Awesome Pretrained StyleGAN2

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

Note the readme is a bit out of date, there are more models linked in the issues.

• For StyleGAN3 models see: https://github.com/justinpinkney/awesome-pretrained-stylegan3
• For StyleGAN1 models see: https://github.com/justinpinkney/awesome-pretrained-stylegan)

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.

Table of Contents

• 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
  • Doors
  • floor plans
  • figure drawings

• Notes

• Contributing

car (config-e)

• 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

car (config-f)

• 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

cat

• 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

church

• 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

faces (FFHQ config-e)

• 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

faces (FFHQ config-e 256x256)

• 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

faces (FFHQ config-f)

• 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

faces (FFHQ config-f 512x512)

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

horse

• 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

Imagenet

• 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

WikiArt

• 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

Anime portraits

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

microscope images

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

wildlife

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

modern art

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

trypophobia

• 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

Abstract art

• 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

Maps

• 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

cakes

• 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

CIFAR 10

• 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

CIFAR 100

• 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

faces (FFHQ slim 256x256)

• 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

obama

• 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

grumpy cat

• 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

panda

• 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

fursona

• 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

my little pony

• 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

painting faces

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

ukiyoe faces

• 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

beetles

• 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

textures

• 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

more abstract art

• 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

flowers

• 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

Doors

• Style mixing example, interpolation video
• Dataset: 5k architectural elements from Barcelona
• Resolution: 256x256 config: f
• Author: Vasily Korf
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan2
• Notes: styleGAN trained on architectural elements to create Art Nouveau doors
• Licence: Unknown
• Source

floor plans

• Style mixing example, interpolation video
• Dataset: Floor Plans (Instagram scrapped)
• Resolution: 1024x1024 config: a
• Author: Mayur Mistry
• Download link
• StyleGAN2 implementation: https://github.com/NVlabs/stylegan
• Licence: Unknown
• Source

figure drawings

• Style mixing example, interpolation video
• Dataset: Previous models made from the drawings from my blog
• Resolution: 1024x1024 config: f
• Author: Krrrl
• Download link
• StyleGAN2 implementation: Runway ML
• Licence: Unknown
• Source

Notes

• 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

Contributing

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.