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# 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:

- [`LoraLoaderMixin`] 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)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`LoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
- [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.

<Tip>

To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.

</Tip>

## LoraLoaderMixin

[[autodoc]] loaders.lora.LoraLoaderMixin

## StableDiffusionXLLoraLoaderMixin

[[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin

## SD3LoraLoaderMixin

[[autodoc]] loaders.lora.SD3LoraLoaderMixin

## AmusedLoraLoaderMixin

[[autodoc]] loaders.lora.AmusedLoraLoaderMixin

## LoraBaseMixin

[[autodoc]] loaders.lora_base.LoraBaseMixin