March 11, 2021

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Discovering Interpretable GAN Controls [NeurIPS 2020]

repo name harskish/ganspace
repo link
language Jupyter Notebook
size (curr.) 47908 kB
stars (curr.) 1240
created 2020-04-02
license Apache License 2.0

GANSpace: Discovering Interpretable GAN Controls

Python 3.7 PyTorch 1.3 Open In Colab teaser

GANSpace: Discovering Interpretable GAN Controls Erik Härkönen1,2, Aaron Hertzmann2, Jaakko Lehtinen1,3, Sylvain Paris2 1Aalto University, 2Adobe Research, 3NVIDIA


See the setup instructions.


This repository includes versions of BigGAN, StyleGAN, and StyleGAN2 modified to support per-layer latent vectors.

Interactive model exploration

# Explore BigGAN-deep husky
python --model=BigGAN-512 --class=husky --layer=generator.gen_z -n=1_000_000

# Explore StyleGAN2 ffhq in W space
python --model=StyleGAN2 --class=ffhq --layer=style --use_w -n=1_000_000 -b=10_000

# Explore StyleGAN2 cars in Z space
python --model=StyleGAN2 --class=car --layer=style -n=1_000_000 -b=10_000
# Apply previously saved edits interactively
python --model=StyleGAN2 --class=ffhq --layer=style --use_w --inputs=out/directions

Visualize principal components

# Visualize StyleGAN2 ffhq W principal components
python --model=StyleGAN2 --class=ffhq --use_w --layer=style -b=10_000

# Create videos of StyleGAN wikiart components (saved to ./out)
python --model=StyleGAN --class=wikiart --use_w --layer=g_mapping -b=10_000 --batch --video


Command line paramaters:
  --model      one of [ProGAN, BigGAN-512, BigGAN-256, BigGAN-128, StyleGAN, StyleGAN2]
  --class      class name; leave empty to list options
  --layer      layer at which to perform PCA; leave empty to list options
  --use_w      treat W as the main latent space (StyleGAN / StyleGAN2)
  --inputs     load previously exported edits from directory
  --sigma      number of stdevs to use in
  -n           number of PCA samples
  -b           override automatic minibatch size detection
  -c           number of components to keep


All figures presented in the main paper can be recreated using the included Jupyter notebooks:

  • Figure 1: figure_teaser.ipynb
  • Figure 2: figure_pca_illustration.ipynb
  • Figure 3: figure_pca_cleanup.ipynb
  • Figure 4: figure_style_content_sep.ipynb
  • Figure 5: figure_supervised_comp.ipynb
  • Figure 6: figure_biggan_style_resampling.ipynb
  • Figure 7: figure_edit_zoo.ipynb

Known issues

  • The interactive viewer sometimes freezes on startup on Ubuntu 18.04. The freeze is resolved by clicking on the terminal window and pressing the control key. Any insight into the issue would be greatly appreciated!

Integrating a new model

  1. Create a wrapper for the model in models/ using the BaseModel interface.
  2. Add the model to get_model() in models/

Importing StyleGAN checkpoints from TensorFlow

It is possible to import trained StyleGAN and StyleGAN2 weights from TensorFlow into GANSpace.


  1. Install TensorFlow: conda install tensorflow-gpu=1.*.
  2. Modify methods __init__(), load_model() in models/ under class StyleGAN.


  1. Follow the instructions in models/stylegan2/stylegan2-pytorch/ Make sure to use the fork in this specific folder when converting the weights for compatibility reasons.
  2. Save the converted checkpoint as checkpoints/stylegan2/<dataset>_<resolution>.pt.
  3. Modify methods __init__(), download_checkpoint() in models/ under class StyleGAN2.


We would like to thank:

  • The authors of the PyTorch implementations of BigGAN, StyleGAN, and StyleGAN2:Thomas Wolf, Piotr Bialecki, Thomas Viehmann, and Kim Seonghyeon.
  • Joel Simon from ArtBreeder for providing us with the landscape model for StyleGAN.(unfortunately we cannot distribute this model)
  • David Bau and colleagues for the excellent GAN Dissection project.
  • Justin Pinkney for the Awesome Pretrained StyleGAN collection.
  • Tuomas Kynkäänniemi for giving us a helping hand with the experiments.
  • The Aalto Science-IT project for providing computational resources for this project.


  title     = {GANSpace: Discovering Interpretable GAN Controls},
  author    = {Erik Härkönen and Aaron Hertzmann and Jaakko Lehtinen and Sylvain Paris},
  booktitle = {Proc. NeurIPS},
  year      = {2020}


The code of this repository is released under the Apache 2.0 license. The directory netdissect is a derivative of the GAN Dissection project, and is provided under the MIT license. The directories models/biggan and models/stylegan2 are provided under the MIT license.

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