Flow matching is a way to train generative models to turn random noise into data along smooth paths, often simpler and faster than classic diffusion training.
In plain words
Like diffusion, flow matching starts from noise and ends at an image or other data. The model learns a direction to move at every point on the way, and a solver follows those directions. Training does not need to simulate the whole journey. Choosing straight paths between noise and data, as rectified flow does, makes sampling efficient, sometimes in very few steps. Researchers have scaled it up to large, high-resolution text-to-image models.
A quick example
Picture each training image paired with a random noise image, joined by a straight line. The model learns which way to move at any point on such lines; to generate, it starts from fresh noise and follows its learned directions.
Where you'll meet it
- Recent high-resolution text-to-image models.
- Research on faster image generation.
Related
Sources
- PaperFlow Matching for Generative Modeling, Lipman et al. (arXiv) · read 29 Sept 2026
- PaperFlow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow, Liu, Gong and Liu (arXiv) · read 29 Sept 2026
- PaperScaling Rectified Flow Transformers for High-Resolution Image Synthesis, Esser et al. (arXiv) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.