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Discovering Interpretable GAN Controls [NeurIPS 2020]
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# GANSpace: Discovering Interpretable GAN Controls
[](https://colab.research.google.com/github/harskish/ganspace/blob/master/notebooks/Ganspace_colab.ipynb)
<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](SETUP.md).
## 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:
* 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 keyExcerpt of 6,751 characters
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matched fp:c7a9a3625581296a, topic:deep-learning, topic:pytorch
matched fp:c7a9a3625581296a, topic:image-generation, topic:gan