  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.
