Unverified50% confidenceFactExact time
CoupledFlow adopts a structure-constrained flow matching algorithm; unlike step-by-step iterative diffusion-style generation, flow matching achieves fast sampling by learning a velocity field, thereby enabling one-step prediction.
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11/10/2026
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UnverifiedFlow matching is a class of methods for training continuous-time generative models that learn a velocity field to transport a prior distribution along ODE trajectories to the data distribution, and at inference time a numerical integrator is used to solve iteratively, with the number of integration steps being the NFE.79% similarVerifiedFlow Matching continuously maps a noise distribution to a data distribution by learning a deterministic velocity field, and both Stable Diffusion 3 and Flux adopt this framework.79% similarVerifiedFlow matching is a generative model training method that directly learns a deterministic ODE path from the noise distribution to the data distribution, defines a linear interpolation path xt=(1-t)x₀+tx₁, and uses a neural network to learn to predict the velocity vector at every point along that path.77% similarUnverifiedThe technical principle behind four-step generation typically comes from accelerated sampling paradigms such as Consistency Models or Flow Matching, which use distillation training to enable the model to accurately predict the denoising direction at sparse time steps.77% similarVerifiedFlow matching models generate images by learning a continuous probability flow (ODE trajectory) from the noise distribution to the data distribution, and are generally more stable to train and require fewer inference steps.76% similar
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