Consistency Regularised Gradient Flows for Inverse Problems

Alessio Spagnoletti; Tim Y. J. Wang; Marcelo Pereyra; O. Deniz Akyildiz

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arXiv · 2605.07907

Consistency Regularised Gradient Flows for Inverse Problems

Alessio Spagnoletti·Tim Y. J. Wang·Marcelo Pereyra·O. Deniz Akyildiz

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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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Alessio Spagnoletti, Tim Y. J. Wang, Marcelo Pereyra, O. Deniz Akyildiz. 2026-05-08. Consistency Regularised Gradient Flows for Inverse Problems. https://arxiv.org/abs/2605.07907

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