Qwen-Image-Edit-2509-LoRAs-Fast-Fusion

Qwen-Image-Edit-2509-LoRAs-Fast-Fusion is a fast, interactive web application built with Gradio that enables advanced image editing using the Qwen/Qwen-Image-Edit-2509 model from Alibaba's Qwen team. It leverages specialized LoRA adapters for efficient, low-step inference (as few as 4 steps) on tasks like texture application, object fusion, and cloth design transfer. Upload a base image and a reference image, provide a prompt, and select an editing style to generate edited results with Lightning-fast acceleration. This app showcases Qwen-Image-Edit's capabilities in inpainting, outpainting, and style transfer, optimized with Flash Attention 3 (if supported) and a custom orange-red themed interface for a vibrant user experience.

Key Features

• Specialized LoRA Adapters: Choose from "Texture Edit" (apply textures), "Fuse-Objects" (blend elements), or "Cloth-Design-Fuse" (transfer designs to clothing).
• Lightning Inference: Uses Qwen-Image-Lightning LoRA for ultra-fast generation (4 steps default).
• Prompt-Guided Edits: Natural language prompts guide the fusion (e.g., "Apply wood texture to the mug").
• Advanced Controls: Adjustable guidance scale, steps, seed randomization, and auto-resizing for optimal resolution (multiples of 16).
• GPU Optimization: Auto-detects CUDA; supports bfloat16 for efficiency.
• Gradio Interface: Dual-image upload with annotated outputs, examples, and progress tracking.
• Custom Theme: Orange-red gradients and shadows for a dynamic, fiery aesthetic.

Requirements

To run this app locally, install the dependencies. Note that it requires recent versions of Hugging Face libraries from Git for compatibility with Qwen-Image-Edit.

pip install torch torchvision numpy gradio spaces sentencepiece huggingface_hub supervision kernels
pip install git+https://github.com/huggingface/accelerate.git
pip install git+https://github.com/huggingface/diffusers.git
pip install git+https://github.com/huggingface/peft.git
pip install git+https://github.com/QwenLM/QwenImage.git  # For qwenimage custom components

Hardware Recommendations

• GPU: NVIDIA GPU with CUDA 11.8+ and at least 8GB VRAM (e.g., RTX 30-series or A100) for fast inference. Falls back to CPU (slower).
• RAM: 16GB+ recommended.
• Storage: ~10GB for model weights and LoRAs (downloaded on first run).

Installation & Setup

1. Clone the Repository:

   git clone https://github.com/PRITHIVSAKTHIUR/Qwen-Image-Edit-2509-LoRAs-Fast-Fusion.git
   cd Qwen-Image-Edit-2509-LoRAs-Fast-Fusion
   

2. Install Dependencies: Run the pip commands from the Requirements section above.

3. Prepare Examples (Optional): Ensure the examples/ folder contains sample images like Cloth1.jpg, Design1.png, Cup1.png, Wood1.png, etc. Download them if needed or replace with your own.

4. Run Locally:

   python app.py
   

   • The app launches a local web server (typically at http://127.0.0.1:7860).
   • Open the URL in your browser to start editing.

Deployment on Hugging Face Spaces

• Optimized for HF Spaces with @spaces.GPU (30-second duration limit per inference).
• Fork the repo, create a new Space on Hugging Face, and link it. Enable GPU in Space settings.
• Models and LoRAs auto-download on startup.

Hardware & Environment Requirements

Usage

1. Upload Images: Select a "Base Image" (e.g., a mug) and a "Reference Image" (e.g., wood texture).
2. Enter Prompt: Describe the edit (e.g., "Apply wood texture to the mug"). Defaults provided for each style.
3. Choose Editing Style: Dropdown for "Texture Edit", "Fuse-Objects", or "Cloth-Design-Fuse".
4. Tune Settings (Optional): Adjust seed, guidance scale (default: 1.0), and steps (default: 4) in the Advanced accordion.
5. Click Edit Image: Generates the output with progress bar. Randomize seed for variations.
6. Test Examples: Use built-in examples for quick demos like shirt design transfer.

Input/Output Format

• Inputs: Two PIL Images (base + reference), Text Prompt, LoRA Style, Seed/Params.
• Output: Edited PIL Image (PNG format) + Updated Seed.

Example Workflows

• Texture Edit: Base: Mug photo; Reference: Wood pattern; Prompt: "Apply wood texture to mug."
• Cloth-Design-Fuse: Base: Person in shirt; Reference: Graphic design; Prompt: "Put this design on their shirt."
• Fuse-Objects: Base: Cat photo; Reference: Glasses; Prompt: "A cat wearing glasses."

Code Structure

• app.py: Main Gradio app with pipeline loading, inference function, and UI setup.
• examples/: Sample image pairs for testing various edits.
• Custom Theme: OrangeRedTheme class for bold, gradient-based styling.
• Key Components:
  • QwenImageEditPlusPipeline: Core editing pipeline with LoRA fusion.
  • infer(...): Handles image preprocessing, adapter selection, and generation.
  • LoRAs: Lightning (speed), Texture, Fusion, Shirt Design—loaded and fused dynamically.

Model Details

• Base: Qwen/Qwen-Image-Edit-2509
• Transformer: linoyts/Qwen-Image-Edit-Rapid-AIO (subfolder: transformer)
• LoRAs: From lightx2v, tarn59, ostris

Troubleshooting

• Model/LoRA Loading Error: Verify internet access and Git installs. Clear cache with huggingface-cli delete-cache.
• CUDA/FA3 Issues: If Flash Attention 3 fails, it uses default attention. Ensure CUDA toolkit matches PyTorch version.
• Out of Memory: Reduce steps or image size; use torch_dtype=torch.float16 for lower VRAM.
• No Output/Blurry Results: Increase guidance scale (try 2.0+) or steps (8+). Ensure prompts are descriptive.
• Gradio Errors: Update Gradio (pip install --upgrade gradio) or check for port conflicts (e.g., --port 7861).

Contributing

Contributions welcome! Fork, branch, PR.

Acknowledgments

• Qwen Team: For the Qwen-Image-Edit model.
• Gradio: Intuitive UI framework.
• Hugging Face Diffusers/PEFT: For pipeline and adapter handling.
• LoRA Creators: lightx2v, tarn59, ostris.

For bugs or ideas, open a GitHub issue. Edit away!