Seonglae ChoFlow Matching (FM) is a training objective (loss/formulation), not a model architecture.
Presents a method to train CNF without simulation, training the model by regressing the Vector Field of fixed conditional probability paths. The path is generally expressed as a probability flow (Vector Field) that varies with time .
This approach improves upon the sampling efficiency issues present in existing diffusion models and enables a more efficient generation process by utilizing diverse probability paths.
Flow Matching with ODE → "following a map and driving in a consistent direction" while Diffusion Model with SDE → "following a map, but random wind blows at each segment". Solver: the rule that determines how often and how precisely to apply steering in reverse direction (deterministic for ODE, stochastic for SDE). Note that diffusion models can also be sampled using ODE solvers (e.g., probability flow ODE).
Flow Matchings