COT-FM: Cluster-wise Optimal Transport Flow Matching
We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved trajectories due to random or batchwise couplings, which increase discretization error and reduce sample quality. COT-FM fixes this by clustering target samples and assigning each cluster a dedicated source distribution obtained by reversing pretrained FM models. This divide-and-conquer strategy yields more accurate local transport and significantly straighter vector fields, all without changing the model architecture. As a plug-and-play approach, COT-FM consistently accelerates sampling and improves generation quality across 2D datasets, image generation benchmarks, and robotic manipulation tasks.
69bcbfdb081b9ad49af572c7
Chiensheng Chiang
69bcbfdb081b9ad49af572c8
Kuan-Hsun Tu
claimed_verified
2026-03-23T10:52:40.073Z
69510b8af089e912cc615907
/avatars/c250ec4ba5df8078ad4576d18539a66a.svg
TU
KUAN-HSUN
user
KUAN-HSUN
69bcbfdb081b9ad49af572c9
Jia-Wei Liao
claimed_verified
2026-03-23T10:52:42.409Z
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Jiawei
jwliao1209
user
jwliao1209
69bcbfdb081b9ad49af572ca
Cheng-Fu Chou
69bcbfdb081b9ad49af572cb
Tsung-Wei Ke
COT-FM: Cluster-wise Optimal Transport Flow Matching
We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved trajectories due to random or batchwise couplings, which increase discretization error and reduce sample quality. COT-FM fixes this by clustering target samples and assigning each cluster a dedicated source distribution obtained by reversing pretrained FM models. This divide-and-conquer strategy yields more accurate local transport and significantly straighter vector fields, all without changing the model architecture. As a plug-and-play approach, COT-FM consistently accelerates sampling and improves generation quality across 2D datasets, image generation benchmarks, and robotic manipulation tasks.
COT-FM enhances flow matching generation by reshaping probability paths through clustering and dedicated source distributions, resulting in faster and more reliable sampling across diverse tasks.
Flow Matching probability path discretization error vector fields clustering source distribution transport sampling generation quality