用多张风格图提升图像风格迁移效果，避免内容混淆。
Leveraging Diffusion Models for Stylization using Multiple Style Images
- 结合多张风格图与注意力层干预，分离内容与风格
- 通过聚类提取代表性特征，提升风格匹配精度
- 适合需要高质量风格迁移的创作者和研究者
近年来，潜在扩散模型在图像风格迁移方面取得了显著进展。然而，现有方法仍存在风格匹配不准、可使用的风格图数量有限以及内容与风格意外纠缠等问题。为此，我们提出利用多张风格图来更好地表征风格特征，并防止风格图中的内容泄露。设计了一种结合图像提示适配器与去噪过程中特征统计对齐的方法，可在去噪UNet的交叉注意力与自注意力层进行干预。统计对齐采用聚类技术，从大量风格样本的注意力值中提炼出少量代表性特征。实验表明，该方法在风格迁移任务中达到了当前最优性能。
原文摘要 · Abstract (English)
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.
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