  Producing quality segmentation masks for images is a fundamental problem in
computer vision. Recent research has explored large-scale supervised training
to enable zero-shot segmentation on virtually any image style and unsupervised
training to enable segmentation without dense annotations. However,
constructing a model capable of segmenting anything in a zero-shot manner
without any annotations is still challenging. In this paper, we propose to
utilize the self-attention layers in stable diffusion models to achieve this
goal because the pre-trained stable diffusion model has learned inherent
concepts of objects within its attention layers. Specifically, we introduce a
simple yet effective iterative merging process based on measuring KL divergence
among attention maps to merge them into valid segmentation masks. The proposed
method does not require any training or language dependency to extract quality
segmentation for any images. On COCO-Stuff-27, our method surpasses the prior
unsupervised zero-shot SOTA method by an absolute 26% in pixel accuracy and 17%
in mean IoU. The project page is at
\url{https://sites.google.com/view/diffseg/home}.
