Zero-shot diffusion posterior sampling offers a flexible framework for inverse problems by accommodating arbitrary degradation operators at test time, but incurs high computational cost due to repeated likelihood-guided updates. In contrast, previous amortized diffusion approaches enable fast inference by replacing likelihood-based sampling with implicit inference models, but at the expense of robustness to unseen degradations. We introduce an amortization strategy for diffusion posterior sampling that preserves explicit likelihood guidance by amortizing the inner optimization problems arising in variational diffusion posterior sampling. This accelerates inference for in-distribution degradations while maintaining robustness to previously unseen operators, thereby improving the trade-off between efficiency and flexibility in diffusion-based inverse problems.

翻译：零样本扩散后验采样通过允许在测试时使用任意退化算子，为逆问题提供了一个灵活的框架，但由于需要重复进行似然引导的更新，计算成本高昂。相比之下，先前的摊销扩散方法通过用隐式推断模型替代基于似然的采样，实现了快速推断，但代价是对未见退化的鲁棒性降低。我们提出了一种用于扩散后验采样的摊销策略，该策略通过摊销变分扩散后验采样中产生的内部优化问题，保留了显式的似然引导。这加速了对分布内退化的推断，同时保持了对先前未见算子的鲁棒性，从而改善了基于扩散的逆问题中效率与灵活性之间的权衡。