Despite the remarkable progress in image style transfer, formulating style in
the context of art is inherently subjective and challenging. In contrast to
existing learning/tuning methods, this study shows that vanilla diffusion
models can directly extract style information and seamlessly integrate the
generative prior into the content image without retraining. Specifically, we
adopt dual denoising paths to represent content/style references in latent
space and then guide the content image denoising process with style latent
codes. We further reveal that the cross-attention mechanism in latent diffusion
models tends to blend the content and style images, resulting in stylized
outputs that deviate from the original content image. To overcome this
limitation, we introduce a cross-attention rearrangement strategy. Through
theoretical analysis and experiments, we demonstrate the effectiveness and
superiority of the diffusion-based $\underline{Z}$ero-shot $\underline{S}$tyle
$\underline{T}$ransfer via $\underline{A}$ttention $\underline{R}$earrangement,
Z-STAR.

通过理论分析和实验，本研究展示了扩散模型在零样式转换的有效性和优越性，并引入了交叉注意力重排策略，使样式信息能够直接提取并无缝集成到内容图像中。