Overview

Implementation of Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization in Tensorflow

Paper

https://arxiv.org/abs/1703.06868

Setup

git clone https://github.com/ftokarev/tf-adain
cd tf-adain
virtualenv venv --python /usr/bin/python3
source venv/bin/activate
pip install -r requirements.txt
bash models/get_vgg.sh

Usage

Basic

Use --content and --style to provide the respective path to the content and style image, for example:

./test.py --content input/content/cornell.jpg --style input/style/woman_with_hat_matisse.jpg --content_size 0 --style_size 0

To run the code on directories of content and style images use --content_dir and --style_dir options. It will save every possible combination of content and styles to the output directory.

./test.py --content_dir input/content --style_dir input/style

Other options:

• --crop: Center crop both content and style images beforehand.
• --content_size: Scale the content image to this size, keeping proportions. Set to zero to keep the original size.
• --style_size: Scale the style image to this size, keeping proportions. Set to zero to keep the original size.

To see all available options, type:

./test.py -h

Content-Style tradeoff

Use --alpha to adjust the degree of stylization. It should be a value between 0 and 1 (default).

Preserve color

Add --preserve_color to preserve the color of the content image. Example usage:

./test.py --content input/content/newyork.jpg --style input/style/brushstrokes.jpg --content_size 0 --style_size 0 --preserve_color

Style Interpolation

To interpolate between several style images, provide them as a comma-separated list. You can control the relative weight of each style by using the --style_interp_weights option. Example usage:

./test.py --content input/content/avril.jpg --style input/style/picasso_self_portrait.jpg,input/style/impronte_d_artista.jpg --style_interp_weights 1,1 --crop --style_size 400

Spatial Control

Use --mask to provide the path to a binary foreground mask. You can transfer the foreground and background of the content image to different styles. Note that you also to need to provide two style images separated be comma, in which the first one is applied to foreground and the second one is applied to background. Example usage:

./test.py --content input/content/blonde_girl.jpg --style input/style/woman_in_peasant_dress_cropped.jpg,input/style/mondrian_cropped.jpg --mask input/mask/mask.png --content_size 0 --style_size 0

Training

• Download the wikiart dataset from Kaggle (https://www.kaggle.com/c/painter-by-numbers/download/train.zip), unzip images into datasets/wikiart/train
• Download the MS COCO dataset using the included downloader:

./datasets/get_coco_train.sh

• Prepare datasets for training

./prepare_dataset.py datasets/coco/train2014/ datasets/coco/
./prepare_dataset.py datasets/wikiart/train/ datasets/wikiart/

• Launch the training script

./train.py

To see all available options, type:

./train.py -h

Acknowledgement

• This implementation is based on the original Torch implementation (https://github.com/xunhuang1995/AdaIN-style)