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Rectified flow is a class of generative models based on ordinary differential equations that learns a velocity field to transport a simple distribution along approximately straight trajectories to the data distribution, and can complete generation with fewer sampling steps than diffusion models.
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11/4/2026
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UnverifiedCompared to score-function-based diffusion models, the flow matching training objective is more concise, and the trajectory can be designed as a rectified flow, enabling high-quality generation in fewer inference steps.76% similarUnverifiedRectified Flow is a specific implementation of flow matching that straightens the path between the source distribution and the target distribution into a straight line, enabling fewer integration steps and higher generation efficiency.76% 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% similarUnverifiedMainstream generative models such as diffusion models and flow matching models are essentially probabilistic generators that produce new samples by sampling from data distributions.74% 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.73% similar
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