  Text-based style transfer is a newly-emerging research topic that uses text
information instead of style image to guide the transfer process, significantly
extending the application scenario of style transfer. However, previous methods
require extra time for optimization or text-image paired data, leading to
limited effectiveness. In this work, we achieve a data-efficient text-based
style transfer method that does not require optimization at the inference
stage. Specifically, we convert text input to the style space of the
pre-trained VGG network to realize a more effective style swap. We also
leverage CLIP's multi-modal embedding space to learn the text-to-style mapping
with the image dataset only. Our method can transfer arbitrary new styles of
text input in real-time and synthesize high-quality artistic images.
