repo_description: 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
repo_topics: deep-learning diffusion flux image-generation image2image image2video latent-diffusion-models pytorch qwen-image score-based-generative-modeling stable-diffusion stable-diffusion-diffusers text2image text2video video2video
code: # coding=utf-8
  # Copyright 2025 The HuggingFace Inc. team.
  # Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ConfigMixin base class and utilities."""
  
  import dataclasses
  import functools
  import importlib
  import inspect
  import json
  import os
  import re
  from collections import OrderedDict
  from pathlib import Path
  from typing import Any
  
  import numpy as np
  from huggingface_hub import DDUFEntry, create_repo, hf_hub_download
  from huggingface_hub.utils import (
      EntryNotFoundError,
      HfHubHTTPError,
      RepositoryNotFoundError,
      RevisionNotFoundError,
      validate_hf_hub_args,
  )
  from typing_extensions import Self
  
  from . import __version__
  from .utils import (
      HUGGINGFACE_CO_RESOLVE_ENDPOINT,
      DummyObject,
      deprecate,
      extract_commit_hash,
      http_user_agent,
      logging,
  )
  
  
  logger = logging.get_logger(__name__)
  
  _re_configuration_file = re.compile(r"config\.(.*)\.json")
  
  
  class FrozenDict(OrderedDict):
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          for key, value in self.items():
              setattr(self, key, value)
  
          self.__frozen = True
  
      def __delitem__(self, *args, **kwargs):
          raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
  
      def setdefault(self, *args, **kwargs):
          raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
  
      def pop(self, *args, **kwargs):
          raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
  
      def update(self, *args, **kwargs):
          raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
  
      def __setattr__(self, name, value):
          if hasattr(self, "__frozen") and self.__frozen:
              raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
          super().__setattr__(name, value)
  
      def __setitem__(self, name, value):
          if hasattr(self, "__frozen") and self.__frozen:
              raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
          super().__setitem__(name, value)
  
  
  class ConfigMixin:
      r"""
      Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also
      provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and
      saving classes that inherit from [`ConfigMixin`].
  
      Class attributes:
          - **config_name** (`str`) -- A filename under which the config should stored when calling
            [`~ConfigMixin.save_config`] (should be overridden by parent class).
          - **ignore_for_config** (`list[str]`) -- A list of attributes that should not be saved in the config (should be
            overridden by subclass).
          - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
          - **_deprecated_kwargs** (`list[str]`) -- Keyword arguments that are deprecated. Note that the `init` function
            should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
            subclass).
      """
  
      config_name = None
      ignore_for_config = []
      has_compatibles = False
  
      _deprecated_kwargs = []
      _auto_class = None
  
      @classmethod
      def register_for_auto_class(cls, auto_class="AutoModel"):
          """
          Register this class with the given auto class so that it can be loaded with `AutoModel.from_pretrained(...,
          trust_remote_code=True)`.
  
          When the config is saved, the resulting `config.json` will include an `auto_map` entry mapping the auto class
          to this class's module and class name.
  
          Args:
              auto_class (`str` or type, *optional*, defaults to `"AutoModel"`):
                  The auto class to register this class with. Can be a string (e.g. `"AutoModel"`) or the class itself.
                  Currently only `"AutoModel"` is supported.
  
          Example:
  
          ```python
          from diffusers import ModelMixin, ConfigMixin
  
  
          class MyCustomModel(ModelMixin, ConfigMixin): ...
  
  
          MyCustomModel.register_for_auto_class("AutoModel")
          ```
          """
          if auto_class != "AutoModel":
              raise ValueError(f"Only 'AutoModel' is supported, got '{auto_class}'.")
  
          cls._auto_class = auto_class
  
      def register_to_config(self, **kwargs):
          if self.config_name is None:
              raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
          # Special case for `kwargs` used in deprecation warning added to schedulers
          # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
          # or solve in a more general way.
          kwargs.pop("kwargs", None)
  
          if not hasattr(self, "_internal_dict"):
              internal_dict = kwargs
          else:
              previous_dict = dict(self._internal_dict)
              internal_dict = {**self._internal_dict, **kwargs}
              logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
  
          self._internal_dict = FrozenDict(internal_dict)
  
      def __getattr__(self, name: str) -> Any:
          """The only reason we overwrite `getattr` here is to gracefully deprecate accessing
          config attributes directly. See https://github.com/huggingface/diffusers/pull/3129
  
          This function is mostly copied from PyTorch's __getattr__ overwrite:
          https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
          """
  
          is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name)
          is_attribute = name in self.__dict__
  
          if is_in_config and not is_attribute:
              deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'scheduler.config.{name}'."
              deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
              return self._internal_dict[name]
  
          raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
  
      def save_config(self, save_directory: str | os.PathLike, push_to_hub: bool = False, **kwargs):
          """
          Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the
          [`~ConfigMixin.from_config`] class method.
  
          Args:
              save_directory (`str` or `os.PathLike`):
                  Directory where the configuration JSON file is saved (will be created if it does not exist).
              push_to_hub (`bool`, *optional*, defaults to `False`):
                  Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
                  repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                  namespace).
              kwargs (`dict[str, Any]`, *optional*):
                  Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
          """
          if os.path.isfile(save_directory):
              raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
  
          os.makedirs(save_directory, exist_ok=True)
  
          # If we save using the predefined names, we can load using `from_config`
          output_config_file = os.path.join(save_directory, self.config_name)
  
          self.to_json_file(output_config_file)
          logger.info(f"Configuration saved in {output_config_file}")
  
          if push_to_hub:
              commit_message = kwargs.pop("commit_message", None)
              private = kwargs.pop("private", None)
              create_pr = kwargs.pop("create_pr", False)
              token = kwargs.pop("token", None)
              repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
              repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
              subfolder = kwargs.pop("subfolder", None)
  
              self._upload_folder(
                  save_directory,
                  repo_id,
                  token=token,
                  commit_message=commit_message,
                  create_pr=create_pr,
                  subfolder=subfolder,
              )
  
      @classmethod
      def from_config(
          cls, config: FrozenDict | dict[str, Any] = None, return_unused_kwargs=False, **kwargs
      ) -> Self | tuple[Self, dict[str, Any]]:
          r"""
          Instantiate a Python class from a config dictionary.
  
          Parameters:
              config (`dict[str, Any]`):
                  A config dictionary from which the Python class is instantiated. Make sure to only load configuration
                  files of compatible classes.
              return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                  Whether kwargs that are not consumed by the Python class should be returned or not.
              kwargs (remaining dictionary of keyword arguments, *optional*):
                  Can be used to update the configuration object (after it is loaded) and initiate the Python class.
                  `**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually
                  overwrite the same named arguments in `config`.
  
          Returns:
              [`ModelMixin`] or [`SchedulerMixin`]:
                  A model or scheduler object instantiated from a config dictionary.
  
          Examples:
  
          ```python
          >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler
  
          >>> # Download scheduler from huggingface.co and cache.
          >>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")
  
          >>> # Instantiate DDIM scheduler class with same config as DDPM
          >>> scheduler = DDIMScheduler.from_config(scheduler.config)
  
          >>> # Instantiate PNDM scheduler class with same config as DDPM
          >>> scheduler = PNDMScheduler.from_config(scheduler.config)
          ```
          """
          # <===== TO BE REMOVED WITH DEPRECATION
          # TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated
          if "pretrained_model_name_or_path" in kwargs:
              config = kwargs.pop("pretrained_model_name_or_path")
  
          if config is None:
              raise ValueError("Please make sure to provide a config as the first positional argument.")
          # ======>
  
          if not isinstance(config, dict):
              deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`."
              if "Scheduler" in cls.__name__:
                  deprecation_message += (
                      f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead."
                      " Otherwise, please make sure to pass a configuration dictionary instead. This functionality will"
                      " be removed in v1.0.0."
                  )
              elif "Model" in cls.__name__:
                  deprecation_message += (
                      f"If you were trying to load a model, please use {cls}.load_config(...) followed by"
                      f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary"
                      " instead. This functionality will be removed in v1.0.0."
                  )
              deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
              config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)
  
          init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)
  
          # Allow dtype to be specified on initialization
          if "dtype" in unused_kwargs:
              init_dict["dtype"] = unused_kwargs.pop("dtype")
  
          # add possible deprecated kwargs
          for deprecated_kwarg in cls._deprecated_kwargs:
              if deprecated_kwarg in unused_kwargs:
                  init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)
  
          # Return model and optionally state and/or unused_kwargs
          model = cls(**init_dict)
  
          # make sure to also save config parameters that might be used for compatible classes
          # update _class_name
          if "_class_name" in hidden_dict:
              hidden_dict["_class_name"] = cls.__name__
  
          model.register_to_config(**hidden_dict)
  
          # add hidden kwargs of compatible classes to unused_kwargs
          unused_kwargs = {**unused_kwargs, **hidden_dict}
  
          if return_unused_kwargs:
              return (model, unused_kwargs)
          else:
              return model
  
      @classmethod
      def get_config_dict(cls, *args, **kwargs):
          deprecation_message = (
              f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be"
              " removed in version v1.0.0"
          )
          deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False)
          return cls.load_config(*args, **kwargs)
  
      @classmethod
      @validate_hf_hub_args
      def load_config(
          cls,
          pretrained_model_name_or_path: str | os.PathLike,
          return_unused_kwargs=False,
          return_commit_hash=False,
          **kwargs,
      ) -> tuple[dict[str, Any], dict[str, Any]]:
          r"""
          Load a model or scheduler configuration.
  
          Parameters:
              pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
                  Can be either:
  
                      - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                        the Hub.
                      - A path to a *directory* (for example `./my_model_directory`) containing model weights saved with
                        [`~ConfigMixin.save_config`].
  
              cache_dir (`str | os.PathLike`, *optional*):
                  Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                  is not used.
              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.
              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.
              subfolder (`str`, *optional*, defaults to `""`):
                  The subfolder location of a model file within a larger model repository on the Hub or locally.
              return_unused_kwargs (`bool`, *optional*, defaults to `False):
                  Whether unused keyword arguments of the config are returned.
              return_commit_hash (`bool`, *optional*, defaults to `False):
                  Whether the `commit_hash` of the loaded configuration are returned.
  
          Returns:
              `dict`:
                  A dictionary of all the parameters stored in a JSON configuration file.
  
          """
          cache_dir = kwargs.pop("cache_dir", None)
          local_dir = kwargs.pop("local_dir", None)
          local_dir_use_symlinks = kwargs.pop("local_dir_use_symlinks", "auto")
          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)
          _ = kwargs.pop("mirror", None)
          subfolder = kwargs.pop("subfolder", None)
          user_agent = kwargs.pop("user_agent", {})
          dduf_entries: dict[str, DDUFEntry] | None = kwargs.pop("dduf_entries", None)
  
          user_agent = {**user_agent, "file_type": "config"}
          user_agent = http_user_agent(user_agent)
  
          pretrained_model_name_or_path = str(pretrained_model_name_or_path)
  
          if cls.config_name is None:
              raise ValueError(
                  "`self.config_name` is not defined. Note that one should not load a config from "
                  "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
              )
          # Custom path for now
          if dduf_entries:
              if subfolder is not None:
                  raise ValueError(
                      "DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
                      "Please check the DDUF structure"
                  )
              config_file = cls._get_config_file_from_dduf(pretrained_model_name_or_path, dduf_entries)
          elif os.path.isfile(pretrained_model_name_or_path):
              config_file = pretrained_model_name_or_path
          elif os.path.isdir(pretrained_model_name_or_path):
              if subfolder is not None and os.path.isfile(
                  os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
              ):
                  config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
              elif os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
                  # Load from a PyTorch checkpoint
                  config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
              else:
                  raise EnvironmentError(
                      f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
                  )
          else:
              try:
                  # Load from URL or cache if already cached
                  config_file = hf_hub_download(
                      pretrained_model_name_or_path,
                      filename=cls.config_name,
                      cache_dir=cache_dir,
                      force_download=force_download,
                      proxies=proxies,
                      local_files_only=local_files_only,
                      token=token,
                      user_agent=user_agent,
                      subfolder=subfolder,
                      revision=revision,
                      local_dir=local_dir,
                      local_dir_use_symlinks=local_dir_use_symlinks,
                  )
              except RepositoryNotFoundError:
                  raise EnvironmentError(
                      f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
                      " listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
                      " token having permission to this repo with `token` or log in with `hf auth login`."
                  )
              except RevisionNotFoundError:
                  raise EnvironmentError(
                      f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
                      " this model name. Check the model page at"
                      f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
                  )
              except EntryNotFoundError:
                  raise EnvironmentError(
                      f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
                  )
              except HfHubHTTPError as err:
                  raise EnvironmentError(
                      "There was a specific connection error when trying to load"
                      f" {pretrained_model_name_or_path}:\n{err}"
                  )
              except ValueError:
                  raise EnvironmentError(
                      f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                      f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                      f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
                      " run the library in offline mode at"
                      " 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
                  )
              except EnvironmentError:
                  raise EnvironmentError(
                      f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                      "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                      f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                      f"containing a {cls.config_name} file"
                  )
          try:
              config_dict = cls._dict_from_json_file(config_file, dduf_entries=dduf_entries)
  
              commit_hash = extract_commit_hash(config_file)
          except (json.JSONDecodeError, UnicodeDecodeError):
              raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
  
          if not (return_unused_kwargs or return_commit_hash):
              return config_dict
  
          outputs = (config_dict,)
  
          if return_unused_kwargs:
              outputs += (kwargs,)
  
          if return_commit_hash:
              outputs += (commit_hash,)
  
          return outputs
  
      @staticmethod
      def _get_init_keys(input_class):
          return set(dict(inspect.signature(input_class.__init__).parameters).keys())
  
      @classmethod
      def extract_init_dict(cls, config_dict, **kwargs):
          # Skip keys that were not present in the original config, so default __init__ values were used
          used_defaults = config_dict.get("_use_default_values", [])
          config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != "_use_default_values"}
  
          # 0. Copy origin config dict
          original_dict = dict(config_dict.items())
  
          # 1. Retrieve expected config attributes from __init__ signature
          expected_keys = cls._get_init_keys(cls)
          expected_keys.remove("self")
          # remove general kwargs if present in dict
          if "kwargs" in expected_keys:
              expected_keys.remove("kwargs")
          # remove flax internal keys
          if hasattr(cls, "_flax_internal_args"):
              for arg in cls._flax_internal_args:
                  expected_keys.remove(arg)
  
          # 2. Remove attributes that cannot be expected from expected config attributes
          # remove keys to be ignored
          if len(cls.ignore_for_config) > 0:
              expected_keys = expected_keys - set(cls.ignore_for_config)
  
          # load diffusers library to import compatible and original scheduler
          diffusers_library = importlib.import_module(__name__.split(".")[0])
  
          if cls.has_compatibles:
              compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]
          else:
              compatible_classes = []
  
          expected_keys_comp_cls = set()
          for c in compatible_classes:
              expected_keys_c = cls._get_init_keys(c)
              expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
          expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
          config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}
  
          # remove attributes from orig class that cannot be expected
          orig_cls_name = config_dict.pop("_class_name", cls.__name__)
          if (
              isinstance(orig_cls_name, str)
              and orig_cls_name != cls.__name__
              and hasattr(diffusers_library, orig_cls_name)
          ):
              orig_cls = getattr(diffusers_library, orig_cls_name)
              unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
              config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}
          elif not isinstance(orig_cls_name, str) and not isinstance(orig_cls_name, (list, tuple)):
              raise ValueError(
                  "Make sure that the `_class_name` is of type string or list of string (for custom pipelines)."
              )
  
          # remove private attributes
          config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
  
          # remove quantization_config
          config_dict = {k: v for k, v in config_dict.items() if k != "quantization_config"}
  
          # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
          init_dict = {}
          for key in expected_keys:
              # if config param is passed to kwarg and is present in config dict
              # it should overwrite existing config dict key
              if key in kwargs and key in config_dict:
                  config_dict[key] = kwargs.pop(key)
  
              if key in kwargs:
                  # overwrite key
                  init_dict[key] = kwargs.pop(key)
              elif key in config_dict:
                  # use value from config dict
                  init_dict[key] = config_dict.pop(key)
  
          # 4. Give nice warning if unexpected values have been passed
          if len(config_dict) > 0:
              logger.warning(
                  f"The config attributes {config_dict} were passed to {cls.__name__}, "
                  "but are not expected and will be ignored. Please verify your "
                  f"{cls.config_name} configuration file."
              )
  
          # 5. Give nice info if config attributes are initialized to default because they have not been passed
          passed_keys = set(init_dict.keys())
          if len(expected_keys - passed_keys) > 0:
              logger.info(
                  f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
              )
  
          # 6. Define unused keyword arguments
          unused_kwargs = {**config_dict, **kwargs}
  
          # 7. Define "hidden" config parameters that were saved for compatible classes
          hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}
  
          return init_dict, unused_kwargs, hidden_config_dict
  
      @classmethod
      def _dict_from_json_file(cls, json_file: str | os.PathLike, dduf_entries: dict[str, DDUFEntry] | None = None):
          if dduf_entries:
              text = dduf_entries[json_file].read_text()
          else:
              with open(json_file, "r", encoding="utf-8") as reader:
                  text = reader.read()
          return json.loads(text)
  
      def __repr__(self):
          return f"{self.__class__.__name__} {self.to_json_string()}"
  
      @property
      def config(self) -> dict[str, Any]:
          """
          Returns the config of the class as a frozen dictionary
  
          Returns:
              `dict[str, Any]`: Config of the class.
          """
          return self._internal_dict
  
      def to_json_string(self) -> str:
          """
          Serializes the configuration instance to a JSON string.
  
          Returns:
              `str`:
                  String containing all the attributes that make up the configuration instance in JSON format.
          """
          config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
          config_dict["_class_name"] = self.__class__.__name__
          config_dict["_diffusers_version"] = __version__
  
          def to_json_saveable(value):
              if isinstance(value, np.ndarray):
                  value = value.tolist()
              elif isinstance(value, Path):
                  value = value.as_posix()
              elif hasattr(value, "to_dict") and callable(value.to_dict):
                  value = value.to_dict()
              elif isinstance(value, list):
                  value = [to_json_saveable(v) for v in value]
              return value
  
          if "quantization_config" in config_dict:
              config_dict["quantization_config"] = (
                  config_dict.quantization_config.to_dict()
                  if not isinstance(config_dict.quantization_config, dict)
                  else config_dict.quantization_config
              )
  
          config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}
          # Don't save "_ignore_files" or "_use_default_values"
          config_dict.pop("_ignore_files", None)
          config_dict.pop("_use_default_values", None)
          # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
          _ = config_dict.pop("_pre_quantization_dtype", None)
  
          if getattr(self, "_auto_class", None) is not None:
              module = self.__class__.__module__.split(".")[-1]
              auto_map = config_dict.get("auto_map", {})
              auto_map[self._auto_class] = f"{module}.{self.__class__.__name__}"
              config_dict["auto_map"] = auto_map
  
          return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
  
      def to_json_file(self, json_file_path: str | os.PathLike):
          """
          Save the configuration instance's parameters to a JSON file.
  
          Args:
              json_file_path (`str` or `os.PathLike`):
                  Path to the JSON file to save a configuration instance's parameters.
          """
          with open(json_file_path, "w", encoding="utf-8") as writer:
              writer.write(self.to_json_string())
  
      @classmethod
      def _get_config_file_from_dduf(cls, pretrained_model_name_or_path: str, dduf_entries: dict[str, DDUFEntry]):
          # paths inside a DDUF file must always be "/"
          config_file = (
              cls.config_name
              if pretrained_model_name_or_path == ""
              else "/".join([pretrained_model_name_or_path, cls.config_name])
          )
          if config_file not in dduf_entries:
              raise ValueError(
                  f"We did not manage to find the file {config_file} in the dduf file. We only have the following files {dduf_entries.keys()}"
              )
          return config_file
  
  
  def register_to_config(init):
      r"""
      Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
      automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
      shouldn't be registered in the config, use the `ignore_for_config` class variable
  
      Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
      """
  
      @functools.wraps(init)
      def inner_init(self, *args, **kwargs):
          # Ignore private kwargs in the init.
          init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
          config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")}
          if not isinstance(self, ConfigMixin):
              raise RuntimeError(
                  f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
                  "not inherit from `ConfigMixin`."
              )
  
          ignore = getattr(self, "ignore_for_config", [])
          # Get positional arguments aligned with kwargs
          new_kwargs = {}
          signature = inspect.signature(init)
          parameters = {
              name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
          }
          for arg, name in zip(args, parameters.keys()):
              new_kwargs[name] = arg
  
          # Then add all kwargs
          new_kwargs.update(
              {
                  k: init_kwargs.get(k, default)
                  for k, default in parameters.items()
                  if k not in ignore and k not in new_kwargs
              }
          )
  
          # Take note of the parameters that were not present in the loaded config
          if len(set(new_kwargs.keys()) - set(init_kwargs)) > 0:
              new_kwargs["_use_default_values"] = list(set(new_kwargs.keys()) - set(init_kwargs))
  
          new_kwargs = {**config_init_kwargs, **new_kwargs}
          getattr(self, "register_to_config")(**new_kwargs)
          init(self, *args, **init_kwargs)
  
      return inner_init
  
  
  def flax_register_to_config(cls):
      original_init = cls.__init__
  
      @functools.wraps(original_init)
      def init(self, *args, **kwargs):
          if not isinstance(self, ConfigMixin):
              raise RuntimeError(
                  f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
                  "not inherit from `ConfigMixin`."
              )
  
          # Ignore private kwargs in the init. Retrieve all passed attributes
          init_kwargs = dict(kwargs.items())
  
          # Retrieve default values
          fields = dataclasses.fields(self)
          default_kwargs = {}
          for field in fields:
              # ignore flax specific attributes
              if field.name in self._flax_internal_args:
                  continue
              if type(field.default) == dataclasses._MISSING_TYPE:
                  default_kwargs[field.name] = None
              else:
                  default_kwargs[field.name] = getattr(self, field.name)
  
          # Make sure init_kwargs override default kwargs
          new_kwargs = {**default_kwargs, **init_kwargs}
          # dtype should be part of `init_kwargs`, but not `new_kwargs`
          if "dtype" in new_kwargs:
              new_kwargs.pop("dtype")
  
          # Get positional arguments aligned with kwargs
          for i, arg in enumerate(args):
              name = fields[i].name
              new_kwargs[name] = arg
  
          # Take note of the parameters that were not present in the loaded config
          if len(set(new_kwargs.keys()) - set(init_kwargs)) > 0:
              new_kwargs["_use_default_values"] = list(set(new_kwargs.keys()) - set(init_kwargs))
  
          getattr(self, "register_to_config")(**new_kwargs)
          original_init(self, *args, **kwargs)
  
      cls.__init__ = init
      return cls
  
  
  class LegacyConfigMixin(ConfigMixin):
      r"""
      A subclass of `ConfigMixin` to resolve class mapping from legacy classes (like `Transformer2DModel`) to more
      pipeline-specific classes (like `DiTTransformer2DModel`).
      """
  
      @classmethod
      def from_config(cls, config: FrozenDict | dict[str, Any] = None, return_unused_kwargs=False, **kwargs):
          # To prevent dependency import problem.
          from .models.model_loading_utils import _fetch_remapped_cls_from_config
  
          # resolve remapping
          remapped_class = _fetch_remapped_cls_from_config(config, cls)
  
          if remapped_class is cls:
              return super(LegacyConfigMixin, remapped_class).from_config(config, return_unused_kwargs, **kwargs)
          else:
              return remapped_class.from_config(config, return_unused_kwargs, **kwargs)
  