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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.06082 |
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| _version_ | 1866915497696559104 |
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| author | Sun, Yanxiao Wu, Jiafu Cao, Yun Xu, Chengming Wang, Yabiao Cao, Weijian Luo, Donghao Wang, Chengjie Fu, Yanwei |
| author_facet | Sun, Yanxiao Wu, Jiafu Cao, Yun Xu, Chengming Wang, Yabiao Cao, Weijian Luo, Donghao Wang, Chengjie Fu, Yanwei |
| contents | Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps, which incurs substantial computational overhead. While many distillation methods that are solely based on trajectory-preserving or distribution-matching have been developed to accelerate video generation models, these approaches often suffer from performance breakdown or increased artifacts under few-step settings. To address these limitations, we propose \textbf{\emph{SwiftVideo}}, a unified and stable distillation framework that combines the advantages of trajectory-preserving and distribution-matching strategies. Our approach introduces continuous-time consistency distillation to ensure precise preservation of ODE trajectories. Subsequently, we propose a dual-perspective alignment that includes distribution alignment between synthetic and real data along with trajectory alignment across different inference steps. Our method maintains high-quality video generation while substantially reducing the number of inference steps. Quantitative evaluations on the OpenVid-1M benchmark demonstrate that our method significantly outperforms existing approaches in few-step video generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06082 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SwiftVideo: A Unified Framework for Few-Step Video Generation through Trajectory-Distribution Alignment Sun, Yanxiao Wu, Jiafu Cao, Yun Xu, Chengming Wang, Yabiao Cao, Weijian Luo, Donghao Wang, Chengjie Fu, Yanwei Computer Vision and Pattern Recognition Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps, which incurs substantial computational overhead. While many distillation methods that are solely based on trajectory-preserving or distribution-matching have been developed to accelerate video generation models, these approaches often suffer from performance breakdown or increased artifacts under few-step settings. To address these limitations, we propose \textbf{\emph{SwiftVideo}}, a unified and stable distillation framework that combines the advantages of trajectory-preserving and distribution-matching strategies. Our approach introduces continuous-time consistency distillation to ensure precise preservation of ODE trajectories. Subsequently, we propose a dual-perspective alignment that includes distribution alignment between synthetic and real data along with trajectory alignment across different inference steps. Our method maintains high-quality video generation while substantially reducing the number of inference steps. Quantitative evaluations on the OpenVid-1M benchmark demonstrate that our method significantly outperforms existing approaches in few-step video generation. |
| title | SwiftVideo: A Unified Framework for Few-Step Video Generation through Trajectory-Distribution Alignment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.06082 |