The goal of this Google Colab notebook is to capture the distribution of Steam banners and sample with a StyleGAN.

• Acquire the data, e.g. as a snapshot called 128x128.zip in another of my repositories,
• Follow the instructions to edit train.py in the official StyleGAN Github repository,
• Run StyleGAN.ipynb to train a StyleGAN.
• To resume training from a checkpoint, you will have to edit training/training_loop.py.

NB: You might have to edit metrics/frechet_inception_distance.py to retrieve the network inception_v3_features.pkl locally if it cannot be downloaded from Google Colab.

The dataset consists of 31,723 Steam banners with RGB channels and resized from 460x215 to 128x128 resolution. Pre-processed data, as .tfrecords files, can be downloaded from Google Drive.

A StyleGAN model was trained on 3,524,000 images, with a decreasing mini-batch size, which is about 111 epochs. A checkpoint of the network can be downloaded from Google Drive.

Caveat: training was manually stopped after roughly 1 day, using 1 Tesla K80 GPU in the cloud. Based on the expected training times for 1024x1024, 512x512 and 256x256 images, 9 days of computation time might be required to get the best results for 128x128 images.

Results obtained with different numbers of images seen during training are shown on the Wiki.

A grid of generated Steam banners after 3,524 kimg:

A grid of real Steam banners:

• StyleGAN2:
  • StyleGAN2
  • Steam-StyleGAN2
• StyleGAN:
  • StyleGAN1
• DCGAN:
  • Steam-DCGAN