Introduction to kohya_ss Project
Kohya's GUI is an innovative graphical user interface designed specifically for Kohya's Stable Diffusion trainers. This tool offers a user-friendly way to set training parameters and generate the necessary command-line interface commands to train machine learning models. The project is primarily built for Windows, with community support extending functionality to Linux systems. While macOS support is not fully developed, it may work under certain conditions.
Colab Integration
There's a Colab notebook available which is not officially developed by the project maintainers but is known to be effective. This notebook allows users to leverage Kohya's GUI on Google Colab, enhancing its accessibility and convenience.
Installation Guidelines
For Windows Users
Windows users need to ensure they have several prerequisites like Python 3.10, CUDA toolkit, Git, and Visual Studio redistributables. After setting these up, users can clone the kohya_ss repository and run the setup script to install the GUI.
Optional Upgrade: Installing CUDNN 8.9.6.50 can enhance training speeds for users with NVIDIA 30X0/40X0 GPUs.
For Linux and macOS Users
Linux users need to ensure they have Python and CUDA installed. The installation involves cloning the repository, making the setup script executable, and running it. The default installation directory is dynamic, adjusting based on permissions and existing installations.
Runpod and Docker Options
Runpod provides a manual and pre-built template for installation, while Docker offers a Dockerfile for users interested in containerized environments. Docker setup supports GPU involvement and requires additional driver installations for both Windows and Linux systems.
How to Upgrade
Users on Windows and Linux can easily upgrade their kohya_ss installation by pulling the latest changes from the repository and running the setup script again.
Launching the GUI
Kohya's GUI can be started using included scripts. On Windows, scripts like gui.ps1 or gui.bat can be used, while Linux/macOS users can use gui.sh. These scripts include several command-line options for customizing the service behavior, like specifying IP addresses, ports, and enabling browser-based interfaces.
Customization and Features
The project supports customization through a default configuration file (config.toml) where users can set paths and preferences. Kohya's GUI also supports training techniques like LoRA and allows sample image generation during training with customizable prompts.
Troubleshooting
The documentation addresses common issues such as GPU utilization problems and module errors, providing solutions like increasing page file limits or reinstalling specific software components.
Advanced Features and Development
While still developing, Kohya's GUI includes support for advanced features like masked loss and SDXL training, showcasing its intent to support robust machine learning needs. Users are encouraged to provide feedback or report issues, helping the community-driven project to improve continuously.
Conclusion
Kohya's GUI stands out as a practical and versatile tool for individuals engaged in machine learning training using Stable Diffusion. With its broad support for various operating systems and containerized environments, it remains accessible to a wide range of users while continuing to evolve.