Dreambooth-Stable-Diffusion

Why we included this project

This is a training toolkit for teaching a Stable Diffusion model to recognize and reproduce a specific face, object, or visual style. It combines the Dreambooth and Textual Inversion methods into one pipeline for fine-tuning on a small set of your own images, with tweaks aimed at keeping faces and objects recognizable rather than generic. Setup guides cover cloud GPU services and local machines, so you can get going even without prior fine-tuning experience. The author, Joe Penna, is a film director who built it to train actors, props, and locations for his movies, and that shows in the debugging notes that walk you through common failure cases like faces that look nothing like the source or training images that repeat too literally. If you want consistent characters or a signature look instead of generic outputs, this gives you the training code and the troubleshooting guidance to get there.