Looped-DiT turns repeated depth into a diffusion scaling axis

Text-to-image diffusion models usually scale through larger networks or additional denoising steps. Researchers from SenseTime Research, Tsinghua University, and Nanyang Technological University propose another option in the Looped-DiT paper: reuse the same transformer blocks several times within each denoising step. The model gains computational depth at inference without adding parameters.

At every denoising timestep, a conventional diffusion transformer processes the current noisy image representation once before advancing the sampler. Looped-DiT applies its shared block stack repeatedly before moving to the next timestep. Loop count becomes a configurable scaling control alongside model size and sampler length.

A smaller weight set reduces model storage and memory bandwidth requirements, while repeated execution spends additional compute on the same parameters. The trade is especially relevant when model weights exceed available VRAM but longer inference remains acceptable.