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Artistic style transfer aims to repaint the content image with the learnedartistic style. Existing artistic style transfer methods can be divided intotwo categories: small model-based approaches and pre-trained large-scalemodel-based approaches. Small model-based approaches can preserve the contentstrucuture, but fail to produce highly realistic stylized images and introduceartifacts and disharmonious patterns; Pre-trained large-scale model-basedapproaches can generate highly realistic stylized images but struggle withpreserving the content structure. To address the above issues, we proposeArtBank, a novel artistic style transfer framework, to generate highlyrealistic stylized images while preserving the content structure of the contentimages. Specifically, to sufficiently dig out the knowledge embedded inpre-trained large-scale models, an Implicit Style Prompt Bank (ISPB), a set oftrainable parameter matrices, is designed to learn and store knowledge from thecollection of artworks and behave as a visual prompt to guide pre-trainedlarge-scale models to generate highly realistic stylized images whilepreserving content structure. Besides, to accelerate training the above ISPB,we propose a novel Spatial-Statistical-based self-Attention Module (SSAM). Thequalitative and quantitative experiments demonstrate the superiority of ourproposed method over state-of-the-art artistic style transfer methods.

ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank