Progressive Growing of GANs for Improved Quality, Stability, and Variation
You would find some helpful comments in some key functions, which may help to find detail instructions from the paper.
- OS: Win10
- Python 3.6.3
- CUDA 8.0
- Pytorch Windows-py3.6-cuda8
- PIL 4.3.0
- numpy 1.13.3
Gen Image dataset: Download the CelebA first, then run "gen_classified_images" function in train.py file.
if __name__ == "__main__":
gen_classified_images(r"E:\workspace\datasets\CelebA\Img\img_align_celeba", centre_crop=True, save_to_local=True)
This function just resizing the original image, if you would like to test the CelebA-HQ dataset, please follow tkarras' instructions.
Training: Open the train.py file again, modify and run the script：
if __name__ == "__main__":
p = PGGAN(resolution=1024, # Final Resolution.
latent_size=512, # Dimensionality of the latent vectors.
criterion_type="GAN" # "GAN" or "WGAN-GP"
)
p.train(r"E:\workspace\datasets\CelebA\Img\img_align_celeba_classified")