Aiming at the problem of memory consumption in ultra-high resolution image processing and the problem of over-stylization in the process of style transfer, a method of ultra-high resolution image style transfer combined with reversible network is proposed. The algorithm used the reversible Glow module as the basic unit to construct a reversible neural network module, and divided the image into small blocks for processing. In the style transfer module, a residual module with a channel attention mechanism and a thumbnail instantiation normalization module (TIN) were used to ensure that the styles of each module were consistent. A global-local loss calculation method was proposed, which could effectively deal with local structural features. Experimental results show that, compared with the current general-purpose neural style transfer network, this algorithm can not only avoid the information loss problem in the process of image encoding and decoding, but also achieve better style transfer performance at a lower memory cost.