Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference

2026-02-10 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new amortization strategy for diffusion posterior sampling, introduced on February 6, 2026, aims to enhance the efficiency and flexibility of diffusion-based inverse problems. Traditional zero-shot diffusion posterior sampling, while flexible for arbitrary degradation operators, suffers from high computational costs due to repeated likelihood-guided updates. Conversely, existing amortized diffusion methods offer faster inference by using implicit inference models but lack robustness to novel degradations. This new approach addresses this trade-off by amortizing the inner optimization problems within variational diffusion posterior sampling, thereby preserving explicit likelihood guidance. This method significantly accelerates inference for in-distribution degradations while simultaneously maintaining robustness when encountering previously unseen operators.

Key takeaway

For research scientists developing diffusion models for inverse problems, this amortization strategy offers a path to significantly faster inference without sacrificing the crucial robustness needed for real-world, varied degradation operators. You should investigate integrating amortized variational inference into your diffusion posterior sampling pipelines to improve both performance and adaptability.

Key insights

Amortized variational inference improves diffusion posterior sampling efficiency while retaining robustness to unseen degradations.

Principles

• Explicit likelihood guidance is crucial for robustness.
• Amortizing inner optimization accelerates inference.

Method

The method amortizes inner optimization problems in variational diffusion posterior sampling to preserve explicit likelihood guidance, accelerating inference for known degradations.

In practice

• Accelerates inverse problem solving.
• Maintains robustness to novel degradations.

Topics

• Diffusion Posterior Sampling
• Amortized Variational Inference
• Inverse Problems
• Likelihood Guidance
• Zero-shot Learning

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.