LoRA This is experimental and the API may change in the future. LoRA (Low-Rank Adaptation of Large Language Models) is a popular and lightweight training technique that significantly reduces the number of trainable parameters. It works by inserting a smaller number of new weights into the model and only these are trained. This makes training with LoRA much faster, memory-efficient, and produces smaller model weights (a few hundred MBs), which are easier to store and share. LoRA can also be combined with other training techniques like DreamBooth to speedup training. LoRA is very versatile and supported for DreamBooth, Kandinsky 2.2, Stable Diffusion XL, text-to-image, and Wuerstchen. This guide will explore the train_text_to_image_lora.py script to help you become more familiar with it, and how you can adapt it for your own use-case. Before running the script, make sure you install the library from source:   Copied git clone https://github.com/huggingface/diffusers