We build our code on the StyleGAN2-ADA, please go to https://github.com/NVlabs/stylegan2-ada-pytorch for environment setup. (You can still run the code if you don't build NVIDIA conv ops, though it might be slower)
git clone https://github.com/Mid-Push/i-stylegan.git
cd i-stylegan
We have modified the dataset_tools.py so it accepts the multi-domain data with different directories.
Download the AFHQ or CELEBA-HQ dataset from https://github.com/clovaai/stargan-v2
python dataset_tool.py --source=datasets/afhq/train --dest=datasets/afhq.zip --height=256 --width=256
python train.py --outdir=training-runs --data=datasets/afhq.zip --gpus=4 --cond_mode=flow --flow_norm=1 --i_dim=256 --lambda_sparse=0.1
For image-to-image translation, after training the AFHQ stylegan model, put your i-stylegan checkpoint into stylegan_dir.
cd stargan
python main.py --mode train --num_domains 3 --w_hpf 0 \
--lambda_reg 1 --lambda_sty 1 --lambda_ds 2 --lambda_cyc 1 \
--batch_size=16\
--lambda_pair=0.1\
--tag=Run\
--train_img_dir ../../datasets/afhq/train \
--val_img_dir ../../datasets/afhq/val \
--stylegan_dir expr/stylegan/afhq
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