Hugging Face's logo

Diffusers documentation

UNet

Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

UNet

Some training methods - like LoRA and Custom Diffusion - typically target the UNet’s attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model’s parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you’re only loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the load_lora_weights() function instead.

The UNet2DConditionLoadersMixin class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.

To learn more about how to load LoRA weights, see the LoRA loading guide.

UNet2DConditionLoadersMixin

class diffusers.loaders.UNet2DConditionLoadersMixin

< >

( )

Load LoRA layers into a UNet2DCondtionModel.

load_attn_procs

< >

( pretrained_model_name_or_path_or_dict: Union **kwargs )

Parameters

  • pretrained_model_name_or_path_or_dict (str or os.PathLike or dict) — 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 the model weights saved with ModelMixin.save_pretrained().
    • A torch state dict.
  • cache_dir (Union[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.
  • 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.
  • network_alphas (Dict[str, float]) — The value of the network alpha used for stable learning and preventing underflow. This value has the same meaning as the --network_alpha option in the kohya-ss trainer script. Refer to this link.
  • adapter_name (str, optional, defaults to None) — Adapter name to be used for referencing the loaded adapter model. If not specified, it will use default_{i} where i is the total number of adapters being loaded.
  • weight_name (str, optional, defaults to None) — Name of the serialized state dict file.
  • low_cpu_mem_usage (bool, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.

Load pretrained attention processor layers into UNet2DConditionModel. Attention processor layers have to be defined in attention_processor.py and be a torch.nn.Module class. Currently supported: LoRA, Custom Diffusion. For LoRA, one must install peft: pip install -U peft.

Example:

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.unet.load_attn_procs(
    "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)

save_attn_procs

< >

( save_directory: Union is_main_process: bool = True weight_name: str = None save_function: Callable = None safe_serialization: bool = True **kwargs )

Parameters

  • save_directory (str or os.PathLike) — Directory to save an attention processor to (will be created if it doesn’t exist).
  • is_main_process (bool, optional, defaults to True) — Whether the process calling this is the main process or not. Useful during distributed training and you 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.
  • save_function (Callable) — The function to use to save the state dictionary. Useful during distributed training when you need to replace torch.save with another method. Can be configured with the environment variable DIFFUSERS_SAVE_MODE.
  • safe_serialization (bool, optional, defaults to True) — Whether to save the model using safetensors or with pickle.

Save attention processor layers to a directory so that it can be reloaded with the load_attn_procs() method.

Example:

import torch
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.unet.save_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
< > Update on GitHub

←Textual Inversion PEFT→