SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916810178166784 |
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| author | Xue, Shuchen Yi, Mingyang Luo, Weijian Zhang, Shifeng Sun, Jiacheng Li, Zhenguo Ma, Zhi-Ming |
| author_facet | Xue, Shuchen Yi, Mingyang Luo, Weijian Zhang, Shifeng Sun, Jiacheng Li, Zhenguo Ma, Zhi-Ming |
| contents | Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-consuming, numerous fast sampling methods built upon improved differential equation solvers are proposed. The majority of such techniques consider solving the diffusion ODE due to its superior efficiency. However, stochastic sampling could offer additional advantages in generating diverse and high-quality data. In this work, we engage in a comprehensive analysis of stochastic sampling from two aspects: variance-controlled diffusion SDE and linear multi-step SDE solver. Based on our analysis, we propose \textit{SA-Solver}, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality. Our experiments show that \textit{SA-Solver} achieves: 1) improved or comparable performance compared with the existing state-of-the-art (SOTA) sampling methods for few-step sampling; 2) SOTA FID on substantial benchmark datasets under a suitable number of function evaluations (NFEs). Code is available at https://github.com/scxue/SA-Solver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05019 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models Xue, Shuchen Yi, Mingyang Luo, Weijian Zhang, Shifeng Sun, Jiacheng Li, Zhenguo Ma, Zhi-Ming Machine Learning Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-consuming, numerous fast sampling methods built upon improved differential equation solvers are proposed. The majority of such techniques consider solving the diffusion ODE due to its superior efficiency. However, stochastic sampling could offer additional advantages in generating diverse and high-quality data. In this work, we engage in a comprehensive analysis of stochastic sampling from two aspects: variance-controlled diffusion SDE and linear multi-step SDE solver. Based on our analysis, we propose \textit{SA-Solver}, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality. Our experiments show that \textit{SA-Solver} achieves: 1) improved or comparable performance compared with the existing state-of-the-art (SOTA) sampling methods for few-step sampling; 2) SOTA FID on substantial benchmark datasets under a suitable number of function evaluations (NFEs). Code is available at https://github.com/scxue/SA-Solver. |
| title | SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.05019 |