DRIT-Tensorflow

Simple Tensorflow implementation of Diverse Image-to-Image Translation via Disentangled Representations (ECCV 2018 Oral)

Pytorch version

• Author_pytorch_code

Requirements

• Tensorflow 1.8
• python 3.6

Usage

Download Dataset

• cat2dog
• portrait
• CycleGAN

├── dataset
   └── YOUR_DATASET_NAME
       ├── trainA
           ├── xxx.jpg (name, format doesn't matter)
           ├── yyy.png
           └── ...
       ├── trainB
           ├── zzz.jpg
           ├── www.png
           └── ...
       ├── testA
           ├── aaa.jpg 
           ├── bbb.png
           └── ...
       └── testB
           ├── ccc.jpg 
           ├── ddd.png
           └── ...
           
├── guide.jpg (example for guided image translation task)

Train

python main.py --phase train --dataset summer2winter --concat True

Test

python main.py --phase test --dataset summer2winter --concat True --num_attribute 3

Guide

python main.py --phase guide --dataset summer2winter --concat True --direction a2b --guide_img ./guide.jpg

Tips

• --concat

  • True : for the shape preserving translation (summer <-> winter) (default)
  • False : for the shape variation translation (cat <-> dog)

• --n_scale

  • Recommend n_scale = 3 (default)
  • Using the n_scale > 1, a.k.a. multiscale discriminator often gets better results

• --n_dis

  • If you use the multi-discriminator, then recommend n_dis = 4 (default)
  • If you don't the use multi-discriminator, then recommend n_dis = 6

• --n_d_con

  • Author use n_d_con = 3 (default)
  • Model can still generate diverse results with n_d_con = 1

• --num_attribute (only for the test phase)

  • If you use the num_attribute > 1, then output images are variously generated

Summary

Comparison

Architecture

Train phase

Test & Guide phase

Results

Related works

• UNIT-Tensorflow
• MUNIT-Tensorflow

Author

Junho Kim