Plug-and-play (PnP) methods are widely used for solving imaging inverse problems by incorporating a denoiser into an optimization algorithm. Score-based diffusion models (SBDMs) have recently demonstrated strong generative performance via a denoiser trained across a wide range of noise levels. Despite their shared reliance on denoisers, it remains unclear how to systematically use SBDMs as priors within the PnP framework without relying on reverse diffusion sampling. In this paper, we establish a score-based interpretation of PnP that justifies using pretrained SBDMs directly within PnP algorithms. Building on this connection, we introduce a stochastic generative PnP (SGPnP) framework that uses stochastic re-noising to better leverage the broad noise-level conditioning of pretrained SBDMs, significantly improving reconstruction quality in severely ill-posed inverse problems. We show that noise injection induces optimization on a Gaussian-smoothed objective and enables avoidance of strict saddle points under suitable conditions. Experiments on challenging inverse tasks, such as multi-coil MRI reconstruction and large-mask natural image inpainting, demonstrate consistent improvement over conventional PnP methods and achieve performance competitive with diffusion-based solvers.

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