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.
@article{arxiv.2601.04791,
title = {Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers},
author = {Lee Hyoseok and Sohwi Lim and Eunju Cha and Tae-Hyun Oh},
journal= {arXiv preprint arXiv:2601.04791},
year = {2026}
}