Reinforcement learning improves diffusion models using rewards on generated images, but repeated denoising and gradient estimation make training expensive. We introduce \method, an efficient method that constructs useful update directions by regressing centered rewards on centered final denoised images. It requires nei...
This work defines an explicit surrogate posterior path and derives the Posterior-Dynamics Implicit--Explicit sampler (PD-IMEX), a stable method using one score evaluation per diffusion scale and an implicit data-consistency update, and derives continuous posterior dynamics.
Zhao-Qiang Liu, T. Pang, Ruibing Wang et al.· 1 citation
Posterior sampling with a pretrained diffusion prior is governed by a conditional score whose intermediate likelihood component is generally intractable. We begin from an ideal one-parameter posterior SDE family in which a stochasticity parameter controls probability-flow transport and stochastic exploration without ch...
Zhaoqiang Liu, T. Pang, Ruibing Wang et al.· 0 citations
This analysis transfers reverse-time discretization errors to the forward corruption law and treats two numerical schemes within a common framework, which identifies a local error, accumulates it through the forward evolution, and inserts the result into a common KL decomposition.
T. Pang, Zuowei Shen, Rui-Tong Zhang· 1 citation
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