#
LoRA

LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet (UNet2DConditionModel, for example) or a Transformer (SD3Transformer2DModel, for example). There are several classes for loading LoRA weights:

- `StableDiffusionLoraLoaderMixin` provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
- `StableDiffusionXLLoraLoaderMixin` is a Stable Diffusion (SDXL) version of the `StableDiffusionLoraLoaderMixin` class for loading and saving LoRA weights. It can only be used with the SDXL model.
- `SD3LoraLoaderMixin` provides similar functions for Stable Diffusion 3.
- `FluxLoraLoaderMixin` provides similar functions for Flux.
- `CogVideoXLoraLoaderMixin` provides similar functions for CogVideoX.
- `Mochi1LoraLoaderMixin` provides similar functions for Mochi.
- `AuraFlowLoraLoaderMixin` provides similar functions for AuraFlow.
- `LTXVideoLoraLoaderMixin` provides similar functions for LTX-Video.
- `SanaLoraLoaderMixin` provides similar functions for Sana.
- `HunyuanVideoLoraLoaderMixin` provides similar functions for HunyuanVideo.
- `Lumina2LoraLoaderMixin` provides similar functions for Lumina2.
- `WanLoraLoaderMixin` provides similar functions for Wan.
- `SkyReelsV2LoraLoaderMixin` provides similar functions for SkyReels-V2.
- `CogView4LoraLoaderMixin` provides similar functions for CogView4.
- `AmusedLoraLoaderMixin` is for the AmusedPipeline.
- `HiDreamImageLoraLoaderMixin` provides similar functions for HiDream Image
- `QwenImageLoraLoaderMixin` provides similar functions for Qwen Image.
- `ZImageLoraLoaderMixin` provides similar functions for Z-Image.
- `Flux2LoraLoaderMixin` provides similar functions for Flux2.
- `LoraBaseMixin` provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.

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

##
LoraBaseMixin

###
class diffusers.loaders.lora_base.LoraBaseMixin

< source >

( )

Utility class for handling LoRAs.

####
delete_adapters

< source >

( adapter_names: typing.Union[typing.List[str], str] )

Parameters

- adapter_names (`Union[List[str], str]`) — The names of the adapters to delete.

Delete an adapter’s LoRA layers from the pipeline.

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

< source >

( )

Disables the active LoRA layers of the pipeline.

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

< source >

( )

Enables the active LoRA layers of the pipeline.

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()

####
enable_lora_hotswap

< source >

( **kwargs )

Parameters

- target_rank (`int`) — The highest rank among all the adapters that will be loaded.
- check_compiled (`str`, optional, defaults to `"error"`) — How to handle a model that is already compiled. The check can return the following messages:
  - “error” (default): raise an error
  - “warn”: issue a warning
  - “ignore”: do nothing

Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are different.

####
fuse_lora

< source >

( components: typing.List[str] = [] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

Parameters

- components — (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
- lora_scale (`float`, defaults to 1.0) — Controls how much to influence the outputs with the LoRA parameters.
- safe_fusing (`bool`, defaults to `False`) — Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
- adapter_names (`List[str]`, optional) — Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.

Fuses the LoRA parameters into the original parameters of the corresponding blocks.

> > This is an experimental API.

Example:

    from diffusers import DiffusionPipeline
    import torch

    pipeline = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
    ).to("cuda")
    pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
    pipeline.fuse_lora(lora_scale=0.7)

####
get_active_adapters

< source >

( )

Gets the list of the current active adapters.

Example:

    from diffusers import DiffusionPipeline

    pipeline = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
    ).to("cuda")
    pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
    pipeline.get_active_adapters()

####
get_list_adapters

< source >

( )

Gets the current list of all available adapters in the pipeline.

####
set_adapters

< source >

( adapter_names: typing.Union[typing.List[str], str] adapter_weights: typing.Union[float, typing.Dict, typing.List[float], typing.List[typing.Dict], NoneType] = 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 pipeline.

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])

####
set_lora_device

< source >

( adapter_names: typing.List[str] device: typing.Union[torch.device, str, int] )

Parameters

- adapter_names (`List[str]`) — List of adapters to send device to.
- device (`Union[torch.device, str, int]`) — Device to send the adapters to. Can be either a torch device, a str or an integer.

Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case you want to load multiple adapters and free some GPU memory.

After offloading the LoRA adapters to CPU, as long as the rest of the model is still on GPU, the LoRA adapters can no longer be used for inference, as that would cause a device mismatch. Remember to set the device back to GPU before using those LoRA adapters for inference.

    >>> pipe.load_lora_weights(path_1, adapter_name="adapter-1")
    >>> pipe.load_lora_weights(path_2, adapter_name="adapter-2")
    >>> pipe.set_adapters("adapter-1")
    >>> image_1 = pipe(**kwargs)
    >>> # switch to adapter-2, offload adapter-1
    >>> pipeline.set_lora_device(adapter_names=["adapter-1"], device="cpu")
    >>> pipeline.set_lora_device(adapter_names=["adapter-2"], device="cuda:0")
    >>> pipe.set_adapters("adapter-2")
    >>> image_2 = pipe(**kwargs)
    >>> # switch back to adapter-1, offload adapter-2
    >>> pipeline.set_lora_device(adapter_names=["adapter-2"], device="cpu")
    >>> pipeline.set_lora_device(adapter_names=["adapter-1"], device="cuda:0")
    >>> pipe.set_adapters("adapter-1")
    >>> ...

####
unfuse_lora

< source >

( components: typing.List[str] = [] **kwargs )

Parameters

- components (`List[str]`) — List of LoRA-injectable components to unfuse LoRA from.
- unfuse_unet (`bool`, defaults to `True`) — Whether to unfuse the UNet LoRA parameters.
- unfuse_text_encoder (`bool`, defaults to `True`) — Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the LoRA parameters then it won’t have any effect.

Reverses the effect of `pipe.fuse_lora()`.

> > This is an experimental API.

####
unload_lora_weights

< source >

( )

Unloads the LoRA parameters.

Examples:

    >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
    >>> pipeline.unload_lora_weights()
    >>> ...

####
write_lora_layers

< source >

( state_dict: typing.Dict[str, torch.Tensor] save_directory: str is_main_process: bool weight_name: str save_function: typing.Callable safe_serialization: bool lora_adapter_metadata: typing.Optional[dict] = None )

Writes the state dict of the LoRA layers (optionally with metadata) to disk.

##
StableDiffusionLoraLoaderMixin

###
class diffusers.loaders.StableDiffusionLoraLoaderMixin

< source >

( )

Load LoRA layers into Stable Diffusion UNet2DConditionModel and `CLIPTextModel`.

####
load_lora_into_text_encoder

< source >

( state_dict network_alphas text_encoder prefix = None lora_scale = 1.0 adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters. The key should be prefixed with an additional `text_encoder` to distinguish between unet lora layers.
- 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.
- text_encoder (`CLIPTextModel`) — The text encoder model to load the LoRA layers into.
- prefix (`str`) — Expected prefix of the `text_encoder` in the `state_dict`.
- lora_scale (`float`) — How much to scale the output of the lora linear layer before it is added with the output of the regular lora layer.
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata. When supplied, the `LoraConfig` arguments of `peft` won’t be derived from the state dict.

This will load the LoRA layers specified in `state_dict` into `text_encoder`

####
load_lora_into_unet

< source >

( state_dict network_alphas unet adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters. The keys can either be indexed directly into the unet or prefixed with an additional `unet` which can be used to distinguish between text encoder lora layers.
- 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.
- unet (`UNet2DConditionModel`) — The UNet model to load the LoRA layers into.
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata. When supplied, the `LoraConfig` arguments of `peft` won’t be derived from the state dict.

This will load the LoRA layers specified in `state_dict` into `unet`.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

Parameters

- pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`) — See lora_state_dict().
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means that, instead of loading an additional adapter, this will take the existing adapter weights and replace them with the weights of the new adapter. This can be faster and more memory efficient. However, the main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new adapter does not require recompilation of the model. When using hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.

  If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an additional method before loading the adapter:

Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and `self.text_encoder`.

All kwargs are forwarded to `self.lora_state_dict`.

See lora_state_dict() for more details on how the state dict is loaded.

See load_lora_into_unet() for more details on how the state dict is loaded into `self.unet`.

See load_lora_into_text_encoder() for more details on how the state dict is loaded into `self.text_encoder`.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **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.
- weight_name (`str`, optional, defaults to None) — Name of the serialized state dict file.
- return_lora_metadata (`bool`, optional, defaults to False) — When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.

Return state dict for lora weights and the network alphas.

> > We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is experimental and might change in the future.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = 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 = True unet_lora_adapter_metadata = None text_encoder_lora_adapter_metadata = None )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to save LoRA parameters to. Will be created if it doesn’t exist.
- unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `unet`.
- text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text encoder LoRA state dict because it comes from 🤗 Transformers.
- 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 the traditional PyTorch way with `pickle`.
- unet_lora_adapter_metadata — LoRA adapter metadata associated with the unet to be serialized with the state dict.
- text_encoder_lora_adapter_metadata — LoRA adapter metadata associated with the text encoder to be serialized with the state dict.

Save the LoRA parameters corresponding to the UNet and text encoder.

##
StableDiffusionXLLoraLoaderMixin

###
class diffusers.loaders.StableDiffusionXLLoraLoaderMixin

< source >

( )

Load LoRA layers into Stable Diffusion XL UNet2DConditionModel, `CLIPTextModel`, and `CLIPTextModelWithProjection`.

####
fuse_lora

< source >

( components: typing.List[str] = ['unet', 'text_encoder', 'text_encoder_2'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_text_encoder

< source >

( state_dict network_alphas text_encoder prefix = None lora_scale = 1.0 adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters. The key should be prefixed with an additional `text_encoder` to distinguish between unet lora layers.
- 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.
- text_encoder (`CLIPTextModel`) — The text encoder model to load the LoRA layers into.
- prefix (`str`) — Expected prefix of the `text_encoder` in the `state_dict`.
- lora_scale (`float`) — How much to scale the output of the lora linear layer before it is added with the output of the regular lora layer.
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata. When supplied, the `LoraConfig` arguments of `peft` won’t be derived from the state dict.

This will load the LoRA layers specified in `state_dict` into `text_encoder`

####
load_lora_into_unet

< source >

( state_dict network_alphas unet adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters. The keys can either be indexed directly into the unet or prefixed with an additional `unet` which can be used to distinguish between text encoder lora layers.
- 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.
- unet (`UNet2DConditionModel`) — The UNet model to load the LoRA layers into.
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata. When supplied, the `LoraConfig` arguments of `peft` won’t be derived from the state dict.

This will load the LoRA layers specified in `state_dict` into `unet`.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **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.
- weight_name (`str`, optional, defaults to None) — Name of the serialized state dict file.
- return_lora_metadata (`bool`, optional, defaults to False) — When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.

Return state dict for lora weights and the network alphas.

> > We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is experimental and might change in the future.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_2_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True unet_lora_adapter_metadata = None text_encoder_lora_adapter_metadata = None text_encoder_2_lora_adapter_metadata = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['unet', 'text_encoder', 'text_encoder_2'] **kwargs )

See `unfuse_lora()` for more details.

##
SD3LoraLoaderMixin

###
class diffusers.loaders.SD3LoraLoaderMixin

< source >

( )

Load LoRA layers into SD3Transformer2DModel, `CLIPTextModel`, and `CLIPTextModelWithProjection`.

Specific to StableDiffusion3Pipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_text_encoder

< source >

( state_dict network_alphas text_encoder prefix = None lora_scale = 1.0 adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters. The key should be prefixed with an additional `text_encoder` to distinguish between unet lora layers.
- 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.
- text_encoder (`CLIPTextModel`) — The text encoder model to load the LoRA layers into.
- prefix (`str`) — Expected prefix of the `text_encoder` in the `state_dict`.
- lora_scale (`float`) — How much to scale the output of the lora linear layer before it is added with the output of the regular lora layer.
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata. When supplied, the `LoraConfig` arguments of `peft` won’t be derived from the state dict.

This will load the LoRA layers specified in `state_dict` into `text_encoder`

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_2_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata = None text_encoder_lora_adapter_metadata = None text_encoder_2_lora_adapter_metadata = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2'] **kwargs )

See `unfuse_lora()` for more details.

##
FluxLoraLoaderMixin

###
class diffusers.loaders.FluxLoraLoaderMixin

< source >

( )

Load LoRA layers into FluxTransformer2DModel, `CLIPTextModel`.

Specific to StableDiffusion3Pipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See lora_state_dict() for more details.

####
load_lora_into_text_encoder

< source >

( state_dict network_alphas text_encoder prefix = None lora_scale = 1.0 adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters. The key should be prefixed with an additional `text_encoder` to distinguish between unet lora layers.
- 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.
- text_encoder (`CLIPTextModel`) — The text encoder model to load the LoRA layers into.
- prefix (`str`) — Expected prefix of the `text_encoder` in the `state_dict`.
- lora_scale (`float`) — How much to scale the output of the lora linear layer before it is added with the output of the regular lora layer.
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata. When supplied, the `LoraConfig` arguments of `peft` won’t be derived from the state dict.

This will load the LoRA layers specified in `state_dict` into `text_encoder`

####
load_lora_into_transformer

< source >

( state_dict network_alphas transformer adapter_name = None metadata = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

Parameters

- pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`) — See lora_state_dict().
- adapter_name (`str`, optional) — 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.
- low_cpu_mem_usage (`bool`, optional) — `Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- kwargs (`dict`, optional) — See lora_state_dict().

Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and `self.text_encoder`.

All kwargs are forwarded to `self.lora_state_dict`.

See lora_state_dict() for more details on how the state dict is loaded.

See `~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer` for more details on how the state dict is loaded into `self.transformer`.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] return_alphas: bool = False **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = 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 = True transformer_lora_adapter_metadata = None text_encoder_lora_adapter_metadata = None )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to save LoRA parameters to. Will be created if it doesn’t exist.
- transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `transformer`.
- text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text encoder LoRA state dict because it comes from 🤗 Transformers.
- 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 the traditional PyTorch way with `pickle`.
- transformer_lora_adapter_metadata — LoRA adapter metadata associated with the transformer to be serialized with the state dict.
- text_encoder_lora_adapter_metadata — LoRA adapter metadata associated with the text encoder to be serialized with the state dict.

Save the LoRA parameters corresponding to the UNet and text encoder.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer', 'text_encoder'] **kwargs )

Parameters

- components (`List[str]`) — List of LoRA-injectable components to unfuse LoRA from.

Reverses the effect of `pipe.fuse_lora()`.

> > This is an experimental API.

####
unload_lora_weights

< source >

( reset_to_overwritten_params = False )

Parameters

- reset_to_overwritten_params (`bool`, defaults to `False`) — Whether to reset the LoRA-loaded modules to their original params. Refer to the Flux documentation to learn more.

Unloads the LoRA parameters.

Examples:

    >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
    >>> pipeline.unload_lora_weights()
    >>> ...

##
Flux2LoraLoaderMixin

###
class diffusers.loaders.Flux2LoraLoaderMixin

< source >

( )

Load LoRA layers into Flux2Transformer2DModel. Specific to Flux2Pipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
CogVideoXLoraLoaderMixin

###
class diffusers.loaders.CogVideoXLoraLoaderMixin

< source >

( )

Load LoRA layers into CogVideoXTransformer3DModel. Specific to CogVideoXPipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
Mochi1LoraLoaderMixin

###
class diffusers.loaders.Mochi1LoraLoaderMixin

< source >

( )

Load LoRA layers into MochiTransformer3DModel. Specific to MochiPipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
AuraFlowLoraLoaderMixin

###
class diffusers.loaders.AuraFlowLoraLoaderMixin

< source >

( )

Load LoRA layers into AuraFlowTransformer2DModel Specific to AuraFlowPipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer', 'text_encoder'] **kwargs )

See `unfuse_lora()` for more details.

##
LTXVideoLoraLoaderMixin

###
class diffusers.loaders.LTXVideoLoraLoaderMixin

< source >

( )

Load LoRA layers into LTXVideoTransformer3DModel. Specific to LTXPipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
SanaLoraLoaderMixin

###
class diffusers.loaders.SanaLoraLoaderMixin

< source >

( )

Load LoRA layers into SanaTransformer2DModel. Specific to SanaPipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
HunyuanVideoLoraLoaderMixin

###
class diffusers.loaders.HunyuanVideoLoraLoaderMixin

< source >

( )

Load LoRA layers into HunyuanVideoTransformer3DModel. Specific to HunyuanVideoPipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
Lumina2LoraLoaderMixin

###
class diffusers.loaders.Lumina2LoraLoaderMixin

< source >

( )

Load LoRA layers into Lumina2Transformer2DModel. Specific to `Lumina2Text2ImgPipeline`.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
CogView4LoraLoaderMixin

###
class diffusers.loaders.CogView4LoraLoaderMixin

< source >

( )

Load LoRA layers into WanTransformer3DModel. Specific to CogView4Pipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
WanLoraLoaderMixin

###
class diffusers.loaders.WanLoraLoaderMixin

< source >

( )

Load LoRA layers into WanTransformer3DModel. Specific to WanPipeline and `[WanImageToVideoPipeline`].

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
SkyReelsV2LoraLoaderMixin

###
class diffusers.loaders.SkyReelsV2LoraLoaderMixin

< source >

( )

Load LoRA layers into SkyReelsV2Transformer3DModel.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
AmusedLoraLoaderMixin

###
class diffusers.loaders.AmusedLoraLoaderMixin

< source >

( )

####
load_lora_into_transformer

< source >

( state_dict network_alphas transformer adapter_name = None metadata = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False )

See load_lora_into_unet() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] text_encoder_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = None transformer_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 = True )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to save LoRA parameters to. Will be created if it doesn’t exist.
- unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `unet`.
- text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text encoder LoRA state dict because it comes from 🤗 Transformers.
- 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 the traditional PyTorch way with `pickle`.

Save the LoRA parameters corresponding to the UNet and text encoder.

##
HiDreamImageLoraLoaderMixin

###
class diffusers.loaders.HiDreamImageLoraLoaderMixin

< source >

( )

Load LoRA layers into HiDreamImageTransformer2DModel. Specific to HiDreamImagePipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
QwenImageLoraLoaderMixin

###
class diffusers.loaders.QwenImageLoraLoaderMixin

< source >

( )

Load LoRA layers into QwenImageTransformer2DModel. Specific to QwenImagePipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
ZImageLoraLoaderMixin

###
class diffusers.loaders.ZImageLoraLoaderMixin

< source >

( )

Load LoRA layers into ZImageTransformer2DModel. Specific to ZImagePipeline.

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

See `fuse_lora()` for more details.

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

See load_lora_into_unet() for more details.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

See load_lora_weights() for more details.

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

See lora_state_dict() for more details.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata: typing.Optional[dict] = None )

See save_lora_weights() for more information.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

See `unfuse_lora()` for more details.

##
KandinskyLoraLoaderMixin

###
class diffusers.loaders.KandinskyLoraLoaderMixin

< source >

( )

Load LoRA layers into `Kandinsky5Transformer3DModel`,

####
fuse_lora

< source >

( components: typing.List[str] = ['transformer'] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

Parameters

- components — (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
- lora_scale (`float`, defaults to 1.0) — Controls how much to influence the outputs with the LoRA parameters.
- safe_fusing (`bool`, defaults to `False`) — Whether to check fused weights for NaN values before fusing.
- adapter_names (`List[str]`, optional) — Adapter names to be used for fusing.

Fuses the LoRA parameters into the original parameters of the corresponding blocks.

Example:

    from diffusers import Kandinsky5T2VPipeline

    pipeline = Kandinsky5T2VPipeline.from_pretrained("ai-forever/Kandinsky-5.0-T2V")
    pipeline.load_lora_weights("path/to/lora.safetensors")
    pipeline.fuse_lora(lora_scale=0.7)

####
load_lora_into_transformer

< source >

( state_dict transformer adapter_name = None _pipeline = None low_cpu_mem_usage = False hotswap: bool = False metadata = None )

Parameters

- state_dict (`dict`) — A standard state dict containing the lora layer parameters.
- transformer (`Kandinsky5Transformer3DModel`) — The transformer model to load the LoRA layers into.
- adapter_name (`str`, optional) — Adapter name to be used for referencing the loaded adapter model.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights.
- hotswap (`bool`, optional) — See load_lora_weights().
- metadata (`dict`) — Optional LoRA adapter metadata.

Load the LoRA layers specified in `state_dict` into `transformer`.

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name: typing.Optional[str] = None hotswap: bool = False **kwargs )

Parameters

- pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`) — See lora_state_dict().
- adapter_name (`str`, optional) — Adapter name to be used for referencing the loaded adapter model.
- hotswap (`bool`, optional) — Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place.
- low_cpu_mem_usage (`bool`, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
- kwargs (`dict`, optional) — See lora_state_dict().

Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer`

####
lora_state_dict

< source >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

Parameters

- pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`) — Can be either:
  - A string, the model id of a pretrained model hosted on the Hub.
  - A path to a directory containing the model weights.
  - A torch state dict.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory where a downloaded pretrained model configuration is cached.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint.
- local_files_only (`bool`, optional, defaults to `False`) — Whether to only load local model weights and configuration files.
- token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files.
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use.
- subfolder (`str`, optional, defaults to `""`) — The subfolder location of a model file within a larger model repository.
- weight_name (`str`, optional, defaults to None) — Name of the serialized state dict file.
- use_safetensors (`bool`, optional) — Whether to use safetensors for loading.
- return_lora_metadata (`bool`, optional, defaults to False) — When enabled, additionally return the LoRA adapter metadata.

Return state dict for lora weights and the network alphas.

####
save_lora_weights

< source >

( save_directory: typing.Union[str, os.PathLike] transformer_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True transformer_lora_adapter_metadata = None )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to save LoRA parameters to.
- transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `transformer`.
- is_main_process (`bool`, optional, defaults to `True`) — Whether the process calling this is the main process.
- save_function (`Callable`) — The function to use to save the state dictionary.
- safe_serialization (`bool`, optional, defaults to `True`) — Whether to save the model using `safetensors` or the traditional PyTorch way.
- transformer_lora_adapter_metadata — LoRA adapter metadata associated with the transformer.

Save the LoRA parameters corresponding to the transformer and text encoders.

####
unfuse_lora

< source >

( components: typing.List[str] = ['transformer'] **kwargs )

Parameters

- components (`List[str]`) — List of LoRA-injectable components to unfuse LoRA from.

Reverses the effect of `pipe.fuse_lora()`.

##
LoraBaseMixin

###
class diffusers.loaders.lora_base.LoraBaseMixin

< source >

( )

Utility class for handling LoRAs.

####
delete_adapters

< source >

( adapter_names: typing.Union[typing.List[str], str] )

Parameters

- adapter_names (`Union[List[str], str]`) — The names of the adapters to delete.

Delete an adapter’s LoRA layers from the pipeline.

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

< source >

( )

Disables the active LoRA layers of the pipeline.

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

< source >

( )

Enables the active LoRA layers of the pipeline.

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()

####
enable_lora_hotswap

< source >

( **kwargs )

Parameters

- target_rank (`int`) — The highest rank among all the adapters that will be loaded.
- check_compiled (`str`, optional, defaults to `"error"`) — How to handle a model that is already compiled. The check can return the following messages:
  - “error” (default): raise an error
  - “warn”: issue a warning
  - “ignore”: do nothing

Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are different.

####
fuse_lora

< source >

( components: typing.List[str] = [] lora_scale: float = 1.0 safe_fusing: bool = False adapter_names: typing.Optional[typing.List[str]] = None **kwargs )

Parameters

- components — (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
- lora_scale (`float`, defaults to 1.0) — Controls how much to influence the outputs with the LoRA parameters.
- safe_fusing (`bool`, defaults to `False`) — Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
- adapter_names (`List[str]`, optional) — Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.

Fuses the LoRA parameters into the original parameters of the corresponding blocks.

> > This is an experimental API.

Example:

    from diffusers import DiffusionPipeline
    import torch

    pipeline = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
    ).to("cuda")
    pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
    pipeline.fuse_lora(lora_scale=0.7)

####
get_active_adapters

< source >

( )

Gets the list of the current active adapters.

Example:

    from diffusers import DiffusionPipeline

    pipeline = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
    ).to("cuda")
    pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
    pipeline.get_active_adapters()

####
get_list_adapters

< source >

( )

Gets the current list of all available adapters in the pipeline.

####
set_adapters

< source >

( adapter_names: typing.Union[typing.List[str], str] adapter_weights: typing.Union[float, typing.Dict, typing.List[float], typing.List[typing.Dict], NoneType] = 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 pipeline.

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])

####
set_lora_device

< source >

( adapter_names: typing.List[str] device: typing.Union[torch.device, str, int] )

Parameters

- adapter_names (`List[str]`) — List of adapters to send device to.
- device (`Union[torch.device, str, int]`) — Device to send the adapters to. Can be either a torch device, a str or an integer.

Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case you want to load multiple adapters and free some GPU memory.

After offloading the LoRA adapters to CPU, as long as the rest of the model is still on GPU, the LoRA adapters can no longer be used for inference, as that would cause a device mismatch. Remember to set the device back to GPU before using those LoRA adapters for inference.

    >>> pipe.load_lora_weights(path_1, adapter_name="adapter-1")
    >>> pipe.load_lora_weights(path_2, adapter_name="adapter-2")
    >>> pipe.set_adapters("adapter-1")
    >>> image_1 = pipe(**kwargs)
    >>> # switch to adapter-2, offload adapter-1
    >>> pipeline.set_lora_device(adapter_names=["adapter-1"], device="cpu")
    >>> pipeline.set_lora_device(adapter_names=["adapter-2"], device="cuda:0")
    >>> pipe.set_adapters("adapter-2")
    >>> image_2 = pipe(**kwargs)
    >>> # switch back to adapter-1, offload adapter-2
    >>> pipeline.set_lora_device(adapter_names=["adapter-2"], device="cpu")
    >>> pipeline.set_lora_device(adapter_names=["adapter-1"], device="cuda:0")
    >>> pipe.set_adapters("adapter-1")
    >>> ...

####
unfuse_lora

< source >

( components: typing.List[str] = [] **kwargs )

Parameters

- components (`List[str]`) — List of LoRA-injectable components to unfuse LoRA from.
- unfuse_unet (`bool`, defaults to `True`) — Whether to unfuse the UNet LoRA parameters.
- unfuse_text_encoder (`bool`, defaults to `True`) — Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the LoRA parameters then it won’t have any effect.

Reverses the effect of `pipe.fuse_lora()`.

> > This is an experimental API.

####
unload_lora_weights

< source >

( )

Unloads the LoRA parameters.

Examples:

    >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
    >>> pipeline.unload_lora_weights()
    >>> ...

####
write_lora_layers

< source >

( state_dict: typing.Dict[str, torch.Tensor] save_directory: str is_main_process: bool weight_name: str save_function: typing.Callable safe_serialization: bool lora_adapter_metadata: typing.Optional[dict] = None )

Writes the state dict of the LoRA layers (optionally with metadata) to disk.

Update on GitHub