  While personalized text-to-image generation has enabled the learning of a
single concept from multiple images, a more practical yet challenging scenario
involves learning multiple concepts within a single image. However, existing
works tackling this scenario heavily rely on extensive human annotations. In
this paper, we introduce a novel task named Unsupervised Concept Extraction
(UCE) that considers an unsupervised setting without any human knowledge of the
concepts. Given an image that contains multiple concepts, the task aims to
extract and recreate individual concepts solely relying on the existing
knowledge from pretrained diffusion models. To achieve this, we present
ConceptExpress that tackles UCE by unleashing the inherent capabilities of
pretrained diffusion models in two aspects. Specifically, a concept
localization approach automatically locates and disentangles salient concepts
by leveraging spatial correspondence from diffusion self-attention; and based
on the lookup association between a concept and a conceptual token, a
concept-wise optimization process learns discriminative tokens that represent
each individual concept. Finally, we establish an evaluation protocol tailored
for the UCE task. Extensive experiments demonstrate that ConceptExpress is a
promising solution to the UCE task. Our code and data are available at:
https://github.com/haoosz/ConceptExpress
