#
Configuration

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

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class diffusers.ConfigMixin

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( )

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

- from_config()
- save_config()

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).

####
from_config

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( config: typing.Union[diffusers.configuration_utils.FrozenDict, typing.Dict[str, typing.Any]] = None return_unused_kwargs = False **kwargs )

Parameters

- 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

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)

####
load_config

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

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`, optional) — Can be either:
  - A string, the model id of a model repo on huggingface.co. 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': '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 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 huggingface.co, 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 huggingface.co 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_config

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

Parameters

- 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.

####
to_json_file

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

Parameters

- 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.

####
to_json_string

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( ) → `str`

Returns

`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!