ManifoldCache: Training-Free Diffusion Acceleration via Constraint Manifold Caching

ManifoldCache is a training‑free, data‑free accelerator for Constraint‑Manifold Diffusion Models (CMDMs), which generate samples that must satisfy hard geometric constraints in scientific domains such as medical imaging, molecular design, and 3D scene generation. By decomposing the conditional score into orthogonal normal and tangential components, the method identifies a noise‑schedule midpoint as a safe caching boundary—caching before this point guarantees bounded error, while caching after it leads to mode confusion. The approach also shows that deeper network blocks allow larger certified cache strides, all without requiring calibration data or additional training.

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• diffusion
• efficiency
• benchmarks