def load_lora_into_transformer(
        cls,
        state_dict,
        network_alphas,
        transformer,
        adapter_name=None,
        _pipeline=None,
        low_cpu_mem_usage=False,
        hotswap: bool = 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.
            hotswap (`bool`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_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.
        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,
            hotswap=hotswap,
        )

        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.
            hotswap (`bool`, *optional*):
                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
        
src/diffusers/loaders/lora_pipeline.py