Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Figure: Drag your GAN.

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

Requirements

Please follow the requirements of https://github.com/NVlabs/stylegan3.

Download pre-trained StyleGAN2 weights

To download pre-trained weights, simply run:

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, LHQ, and put them under ./checkpoints.

Feel free to try other pretrained StyleGAN.

Run DragGAN GUI

To start the DragGAN GUI, simply run:

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. Then load the new latent code and model weights to the GUI.

You can run DragGAN Gradio demo as well:

python visualizer_drag_gradio.py

Acknowledgement

This code is developed based on StyleGAN3. Part of the code is borrowed from StyleGAN-Human.

License

The code related to the DragGAN algorithm is licensed under CC-BY-NC. However, most of this project are available under a separate license terms: all codes used or modified from StyleGAN3 is under the Nvidia Source Code License.

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

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}
}

Madankh/DragGAN