I used the pastiche style transfer program—discussed in a prior post—to create the video shown above. The content image is a photo I took in Boston in 2015, and the style images were randomly sampled from the test images of the Painter by Numbers Kaggle competition.
The frames used in the video were retained during gradient descent by using pastiche‘s --workspace option.
The Python script for generating the video is on GitHub:
https://gist.github.com/dstein64/5dcc67fa43cc0d13d6d4d544095a1382
pastiche A literary, artistic, musical, or architectural work that imitates the style of previous work.
―Merriam-Webster dictionary
Update 1/20/2021: The command line usage snippets were updated in accordance with v1.1.0.
I recently implemented pastiche, a PyTorch-based Python program for applying neural style transfer [1]. Given a content image C and a style image S, neural style transfer (NST) synthesizes a new image I that retains the content from C and style from S. This is achieved by iteratively updating I so that relevant properties of its representation within the VGG neural network [3] approach the corresponding properties for C and S.
The library is available on PyPI and can be installed with pip.
$ pip3 install pasticheThe example image above was synthesized by applying the style from Vincent van Gogh’s The Starry Night to a photo I took in Boston in 2015.