Implementation of the paper ``Large Scale Image Completion via Co-Modulated Generative Adversarial Networks"

official tensorflow version: https://github.com/zsyzzsoft/co-mod-gan

Input image Mask Result

conda install pytorch torchvision cudatoolkit=11 -c pytorch conda install matplotlib jinja2 ninja dill pip install git+https://github.com/zengxianyu/pytorch-fid 

Download the code:

git clone https://github.com/zengxianyu/co-mod-gan-pytorch git checkout train git submodule init git submodule update 

1. download pretrained model using ``download/*.sh" (converted from the tensorflow pretrained model)

e.g. ffhq512

./download/ffhq512.sh 

converted model:

• FFHQ 512 checkpoints/comod-ffhq-512/co-mod-gan-ffhq-9-025000_net_G_ema.pth
• FFHQ 1024 checkpoints/comod-ffhq-1024/co-mod-gan-ffhq-10-025000_net_G_ema.pth
• Places 512 checkpoints/comod-places-512/co-mod-gan-places2-050000_net_G_ema.pth

2. use the following command as a minimal example of usage

./test.sh 

1. download example datasets for training and validation

./download/data.sh 

2. use the following command as a minimal example of usage

./train.sh 

Coming soon

[1] official tensorflow version: https://github.com/zsyzzsoft/co-mod-gan

[2] stylegan2-pytorch https://github.com/rosinality/stylegan2-pytorch

[3] pix2pixHD https://github.com/NVIDIA/pix2pixHD

[4] SPADE https://github.com/NVlabs/SPADE