Low-rank adaptation (LoRA) is a technique for fine-tuning models that has some advantages over previous methods:
It is faster and uses less memory, which means it can run on consumer hardware.
The output is much smaller (megabytes, not gigabytes).
You can combine multiple fine-tuned models together at runtime.
Last month we blogged about faster fine-tuning of Stable Diffusion with LoRA. Our friend Simon Ryu (aka @cloneofsimo) applied the LoRA technique to Stable diffusion, allowing people to create custom trained styles from just a handful of training images, then mix and match those styles at prediction time to create highly customized images.
