Provable diffusion-based posterior sampling for linear inverse problems via DDIM

• URL: http://arxiv.org/abs/2607.19333v1
• Date: Tue, 21 Jul 2026 17:53:36 GMT
• Title: Provable diffusion-based posterior sampling for linear inverse problems via DDIM
• Abstract summary: We propose a simple and efficient algorithm, called pddim, for solving linear inverse problems with diffusion priors via a DDIM-type sampler.<n>Our results convert posterior sampling for noisy linear inverse problems to simple coordinate-wise DDIM updates, yielding an efficient, easy-to-implement algorithm with provable posterior consistency.
• Score: 22.029416455468063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantees or incur substantial computational overhead. We propose a simple and efficient algorithm, called \pddim, for solving linear inverse problems with diffusion priors via a DDIM-type sampler. Our method requires only lightweight, coordinate-wise modifications to the standard DDIM update, while explicitly incorporating the measurement model. The key idea is to perform posterior sampling separately along each singular direction of the measurement operator: for each direction, the sampler follows the learned diffusion prior when the observation signal-to-noise ratio (SNR) is below the corresponding diffusion SNR, and switches to a calibrated measurement-based predictor otherwise. We prove that the proposed sampler converges to the Bayesian posterior conditioned on the measurements. Empirical results show that the proposed sampler performs favorably against existing diffusion-based posterior samplers across a range of image restoration tasks, achieving the best performance on the majority of evaluation metrics considered. Overall, our results convert posterior sampling for noisy linear inverse problems to simple coordinate-wise DDIM updates, yielding an efficient, easy-to-implement algorithm with provable posterior consistency.

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