  This paper focuses on face stylization with a single artistic target.
Existing works for this task often fail to retain the source content while
achieving geometry variation. Here, we present a novel StyO model, ie. Stylize
the face in only One-shot, to solve the above problem. In particular, StyO
exploits a disentanglement and recombination strategy. It first disentangles
the content and style of source and target images into identifiers, which are
then recombined in a cross manner to derive the stylized face image. In this
way, StyO decomposes complex images into independent and specific attributes,
and simplifies one-shot face stylization as the combination of different
attributes from input images, thus producing results better matching face
geometry of target image and content of source one. StyO is implemented with
latent diffusion models (LDM) and composed of two key modules: 1) Identifier
Disentanglement Learner (IDL) for disentanglement phase. It represents
identifiers as contrastive text prompts, ie. positive and negative
descriptions. And it introduces a novel triple reconstruction loss to fine-tune
the pre-trained LDM for encoding style and content into corresponding
identifiers; 2) Fine-grained Content Controller (FCC) for the recombination
phase. It recombines disentangled identifiers from IDL to form an augmented
text prompt for generating stylized faces. In addition, FCC also constrains the
cross-attention maps of latent and text features to preserve source face
details in results. The extensive evaluation shows that StyO produces
high-quality images on numerous paintings of various styles and outperforms the
current state-of-the-art. Code will be released upon acceptance.
