arXiv Computer Vision
Smoother Flow Matching via Contrastive Trajectory Repulsion
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Continuous Adversarial MeanFlow Transfer
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costly multi-step sampling unaddressed, and existing acceleration methods are tied to the source parameterization--$ε$, $x$, $v$, or $u$--leaving heterogeneous pretrained models with no common acceleration target.
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