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arXiv · 2601.04791

Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers

Lee Hyoseok·Sohwi Lim·Eunju Cha·Tae-Hyun Oh
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Abstract

While latent diffusion models (LDMs) have emerged as powerful priors for inverse problems, existing LDM-based solvers frequently suffer from instability. In this work, we first identify the instability as a discrepancy between the solver dynamics and stable reverse diffusion dynamics learned by the diffusion model, and show that reducing this gap stabilizes the solver. Building on this, we introduce \textit{Measurement-Consistent Langevin Corrector (MCLC)}, a theoretically grounded plug-and-play stabilization module that remedies the LDM-based inverse problem solvers through measurement-consistent Langevin updates. Compared to prior approaches that rely on linear manifold assumptions, which often fail to hold in latent space, MCLC provides a principled stabilization mechanism, leading to more stable and reliable behavior in latent space.

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Lee Hyoseok, Sohwi Lim, Eunju Cha, Tae-Hyun Oh. 2026-01-08. Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers. https://arxiv.org/abs/2601.04791

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