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LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions

stylegan_ours

https://arxiv.org/abs/2104.00820

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.

Project website

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 runfolder 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}
}