not in state_dict:
                    continue

                in_features = state_dict[lora_A_weight_name].shape[1]
                out_features = state_dict[lora_B_weight_name].shape[0]

                # Model maybe loaded with different quantization schemes which may flatten the params.
                # `bitsandbytes`, for example, flatten the weights when using 4bit. 8bit bnb models
                # preserve weight shape.
                module_weight_shape = cls._calculate_module_shape(model=transformer, base_module=module)

                # This means there's no need for an expansion in the params, so we simply skip.
                if tuple(module_weight_shape) == (out_features, in_features):
                    continue

                # TODO (sayakpaul): We still need to consider if the module we're expanding is
                # quantized and handle it accordingly if that is the case.
                module_out_features, module_in_features = module_weight.shape
                debug_message = ""
                if in_features > module_in_features:
                    debug_message += (
                        f'Expanding the nn.Linear input/output features for module="{name}" because the provided LoRA '
                        f"checkpoint contains higher number of features than expected. The number of input_features will be "
                        f"expanded from {module_in_features} to {in_features}"
                    )
                if out_features > module_out_features:
                    debug_message += (
                        ", and the number of output features will be "
                        f"expanded from {module_out_features} to {out_features}."
                    )
                else:
                    debug_message += "."
                if debug_message:
                    logger.debug(debug_message)

                if out_features > module_out_features or in_features > module_in_features:
                    has_param_with_shape_update = True
                    parent_module_name, _, current_module_name = name.rpartition(".")
                    parent_module = transformer.get_submodule(parent_module_name)

                    with torch.device("meta"):
                        expanded_module = torch.nn.Linear(
                            in_features, out_features, bias=bias, dtype=module_weight.dtype
                        )
                    # Only weights are expanded and biases are not. This is because only the input dimensions
                    # are changed while the output dimensions remain the same. The shape of the weight tensor
                    # is (out_features, in_features), while the shape of bias tensor is (out_features,), which
                    # explains the reason why only weights are expanded.
                    new_weight = torch.zeros_like(
                        expanded_module.weight.data, device=module_weight.device, dtype=module_weight.dtype
                    )
                    slices = tuple(slice(0, dim) for dim in module_weight.shape)
                    new_weight[slices] = module_weight
                    tmp_state_dict = {"weight": new_weight}
                    if module_bias is not None:
                        tmp_state_dict["bias"] = module_bias
                    expanded_module.load_state_dict(tmp_state_dict, strict=True, assign=True)

                    setattr(parent_module, current_module_name, expanded_module)

                    del tmp_state_dict

                    if current_module_name in _MODULE_NAME_TO_ATTRIBUTE_MAP_FLUX:
                        attribute_name = _MODULE_NAME_TO_ATTRIBUTE_MAP_FLUX[current_module_name]
                        new_value = int(expanded_module.weight.data.shape[1])
                        old_value = getattr(transformer.config, attribute_name)
                        setattr(transformer.config, attribute_name, new_value)
                        logger.info(
                            f"Set the {attribute_name} attribute of the model to {new_value} from {old_value}."
                        )

                    # For `unload_lora_weights()`.
                    # TODO: this could lead to more memory overhead if the number of overwritten params
                    # are large. Should be revisited later and tackled through a `discard_original_layers` arg.
                    overwritten_params[f"{current_module_name}.weight"] = module_weight
                    if module_bias is not None:
                        overwritten_params[f"{current_module_name}.bias"] = module_bias

        if len(overwritten_params) > 0:
            transformer._overwritten_params = overwritten_params

        return has_param_with_shape_update

    @classmethod
    def _maybe_expand_lora_state_dict(cls, transformer, lora_state_dict):
        expanded_module_names = set()
        transformer_state_dict = transformer.state_dict()
        prefix = f"{cls.transformer_name}."

        lora_module_names = [
            key[: -len(".lora_A.weight")] for key in lora_state_dict if key.endswith(".lora_A.weight")
        ]
        lora_module_names = [name[len(prefix) :] for name in lora_module_names if name.startswith(prefix)]
        lora_module_names = sorted(set(lora_module_names))
        transformer_module_names = sorted({name for name, _ in transformer.named_modules()})
        unexpected_modules = set(lora_module_names) - set(transformer_module_names)
        if unexpected_modules:
            logger.debug(f"Found unexpected modules: {unexpected_modules}. These will be ignored.")

        is_peft_loaded = getattr(transformer, "peft_config", None) is not None
        for k in lora_module_names:
            if k in unexpected_modules:
                continue

            base_param_name = (
                f"{k.replace(prefix, '')}.base_layer.weight"
                if is_peft_loaded and f"{k.replace(prefix, '')}.base_layer.weight" in transformer_state_dict
                else f"{k.replace(prefix, '')}.weight"
            )
            base_weight_param = transformer_state_dict[base_param_name]
            lora_A_param = lora_state_dict[f"{prefix}{k}.lora_A.weight"]

            # TODO (sayakpaul): Handle the cases when we actually need to expand when using quantization.
            base_module_shape = cls._calculate_module_shape(model=transformer, base_weight_param_name=base_param_name)

            if base_module_shape[1] > lora_A_param.shape[1]:
                shape = (lora_A_param.shape[0], base_weight_param.shape[1])
                expanded_state_dict_weight = torch.zeros(shape, device=base_weight_param.device)
                expanded_state_dict_weight[:, : lora_A_param.shape[1]].copy_(lora_A_param)
                lora_state_dict[f"{prefix}{k}.lora_A.weight"] = expanded_state_dict_weight
                expanded_module_names.add(k)
            elif base_module_shape[1] < lora_A_param.shape[1]:
                raise NotImplementedError(
                    f"This LoRA param ({k}.lora_A.weight) has an incompatible shape {lora_A_param.shape}. Please open an issue to file for a feature request - https://github.com/huggingface/diffusers/issues/new."
                )

        if expanded_module_names:
            logger.info(
                f"The following LoRA modules were zero padded to match the state dict of {cls.transformer_name}: {expanded_module_names}. Please open an issue if you think this was unexpected - https://github.com/huggingface/diffusers/issues/new."
            )

        return lora_state_dict

    @staticmethod
    def _calculate_module_shape(
        model: "torch.nn.Module",
        base_module: "torch.nn.Linear" = None,
        base_weight_param_name: str = None,
    ) -> "torch.Size":
        def _get_weight_shape(weight: torch.Tensor):
            return weight.quant_state.shape if weight.__class__.__name__ == "Params4bit" else weight.shape

        if base_module is not None:
            return _get_weight_shape(base_module.weight)
        elif base_weight_param_name is not None:
            if not base_weight_param_name.endswith(".weight"):
                raise ValueError(
                    f"Invalid `base_weight_param_name` passed as it does not end with '.weight' {base_weight_param_name=}."
                )
            module_path = base_weight_param_name.rsplit(".weight", 1)[0]
            submodule = get_submodule_by_name(model, module_path)
            return _get_weight_shape(submodule.weight)

        raise ValueError("Either `base_module` or `base_weight_param_name` must be provided.")
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py