Optimizing Few-Step Sampler for Diffusion Probabilistic Model
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929630250795008 |
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| author | Huang, Jen-Yuan |
| author_facet | Huang, Jen-Yuan |
| contents | Diffusion Probabilistic Models (DPMs) have demonstrated exceptional capability of generating high-quality and diverse images, but their practical application is hindered by the intensive computational cost during inference. The DPM generation process requires solving a Probability-Flow Ordinary Differential Equation (PF-ODE), which involves discretizing the integration domain into intervals for numerical approximation. This corresponds to the sampling schedule of a diffusion ODE solver, and we notice the solution from a first-order solver can be expressed as a convex combination of model outputs at all scheduled time-steps. We derive an upper bound for the discretization error of the sampling schedule, which can be efficiently optimized with Monte-Carlo estimation. Building on these theoretical results, we purpose a two-phase alternating optimization algorithm. In Phase-1, the sampling schedule is optimized for the pre-trained DPM; in Phase-2, the DPM further tuned on the selected time-steps. Experiments on a pre-trained DPM for ImageNet64 dataset demonstrate the purposed method consistently improves the baseline across various number of sampling steps. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_10786 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Few-Step Sampler for Diffusion Probabilistic Model Huang, Jen-Yuan Computer Vision and Pattern Recognition Artificial Intelligence Diffusion Probabilistic Models (DPMs) have demonstrated exceptional capability of generating high-quality and diverse images, but their practical application is hindered by the intensive computational cost during inference. The DPM generation process requires solving a Probability-Flow Ordinary Differential Equation (PF-ODE), which involves discretizing the integration domain into intervals for numerical approximation. This corresponds to the sampling schedule of a diffusion ODE solver, and we notice the solution from a first-order solver can be expressed as a convex combination of model outputs at all scheduled time-steps. We derive an upper bound for the discretization error of the sampling schedule, which can be efficiently optimized with Monte-Carlo estimation. Building on these theoretical results, we purpose a two-phase alternating optimization algorithm. In Phase-1, the sampling schedule is optimized for the pre-trained DPM; in Phase-2, the DPM further tuned on the selected time-steps. Experiments on a pre-trained DPM for ImageNet64 dataset demonstrate the purposed method consistently improves the baseline across various number of sampling steps. |
| title | Optimizing Few-Step Sampler for Diffusion Probabilistic Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.10786 |