  While GAN-based models have been successful in image stylization tasks, they
often struggle with structure preservation while stylizing a wide range of
input images. Recently, diffusion models have been adopted for image
stylization but still lack the capability to maintain the original quality of
input images. Building on this, we propose OSASIS: a novel one-shot stylization
method that is robust in structure preservation. We show that OSASIS is able to
effectively disentangle the semantics from the structure of an image, allowing
it to control the level of content and style implemented to a given input. We
apply OSASIS to various experimental settings, including stylization with
out-of-domain reference images and stylization with text-driven manipulation.
Results show that OSASIS outperforms other stylization methods, especially for
input images that were rarely encountered during training, providing a
promising solution to stylization via diffusion models.
