Method save_pretrained

src/diffusers/models/adapter.py:105–145

Save a model and its configuration file to a specified directory, allowing it to be re-loaded with the `[`~models.adapter.MultiAdapter.from_pretrained`]` class method. Args: save_directory (`str` or `os.PathLike`): The directory where the model w

(
        self,
        save_directory: str | os.PathLike,
        is_main_process: bool = True,
        save_function: Callable = None,
        safe_serialization: bool = True,
        variant: str | None = None,
    )

        return accume_state

    def save_pretrained(
        self,
        save_directory: str | os.PathLike,
        is_main_process: bool = True,
        save_function: Callable = None,
        safe_serialization: bool = True,
        variant: str | None = None,
    ):
        """
        Save a model and its configuration file to a specified directory, allowing it to be re-loaded with the
        `[`~models.adapter.MultiAdapter.from_pretrained`]` class method.

        Args:
            save_directory (`str` or `os.PathLike`):
                The directory where the model will be saved. If the directory does not exist, it will be created.
            is_main_process (`bool`, optional, defaults=True):
                Indicates whether current process is the main process or not. Useful for distributed training (e.g.,
                TPUs) and need to call this function on all processes. In this case, set `is_main_process=True` only
                for the main process to avoid race conditions.
            save_function (`Callable`):
                Function used to save the state dictionary. Useful for distributed training (e.g., TPUs) to replace
                `torch.save` with another method. Can also be configured using`DIFFUSERS_SAVE_MODE` environment
                variable.
            safe_serialization (`bool`, optional, defaults=True):
                If `True`, save the model using `safetensors`. If `False`, save the model with `pickle`.
            variant (`str`, *optional*):
                If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
        """
        idx = 0
        model_path_to_save = save_directory
        for adapter in self.adapters:
            adapter.save_pretrained(
                model_path_to_save,
                is_main_process=is_main_process,
                save_function=save_function,
                safe_serialization=safe_serialization,
                variant=variant,
            )

            idx += 1
            model_path_to_save = model_path_to_save + f"_{idx}"

    @classmethod
    def from_pretrained(cls, pretrained_model_path: str | os.PathLike | None, **kwargs):