Swapping-Autoencoder-tf

The unofficial tensorflow implementation of Swapping Autoencoder for Deep Image Manipulation. Pdf linking: Swapping AutoEncoder

Differences

This implementation has three main differences with original paper.

• trained on 256x256 images, not 512

• Use AdaIn, not modulation/demodulation layer. We will update it in the next few days.

Dependencies

Python=3.6
tensorflow=1.14
pip install -r requirements.txt

Or Using Conda

-conda create -name SAE python=3.6
-conda install tensorflow-gpu=1.14 or higher

Other packages installed by pip.

Usage

• Clone this repo:

git clone https://github.com/zhangqianhui/Swapping-Autoencoder-tf
cd Swapping-Autoencoder-tf

• Download the CelebAHQ dataset

  Download the tar of CelebAHQ dataset from Google Driver Linking.

• Train the model using command line with python

python train.py --gpu_id=0 --exper_name='log10_10_1' --data_dir='../dataset/CelebAMask-HQ/CelebA-HQ-img/'

• Test the model

python test.py --gpu_id=0 --exper_name='log10_10_1' --data_dir='../dataset/CelebAMask-HQ/CelebA-HQ-img/'

Or Using scripts for training

bash scripts/train_log10_10_1.sh

For testing

bash scripts/test_log10_10_1.sh

Experiment Result with 50000 iterations

Training results on CelebAHQ. 1st-4th colums are structure input, texture input, reconstruction, swapped

Testing results on CelebAHQ. 1st-4th colums are structure input, texture input, reconstruction, swapped