Flow Matching for Generative Modeling

Formulated continuous normalizing flows via simulation-free conditional regression on target vector fields, establishing straight trajectories and faster sampling than diffusion.

Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-image-generation.

Assumptions

• Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support

Limitations

• Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology