There are many ways to train adapter neural networks for diffusion models, such as
Such adapter neural networks often only consist of a fraction of the number of weights compared
to the pretrained model and as such are very portable. The Diffusers library offers an easy-to-use
API to load such adapter neural networks via the loaders.py
module.
Note: This module is still highly experimental and prone to future changes.
( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )
Parameters
str
or os.PathLike
or dict
) —
Can be either:
google/ddpm-celebahq-256
.~ModelMixin.save_config
, e.g.,
./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 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 diffusers-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.
str
, optional) —
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
Load pretrained attention processor layers into UNet2DConditionModel
. Attention processor layers have to be
defined in
cross_attention.py
and be a torch.nn.Module
class.
This function is experimental and might change in the future.
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_directory: typing.Union[str, os.PathLike] is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory to which to save. Will be created if it doesn’t exist.
bool
, optional, defaults to True
) —
Whether the process calling this is the main process or not. Useful when in distributed training like
TPUs and need to call this function on all processes. In this case, set is_main_process=True
only on
the main process to avoid race conditions.
Callable
) —
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace torch.save
by another method. Can be configured with the environment variable
DIFFUSERS_SAVE_MODE
.
Save an attention processor to a directory, so that it can be re-loaded using the
[load_attn_procs()](/docs/diffusers/pr_2715/en/api/loaders#diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs)
method.