Advances in high-fidelity image generation have been spearheaded by denoising diffusion probabilistic models (DDPMs). However, there remain considerable computational challenges when scaling current DDPM architectures to high-resolutions, due to the use of attention either in UNet architectures or Transformer variants. To make models tractable, it is common to employ lossy compression techniques in hidden space, such as patchifying, which trade-off representational capacity for efficiency. We propose Diffusion State Space Model (DiffuSSMs), an architecture that replaces attention with a more efficient state space model backbone. The model avoids global compression which enables longer, more fine-grained image representation in the diffusion process. Our validation on ImageNet indicates superior performance in terms of FiD and Inception Score at reduced total FLOP usage compared to previous diffusion models using attention.