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---
language:
- en
license: mit
license_name: flux-1-dev
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
tags:
- image-restoration
- diffusion
- computer-vision
- flux
pipeline_tag: image-to-image
library_name: diffusers
---

<div align="center">
<h1>🎨 LucidFlux:<br/>Caption-Free Universal Image Restoration with a Large-Scale Diffusion Transformer</h1>

### 
[**🌍 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) 
</div>

---
<img width="1420" height="1116" alt="abs_image" src="https://github.com/user-attachments/assets/791c0c60-29a6-4497-86a9-5716049afe9a" />

---
## News & Updates

---

Let us know if this works!

## 👥 Authors

> [**Song Fei**](https://github.com/FeiSong123)<sup>1</sup>\*, [**Tian Ye**](https://owen718.github.io/)<sup>1</sup>\*‡, [**Lei Zhu**](https://sites.google.com/site/indexlzhu/home)<sup>1,2</sup>†
>
> <sup>1</sup>The Hong Kong University of Science and Technology (Guangzhou)  
> <sup>2</sup>The Hong Kong University of Science and Technology  
>
> \*Equal Contribution, ‡Project Leader, †Corresponding Author

---

## 🌟 What is LucidFlux?

<!-- <div align="center">
<img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/demo/demo2.png" alt="What is LucidFlux - Quick Prompt Demo" width="1200"/>
<br>
</div> -->

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.

<!-- ## 🚀 Quick Start

### 🔧 Installation

```bash
# Clone the repository
git clone https://github.com/ephemeral182/LucidFlux.git
cd LucidFlux

# Create conda environment
conda create -n postercraft python=3.11
conda activate postercraft

# Install dependencies
pip install -r requirements.txt

``` -->

<!-- ### 🚀 Quick Generation

Generate high-quality aesthetic posters from your prompt with `BF16` precision:

```bash
python inference.py \
  --prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \
  --enable_recap \
  --num_inference_steps 28 \
  --guidance_scale 3.5 \
  --seed 42 \
  --pipeline_path "black-forest-labs/FLUX.1-dev" \
  --custom_transformer_path "LucidFlux/LucidFlux-v1_RL" \
  --qwen_model_path "Qwen/Qwen3-8B"
```

If you are running on a GPU with limited memory, you can use `inference_offload.py` to offload some components to the CPU:

```bash
python inference_offload.py \
  --prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \
  --enable_recap \
  --num_inference_steps 28 \
  --guidance_scale 3.5 \
  --seed 42 \
  --pipeline_path "black-forest-labs/FLUX.1-dev" \
  --custom_transformer_path "LucidFlux/LucidFlux-v1_RL" \
  --qwen_model_path "Qwen/Qwen3-8B"
``` -->
<!-- 
### 💻 Gradio Web UI

We provide a Gradio web UI for LucidFlux. 

```bash
python demo_gradio.py
``` -->


## 📊 Performance Benchmarks

<div align="center">

### 📈 Quantitative Results

<table>
<thead>
  <tr>
    <th>Benchmark</th>
    <th>Metric</th>
    <th>ResShift</th>
    <th>StableSR</th>
    <th>SinSR</th>
    <th>SeeSR</th>
    <th>DreamClear</th>
    <th>SUPIR</th>
    <th>LucidFlux<br/>(Ours)</th>
  </tr>
</thead>
<tbody>
  <tr>
    <td rowspan="7" style="text-align:center; vertical-align:middle;">RealSR</td>
    <td style="white-space: nowrap;">CLIP-IQA+ ↑</td>
    <td>0.5005</td>
    <td>0.4408</td>
    <td>0.5416</td>
    <td>0.6731</td>
    <td>0.5331</td>
    <td>0.5640</td>
    <td><b>0.7074</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">Q-Align ↑</td>
    <td>3.1045</td>
    <td>2.5087</td>
    <td>3.3615</td>
    <td>3.6073</td>
    <td>3.0044</td>
    <td>3.4682</td>
    <td><b>3.7555</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">MUSIQ ↑</td>
    <td>49.50</td>
    <td>39.98</td>
    <td>57.95</td>
    <td>67.57</td>
    <td>49.48</td>
    <td>55.68</td>
    <td><b>70.20</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">MANIQA ↑</td>
    <td>0.2976</td>
    <td>0.2356</td>
    <td>0.3753</td>
    <td>0.5087</td>
    <td>0.3092</td>
    <td>0.3426</td>
    <td><b>0.5437</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">NIMA ↑</td>
    <td>4.7026</td>
    <td>4.3639</td>
    <td>4.8282</td>
    <td>4.8957</td>
    <td>4.4948</td>
    <td>4.6401</td>
    <td><b>5.1072</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">CLIP-IQA ↑</td>
    <td>0.5283</td>
    <td>0.3521</td>
    <td>0.6601</td>
    <td><b>0.6993</b></td>
    <td>0.5390</td>
    <td>0.4857</td>
    <td>0.6783</td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">NIQE ↓</td>
    <td>9.0674</td>
    <td>6.8733</td>
    <td>6.4682</td>
    <td>5.4594</td>
    <td>5.2873</td>
    <td>5.2819</td>
    <td><b>4.2893</b></td>
  </tr>
  <tr>
    <td rowspan="7" style="text-align:center; vertical-align:middle;">RealLQ250</td>
    <td style="white-space: nowrap;">CLIP-IQA+ ↑</td>
    <td>0.5529</td>
    <td>0.5804</td>
    <td>0.6054</td>
    <td>0.7034</td>
    <td>0.6810</td>
    <td>0.6532</td>
    <td><b>0.7406</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">Q-Align ↑</td>
    <td>3.6318</td>
    <td>3.5586</td>
    <td>3.7451</td>
    <td>4.1423</td>
    <td>4.0640</td>
    <td>4.1347</td>
    <td><b>4.3935</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">MUSIQ ↑</td>
    <td>59.50</td>
    <td>57.25</td>
    <td>65.45</td>
    <td>70.38</td>
    <td>67.08</td>
    <td>65.81</td>
    <td><b>73.01</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">MANIQA ↑</td>
    <td>0.3397</td>
    <td>0.2937</td>
    <td>0.4230</td>
    <td>0.4895</td>
    <td>0.4400</td>
    <td>0.3826</td>
    <td><b>0.5589</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">NIMA ↑</td>
    <td>5.0624</td>
    <td>5.0538</td>
    <td>5.2397</td>
    <td>5.3146</td>
    <td>5.2200</td>
    <td>5.0806</td>
    <td><b>5.4836</b></td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">CLIP-IQA ↑</td>
    <td>0.6129</td>
    <td>0.5160</td>
    <td><b>0.7166</b></td>
    <td>0.7063</td>
    <td>0.6950</td>
    <td>0.5767</td>
    <td>0.7122</td>
  </tr>
  <tr>
    <td style="white-space: nowrap;">NIQE ↓</td>
    <td>6.6326</td>
    <td>4.6236</td>
    <td>5.4425</td>
    <td>4.4383</td>
    <td>3.8700</td>
    <td><b>3.6591</b></td>
    <td>3.6742</td>
  </tr>
</tbody>
</table>



<!-- <img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/user_study/hpc.png" alt="User Study Results" width="1200"/> -->

</div>

---

## 🎭 Gallery & Examples

<div align="center">

### 🎨 LucidFlux Gallery

---

### 🔍 Comparison with Open-Source Methods

<table>
<tr align="center">
    <td width="200"><b>LQ</b></td>
    <td width="200"><b>SinSR</b></td>
    <td width="200"><b>SeeSR</b></td>
    <td width="200"><b>SUPIR</b></td>
    <td width="200"><b>DreamClear</b></td>
    <td width="200"><b>Ours</b></td>
</tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/040.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/041.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/111.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/123.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/160.jpg" width="1200"></td></tr>
</table>

<details>
<summary>Show more examples</summary>

<table>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/013.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/079.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/082.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/137.jpg" width="1200"></td></tr>
<tr align="center"><td colspan="6"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/comparison/166.jpg" width="1200"></td></tr>
</table>

</details>

---

### 💼 Comparison with Commercial Models

<table>
<tr align="center">
    <td width="200"><b>LQ</b></td>
    <td width="200"><b>HYPIR</b></td>
    <td width="200"><b>Topaz</b></td>
    <td width="200"><b>SeeDream 4.0</b></td>
    <td width="200"><b>Gemini-NanoBanana</b></td>
    <td width="200"><b>GPT-4o</b></td>
    <td width="200"><b>Ours</b></td>
</tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_061.jpg" width="1400"></td></tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_094.jpg" width="1400"></td></tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_205.jpg" width="1400"></td></tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_209.jpg" width="1400"></td></tr>
</table>

<details>
<summary>Show more examples</summary>

<table>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_062.jpg" width="1400"></td></tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_160.jpg" width="1400"></td></tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_111.jpg" width="1400"></td></tr>
<tr align="center"><td colspan="7"><img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/commercial_comparison/commercial_123.jpg" width="1400"></td></tr>
</table>

</details>
</div>

---

## 🏗️ Model Architecture

<div align="center">
<img src="https://raw.githubusercontent.com/W2GenAI-Lab/LucidFlux/main/images/framework/framework.png" alt="LucidFlux Framework Overview" width="1200"/>
<br>
<em><strong>Caption-Free Universal Image Restoration with a Large-Scale Diffusion Transformer</strong></em>
</div>

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**: `[email protected]`
- **Tian Ye**: `[email protected]`

## 🧑‍🤝‍🧑 WeChat Group
<details>
  <summary>点击展开二维码（WeChat Group QR Code）</summary>

  <br>

  <img src="https://github.com/user-attachments/assets/047faa4e-da63-415c-97a0-8dbe8045a839"
       alt="WeChat Group QR"
       width="320">
</details>