Difficulty-Calibrated Interpolation Paths for Conditional Flow Matching
Conditional Flow Matching trains generative models by regressing a network onto the velocity of a prescribed noise-to-data interpolation path. The interpolation schedule that shapes this path is known to affect convergence and sample quality, yet it is invariably fixed in advance, independent of both the data and the model. We show that the regression difficulty of Conditional Flow Matching varies systematically along the path, and we propose Difficulty-Calibrated Flow Matching, which derives the schedule from the model itself: a short pilot run with the linear path records the per-time loss, and the schedule is set to the quantile function of this difficulty profile, so the trajectory li...
| Authors | Airin Akter Tania, Md Raihan Khan |
|---|---|
| Primary category | cs.CV |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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