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News & Updates

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Let us know if this works!

👥 Authors

Song Fei¹*, Tian Ye¹*‡, Lei Zhu^1,2†

¹The Hong Kong University of Science and Technology (Guangzhou)
²The Hong Kong University of Science and Technology

*Equal Contribution, ‡Project Leader, †Corresponding Author

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🌟 What is LucidFlux?

LucidFlux is a framework designed to perform high-fidelity image restoration across a wide range of degradations without requiring textual captions. By combining a Flux-based DiT backbone with Light-weight Condition Module and SigLIP semantic alignment, LucidFlux enables caption-free guidance while preserving structural and semantic consistency, achieving superior restoration quality.

📊 Performance Benchmarks

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🎭 Gallery & Examples

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🏗️ Model Architecture

Our unified framework consists of four critical components in the training workflow:

🔤 Scaling Up Real-world High-Quality Data for Universal Image Restoration

🎨 Two Parallel Light-weight Condition Module Branches for Low-Quality Image Conditioning

🎯 Timestep and Layer-Adaptive Condition Injection

🔄 Semantic Priors from Siglip for Caption-Free Semantic Alignment

🚀 Quick Start

🔧 Installation

# Clone the repository
git clone https://github.com/W2GenAI-Lab/LucidFlux.git
cd LucidFlux

# Create conda environment
conda create -n lucidflux python=3.9
conda activate lucidflux

# Install dependencies
pip install -r requirements.txt

Inference

Prepare models in 2 steps, then run a single command.

1. Login to Hugging Face (required for gated FLUX.1-dev). Skip if already logged-in.

python -m tools.hf_login --token "$HF_TOKEN"

2. Download required weights to fixed paths and export env vars

# FLUX.1-dev (flow+ae), SwinIR prior, T5, CLIP, SigLIP and LucidFlux checkpoint to ./weights
python -m tools.download_weights --dest weights

# Exports FLUX_DEV_FLOW/FLUX_DEV_AE to your shell
source weights/env.sh

Run inference (uses fixed relative paths):

bash inference.sh

You can also obtain results of LucidFlux on RealSR and RealLQ250 from Hugging Face: LucidFlux.

🪪 License

The provided code and pre-trained weights are licensed under the FLUX.1 [dev].

🙏 Acknowledgments

• This code is based on FLUX. Some code are brought from DreamClear, x-flux. We thank the authors for their awesome work.

• 🏛️ Thanks to our affiliated institutions for their support.

• 🤝 Special thanks to the open-source community for inspiration.

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📬 Contact

For any questions or inquiries, please reach out to us:

• Song Fei: sfei285@connect.hkust-gz.edu.cn
• Tian Ye: tye610@connect.hkust-gz.edu.cn

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