Z-STAR+: 一种通过调整风格分布实现的零样本风格迁移方法
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution
AI总结：
针对风格迁移中预定义风格表示限制表达的问题，提出Z-STAR+方法，利用扩散模型潜在特征的自然分布，通过双去噪路径和交叉注意力重加权模块等，实现无需重训练的零样本风格迁移，提升效果。
摘要：
风格迁移面临重大挑战，主要集中在识别合适的风格表示。传统方法采用由二阶统计量或对比学习得到的风格损失，来约束风格化结果中的风格表示。然而，这些预定义的风格表示往往限制风格表达，导致伪影。与现有方法不同，我们发现普通扩散模型中的潜在特征本身包含自然的风格和内容分布，这使得无需重新训练即可直接提取风格信息，并将生成先验无缝集成到内容图像中。我们的方法采用双去噪路径在潜在空间中表示内容和风格参考，随后用风格潜在编码引导内容图像去噪过程。我们引入交叉注意力重加权模块，利用局部内容特征查询最适合输入块的风格图像信息，从而使风格化结果的风格分布与风格图像对齐。此外，我们设计了缩放自适应实例归一化，以在全局范围内缓解风格与风格化图像之间的颜色分布不一致。通过理论分析和大量实验，我们证明了基于扩散的零样本风格迁移方法（通过调整风格分布，称为Z-STAR+）的有效性和优越性。
英文摘要：
Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. Conventional methods employ style loss, derived from second-order statistics or contrastive learning, to constrain style representation in the stylized result. However, these pre-defined style representations often limit stylistic expression, leading to artifacts. In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions. This allows for direct extraction of style information and seamless integration of generative priors into the content image without necessitating retraining. Our method adopts dual denoising paths to represent content and style references in latent space, subsequently guiding the content image denoising process with style latent codes. We introduce a Cross-attention Reweighting module that utilizes local content features to query style image information best suited to the input patch, thereby aligning the style distribution of the stylized results with that of the style image. Furthermore, we design a scaled adaptive instance normalization to mitigate inconsistencies in color distribution between style and stylized images on a global scale. Through theoretical analysis and extensive experimentation, we demonstrate the effectiveness and superiority of our diffusion-based \uline{z}ero-shot \uline{s}tyle \uline{t}ransfer via \uline{a}djusting style dist\uline{r}ibution, termed Z-STAR+.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
