提出新方法，在保持精度的同时大幅提升扩散模型逆问题求解速度。
Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference
- 用可学习的近似优化替代重复计算，加速推理过程。
- 在常见退化条件下速度提升显著，且对未知退化仍保持稳定性能。
- 适合需要快速、可靠重建的图像修复与逆问题场景。
零样本扩散后验采样为逆问题提供了灵活框架，可在测试时适配任意退化算子，但因需反复进行似然引导更新而计算成本高。以往的摊销扩散方法通过隐式推理模型替代基于似然的采样实现快速推理，但对未见退化缺乏鲁棒性。本文提出一种扩散后验采样的摊销策略，通过摊销变分扩散后验采样中的内层优化问题，保留显式的似然引导。该方法在分布内退化下加速推理，同时对未见过的退化算子仍保持鲁棒性，从而改善了扩散模型在逆问题中效率与灵活性的权衡。
原文摘要 · Abstract (English)
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.
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