Sayak Paul
6d11129c5a
* introduce to promote reusability. * up * add more tests * up * remove comments. * fix fuse_nan test * clarify the scope of fuse_lora and unfuse_lora * remove space * rewrite fuse_lora a bit. * feedback * copy over load_lora_into_text_encoder. * address dhruv's feedback. * fix-copies * fix issubclass. * num_fused_loras * fix * fix * remove mapping * up * fix * style * fix-copies * change to SD3TransformerLoRALoadersMixin * Apply suggestions from code review Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> * up * handle wuerstchen * up * move lora to lora_pipeline.py * up * fix-copies * fix documentation. * comment set_adapters(). * fix-copies * fix set_adapters() at the model level. * fix? * fix * loraloadermixin. --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
1.5 KiB
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 [~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.
To learn more about how to load LoRA weights, see the LoRA loading guide.
UNet2DConditionLoadersMixin
autodoc loaders.unet.UNet2DConditionLoadersMixin