---
title: LucidFlux
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/LucidFlux"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/LucidFlux.md"
repository: AI-ModelScope/LucidFlux
last_updated: 2026-05-26
license: other
pipeline_tag: image-to-image
tasks:
- image-to-image
library_name:
- pytorch
frameworks:
- pytorch
language:
- en
downloads: 110
stars: 1
tags:
- image-restoration
- diffusion
- computer-vision
- flux
---
# LucidFlux
> LucidFlux - AI-ModelScope 在 ModelScope 开源的模型。🎨 LucidFlux: Caption-Free Universal Image Restoration with a Large-Scale Diffusion Transformer
AI-ModelScope/LucidFlux 是 ModelScope 魔搭社区上的image-to-image模型，采用 other 许可。
- **Repository**: AI-ModelScope/LucidFlux
- **License**: other
- **Tasks**: image-to-image
- **Tags**: image-restoration, diffusion, computer-vision, flux
- **Downloads**: 110
- **Stars**: 1
- **Last updated**: 2026-05-26
Source: https://www.modelscope.cn/models/AI-ModelScope/LucidFlux
---
🎨 LucidFlux:
Caption-Free Universal Image Restoration with a Large-Scale Diffusion Transformer
###
[**🌍 Website**](https://w2genai-lab.github.io/LucidFlux/) | [**📄 Paper**](https://huggingface.co/papers/2509.22414) | [**💻 Code**](https://github.com/W2GenAI-Lab/LucidFlux) | [**🧩 Models**](https://huggingface.co/W2GenAI/LucidFlux)
---
---
## News & Updates
---
Let us know if this works!
## 👥 Authors
> [**Song Fei**](https://github.com/FeiSong123)1\*, [**Tian Ye**](https://owen718.github.io/)1\*‡, [**Lei Zhu**](https://sites.google.com/site/indexlzhu/home)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
---
## 🌟 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
### 📈 Quantitative Results
| Benchmark |
Metric |
ResShift |
StableSR |
SinSR |
SeeSR |
DreamClear |
SUPIR |
LucidFlux
(Ours) |
| RealSR |
CLIP-IQA+ ↑ |
0.5005 |
0.4408 |
0.5416 |
0.6731 |
0.5331 |
0.5640 |
0.7074 |
| Q-Align ↑ |
3.1045 |
2.5087 |
3.3615 |
3.6073 |
3.0044 |
3.4682 |
3.7555 |
| MUSIQ ↑ |
49.50 |
39.98 |
57.95 |
67.57 |
49.48 |
55.68 |
70.20 |
| MANIQA ↑ |
0.2976 |
0.2356 |
0.3753 |
0.5087 |
0.3092 |
0.3426 |
0.5437 |
| NIMA ↑ |
4.7026 |
4.3639 |
4.8282 |
4.8957 |
4.4948 |
4.6401 |
5.1072 |
| CLIP-IQA ↑ |
0.5283 |
0.3521 |
0.6601 |
0.6993 |
0.5390 |
0.4857 |
0.6783 |
| NIQE ↓ |
9.0674 |
6.8733 |
6.4682 |
5.4594 |
5.2873 |
5.2819 |
4.2893 |
| RealLQ250 |
CLIP-IQA+ ↑ |
0.5529 |
0.5804 |
0.6054 |
0.7034 |
0.6810 |
0.6532 |
0.7406 |
| Q-Align ↑ |
3.6318 |
3.5586 |
3.7451 |
4.1423 |
4.0640 |
4.1347 |
4.3935 |
| MUSIQ ↑ |
59.50 |
57.25 |
65.45 |
70.38 |
67.08 |
65.81 |
73.01 |
| MANIQA ↑ |
0.3397 |
0.2937 |
0.4230 |
0.4895 |
0.4400 |
0.3826 |
0.5589 |
| NIMA ↑ |
5.0624 |
5.0538 |
5.2397 |
5.3146 |
5.2200 |
5.0806 |
5.4836 |
| CLIP-IQA ↑ |
0.6129 |
0.5160 |
0.7166 |
0.7063 |
0.6950 |
0.5767 |
0.7122 |
| NIQE ↓ |
6.6326 |
4.6236 |
5.4425 |
4.4383 |
3.8700 |
3.6591 |
3.6742 |
Caption-Free Universal Image Restoration with a Large-Scale Diffusion Transformer
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
```bash
# 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.
```bash
python -m tools.hf_login --token "$HF_TOKEN"
```
2) Download required weights to fixed paths and export env vars
```bash
# 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
bash inference.sh
```
You can also obtain results of LucidFlux on RealSR and RealLQ250 from Hugging Face: [**LucidFlux**](https://huggingface.co/W2GenAI/LucidFlux).
## 🪪 License
The provided code and pre-trained weights are licensed under the [FLUX.1 [dev]](LICENSE).
## 🙏 Acknowledgments
- This code is based on [FLUX](https://github.com/black-forest-labs/flux). Some code are brought from [DreamClear](https://github.com/shallowdream204/DreamClear), [x-flux](https://github.com/XLabs-AI/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.
---
## 📬 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`
## 🧑🤝🧑 WeChat Group
点击展开二维码（WeChat Group QR Code）
