260M-parameter looped diffusion transformer beats 6x larger baselines with 5x less compute
burkov · x · 2026-10-16
A looped diffusion transformer text-to-image model with just 260M parameters outperforms baselines over 6x larger while cutting inference compute nearly 5x — showing the promise of reusing the same transformer through repeated loops instead of stacking parameters.
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