A 260M diffusion model beats one 6.5× larger, using looped compute
Looped-DiT lets a 260M-parameter text-to-image model outperform a model 6.5 times larger across multiple benchmarks, while using 4.9 times less inference compute. Instead of adding parameters, it runs the same Transformer blocks repeatedly inside each denoising step, with supervision and self-modulating attention keeping those updates stable. The loop depth can also be increased at sampling time, and the paper reports bigger gains than adding denoising steps under a fixed compute budget.