Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds

• URL: http://arxiv.org/abs/2510.21608v1
• Date: Fri, 24 Oct 2025 16:14:31 GMT
• Title: Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
• Authors: Oscar Davis, Michael S. Albergo, Nicholas M. Boffi, Michael M. Bronstein, Avishek Joey Bose,
• Abstract summary: Generalised Flow Maps (GFM) is a new class of few-step generative models.<n>We benchmark GFMs against other geometric generative models on a suite of geometric datasets.
• Score: 32.40675406199536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geometric data and purpose-built generative models on them have become ubiquitous in high-impact deep learning application domains, ranging from protein backbone generation and computational chemistry to geospatial data. Current geometric generative models remain computationally expensive at inference -- requiring many steps of complex numerical simulation -- as they are derived from dynamical measure transport frameworks such as diffusion and flow-matching on Riemannian manifolds. In this paper, we propose Generalised Flow Maps (GFM), a new class of few-step generative models that generalises the Flow Map framework in Euclidean spaces to arbitrary Riemannian manifolds. We instantiate GFMs with three self-distillation-based training methods: Generalised Lagrangian Flow Maps, Generalised Eulerian Flow Maps, and Generalised Progressive Flow Maps. We theoretically show that GFMs, under specific design decisions, unify and elevate existing Euclidean few-step generative models, such as consistency models, shortcut models, and meanflows, to the Riemannian setting. We benchmark GFMs against other geometric generative models on a suite of geometric datasets, including geospatial data, RNA torsion angles, and hyperbolic manifolds, and achieve state-of-the-art sample quality for single- and few-step evaluations, and superior or competitive log-likelihoods using the implicit probability flow.

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