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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.17403 |
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| _version_ | 1866910890552459264 |
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| author | Wang, Kai Shi, Mingjia Zhou, Yukun Li, Zekai Yuan, Zhihang Shang, Yuzhang Peng, Xiaojiang Zhang, Hanwang You, Yang |
| author_facet | Wang, Kai Shi, Mingjia Zhou, Yukun Li, Zekai Yuan, Zhihang Shang, Yuzhang Peng, Xiaojiang Zhang, Hanwang You, Yang |
| contents | Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps can be empirically divided into acceleration, deceleration, and convergence areas based on the process increment. ii) These time steps are imbalanced, with many concentrated in the convergence area. iii) The concentrated steps provide limited benefits for diffusion training. To address this, we design an asymmetric sampling strategy that reduces the frequency of steps from the convergence area while increasing the sampling probability for steps from other areas. Additionally, we propose a weighting strategy to emphasize the importance of time steps with rapid-change process increments. As a plug-and-play and architecture-agnostic approach, SpeeD consistently achieves 3-times acceleration across various diffusion architectures, datasets, and tasks. Notably, due to its simple design, our approach significantly reduces the cost of diffusion model training with minimal overhead. Our research enables more researchers to train diffusion models at a lower cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17403 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training Wang, Kai Shi, Mingjia Zhou, Yukun Li, Zekai Yuan, Zhihang Shang, Yuzhang Peng, Xiaojiang Zhang, Hanwang You, Yang Machine Learning Artificial Intelligence I.2 Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps can be empirically divided into acceleration, deceleration, and convergence areas based on the process increment. ii) These time steps are imbalanced, with many concentrated in the convergence area. iii) The concentrated steps provide limited benefits for diffusion training. To address this, we design an asymmetric sampling strategy that reduces the frequency of steps from the convergence area while increasing the sampling probability for steps from other areas. Additionally, we propose a weighting strategy to emphasize the importance of time steps with rapid-change process increments. As a plug-and-play and architecture-agnostic approach, SpeeD consistently achieves 3-times acceleration across various diffusion architectures, datasets, and tasks. Notably, due to its simple design, our approach significantly reduces the cost of diffusion model training with minimal overhead. Our research enables more researchers to train diffusion models at a lower cost. |
| title | A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training |
| topic | Machine Learning Artificial Intelligence I.2 |
| url | https://arxiv.org/abs/2405.17403 |