Buckets:
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
hf auth login.
ConfigMixin[[diffusers.ConfigMixin]]
diffusers.ConfigMixin[[diffusers.ConfigMixin]]
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 theinitfunction should only have akwargsargument if at least one argument is deprecated (should be overridden by subclass).
load_config[[diffusers.ConfigMixin.load_config]]
load_config(pretrained_model_name_or_path: str | os.PathLike, return_unused_kwargs = False, return_commit_hash = False, **kwargs)
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 (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[[diffusers.ConfigMixin.from_config]]
from_config(config: diffusers.configuration_utils.FrozenDict | dict[str, typing.Any] = None, return_unused_kwargs = False, **kwargs)
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[[diffusers.ConfigMixin.save_config]]
save_config(save_directory: str | os.PathLike, 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[[diffusers.ConfigMixin.to_json_file]]
to_json_file(json_file_path: str | os.PathLike)
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[[diffusers.ConfigMixin.to_json_string]]
to_json_string()
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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