The existing image style transfer algorithms can only handle a limited number of styles. In some common application scenarios, large-scale image style transfer is required to generate differentiated images, and a limited number of styles are clearly not sufficient to cope with such scenarios. In response to the above issues, this chapter proposes a style transfer algorithm based on variational auto-encoder. This algorithm encodes the styles in the training set into a high-dimensional continuous space, randomly sampling a point from this space to obtain a style encoding. Based on content information, the encoded and decoded images can be stylized. Because the space where style encoding is located is continuous, the number of styles is infinite. This article conducted comparative experiments and style sampling experiments on the COCO and WikiArt datasets. The experimental results showed that the VST algorithm has the ability to sample styles, fix content, and quickly synthesize high-quality images for style sampling; In addition, when the synthesis effect is similar, the synthesis speed of VST is 50 times that of GST.