  The artistic style within a painting is the means of expression, which
includes not only the painting material, colors, and brushstrokes, but also the
high-level attributes including semantic elements, object shapes, etc. Previous
arbitrary example-guided artistic image generation methods often fail to
control shape changes or convey elements. The pre-trained text-to-image
synthesis diffusion probabilistic models have achieved remarkable quality, but
it often requires extensive textual descriptions to accurately portray
attributes of a particular painting. We believe that the uniqueness of an
artwork lies precisely in the fact that it cannot be adequately explained with
normal language. Our key idea is to learn artistic style directly from a single
painting and then guide the synthesis without providing complex textual
descriptions. Specifically, we assume style as a learnable textual description
of a painting. We propose an inversion-based style transfer method (InST),
which can efficiently and accurately learn the key information of an image,
thus capturing and transferring the artistic style of a painting. We
demonstrate the quality and efficiency of our method on numerous paintings of
various artists and styles. Code and models are available at
https://github.com/zyxElsa/InST.
