Qwen-Image-Edit-2509 StreamDiffusion WebUI

Fast real-time image editing WebUI using Qwen-Image-Edit-2509 model. Inspired by StreamDiffusion and StreamDiffusion2

Work in Progress

Speed Improvements

Achieved 20x speedup with Lightning LoRA optimization

Architecture

Two deployment options:

Option 1: Gradio (Simple)

Single-file WebUI, good for single user testing.

Option 2: Client-Server (Production)

Separated backend API and React frontend, supports multiple clients.

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│   React     │────▶│   FastAPI    │────▶│    GPU      │
│  Frontend   │◀────│   Backend    │◀────│   (A100)    │
└─────────────┘     └──────────────┘     └─────────────┘
   Port 3000           Port 8086            CUDA
                    (torch.compile)

Requirements

• NVIDIA GPU (80GB VRAM recommended, e.g., A100)
• Python 3.11+
• CUDA 12.x
• Node.js 18+ (for React frontend)

Installation

Backend

pip install torch diffusers transformers accelerate fastapi uvicorn

Frontend

cd frontend
npm install

Usage

Option 1: Gradio (Simple)

CUDA_VISIBLE_DEVICES=0 python webui_realtime.py

Open http://localhost:7865

Option 2: Client-Server (Production)

1. Start API Server:

CUDA_VISIBLE_DEVICES=0 python server.py

2. Start React Frontend:

cd frontend
REACT_APP_API_URL=http://your-gpu-server:8086 npm start

For webcam access, HTTPS is required:

ngrok http 3000  # For frontend

Features

• Real-time webcam image editing
• Image upload editing
• Two-image compositing with editing
• Custom prompts
• Multi-client support (with server.py)

API Endpoints

• GET /health - Health check
• POST /edit - Edit image
  • image: Base64 encoded image
  • prompt: Edit instruction
  • steps: Inference steps (2-8)
  • ref_image: Optional reference image for compositing
  • blend_ratio: Blend ratio (0-1)

Notes

• 1-step inference is numerically unstable (NaN), minimum 2 steps required
• Model size is approximately 67GB (transformer 58GB + VAE 9GB)
• Server queues requests - only one GPU inference at a time
• Lightning LoRA (lightx2v/Qwen-Image-Lightning) provides stable 5.6s inference
• torch.compile is incompatible with LoRA (causes recompilation issues)

Python Files

Core Application

Pipeline Implementations

Quantization

Benchmarks

Utilities & Tests

Acknowledgements

This project builds upon the following excellent works:

• StreamDiffusionV2 - A streaming system for dynamic and interactive video generation
• StreamDiffusion - Pipeline-level solution for real-time interactive generation
• Qwen-Image-Edit-2509 - Image editing model by Qwen team
• Qwen-Image-Lightning - Lightning LoRA for fast 2-step inference

Citation

If you use this project, please cite the following works:

StreamDiffusionV2

@article{feng2025streamdiffusionv2,
  title={StreamDiffusionV2: A Streaming System for Dynamic and Interactive Video Generation},
  author={Feng, Tianrui and Li, Zhi and Yang, Shuo and Xi, Haocheng and Li, Muyang and Li, Xiuyu and Zhang, Lvmin and Yang, Keting and Peng, Kelly and Han, Song and others},
  journal={arXiv preprint arXiv:2511.07399},
  year={2025}
}

StreamDiffusion

@article{kodaira2023streamdiffusion,
  title={StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation},
  author={Akio Kodaira and Chenfeng Xu and Toshiki Hazama and Takanori Yoshimoto and Kohei Ohno and Shogo Mitsuhori and Soichi Sugano and Hanying Cho and Zhijian Liu and Kurt Keutzer},
  year={2023},
  eprint={2312.12491},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Qwen-Image

@misc{wu2025qwenimagetechnicalreport,
  title={Qwen-Image Technical Report},
  author={Wu, Chenfei and Li, Jiahao and Zhou, Jingren and others},
  year={2025},
  eprint={2508.02324},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

License

Apache License 2.0