What question did this study set out to answer?

The study aims to optimize parameterization during sampling for diffusion probabilistic models to enhance efficiency and image quality.

October 20, 2023Open Access

DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics

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Authors

KZKaiwen ZhengFujian Medical University CLCheng LüNorth China Electric Power University JCJianfei ChenFujian Normal University

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Implication

Randomized trial demonstrates improved image generation performance in diffusion probabilistic models, suggesting a faster and more efficient sampling approach.

Key Points

The study aims to optimize parameterization during sampling for diffusion probabilistic models to enhance efficiency and image quality.
Proposed DPM-Solver-v3 with new fast ODE solver techniques.
Introduced empirical model statistics for coefficient computation.
Implemented multistep methods and a predictor-corrector framework for sample quality improvement.
Achieved FIDs of 12.21 (5 NFE) and 2.51 (10 NFE) on unconditional CIFAR10.
Attained MSE of 0.55 (5 NFE, 7.5 guidance scale) on Stable Diffusion.
Realized 15%-30% speed-up compared to previous training-free methods.

Cite This Study

Zheng et al. (2023) studied this question.

synapsesocial.com/papers/6a0a513ca9576e6c7db4eaf2 https://doi.org/10.48550/arxiv.2310.13268

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Implication

Randomized trial demonstrates improved image generation performance in diffusion probabilistic models, suggesting a faster and more efficient sampling approach.

DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics

Discussion

Member takes

Implication

Key Points

Cite This Study

DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics

Discussion

Member takes

Implication

Key Points

Cite This Study

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