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')

For a quick demo, see DEMO.

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.

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).