NullFlow: One-Step Generative Reconstruction
We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measurement-consistent subspace. Because the flow never leaves this subspace, NullFlow needs no separate data-fidelity corrections, unlike existing solvers. NullFlow samples in a single network evaluation by learning the flow's average velocity, avoiding the step-by-step integration of traditional flow matching methods. We prove that the average velocity of this constrained flow yields a training objective whose global minimizer is a one-step posterior sampler. We show on image inpainting that NullFlow matches state-of-the-art diffusion solvers whil...
| Authors | Xiao Shi, Edward P. Chandler, Chicago Y. Park et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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