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Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

ICLR 2022 · Image generation › Fast diffusion sampling · approach: Diffusion theory and design space · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace direct data-space diffusion with critically-damped Langevin diffusionScore-based generative models · CIFAR-10 unconditionalFID: 2.25 vs 3.08Helped
Use SSCS-QS instead of EM-QSCLD sampler · CIFAR-10 unconditional, 50 NFEsFID: 52.7 -> 20.5Helped
Use SSCS-QS instead of EM-QSCLD sampler · CIFAR-10 unconditional, 150 NFEsFID: 7.00 -> 3.07Helped
Use SSCS-QS instead of EM-QSCLD sampler · CIFAR-10 unconditional, 275 NFEsFID: 3.24 -> 2.38Helped
Use SSCS-QS instead of EM-QSCLD sampler · CIFAR-10 unconditional, 500 NFEsFID: 2.41 -> 2.25Helped
Use SSCS-QS instead of EM-QSCLD sampler · CIFAR-10 unconditional, 1000 NFEsFID: 2.27 -> 2.30Hurt
Use SSCS-QS instead of EM-QSCLD sampler · CIFAR-10 unconditional, 2000 NFEsFID: 2.23 -> 2.29Hurt
Use probability-flow ODE instead of GGFCLD sampler · CIFAR-10 unconditional, similar adaptive-solver computeFID: 2.25 vs 2.56Helped
Vary reciprocal mass across tested values (ablation)CLD-SGM · CIFAR-10 unconditional, smaller modelFID: 3.23; 3.14; 3.16No clear effect
Increase initial velocity variance scaling from 0.04 to 0.4 or 1 (ablation)CLD-SGM · CIFAR-10 unconditional, smaller modelFID: 3.14 -> 3.21; 3.14 -> 3.27Hurt
Add mixed score parameterization (ablation)CLD-SGM · CIFAR-10 unconditional, smaller modelFID: 3.56 -> 3.14Helped
Replace HSM with regular DSM (ablation)CLD-SGM · CLD trainingtraining stabilityHurt

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