Computational and Statistical Guarantees of the \textit{c}-Rectified flow
Recently, rectified flow has emerged as a fundamental framework for large-scale image generation, powering state-of-the-art systems such as FLUX.1 and Stable Diffusion 3. Despite its remarkable empirical success, the computational and statistical guarantees of iterative rectified flow have remained largely unexplored. We address this problem by studying \textit{c}-rectified flow, a cost-aware class of rectified flow that projects velocity fields onto a gradient class while preserving endpoint marginals. The ordinary rectified flow can fail to recover the optimal transport coupling: in a Gaussian case study, the iteration converges to the optimal coupling if and only if the source and targ...
| Authors | Leda Wang, Zhehao Xu, Qiang Liu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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