FluxGym - AI Training Tools Tool
Overview
FluxGym is an open-source, minimal web UI focused on making LoRA fine-tuning accessible on consumer-class GPUs. According to the GitHub repository, FluxGym provides a dead-simple Gradio-based frontend (a fork of AI-Toolkit) that delegates training to Kohya Scripts, simplifying the end-to-end workflow for training FLUX LoRA models. The project specifically targets low-VRAM environments and includes optimizations and sensible defaults for systems with 12GB, 16GB, and 20GB of VRAM, lowering the barrier for users who cannot run large native trainings. FluxGym’s design emphasizes straightforward configuration and quick iteration: the Gradio UI exposes the essential training controls while Kohya Scripts handle the underlying training steps. Because it’s an open-source GitHub project, users can inspect the code, adapt the UI, or extend the integration with additional Kohya options. FluxGym is suited for hobbyists and practitioners who want a lightweight, browser-accessible way to fine-tune LoRA adapters without managing complex command-line orchestration.
Key Features
- Gradio-based web UI providing a simplified interface for LoRA training sessions
- Low-VRAM support and optimizations targeting 12GB, 16GB, and 20GB GPUs
- Powered by Kohya Scripts as the training backend for established LoRA workflows
- Forked from AI-Toolkit to preserve streamlined workflows and familiar UI patterns
- Open-source repository on GitHub for code inspection, contributions, and forking
Community
FluxGym is hosted on GitHub where users can open issues, file pull requests, and review code. The project attracts practitioners familiar with Kohya Scripts and low-VRAM training; community engagement typically centers on troubleshooting configurations, sharing dataset tips, and proposing UI or training improvements. For the latest activity, contributors and users should consult the repository’s issue tracker and pull request history.
Key Information
- Category: Training Tools
- Type: AI Training Tools Tool