Minimizing averaged squared Lipschitzness of the drift produces interpolation schedules that improve numerical accuracy and mitigate mode collapse in generative models, with closed-form optima for Gaussians and validation on stochastic PDEs.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
3 Pith papers cite this work. Polarity classification is still indexing.
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2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.
HRSino adaptively allocates diffusion inference effort across spatial regions and scales for efficient high-resolution sinogram completion without training.
citing papers explorer
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Lipschitz-Guided Design of Interpolation Schedules in Generative Models
Minimizing averaged squared Lipschitzness of the drift produces interpolation schedules that improve numerical accuracy and mitigate mode collapse in generative models, with closed-form optima for Gaussians and validation on stochastic PDEs.
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2ndMatch: Finetuning Pruned Diffusion Models via Second-Order Jacobian Matching
2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.
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Training-Free Inference for High-Resolution Sinogram Completion
HRSino adaptively allocates diffusion inference effort across spatial regions and scales for efficient high-resolution sinogram completion without training.