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Ready-to-use artistic deep learning algorithms

Project description


A ready-to-use implementation of various Artistic Deep Learning Algorithms.

  • Image Style Transfer Using Convolutional Neural Networks, Gatys et. al, 2016
  • Controlling Perceptual Factors in Neural Style Transfer, Gatys et. al, 2016


# It is recommended to install torch/torchvision manually before this command, according to your hardware configuration (see below)
pip install neurartist

Please note that the use of a GPU is recommended, as CNN computations are pretty slow on a CPU.

NB for GPU users: pip ships torch/torchvision with the Cuda Toolkit 9.0. If you use a more recent version of the Cuda Toolkit, see the PyTorch website for instructions on PyTorch installation with another version of the toolkit.


Console entrypoint

# Then see the builtin help for usage details
neurartist --help


import neurartist

To be added.


  • Basic usage: apply the style of an image to a content image, while preserving the semantic content.
neurartist -c content.jpg -s style.jpg
  • Color control: apply a style, but preserve the color of the content image.
# Luminance only
neurartist -c content.jpg -s style.jpg --color-control luminance_only
# Luminance only, luma normalized
neurartist -c content.jpg -s style.jpg --color-control luminance_only --cc-luminance-only-normalize
# Color histogram matching
neurartist -c content.jpg -s style.jpg --color-control histogram_matching
  • Style mixin: mix the coarse scale information of style1 (higher layers) with the fine scale information of style2 (lower layers), to create a mixed style to apply to a content image.
neurartist -c style1.jpg -s style2.jpg -o mixed.png --content-layers [22,29] --style-layers [1,6]
neurartist -c content.jpg -s mixed.png
  • Efficient high resolution: first pass is a low resolution style transfer that efficiently catches coarse scale style features, second pass is a high resolution style transfer that upscales the result of the first pass and fills the lost fine information using fine scale style features.
neurartist -c content.jpg -s style.jpg -o lowres.png -S 500
neurartist -c content.jpg -s style.jpg -o highres.png -S 1000 --init-image-path lowres.png


Anaconda is strongly recommended:

conda create python=3.7 --name neurartist_env
conda activate neurartist_env

# with gpu
conda install pytorch torchvision cudatoolkit=<your cudatoolkit version> -c pytorch
conda install --file requirements.txt

# with cpu
conda install pytorch-cpu torchvision-cpu -c pytorch
conda install --file requirements.txt

You can then run the main entrypoint directly using:

python -m neurartist --help

Or build and install the wheel file with the --editable flag.


  • Be more consistent with batchsize/no batchsize, especially in covariance_matrix(), add squeeze/unsqueeze steps in transforms
  • Option to initialize the optimizer with another image (and depreciate –hr options, because with this one we can do the lowres and highres passes with two separate console calls)
  • Documentation.
  • Implement the remaining parts of the jupyter notebook.
  • Semantic segmentation as described in this article as to limit spillovers: different approach than guided gram matrices, but same idea of using spatial guidance channels that describe a semantic segmentation of our images.
  • More deep-artistic algorithms.

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