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Style transfer aims to render the style of a given image for style referenceto another given image for content reference, and has been widely adopted inartistic generation and image editing. Existing approaches either apply theholistic style of the style image in a global manner, or migrate local colorsand textures of the style image to the content counterparts in a pre-definedway. In either case, only one result can be generated for a specific pair ofcontent and style images, which therefore lacks flexibility and is hard tosatisfy different users with different preferences. We propose here a novelstrategy termed Any-to-Any Style Transfer to address this drawback, whichenables users to interactively select styles of regions in the style image andapply them to the prescribed content regions. In this way, personalizable styletransfer is achieved through human-computer interaction. At the heart of ourapproach lies in (1) a region segmentation module based on Segment Anything,which supports region selection with only some clicks or drawing on images andthus takes user inputs conveniently and flexibly; (2) and an attention fusionmodule, which converts inputs from users to controlling signals for the styletransfer model. Experiments demonstrate the effectiveness for personalizablestyle transfer. Notably, our approach performs in a plug-and-play mannerportable to any style transfer method and enhance the controllablity. Our codeis available \href{https://github.com/Huage001/Transfer-Any-Style}{here}.

Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate