kohya_ss is a graphical user interface and workbench for fine-tuning diffusion models, specifically designed for Stable Diffusion. It provides a suite of tools for training generative AI models, including specialized interfaces for creating Low-Rank Adaptation weights and training ControlNet spatial control networks.
The project distinguishes itself through integrated VRAM usage optimization and hardware acceleration, featuring specific support for Intel GPUs via XPU-accelerated libraries. It implements parameter-efficient training methods and memory-saving techniques like gradient checkpointing to enable the training of large models on consumer hardware.
The platform covers the entire training lifecycle, from dataset preparation with image bucket organization and caption control to the execution of fine-tuning scripts. It includes capabilities for real-time progress monitoring through in-training sample generation, state recovery via model checkpointing, and the application of advanced training techniques such as masked loss and custom learning schedules.
The software includes automation for environment bootstrapping, dependency management, and containerized deployment options.
