Consistency Regularised Gradient Flows for Inverse Problems

• URL: http://arxiv.org/abs/2605.07907v1
• Date: Fri, 08 May 2026 15:45:34 GMT
• Title: Consistency Regularised Gradient Flows for Inverse Problems
• Abstract summary: Vision-Language Latent Diffusion Models (LDMs) provide powerful generative priors for inverse problems.<n>Existing LDM-based inverse solvers require a large number of neural function evaluations (NFEs) and backpropagation through large pretrained components.<n>We propose a unified Euclidean-Wasserstein-2 gradient-flow framework that jointly performs posterior sampling and prompt optimization in the latent space.
• Score: 0.9974630621313313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Latent Diffusion Models (LDMs) (Rombach et al., 2022) provide powerful generative priors for inverse problems. However, existing LDM-based inverse solvers typically require a large number of neural function evaluations (NFEs) and backpropagation through large pretrained components, leading to substantial computational costs and, in some cases, degraded reconstruction quality. We propose a unified Euclidean-Wasserstein-2 gradient-flow framework that jointly performs posterior sampling and prompt optimization in the latent space through a single flow that aligns the prior and posterior with the observed data. Combined with few-step latent text-to-image models, this formulation enables low-NFE inference without backpropagation through autoencoders. Experiments across several canonical imaging inverse problems show that our method achieves state-of-the-art performance with significantly reduced computational cost.

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