Looped Diffusion Transformer: the same blocks run several times inside each denoising step, and loop depth can be changed at sampling time [R]

Key points

• Instead of making a text-to-image DiT bigger, it runs a shared group of transformer blocks several times inside each denoising step.1
• Their headline claims: a 260M looped model beats a model 6.5x larger on several benchmarks with 4.9x less inference compute, and at a fixed inference budget, more loop depth helps more than more denoising steps.1
• The B/16 code and weights are MIT (the weights are on Hugging Face under sensenova), so I ran it on an M5 Pro: PyTorch MPS in fp32, 100 Euler steps, CFG 6.0, and one seed per prompt reused at loop depths 1 to 4.1
• The interesting part to me is that depth is picked at sampling time, the way the iteration count is in looped and recurrent-depth language models, so you can trade compute for refinement per image.1

Sources (1)

• [1]Looped Diffusion Transformer: the same blocks run several times inside each denoising step, and loop depth can be changed at sampling time [R]
  r/MachineLearning (top, daily) · Oct 12, 06:13 PM

  Paper: Looped Diffusion Transformer (arXiv 2609.40305, 30 Sep).

  Instead of making a text-to-image DiT bigger, it runs a shared group of transformer blocks several times inside each denoising step.

Extractive summary: sentences quoted from the sources.