Authors official PyTorch implementation of the WarpedGANSpace: Finding non-linear RBF paths in GAN latent space (ICCV 2021). If you use this code for your research, please cite our paper.

In this work, we try to discover non-linear interpretable paths in GAN latent space in an unsupervised and model-agnostic manner. For doing so, we model non-linear paths using RBF-based warping functions, which by warping the latent space, endow it with vector fields (i.e., their gradients). We use the latter to traverse the latent space across the paths determined by the aforementioned vector fields for any given latent code.

Each warping function is defined by a set of N support vectors (which form a "support set") and its gradient is given analytically as shown above. For a given warping function f^k and a given latent code z, we traverse the latent space as illustrated below:

Each warping function gives rise to a family of non-linear paths. We learn a set of such warping functions (implemented by the Warping Network), i.e., a set of such non-linear path families, so as the image transformations that they produce are distinguishable to each other by a discriminator network (the Reconstructor). An overview of the method is given below.

We recommend installing the required packages using python's native virtual environment. For Python 3.4+, this can be done as follows:

$ python -m venv warped-gan-space
$ source warped-gan-space/bin/activate
(warped-gan-space) $ pip install --upgrade pip
(warped-gan-space) $ pip install -r requirements.txt

Download the prerequisite pretrained models (i.e., GAN generators, face detector, pose estimator, and other attribute detectors), as well as pre-trained WarpedGANSpace models (optionally, by passing -m), as follows:

(warped-gan-space) $ python download.py	

This will create a directory models/pretrained with the following sub-directories (~2.0 GiB):

./models/pretrained/
├── au_detector/
├── generators/
├── arcface/
├── fairface/
├── hopenet/
└── sfd/

as well as, a directory experiments/complete/ (if not already created by the user upon an experiment's completion) for downloading the WarpedGANSpace pretrained models (if selected) with the following sub-directories (~??? GiB):

.experiments/complete/
├── SNGAN_AnimeFaces-LeNet-K64-D128-LearnGammas-eps0.25_0.35/
├── SNGAN_MNIST-LeNet-K64-D128-LearnGammas-eps0.15_0.25/
├── BigGAN-239-ResNet-K120-D256-LearnGammas-eps0.15_0.25/
└── ProgGAN-ResNet-K200-D512-LearnGammas-eps0.1_0.2/

For training a WarpedGANSpace model you need to use train.py (check its basic usage by running python train.py -h).

For example, in order to train a WarpedGANSpace model on the ProgGAN pre-trained (on CelebA) generator for discovering K=128 interpretable paths (latent warping functions) with N=32 support dipoles each (i.e., 32 pairs of bipolar RBFs) run the following command:

(warped-gan-space) $ python train.py -v --gan-type=ProgGAN --reconstructor-type=ResNet --learn-gammas --num-support-sets=128 --num-support-dipoles=32 --min-shift-magnitude=0.15 --max-shift-magnitude=0.25 --batch-size=8 --max-iter=200000

In the example above, batch size is set to 8 and the training will be conducted for 200000 iterations. Minimum and maximum shift magnitudes are set to 0.15 and 0.25, respectively (please see Sect. 3.2 in the paper for more details). A set of auxiliary training scripts (for all available GAN generators) can be found under scripts/train/.

The training script will create a directory with the following name format:

<gan_type>(-<stylegan2_resolution>)-<reconstructor_type>-K<num_support_sets>-N<num_support_dipoles>(-LearnAlphas)(-LearnGammas)-eps<min_shift_magnitude>_<max_shift_magnitude>

For instance, ProgGAN-ResNet-K128-N128-LearnGammas-eps0.15_0.25, under experiments/wip/ while training is in progress, which after training completion, will be copied under experiments/complete/. This directory has the following structure:

├── models/
├── tensorboard/
├── args.json
├── stats.json
└── command.sh

where models/ contains the weights for the reconstructor (reconstructor.pt) and the support sets (support_sets.pt). While training is in progress (i.e., while this directory is found under experiments/wip/), the corresponding models/ directory contains a checkpoint file (checkpoint.pt) containing the last iteration, and the weights for the reconstructor and the support sets, so as to resume training. Re-run the same command, and if the last iteration is less than the given maximum number of iterations, training will resume from the last iteration. This directory will be referred to as EXP_DIR for the rest of this document.

After a WarpedGANSpace model is trained, the corresponding experiment's directory (i.e., EXP_DIR) can be found under experiments/complete/. The evaluation of the model includes the following steps:

• Latent space traversals For a given set of latent codes, we first generate images for all K paths (warping functions) and save the traversals (path latent codes and generated image sequences).
• Attribute space traversals In the case of facial images (i.e., ProgGAN and StyleGAN2), for the latent traversals above, we calculate the corresponding attribute paths (i.e., pose, id scores, action units intensities, etc.).
• Interpretable paths discovery and ranking For the attribute traversals above, we rank the discovered paths based on the how much correlated each path is with each attribute path.

Before calculating latent space traversals, we need to create a pool of latent codes/images for the corresponding GAN type. This can be done using sample_gan.py. The name of the pool can be passed using --pool; if left empty <gan_type>_<num_samples> will be used instead. The pool of latent codes/images will be stored under experiments/latent_codes/<gan_type>/. We will be referring to it as POOL for the rest of this document.

For example, the following command will create a pool named ProgGAN_4 under experiments/latent_codes/ProgGAN/:

python sample_gan.py -v --gan-type=ProgGAN --num-samples=4

Latent space traversals can be calculated using the script traverse_latent_space.py (please check its basic usage by running traverse_latent_space.py -h) for a given model and a given POOL. Upon completion, results (i.e., latent traversals) will be stored under the following directory:

experiments/complete/EXP_DIR/results/POOL/<2*shift_steps>_<eps>_<total_length>

where eps, shift_steps, and total_length denote respectively the shift magnitude (of a single step on the path), the number of such steps, and the total traversal length. We will be referring to a directory <2*shift_steps>_<eps>_<total_length> as TRAVERSAL_CONFIG for the rest of this document.

If --gif is set, a directory experiments/complete/EXP_DIR/results/POOL/TRAVERSAL_CONFIG/paths_gifs/ will be created and populated by GIF images for all latent codes (original images) in POOL and for all discovered paths.

Granted that the GAN at hand generates facial images (i.e., ProgGAN or StyleGAN2), we can traverse an attribute space by running traverse_attribute_space.py These attributes include the facial bounding box (in terms of width and height), an identity score, age, race, and gender estimation, pose estimation in terms of yaw, pitch, and roll angles, 12 facial action units predictions, and 5 CelebA attributes. For more details, please check its basic usage by running traverse_attribute_space.py -h.

This script needs a TRAVERSAL_CONFIG found under experiments/complete/EXP_DIR/results/POOL/. Upon completion, the corresponding attribute paths will be stored under the same directory.

After generating the attribute traversals, as described above, the discovered latent paths can be ranked based on the correlation they exhibit with respect to a set of attributes. In other words, based on how certain attributes change when traversing the discovered latent paths.

This can be done by using rank_interpretable_paths.py for a given group of attributes. An attribute group, to which we will be referring to it as ATTR_GROUP for the rest of this document, is a subset of the set of all available attributes (see ATTRIBUTE_GROUPS dictionary in rank_interpretable_paths.py and/or run rank_interpretable_paths.py -h for more details). The ranking results will be stored under experiments/complete/EXP_DIR/results/POOL/interpretable_paths/ATTR_GROUP/ for the chosen group of attributes ATTR_GROUP. A markdown file the summarizes the results will be created under experiments/complete/EXP_DIR/results/POOL/interpretable_paths/ATTR_GROUP/ATTRIBUTE for each ATTRIBUTE in ATTR_GROUP. An example of such summarizing file is given here.

[1] Christos Tzelepis, Georgios Tzimiropoulos, and Ioannis Patras. WarpedGANSpace: Finding non-linear rbf paths in gan latent space. IEEE International Conference on Computer Vision (ICCV), 2021.

Bibtex entry:

@InProceedings{Tzelepis_2021_ICCV,
    author    = {Tzelepis, Christos and Tzimiropoulos, Georgios and Patras, Ioannis},
    title     = {{WarpedGANSpace}: Finding Non-Linear RBF Paths in {GAN} Latent Space},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6393-6402}
}

This research was supported by the EU's Horizon 2020 programme H2020-951911 AI4Media project.