#
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

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( )

Load LoRA layers into a `UNet2DCondtionModel`.

####
delete_adapters

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( adapter_names: Union )

Parameters

- adapter_names (`Union[List[str], str]`) — The names (single string or list of strings) of the adapter to delete.

Delete an adapter’s LoRA layers from the UNet.

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.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
    )
    pipeline.delete_adapters("cinematic")

####
disable_lora

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( )

Disable the UNet’s active LoRA layers.

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.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
    )
    pipeline.disable_lora()

####
enable_lora

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( )

Enable the UNet’s active LoRA layers.

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.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
    )
    pipeline.enable_lora()

####
load_attn_procs

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( 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.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to resume downloading the model weights and configuration files. If set to `False`, any incompletely downloaded files are deleted.
- 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.
- low_cpu_mem_usage (`bool`, optional, defaults to `True` if torch version >= 1.9.0 else `False`) — Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error.
- 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.
- mirror (`str`, optional) — Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information.

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.

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

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( 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")

####
set_adapters

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( adapter_names: Union weights: Union = None )

Parameters

- adapter_names (`List[str]` or `str`) — The names of the adapters to use.
- adapter_weights (`Union[List[float], float]`, optional) — The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the adapters.

Set the currently active adapters for use in the UNet.

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.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
    )
    pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
    pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])