Abstract
Recent advances in latent diffusion models have enabled exciting progress in image style transfer. However, several key issues remain. For example, existing methods still struggle to accurately match styles. They are often limited in the number of style images that can be used. Furthermore, they tend to entangle content and style in undesired ways. To address this, we propose leveraging multiple style images which helps better represent style features and prevent content leaking from the style images. We design a method that leverages both image prompt adapters and statistical alignment of the features during the denoising process. With this, our approach is designed such that it can intervene both at the cross-attention and the self-attention layers of the denoising UNet. For the statistical alignment, we employ clustering to distill a small representative set of attention features from the large number of attention values extracted from the style samples. As demonstrated in our experimental section, the resulting method achieves state-of-the-art results for stylization.
Abstract (translated)
最近在潜在扩散模型方面的进展已经在图像风格转换领域取得了令人兴奋的成果。然而，仍存在一些关键问题亟待解决。例如，现有的方法仍然难以准确匹配样式特征，并且通常局限于使用有限数量的样例图片。此外，它们倾向于以非预期的方式纠缠内容和样式信息。为了解决这些问题，我们提出了一种利用多张风格图像的方法，这有助于更好地表示风格特性并防止从风格图像中泄露内容。为此，我们设计了一种方法，该方法结合了图像提示适配器以及在去噪过程中对特征的统计一致性处理。 我们的方法被特别设计为可以在扩散UNet中的交叉注意力层和自我注意力层进行干预。对于统计一致性处理部分，我们采用了聚类技术来从大量的样例图片中提取的关注值中提炼出一小批具有代表性的关注特性。实验结果表明，所提出的方法在风格化任务上取得了当前最先进的性能表现。
URL
https://arxiv.org/abs/2508.12784
