Class DiffusionPipeline

diffusers/src/diffusers/pipelines/pipeline_utils.py:130–1850

r""" Base class for all pipelines. [`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and provides methods for loading, downloading and saving models. It also includes methods to: - move all PyTorch modules to the device of

class DiffusionPipeline(ConfigMixin, PushToHubMixin):
    r"""
    Base class for all pipelines.

    [`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
    provides methods for loading, downloading and saving models. It also includes methods to:

        - move all PyTorch modules to the device of your choice
        - enable/disable the progress bar for the denoising iteration

    Class attributes:

        - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
          diffusion pipeline's components.
        - **_optional_components** (`List[str]`) -- List of all optional components that don't have to be passed to the
          pipeline to function (should be overridden by subclasses).
    """

    config_name = "model_index.json"
    model_cpu_offload_seq = None
    hf_device_map = None
    _optional_components = []
    _exclude_from_cpu_offload = []
    _load_connected_pipes = False
    _is_onnx = False

    def register_modules(self, **kwargs):
        for name, module in kwargs.items():
            # retrieve library
            if module is None or isinstance(module, (tuple, list)) and module[0] is None:
                register_dict = {name: (None, None)}
            else:
                library, class_name = _fetch_class_library_tuple(module)
                register_dict = {name: (library, class_name)}

            # save model index config
            self.register_to_config(**register_dict)

            # set models
            setattr(self, name, module)

    def __setattr__(self, name: str, value: Any):
        if name in self.__dict__ and hasattr(self.config, name):
            # We need to overwrite the config if name exists in config
            if isinstance(getattr(self.config, name), (tuple, list)):
                if value is not None and self.config[name][0] is not None:
                    class_library_tuple = _fetch_class_library_tuple(value)
                else:
                    class_library_tuple = (None, None)

                self.register_to_config(**{name: class_library_tuple})
            else:
                self.register_to_config(**{name: value})

        super().__setattr__(name, value)

    def save_pretrained(
        self,