Awesome

AdaIN-TF

This is a TensorFlow/Keras implementation of Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.

Requirements

Python 3.x
tensorflow 1.2.1+
keras 2.0.x
torchfile

Optionally:

OpenCV with contrib modules (for webcam.py)
- MacOS install http://www.pyimagesearch.com/2016/12/05/macos-install-opencv-3-and-python-3-5/
- Linux install http://www.pyimagesearch.com/2016/10/24/ubuntu-16-04-how-to-install-opencv/
ffmpeg (for video stylization)

Training

Download MS COCO images and Wikiart images.
Download VGG19 model: bash models/download_models.sh
python train.py --content-path /path/to/coco --style-path /path/to/wikiart --batch-size 8 --content-weight 1 --style-weight 1e-2 --tv-weight 0 --checkpoint /path/to/checkpointdir --learning-rate 1e-4 --lr-decay 1e-5
Monitor training with TensorBoard: tensorboard --logdir /path/to/checkpointdir

Running a trained model

To stylize images captured from a webcam:

python webcam.py --checkpoint /path/to/checkpointdir --style-path /path/to/wikiart

This script contains a few options:

--source Use a different camera input
--width and --height Set the size of camera frames
--video-out Write stylized frames to .mp4 out path
--fps Frames Per Second for video out
--scale Resize content images by this factor before stylizing
--keep-colors Apply CORAL transform to preserve colors of content
--device Device to perform compute on, default /gpu:0
--style-size Resize small side of style image to this before cropping a 256x256 square
--alpha Control blending of content features + AdaIN transformed features
--concat Append the style image to the stylized output
--interpolate Interpolate between AdaIN features of two random images
--noise Generate textures from random noise image instead of webcam
--random Load a new random image every # of frames

There are also three keyboard shortcuts:

r Load random image from style folder
c Toggle color preservation
q Quit cleanly and close streams

Additionally, stylize.py will stylize image files. The options are the same as for the webcam script with the addition of --content-path, which can be a single image file or folder. Each style in --style-path will be applied to each content image.

Notes

I tried to stay as faithful as possible to the paper and the author's implementation. This includes the decoder architecture, default hyperparams, image preprocessing, use of reflection padding in all conv layers, and bilinear upsampling + conv instead of transposed convs in the decoder. The latter two techniques help to avoid border artifacts and checkerboard patterns as described in https://distill.pub/2016/deconv-checkerboard/.
The same normalised VGG19 is also used with weights loaded from vgg_normalised.t7 and then translated into Keras layers. A version that uses a modified keras.applications.VGG19 can be found in the vgg_keras branch.
coral.py implements CORellation ALignment to transfer colors from the content image to the style image in order to preserve colors in the stylized output. The default method uses numpy, and I have also translated the author's CORAL code from Torch to PyTorch.

Acknowledgments

Many thanks to the author Xun Huang for the excellent original Torch implementation that saved me countless hours of frustation. I also drew inspiration from Jon Rei's TF implementation for the .t7 pre-trained decoder and borrowed the clean TF code for the AdaIN transform.