  Foundation models have emerged as powerful tools across various domains
including language, vision, and multimodal tasks. While prior works have
addressed unsupervised image segmentation, they significantly lag behind
supervised models. In this paper, we use a diffusion UNet encoder as a
foundation vision encoder and introduce DiffCut, an unsupervised zero-shot
segmentation method that solely harnesses the output features from the final
self-attention block. Through extensive experimentation, we demonstrate that
the utilization of these diffusion features in a graph based segmentation
algorithm, significantly outperforms previous state-of-the-art methods on
zero-shot segmentation. Specifically, we leverage a recursive Normalized Cut
algorithm that softly regulates the granularity of detected objects and
produces well-defined segmentation maps that precisely capture intricate image
details. Our work highlights the remarkably accurate semantic knowledge
embedded within diffusion UNet encoders that could then serve as foundation
vision encoders for downstream tasks. Project page at
https://diffcut-segmentation.github.io
