Gradient-guided diffusion sampling reduces extreme precipitation forecasts in data-driven weather models while producing more physically plausible changes than adversarial perturbations.
Elucidating the design space of diffusion-based generative models
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CCVFM uses an entropic Sinkhorn coreset to induce a closed-form Gaussian mixture source for hierarchical rectified flow matching, then trains a lightweight correction flow on the residual, achieving competitive few-step image generation.
A short teacher-alignment repair stage between structured pruning and one-step distillation yields a 20% pruned one-step generator that improves FID from 3.53 to 3.12 on ImageNet-512 while reducing NFE from 63 to 1.
Venom is an educational PyTorch toolkit that packages multiple generative modeling families under a single MNIST-first interface with reproducible scripts and tutorials.
A tutorial that unifies diffusion probabilistic models, score-based generative modeling, and SDE methods by deriving forward and reverse dynamics from a shared Gaussian noising process.
citing papers explorer
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Guided Diffusion Sampling for Precipitation Forecast Interventions
Gradient-guided diffusion sampling reduces extreme precipitation forecasts in data-driven weather models while producing more physically plausible changes than adversarial perturbations.
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Coreset-Induced Conditional Velocity Flow Matching
CCVFM uses an entropic Sinkhorn coreset to induce a closed-form Gaussian mixture source for hierarchical rectified flow matching, then trains a lightweight correction flow on the residual, achieving competitive few-step image generation.
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Bridging Diffusion Pruning and Step Distillation with Teacher-Aligned Repair
A short teacher-alignment repair stage between structured pruning and one-step distillation yields a 20% pruned one-step generator that improves FID from 3.53 to 3.12 on ImageNet-512 while reducing NFE from 63 to 1.
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Venom: A PyTorch Generative Modeling Toolkit
Venom is an educational PyTorch toolkit that packages multiple generative modeling families under a single MNIST-first interface with reproducible scripts and tutorials.
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A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models
A tutorial that unifies diffusion probabilistic models, score-based generative modeling, and SDE methods by deriving forward and reverse dynamics from a shared Gaussian noising process.