Schedulers from SchedulerMixin and models from ModelMixin inherit from ConfigMixin which conveniently takes care of storing all the parameters that are
passed to their respective __init__
methods in a JSON-configuration file.
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:
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 )
Parameters
str
or os.PathLike
, optional) —
Can be either:
google/ddpm-celebahq-256
../my_model_directory/
.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.
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 delete incompletely received files. Will attempt to resume the download if such a
file exists.
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.
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 or not to only look at local files (i.e., do not try to download the model).
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
).
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.
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.
bool
, optional, defaults to `False) —
Whether unused keyword arguments of the config shall be returned.
bool
, optional, defaults to `False) —
Whether the commit_hash of the loaded configuration shall be returned.
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.
( config: typing.Union[diffusers.configuration_utils.FrozenDict, typing.Dict[str, typing.Any]] = None return_unused_kwargs = False **kwargs )
Parameters
Dict[str, Any]
) —
A config dictionary from which the Python class will be 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
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)
( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )
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] )
Save this instance to a JSON file.
(
)
→
str
Returns
str
String containing all the attributes that make up this configuration instance in JSON format.
Serializes this instance to a JSON string.