  This technical report outlines our method for generating a synthetic dataset
for semantic segmentation using a latent diffusion model. Our approach
eliminates the need for additional models specifically trained on segmentation
data and is part of our submission to the CVPR 2024 workshop challenge,
entitled CVPR 2024 workshop challenge "SyntaGen Harnessing Generative Models
for Synthetic Visual Datasets". Our methodology uses self-attentions to
facilitate a novel head-wise semantic information condensation, thereby
enabling the direct acquisition of class-agnostic image segmentation from the
Stable Diffusion latents. Furthermore, we employ non-prompt-influencing
cross-attentions from text to pixel, thus facilitating the classification of
the previously generated masks. Finally, we propose a mask refinement step by
using only the output image by Stable Diffusion.
