BlackSkye View Providers

Introduction

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Elit nisi in eleifend sed nisi. Pulvinar at orci, proin imperdiet commodo consectetur convallis risus. Sed condimentum enim dignissim adipiscing faucibus consequat, urna. Viverra purus et erat auctor aliquam. Risus, volutpat vulputate posuere purus sit congue convallis aliquet. Arcu id augue ut feugiat donec porttitor neque. Mauris, neque ultricies eu vestibulum, bibendum quam lorem id. Dolor lacus, eget nunc lectus in tellus, pharetra, porttitor.

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Conclusion

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GPU Providers

Kohya GUI on RunPod: LoRA Training Made Easy

Installing Kohya GUI on RunPod for Efficient LoRA Training
Daniel
June 14, 2025
•
5 min read
A server rack with glowing GPU cards, representing cloud computing and AI training.
Looking to train LoRA models using powerful GPUs for AI without breaking the bank? This comprehensive guide walks you through setting up Kohya GUI on RunPod's cloud platform, giving you access to high-performance computing resources like the RTX 3090 for efficient AI model training. First, you'll need to access RunPod's platform and select appropriate GPU resources. Navigate to RunPod's community cloud. Select an RTX 3090 (30GB RAM) or similar powerful GPU. Click "Deploy" and choose the web automatic template (version 6.0.1 or higher). Edit template overrides: Set container disk to 10GB and configure volume disk as needed. Click "Continue" and "Deploy". Once the container starts, connect via JupyterLab. After connecting to JupyterLab, follow these steps to install Kohya GUI. First, clone the Kohya_SS repository by running git clone https://github.com/bmaltais/kohya_ss.git in the workspace terminal. Then, navigate into the new Kohya directory using cd kohya_ss. Next, create a virtual environment with python -m venv venv and activate it by running source venv/bin/activate. Finally, install the required packages using pip install -r requirements.txt. If you encounter errors during installation, simply retry the command. If you encounter tkinter errors (very common), run these commands while the virtual environment is activated. To install tkinter, run apt-get update && apt-get install -y python3-tk. For optimal performance, install the latest PyTorch using pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url https://download.pytorch.org/whl/cu118. Note that you can ignore any xformers-related messages as they are not needed for training. To start the Kohya interface, open a new terminal in the Kohya_SS directory. Run the launch command cd /workspace/kohya_ss && python kohya_gui.py. Then, open the provided Gradio link to access the interface. For successful training, first download a base model like Realistic Vision. Prepare high-quality, diverse training images. Configure the training parameters: specify model path and image directories, set network rank (128-256 works well), disable xformers for better performance, and configure epochs and batch size. Finally, save your configuration for future use. For best results, avoid repetitive backgrounds in training images. Reduce the "repeating" parameter for more frequent checkpoints. Experiment with different network ranks (128 for lighter models). Try various optimizers based on your specific use case. Monitor GPU memory usage and adjust batch size accordingly. With proper setup, you can complete training in minutes rather than hours, depending on your dataset size and chosen parameters. BlackSkye's decentralized GPU marketplace offers an excellent alternative for accessing powerful computing resources for AI model training at competitive rates. By connecting users with GPU providers directly, BlackSkye helps democratize access to the computing power needed for advanced AI projects like LoRA training.