  Language-guided image editing has achieved great success recently. In this
paper, for the first time, we investigate exemplar-guided image editing for
more precise control. We achieve this goal by leveraging self-supervised
training to disentangle and re-organize the source image and the exemplar.
However, the naive approach will cause obvious fusing artifacts. We carefully
analyze it and propose an information bottleneck and strong augmentations to
avoid the trivial solution of directly copying and pasting the exemplar image.
Meanwhile, to ensure the controllability of the editing process, we design an
arbitrary shape mask for the exemplar image and leverage the classifier-free
guidance to increase the similarity to the exemplar image. The whole framework
involves a single forward of the diffusion model without any iterative
optimization. We demonstrate that our method achieves an impressive performance
and enables controllable editing on in-the-wild images with high fidelity.
