ConceptLab: Creative Generation using Diffusion Prior Constraints
Authors: name: Elad Richardson, name: Kfir Goldberg, name: Yuval Alaluf, name: Daniel Cohen-Or

Recent text-to-image generative models have enabled us to transform our words
into vibrant, captivating imagery. The surge of personalization techniques that
has followed has also allowed us to imagine unique concepts in new scenes.
However, an intriguing question remains: How can we generate a new, imaginary
concept that has never been seen before? In this paper, we present the task of
creative text-to-image generation, where we seek to generate new members of a
broad category (e.g., generating a pet that differs from all existing pets). We
leverage the under-studied Diffusion Prior models and show that the creative
generation problem can be formulated as an optimization process over the output
space of the diffusion prior, resulting in a set of "prior constraints". To
keep our generated concept from converging into existing members, we
incorporate a question-answering model that adaptively adds new constraints to
the optimization problem, encouraging the model to discover increasingly more
unique creations. Finally, we show that our prior constraints can also serve as
a strong mixing mechanism allowing us to create hybrids between generated
concepts, introducing even more flexibility into the creative process.