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

2026-01-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

The paper introduces the Measurement-Consistent Langevin Corrector (MCLC), a novel plug-and-play stabilization module designed to address instability in latent diffusion model (LDM)-based inverse problem solvers. Researchers identified this instability as a discrepancy between solver dynamics and the stable reverse diffusion dynamics learned by the diffusion model. MCLC remedies this issue by employing theoretically grounded, measurement-consistent Langevin updates. Unlike prior methods that often rely on linear manifold assumptions, which frequently fail in latent space, MCLC offers a principled stabilization mechanism. This approach leads to more stable and reliable behavior for LDM-based solvers in latent space, enhancing their performance for various inverse problems, and was presented at ICML 2026.

Key takeaway

For Computer Vision Engineers developing or deploying latent diffusion model (LDM) inverse problem solvers, you should consider integrating the Measurement-Consistent Langevin Corrector (MCLC). This module directly addresses solver instability by providing a theoretically sound stabilization mechanism, moving beyond unreliable linear manifold assumptions. Implementing MCLC can significantly enhance the reliability and stability of your LDM-based solutions, leading to more robust performance in real-world applications.

Key insights

Latent diffusion model solver instability stems from dynamic discrepancies, remedied by measurement-consistent Langevin updates.

Principles

• Solver stability improves by aligning dynamics with reverse diffusion.
• Linear manifold assumptions often fail in latent space.
• Theoretically grounded updates offer principled stabilization.

Method

MCLC is a plug-and-play module that stabilizes LDM-based inverse problem solvers by applying measurement-consistent Langevin updates, addressing dynamic discrepancies in latent space.

In practice

• Integrate MCLC into existing LDM inverse solvers.
• Improve reliability of image reconstruction tasks.

Topics

• Latent Diffusion Models
• Inverse Problems
• Langevin Dynamics
• Model Stabilization
• Computer Vision
• Machine Learning

Best for: AI Scientist, Computer Vision Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.