pystiche (pronounced /ˈpaɪˈstiʃ/ ) is a framework for Neural Style Transfer (NST) built upon PyTorch. The name of the project is a pun on pastiche meaning:

A pastiche is a work of visual art [...] that imitates the style or character of the work of one or more other artists. Unlike parody, pastiche celebrates, rather than mocks, the work it imitates.

pystiche has similar goals as Deep Learning (DL) frameworks such as PyTorch:

  1. **Accessibility**Starting off with NST can be quite overwhelming due to the sheer amount of techniques one has to know and be able to deploy. pystiche aims to provide an easy-to-use interface that reduces the necessary prior knowledge about NST and DL to a minimum.
  2. **Reproducibility**Implementing NST from scratch is not only inconvenient but also error-prone. pystiche aims to provide reusable tools that let developers focus on their ideas rather than worrying about bugs in everything around it.


pystiche is a proper Python package and can be installed with pip. The latest release can be installed with

pip install pystiche


pystiche makes it easy to define the optimization criterion for an NST task fully compatible with PyTorch. For example, the banner above was generated with the following criterion:

from pystiche import enc, loss, ops

multi_layer_encoder = enc.vgg19_multi_layer_encoder()

criterion = loss.PerceptualLoss(
        ("relu1_1", "relu2_1", "relu3_1", "relu4_1", "relu5_1"),
        lambda encoder, layer_weight: ops.GramOperator(
            encoder, score_weight=layer_weight

For the full example, head over to the example NST with pystiche.


If you use this software, please cite it as

  author  = {Meier, Philip and Lohweg, Volker},
  journal = {Journal of Open Source Software {JOSS}},
  title   = {pystiche: A Framework for Neural Style Transfer},
  year    = {2020},
  doi     = {10.21105/joss.02761},