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97 lines
4.3 KiB
Markdown
97 lines
4.3 KiB
Markdown
<p align="center">
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<h1 align="center">Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold</h1>
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<p align="center">
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<a href="https://xingangpan.github.io/"><strong>Xingang Pan</strong></a>
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<a href="https://ayushtewari.com/"><strong>Ayush Tewari</strong></a>
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<a href="https://people.mpi-inf.mpg.de/~tleimkue/"><strong>Thomas Leimkühler</strong></a>
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<a href="https://lingjie0206.github.io/"><strong>Lingjie Liu</strong></a>
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<a href="https://www.meka.page/"><strong>Abhimitra Meka</strong></a>
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<a href="http://www.mpi-inf.mpg.de/~theobalt/"><strong>Christian Theobalt</strong></a>
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</p>
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<h2 align="center">SIGGRAPH 2023 Conference Proceedings</h2>
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<div align="center">
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<img src="DragGAN.gif", width="600">
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</div>
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<p align="center">
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<br>
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<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
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<a href="https://twitter.com/XingangP"><img alt='Twitter' src="https://img.shields.io/twitter/follow/XingangP?label=%40XingangP"></a>
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<a href="https://arxiv.org/abs/2305.10973">
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<img src='https://img.shields.io/badge/Paper-PDF-green?style=for-the-badge&logo=adobeacrobatreader&logoWidth=20&logoColor=white&labelColor=66cc00&color=94DD15' alt='Paper PDF'>
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</a>
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<a href='https://vcai.mpi-inf.mpg.de/projects/DragGAN/'>
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<img src='https://img.shields.io/badge/DragGAN-Page-orange?style=for-the-badge&logo=Google%20chrome&logoColor=white&labelColor=D35400' alt='Project Page'></a>
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<a href="https://huggingface.co/spaces/radames/DragGan"><img alt="Huggingface" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DragGAN-orange"></a>
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<a href="https://colab.research.google.com/drive/1mey-IXPwQC_qSthI5hO-LTX7QL4ivtPh?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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</p>
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</p>
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## Requirements
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If you have CUDA graphic card, please follow the requirements of [https://github.com/NVlabs/stylegan3](https://github.com/NVlabs/stylegan3).
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Otherwise (for GPU acceleration on MacOS with Silicon Mac M1/M2, or just CPU) try the following:
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```sh
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cat environment.yml | \
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grep -v -E 'nvidia|cuda' > environment-no-nvidia.yml && \
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conda env create -f environment-no-nvidia.yml
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conda activate stylegan3
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# On MacOS
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export PYTORCH_ENABLE_MPS_FALLBACK=1
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```
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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 showing "AI Generated".
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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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