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Unofficial implementation of "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold"

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DragGAN

💥 Colab Demo

Note for Colab, remember to select a GPU via Runtime/Change runtime type (代码执行程序/更改运行时类型).

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

demo

🌟 Features

  • Tweak performance.
  • Automatically determining the number of iterations.
  • Custom Image with GAN inversion.
  • Download generated image and generation trajectory.
  • Controling generation process with GUI.
  • Automatically download stylegan2 checkpoint.
  • Support movable region, mutliple handle points.
  • Gradio and Colab Demo.

Demo

demo.mp4

Usage

Ensure you have PyTorch, Gradio, and tqdm installed.

pip install -r requirements.txt

Lanuch the Gradio demo

python gradio_app.py

If you have any issuse for downloading the checkpoint, you could manually download it from here and put it into the folder checkpoints.

Acknowledgement

Official DragGANStyleGAN2StyleGAN2-pytorch

Citation

@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}
}

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Unofficial implementation of "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold"

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  • Python 83.1%
  • Cuda 13.1%
  • C++ 2.3%
  • Jupyter Notebook 1.5%