---
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 reasoning photo retouching. It utilizes a 0.5B Vision-Language Model (VLM) to analyze image defects and formulate plans, which are then executed by a custom differentiable Retouch Renderer.

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

## Overview
Existing photo retouching approaches often rely on non-differentiable external software, creating optimization barriers. VeraRetouch overcomes this with a fully differentiable Retouch Renderer, enabling direct end-to-end pixel-level training. It supports several modes:
- **Auto Mode:** Analyzes image defects and enhances them automatically.
- **Style Mode:** Retouches images based on user-provided text prompts.
- **Param Mode:** Executes retouching based on specific operator parameters.

## Usage

### Installation
```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 Examples
The model supports three inference modes. First, download the weights and place them in the `./checkpoints` directory.

**Auto Retouch Mode:**
```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 Mode:**
```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 Mode:**
```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}
}
```