Shai Avidan

Diffusion models can achieve up to $6.9\times$ speedup by adaptively adjusting step sizes based on a staleness signal's "headroom," effectively jumping over scheduled inference steps.

Mixing the right local appearances, not just blending pixels, unlocks significantly higher quality texture interpolations.

Even the most advanced vision-language models fail to leverage global scene geometry, relying heavily on target identity instead.