A clean, simple and readable implementation of ProGAN in PyTorch. I've tried to replicate the original paper as closely as possible, so if you read the paper the implementation should be pretty much identical. The results from this implementation I would say is on par with the paper, I'll include some examples results below.

Results

The model was trained on the Celeb-HQ dataset up to 256x256 image size. After that point I felt it was enough as it would take quite a while to train to 1024^2.

First is 64 random examples (not cherry picked) and second is more cherry picked examples.

Celeb-HQ dataset

The dataset can be downloaded from Kaggle: link.

Download pretrained weights

Pretrained weights here.

Extract the zip file and put the pth.tar files in the directory with all the python files. Make sure you put LOAD_MODEL=True in the config.py file.

Training

Edit the config.py file to match the setup you want to use. Then run train.py

ProGAN paper

Progressive Growing of GANs for Improved Quality, Stability, and Variation by Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen

Abstract

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

@misc{karras2018progressive,
      title={Progressive Growing of GANs for Improved Quality, Stability, and Variation}, 
      author={Tero Karras and Timo Aila and Samuli Laine and Jaakko Lehtinen},
      year={2018},
      eprint={1710.10196},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}