Method from_pretrained

src/diffusers/models/adapter.py:148–213

r""" Instantiate a pretrained `MultiAdapter` model from multiple pre-trained adapter models. The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train the model, set it back to training mode using `model.train()`.

(cls, pretrained_model_path: str | os.PathLike | None, **kwargs)

    @classmethod
    def from_pretrained(cls, pretrained_model_path: str | os.PathLike | None, **kwargs):
        r"""
        Instantiate a pretrained `MultiAdapter` model from multiple pre-trained adapter models.

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, set it back to training mode using `model.train()`.

        Warnings:
            *Weights from XXX not initialized from pretrained model* means that the weights of XXX are not pretrained
            with the rest of the model. It is up to you to train those weights with a downstream fine-tuning. *Weights
            from XXX not used in YYY* means that the layer XXX is not used by YYY, so those weights are discarded.

        Args:
            pretrained_model_path (`os.PathLike`):
                A path to a *directory* containing model weights saved using
                [`~diffusers.models.adapter.MultiAdapter.save_pretrained`], e.g., `./my_model_directory/adapter`.
            torch_dtype (`torch.dtype`, *optional*):
                Override the default `torch.dtype` and load the model under this dtype.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            device_map (`str` or `dict[str, int | str | torch.device]`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be refined to each
                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
                same device.

                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            max_memory (`Dict`, *optional*):
                A dictionary mapping device identifiers to their maximum memory. Default to the maximum memory
                available for each GPU and the available CPU RAM if unset.
            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
                Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
                also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
                model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
                setting this argument to `True` will raise an error.
            variant (`str`, *optional*):
                If specified, load weights from a `variant` file (*e.g.* pytorch_model.<variant>.bin). `variant` will
                be ignored when using `from_flax`.
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If `None`, the `safetensors` weights will be downloaded if available **and** if`safetensors` library is
                installed. If `True`, the model will be forcibly loaded from`safetensors` weights. If `False`,
                `safetensors` is not used.
        """
        idx = 0
        adapters = []

        # load adapter and append to list until no adapter directory exists anymore
        # first adapter has to be saved under `./mydirectory/adapter` to be compliant with `DiffusionPipeline.from_pretrained`
        # second, third, ... adapters have to be saved under `./mydirectory/adapter_1`, `./mydirectory/adapter_2`, ...
        model_path_to_load = pretrained_model_path
        while os.path.isdir(model_path_to_load):
            adapter = T2IAdapter.from_pretrained(model_path_to_load, **kwargs)
            adapters.append(adapter)

            idx += 1
            model_path_to_load = pretrained_model_path + f"_{idx}"