---
title: shuangzizuoDragGAN
app_file: visualizer_drag_gradio.py
sdk: gradio
sdk_version: 3.35.2
---
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
## Web Demos
[](https://openxlab.org.cn/apps/detail/XingangPan/DragGAN)
## Requirements
If you have CUDA graphic card, please follow the requirements of [NVlabs/stylegan3](https://github.com/NVlabs/stylegan3#requirements).
The usual installation steps involve the following commands, they should set up the correct CUDA version and all the python packages
```
conda env create -f environment.yml
conda activate stylegan3
```
Then install the additional requirements
```
pip install -r requirements.txt
```
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
```
## Run Gradio visualizer in Docker
Provided docker image is based on NGC PyTorch repository. To quickly try out visualizer in Docker, run the following:
```sh
# before you build the docker container, make sure you have cloned this repo, and downloaded the pretrained model by `python scripts/download_model.py`.
docker build . -t draggan:latest
docker run -p 7860:7860 -v "$PWD":/workspace/src -it draggan:latest bash
# (Use GPU)if you want to utilize your Nvidia gpu to accelerate in docker, please add command tag `--gpus all`, like:
# docker run --gpus all -p 7860:7860 -v "$PWD":/workspace/src -it draggan:latest bash
cd src && python visualizer_drag_gradio.py --listen
```
Now you can open a shared link from Gradio (printed in the terminal console).
Beware the Docker image takes about 25GB of disk space!
## Download pre-trained StyleGAN2 weights
To download pre-trained weights, simply run:
```
python scripts/download_model.py
```
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
```
If you are using windows, you can run:
```
.\scripts\gui.bat
```
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, this is universal for both windows and linux:
```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).
(cheers to the community as well)
## 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}
}
```
