  We make the distinction between (i) style transfer, in which a source image
is manipulated to match the textures and colors of a target image, and (ii)
essence transfer, in which one edits the source image to include high-level
semantic attributes from the target. Crucially, the semantic attributes that
constitute the essence of an image may differ from image to image. Our blending
operator combines the powerful StyleGAN generator and the semantic encoder of
CLIP in a novel way that is simultaneously additive in both latent spaces,
resulting in a mechanism that guarantees both identity preservation and
high-level feature transfer without relying on a facial recognition network. We
present two variants of our method. The first is based on optimization, while
the second fine-tunes an existing inversion encoder to perform essence
extraction. Through extensive experiments, we demonstrate the superiority of
our methods for essence transfer over existing methods for style transfer,
domain adaptation, and text-based semantic editing. Our code is available at
https://github.com/hila-chefer/TargetCLIP.
