This repository provides an open-source implementation for training Low-Rank Adaptation (LoRA) layers for Qwen-Image and Qwen-Image-Edit models, enabling efficient fine-tuning for text-to-image generation and control-based image editing. It is targeted at GenAI developers and researchers looking to customize these powerful diffusion models. The primary benefit is the ability to achieve significant model adaptation with reduced computational resources and training time compared to full fine-tuning.

How It Works

The project leverages LoRA, a parameter-efficient fine-tuning technique that injects trainable low-rank matrices into the existing model weights. This approach significantly reduces the number of trainable parameters, leading to faster training and smaller model checkpoints. The implementation is built on Hugging Face's diffusers library, ensuring compatibility and ease of use within the existing ecosystem. It supports both standard text-to-image generation with Qwen-Image and control-based editing with Qwen-Image-Edit, offering flexibility for different use cases.

Quick Start & Requirements

• Installation: Clone the repository, navigate into it, and install requirements using pip install -r requirements.txt. Install the latest diffusers from GitHub: pip install git+https://github.com/huggingface/diffusers.
• Prerequisites: Python 3.10. Optional: Download pre-trained LoRA weights from Hugging Face.
• Setup: Basic setup involves cloning and installing packages, estimated to take a few minutes.
• Links: Official Website, Documentation, Discord Community, X (Twitter), LinkedIn, YouTube, Instagram.

Highlighted Details

• Supports both Qwen-Image and Qwen-Image-Edit models.
• Features easy configuration via YAML files.
• Includes comprehensive dataset preparation instructions and a validation script.
• Offers a ComfyUI workflow for simplified inference.

Maintenance & Community

The project is actively under development with recent updates adding support for Qwen-Image-Edit and optimizing for lower VRAM GPUs. Community support is available via Discord, and the project maintains an active presence on X, LinkedIn, YouTube, and Instagram.

Licensing & Compatibility

The repository is open-source, facilitating use and modification. Specific license details are not explicitly stated in the README, but the open-source nature suggests broad compatibility for research and development.

Limitations & Caveats

The project is marked as "Under Development," with ongoing work on performance optimization and test coverage. While functional, users should be aware that it is in a refinement stage and may encounter evolving features or potential bugs.