Decoupled Video Generation with Chain of Training-free Diffusion Model Experts
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910763713560576 |
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| author | Li, Wenhao Cao, Yichao Su, Xiu Lin, Xi You, Shan Zheng, Mingkai Chen, Yi Xu, Chang |
| author_facet | Li, Wenhao Cao, Yichao Su, Xiu Lin, Xi You, Shan Zheng, Mingkai Chen, Yi Xu, Chang |
| contents | Video generation models hold substantial potential in areas such as filmmaking. However, current video diffusion models need high computational costs and produce suboptimal results due to extreme complexity of video generation task. In this paper, we propose \textbf{ConFiner}, an efficient video generation framework that decouples video generation into easier subtasks: structure \textbf{con}trol and spatial-temporal re\textbf{fine}ment. It can generate high-quality videos with chain of off-the-shelf diffusion model experts, each expert responsible for a decoupled subtask. During the refinement, we introduce coordinated denoising, which can merge multiple diffusion experts' capabilities into a single sampling. Furthermore, we design ConFiner-Long framework, which can generate long coherent video with three constraint strategies on ConFiner. Experimental results indicate that with only 10\% of the inference cost, our ConFiner surpasses representative models like Lavie and Modelscope across all objective and subjective metrics. And ConFiner-Long can generate high-quality and coherent videos with up to 600 frames. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13423 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoupled Video Generation with Chain of Training-free Diffusion Model Experts Li, Wenhao Cao, Yichao Su, Xiu Lin, Xi You, Shan Zheng, Mingkai Chen, Yi Xu, Chang Computer Vision and Pattern Recognition Video generation models hold substantial potential in areas such as filmmaking. However, current video diffusion models need high computational costs and produce suboptimal results due to extreme complexity of video generation task. In this paper, we propose \textbf{ConFiner}, an efficient video generation framework that decouples video generation into easier subtasks: structure \textbf{con}trol and spatial-temporal re\textbf{fine}ment. It can generate high-quality videos with chain of off-the-shelf diffusion model experts, each expert responsible for a decoupled subtask. During the refinement, we introduce coordinated denoising, which can merge multiple diffusion experts' capabilities into a single sampling. Furthermore, we design ConFiner-Long framework, which can generate long coherent video with three constraint strategies on ConFiner. Experimental results indicate that with only 10\% of the inference cost, our ConFiner surpasses representative models like Lavie and Modelscope across all objective and subjective metrics. And ConFiner-Long can generate high-quality and coherent videos with up to 600 frames. |
| title | Decoupled Video Generation with Chain of Training-free Diffusion Model Experts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.13423 |