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gDDIM: Generalized denoising diffusion implicit models

ICLR 2023 · Image generation › Fast diffusion sampling · approach: Denoising diffusion probabilistic models · 27 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use gDDIM instead of probability-flow RK45DDPM · CIFAR10, NFE=50FID: 6.62 -> 2.59Helped
Use gDDIM instead of probability-flow RK45BDM · CIFAR10, NFE=50FID: 7.12 -> 2.49Helped
Use gDDIM instead of probability-flow RK45CLD · CIFAR10, NFE=50FID: 31.7 -> 2.26Helped
Use the gDDIM parameterization K=R rather than K=L (ablation)CLD · CIFAR10, NFE=50FID: 3.31 -> 2.26Helped
Use the gDDIM parameterization K=R rather than K=L (ablation)CLD · CIFAR10, NFE=20FID: 368 -> 3.90Helped
Increase lambda from 0.0 to 0.1 (ablation)gDDIM · CIFAR10, NFE=50FID: 5.17 -> 5.51Hurt
Increase lambda from 0.0 to 0.3 (ablation)gDDIM · CIFAR10, NFE=50FID: 5.17 -> 12.13Hurt
Increase lambda from 0.0 to 0.5 (ablation)gDDIM · CIFAR10, NFE=50FID: 5.17 -> 33Hurt
Increase lambda from 0.0 to 0.7 (ablation)gDDIM · CIFAR10, NFE=50FID: 5.17 -> 41Hurt
Increase lambda from 0.0 to 1.0 (ablation)gDDIM · CIFAR10, NFE=50FID: 5.17 -> 49Hurt
Use stochastic gDDIM rather than Euler-Maruyama at lambda 0.1CLD · CIFAR10, NFE=50, lambda=0.1FID: 168 -> 5.51Helped

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