This paper presents a novel training-free semantic segmentation method that leverages a pre-trained large-scale image generation model incorporating the Multi-modal Diffusion Transformer (MM-DiT) architecture. Inspired by training-free segmentation techniques using the U-Net-based noise removal model in the Stable Diffusion framework, our approach extracts cross-attention maps between textual and visual features during the inference stages of the MM-DiT to generate mask images. Experimental results demonstrate that our method achieves segmentation accuracy comparable to CLIP-based and U-Net-based stable diffusion methods. While the direct segmentation scores are relatively modest, the significance of our work lies in the exploration of cross-attention maps within the DiT. This investigation provides critical insights that could advance training-free segmentation methodologies and enhance the interpretability of diffusion-based models.