AutoPipeline

The AutoPipeline is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the AutoPipeline automatically retrieves the correct pipeline class from the checkpoint model_index.json file.

Tip

Check out the AutoPipeline tutorial to learn how to use this API!

mindone.diffusers.AutoPipelineForText2Image

Bases: ConfigMixin

[AutoPipelineForText2Image] is a generic pipeline class that instantiates a text-to-image pipeline class. The specific underlying pipeline class is automatically selected from either the [~AutoPipelineForText2Image.from_pretrained] or [~AutoPipelineForText2Image.from_pipe] methods.

This class cannot be instantiated using __init__() (throws an error).

Class attributes:

- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
  diffusion pipeline's components.

Source code in mindone/diffusers/pipelines/auto_pipeline.py

class AutoPipelineForText2Image(ConfigMixin):
    r"""

    [`AutoPipelineForText2Image`] is a generic pipeline class that instantiates a text-to-image pipeline class. The
    specific underlying pipeline class is automatically selected from either the
    [`~AutoPipelineForText2Image.from_pretrained`] or [`~AutoPipelineForText2Image.from_pipe`] methods.

    This class cannot be instantiated using `__init__()` (throws an error).

    Class attributes:

        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
          diffusion pipeline's components.

    """

    config_name = "model_index.json"

    def __init__(self, *args, **kwargs):
        raise EnvironmentError(
            f"{self.__class__.__name__} is designed to be instantiated "
            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
        )

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
        r"""
        Instantiates a text-to-image MindSpore diffusion pipeline from pretrained pipeline weight.

        The from_pretrained() method takes care of returning the correct pipeline class instance by:
            1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
               config object
            2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
               name.

        If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object.

        The pipeline is set in evaluation mode (`model.eval()`) by default.

        If you get the error message below, you need to finetune the weights for your downstream task:

        ```
        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
        - conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
        ```

        Parameters:
            pretrained_model_or_path (`str` or `os.PathLike`, *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 pipeline weights
                      saved using
                    [`~DiffusionPipeline.save_pretrained`].
            mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
                Override the default `mindspore.dtype` and load the model with another dtype. If "auto" is passed, the
                dtype is automatically derived from the model's weights.
            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.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            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.
            custom_revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
            mirror (`str`, *optional*):
                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                information.
            max_memory (`Dict`, *optional*):
                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
                each NPU and the available CPU RAM if unset.
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                weights. If set to `False`, safetensors weights are not loaded.
            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.
            variant (`str`, *optional*):
                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                loading `from_flax`.

        <Tip>

        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
        `huggingface-cli login`.

        </Tip>

        Examples:

        ```py
        >>> from mindone.diffusers import AutoPipelineForText2Image

        >>> pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> image = pipeline(prompt)[0][0]
        ```
        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        token = kwargs.pop("token", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)

        load_config_kwargs = {
            "cache_dir": cache_dir,
            "force_download": force_download,
            "proxies": proxies,
            "token": token,
            "local_files_only": local_files_only,
            "revision": revision,
        }

        config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
        orig_class_name = config["_class_name"]

        if "controlnet" in kwargs:
            orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
        if "enable_pag" in kwargs:
            enable_pag = kwargs.pop("enable_pag")
            if enable_pag:
                orig_class_name = orig_class_name.replace("Pipeline", "PAGPipeline")

        text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, orig_class_name)

        kwargs = {**load_config_kwargs, **kwargs}
        return text_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)

    @classmethod
    def from_pipe(cls, pipeline, **kwargs):
        r"""
        Instantiates a text-to-image MindSpore diffusion pipeline from another instantiated diffusion pipeline class.

        The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image
        pipeline linked to the pipeline class using pattern matching on pipeline class name.

        All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
        additional memory.

        The pipeline is set in evaluation mode (`model.eval()`) by default.

        Parameters:
            pipeline (`DiffusionPipeline`):
                an instantiated `DiffusionPipeline` object

        ```py
        >>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

        >>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
        ...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
        ... )

        >>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i)
        >>> image = pipe_t2i(prompt)[0][0]
        ```
        """

        original_config = dict(pipeline.config)
        original_cls_name = pipeline.__class__.__name__

        # derive the pipeline class to instantiate
        text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, original_cls_name)

        if "controlnet" in kwargs:
            if kwargs["controlnet"] is not None:
                to_replace = "PAGPipeline" if "PAG" in text_2_image_cls.__name__ else "Pipeline"
                text_2_image_cls = _get_task_class(
                    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                    text_2_image_cls.__name__.replace("ControlNet", "").replace(to_replace, "ControlNet" + to_replace),
                )
            else:
                text_2_image_cls = _get_task_class(
                    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                    text_2_image_cls.__name__.replace("ControlNet", ""),
                )

        if "enable_pag" in kwargs:
            enable_pag = kwargs.pop("enable_pag")
            if enable_pag:
                text_2_image_cls = _get_task_class(
                    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                    text_2_image_cls.__name__.replace("PAG", "").replace("Pipeline", "PAGPipeline"),
                )
            else:
                text_2_image_cls = _get_task_class(
                    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                    text_2_image_cls.__name__.replace("PAG", ""),
                )

        # define expected module and optional kwargs given the pipeline signature
        expected_modules, optional_kwargs = text_2_image_cls._get_signature_keys(text_2_image_cls)

        pretrained_model_name_or_path = original_config.pop("_name_or_path", None)

        # allow users pass modules in `kwargs` to override the original pipeline's components
        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
        original_class_obj = {
            k: pipeline.components[k]
            for k, v in pipeline.components.items()
            if k in expected_modules and k not in passed_class_obj
        }

        # allow users pass optional kwargs to override the original pipelines config attribute
        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
        original_pipe_kwargs = {
            k: original_config[k]
            for k, v in original_config.items()
            if k in optional_kwargs and k not in passed_pipe_kwargs
        }

        # config that were not expected by original pipeline is stored as private attribute
        # we will pass them as optional arguments if they can be accepted by the pipeline
        additional_pipe_kwargs = [
            k[1:]
            for k in original_config.keys()
            if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
        ]
        for k in additional_pipe_kwargs:
            original_pipe_kwargs[k] = original_config.pop(f"_{k}")

        text_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}

        # store unused config as private attribute
        unused_original_config = {
            f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
            for k, v in original_config.items()
            if k not in text_2_image_kwargs
        }

        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(text_2_image_kwargs.keys())

        if len(missing_modules) > 0:
            raise ValueError(
                f"Pipeline {text_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"  # noqa: E501
            )

        model = text_2_image_cls(**text_2_image_kwargs)
        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
        model.register_to_config(**unused_original_config)

        return model

mindone.diffusers.AutoPipelineForText2Image.from_pipe(pipeline, **kwargs) classmethod

Instantiates a text-to-image MindSpore diffusion pipeline from another instantiated diffusion pipeline class.

The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.

All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory.

The pipeline is set in evaluation mode (model.eval()) by default.

PARAMETER	DESCRIPTION
pipeline	an instantiated DiffusionPipeline object TYPE: `DiffusionPipeline`

>>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

>>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... )

>>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i)
>>> image = pipe_t2i(prompt)[0][0]

Source code in mindone/diffusers/pipelines/auto_pipeline.py

@classmethod
def from_pipe(cls, pipeline, **kwargs):
    r"""
    Instantiates a text-to-image MindSpore diffusion pipeline from another instantiated diffusion pipeline class.

    The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image
    pipeline linked to the pipeline class using pattern matching on pipeline class name.

    All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
    additional memory.

    The pipeline is set in evaluation mode (`model.eval()`) by default.

    Parameters:
        pipeline (`DiffusionPipeline`):
            an instantiated `DiffusionPipeline` object

    ```py
    >>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

    >>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
    ...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
    ... )

    >>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i)
    >>> image = pipe_t2i(prompt)[0][0]
    ```
    """

    original_config = dict(pipeline.config)
    original_cls_name = pipeline.__class__.__name__

    # derive the pipeline class to instantiate
    text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, original_cls_name)

    if "controlnet" in kwargs:
        if kwargs["controlnet"] is not None:
            to_replace = "PAGPipeline" if "PAG" in text_2_image_cls.__name__ else "Pipeline"
            text_2_image_cls = _get_task_class(
                AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                text_2_image_cls.__name__.replace("ControlNet", "").replace(to_replace, "ControlNet" + to_replace),
            )
        else:
            text_2_image_cls = _get_task_class(
                AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                text_2_image_cls.__name__.replace("ControlNet", ""),
            )

    if "enable_pag" in kwargs:
        enable_pag = kwargs.pop("enable_pag")
        if enable_pag:
            text_2_image_cls = _get_task_class(
                AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                text_2_image_cls.__name__.replace("PAG", "").replace("Pipeline", "PAGPipeline"),
            )
        else:
            text_2_image_cls = _get_task_class(
                AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
                text_2_image_cls.__name__.replace("PAG", ""),
            )

    # define expected module and optional kwargs given the pipeline signature
    expected_modules, optional_kwargs = text_2_image_cls._get_signature_keys(text_2_image_cls)

    pretrained_model_name_or_path = original_config.pop("_name_or_path", None)

    # allow users pass modules in `kwargs` to override the original pipeline's components
    passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
    original_class_obj = {
        k: pipeline.components[k]
        for k, v in pipeline.components.items()
        if k in expected_modules and k not in passed_class_obj
    }

    # allow users pass optional kwargs to override the original pipelines config attribute
    passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
    original_pipe_kwargs = {
        k: original_config[k]
        for k, v in original_config.items()
        if k in optional_kwargs and k not in passed_pipe_kwargs
    }

    # config that were not expected by original pipeline is stored as private attribute
    # we will pass them as optional arguments if they can be accepted by the pipeline
    additional_pipe_kwargs = [
        k[1:]
        for k in original_config.keys()
        if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
    ]
    for k in additional_pipe_kwargs:
        original_pipe_kwargs[k] = original_config.pop(f"_{k}")

    text_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}

    # store unused config as private attribute
    unused_original_config = {
        f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
        for k, v in original_config.items()
        if k not in text_2_image_kwargs
    }

    missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(text_2_image_kwargs.keys())

    if len(missing_modules) > 0:
        raise ValueError(
            f"Pipeline {text_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"  # noqa: E501
        )

    model = text_2_image_cls(**text_2_image_kwargs)
    model.register_to_config(_name_or_path=pretrained_model_name_or_path)
    model.register_to_config(**unused_original_config)

    return model

mindone.diffusers.AutoPipelineForText2Image.from_pretrained(pretrained_model_or_path, **kwargs) classmethod

Instantiates a text-to-image MindSpore diffusion pipeline from pretrained pipeline weight.

The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.

If a controlnet argument is passed, it will instantiate a [StableDiffusionControlNetPipeline] object.

The pipeline is set in evaluation mode (model.eval()) by default.

If you get the error message below, you need to finetune the weights for your downstream task:

Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
- conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

PARAMETER	DESCRIPTION
pretrained_model_or_path	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 pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. TYPE: `str` or `os.PathLike`, *optional*
mindspore_dtype	Override the default mindspore.dtype and load the model with another dtype. If "auto" is passed, the dtype is automatically derived from the model's weights. 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*
output_loading_info(`bool`,	Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. TYPE: *optional*, defaults to `False`
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"`
custom_revision	The specific model version to use. It can be a branch name, a tag name, or a commit id similar to revision when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to "main" when loading from the Hub. TYPE: `str`, *optional*, defaults to `"main"`
mirror	Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. TYPE: `str`, *optional*
max_memory	A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each NPU and the available CPU RAM if unset. TYPE: `Dict`, *optional*
use_safetensors	If set to None, the safetensors weights are downloaded if they're available and if the safetensors library is installed. If set to True, the model is forcibly loaded from safetensors weights. If set to False, safetensors weights are not loaded. TYPE: `bool`, *optional*, defaults to `None`
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: {}
variant	Load weights from a specified variant filename such as "fp16" or "ema". This is ignored when loading from_flax. TYPE: `str`, *optional*

To use private or gated models, log-in with huggingface-cli login.

Examples:

>>> from mindone.diffusers import AutoPipelineForText2Image

>>> pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> image = pipeline(prompt)[0][0]

Source code in mindone/diffusers/pipelines/auto_pipeline.py

@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
    r"""
    Instantiates a text-to-image MindSpore diffusion pipeline from pretrained pipeline weight.

    The from_pretrained() method takes care of returning the correct pipeline class instance by:
        1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
           config object
        2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
           name.

    If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object.

    The pipeline is set in evaluation mode (`model.eval()`) by default.

    If you get the error message below, you need to finetune the weights for your downstream task:

    ```
    Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
    - conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
    You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
    ```

    Parameters:
        pretrained_model_or_path (`str` or `os.PathLike`, *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 pipeline weights
                  saved using
                [`~DiffusionPipeline.save_pretrained`].
        mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
            Override the default `mindspore.dtype` and load the model with another dtype. If "auto" is passed, the
            dtype is automatically derived from the model's weights.
        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.
        output_loading_info(`bool`, *optional*, defaults to `False`):
            Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
        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.
        custom_revision (`str`, *optional*, defaults to `"main"`):
            The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
            `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
            custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
        mirror (`str`, *optional*):
            Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
            guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
            information.
        max_memory (`Dict`, *optional*):
            A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
            each NPU and the available CPU RAM if unset.
        use_safetensors (`bool`, *optional*, defaults to `None`):
            If set to `None`, the safetensors weights are downloaded if they're available **and** if the
            safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
            weights. If set to `False`, safetensors weights are not loaded.
        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.
        variant (`str`, *optional*):
            Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
            loading `from_flax`.

    <Tip>

    To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
    `huggingface-cli login`.

    </Tip>

    Examples:

    ```py
    >>> from mindone.diffusers import AutoPipelineForText2Image

    >>> pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> image = pipeline(prompt)[0][0]
    ```
    """
    cache_dir = kwargs.pop("cache_dir", None)
    force_download = kwargs.pop("force_download", False)
    proxies = kwargs.pop("proxies", None)
    token = kwargs.pop("token", None)
    local_files_only = kwargs.pop("local_files_only", False)
    revision = kwargs.pop("revision", None)

    load_config_kwargs = {
        "cache_dir": cache_dir,
        "force_download": force_download,
        "proxies": proxies,
        "token": token,
        "local_files_only": local_files_only,
        "revision": revision,
    }

    config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
    orig_class_name = config["_class_name"]

    if "controlnet" in kwargs:
        orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
    if "enable_pag" in kwargs:
        enable_pag = kwargs.pop("enable_pag")
        if enable_pag:
            orig_class_name = orig_class_name.replace("Pipeline", "PAGPipeline")

    text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, orig_class_name)

    kwargs = {**load_config_kwargs, **kwargs}
    return text_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)

mindone.diffusers.AutoPipelineForImage2Image

Bases: ConfigMixin

[AutoPipelineForImage2Image] is a generic pipeline class that instantiates an image-to-image pipeline class. The specific underlying pipeline class is automatically selected from either the [~AutoPipelineForImage2Image.from_pretrained] or [~AutoPipelineForImage2Image.from_pipe] methods.

This class cannot be instantiated using __init__() (throws an error).

Class attributes:

- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
  diffusion pipeline's components.

Source code in mindone/diffusers/pipelines/auto_pipeline.py

class AutoPipelineForImage2Image(ConfigMixin):
    r"""

    [`AutoPipelineForImage2Image`] is a generic pipeline class that instantiates an image-to-image pipeline class. The
    specific underlying pipeline class is automatically selected from either the
    [`~AutoPipelineForImage2Image.from_pretrained`] or [`~AutoPipelineForImage2Image.from_pipe`] methods.

    This class cannot be instantiated using `__init__()` (throws an error).

    Class attributes:

        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
          diffusion pipeline's components.

    """

    config_name = "model_index.json"

    def __init__(self, *args, **kwargs):
        raise EnvironmentError(
            f"{self.__class__.__name__} is designed to be instantiated "
            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
        )

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
        r"""
        Instantiates a image-to-image MindSpore diffusion pipeline from pretrained pipeline weight.

        The from_pretrained() method takes care of returning the correct pipeline class instance by:
            1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
               config object
            2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
               name.

        If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetImg2ImgPipeline`]
        object.

        The pipeline is set in evaluation mode (`model.eval()`) by default.

        If you get the error message below, you need to finetune the weights for your downstream task:

        ```
        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
        - conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
        ```

        Parameters:
            pretrained_model_or_path (`str` or `os.PathLike`, *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 pipeline weights
                      saved using
                    [`~DiffusionPipeline.save_pretrained`].
            mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
                Override the default `mindspore.dtype` and load the model with another dtype. If "auto" is passed, the
                dtype is automatically derived from the model's weights.
            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.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            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.
            custom_revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
            mirror (`str`, *optional*):
                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                information.
            max_memory (`Dict`, *optional*):
                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
                each NPU and the available CPU RAM if unset.
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                weights. If set to `False`, safetensors weights are not loaded.
            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.
            variant (`str`, *optional*):
                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                loading `from_flax`.

        <Tip>

        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
        `huggingface-cli login`.

        </Tip>

        Examples:

        ```py
        >>> from mindone.diffusers import AutoPipelineForImage2Image

        >>> pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> image = pipeline(prompt, image)[0][0]
        ```
        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        token = kwargs.pop("token", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)

        load_config_kwargs = {
            "cache_dir": cache_dir,
            "force_download": force_download,
            "proxies": proxies,
            "token": token,
            "local_files_only": local_files_only,
            "revision": revision,
        }

        config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
        orig_class_name = config["_class_name"]

        # the `orig_class_name` can be:
        # `- *Pipeline` (for regular text-to-image checkpoint)
        # `- *Img2ImgPipeline` (for refiner checkpoint)
        to_replace = "Img2ImgPipeline" if "Img2Img" in config["_class_name"] else "Pipeline"

        if "controlnet" in kwargs:
            orig_class_name = orig_class_name.replace(to_replace, "ControlNet" + to_replace)
        if "enable_pag" in kwargs:
            enable_pag = kwargs.pop("enable_pag")
            if enable_pag:
                orig_class_name = orig_class_name.replace(to_replace, "PAG" + to_replace)

        image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, orig_class_name)

        kwargs = {**load_config_kwargs, **kwargs}
        return image_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)

    @classmethod
    def from_pipe(cls, pipeline, **kwargs):
        r"""
        Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.

        The from_pipe() method takes care of returning the correct pipeline class instance by finding the
        image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.

        All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
        additional memory.

        The pipeline is set in evaluation mode (`model.eval()`) by default.

        Parameters:
            pipeline (`DiffusionPipeline`):
                an instantiated `DiffusionPipeline` object

        Examples:

        ```py
        >>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

        >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
        ...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
        ... )

        >>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)
        >>> image = pipe_i2i(prompt, image)[0][0]
        ```
        """

        original_config = dict(pipeline.config)
        original_cls_name = pipeline.__class__.__name__

        # derive the pipeline class to instantiate
        image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name)

        if "controlnet" in kwargs:
            if kwargs["controlnet"] is not None:
                to_replace = "Img2ImgPipeline"
                if "PAG" in image_2_image_cls.__name__:
                    to_replace = "PAG" + to_replace
                image_2_image_cls = _get_task_class(
                    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                    image_2_image_cls.__name__.replace("ControlNet", "").replace(to_replace, "ControlNet" + to_replace),
                )
            else:
                image_2_image_cls = _get_task_class(
                    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                    image_2_image_cls.__name__.replace("ControlNet", ""),
                )

        if "enable_pag" in kwargs:
            enable_pag = kwargs.pop("enable_pag")
            if enable_pag:
                image_2_image_cls = _get_task_class(
                    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                    image_2_image_cls.__name__.replace("PAG", "").replace("Img2ImgPipeline", "PAGImg2ImgPipeline"),
                )
            else:
                image_2_image_cls = _get_task_class(
                    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                    image_2_image_cls.__name__.replace("PAG", ""),
                )

        # define expected module and optional kwargs given the pipeline signature
        expected_modules, optional_kwargs = image_2_image_cls._get_signature_keys(image_2_image_cls)

        pretrained_model_name_or_path = original_config.pop("_name_or_path", None)

        # allow users pass modules in `kwargs` to override the original pipeline's components
        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
        original_class_obj = {
            k: pipeline.components[k]
            for k, v in pipeline.components.items()
            if k in expected_modules and k not in passed_class_obj
        }

        # allow users pass optional kwargs to override the original pipelines config attribute
        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
        original_pipe_kwargs = {
            k: original_config[k]
            for k, v in original_config.items()
            if k in optional_kwargs and k not in passed_pipe_kwargs
        }

        # config attribute that were not expected by original pipeline is stored as its private attribute
        # we will pass them as optional arguments if they can be accepted by the pipeline
        additional_pipe_kwargs = [
            k[1:]
            for k in original_config.keys()
            if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
        ]
        for k in additional_pipe_kwargs:
            original_pipe_kwargs[k] = original_config.pop(f"_{k}")

        image_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}

        # store unused config as private attribute
        unused_original_config = {
            f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
            for k, v in original_config.items()
            if k not in image_2_image_kwargs
        }

        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(image_2_image_kwargs.keys())

        if len(missing_modules) > 0:
            raise ValueError(
                f"Pipeline {image_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"  # noqa: E501
            )

        model = image_2_image_cls(**image_2_image_kwargs)
        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
        model.register_to_config(**unused_original_config)

        return model

mindone.diffusers.AutoPipelineForImage2Image.from_pipe(pipeline, **kwargs) classmethod

Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.

The from_pipe() method takes care of returning the correct pipeline class instance by finding the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.

All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory.

The pipeline is set in evaluation mode (model.eval()) by default.

PARAMETER	DESCRIPTION
pipeline	an instantiated DiffusionPipeline object TYPE: `DiffusionPipeline`

>>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... )

>>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)
>>> image = pipe_i2i(prompt, image)[0][0]

Source code in mindone/diffusers/pipelines/auto_pipeline.py

@classmethod
def from_pipe(cls, pipeline, **kwargs):
    r"""
    Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.

    The from_pipe() method takes care of returning the correct pipeline class instance by finding the
    image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.

    All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
    additional memory.

    The pipeline is set in evaluation mode (`model.eval()`) by default.

    Parameters:
        pipeline (`DiffusionPipeline`):
            an instantiated `DiffusionPipeline` object

    Examples:

    ```py
    >>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

    >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
    ...     "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
    ... )

    >>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)
    >>> image = pipe_i2i(prompt, image)[0][0]
    ```
    """

    original_config = dict(pipeline.config)
    original_cls_name = pipeline.__class__.__name__

    # derive the pipeline class to instantiate
    image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name)

    if "controlnet" in kwargs:
        if kwargs["controlnet"] is not None:
            to_replace = "Img2ImgPipeline"
            if "PAG" in image_2_image_cls.__name__:
                to_replace = "PAG" + to_replace
            image_2_image_cls = _get_task_class(
                AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                image_2_image_cls.__name__.replace("ControlNet", "").replace(to_replace, "ControlNet" + to_replace),
            )
        else:
            image_2_image_cls = _get_task_class(
                AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                image_2_image_cls.__name__.replace("ControlNet", ""),
            )

    if "enable_pag" in kwargs:
        enable_pag = kwargs.pop("enable_pag")
        if enable_pag:
            image_2_image_cls = _get_task_class(
                AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                image_2_image_cls.__name__.replace("PAG", "").replace("Img2ImgPipeline", "PAGImg2ImgPipeline"),
            )
        else:
            image_2_image_cls = _get_task_class(
                AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
                image_2_image_cls.__name__.replace("PAG", ""),
            )

    # define expected module and optional kwargs given the pipeline signature
    expected_modules, optional_kwargs = image_2_image_cls._get_signature_keys(image_2_image_cls)

    pretrained_model_name_or_path = original_config.pop("_name_or_path", None)

    # allow users pass modules in `kwargs` to override the original pipeline's components
    passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
    original_class_obj = {
        k: pipeline.components[k]
        for k, v in pipeline.components.items()
        if k in expected_modules and k not in passed_class_obj
    }

    # allow users pass optional kwargs to override the original pipelines config attribute
    passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
    original_pipe_kwargs = {
        k: original_config[k]
        for k, v in original_config.items()
        if k in optional_kwargs and k not in passed_pipe_kwargs
    }

    # config attribute that were not expected by original pipeline is stored as its private attribute
    # we will pass them as optional arguments if they can be accepted by the pipeline
    additional_pipe_kwargs = [
        k[1:]
        for k in original_config.keys()
        if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
    ]
    for k in additional_pipe_kwargs:
        original_pipe_kwargs[k] = original_config.pop(f"_{k}")

    image_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}

    # store unused config as private attribute
    unused_original_config = {
        f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
        for k, v in original_config.items()
        if k not in image_2_image_kwargs
    }

    missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(image_2_image_kwargs.keys())

    if len(missing_modules) > 0:
        raise ValueError(
            f"Pipeline {image_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"  # noqa: E501
        )

    model = image_2_image_cls(**image_2_image_kwargs)
    model.register_to_config(_name_or_path=pretrained_model_name_or_path)
    model.register_to_config(**unused_original_config)

    return model

mindone.diffusers.AutoPipelineForImage2Image.from_pretrained(pretrained_model_or_path, **kwargs) classmethod

Instantiates a image-to-image MindSpore diffusion pipeline from pretrained pipeline weight.

The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.

If a controlnet argument is passed, it will instantiate a [StableDiffusionControlNetImg2ImgPipeline] object.

The pipeline is set in evaluation mode (model.eval()) by default.

If you get the error message below, you need to finetune the weights for your downstream task:

Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
- conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

PARAMETER	DESCRIPTION
pretrained_model_or_path	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 pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. TYPE: `str` or `os.PathLike`, *optional*
mindspore_dtype	Override the default mindspore.dtype and load the model with another dtype. If "auto" is passed, the dtype is automatically derived from the model's weights. 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*
output_loading_info(`bool`,	Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. TYPE: *optional*, defaults to `False`
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"`
custom_revision	The specific model version to use. It can be a branch name, a tag name, or a commit id similar to revision when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to "main" when loading from the Hub. TYPE: `str`, *optional*, defaults to `"main"`
mirror	Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. TYPE: `str`, *optional*
max_memory	A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each NPU and the available CPU RAM if unset. TYPE: `Dict`, *optional*
use_safetensors	If set to None, the safetensors weights are downloaded if they're available and if the safetensors library is installed. If set to True, the model is forcibly loaded from safetensors weights. If set to False, safetensors weights are not loaded. TYPE: `bool`, *optional*, defaults to `None`
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: {}
variant	Load weights from a specified variant filename such as "fp16" or "ema". This is ignored when loading from_flax. TYPE: `str`, *optional*

To use private or gated models, log-in with huggingface-cli login.

Examples:

>>> from mindone.diffusers import AutoPipelineForImage2Image

>>> pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> image = pipeline(prompt, image)[0][0]

Source code in mindone/diffusers/pipelines/auto_pipeline.py

@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
    r"""
    Instantiates a image-to-image MindSpore diffusion pipeline from pretrained pipeline weight.

    The from_pretrained() method takes care of returning the correct pipeline class instance by:
        1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
           config object
        2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
           name.

    If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetImg2ImgPipeline`]
    object.

    The pipeline is set in evaluation mode (`model.eval()`) by default.

    If you get the error message below, you need to finetune the weights for your downstream task:

    ```
    Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
    - conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
    You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
    ```

    Parameters:
        pretrained_model_or_path (`str` or `os.PathLike`, *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 pipeline weights
                  saved using
                [`~DiffusionPipeline.save_pretrained`].
        mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
            Override the default `mindspore.dtype` and load the model with another dtype. If "auto" is passed, the
            dtype is automatically derived from the model's weights.
        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.
        output_loading_info(`bool`, *optional*, defaults to `False`):
            Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
        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.
        custom_revision (`str`, *optional*, defaults to `"main"`):
            The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
            `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
            custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
        mirror (`str`, *optional*):
            Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
            guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
            information.
        max_memory (`Dict`, *optional*):
            A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
            each NPU and the available CPU RAM if unset.
        use_safetensors (`bool`, *optional*, defaults to `None`):
            If set to `None`, the safetensors weights are downloaded if they're available **and** if the
            safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
            weights. If set to `False`, safetensors weights are not loaded.
        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.
        variant (`str`, *optional*):
            Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
            loading `from_flax`.

    <Tip>

    To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
    `huggingface-cli login`.

    </Tip>

    Examples:

    ```py
    >>> from mindone.diffusers import AutoPipelineForImage2Image

    >>> pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> image = pipeline(prompt, image)[0][0]
    ```
    """
    cache_dir = kwargs.pop("cache_dir", None)
    force_download = kwargs.pop("force_download", False)
    proxies = kwargs.pop("proxies", None)
    token = kwargs.pop("token", None)
    local_files_only = kwargs.pop("local_files_only", False)
    revision = kwargs.pop("revision", None)

    load_config_kwargs = {
        "cache_dir": cache_dir,
        "force_download": force_download,
        "proxies": proxies,
        "token": token,
        "local_files_only": local_files_only,
        "revision": revision,
    }

    config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
    orig_class_name = config["_class_name"]

    # the `orig_class_name` can be:
    # `- *Pipeline` (for regular text-to-image checkpoint)
    # `- *Img2ImgPipeline` (for refiner checkpoint)
    to_replace = "Img2ImgPipeline" if "Img2Img" in config["_class_name"] else "Pipeline"

    if "controlnet" in kwargs:
        orig_class_name = orig_class_name.replace(to_replace, "ControlNet" + to_replace)
    if "enable_pag" in kwargs:
        enable_pag = kwargs.pop("enable_pag")
        if enable_pag:
            orig_class_name = orig_class_name.replace(to_replace, "PAG" + to_replace)

    image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, orig_class_name)

    kwargs = {**load_config_kwargs, **kwargs}
    return image_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)

mindone.diffusers.AutoPipelineForInpainting

Bases: ConfigMixin

[AutoPipelineForInpainting] is a generic pipeline class that instantiates an inpainting pipeline class. The specific underlying pipeline class is automatically selected from either the [~AutoPipelineForInpainting.from_pretrained] or [~AutoPipelineForInpainting.from_pipe] methods.

This class cannot be instantiated using __init__() (throws an error).

Class attributes:

- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
  diffusion pipeline's components.

Source code in mindone/diffusers/pipelines/auto_pipeline.py

class AutoPipelineForInpainting(ConfigMixin):
    r"""

    [`AutoPipelineForInpainting`] is a generic pipeline class that instantiates an inpainting pipeline class. The
    specific underlying pipeline class is automatically selected from either the
    [`~AutoPipelineForInpainting.from_pretrained`] or [`~AutoPipelineForInpainting.from_pipe`] methods.

    This class cannot be instantiated using `__init__()` (throws an error).

    Class attributes:

        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
          diffusion pipeline's components.

    """

    config_name = "model_index.json"

    def __init__(self, *args, **kwargs):
        raise EnvironmentError(
            f"{self.__class__.__name__} is designed to be instantiated "
            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
        )

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
        r"""
        Instantiates a inpainting MindSpore diffusion pipeline from pretrained pipeline weight.

        The from_pretrained() method takes care of returning the correct pipeline class instance by:
            1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
               config object
            2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.

        If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetInpaintPipeline`]
        object.

        The pipeline is set in evaluation mode (`model.eval()`) by default.

        If you get the error message below, you need to finetune the weights for your downstream task:

        ```
        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
        - conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
        ```

        Parameters:
            pretrained_model_or_path (`str` or `os.PathLike`, *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 pipeline weights
                      saved using
                    [`~DiffusionPipeline.save_pretrained`].
            mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
                Override the default `mindspore.dtype` and load the model with another dtype. If "auto" is passed, the
                dtype is automatically derived from the model's weights.
            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.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            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.
            custom_revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
                `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
                custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
            mirror (`str`, *optional*):
                Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                information.
            max_memory (`Dict`, *optional*):
                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
                each NPU and the available CPU RAM if unset.
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                weights. If set to `False`, safetensors weights are not loaded.
            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.
            variant (`str`, *optional*):
                Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                loading `from_flax`.

        <Tip>

        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
        `huggingface-cli login`.

        </Tip>

        Examples:

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

        >>> pipeline = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> image = pipeline(prompt, image=init_image, mask_image=mask_image)[0][0]
        ```
        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        token = kwargs.pop("token", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)

        load_config_kwargs = {
            "cache_dir": cache_dir,
            "force_download": force_download,
            "proxies": proxies,
            "token": token,
            "local_files_only": local_files_only,
            "revision": revision,
        }

        config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
        orig_class_name = config["_class_name"]

        # The `orig_class_name`` can be:
        # `- *InpaintPipeline` (for inpaint-specific checkpoint)
        #  - or *Pipeline (for regular text-to-image checkpoint)
        to_replace = "InpaintPipeline" if "Inpaint" in config["_class_name"] else "Pipeline"

        if "controlnet" in kwargs:
            orig_class_name = orig_class_name.replace(to_replace, "ControlNet" + to_replace)
        if "enable_pag" in kwargs:
            enable_pag = kwargs.pop("enable_pag")
            if enable_pag:
                orig_class_name = orig_class_name.replace(to_replace, "PAG" + to_replace)
        inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, orig_class_name)

        kwargs = {**load_config_kwargs, **kwargs}
        return inpainting_cls.from_pretrained(pretrained_model_or_path, **kwargs)

    @classmethod
    def from_pipe(cls, pipeline, **kwargs):
        r"""
        Instantiates a inpainting MindSpore diffusion pipeline from another instantiated diffusion pipeline class.

        The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting
        pipeline linked to the pipeline class using pattern matching on pipeline class name.

        All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating
        additional memory.

        The pipeline is set in evaluation mode (`model.eval()`) by default.

        Parameters:
            pipeline (`DiffusionPipeline`):
                an instantiated `DiffusionPipeline` object

        Examples:

        ```py
        >>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting

        >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
        ...     "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
        ... )

        >>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
        >>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image)[0][0]
        ```
        """
        original_config = dict(pipeline.config)
        original_cls_name = pipeline.__class__.__name__

        # derive the pipeline class to instantiate
        inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, original_cls_name)

        if "controlnet" in kwargs:
            if kwargs["controlnet"] is not None:
                inpainting_cls = _get_task_class(
                    AUTO_INPAINT_PIPELINES_MAPPING,
                    inpainting_cls.__name__.replace("ControlNet", "").replace(
                        "InpaintPipeline", "ControlNetInpaintPipeline"
                    ),
                )
            else:
                inpainting_cls = _get_task_class(
                    AUTO_INPAINT_PIPELINES_MAPPING,
                    inpainting_cls.__name__.replace("ControlNetInpaintPipeline", "InpaintPipeline"),
                )

        if "enable_pag" in kwargs:
            enable_pag = kwargs.pop("enable_pag")
            if enable_pag:
                inpainting_cls = _get_task_class(
                    AUTO_INPAINT_PIPELINES_MAPPING,
                    inpainting_cls.__name__.replace("PAG", "").replace("InpaintPipeline", "PAGInpaintPipeline"),
                )
            else:
                inpainting_cls = _get_task_class(
                    AUTO_INPAINT_PIPELINES_MAPPING,
                    inpainting_cls.__name__.replace("PAGInpaintPipeline", "InpaintPipeline"),
                )

        # define expected module and optional kwargs given the pipeline signature
        expected_modules, optional_kwargs = inpainting_cls._get_signature_keys(inpainting_cls)

        pretrained_model_name_or_path = original_config.pop("_name_or_path", None)

        # allow users pass modules in `kwargs` to override the original pipeline's components
        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
        original_class_obj = {
            k: pipeline.components[k]
            for k, v in pipeline.components.items()
            if k in expected_modules and k not in passed_class_obj
        }

        # allow users pass optional kwargs to override the original pipelines config attribute
        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
        original_pipe_kwargs = {
            k: original_config[k]
            for k, v in original_config.items()
            if k in optional_kwargs and k not in passed_pipe_kwargs
        }

        # config that were not expected by original pipeline is stored as private attribute
        # we will pass them as optional arguments if they can be accepted by the pipeline
        additional_pipe_kwargs = [
            k[1:]
            for k in original_config.keys()
            if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
        ]
        for k in additional_pipe_kwargs:
            original_pipe_kwargs[k] = original_config.pop(f"_{k}")

        inpainting_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}

        # store unused config as private attribute
        unused_original_config = {
            f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
            for k, v in original_config.items()
            if k not in inpainting_kwargs
        }

        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(inpainting_kwargs.keys())

        if len(missing_modules) > 0:
            raise ValueError(
                f"Pipeline {inpainting_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"  # noqa: E501
            )

        model = inpainting_cls(**inpainting_kwargs)
        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
        model.register_to_config(**unused_original_config)

        return model

mindone.diffusers.AutoPipelineForInpainting.from_pipe(pipeline, **kwargs) classmethod

Instantiates a inpainting MindSpore diffusion pipeline from another instantiated diffusion pipeline class.

The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.

All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating additional memory.

The pipeline is set in evaluation mode (model.eval()) by default.

PARAMETER	DESCRIPTION
pipeline	an instantiated DiffusionPipeline object TYPE: `DiffusionPipeline`

>>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting

>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
...     "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
... )

>>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
>>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image)[0][0]

Source code in mindone/diffusers/pipelines/auto_pipeline.py

@classmethod
def from_pipe(cls, pipeline, **kwargs):
    r"""
    Instantiates a inpainting MindSpore diffusion pipeline from another instantiated diffusion pipeline class.

    The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting
    pipeline linked to the pipeline class using pattern matching on pipeline class name.

    All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating
    additional memory.

    The pipeline is set in evaluation mode (`model.eval()`) by default.

    Parameters:
        pipeline (`DiffusionPipeline`):
            an instantiated `DiffusionPipeline` object

    Examples:

    ```py
    >>> from mindone.diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting

    >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
    ...     "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
    ... )

    >>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
    >>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image)[0][0]
    ```
    """
    original_config = dict(pipeline.config)
    original_cls_name = pipeline.__class__.__name__

    # derive the pipeline class to instantiate
    inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, original_cls_name)

    if "controlnet" in kwargs:
        if kwargs["controlnet"] is not None:
            inpainting_cls = _get_task_class(
                AUTO_INPAINT_PIPELINES_MAPPING,
                inpainting_cls.__name__.replace("ControlNet", "").replace(
                    "InpaintPipeline", "ControlNetInpaintPipeline"
                ),
            )
        else:
            inpainting_cls = _get_task_class(
                AUTO_INPAINT_PIPELINES_MAPPING,
                inpainting_cls.__name__.replace("ControlNetInpaintPipeline", "InpaintPipeline"),
            )

    if "enable_pag" in kwargs:
        enable_pag = kwargs.pop("enable_pag")
        if enable_pag:
            inpainting_cls = _get_task_class(
                AUTO_INPAINT_PIPELINES_MAPPING,
                inpainting_cls.__name__.replace("PAG", "").replace("InpaintPipeline", "PAGInpaintPipeline"),
            )
        else:
            inpainting_cls = _get_task_class(
                AUTO_INPAINT_PIPELINES_MAPPING,
                inpainting_cls.__name__.replace("PAGInpaintPipeline", "InpaintPipeline"),
            )

    # define expected module and optional kwargs given the pipeline signature
    expected_modules, optional_kwargs = inpainting_cls._get_signature_keys(inpainting_cls)

    pretrained_model_name_or_path = original_config.pop("_name_or_path", None)

    # allow users pass modules in `kwargs` to override the original pipeline's components
    passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
    original_class_obj = {
        k: pipeline.components[k]
        for k, v in pipeline.components.items()
        if k in expected_modules and k not in passed_class_obj
    }

    # allow users pass optional kwargs to override the original pipelines config attribute
    passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
    original_pipe_kwargs = {
        k: original_config[k]
        for k, v in original_config.items()
        if k in optional_kwargs and k not in passed_pipe_kwargs
    }

    # config that were not expected by original pipeline is stored as private attribute
    # we will pass them as optional arguments if they can be accepted by the pipeline
    additional_pipe_kwargs = [
        k[1:]
        for k in original_config.keys()
        if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
    ]
    for k in additional_pipe_kwargs:
        original_pipe_kwargs[k] = original_config.pop(f"_{k}")

    inpainting_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}

    # store unused config as private attribute
    unused_original_config = {
        f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
        for k, v in original_config.items()
        if k not in inpainting_kwargs
    }

    missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(inpainting_kwargs.keys())

    if len(missing_modules) > 0:
        raise ValueError(
            f"Pipeline {inpainting_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"  # noqa: E501
        )

    model = inpainting_cls(**inpainting_kwargs)
    model.register_to_config(_name_or_path=pretrained_model_name_or_path)
    model.register_to_config(**unused_original_config)

    return model

mindone.diffusers.AutoPipelineForInpainting.from_pretrained(pretrained_model_or_path, **kwargs) classmethod

Instantiates a inpainting MindSpore diffusion pipeline from pretrained pipeline weight.

The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.

If a controlnet argument is passed, it will instantiate a [StableDiffusionControlNetInpaintPipeline] object.

The pipeline is set in evaluation mode (model.eval()) by default.

If you get the error message below, you need to finetune the weights for your downstream task:

Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
- conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

PARAMETER	DESCRIPTION
pretrained_model_or_path	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 pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. TYPE: `str` or `os.PathLike`, *optional*
mindspore_dtype	Override the default mindspore.dtype and load the model with another dtype. If "auto" is passed, the dtype is automatically derived from the model's weights. 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*
output_loading_info(`bool`,	Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. TYPE: *optional*, defaults to `False`
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"`
custom_revision	The specific model version to use. It can be a branch name, a tag name, or a commit id similar to revision when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to "main" when loading from the Hub. TYPE: `str`, *optional*, defaults to `"main"`
mirror	Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. TYPE: `str`, *optional*
max_memory	A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each NPU and the available CPU RAM if unset. TYPE: `Dict`, *optional*
use_safetensors	If set to None, the safetensors weights are downloaded if they're available and if the safetensors library is installed. If set to True, the model is forcibly loaded from safetensors weights. If set to False, safetensors weights are not loaded. TYPE: `bool`, *optional*, defaults to `None`
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: {}
variant	Load weights from a specified variant filename such as "fp16" or "ema". This is ignored when loading from_flax. TYPE: `str`, *optional*

To use private or gated models, log-in with huggingface-cli login.

Examples:

>>> from mindone.diffusers import AutoPipelineForInpainting

>>> pipeline = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> image = pipeline(prompt, image=init_image, mask_image=mask_image)[0][0]

Source code in mindone/diffusers/pipelines/auto_pipeline.py

@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
    r"""
    Instantiates a inpainting MindSpore diffusion pipeline from pretrained pipeline weight.

    The from_pretrained() method takes care of returning the correct pipeline class instance by:
        1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
           config object
        2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.

    If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetInpaintPipeline`]
    object.

    The pipeline is set in evaluation mode (`model.eval()`) by default.

    If you get the error message below, you need to finetune the weights for your downstream task:

    ```
    Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:  # noqa: E501
    - conv_in.weight: found shape [320, 4, 3, 3] in the checkpoint and [320, 9, 3, 3] in the model instantiated
    You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
    ```

    Parameters:
        pretrained_model_or_path (`str` or `os.PathLike`, *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 pipeline weights
                  saved using
                [`~DiffusionPipeline.save_pretrained`].
        mindspore_dtype (`str` or `mindspore.dtype`, *optional*):
            Override the default `mindspore.dtype` and load the model with another dtype. If "auto" is passed, the
            dtype is automatically derived from the model's weights.
        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.
        output_loading_info(`bool`, *optional*, defaults to `False`):
            Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
        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.
        custom_revision (`str`, *optional*, defaults to `"main"`):
            The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
            `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
            custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
        mirror (`str`, *optional*):
            Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
            guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
            information.
        max_memory (`Dict`, *optional*):
            A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
            each NPU and the available CPU RAM if unset.
        use_safetensors (`bool`, *optional*, defaults to `None`):
            If set to `None`, the safetensors weights are downloaded if they're available **and** if the
            safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
            weights. If set to `False`, safetensors weights are not loaded.
        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.
        variant (`str`, *optional*):
            Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
            loading `from_flax`.

    <Tip>

    To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
    `huggingface-cli login`.

    </Tip>

    Examples:

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

    >>> pipeline = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> image = pipeline(prompt, image=init_image, mask_image=mask_image)[0][0]
    ```
    """
    cache_dir = kwargs.pop("cache_dir", None)
    force_download = kwargs.pop("force_download", False)
    proxies = kwargs.pop("proxies", None)
    token = kwargs.pop("token", None)
    local_files_only = kwargs.pop("local_files_only", False)
    revision = kwargs.pop("revision", None)

    load_config_kwargs = {
        "cache_dir": cache_dir,
        "force_download": force_download,
        "proxies": proxies,
        "token": token,
        "local_files_only": local_files_only,
        "revision": revision,
    }

    config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
    orig_class_name = config["_class_name"]

    # The `orig_class_name`` can be:
    # `- *InpaintPipeline` (for inpaint-specific checkpoint)
    #  - or *Pipeline (for regular text-to-image checkpoint)
    to_replace = "InpaintPipeline" if "Inpaint" in config["_class_name"] else "Pipeline"

    if "controlnet" in kwargs:
        orig_class_name = orig_class_name.replace(to_replace, "ControlNet" + to_replace)
    if "enable_pag" in kwargs:
        enable_pag = kwargs.pop("enable_pag")
        if enable_pag:
            orig_class_name = orig_class_name.replace(to_replace, "PAG" + to_replace)
    inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, orig_class_name)

    kwargs = {**load_config_kwargs, **kwargs}
    return inpainting_cls.from_pretrained(pretrained_model_or_path, **kwargs)