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The artistic style within a painting is the means of expression, whichincludes not only the painting material, colors, and brushstrokes, but also thehigh-level attributes including semantic elements, object shapes, etc. Previousarbitrary example-guided artistic image generation methods often fail tocontrol shape changes or convey elements. The pre-trained text-to-imagesynthesis diffusion probabilistic models have achieved remarkable quality, butit often requires extensive textual descriptions to accurately portrayattributes of a particular painting. We believe that the uniqueness of anartwork lies precisely in the fact that it cannot be adequately explained withnormal language. Our key idea is to learn artistic style directly from a singlepainting and then guide the synthesis without providing complex textualdescriptions. Specifically, we assume style as a learnable textual descriptionof 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. Wedemonstrate the quality and efficiency of our method on numerous paintings ofvarious artists and styles. Code and models are available athttps://github.com/zyxElsa/InST.

Inversion-Based Style Transfer with Diffusion Models