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LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable controllable image generation and support a wide range of semantic editing operations, such as zoom or rotation. The discovery of such directions is often done in a supervised or semi-supervised manner and requires manual annotations which limits their use in practice. In comparison, unsupervised discovery allows finding subtle directions that are difficult to detect a priori. In this work, we propose a contrastive learning-based approach to discover semantic directions in the latent space of pre-trained GANs in a selfsupervised manner. Our approach finds semantically meaningful dimensions compatible with state-of-the-art methods.
Installation
Install the dependencies in env.yml
$ conda env create -f env.yml
$ conda activate latentclr-env
and download the wordnet as follows:
import nltk
nltk.download('wordnet')
Quickstart
For a quick demo, see DEMO.
Hydra Usage
The repository uses Hydra framework to manage experiments. We provide three main experiments:
train.py
: Trains a model from scratch.
eval.py
: Evaluates a pre-trained model.
gen.py
: Generates samples with the given model.
Use --help
to list all experiment parameters and help on Hydra usage:
python train.py --help
Hydra will output experiment results under outputs
folder.
Outputs of train and eval tasks will be under run
folder while results of generation experiments will be scaffolded first by date and then time of the day.
Examples
Train 32 nonlinear directions with BigGAN
and class bulbul
by targeting feature layer generator.layers.4
.
python train.py k=32 generator=biggan generator.feature_layer=generator.layers.4 generator.class_name=bulbul model=nonlinear hparams.batch_size=16 model.alpha='[-3.0,+3.0]'
Train 100 nonlinear directions with StyleGAN2
and class ffhq
by targeting feature layer conv1
.
python train.py k=100 generator=stylegan2 generator.feature_layer=conv1 hparams.batch_size=8 generator.class_name=ffhq model=nonlinear model.alpha=1
Generate 5 images from the first 4 directions of a pre-trained model located at $PATH
python gen.py \
--config-path="$PATH/.hydra"
--config-name=config \
checkpoint="$PATH/best_model.pt"
+n_samples=5 \
+alphas="[-15,-10,-5,5,10,15]" \
+iterative=False \
+image_size=256 \
+n_dirs=[0,1,2,3] \
You can also use feed_layers
(takes a list of layer indices) option to only activate changes in certain layers of the generator (both in training and generation).
Citation
If you use this code for your research, please cite our paper:
@inproceedings{yuksel2021latentclr,
title={LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions},
author={Y{\"u}ksel, O{\u{g}}uz Kaan and Simsar, Enis and Er, Ezgi G{\"u}lperi and Yanardag, Pinar},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month={October},
year={2021},
pages={14263-14272}
}