AINeutralarXiv – CS AI · 8h ago6/10
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Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching
Researchers propose learning condition-dependent source distributions for flow matching in generative models, demonstrating that optimizing the source distribution—rather than defaulting to standard Gaussian—significantly improves text-to-image generation performance. The approach achieves up to 3x faster convergence in FID scores while addressing stability challenges through variance regularization and directional alignment techniques.