  Fast Style Transfer is a series of Neural Style Transfer algorithms that use
feed-forward neural networks to render input images. Because of the high
dimension of the output layer, these networks require much memory for
computation. Therefore, for high-resolution images, most mobile devices and
personal computers cannot stylize them, which greatly limits the application
scenarios of Fast Style Transfer. At present, the two existing solutions are
purchasing more memory and using the feathering-based method, but the former
requires additional cost, and the latter has poor image quality. To solve this
problem, we propose a novel image synthesis method named \emph{block shuffle},
which converts a single task with high memory consumption to multiple subtasks
with low memory consumption. This method can act as a plug-in for Fast Style
Transfer without any modification to the network architecture. We use the most
popular Fast Style Transfer repository on GitHub as the baseline. Experiments
show that the quality of high-resolution images generated by our method is
better than that of the feathering-based method. Although our method is an
order of magnitude slower than the baseline, it can stylize high-resolution
images with limited memory, which is impossible with the baseline. The code and
models will be made available on \url{https://github.com/czczup/block-shuffle}.
