Morph-UGATIT

a morph transfer UGATIT for image translation.

image image image image

Introduction

中文技术文档

This is Pytorch implementation of UGATIT, paper “U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation”.

Additionally, I DIY the model by adding two modules, a MLP module to learn a latent zone and an identity preserving loss. These two factors make UGATIT to achieve a progressive domain transfer for image translation. I call this method Morph UGATIT.

My work has two aspects:

  • Firstly, according to official TensorFlow code of UGATIT, I use PyTorch to reimplement it, very close to original TF model including network, training hyper parameters.
  • I add a MLP module, introducing a latent code for generator. And an identity preserving loss is used to learn more common feature for different domains.

I train model on two datasets, “adult2child” and “selfie2anime”.

Requirements

  • python3.7
  • Pytorch >= 1.6
  • dlib. Before installing dlib, you should install Cmake and Boost
pip install Cmake
pip install Boost
pip install dlib
  • other common-used libraries.

How to Use

There are many models in my repo, but you just need two models and corresponding python script files.

  • UGATIT: “configs/cfgs_ugatit.py”, “models/ugatit.py”, “tool/train_ugatit.py”, “tool/demo_ugatit.py”
  • Morph UGATIT: “configs/cfgs_s_ugatit_plus.py”, “models/s_ugatit_plus.py”, “tool/train_s_ugatit_plus.py”, “tool/demo_morph_ugatit.py”

train step

  1. getting dataset. The “adult2child” dataset comes from G-Lab, which is generated by StyleGAN. You can download here image

The “selfie2anime” dataset comes from official UGATIT repo.

  1. set configurations. configuration files can be found “configs” dir. You just focus on “cfgs_ugatit.py” and “cfgs_s_ugatit_plus.py”. Please change:
  • dirA: domain A dataset path.
  • dirB: domain B dataset path.
  • anime: whether dataset is “selfie2anime”.
  • tensorboard: tensorboard log path.
  • saved_dir: save model weight into “saved_dir”.
  1. start to train.
cd tool
python train_ugatit.py   # ugatit
python train_s_ugatit_plus.py   #  morph ugatit

you can also use tensorboard to check loss curves and some visualizations.

evaluation step

Since dlib is necessary, you should download dlib model weight here. change “alignment_loc” at “tool/demo_xxxx.py”. “xxx” means “ugatit” or “morph_ugatit” to your dlib model weight path. Then put a test image into a dir.

cd tool
python demo_ugatit.py --type ugatit --resume ${ckpt path}$ --input ${image dir}$ --saved-dir ${result location}$ --align
python demo_morph_ugatit.py --resume ${ckpt path}$ --input ${image dir}$ --saved-dir ${result location}$ --align

Note: if you want to try “selfie2anime”, please add a extra term “–anime”.

Here I provide my pretrained model weights.

for “adult2child” dataset

ugatit

morph ugatit

for “selfie2anime” dataset

ugatit

More results can be seen here

References

  • official UGATIT repo
  • official CycleGAN repo
  • GLab, http://www.seeprettyface.com/
  • paper “Lifespan age transformation synthesis” and its’ official code.

GitHub

https://github.com/shoutOutYangJie/Morph-UGATIT