This library applies Low-rank Adaptation (LoRA) to accelerate and optimize fine-tuning of diffusion models like Stable Diffusion. Compared to traditional DreamBooth methods, it achieves roughly 2x faster training while producing compact model files (1-6MB) that are easy to share. The approach uses matrix decomposition to update only low-rank components of the model weights, maintaining quality while reducing computational overhead. It integrates with the diffusers library and supports various training pipelines including CLIP, UNet, and token fine-tuning.