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# The Rise of Single-Step Generative Models [MeanFlow]

**[Jia-Bin Huang](https://daily.dev/sources/jbhuang0604)** · 9 min read · 0 upvotes · 0 comments

## Summary

MeanFlow is a new formulation for generative models that enables single-step sample generation without iterative ODE solving. Unlike standard flow matching which predicts instantaneous velocity and requires multiple integration steps, MeanFlow trains a neural network to predict the average velocity over a time interval. The key insight is the MeanFlow identity, derived using the fundamental theorem of calculus and product rule, which relates average velocity, instantaneous velocity, and a time derivative computed via Jacobian-vector products. This allows generating samples in one forward pass with competitive FID scores, and requires no pre-training, distillation, or curriculum training tricks.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=swKdn-qT47Q>

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#genai](https://daily.dev/tags/genai), [#neural-networks](https://daily.dev/tags/neural-networks), [#diffusion-models](https://daily.dev/tags/diffusion-models)

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