  We propose PARASOL, a multi-modal synthesis model that enables disentangled,
parametric control of the visual style of the image by jointly conditioning
synthesis on both content and a fine-grained visual style embedding. We train a
latent diffusion model (LDM) using specific losses for each modality and adapt
the classifier-free guidance for encouraging disentangled control over
independent content and style modalities at inference time. We leverage
auxiliary semantic and style-based search to create training triplets for
supervision of the LDM, ensuring complementarity of content and style cues.
PARASOL shows promise for enabling nuanced control over visual style in
diffusion models for image creation and stylization, as well as generative
search where text-based search results may be adapted to more closely match
user intent by interpolating both content and style descriptors.
