Flux Matching generalizes score-based generative modeling by using a weaker objective that admits infinitely many non-conservative vector fields with the data as stationary distribution, enabling new design choices beyond traditional score matching.
Improving flow matching by aligning flow divergence.arXiv preprint arXiv:2602.00869,
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Divergence-suppressing couplings attenuate the divergent part of the velocity field when generating training couplings for Rectified Flow, yielding straighter paths and better generation quality at no extra inference cost.
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Generative Modeling with Flux Matching
Flux Matching generalizes score-based generative modeling by using a weaker objective that admits infinitely many non-conservative vector fields with the data as stationary distribution, enabling new design choices beyond traditional score matching.
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Divergence-Suppressing Couplings for Rectified Flow
Divergence-suppressing couplings attenuate the divergent part of the velocity field when generating training couplings for Rectified Flow, yielding straighter paths and better generation quality at no extra inference cost.