base on Official Code for DragGAN (SIGGRAPH 2023) <p align="center"> <h1 align="center">Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold</h1> <p align="center"> <a href="https://xingangpan.github.io/"><strong>Xingang Pan</strong></a> · <a href="https://ayushtewari.com/"><strong>Ayush Tewari</strong></a> · <a href="https://people.mpi-inf.mpg.de/~tleimkue/"><strong>Thomas Leimkühler</strong></a> · <a href="https://lingjie0206.github.io/"><strong>Lingjie Liu</strong></a> · <a href="https://www.meka.page/"><strong>Abhimitra Meka</strong></a> · <a href="http://www.mpi-inf.mpg.de/~theobalt/"><strong>Christian Theobalt</strong></a> </p> <h2 align="center">SIGGRAPH 2023 Conference Proceedings</h2> <div align="center"> <img src="DragGAN.gif", width="600"> </div> <p align="center"> <br> <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> <a href="https://twitter.com/XingangP"><img alt='Twitter' src="https://img.shields.io/twitter/follow/XingangP?label=%40XingangP"></a> <a href="https://arxiv.org/abs/2305.10973"> <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'> </a> <a href='https://vcai.mpi-inf.mpg.de/projects/DragGAN/'> <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> <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> </p> </p> ## Web Demos [](https://openxlab.org.cn/apps/detail/XingangPan/DragGAN) <p align="left"> <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> </p> ## 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} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"4030","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"