  Artistic style transfer aims to transfer the learned artistic style onto an
arbitrary content image, generating artistic stylized images. Existing
generative adversarial network-based methods fail to generate highly realistic
stylized images and always introduce obvious artifacts and disharmonious
patterns. Recently, large-scale pre-trained diffusion models opened up a new
way for generating highly realistic artistic stylized images. However,
diffusion model-based methods generally fail to preserve the content structure
of input content images well, introducing some undesired content structure and
style patterns. To address the above problems, we propose a novel pre-trained
diffusion-based artistic style transfer method, called LSAST, which can
generate highly realistic artistic stylized images while preserving the content
structure of input content images well, without bringing obvious artifacts and
disharmonious style patterns. Specifically, we introduce a Step-aware and
Layer-aware Prompt Space, a set of learnable prompts, which can learn the style
information from the collection of artworks and dynamically adjusts the input
images' content structure and style pattern. To train our prompt space, we
propose a novel inversion method, called Step-ware and Layer-aware Prompt
Inversion, which allows the prompt space to learn the style information of the
artworks collection. In addition, we inject a pre-trained conditional branch of
ControlNet into our LSAST, which further improved our framework's ability to
maintain content structure. Extensive experiments demonstrate that our proposed
method can generate more highly realistic artistic stylized images than the
state-of-the-art artistic style transfer methods.
