Multi-style transfer based on the fast stilization paper

To train the network end-to-end only do:

python train.py

...after installing the dependencies. :)

Docker

The model can be run in a dockerized form either by building it or by downloading it:

The docker image is hosted on DockerHub as well:

Download

sudo docker pull qbear666/multi_style
sudo docker tag qbear666/multi_style:latest multi_style # rename it just for generality of running

Build

sudo docker built -t multi_style . # in project directory, or use the pulled image

Run

sudo docker run \
--gpus all \
-v <path-to-the-movie-file>:/app/movie \
-v <your-desired-output-directory-for-the-movies>:/app/output/ multi_style

It will launch the run.py script and will access your video and GPU device. You'll need to have nvidia-docker installed on your machine and the desired video to be translated in 8 different styles. The docker image will produce the styled videos into the output folder.

Demo videos

Live demo:

• the live demo can be tried via installing all dependencies to your machine since there is some issues with opencv using the webcam in docker which I didn't wank to solve (~20 hours)

• the provided run.py script opens up your webcam, you can quit with pressing q or change styles with the press of button c

Demo videos

@Regards, Alex