lora

Why we included this project

This is the repository that introduced many people to applying low-rank adaptation to diffusion models. Instead of fine-tuning all the weights, it trains a small residual adapter, so a finished style or character comes out as a one-to-few-megabyte file rather than a multi-gigabyte checkpoint. That makes it practical to share and swap fine-tuned models, and the project bundles DreamBooth, textual inversion, and pivotal tuning (a combination of both), plus tools to merge multiple LoRAs and convert them to diffusers-compatible formats. It reads as a reference implementation rather than a polished product, so expect to wire up your own training runs. If you are comfortable with that and want to keep many distinct fine-tunes without heavy storage or GPU demands, this is a solid base.