  We present an extremely simple Ultra-Resolution Style Transfer framework,
termed URST, to flexibly process arbitrary high-resolution images (e.g.,
10000x10000 pixels) style transfer for the first time. Most of the existing
state-of-the-art methods would fall short due to massive memory cost and small
stroke size when processing ultra-high resolution images. URST completely
avoids the memory problem caused by ultra-high resolution images by 1) dividing
the image into small patches and 2) performing patch-wise style transfer with a
novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract
thumbnail's normalization statistics and apply them to small patches, ensuring
the style consistency among different patches. Overall, the URST framework has
three merits compared to prior arts. 1) We divide input image into small
patches and adopt TIN, successfully transferring image style with arbitrary
high-resolution. 2) Experiments show that our URST surpasses existing SOTA
methods on ultra-high resolution images benefiting from the effectiveness of
the proposed stroke perceptual loss in enlarging the stroke size. 3) Our URST
can be easily plugged into most existing style transfer methods and directly
improve their performance even without training. Code is available at
https://github.com/czczup/URST.
