Figure 3. This diagram illustrates our multimodal super-resolution pipeline. Starting with a low-resolution (LR) image, we extract modalities
like depth and semantic segmentation maps. These modalities are encoded into tokens and transformed into multimodal latent tokens (m).
Our diffusion model uses these tokens and the LR input to generate a high-resolution (SR) output. A multimodal classifier-free guidance
(m-cfg) refines the SR image for enhanced quality.