Single files¶

The from_single_file method allows you to load:

a model stored in a single file, which is useful if you're working with models from the diffusion ecosystem, like Automatic1111, and commonly rely on a single-file layout to store and share models
a model stored in their originally distributed layout, which is useful if you're working with models finetuned with other services, and want to load it directly into Diffusers model objects and pipelines

Tip

Read the Model files and layouts guide to learn more about the Diffusers-multifolder layout versus the single-file layout, and how to load models stored in these different layouts.

Supported pipelines¶

Supported models¶

`mindone.diffusers.loaders.single_file.FromSingleFileMixin` ¶

Load model weights saved in the .ckpt format into a [DiffusionPipeline].

Source code in mindone/diffusers/loaders/single_file.py

class FromSingleFileMixin:
    """
    Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].
    """

    @classmethod
    @validate_hf_hub_args
    def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
        r"""
        Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.ckpt` or `.safetensors`
        format. The pipeline is set in evaluation mode (`model.eval()`) by default.

        Parameters:
            pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
                Can be either:
                    - A link to the `.ckpt` file (for example
                      `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
                    - A path to a *file* containing all pipeline weights.
            mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
                Override the default `mindspore.dtype` and load the model with another dtype.
            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.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.

            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.
            original_config_file (`str`, *optional*):
                The path to the original config file that was used to train the model. If not provided, the config file
                will be inferred from the checkpoint file.
            config (`str`, *optional*):
                Can be either:
                    - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
                      hosted on the Hub.
                    - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline
                      component configs in Diffusers format.
            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
                class). The overwritten components are passed directly to the pipelines `__init__` method. See example
                below for more information.

        Examples:

        ```py
        >>> from mindone.diffusers import StableDiffusionPipeline

        >>> # Download pipeline from huggingface.co and cache.
        >>> pipeline = StableDiffusionPipeline.from_single_file(
        ...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
        ... )

        >>> # Download pipeline from local file
        >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
        >>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly")
        ```

        """
        original_config_file = kwargs.pop("original_config_file", None)
        config = kwargs.pop("config", None)
        original_config = kwargs.pop("original_config", None)

        if original_config_file is not None:
            deprecation_message = (
                "`original_config_file` argument is deprecated and will be removed in future versions."
                "please use the `original_config` argument instead."
            )
            deprecate("original_config_file", "1.0.0", deprecation_message)
            original_config = original_config_file

        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        token = kwargs.pop("token", None)
        cache_dir = kwargs.pop("cache_dir", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)
        mindspore_dtype = kwargs.pop("mindspore_dtype", None)

        is_legacy_loading = False

        # We shouldn't allow configuring individual models components through a Pipeline creation method
        # These model kwargs should be deprecated
        scaling_factor = kwargs.get("scaling_factor", None)
        if scaling_factor is not None:
            deprecation_message = (
                "Passing the `scaling_factor` argument to `from_single_file is deprecated "
                "and will be ignored in future versions."
            )
            deprecate("scaling_factor", "1.0.0", deprecation_message)

        if original_config is not None:
            original_config = fetch_original_config(original_config, local_files_only=local_files_only)

        from ..pipelines.pipeline_utils import _get_pipeline_class

        pipeline_class = _get_pipeline_class(cls, config=None)

        checkpoint = load_single_file_checkpoint(
            pretrained_model_link_or_path,
            force_download=force_download,
            proxies=proxies,
            token=token,
            cache_dir=cache_dir,
            local_files_only=local_files_only,
            revision=revision,
        )

        if config is None:
            config = fetch_diffusers_config(checkpoint)
            default_pretrained_model_config_name = config["pretrained_model_name_or_path"]
        else:
            default_pretrained_model_config_name = config

        if not os.path.isdir(default_pretrained_model_config_name):
            # Provided config is a repo_id
            if default_pretrained_model_config_name.count("/") > 1:
                raise ValueError(
                    f'The provided config "{config}"'
                    " is neither a valid local path nor a valid repo id. Please check the parameter."
                )
            try:
                # Attempt to download the config files for the pipeline
                cached_model_config_path = _download_diffusers_model_config_from_hub(
                    default_pretrained_model_config_name,
                    cache_dir=cache_dir,
                    revision=revision,
                    proxies=proxies,
                    force_download=force_download,
                    local_files_only=local_files_only,
                    token=token,
                )
                config_dict = pipeline_class.load_config(cached_model_config_path)

            except LocalEntryNotFoundError:
                # `local_files_only=True` but a local diffusers format model config is not available in the cache
                # If `original_config` is not provided, we need override `local_files_only` to False
                # to fetch the config files from the hub so that we have a way
                # to configure the pipeline components.

                if original_config is None:
                    logger.warning(
                        "`local_files_only` is True but no local configs were found for this checkpoint.\n"
                        "Attempting to download the necessary config files for this pipeline.\n"
                    )
                    cached_model_config_path = _download_diffusers_model_config_from_hub(
                        default_pretrained_model_config_name,
                        cache_dir=cache_dir,
                        revision=revision,
                        proxies=proxies,
                        force_download=force_download,
                        local_files_only=False,
                        token=token,
                    )
                    config_dict = pipeline_class.load_config(cached_model_config_path)

                else:
                    # For backwards compatibility
                    # If `original_config` is provided, then we need to assume we are using legacy loading for pipeline components
                    logger.warning(
                        "Detected legacy `from_single_file` loading behavior. Attempting to create the pipeline based on inferred components.\n"
                        "This may lead to errors if the model components are not correctly inferred. \n"
                        "To avoid this warning, please explicity pass the `config` argument to `from_single_file` with a path to a local diffusers model repo \n"  # noqa E501
                        "e.g. `from_single_file(<my model checkpoint path>, config=<path to local diffusers model repo>) \n"
                        "or run `from_single_file` with `local_files_only=False` first to update the local cache directory with "
                        "the necessary config files.\n"
                    )
                    is_legacy_loading = True
                    cached_model_config_path = None

                    config_dict = _infer_pipeline_config_dict(pipeline_class)
                    config_dict["_class_name"] = pipeline_class.__name__

        else:
            # Provided config is a path to a local directory attempt to load directly.
            cached_model_config_path = default_pretrained_model_config_name
            config_dict = pipeline_class.load_config(cached_model_config_path)

        #   pop out "_ignore_files" as it is only needed for download
        config_dict.pop("_ignore_files", None)

        expected_modules, optional_kwargs = pipeline_class._get_signature_keys(cls)
        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}

        init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
        init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}
        init_kwargs = {**init_kwargs, **passed_pipe_kwargs}

        from mindone.diffusers import pipelines

        # remove `null` components
        def load_module(name, value):
            if value[0] is None:
                return False
            if name in passed_class_obj and passed_class_obj[name] is None:
                return False
            if name in SINGLE_FILE_OPTIONAL_COMPONENTS:
                return False

            return True

        init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}

        for name, (library_name, class_name) in logging.tqdm(
            sorted(init_dict.items()), desc="Loading pipeline components..."
        ):
            loaded_sub_model = None
            is_pipeline_module = hasattr(pipelines, library_name)

            if name in passed_class_obj:
                loaded_sub_model = passed_class_obj[name]

            else:
                try:
                    loaded_sub_model = load_single_file_sub_model(
                        library_name=library_name,
                        class_name=class_name,
                        name=name,
                        checkpoint=checkpoint,
                        is_pipeline_module=is_pipeline_module,
                        cached_model_config_path=cached_model_config_path,
                        pipelines=pipelines,
                        mindspore_dtype=mindspore_dtype,
                        original_config=original_config,
                        local_files_only=local_files_only,
                        is_legacy_loading=is_legacy_loading,
                        **kwargs,
                    )
                except SingleFileComponentError as e:
                    raise SingleFileComponentError(
                        (
                            f"{e.message}\n"
                            f"Please load the component before passing it in as an argument to `from_single_file`.\n"
                            f"\n"
                            f"{name} = {class_name}.from_pretrained('...')\n"
                            f"pipe = {pipeline_class.__name__}.from_single_file(<checkpoint path>, {name}={name})\n"
                            f"\n"
                        )
                    )

            init_kwargs[name] = loaded_sub_model

        missing_modules = set(expected_modules) - set(init_kwargs.keys())
        passed_modules = list(passed_class_obj.keys())
        optional_modules = pipeline_class._optional_components

        if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
            for module in missing_modules:
                init_kwargs[module] = passed_class_obj.get(module, None)
        elif len(missing_modules) > 0:
            passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
            raise ValueError(
                f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
            )

        # deprecated kwargs
        load_safety_checker = kwargs.pop("load_safety_checker", None)
        if load_safety_checker is not None:
            deprecation_message = (
                "Please pass instances of `StableDiffusionSafetyChecker` and `AutoImageProcessor`"
                "using the `safety_checker` and `feature_extractor` arguments in `from_single_file`"
            )
            deprecate("load_safety_checker", "1.0.0", deprecation_message)

            safety_checker_components = _legacy_load_safety_checker(local_files_only, mindspore_dtype)
            init_kwargs.update(safety_checker_components)

        pipe = pipeline_class(**init_kwargs)

        return pipe

`mindone.diffusers.loaders.single_file.FromSingleFileMixin.from_single_file(pretrained_model_link_or_path, **kwargs)` `classmethod` ¶

Instantiate a [DiffusionPipeline] from pretrained pipeline weights saved in the .ckpt or .safetensors format. The pipeline is set in evaluation mode (model.eval()) by default.

PARAMETER	DESCRIPTION
`pretrained_model_link_or_path`	Can be either: - A link to the `.ckpt` file (for example `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub. - A path to a file containing all pipeline weights. TYPE: `str` or `os.PathLike`, optional
`mindspore_dtype`	Override the default `mindspore.dtype` and load the model with another dtype. TYPE: `str` or `mindspore.dtype`, optional
`force_download`	Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. TYPE: `bool`, optional, defaults to `False`
`cache_dir`	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. TYPE: `Union[str, os.PathLike]`, optional
`proxies`	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. TYPE: `Dict[str, str]`, optional
`local_files_only`	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. TYPE: `bool`, optional, defaults to `False`
`token`	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. TYPE: `str` or bool, optional
`revision`	The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. TYPE: `str`, optional, defaults to `"main"`
`original_config_file`	The path to the original config file that was used to train the model. If not provided, the config file will be inferred from the checkpoint file. TYPE: `str`, optional
`config`	Can be either: - A string, the repo id (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. - A path to a directory (for example `./my_pipeline_directory/`) containing the pipeline component configs in Diffusers format. TYPE: `str`, optional
`kwargs`	Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below for more information. TYPE: `remaining dictionary of keyword arguments, optional` DEFAULT: `{}`

>>> from mindone.diffusers import StableDiffusionPipeline

>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = StableDiffusionPipeline.from_single_file(
...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
... )

>>> # Download pipeline from local file
>>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
>>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly")

Source code in mindone/diffusers/loaders/single_file.py

@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
    r"""
    Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.ckpt` or `.safetensors`
    format. The pipeline is set in evaluation mode (`model.eval()`) by default.

    Parameters:
        pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
            Can be either:
                - A link to the `.ckpt` file (for example
                  `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
                - A path to a *file* containing all pipeline weights.
        mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
            Override the default `mindspore.dtype` and load the model with another dtype.
        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.
        cache_dir (`Union[str, os.PathLike]`, *optional*):
            Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
            is not used.

        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.
        original_config_file (`str`, *optional*):
            The path to the original config file that was used to train the model. If not provided, the config file
            will be inferred from the checkpoint file.
        config (`str`, *optional*):
            Can be either:
                - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
                  hosted on the Hub.
                - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline
                  component configs in Diffusers format.
        kwargs (remaining dictionary of keyword arguments, *optional*):
            Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
            class). The overwritten components are passed directly to the pipelines `__init__` method. See example
            below for more information.

    Examples:

    ```py
    >>> from mindone.diffusers import StableDiffusionPipeline

    >>> # Download pipeline from huggingface.co and cache.
    >>> pipeline = StableDiffusionPipeline.from_single_file(
    ...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
    ... )

    >>> # Download pipeline from local file
    >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
    >>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly")
    ```

    """
    original_config_file = kwargs.pop("original_config_file", None)
    config = kwargs.pop("config", None)
    original_config = kwargs.pop("original_config", None)

    if original_config_file is not None:
        deprecation_message = (
            "`original_config_file` argument is deprecated and will be removed in future versions."
            "please use the `original_config` argument instead."
        )
        deprecate("original_config_file", "1.0.0", deprecation_message)
        original_config = original_config_file

    force_download = kwargs.pop("force_download", False)
    proxies = kwargs.pop("proxies", None)
    token = kwargs.pop("token", None)
    cache_dir = kwargs.pop("cache_dir", None)
    local_files_only = kwargs.pop("local_files_only", False)
    revision = kwargs.pop("revision", None)
    mindspore_dtype = kwargs.pop("mindspore_dtype", None)

    is_legacy_loading = False

    # We shouldn't allow configuring individual models components through a Pipeline creation method
    # These model kwargs should be deprecated
    scaling_factor = kwargs.get("scaling_factor", None)
    if scaling_factor is not None:
        deprecation_message = (
            "Passing the `scaling_factor` argument to `from_single_file is deprecated "
            "and will be ignored in future versions."
        )
        deprecate("scaling_factor", "1.0.0", deprecation_message)

    if original_config is not None:
        original_config = fetch_original_config(original_config, local_files_only=local_files_only)

    from ..pipelines.pipeline_utils import _get_pipeline_class

    pipeline_class = _get_pipeline_class(cls, config=None)

    checkpoint = load_single_file_checkpoint(
        pretrained_model_link_or_path,
        force_download=force_download,
        proxies=proxies,
        token=token,
        cache_dir=cache_dir,
        local_files_only=local_files_only,
        revision=revision,
    )

    if config is None:
        config = fetch_diffusers_config(checkpoint)
        default_pretrained_model_config_name = config["pretrained_model_name_or_path"]
    else:
        default_pretrained_model_config_name = config

    if not os.path.isdir(default_pretrained_model_config_name):
        # Provided config is a repo_id
        if default_pretrained_model_config_name.count("/") > 1:
            raise ValueError(
                f'The provided config "{config}"'
                " is neither a valid local path nor a valid repo id. Please check the parameter."
            )
        try:
            # Attempt to download the config files for the pipeline
            cached_model_config_path = _download_diffusers_model_config_from_hub(
                default_pretrained_model_config_name,
                cache_dir=cache_dir,
                revision=revision,
                proxies=proxies,
                force_download=force_download,
                local_files_only=local_files_only,
                token=token,
            )
            config_dict = pipeline_class.load_config(cached_model_config_path)

        except LocalEntryNotFoundError:
            # `local_files_only=True` but a local diffusers format model config is not available in the cache
            # If `original_config` is not provided, we need override `local_files_only` to False
            # to fetch the config files from the hub so that we have a way
            # to configure the pipeline components.

            if original_config is None:
                logger.warning(
                    "`local_files_only` is True but no local configs were found for this checkpoint.\n"
                    "Attempting to download the necessary config files for this pipeline.\n"
                )
                cached_model_config_path = _download_diffusers_model_config_from_hub(
                    default_pretrained_model_config_name,
                    cache_dir=cache_dir,
                    revision=revision,
                    proxies=proxies,
                    force_download=force_download,
                    local_files_only=False,
                    token=token,
                )
                config_dict = pipeline_class.load_config(cached_model_config_path)

            else:
                # For backwards compatibility
                # If `original_config` is provided, then we need to assume we are using legacy loading for pipeline components
                logger.warning(
                    "Detected legacy `from_single_file` loading behavior. Attempting to create the pipeline based on inferred components.\n"
                    "This may lead to errors if the model components are not correctly inferred. \n"
                    "To avoid this warning, please explicity pass the `config` argument to `from_single_file` with a path to a local diffusers model repo \n"  # noqa E501
                    "e.g. `from_single_file(<my model checkpoint path>, config=<path to local diffusers model repo>) \n"
                    "or run `from_single_file` with `local_files_only=False` first to update the local cache directory with "
                    "the necessary config files.\n"
                )
                is_legacy_loading = True
                cached_model_config_path = None

                config_dict = _infer_pipeline_config_dict(pipeline_class)
                config_dict["_class_name"] = pipeline_class.__name__

    else:
        # Provided config is a path to a local directory attempt to load directly.
        cached_model_config_path = default_pretrained_model_config_name
        config_dict = pipeline_class.load_config(cached_model_config_path)

    #   pop out "_ignore_files" as it is only needed for download
    config_dict.pop("_ignore_files", None)

    expected_modules, optional_kwargs = pipeline_class._get_signature_keys(cls)
    passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
    passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}

    init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
    init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}
    init_kwargs = {**init_kwargs, **passed_pipe_kwargs}

    from mindone.diffusers import pipelines

    # remove `null` components
    def load_module(name, value):
        if value[0] is None:
            return False
        if name in passed_class_obj and passed_class_obj[name] is None:
            return False
        if name in SINGLE_FILE_OPTIONAL_COMPONENTS:
            return False

        return True

    init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}

    for name, (library_name, class_name) in logging.tqdm(
        sorted(init_dict.items()), desc="Loading pipeline components..."
    ):
        loaded_sub_model = None
        is_pipeline_module = hasattr(pipelines, library_name)

        if name in passed_class_obj:
            loaded_sub_model = passed_class_obj[name]

        else:
            try:
                loaded_sub_model = load_single_file_sub_model(
                    library_name=library_name,
                    class_name=class_name,
                    name=name,
                    checkpoint=checkpoint,
                    is_pipeline_module=is_pipeline_module,
                    cached_model_config_path=cached_model_config_path,
                    pipelines=pipelines,
                    mindspore_dtype=mindspore_dtype,
                    original_config=original_config,
                    local_files_only=local_files_only,
                    is_legacy_loading=is_legacy_loading,
                    **kwargs,
                )
            except SingleFileComponentError as e:
                raise SingleFileComponentError(
                    (
                        f"{e.message}\n"
                        f"Please load the component before passing it in as an argument to `from_single_file`.\n"
                        f"\n"
                        f"{name} = {class_name}.from_pretrained('...')\n"
                        f"pipe = {pipeline_class.__name__}.from_single_file(<checkpoint path>, {name}={name})\n"
                        f"\n"
                    )
                )

        init_kwargs[name] = loaded_sub_model

    missing_modules = set(expected_modules) - set(init_kwargs.keys())
    passed_modules = list(passed_class_obj.keys())
    optional_modules = pipeline_class._optional_components

    if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
        for module in missing_modules:
            init_kwargs[module] = passed_class_obj.get(module, None)
    elif len(missing_modules) > 0:
        passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
        raise ValueError(
            f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
        )

    # deprecated kwargs
    load_safety_checker = kwargs.pop("load_safety_checker", None)
    if load_safety_checker is not None:
        deprecation_message = (
            "Please pass instances of `StableDiffusionSafetyChecker` and `AutoImageProcessor`"
            "using the `safety_checker` and `feature_extractor` arguments in `from_single_file`"
        )
        deprecate("load_safety_checker", "1.0.0", deprecation_message)

        safety_checker_components = _legacy_load_safety_checker(local_files_only, mindspore_dtype)
        init_kwargs.update(safety_checker_components)

    pipe = pipeline_class(**init_kwargs)

    return pipe