arXiv Computer Vision September 18, 2026 By Tianao Li, Xinhui Qian, Emma Alexander

FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants

Read the original on arXiv Computer Vision →

FlowSGS introduces a flow-based posterior sampling method that combines Split Gibbs Sampling (SGS) with Langevin dynamics for the likelihood step and Stochastic Interpolants (SI) for the prior step. By integrating a pretrained flow model into the prior step via SI's reverse-time SDE and a novel timestep correction, FlowSGS reduces the number of network evaluations compared to plug‑and‑play diffusion samplers. Experiments demonstrate state‑of‑the‑art performance on various inverse problems, including the first flow‑based solution to a nonlinear inverse problem (Fourier phase retrieval).

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