liuguoyou / PhotoWCT2

PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals

PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals

Code for our WACV 2022 paper PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals

• Our PhotoWCT2 transfers stronger effects than WCT2 and comparable effects to those transferred by PhotoWCT.
• Our PhotoWCT2 preserves better high-frequency details than PhotoWCT (three examples are shown below).

Models and files

We apply the proposed blockwise training to train two models:

1. A pre-trained VGG-19 encoder (from input layer to conv41 layer; fixed during training) and a blockwisely trained decoder which can reproduce the conv31, conv21, conv11 features and the input image. The ZCA transformations are embedded at the bottleneck and the reproduced convN1 layers in the decoder.

   • The model is in utils/model_conv.py and the associated checkpoint is in ckpts/ckpts-conv.
   • A demo that uses this model to stylize example images in figures/ is shown in conv_demo.ipynb. The resulting stylized images are in results/conv.

2. A pre-trained VGG-19 encoder (from input layer to relu41 layer; fixed during training) and a blockwisely trained decoder which can reproduce the relu31, relu21, relu11 features and the input image. The ZCA transformations are embedded at the bottleneck and the reproduced reluN1 layers in the decoder.

   • The model is in utils/model_relu.py and the associated checkpoint is in ckpts/ckpts-relu.
   • A demo that uses this model to stylize example images in figures/ is shown in relu_demo.ipynb. The resulting stylized images are in results/relu.

Stylization with both models requires guided filtering in utils/photo_gif.py as the post-processing. The file is adapted from the one used in the PhotoWCT code.

The performance of these two models is similar. Please choose whichever you would like to use.

Training

train.py is the training code for our PhotoWCT2 model. The usage is provided in the file.

Requirements

• tensorflow v2.0.0 or above (we developed the models with tf-v2.4.1 and we also tested them in tf-v2.0.0)

Citation

If you find this repo useful, please cite our paper PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals published in WACV 2022.