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.

我们提出了ArtBank，一种新颖的艺术风格转换框架，旨在生成高度逼真且保留内容结构的风格化图像。通过设计可训练的参数矩阵集合-隐式风格提示库（ISPB），从艺术作品的集合中学习和存储知识，作为视觉提示，指导预训练大规模模型生成高度逼真的风格化图像，同时保留内容结构。此外，我们提出了一种基于空间统计的自注意力模块（SSAM），加速训练ISPB。定性和定量实验证明了我们提出的方法优于现有的艺术风格转换方法。

ArtBank: 利用预训练扩散模型和隐式风格提示库实现艺术风格转换