Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity field of flow matching. However, discrete state spaces lack smooth trajectories or spatial derivatives, so the continuous formulation does not directly apply. We introduce Discrete MeanFlow, a theoretical finite-state formulation that replaces point motion with probability-mass transport. Our key object is the conditional transition kernel of a continuous-time Markov chain (CTMC), from which we define a mean discrete rate that measures average transition-probability change over a finite interval. We prove a Discrete MeanFlow identity that re...
| Authors | Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed et al. |
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| Primary category | cs.LG |
| Published | 12 May 2026 |
| Last updated at source | 12 October 2026 |
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