Framework of GAN Inversion

Introcuction

You can implement your own inversion idea using our repo. We offer a full range of tuning settings (in hparams.py), some excellent backbones and classics loss functions. You can modify the arch of network or loss easily.

Recent Updates

2021.9.1 The simplfied framework of GAN Inversion is released.

Requirements

pip install git+git://github.com/lehduong/torch-warmup-lr.git
PyTorch1.7

Train

Without DDP

python train.py

With DDP

python -m torch.distributed.launch --nproc_per_node=nums_gpus train.py

Done

Tuning Setting

Apply_init
Optimizer_mode
Scheduler_mode
Open_warn_up

Backbone

GradualStyleEncoder from restyle-encoder and pixel2style2pixel
ResNetGradualStyleEncoder from restyle-encoder and pixel2style2pixel

Loss

MSE
LPIPS
ID loss from restyle-encoder and pixel2style2pixel
Moco loss from restyle-encoder and pixel2style2pixel

TODO

DDP
More Backbones
Metrics

Acknowledgements

This repository is an unoffical PyTorch Framework of GAN Inversion and highly based on restyle-encoder, pixel2style2pixel, stylegan2-pytorch, AliProducts and stylegan2. Jointly developed with Prof. Shuang Song. Thank you for the above repo. Thank you to Daiheng Gao and Jie Zhang for all the help I received.