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| # LoRA |
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| 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 UNet, text encoder or both. There are two classes for loading LoRA weights: |
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| - [`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. |
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| <Tip> |
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| To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide. |
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| </Tip> |
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| ## LoraLoaderMixin |
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| [[autodoc]] loaders.lora.LoraLoaderMixin |
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| ## StableDiffusionXLLoraLoaderMixin |
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| [[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin |
