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.

翻译：神经风格迁移（NST）是通过神经技术修改内容图像的艺术风格以匹配参考风格图像风格的研究领域。传统NST方法侧重于基于纹理的图像编辑，主要影响低级信息并保持大部分图像结构不变。然而，基于风格的内容变形在某些风格中具有需求，尤其是当风格较为抽象或其主要概念体现在对内容的扭曲再现时。随着扩散模型（如Stable Diffusion）的近期引入，我们能获取更强大的图像生成技术，从而开辟新的可能性。本文提出利用这类新模型实现风格迁移，同时具备可变形风格迁移能力——这是先前模型难以实现的功能。我们展示了如何利用这些模型的先验知识在推理阶段实现新的艺术控制，并记录了探索这一风格迁移新方向的研究发现。