This is a Pytorch implementation of the "Universal Style Transfer via Feature Trasforms" NIPS17 paper.

Given a content image and an arbitrary style image, the program attempts to transfer the visual style characteristics extracted from the style image to the content image generating stylized ouput.

The core architecture is a VGG19 Convolutional Autoencoder performing Whitening and Coloring Transformation on the content and style features in the bottleneck layer.

• Needed Python packages can be installed using conda package manager by running conda env create -f environment.yaml

python main.py ARGS

Possible ARGS are:

• --content CONTENT path of the content image (or a directory containing images) to be trasformed;
• --style STYLE path of the style image (or a directory containing images) to use;
• --contentSize CONTENTSIZE reshape content image to have the new specified maximum size (keeping aspect ratio);
• --styleSize STYLESIZE reshape style image to have the new specified maximum size (keeping aspect ratio);
• --outDir OUTDIR path of the directory where stylized results will be saved (default is outputs/);
• --alpha ALPHA hyperparameter balancing the blending between original content features and WCT-transformed features (default is 0.2);
• --no-cuda flag to enable CPU-only computations (default is False i.e. GPU (CUDA) accelaration);

An example

python main.py --content inputs/contents/in4.jpg --style inputs/styles/candy.jpg