Convergence rates for generative drifting flows: fixed-scale obstructions and multihead acceleration
Drifting models offer a promising route to faster generative AI: they perform gradual transport during training, while generating new samples in a single step. This paper asks whether the underlying drifting process can converge rapidly to a target distribution under ideal conditions, before finite-data or optimization effects are introduced. We show that its convergence rate depends critically on how it handles spatial scale. With a single fixed resolution, fine-scale features of the target can become nearly invisible, leading to extremely slow convergence. We introduce a multihead approach that combines scale-normalized information across a continuum of resolutions. We prove that this m...
| Authors | Arthur Stéphanovitch, Eddie Aamari |
|---|---|
| Primary category | cs.LG |
| Published | 14 September 2026 |
| Last updated at source | 13 October 2026 |
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