  Artistic style transfer aims to repaint the content image with the learned
artistic style. Existing artistic style transfer methods can be divided into
two categories: small model-based approaches and pre-trained large-scale
model-based approaches. Small model-based approaches can preserve the content
strucuture, but fail to produce highly realistic stylized images and introduce
artifacts and disharmonious patterns; Pre-trained large-scale model-based
approaches can generate highly realistic stylized images but struggle with
preserving the content structure. To address the above issues, we propose
ArtBank, a novel artistic style transfer framework, to generate highly
realistic stylized images while preserving the content structure of the content
images. Specifically, to sufficiently dig out the knowledge embedded in
pre-trained large-scale models, an Implicit Style Prompt Bank (ISPB), a set of
trainable parameter matrices, is designed to learn and store knowledge from the
collection of artworks and behave as a visual prompt to guide pre-trained
large-scale models to generate highly realistic stylized images while
preserving content structure. Besides, to accelerate training the above ISPB,
we propose a novel Spatial-Statistical-based self-Attention Module (SSAM). The
qualitative and quantitative experiments demonstrate the superiority of our
proposed method over state-of-the-art artistic style transfer methods.
