---
title: DragGan
canonical_url: "https://www.modelscope.cn/models/thomas/DragGan"
md_url: "https://www.modelscope.cn/models/thomas/DragGan.md"
repository: thomas/DragGan
last_updated: 2023-06-28
license: "Apache License 2.0"
pipeline_tag: vision-efficient-tuning
tasks:
  - vision-efficient-tuning
library_name:
  - pytorch
frameworks:
  - Pytorch
downloads: 139
stars: 3
---

# DragGan

> DragGan - thomas 在 ModelScope 开源的模型。Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold Xingang Pan · Ayush Tewari · Thomas Leimkühler · Lingjie Liu · Abhimitra Meka · Christian Theobalt SIGGRAPH 2023 Conference Proceedings

thomas/DragGan 是 ModelScope 魔搭社区上的vision-efficient-tuning模型，采用 Apache License 2.0 许可。

- **Repository**: thomas/DragGan
- **License**: Apache License 2.0
- **Tasks**: vision-efficient-tuning
- **Downloads**: 139
- **Stars**: 3
- **Last updated**: 2023-06-28

Source: https://www.modelscope.cn/models/thomas/DragGan

---

<p align="center">

  <h1 align="center">Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold</h1>
  <p align="center">
    <a href="https://xingangpan.github.io/"><strong>Xingang Pan</strong></a>
    ·
    <a href="https://ayushtewari.com/"><strong>Ayush Tewari</strong></a>
    ·
    <a href="https://people.mpi-inf.mpg.de/~tleimkue/"><strong>Thomas Leimkühler</strong></a>
    ·
    <a href="https://lingjie0206.github.io/"><strong>Lingjie Liu</strong></a>
    ·
    <a href="https://www.meka.page/"><strong>Abhimitra Meka</strong></a>
    ·
    <a href="http://www.mpi-inf.mpg.de/~theobalt/"><strong>Christian Theobalt</strong></a>
  </p>
  <h2 align="center">SIGGRAPH 2023 Conference Proceedings</h2>
  <div align="center">
    <img src="DragGAN.gif", width="600">
  </div>

  <p align="center">
  <br>
    <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
    <a href="https://twitter.com/XingangP"><img alt='Twitter' src="https://img.shields.io/twitter/follow/XingangP?label=%40XingangP"></a>
    <a href="https://arxiv.org/abs/2305.10973">
      <img src='https://img.shields.io/badge/Paper-PDF-green?style=for-the-badge&logo=adobeacrobatreader&logoWidth=20&logoColor=white&labelColor=66cc00&color=94DD15' alt='Paper PDF'>
    </a>
    <a href='https://vcai.mpi-inf.mpg.de/projects/DragGAN/'>
      <img src='https://img.shields.io/badge/DragGAN-Page-orange?style=for-the-badge&logo=Google%20chrome&logoColor=white&labelColor=D35400' alt='Project Page'></a>
    <a href="https://colab.research.google.com/drive/1mey-IXPwQC_qSthI5hO-LTX7QL4ivtPh?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
  </p>
</p>

## Web Demos

[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/XingangPan/DragGAN)

<p align="left">
  <a href="https://huggingface.co/spaces/radames/DragGan"><img alt="Huggingface" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DragGAN-orange"></a>
</p>

## Requirements

If you have CUDA graphic card, please follow the requirements of [NVlabs/stylegan3](https://github.com/NVlabs/stylegan3#requirements).  

Otherwise (for GPU acceleration on MacOS with Silicon Mac M1/M2, or just CPU) try the following:

```sh
cat environment.yml | \
  grep -v -E 'nvidia|cuda' > environment-no-nvidia.yml && \
    conda env create -f environment-no-nvidia.yml
conda activate stylegan3

# On MacOS
export PYTORCH_ENABLE_MPS_FALLBACK=1
```

## Download pre-trained StyleGAN2 weights

To download pre-trained weights, simply run:
```sh
sh scripts/download_model.sh
```
If you want to try StyleGAN-Human and the Landscapes HQ (LHQ) dataset, please download weights from these links: [StyleGAN-Human](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing), [LHQ](https://drive.google.com/file/d/16twEf0T9QINAEoMsWefoWiyhcTd-aiWc/view?usp=sharing), and put them under `./checkpoints`.

Feel free to try other pretrained StyleGAN.

## Run DragGAN GUI

To start the DragGAN GUI, simply run:
```sh
sh scripts/gui.sh
```

This GUI supports editing GAN-generated images. To edit a real image, you need to first perform GAN inversion using tools like [PTI](https://github.com/danielroich/PTI). Then load the new latent code and model weights to the GUI.

You can run DragGAN Gradio demo as well:
```sh
python visualizer_drag_gradio.py
```

## Acknowledgement

This code is developed based on [StyleGAN3](https://github.com/NVlabs/stylegan3). Part of the code is borrowed from [StyleGAN-Human](https://github.com/stylegan-human/StyleGAN-Human).

## License

The code related to the DragGAN algorithm is licensed under [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/).
However, most of this project are available under a separate license terms: all codes used or modified from [StyleGAN3](https://github.com/NVlabs/stylegan3) is under the [Nvidia Source Code License](https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt).

Any form of use and derivative of this code must preserve the watermarking functionality showing "AI Generated".

## BibTeX

```bibtex
@inproceedings{pan2023draggan,
    title={Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold},
    author={Pan, Xingang and Tewari, Ayush, and Leimk{\"u}hler, Thomas and Liu, Lingjie and Meka, Abhimitra and Theobalt, Christian},
    booktitle = {ACM SIGGRAPH 2023 Conference Proceedings},
    year={2023}
}
```

#### Clone with HTTP
```bash
 git clone https://www.modelscope.cn/thomas/DragGan.git
```