  Diffusion models gain increasing popularity for their generative
capabilities. Recently, there have been surging needs to generate customized
images by inverting diffusion models from exemplar images. However, existing
inversion methods mainly focus on capturing object appearances. How to invert
object relations, another important pillar in the visual world, remains
unexplored. In this work, we propose ReVersion for the Relation Inversion task,
which aims to learn a specific relation (represented as "relation prompt") from
exemplar images. Specifically, we learn a relation prompt from a frozen
pre-trained text-to-image diffusion model. The learned relation prompt can then
be applied to generate relation-specific images with new objects, backgrounds,
and styles. Our key insight is the "preposition prior" - real-world relation
prompts can be sparsely activated upon a set of basis prepositional words.
Specifically, we propose a novel relation-steering contrastive learning scheme
to impose two critical properties of the relation prompt: 1) The relation
prompt should capture the interaction between objects, enforced by the
preposition prior. 2) The relation prompt should be disentangled away from
object appearances. We further devise relation-focal importance sampling to
emphasize high-level interactions over low-level appearances (e.g., texture,
color). To comprehensively evaluate this new task, we contribute ReVersion
Benchmark, which provides various exemplar images with diverse relations.
Extensive experiments validate the superiority of our approach over existing
methods across a wide range of visual relations.
