Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation contains a lot of my guesses, so I think there are many differences to the official implementations
First create lmdb datasets:
python prepare_data.py --out LMDB_PATH --n_worker N_WORKER --size SIZE1,SIZE2,SIZE3,... DATASET_PATH
This will convert images to jpeg and pre-resizes it. This implementation does not use progressive growing, but you can create multiple resolution datasets using size arguments with comma separated lists, for the cases that you want to try another resolutions later.
Then you can train model in distributed settings
python train.py --n_gpu N_GPU --conf config/config-t.jsonnet training.batch=BATCH_SIZE path=LMDB_PATH
train.py supports Weights & Biases logging. If you want to use it, add wandb=true arguments to the script.
