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 [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](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} } ```