Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
arXiv:2601.04791v4 Announce Type: replace-cross 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 LD
The rapid advancement and widespread adoption of latent diffusion models necessitate ongoing research into their stability and practical applicability in inverse problems.
This development addresses a critical instability in powerful latent diffusion models, potentially broadening their utility in critical applications from medical imaging to scientific research.
The introduction of MCLC provides a robust method to stabilize LDM-based solvers, making them more reliable and practical for complex inverse problems.
- · AI researchers
- · Machine learning engineers
- · Healthcare sector (imaging)
- · Scientific research institutions
- · Methods relying on unstable LDM implementations
Latent diffusion models become significantly more reliable for solving inverse problems across various fields.
Improved reliability and performance could accelerate the deployment of AI in sensitive applications requiring high accuracy and consistency.
The enhanced foundational stability might lead to new AI applications currently constrained by model robustness issues.
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Read at arXiv cs.LG