Function _get_model_file

src/diffusers/utils/hub_utils.py:228–356

(
    pretrained_model_name_or_path: str | Path,
    *,
    weights_name: str,
    subfolder: str | None = None,
    cache_dir: str | None = None,
    force_download: bool = False,
    proxies: dict | None = None,
    local_files_only: bool = False,
    token: str | None = None,
    user_agent: dict | str | None = None,
    revision: str | None = None,
    commit_hash: str | None = None,
    dduf_entries: dict[str, DDUFEntry] | None = None,
)

@validate_hf_hub_args
def _get_model_file(
    pretrained_model_name_or_path: str | Path,
    *,
    weights_name: str,
    subfolder: str | None = None,
    cache_dir: str | None = None,
    force_download: bool = False,
    proxies: dict | None = None,
    local_files_only: bool = False,
    token: str | None = None,
    user_agent: dict | str | None = None,
    revision: str | None = None,
    commit_hash: str | None = None,
    dduf_entries: dict[str, DDUFEntry] | None = None,
):
    pretrained_model_name_or_path = str(pretrained_model_name_or_path)

    if dduf_entries:
        if subfolder is not None:
            raise ValueError(
                "DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
                "Please check the DDUF structure"
            )
        model_file = (
            weights_name
            if pretrained_model_name_or_path == ""
            else "/".join([pretrained_model_name_or_path, weights_name])
        )
        if model_file in dduf_entries:
            return model_file
        else:
            raise EnvironmentError(f"Error no file named {weights_name} found in archive {dduf_entries.keys()}.")
    elif os.path.isfile(pretrained_model_name_or_path):
        return pretrained_model_name_or_path
    elif os.path.isdir(pretrained_model_name_or_path):
        if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
            # Load from a PyTorch checkpoint
            model_file = os.path.join(pretrained_model_name_or_path, weights_name)
            return model_file
        elif subfolder is not None and os.path.isfile(
            os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
        ):
            model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
            return model_file
        else:
            raise EnvironmentError(
                f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
            )
    else:
        # 1. First check if deprecated way of loading from branches is used
        if (
            revision in DEPRECATED_REVISION_ARGS
            and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
            and version.parse(version.parse(__version__).base_version) >= version.parse("0.22.0")
        ):
            try:
                model_file = hf_hub_download(
                    pretrained_model_name_or_path,