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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
The usual installation steps involve the following commands, you should set up the correct CUDA version and all the python packages
```
conda env create -f environment.yml
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## Run Gradio visualizer in Docker
Provided docker image is based on NGC PyTorch repository. To quickly try out visualizer in Docker, run the following:
Provided docker image is based on NGC PyTorch repository. To quickly try out the visualizer in Docker, run the following:
```sh
docker build . -t draggan:latest
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## 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).
However, most parts 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".
Any form of use and derivative of this code must preserve the watermarking functionality showing "AI-Generated".
## BibTeX

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