EmerDiff is a novel diffusion model designed to generate fine-grained segmentation maps by leveraging semantic knowledge extracted from diffusion models, without requiring additional training. This model leverages semantic knowledge extracted from Stable Diffusion (SD) to overcome the challenge of directly extracting pixel-level semantic relationships from low-dimensional feature maps. It utilizes these relationships to construct segmentation maps at the image resolution. Extensive experiments have demonstrated that the generated segmentation maps are clear and capture detailed aspects of images, indicating the presence of highly accurate pixel-level semantic knowledge within diffusion models.