34 citations · 66 across the 13 of their papers we have counts for
5 papers · 1 filter
Normalizing Flow Maps
Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng +6
Scalable approaches to learning neural dynamical systems have placed diffusion, flow matching, and few-step generators in flow maps at the forefront of generative modeling. Such st…
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
Global Transport Couplings for Classifier-Free Guided Flows
Katarina Petrović, Zander W. Blasingame, Danyal Rehman +5
Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains unclear. A natural approach c…
Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
Shucheng Li, Iolo Jones, Alexander Tong +1
Distribution Matching Distillation (DMD) compresses pretrained diffusion models into efficient few-step generators by aligning their noised distributions across all scales. In prin…
Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers
Bruno Trentini, Dejan Stancevic, Michael M. Bronstein +2
For a fixed flow-based generative model under a small inference budget, sample quality can depend strongly on where the sampler spends its few function evaluations. Flow matching a…