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The handling of configurations in Diffusers is with the ConfigMixin class.

class diffusers.ConfigMixin

< >

( )

Base class for all configuration classes. Stores all configuration parameters under self.config Also handles all methods for loading/downloading/saving classes inheriting from ConfigMixin with

Class attributes:

  • config_name (str) — A filename under which the config should stored when calling 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: typing.Union[diffusers.configuration_utils.FrozenDict, typing.Dict[str, typing.Any]] = None return_unused_kwargs = False **kwargs )


  • config (Dict[str, Any]) — A config dictionary from which the Python class will be 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 being loaded) and initiate the Python class. **kwargs will be directly passed to the underlying scheduler/model’s __init__ method and eventually overwrite same named arguments of config.

Instantiate a Python class from a config dictionary


>>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler

>>> # Download scheduler from 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)


< >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] return_unused_kwargs = False **kwargs )


  • pretrained_model_name_or_path (str or os.PathLike, optional) — Can be either:

    • A string, the model id of a model repo on Valid model ids should have an organization name, like google/ddpm-celebahq-256.
    • A path to a directory containing model weights saved using save_config(), e.g., ./my_model_directory/.
  • cache_dir (Union[str, os.PathLike], optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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.
  • resume_download (bool, optional, defaults to False) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': '', 'http://hostname': ''}. 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 or not to only look at local files (i.e., do not try to download the model).
  • use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running transformers-cli login (stored in ~/.huggingface).
  • 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, since we use a git-based system for storing models and other artifacts on, so revision can be any identifier allowed by git.
  • subfolder (str, optional, defaults to "") — In case the relevant files are located inside a subfolder of the model repo (either remote in or downloaded locally), you can specify the folder name here.

Instantiate a Python class from a config dictionary

It is required to be logged in (huggingface-cli login) when you want to use private or gated models.

Activate the special “offline-mode” to use this method in a firewalled environment.


< >

( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )


  • save_directory (str or os.PathLike) — Directory where the configuration JSON file will be saved (will be created if it does not exist).

Save a configuration object to the directory save_directory, so that it can be re-loaded using the from_config() class method.


< >

( json_file_path: typing.Union[str, os.PathLike] )


  • json_file_path (str or os.PathLike) — Path to the JSON file in which this configuration instance’s parameters will be saved.

Save this instance to a JSON file.


< >

( ) str



String containing all the attributes that make up this configuration instance in JSON format.

Serializes this instance to a JSON string.

Under further construction 🚧, open a PR if you want to contribute!