Method load_lora_adapter

src/diffusers/loaders/peft.py:80–382

r""" Loads a LoRA adapter into the underlying model. 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 pretrai

(
        self, pretrained_model_name_or_path_or_dict, prefix="transformer", hotswap: bool = False, **kwargs
    )

        return _func_optionally_disable_offloading(_pipeline=_pipeline)

    def load_lora_adapter(
        self, pretrained_model_name_or_path_or_dict, prefix="transformer", hotswap: bool = False, **kwargs
    ):
        r"""
        Loads a LoRA adapter into the underlying model.

        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).

            prefix (`str`, *optional*): Prefix to filter the state dict.

            cache_dir (`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.
            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).
            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*)
                Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
                in-place. This means that, instead of loading an additional adapter, this will take the existing
                adapter weights and replace them with the weights of the new adapter. This can be faster and more
                memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
                torch.compile, loading the new adapter does not require recompilation of the model. When using
                hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.

                If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
                to call an additional method before loading the adapter:

                ```py