An implementation of the paper 'A Neural Algorithm of Artistic Style'.

Subscribe to updates I use neuralart

Statistics on neuralart

Number of watchers on Github 2180
Number of open issues 6
Average time to close an issue 4 days
Main language Lua
Average time to merge a PR about 21 hours
Open pull requests 1+
Closed pull requests 0+
Last commit about 1 year ago
Repo Created over 2 years ago
Repo Last Updated 3 months ago
Size 27.2 MB
Organization / Authorkaishengtai
Page Updated
Do you use neuralart? Leave a review!
View open issues (6)
View neuralart activity
View on github
Latest Open Source Launches
Trendy new open source projects in your inbox! View examples

Subscribe to our mailing list

Evaluating neuralart for your project? Score Explanation
Commits Score (?)
Issues & PR Score (?)

Implementation of 'A Neural Algorithm of Artistic Style'

This is a Torch7 implementation of the method described in the paper 'A Neural Algorithm of Artistic Style' by Leon Gatys, Alexander Ecker, and Matthias Bethge (

(Longer animation)


  • Torch7
  • imagine-nn (for Inception network)
  • CUDA 6.5+ (unless running on CPU -- see below)

imagine-nn (and any other Torch packages you're missing) can be installed via Luarocks:

luarocks install inn


First, download the models by running the download script:


This downloads the model weights for the VGG and Inception networks.

Basic usage:

qlua main.lua --style <style.jpg> --content <content.jpg> --style_factor <factor>

where style.jpg is the image that provides the style of the final generated image, and content.jpg is the image that provides the content. style_factor is a constant that controls the degree to which the generated image emphasizes style over content. By default it is set to 2E9.

This generates an image using the VGG-19 network by Karen Simonyan and Andrew Zisserman (

Other options:

  • model: {inception, vgg}. Convnet model to use. Inception refers to Google's Inception architecture. Default is VGG.
  • num_iters: Number of optimization steps. Default is 500.
  • size: Long edge dimension of the generated image. Set to 0 to use the size of the content image. Default is 500.
  • display_interval: Number of iterations between image displays. Set to 0 to suppress image display. Default is 20.
  • smoothness: Constant that controls the smoothness of the generated image (total variation norm regularization strength). Useful when using Inception model (set to ~5E-3). Default is 0.
  • init: {image, random}. Initialization mode for the optimized image. image initializes with the content image; random initializes with random Gaussian noise. Default is image.
  • backend: {cunn, cudnn}. Neural network CUDA backend. cudnn requires the Torch bindings for CuDNN R3.
  • optimizer: {sgd, lbfgs}. Optimization algorithm. lbfgs is slower per iteration and consumes more memory, but may yield better results. Default is lbfgs.
  • cpu: Optimize on CPU instead of GPU (only VGG model supported).

Out of memory?

The VGG network with the default L-BFGS optimizer gives the best results. However, this setting also requires a lot of GPU memory. If you run into CUDA out-of-memory errors, try running with the Inception architecture or with the SGD optimizer:

qlua main.lua --style <style.jpg> --content <content.jpg> --model inception --optimizer sgd

You can also try reducing the size of the generated image:

qlua main.lua --style <style.jpg> --content <content.jpg> --size 300

If all else fails (or if you don't have a CUDA-compatible GPU), you can optimize on CPU:

qlua main.lua --style <style.jpg> --content <content.jpg> --cpu


The Eiffel Tower in the style of Edvard Munch's The Scream:

(Longer animation)

Picasso-fied Obama:

(Longer animation)

Implementation Details

When using the Inception network, the outputs of the following layers are used to optimize for style: conv1/7x7_s2, conv2/3x3, inception_3a, inception_3b, inception_4a, inception_4b, inception_4c, inception_4d, inception_4e.

The outputs of the following layers are used to optimize for content: inception_3a, inception_4a.

By default, the optimized image is initialized using the content image; the implementation also works with white noise initialization, as described in the paper.

In order to reduce high-frequency screen door noise in the generated image (especially when using the Inception network), total variation regularization is applied (idea from cnn-vis by jcjohnson).


The weights for the Inception network used in this implementation were ported to Torch from the publicly-available Caffe distribution.

Thanks to the Bethge Group for providing the weights to the normalized VGG network used here.

neuralart open issues Ask a question     (View All Issues)
  • about 2 years How would you install this on a Raspberry Pi?
  • about 2 years how to install on os x
  • about 2 years How to install on Windows?
  • over 2 years How to train models?
  • over 2 years Very High Resolution
  • over 2 years Normalized options
neuralart open pull requests (View All Pulls)
  • Fix default optimizer in
neuralart list of languages used
More projects by kaishengtai View all
Other projects in Lua