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GANSpace: Discovering Interpretable GAN Controls

Python 3.7 PyTorch 1.3 Open In Colab teaser

<p align="justify"><b>Figure 1:</b> Sequences of image edits performed using control discovered with our method, applied to three different GANs. The white insets specify the particular edits using notation explained in Section 3.4 ('Layer-wise Edits').</p>

GANSpace: Discovering Interpretable GAN Controls<br> Erik Härkönen<sup>1,2</sup>, Aaron Hertzmann<sup>2</sup>, Jaakko Lehtinen<sup>1,3</sup>, Sylvain Paris<sup>2</sup><br> <sup>1</sup>Aalto University, <sup>2</sup>Adobe Research, <sup>3</sup>NVIDIA<br> https://arxiv.org/abs/2004.02546

<p align="justify"><b>Abstract:</b> <i>This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied in activation space. Then, we show that interpretable edits can be defined based on layer-wise application of these edit directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. A user may identify a large number of interpretable controls with these mechanisms. We demonstrate results on GANs from various datasets.</i></p> <p align="justify"><b>Video:</b> https://youtu.be/jdTICDa_eAI

Setup

See the setup instructions.

Usage

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

Interactive model exploration

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

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

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

Visualize principal components

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

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

Options

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 visualize.py
  -n           number of PCA samples
  -b           override automatic minibatch size detection
  -c           number of components to keep

Reproducibility

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

Known issues

Integrating a new model

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

Importing StyleGAN checkpoints from TensorFlow

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

StyleGAN

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

StyleGAN2

  1. Follow the instructions in models/stylegan2/stylegan2-pytorch/README.md. 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/wrappers.py under class StyleGAN2.

Acknowledgements

We would like to thank:

Citation

@inproceedings{härkönen2020ganspace,
  title     = {GANSpace: Discovering Interpretable GAN Controls},
  author    = {Erik Härkönen and Aaron Hertzmann and Jaakko Lehtinen and Sylvain Paris},
  booktitle = {Proc. NeurIPS},
  year      = {2020}
}

License

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