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.
( 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() method.
Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder.
( pretrained_model_name_or_path: typing.Union[str, typing.Dict[str, torch.Tensor]] token: typing.Optional[str] = None **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
"sd-concepts-library/low-poly-hd-logos-icons"
../my_text_inversion_directory/
.str
, optional) —
Name of a custom weight file. This should be used in two cases:
diffusers
format, but was saved under a specific weight
name, such as text_inv.bin
.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 textual inversion embeddings into the text encoder of stable diffusion pipelines. Both diffusers
and
Automatic1111
formats are supported (see example below).
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.
Example:
To load a textual inversion embedding vector in diffusers
format:
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.load_textual_inversion("sd-concepts-library/cat-toy")
prompt = "A <cat-toy> backpack"
image = pipe(prompt, num_inference_steps=50).images[0]
image.save("cat-backpack.png")
To load a textual inversion embedding vector in Automatic1111 format, make sure to first download the vector,
e.g. from civitAI and then load the vector locally:
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.load_textual_inversion("./charturnerv2.pt")
prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."
image = pipe(prompt, num_inference_steps=50).images[0]
image.save("character.png")
(
prompt: typing.Union[str, typing.List[str]]
tokenizer: PreTrainedTokenizer
)
→
str
or list of str
Maybe convert a prompt into a “multi vector”-compatible prompt. If the prompt includes a token that corresponds to a multi-vector textual inversion embedding, this function will process the prompt so that the special token is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.
Utility class for handling the loading LoRA layers into UNet (of class UNet2DConditionModel) and Text Encoder
(of class CLIPTextModel
).
This function is experimental and might change in the future.
(
pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]]
**kwargs
)
→
Dict[name, LoRAAttnProcessor]
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.
Returns
Dict[name, LoRAAttnProcessor]
Mapping between the module names and their corresponding
LoRAAttnProcessor
.
Load pretrained attention processor layers for
CLIPTextModel
.
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.
( 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 (such as LoRA) into UNet2DConditionModel and
CLIPTextModel
).
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.
( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = None text_encoder_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = False )
Parameters
str
or os.PathLike
) —
Directory to which to save. Will be created if it doesn’t exist.
Dict[str, torch.nn.Module
]) —
State dict of the LoRA layers corresponding to the UNet. Specifying this helps to make the
serialization process easier and cleaner.
Dict[str, torch.nn.Module
]) —
State dict of the LoRA layers corresponding to the text_encoder
. Since the text_encoder
comes from
transformers
, we cannot rejig it. That is why we have to explicitly pass the text encoder LoRA state
dict.
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 the LoRA parameters corresponding to the UNet and the text encoder.