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--- |
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title: DragGan - Drag Your GAN |
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emoji: 👆🐉 |
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colorFrom: purple |
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colorTo: pink |
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sdk: gradio |
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sdk_version: 3.35.2 |
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app_file: visualizer_drag_gradio.py |
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pinned: false |
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duplicated_from: DragGan/DragGan |
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--- |
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# Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold |
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https://arxiv.org/abs/2305.10973 |
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https://huggingface.co/DragGan/DragGan-Models |
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<p align="center"> |
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<img src="DragGAN.gif", width="700"> |
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</p> |
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**Figure:** *Drag your GAN.* |
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> **Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold** <br> |
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> Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, Christian Theobalt<br> |
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> *SIGGRAPH 2023 Conference Proceedings* |
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## Requirements |
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Please follow the requirements of [https://github.com/NVlabs/stylegan3](https://github.com/NVlabs/stylegan3). |
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## Download pre-trained StyleGAN2 weights |
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To download pre-trained weights, simply run: |
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```sh |
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sh scripts/download_model.sh |
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``` |
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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`. |
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Feel free to try other pretrained StyleGAN. |
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## Run DragGAN GUI |
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To start the DragGAN GUI, simply run: |
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```sh |
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sh scripts/gui.sh |
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``` |
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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. |
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You can run DragGAN Gradio demo as well: |
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```sh |
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python visualizer_drag_gradio.py |
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``` |
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## Acknowledgement |
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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). |
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## License |
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The code related to the DragGAN algorithm is licensed under [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/). |
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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). |
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Any form of use and derivative of this code must preserve the watermarking functionality. |
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## BibTeX |
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```bibtex |
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@inproceedings{pan2023draggan, |
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title={Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold}, |
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author={Pan, Xingang and Tewari, Ayush, and Leimk{\"u}hler, Thomas and Liu, Lingjie and Meka, Abhimitra and Theobalt, Christian}, |
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booktitle = {ACM SIGGRAPH 2023 Conference Proceedings}, |
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year={2023} |
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} |
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``` |
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