class_docstr: 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).
class_docstr_tok: ["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","do","n't","have","to","be","passed","to","the","pipeline","to","function","(","should","be","overridden","by","subclasses",")","."]
docstr: Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.
  
  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:
  - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([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_name_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`].
      torch_dtype (`str` or `torch.dtype`, *optional*):
          Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
          dtype is automatically derived from the model's weights.
      custom_pipeline (`str`, *optional*):
  
          <Tip warning={true}>
  
          🧪 This is an experimental feature and may change in the future.
  
          </Tip>
  
          Can be either:
  
              - A string, the *repo id* (for example `hf-internal-testing/diffusers-dummy-pipeline`) of a custom
                pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines
                the custom pipeline.
              - A string, the *file name* of a community pipeline hosted on GitHub under
                [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
                names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
                instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
                current main branch of GitHub.
              - A path to a directory (`./my_pipeline_directory/`) containing a custom pipeline. The directory
                must contain a file called `pipeline.py` that defines the custom pipeline.
  
          For more information on how to load and create custom pipelines, please have a look at [Loading and
          Adding Custom
          Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)
      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.
      resume_download (`bool`, *optional*, defaults to `False`):
          Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
          incompletely downloaded files are deleted.
      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.
      device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
          A map that specifies where each submodule should go. It doesn’t need to be defined for each
          parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
          same device.
  
          Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
          more information about each option see [designing a device
          map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
      max_memory (`Dict`, *optional*):
          A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
          each GPU and the available CPU RAM if unset.
      offload_folder (`str` or `os.PathLike`, *optional*):
          The path to offload weights if device_map contains the value `"disk"`.
      offload_state_dict (`bool`, *optional*):
          If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
          the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
          when there is some disk offload.
      low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
          Speed up model loading only loading the pretrained weights and not initializing the weights. This also
          tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
          Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
          argument to `True` will raise an error.
      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.
      use_onnx (`bool`, *optional*, defaults to `None`):
          If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
          will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
          `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
          with `.onnx` and `.pb`.
      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 diffusers import DiffusionPipeline
  
  >>> # Download pipeline from huggingface.co and cache.
  >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
  
  >>> # Download pipeline that requires an authorization token
  >>> # For more information on access tokens, please refer to this section
  >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
  >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
  
  >>> # Use a different scheduler
  >>> from diffusers import LMSDiscreteScheduler
  
  >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
  >>> pipeline.scheduler = scheduler
  ```
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code: def from_pretrained(
      cls,
      pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
      **kwargs,
  ):
      r"""
      Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.
  
      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:
      - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([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_name_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`].
          torch_dtype (`str` or `torch.dtype`, *optional*):
              Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
              dtype is automatically derived from the model's weights.
          custom_pipeline (`str`, *optional*):
  
              <Tip warning={true}>
  
              🧪 This is an experimental feature and may change in the future.
  
              </Tip>
  
              Can be either:
  
                  - A string, the *repo id* (for example `hf-internal-testing/diffusers-dummy-pipeline`) of a custom
                    pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines
                    the custom pipeline.
                  - A string, the *file name* of a community pipeline hosted on GitHub under
                    [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
                    names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
                    instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
                    current main branch of GitHub.
                  - A path to a directory (`./my_pipeline_directory/`) containing a custom pipeline. The directory
                    must contain a file called `pipeline.py` that defines the custom pipeline.
  
              For more information on how to load and create custom pipelines, please have a look at [Loading and
              Adding Custom
              Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)
          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.
          resume_download (`bool`, *optional*, defaults to `False`):
              Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
              incompletely downloaded files are deleted.
          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.
          device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
              A map that specifies where each submodule should go. It doesn’t need to be defined for each
              parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
              same device.
  
              Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
              more information about each option see [designing a device
              map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
          max_memory (`Dict`, *optional*):
              A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
              each GPU and the available CPU RAM if unset.
          offload_folder (`str` or `os.PathLike`, *optional*):
              The path to offload weights if device_map contains the value `"disk"`.
          offload_state_dict (`bool`, *optional*):
              If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
              the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
              when there is some disk offload.
          low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
              Speed up model loading only loading the pretrained weights and not initializing the weights. This also
              tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
              Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
              argument to `True` will raise an error.
          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.
          use_onnx (`bool`, *optional*, defaults to `None`):
              If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
              will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
              `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
              with `.onnx` and `.pb`.
          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 diffusers import DiffusionPipeline
  
      >>> # Download pipeline from huggingface.co and cache.
      >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
  
      >>> # Download pipeline that requires an authorization token
      >>> # For more information on access tokens, please refer to this section
      >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
      >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
  
      >>> # Use a different scheduler
      >>> from diffusers import LMSDiscreteScheduler
  
      >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
      >>> pipeline.scheduler = scheduler
      ```
      """
      cache_dir = kwargs.pop("cache_dir", None)
      resume_download = kwargs.pop("resume_download", False)
      force_download = kwargs.pop("force_download", False)
      proxies = kwargs.pop("proxies", None)
      local_files_only = kwargs.pop("local_files_only", None)
      token = kwargs.pop("token", None)
      revision = kwargs.pop("revision", None)
      from_flax = kwargs.pop("from_flax", False)
      torch_dtype = kwargs.pop("torch_dtype", None)
      custom_pipeline = kwargs.pop("custom_pipeline", None)
      custom_revision = kwargs.pop("custom_revision", None)
      provider = kwargs.pop("provider", None)
      sess_options = kwargs.pop("sess_options", None)
      device_map = kwargs.pop("device_map", None)
      max_memory = kwargs.pop("max_memory", None)
      offload_folder = kwargs.pop("offload_folder", None)
      offload_state_dict = kwargs.pop("offload_state_dict", False)
      low_cpu_mem_usage = kwargs.pop(
          "low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT
      )
      variant = kwargs.pop("variant", None)
      use_safetensors = kwargs.pop("use_safetensors", None)
      use_onnx = kwargs.pop("use_onnx", None)
      load_connected_pipeline = kwargs.pop(
          "load_connected_pipeline", False
      )
  
      # 1. Download the checkpoints and configs
      # use snapshot download here to get it working from from_pretrained
      if not os.path.isdir(pretrained_model_name_or_path):
          if pretrained_model_name_or_path.count("/") > 1:
              raise ValueError(
                  f'The provided pretrained_model_name_or_path "{pretrained_model_name_or_path}"'
                  " is neither a valid local path nor a valid repo id. Please check the parameter."
              )
          cached_folder = cls.download(
              pretrained_model_name_or_path,
              cache_dir=cache_dir,
              resume_download=resume_download,
              force_download=force_download,
              proxies=proxies,
              local_files_only=local_files_only,
              token=token,
              revision=revision,
              from_flax=from_flax,
              use_safetensors=use_safetensors,
              use_onnx=use_onnx,
              custom_pipeline=custom_pipeline,
              custom_revision=custom_revision,
              variant=variant,
              load_connected_pipeline=load_connected_pipeline,
              **kwargs,
          )
      else:
          cached_folder = pretrained_model_name_or_path
  
      config_dict = cls.load_config(cached_folder)
  
      # pop out "_ignore_files" as it is only needed for download
      config_dict.pop("_ignore_files", None)
  
      # 2. Define which model components should load variants
      # We retrieve the information by matching whether variant
      # model checkpoints exist in the subfolders
      model_variants = {}
      if variant is not None:
          for folder in os.listdir(cached_folder):
              folder_path = os.path.join(cached_folder, folder)
              is_folder = (
                  os.path.isdir(folder_path) and folder in config_dict
              )
              variant_exists = is_folder and any(
                  p.split(".")[1].startswith(variant)
                  for p in os.listdir(folder_path)
              )
              if variant_exists:
                  model_variants[folder] = variant
  
      # 3. Load the pipeline class, if using custom module then load it from the hub
      # if we load from explicit class, let's use it
      custom_class_name = None
      if os.path.isfile(
          os.path.join(cached_folder, f"{custom_pipeline}.py")
      ):
          custom_pipeline = os.path.join(
              cached_folder, f"{custom_pipeline}.py"
          )
      elif isinstance(
          config_dict["_class_name"], (list, tuple)
      ) and os.path.isfile(
          os.path.join(
              cached_folder, f"{config_dict['_class_name'][0]}.py"
          )
      ):
          custom_pipeline = os.path.join(
              cached_folder, f"{config_dict['_class_name'][0]}.py"
          )
          custom_class_name = config_dict["_class_name"][1]
  
      pipeline_class = _get_pipeline_class(
          cls,
          config_dict,
          load_connected_pipeline=load_connected_pipeline,
          custom_pipeline=custom_pipeline,
          class_name=custom_class_name,
          cache_dir=cache_dir,
          revision=custom_revision,
      )
  
      # DEPRECATED: To be removed in 1.0.0
      if (
          pipeline_class.__name__ == "StableDiffusionInpaintPipeline"
          and version.parse(
              version.parse(
                  config_dict["_diffusers_version"]
              ).base_version
          )
          <= version.parse("0.5.1")
      ):
          from diffusers import (
              StableDiffusionInpaintPipeline,
              StableDiffusionInpaintPipelineLegacy,
          )
  
          pipeline_class = StableDiffusionInpaintPipelineLegacy
  
          deprecation_message = (
              "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
              f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
              " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
              " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
              f" checkpoint {pretrained_model_name_or_path} to the format of"
              " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
              " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
          )
          deprecate(
              "StableDiffusionInpaintPipelineLegacy",
              "1.0.0",
              deprecation_message,
              standard_warn=False,
          )
  
      # 4. Define expected modules given pipeline signature
      # and define non-None initialized modules (=`init_kwargs`)
  
      # some modules can be passed directly to the init
      # in this case they are already instantiated in `kwargs`
      # extract them here
      expected_modules, optional_kwargs = cls._get_signature_keys(
          pipeline_class
      )
      passed_class_obj = {
          k: kwargs.pop(k) for k in expected_modules if k in kwargs
      }
      passed_pipe_kwargs = {
          k: kwargs.pop(k) for k in optional_kwargs if k in kwargs
      }
  
      init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(
          config_dict, **kwargs
      )
  
      # define init kwargs and make sure that optional component modules are filtered out
      init_kwargs = {
          k: init_dict.pop(k)
          for k in optional_kwargs
          if k in init_dict
          and k not in pipeline_class._optional_components
      }
      init_kwargs = {**init_kwargs, **passed_pipe_kwargs}
  
      # remove `null` components
      def load_module(name, value):
          if value[0] is None:
              return False
          if (
              name in passed_class_obj
              and passed_class_obj[name] is None
          ):
              return False
          return True
  
      init_dict = {
          k: v for k, v in init_dict.items() if load_module(k, v)
      }
  
      # Special case: safety_checker must be loaded separately when using `from_flax`
      if (
          from_flax
          and "safety_checker" in init_dict
          and "safety_checker" not in passed_class_obj
      ):
          raise NotImplementedError(
              "The safety checker cannot be automatically loaded when loading weights `from_flax`."
              " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker"
              " separately if you need it."
          )
  
      # 5. Throw nice warnings / errors for fast accelerate loading
      if len(unused_kwargs) > 0:
          logger.warning(
              f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
          )
  
      if low_cpu_mem_usage and not is_accelerate_available():
          low_cpu_mem_usage = False
          logger.warning(
              "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
              " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
              " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
              " install accelerate\n```\n."
          )
  
      if device_map is not None and not is_torch_version(">=", "1.9.0"):
          raise NotImplementedError(
              "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
              " `device_map=None`."
          )
  
      if low_cpu_mem_usage is True and not is_torch_version(
          ">=", "1.9.0"
      ):
          raise NotImplementedError(
              "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
              " `low_cpu_mem_usage=False`."
          )
  
      if low_cpu_mem_usage is False and device_map is not None:
          raise ValueError(
              f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
              " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
          )
  
      # import it here to avoid circular import
      from diffusers import pipelines
  
      # 6. Load each module in the pipeline
      for name, (library_name, class_name) in logging.tqdm(
          init_dict.items(), desc="Loading pipeline components..."
      ):
          # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
          class_name = (
              class_name[4:]
              if class_name.startswith("Flax")
              else class_name
          )
  
          # 6.2 Define all importable classes
          is_pipeline_module = hasattr(pipelines, library_name)
          importable_classes = ALL_IMPORTABLE_CLASSES
          loaded_sub_model = None
  
          # 6.3 Use passed sub model or load class_name from library_name
          if name in passed_class_obj:
              # if the model is in a pipeline module, then we load it from the pipeline
              # check that passed_class_obj has correct parent class
              maybe_raise_or_warn(
                  library_name,
                  library,
                  class_name,
                  importable_classes,
                  passed_class_obj,
                  name,
                  is_pipeline_module,
              )
  
              loaded_sub_model = passed_class_obj[name]
          else:
              # load sub model
              loaded_sub_model = load_sub_model(
                  library_name=library_name,
                  class_name=class_name,
                  importable_classes=importable_classes,
                  pipelines=pipelines,
                  is_pipeline_module=is_pipeline_module,
                  pipeline_class=pipeline_class,
                  torch_dtype=torch_dtype,
                  provider=provider,
                  sess_options=sess_options,
                  device_map=device_map,
                  max_memory=max_memory,
                  offload_folder=offload_folder,
                  offload_state_dict=offload_state_dict,
                  model_variants=model_variants,
                  name=name,
                  from_flax=from_flax,
                  variant=variant,
                  low_cpu_mem_usage=low_cpu_mem_usage,
                  cached_folder=cached_folder,
                  revision=revision,
              )
              logger.info(
                  f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
              )
  
          init_kwargs[
              name
          ] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
  
      if pipeline_class._load_connected_pipes and os.path.isfile(
          os.path.join(cached_folder, "README.md")
      ):
          modelcard = ModelCard.load(
              os.path.join(cached_folder, "README.md")
          )
          connected_pipes = {
              prefix: getattr(modelcard.data, prefix, [None])[0]
              for prefix in CONNECTED_PIPES_KEYS
          }
          load_kwargs = {
              "cache_dir": cache_dir,
              "resume_download": resume_download,
              "force_download": force_download,
              "proxies": proxies,
              "local_files_only": local_files_only,
              "token": token,
              "revision": revision,
              "torch_dtype": torch_dtype,
              "custom_pipeline": custom_pipeline,
              "custom_revision": custom_revision,
              "provider": provider,
              "sess_options": sess_options,
              "device_map": device_map,
              "max_memory": max_memory,
              "offload_folder": offload_folder,
              "offload_state_dict": offload_state_dict,
              "low_cpu_mem_usage": low_cpu_mem_usage,
              "variant": variant,
              "use_safetensors": use_safetensors,
          }
  
          def get_connected_passed_kwargs(prefix):
              connected_passed_class_obj = {
                  k.replace(f"{prefix}_", ""): w
                  for k, w in passed_class_obj.items()
                  if k.split("_")[0] == prefix
              }
              connected_passed_pipe_kwargs = {
                  k.replace(f"{prefix}_", ""): w
                  for k, w in passed_pipe_kwargs.items()
                  if k.split("_")[0] == prefix
              }
  
              connected_passed_kwargs = {
                  **connected_passed_class_obj,
                  **connected_passed_pipe_kwargs,
              }
              return connected_passed_kwargs
  
          connected_pipes = {
              prefix: DiffusionPipeline.from_pretrained(
                  repo_id,
                  **load_kwargs.copy(),
                  **get_connected_passed_kwargs(prefix),
              )
              for prefix, repo_id in connected_pipes.items()
              if repo_id is not None
          }
  
          for prefix, connected_pipe in connected_pipes.items():
              # add connected pipes to `init_kwargs` with <prefix>_<component_name>, e.g. "prior_text_encoder"
              init_kwargs.update(
                  {
                      "_".join([prefix, name]): component
                      for name, component in connected_pipe.components.items()
                  }
              )
  
      # 7. Potentially add passed objects if expected
      missing_modules = set(expected_modules) - set(init_kwargs.keys())
      passed_modules = list(passed_class_obj.keys())
      optional_modules = pipeline_class._optional_components
      if len(missing_modules) > 0 and missing_modules <= set(
          passed_modules + optional_modules
      ):
          for module in missing_modules:
              init_kwargs[module] = passed_class_obj.get(module, None)
      elif len(missing_modules) > 0:
          passed_modules = (
              set(
                  list(init_kwargs.keys())
                  + list(passed_class_obj.keys())
              )
              - optional_kwargs
          )
          raise ValueError(
              f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
          )
  
      # 8. Instantiate the pipeline
      model = pipeline_class(**init_kwargs)
  
      # 9. Save where the model was instantiated from
      model.register_to_config(
          _name_or_path=pretrained_model_name_or_path
      )
      return model
  
imports: import fnmatch
  import importlib
  import inspect
  import os
  import re
  import sys
  import warnings
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import requests
  import torch
  from huggingface_hub import ModelCard, create_repo, hf_hub_download, model_info, snapshot_download
  from huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args
  from packaging import version
  from requests.exceptions import HTTPError
  from tqdm.auto import tqdm
  from ..None import __version__
  from ..configuration_utils import ConfigMixin
  from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
  from ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
  from ..utils import CONFIG_NAME, DEPRECATED_REVISION_ARGS, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, deprecate, get_class_from_dynamic_module, is_accelerate_available, is_accelerate_version, is_peft_available, is_torch_version, is_transformers_available, logging, numpy_to_pil
  from ..utils.torch_utils import is_compiled_module
  from ..utils import FLAX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, PushToHubMixin
label_desc: Use image node_id 7 for calling the DiffusionPipeline obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: model, True, False, False, connected_passed_kwargs