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Configuration

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Configuration

Schedulers from SchedulerMixin and models from ModelMixin inherit from ConfigMixin which stores all the parameters that are passed to their respective __init__ methods in a JSON-configuration file.

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

ConfigMixin

class diffusers.ConfigMixin

< >

( )

Base class for all configuration classes. All configuration parameters are stored under self.config. Also provides the from_config() and 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 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).

load_config

< >

( pretrained_model_name_or_path: Union return_unused_kwargs = False return_commit_hash = False **kwargs ) → dict

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 save_config().
  • 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.
  • 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.

Load a model or scheduler configuration.

from_config

< >

( config: Union = None return_unused_kwargs = False **kwargs ) → ModelMixin or SchedulerMixin

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.

Instantiate a Python class from a config dictionary.

Examples:

>>> 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)

save_config

< >

( save_directory: Union push_to_hub: bool = False **kwargs )

Parameters

  • 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 push_to_hub() method.

Save a configuration object to the directory specified in save_directory so that it can be reloaded using the from_config() class method.

to_json_file

< >

( json_file_path: Union )

Parameters

  • json_file_path (str or os.PathLike) — Path to the JSON file to save a configuration instance’s parameters.

Save the configuration instance’s parameters to a JSON file.

to_json_string

< >

( ) → str

Returns

str

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

Serializes the configuration instance to a JSON string.

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