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 pastiche

The 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.

The command line usage is shown below. Use --help to access documentation for the additional options (e.g., --device for controlling whether to use a CPU or GPU).

$ pastiche                    \
    --content CONTENT         \
    --styles STYLE [STYLE ..] \
    --output OUTPUT

CONTENT is the path to the content image, STYLE is a path to a style image, and OUTPUT is the path to save the synthesized pastiche PNG file.

If the launcher script was not installed within a directory on your PATH, pastiche can be launched by passing its module name to Python.

$ python3 -m pastiche         \
    --content CONTENT         \
    --styles STYLE [STYLE ..] \
    --output OUTPUT

The source code is available on GitHub:

The README includes an example showing how to generate a high-resolution image incrementally, with increasing resolution, using the coarse-to-fine approach described in [2].

The --preserve-color option can be used to retain colors from the content image.


[1] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A Neural Algorithm of Artistic Style.” ArXiv:1508.06576 [Cs, q-Bio], August 26, 2015.

[2] Gatys, Leon A., Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman. “Controlling Perceptual Factors in Neural Style Transfer.” ArXiv:1611.07865 [Cs], November 23, 2016.

[3] Simonyan, Karen, and Andrew Zisserman. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” ArXiv:1409.1556 [Cs], September 4, 2014.

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