Deriving the optimal coefficient for the conditional velocity field in MeanFlow training reduces gradient variance and improves sample quality in one-step generative models.
Progressive distillation for fast sampling of diffusion models
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A reusable architecture for joint spatiotemporal super-resolution of precipitation that adapts to scaling factors from 1-25 in space and 1-6 in time via hyperparameter retuning and optional mass conservation.
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On Variance Reduction in Learning Mean Flows
Deriving the optimal coefficient for the conditional velocity field in MeanFlow training reduces gradient variance and improves sample quality in one-step generative models.
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A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models
A reusable architecture for joint spatiotemporal super-resolution of precipitation that adapts to scaling factors from 1-25 in space and 1-6 in time via hyperparameter retuning and optional mass conservation.