A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution.

See this repo for pretrained models for StyleGAN 1

If you have a publically accessible model which you know of, or would like to share please see the contributing section. Hint: the simplest way to submit a model is to fill in this form.

• Models

  • car (config-e)
  • car (config-f)
  • cat
  • church
  • faces (FFHQ config-e)
  • faces (FFHQ config-e 256x256)
  • faces (FFHQ config-f)
  • faces (FFHQ config-f 512x512)
  • horse
  • Imagenet
  • WikiArt
  • Anime portraits
  • microscope images
  • wildlife
  • modern art
  • trypophobia
  • Abstract art
  • Maps
  • cakes
  • CIFAR 10
  • CIFAR 100
  • faces (FFHQ slim 256x256)
  • obama
  • grumpy cat
  • panda
  • fursona
  • my little pony
  • painting faces
  • ukiyoe faces
  • beetles
  • textures
  • more abstract art
  • flowers

• Notes

• Contributing

• Style mixing example, interpolation video
• Dataset: LSUN Car
• Resolution: 512x512 config: e
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: LSUN Car
• Resolution: 512x512 config: f
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: LSUN Cat
• Resolution: 256x256 config: f
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: LSUN Church
• Resolution: 256x256 config: f
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: FFHQ
• Resolution: 1024x1024 config: e
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: FFHQ
• Resolution: 256x256 config: e
• Author: Justin Pinkney
• Download link
• StyleGAN2 implementation: https://github.com/justinpinkney/stylegan2
• Notes: Trained to FID 11.2 from scratch for 3810 kimg
• Licence: CC BY-NC-SA 4.0
• Source

• Style mixing example, interpolation video
• Dataset: FFHQ
• Resolution: 1024x1024 config: f
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: FFHQ
• Resolution: 512x512 config: f
• Author: aydao
• Download link
• StyleGAN2 implementation:
• Licence: Public Domain
• Source

• Style mixing example, interpolation video
• Dataset: LSUN Horse
• Resolution: 256x256 config: f
• Author: NVIDIA
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: Image net
• Resolution: 512x512 config: Unknown
• Author: Shawn Presser
• Download link
• StyleGAN2 implementation: Unknown
• Notes: Trained using TPUs
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: WikiArt
• Resolution: Unknown config: Unknown
• Author: Peter Baylies
• Download link
• StyleGAN2 implementation: https://github.com/pbaylies/stylegan2
• Notes: Conditional
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Danboru
• Resolution: 512x512 config: f
• Author: Aaron Gokaslan
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Unknown
• Resolution: 512x512 config: Unknown
• Author: Michael Friesen
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Unknown
• Resolution: Unknown config: Unknown
• Author: Michael Friesen
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Unknown
• Resolution: Unknown config: Unknown
• Author: Michael Friesen
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: https://drive.google.com/file/d/1u_fLHmO6JuJlBTQIKRGgl4PeBKbBu9GJ/view
• Resolution: 1024x1024 config: f
• Author: Sid Black
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Nvidia Source Code License-NC
• Source

• Style mixing example, interpolation video
• Dataset: Frea Buckler artwork
• Resolution: 1024x1024 config: f
• Author: Derrick Schultz
• Download link
• StyleGAN2 implementation: RunwayML
• Notes: Based on Frea Buckler’s artwork from her Instagram account (purposefully undertrained to be abstract and not infringe on the artist’s own work)
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Maps
• Resolution: 1024x1024 config: f
• Author: Topi Tjukanov
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Internet scraped cakes
• Resolution: 256x256 config: e
• Author: Justin Pinkney
• Download link
• StyleGAN2 implementation: https://github.com/justinpinkney/stylegan2
• Notes: Trained from scratch to FID 13.6
• Licence: CC BY-NC-SA 4.0
• Source

• Style mixing example, interpolation video
• Dataset: CIFAR 10
• Resolution: 32x32 config: see paper
• Author: mit-han-lab
• Download link
• StyleGAN2 implementation: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
• Notes: Trained with DiffAugment, FID (10k) = 9.89
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: CIFAR 100
• Resolution: 32x32 config: see paper
• Author: mit-han-lab
• Download link
• StyleGAN2 implementation: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
• Notes: Trained with DiffAugment, FID (10k) = 15.22
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: FFHQ
• Resolution: 256x256 config: slim
• Author: mit-han-lab
• Download link
• StyleGAN2 implementation: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
• Notes: Trained with DiffAugment, FID = 3.81
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: 100 images of Barack Obama
• Resolution: 256x256 config: f
• Author: mit-han-lab
• Download link
• StyleGAN2 implementation: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
• Notes: Trained with DiffAugment, FID (5k) = 46.87
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: 100 images of Grumpy Cats
• Resolution: 256x256 config: f
• Author: mit-han-lab
• Download link
• StyleGAN2 implementation: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
• Notes: Trained with DiffAugment, FID (5k) = 27.08
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: 100 images of pandas
• Resolution: 256x256 config: f
• Author: mit-han-lab
• Download link
• StyleGAN2 implementation: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
• Notes: Trained with DiffAugment, FID (5k) = 12.06
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: ~55k SFW images from e621.net
• Resolution: 512x512 config: TBC
• Author: arfa
• Download link
• StyleGAN2 implementation: Unknown
• Notes: Trained using TPUs
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: ~104k SFW images from Derpibooru
• Resolution: 1024x1024 config: TBC
• Author: arfa
• Download link
• StyleGAN2 implementation: Unknown
• Notes: Trained using TPUs
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: MetFaces
• Resolution: 1024x1024 config: f
• Author: AK
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: 5000 faces aligned and detected from ukiyoe images
• Resolution: 256x256 config: slim
• Author: Justin Pinkney
• Download link
• StyleGAN2 implementation: https://github.com/justinpinkney/data-efficient-gans/
• Notes: Fine tuned from ffhq-256-slim, used DiffAugment for training, FID = 12.74
• Licence: CC BY-NC-SA 4.0
• Source

• Style mixing example, interpolation video
• Dataset: Biologia Centrali-Americana :zoology, botany and archaeology
• Resolution: 1024x1024 config: f
• Author: Bernat Cuni
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Describable Textures Dataset (DTD)
• Resolution: 1024x1024 config: f
• Author: Bernat Cuni
• Download link
• StyleGAN2 implementation: Unknown
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: 14,305 abstract paintings
• Resolution: 512x512 config: f
• Author: Nick Saraev
• Download link
• StyleGAN2 implementation: Unknown
• Notes: Fine tuned from FFHQ 512 model
• Licence: Unknown
• Source

• Style mixing example, interpolation video
• Dataset: Oxford flowers 102 prepped with u^2net
• Resolution: 256x256 config: slim
• Author: Justin Pinkney
• Download link
• StyleGAN2 implementation: https://github.com/justinpinkney/data-efficient-gans/
• Notes: Fine tuned from ffhq-256-slim, used DiffAugment for training, FID = 12.20
• Licence: CC BY-NC-SA 4.0
• Source

• The configuration "slim" refers to the reduced feature map model used in the Karras limited data and Zhao data efficient papers.
• Each row in the sample grids above use a different level of trunction: 0.25, 0.5, 0.75, 1 from top to bottom.
• Style mixing figure and interpolation video generated using truncation of 0.75

TLDR: You can either edit the models.json file or fill out this form.

This readme is automatically generated using Jinja, please do not try and edit it directly. Information about the models is stored in models.json please add your model to this file. Preview images are generated automatically and the process is used to test the link so please only edit the json file.