Consistency Regularised Gradient Flows for Inverse Problems

2026-05-11 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

A new unified Euclidean-Wasserstein-2 gradient-flow framework has been developed to address inverse problems using Vision-Language Latent Diffusion Models (LDMs). Existing LDM-based solvers often suffer from high computational costs due to numerous neural function evaluations (NFEs) and extensive backpropagation through large pretrained components, which can also degrade reconstruction quality. This proposed framework jointly performs posterior sampling and prompt optimization within the latent space via a single flow, effectively aligning the prior and posterior with observed data. When integrated with few-step latent text-to-image models, this approach facilitates low-NFE inference without requiring backpropagation through autoencoders. Experimental results across various canonical imaging inverse problems demonstrate that this method achieves state-of-the-art performance while significantly reducing computational expenses.

Key takeaway

For research scientists developing inverse problem solvers with Latent Diffusion Models, you should investigate this Euclidean-Wasserstein-2 gradient-flow framework. It offers a path to significantly reduce computational costs and improve reconstruction quality by minimizing neural function evaluations and eliminating backpropagation through autoencoders, potentially accelerating your development cycles and deployment.

Key insights

A new gradient-flow framework improves LDM-based inverse problem solving by reducing NFEs and backpropagation.

Principles

• Align prior and posterior with observed data.
• Jointly optimize sampling and prompt in latent space.

Method

The method uses a unified Euclidean-Wasserstein-2 gradient-flow to align prior and posterior with data, enabling low-NFE inference without autoencoder backpropagation.

In practice

• Apply to imaging inverse problems.
• Reduce computational cost in LDM solvers.

Topics

• Latent Diffusion Models
• Inverse Problems
• Gradient Flows
• Euclidean-Wasserstein-2
• Posterior Sampling

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

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