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
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 theinit
function should only have akwargs
argument if at least one argument is deprecated (should be overridden by subclass).
load_config
< source >( pretrained_model_name_or_path: Union return_unused_kwargs = False return_commit_hash = False **kwargs ) → dict
Parameters
- pretrained_model_name_or_path (
str
oros.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().
- A string, the model id (for example
- 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 toFalse
) — 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 toFalse
) — Whether or not to resume downloading the model weights and configuration files. If set toFalse
, 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 toFalse
) — Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. - local_files_only (
bool
, optional, defaults toFalse
) — Whether to only load local model weights and configuration files or not. If set toTrue
, 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. IfTrue
, the token generated fromdiffusers-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 toFalse) -- 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
< source >( 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 toFalse
) — 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 inconfig
.
Returns
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
< source >( save_directory: Union push_to_hub: bool = False **kwargs )
Parameters
- save_directory (
str
oros.PathLike
) — Directory where the configuration JSON file is saved (will be created if it does not exist). - push_to_hub (
bool
, optional, defaults toFalse
) — 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 withrepo_id
(will default to the name ofsave_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
< source >( json_file_path: Union )
Save the configuration instance’s parameters to a JSON file.
to_json_string
< source >( ) → 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.