diffusion-iterative-denoising-vs-autoregressive-token-sampling

Summary

Image and video generators like Stable Diffusion and Sora build their output by starting from random static noise and gradually sculpting it into structure over many refinement steps, whereas language models produce output strictly left-to-right, each token conditioned on everything that came before. This matters because the two approaches have different strengths, failure modes, and design trade-offs, so you cannot treat them as interchangeable flavors of the same idea or reason about one using the other's framework.