Robust Posterior Diffusion-based Sampling via Adaptive Guidance Scale

• URL: http://arxiv.org/abs/2511.18471v1
• Date: Sun, 23 Nov 2025 14:37:59 GMT
• Title: Robust Posterior Diffusion-based Sampling via Adaptive Guidance Scale
• Authors: Liav Hen, Tom Tirer, Raja Giryes, Shady Abu-Hussein,
• Abstract summary: We propose an adaptive likelihood step-size strategy to guide the diffusion process for inverse-problem formulations.<n>The resulting approach, Adaptive Posterior diffusion Sampling (AdaPS), is hyper-free and improves reconstruction quality across diverse imaging tasks.
• Score: 39.27744518020771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have recently emerged as powerful generative priors for solving inverse problems, achieving state-of-the-art results across various imaging tasks. A central challenge in this setting lies in balancing the contribution of the prior with the data fidelity term: overly aggressive likelihood updates may introduce artifacts, while conservative updates can slow convergence or yield suboptimal reconstructions. In this work, we propose an adaptive likelihood step-size strategy to guide the diffusion process for inverse-problem formulations. Specifically, we develop an observation-dependent weighting scheme based on the agreement between two different approximations of the intractable intermediate likelihood gradients, that adapts naturally to the diffusion schedule, time re-spacing, and injected stochasticity. The resulting approach, Adaptive Posterior diffusion Sampling (AdaPS), is hyperparameter-free and improves reconstruction quality across diverse imaging tasks - including super-resolution, Gaussian deblurring, and motion deblurring - on CelebA-HQ and ImageNet-256 validation sets. AdaPS consistently surpasses existing diffusion-based baselines in perceptual quality with minimal or no loss in distortion, without any task-specific tuning. Extensive ablation studies further demonstrate its robustness to the number of diffusion steps, observation noise levels, and varying stochasticity.

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