All images on this site were generated by a machine learning model.

I claim no legal ownership or rights to any of the images generated by this model. However, all silhouettes and logos depicted are property of their respective owners.

Upcoming content

In the short term, I am working on a much better editor and adding more sneaker styles in addition to the current Futuristic style. In the long term, I am planning to train a bigger model for more diverse and realistic images.

The grid currently contains only 2000 unique sneakers, but this will increase as sneaker styles are added.

Feel free to reach out to me if you have any ideas or suggestions!

Technical Details


  • NVIDIA’s Stylegan2-ada architecture.
  • Increased feature maps for both generator and discriminator, inspired by l4rz (NSFW) and Gwern. As noted by l4rz, increasing high-resolution feature maps greatly increases quality in images with fine details. Moreover, mixed-precision training makes these high-resolution layers cheap in compute and memory.
  • Disabled path-length regularization.
  • Gamma (R1-regularization) lowered from 10 to 1.
  • Minibatch standard deviation increased to 20.
  • Disabled style mixing.
  • Model trained with inverted colors. (see “General notes on training”)
  • Custom non-square resolution output images (384w * 512h). Code adapted from: eps696.
  • Model trained for about 20 days on a RTX 3090 GPU
  • “Future” style sneakers were made by fine-tuning the model on a curated subset of sneakers.
  • Color sliders were made using SeFa.


  • Training dataset consists of ~50000 images of sneakers scraped from web shops and sneaker marketplaces.
  • All images were standardized using simple image manipulation programs usin Pillow and OpenCV.
  • Silhouettes with large amounts of colorways (Vans, Nike Jordan 1) were partially filtered out.
  • Boring sneakers were largely filtered out.
  • Large amounts of additional interesting sneakers were found using the Wayback Machine on old webshops. These searches retrieved sneakers of short-lived trends from some years ago.
  • Weird and fun sneaker brands were found by following lookatsangi on instagram.
  • A painful amount of dataset filtering was done manually.

General notes on training

  • White backgrounds on images appear to destabilize training. I am not completely sure why this happens, but it seems to happen to other people as well ( ). This problem seems to appear when augmentations are enabled. Making backgrounds black via naive background removal caused artifacts, and out-of-the-box background removal tools were poorly optimized for these specific images. It took me a surprisingly long time to solve all my issues by simply inverting the colors of the images for training, and reverting them after generation.
  • Lowering gamma and path-length regularization slightly decreased perceptual quality of images but improved their variety. For the intended purpose of this site, the tradeoff between variance and quality was well worth it.
  • Achieving both varied and high quality results on sneaker images using GANs is surprisingly difficult.


Special thanks: