Generalizes MeanFlow to learn finite-horizon minimum-energy control coefficients for linear swarm systems via a differential identity and stop-gradient regression objective.
Classifier-Free Diffusion Guidance
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Improved MeanFlow (iMF) reaches 1.72 FID on ImageNet 256x256 with one function evaluation by reformulating the training objective as a regression on instantaneous velocity and treating guidance as flexible conditioning variables.
A monograph that unifies variational, score-based, and flow-based views of diffusion models around a common time-dependent velocity field whose flow is solved as a differential equation to generate data from noise.
citing papers explorer
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Learning Sampled-data Control for Swarms via MeanFlow
Generalizes MeanFlow to learn finite-horizon minimum-energy control coefficients for linear swarm systems via a differential identity and stop-gradient regression objective.
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Improved Mean Flows: On the Challenges of Fastforward Generative Models
Improved MeanFlow (iMF) reaches 1.72 FID on ImageNet 256x256 with one function evaluation by reformulating the training objective as a regression on instantaneous velocity and treating guidance as flexible conditioning variables.
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The Principles of Diffusion Models
A monograph that unifies variational, score-based, and flow-based views of diffusion models around a common time-dependent velocity field whose flow is solved as a differential equation to generate data from noise.