Neural Style Transfer (NST) is the field of study applying neural techniques
to modify the artistic appearance of a content image to match the style of a
reference style image. Traditionally, NST methods have focused on texture-based
image edits, affecting mostly low level information and keeping most image
structures the same. However, style-based deformation of the content is
desirable for some styles, especially in cases where the style is abstract or
the primary concept of the style is in its deformed rendition of some content.
With the recent introduction of diffusion models, such as Stable Diffusion, we
can access far more powerful image generation techniques, enabling new
possibilities. In our work, we propose using this new class of models to
perform style transfer while enabling deformable style transfer, an elusive
capability in previous models. We show how leveraging the priors of these
models can expose new artistic controls at inference time, and we document our
findings in exploring this new direction for the field of style transfer.

神经风格迁移（NST）是将神经技术应用于修改内容图像的艺术外观以匹配参考样式图像的研究领域。我们提出使用基于扩散模型的新类模型来执行风格迁移并实现可变形式的风格迁移，展示了如何在推理时利用这些模型的先验知识，探索了该领域新方向的研究成果。

DIFF-NST：用于可塑性神经风格迁移的扩散交错

DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer

Both geometry and texture are fundamental aspects of visual style. Existing
style transfer methods, however, primarily focus on texture, almost entirely
ignoring geometry. We propose deformable style transfer (DST), an
optimization-based approach that jointly stylizes the texture and geometry of a
content image to better match a style image. Unlike previous geometry-aware
stylization methods, our approach is neither restricted to a particular domain
(such as human faces), nor does it require training sets of matching
style/content pairs. We demonstrate our method on a diverse set of content and
style images including portraits, animals, objects, scenes, and paintings. Code
has been made publicly available at this https URL

提出了一种基于优化的 “可变形风格转移” 方法，可以联合处理内容图像的几何形状和纹理来更好地匹配风格图像，并且不需要训练一整个匹配的样本数据集。