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 [~loaders.StableDiffusionLoraLoaderMixin.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.
[!TIP] To learn more about how to load LoRA weights, see the LoRA guide.
[[autodoc]] loaders.unet.UNet2DConditionLoadersMixin