Conservative Flows: A New Paradigm of Generative Models

• URL: http://arxiv.org/abs/2605.06905v1
• Date: Thu, 07 May 2026 20:06:08 GMT
• Title: Conservative Flows: A New Paradigm of Generative Models
• Abstract summary: We propose an alternative paradigm in which generation is performed by a discrete dynamics that leaves the data distribution invariant.<n>We develop two probability-preserving sampling mechanisms, a corrected Langevin dynamics with a adjustment and a predictor-corrector flow, that operate directly on existing checkpoints.<n>We validate the framework on a synthetic Swiss-roll target, ImageNet-256 and Oxford Flowers-102, where our samplers consistently improve over the original generation procedures.
• Score: 17.481852615249124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by a discrete stochastic dynamics that leaves the data distribution invariant, initialized from data-supported states rather than from noise. The framework can utilize any pretrained flow model. We develop two probability-preserving sampling mechanisms, a corrected Langevin dynamics with a Metropolis adjustment and a predictor-corrector flow, that operate directly on existing checkpoints. We validate the framework on a synthetic Swiss-roll target, ImageNet-256 and Oxford Flowers-102, where our samplers consistently improve over the original generation procedures.

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