

Flux Gym
Overview :
Flux Gym is a streamlined Web UI designed for FLUX LoRA model training, particularly suited for devices with 12GB, 16GB, or 20GB VRAM. It combines the user-friendliness of the AI-Toolkit project with the flexibility of Kohya Scripts, enabling users to train models without complex terminal operations. Flux Gym allows users to upload images and add descriptions through a simple interface, then start the training process.
Target Users :
Flux Gym is ideal for researchers and developers who need to train AI models on devices with limited resources. It simplifies the training process, allowing users without a strong programming background to easily get started.
Use Cases
Researchers use Flux Gym to train text-to-image generation models on limited hardware resources.
Developers leverage Flux Gym for rapid testing and iteration of their LoRA models to optimize performance.
Educational institutions use Flux Gym as a teaching tool, allowing students to learn AI model training without increasing hardware costs.
Features
Supports low VRAM configurations (12GB/16GB/20GB).
Front-end Web UI based on AI-Toolkit, providing an intuitive user interface.
Backend training scripts powered by Kohya Scripts, offering high flexibility.
One-click installation and launch, simplifying the installation process.
Allows users to upload images and add descriptions to trigger model training.
Provides comprehensive installation and usage guides for quick user onboarding.
Supports downloading and utilizing multiple model checkpoints, enhancing training variety.
How to Use
First, install Flux Gym and its dependencies using the Pinokio 1-click launcher or manually.
Clone Fluxgym and kohya-ss/sd-scripts to your local machine.
Activate the virtual environment according to your operating system, and install the necessary dependencies.
Download and place the required model checkpoints into the designated folder.
Once in the virtual environment, run `python app.py` to start Flux Gym.
Enter LoRA information in the Web UI, upload images, and add descriptions.
Click the 'Start' button to initiate model training.
Monitor the training progress and review the results after completion.
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