Skip to main content

A PyTorch implementation of Neural Style Transfer (NST)

Project description

pastiche

A PyTorch-based Python implementation of Neural Style Transfer [1].


Features

  • Support for saving intermediate images during optimization
  • An option for preserving colors from the content image

Installation

Requirements

  • Python 3.5 or greater

Install

$ pip3 install pastiche

Update

$ pip3 install --upgrade pastiche

Usage

The program is intended to be used from the command line.

The general command line usage is shown below.

$ pastiche CONTENT STYLE OUTPUT

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

There are various options, including but not limited to:

  • Device (CPU versus GPU)
  • Number of optimization iterations
  • VGG layers to utilize
  • Loss function term weights

For the full list of options and the corresponding documentation, see the source code or use --help.

$ pastiche --help

Example

The image above was generated by applying the style from Vincent van Gogh's The Starry Night to a photo I took in Boston in 2015. The high-resolution image was generated incrementally, with increasing resolution, using the coarse-to-fine approach described in [2]. The commands are shown below. Depending on GPU memory availability, the commands may necessitate execution on a CPU (--device cpu).

$ pastiche \
    --num-steps 2000 \
    boston.jpg \
    vangogh_starry_night.jpg \
    pastiche0.png

$ pastiche \
    --size 1024 \
    --num-steps 1000 \
    --init pastiche0.png \
    boston.jpg \
    vangogh_starry_night.jpg \
    pastiche1.png

$ pastiche \
    --size 2048 \
    --num-steps 500 \
    --init pastiche1.png \
    boston.jpg \
    vangogh_starry_night.jpg \
    pastiche2.png

$ pastiche \
    --size 4096 \
    --num-steps 100 \
    --init pastiche2.png \
    boston.jpg \
    vangogh_starry_night.jpg \
    pastiche3.png

$ convert pastiche3.png pastiche.jpg  # requires ImageMagick

The --preserve-color option can be used to retain colors from the content image. The image below was generated using the same commands as above (up to --size 2048), with the addition of --preserve-color.

License

The source code has an MIT License.

See LICENSE.

References

[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. http://arxiv.org/abs/1508.06576.

[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. http://arxiv.org/abs/1611.07865.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pastiche, version 0.2.0
Filename, size File type Python version Upload date Hashes
Filename, size pastiche-0.2.0.tar.gz (18.5 MB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page