ContentV: Efficient Training of Video Generation Models with Limited Compute
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910999832952832 |
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| author | Lin, Wenfeng Chen, Renjie Liu, Boyuan Yan, Shiyue Feng, Ruoyu Wei, Jiangchuan Zhang, Yichen Zhou, Yimeng Feng, Chao Ran, Jiao Wu, Qi Liu, Zuotao Guo, Mingyu |
| author_facet | Lin, Wenfeng Chen, Renjie Liu, Boyuan Yan, Shiyue Feng, Ruoyu Wei, Jiangchuan Zhang, Yichen Zhou, Yimeng Feng, Chao Ran, Jiao Wu, Qi Liu, Zuotao Guo, Mingyu |
| contents | Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter text-to-video model that achieves state-of-the-art performance (85.14 on VBench) after training on 256 x 64GB Neural Processing Units (NPUs) for merely four weeks. ContentV generates diverse, high-quality videos across multiple resolutions and durations from text prompts, enabled by three key innovations: (1) A minimalist architecture that maximizes reuse of pre-trained image generation models for video generation; (2) A systematic multi-stage training strategy leveraging flow matching for enhanced efficiency; and (3) A cost-effective reinforcement learning with human feedback framework that improves generation quality without requiring additional human annotations. All the code and models are available at: https://contentv.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ContentV: Efficient Training of Video Generation Models with Limited Compute Lin, Wenfeng Chen, Renjie Liu, Boyuan Yan, Shiyue Feng, Ruoyu Wei, Jiangchuan Zhang, Yichen Zhou, Yimeng Feng, Chao Ran, Jiao Wu, Qi Liu, Zuotao Guo, Mingyu Computer Vision and Pattern Recognition Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter text-to-video model that achieves state-of-the-art performance (85.14 on VBench) after training on 256 x 64GB Neural Processing Units (NPUs) for merely four weeks. ContentV generates diverse, high-quality videos across multiple resolutions and durations from text prompts, enabled by three key innovations: (1) A minimalist architecture that maximizes reuse of pre-trained image generation models for video generation; (2) A systematic multi-stage training strategy leveraging flow matching for enhanced efficiency; and (3) A cost-effective reinforcement learning with human feedback framework that improves generation quality without requiring additional human annotations. All the code and models are available at: https://contentv.github.io. |
| title | ContentV: Efficient Training of Video Generation Models with Limited Compute |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05343 |