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This is a reimplementation of NVidia's stylegan https://github.com/NVlabs/stylegan I did for learning purposes. My priority was testing out changes such as non-square images and conditioning on labels (using acgan and projection discriminator) rather than keeping the code clean so right now it is a bit messy.

This has been tested, and supports features like rectangular images, but I am currently waiting on the most recent training session to finish before uploading results. Training and sample generation are both performed using 'run.py'.

It is mostly original, but includes a couple functions from the official implementation for comparison testing.

To run, see the comments in start_training.sh (and then run start_training.sh). This has only been tested in one environment, so feel free to create a github issue if you encounter a problem. This model can handle non-square images and I plan on organizing and including the tools I've made/modified to generate datasets like that soonish.

Blogs related to a tool I made to interact with StyleGAN models:

https://towardsdatascience.com/animating-ganime-with-stylegan-part-1-4cf764578e https://towardsdatascience.com/animating-ganime-with-stylegan-the-tool-c5a2c31379d

Original Paper: Karras, T., Laine, S., and Aila, T. A style-based generator architecture for generative adversarial networks

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Reimplementation of https://arxiv.org/abs/1812.04948

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