Transformers have demonstrated impressive performance on class-conditional ImageNet benchmarks, achieving state-of-the-art FID scores. However, their computational complexity increases with transformer depth/width or the number of input tokens and requires patchy approximation to operate on even latent input sequences. In this paper, we address these issues by presenting a novel approach to enhance the efficiency and scalability of image generation models, incorporating state space models (SSMs) as the core component and deviating from the widely adopted transformer-based and U-Net…Apple Machine Learning Research