kohya_ss
A browser-based graphical interface for fine-tuning Stable Diffusion image AI models using LoRA and Dreambooth, letting you train on your own images without writing command-line scripts.
Kohya's GUI is a graphical user interface for training and customizing Stable Diffusion image generation models. Stable Diffusion is an AI system that creates images from text descriptions. Fine-tuning it, meaning training it on a set of your own images so it learns a specific person, art style, or subject, is a technically demanding process that normally requires running scripts from the command line with many configuration options.
This project wraps those training scripts in a form-based interface built with Gradio, a Python library for creating browser-based panels. Instead of typing commands, users fill out fields for model paths, dataset locations, training settings, and output folders, then click a button to start the process. The GUI generates the appropriate command-line arguments automatically.
It supports several training methods. LoRA (Low-Rank Adaptation) is the most popular: rather than retraining the entire model, it produces a small add-on file that teaches the base model a new concept. Dreambooth is another approach that trains the model to consistently recognize a specific subject. The GUI also supports SDXL training, which targets the higher-resolution SDXL variant of Stable Diffusion.
The tool can run on a local machine with a supported GPU, or on cloud platforms like Google Colab, Runpod, and Novita for users who do not have the required hardware. Docker is also supported for developers who prefer containerized setups. Installation guides are available for Windows, Linux, and macOS.
A configuration file allows users to save their preferred paths and settings so they do not have to re-enter them each time the GUI opens.
Where it fits
- Train a LoRA file on your own photos so Stable Diffusion can generate images of a specific person, art style, or object
- Fine-tune an SDXL model on a custom image dataset using a form-based interface instead of command-line arguments
- Run Stable Diffusion training on Google Colab or Runpod when you do not have a local GPU