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
.
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:
str
) — A filename under which the config should stored when calling
save_config() (should be overridden by parent class).List[str]
) — A list of attributes that should not be saved in the config (should be
overridden by subclass).bool
) — Whether the class has compatible classes (should be overridden by subclass).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).( pretrained_model_name_or_path: typing.Union[str, os.PathLike] return_unused_kwargs = False return_commit_hash = False **kwargs ) → dict
Parameters
str
or os.PathLike
, optional) —
Can be either:
google/ddpm-celebahq-256
) of a pretrained model hosted on
the Hub../my_model_directory
) containing model weights saved with
save_config().Union[str, os.PathLike]
, optional) —
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used. 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. bool
, optional, defaults to False
) —
Whether or not to resume downloading the model weights and configuration files. If set to False
, any
incompletely downloaded files are deleted. 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. bool
, optional, defaults to False
) —
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. 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. 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. 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. str
, optional, defaults to ""
) —
The subfolder location of a model file within a larger model repository on the Hub or locally. bool
, optional, defaults to `False) —
Whether unused keyword arguments of the config are returned. 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.
( config: typing.Union[diffusers.configuration_utils.FrozenDict, typing.Dict[str, typing.Any]] = None return_unused_kwargs = False **kwargs ) → ModelMixin or SchedulerMixin
Parameters
Dict[str, Any]
) —
A config dictionary from which the Python class is instantiated. Make sure to only load configuration
files of compatible classes. bool
, optional, defaults to False
) —
Whether kwargs that are not consumed by the Python class should be returned or not. **kwargs
are passed directly to the underlying scheduler/model’s __init__
method and eventually
overwrite the same named arguments in config
. 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_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory where the configuration JSON file is saved (will be created if it does not exist). 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). 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.
( json_file_path: typing.Union[str, os.PathLike] )
Save the configuration instance’s parameters to a JSON file.
( ) → 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.