README.md

---
license: apache-2.0
pipeline_tag: image-to-image
---

# VeraRetouch: A Lightweight Fully Differentiable Framework for Multi-Task Reasoning Photo Retouching

VeraRetouch is a lightweight and fully differentiable framework for multi-task photo retouching. It employs a 0.5B Vision-Language Model (VLM) as the central intelligence to formulate retouching plans based on instructions and scene semantics, combined with a fully differentiable Retouch Renderer for direct end-to-end pixel-level training.

[**Paper**](https://huggingface.co/papers/2604.27375) | [**Project Page**](https://apollo-yi.github.io/VeraRetouch/) | [**GitHub**](https://github.com/OpenVeraTeam/VeraRetouch)

## 🚀 Quick Start

### ⚙️ Environment Setup
```bash
# Clone the repository
git clone https://github.com/OpenVeraTeam/VeraRetouch.git
cd VeraRetouch

# Create and activate conda environment
conda create -n vera-retouch python=3.10
conda activate vera-retouch
pip install -r requirements.txt
```

### 🎨 Inference Modes
VeraRetouch supports three primary inference modes via `inference.py`. Ensure you have downloaded the weights and placed them in the `./checkpoints` directory.

#### Auto Retouch
Automatically enhances an image based on scene analysis.
```bash
python inference.py --mode auto \
                    --model-path ./checkpoints/VeraRetouch \
                    --img_paths ./data_samples/input/sample_flower.jpg \
                    --save_dir ./data_samples/output/
```

#### Style Retouch
Retouches an image based on a specific user prompt.
```bash
python inference.py --mode style \
                    --prompt "I want a dreamy bright pink style." \
                    --model-path ./checkpoints/VeraRetouch \
                    --img_paths ./data_samples/input/sample_flower.jpg \
                    --save_dir ./data_samples/output/
```

#### Param Retouch
Applies retouching based on specific operator parameters.
```bash
python inference.py --mode style \
                    --instruction_path ./data_samples/param.json \
                    --model-path ./checkpoints/VeraRetouch \
                    --img_paths ./data_samples/input/sample_flower.jpg \
                    --save_dir ./data_samples/output/
```

## Citation
```bibtex
@article{guo2026veraretouch,
  title={VeraRetouch: A Lightweight Fully Differentiable Framework for Multi-Task Reasoning Photo Retouching},
  author={Guo, Yihong and Lyu, Youwei and Tang, Jiajun and Zhou, Yizhuo and Wang, Hongliang and Chen, Jinwei and Zou, Changqing and Fan, Qingnan},
  journal={arXiv preprint arXiv:2604.27375},
  year={2026}
}
```