An implementation of progressive growing of GANs, purely in TensorFlow 2.0.
The code currently supports both 2D and 3D image generation.
pip install -r requirements.txt
python main.py prepare
--dataset path/to/data
--tf_record_save_dir path/to/save/tfrecords
--dimensionality 2/3
If label conditioning is required, the label mapping from filename for it must be added in dataset.py
python main.py train
--dataset path/to/tfrecord/file
--run_id path/to/save
--dimensionality 2/3
--latent_size latent_size
--kiters_per_resolution 10
--kiters_per_transition 10
--gpus '/gpu:0' '/gpu:1' '/gpu:2' '/gpu:3'
Check opts.py for more parameters to configure for training
Add --label_size x for x labels in training
python main.py generate
--run_id path/to/load
--dimensionality 2/3
python main.py test
--test_name [interpolation | nearest_neighbor]
--model_file path/to/model/file
--save_dir path/to/save/results
--latent_size latent_size
--dimensionality 2/3
Check opts.py for more parameters to configure for specific tests