taki0112/DRIT-Tensorflow taki0112/DRIT-Tensorflow

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    Python
  • License
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  • Created about 6 years ago
  • Updated over 5 years ago
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taki0112/DRIT-Tensorflow
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Repository Details

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

DRIT-Tensorflow

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

Pytorch version

Requirements

  • Tensorflow 1.8
  • python 3.6

Usage

Download Dataset

β”œβ”€β”€ dataset
Β Β  └── YOUR_DATASET_NAME
Β Β      β”œβ”€β”€ trainA
 Β  Β  Β  Β  Β  β”œβ”€β”€ xxx.jpg (name, format doesn't matter)
           β”œβ”€β”€ yyy.png
           └── ...
Β Β      β”œβ”€β”€ trainB
           β”œβ”€β”€ zzz.jpg
           β”œβ”€β”€ www.png
           └── ...
Β Β      β”œβ”€β”€ testA
        Β  Β β”œβ”€β”€ aaa.jpg 
           β”œβ”€β”€ bbb.png
           └── ...
Β Β      └── testB
           β”œβ”€β”€ ccc.jpg 
           β”œβ”€β”€ ddd.png
           └── ...
           
β”œβ”€β”€ 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

comparison

Architecture

true false

Train phase

train_1 train_2

Test & Guide phase

test

Results

result_1 result_2

Related works

Author

Junho Kim

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