Neural style in TensorFlow! 🎨


An implementation of neural style in TensorFlow.

This implementation is a lot simpler than a lot of the other ones out there, thanks to TensorFlow's really nice API and automatic differentiation.

TensorFlow doesn't support L-BFGS (which is what the original authors used), so we use Adam. This may require a little bit more hyperparameter tuning to get nice results.

See here for an implementation of fast (feed-forward) neural style in TensorFlow.


python --content <content file> --styles <style file> --output <output file>

Run python --help to see a list of all options.

Use --checkpoint-output and --checkpoint-iterations to save checkpoint images.

Use --iterations to change the number of iterations (default 1000). For a 512×512 pixel content file, 1000 iterations take 2.5 minutes on a GeForce GTX Titan X GPU, or 90 minutes on an Intel Core i7-5930K CPU.

Example 1

Running it for 500-2000 iterations seems to produce nice results. With certain images or output sizes, you might need some hyperparameter tuning (especially --content-weight, --style-weight, and --learning-rate).

The following example was run for 1000 iterations to produce the result (with default parameters):


These were the input images used (me sleeping at a hackathon and Starry Night):



Example 2

The following example demonstrates style blending, and was run for 1000 iterations to produce the result (with style blend weight parameters 0.8 and 0.2):


The content input image was a picture of the Stata Center at MIT:


The style input images were Picasso's "Dora Maar" and Starry Night, with the Picasso image having a style blend weight of 0.8 and Starry Night having a style blend weight of 0.2:

input-style input-style



If you use this implementation in your work, please cite the following:

  author = {Anish Athalye},
  title = {Neural Style},
  year = {2015},
  howpublished = {\url{}},
  note = {commit xxxxxxx}


Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3. See LICENSE.txt for details.