BlackSkye View Providers

Introduction

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Conclusion

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LoRA Training: Kohya GUI on RunPod Setup Guide

Complete Guide to Installing Kohya GUI on RunPod for LoRA Training
Daniel
June 19, 2025
•
5 min read
GPU icon and a user interface for AI model training, representing Kohya GUI on RunPod.
Looking to maximize your GPU for AI training workflows? This comprehensive guide walks you through setting up Kohya GUI on RunPod for efficient LoRA training, allowing you to leverage powerful cloud GPUs without complex setup procedures. Selecting the Right RunPod Environment Access RunPod.io and navigate to Community Cloud. Select an RTX 3090 (or equivalent powerful GPU). Click "Deploy" and choose the Web Automatic template. Configure resources: Set container disk to 10GB, adjust volume disk as needed, click "Set overrides" then "Continue", and finalize by clicking "Deploy". Setting Up Kohya SS Environment Once your pod is running, connect via JupyterLab and execute commands in the terminal. First, clone the repository and navigate into its directory. Then, create and activate a virtual environment. Install dependencies; if a tkinter error appears, run the specified `apt-get` command. Finally, install the latest PyTorch version using the provided `pip install` command. Launching Kohya GUI Open a new terminal in the Kohya SS directory and run the `gui.sh` script. This will generate a Gradio link that you can open to access the Kohya GUI web interface. Preparing for LoRA Training To prepare, download a base model (like Realistic Vision) to your Stable Diffusion models folder. Download a VAE file if needed. Prepare your training images and classification images. In the Kohya GUI, set the model path manually or select from the dropdown, configure training and regularization image directories, and set the output destination. Configuring Training Parameters For optimal results, set network rank to 128-256. Disable xformers, which is important for best performance. Configure epoch count (10-15 recommended for testing). Set appropriate batch size based on your GPU memory. Save your configuration as JSON for future use. Training Best Practices Verify image count before training begins. Monitor iterations per second (typically 5+ on RTX 3090). Save checkpoints frequently for testing. Watch for signs of overtraining, such as memorization of backgrounds or poses. Consider using fewer repeating steps for more granular checkpoints. Testing Your Trained Model Move checkpoint files to your Stable Diffusion LoRA folder. Refresh models in the web UI. Test different checkpoint versions. Use appropriate prompts and settings. Evaluate results and adjust training parameters as needed. BlackSkye's decentralized GPU marketplace could significantly reduce costs for AI training workflows like this by connecting you with affordable GPU resources. With BlackSkye's pay-per-job billing model, you only pay for the exact compute time needed for your LoRA training sessions.