CacheScope: Exact-Compute Allocation for Training-Free Diffusion Acceleration

CacheScope is a training‑free method that accelerates diffusion transformer inference by selectively replacing full denoising steps with cached approximations. It identifies high‑risk reuse steps online by normalizing probe‑output variation with input displacement, allowing the system to fall back to exact computation only when necessary. The approach achieves up to 3.57× speed‑up and reduces median LPIPS by up to 70.1% on several state‑of‑the‑art models without requiring additional training or offline scheduling.

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• llms
• diffusion