Seedance 1.0: Exploring the Boundaries of Video Generation Models
Notable breakthroughs in diffusion modeling have propelled rapid improvements
in video generation, yet current foundational model still face critical
challenges in simultaneously balancing prompt following, motion plausibility,
and visual quality. In this report, we introduce Seedance 1.0, a
high-performance and inference-efficient video foundation generation model that
integrates several core technical improvements: (i) multi-source data curation
augmented with precision and meaningful video captioning, enabling
comprehensive learning across diverse scenarios; (ii) an efficient architecture
design with proposed training paradigm, which allows for natively supporting
multi-shot generation and jointly learning of both text-to-video and
image-to-video tasks. (iii) carefully-optimized post-training approaches
leveraging fine-grained supervised fine-tuning, and video-specific RLHF with
multi-dimensional reward mechanisms for comprehensive performance improvements;
(iv) excellent model acceleration achieving ~10x inference speedup through
multi-stage distillation strategies and system-level optimizations. Seedance
1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds
(NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance
1.0 stands out with high-quality and fast video generation having superior
spatiotemporal fluidity with structural stability, precise instruction
adherence in complex multi-subject contexts, native multi-shot narrative
coherence with consistent subject representation.
684a3b0a9b38e1e5a33a683f
Yu Gao
claimed_verified
2025-06-12T12:42:01.191Z
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gaoyu
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gaooyu1520
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Haoyuan Guo
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Tuyen Hoang
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Weilin Huang
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Lu Jiang
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Fangyuan Kong
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Huixia Li
claimed_verified
2025-06-15T07:04:04.308Z
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HXLee
user
HXLee
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Jiashi Li
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Liang Li
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Xiaojie Li
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Xunsong Li
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Yifu Li
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Shanchuan Lin
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Zhijie Lin
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Jiawei Liu
claimed_verified
2025-06-12T12:41:55.061Z
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Jiawei Liu
user
jwliu-cc
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Shu Liu
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Xiaonan Nie
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Zhiwu Qing
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Yuxi Ren
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Li Sun
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Zhi Tian
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Rui Wang
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Sen Wang
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Guoqiang Wei
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Guohong Wu
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Jie Wu
claimed_verified
2025-06-12T12:42:05.891Z
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wujie10558@gmail.com
user
wujie10
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Ruiqi Xia
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Fei Xiao
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Xuefeng Xiao
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Jiangqiao Yan
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Ceyuan Yang
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Jianchao Yang
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Runkai Yang
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Tao Yang
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Yihang Yang
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Zilyu Ye
claimed_verified
2025-06-12T12:41:58.552Z
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Zilyu Ye
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YeLuoSuiYou
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Xuejiao Zeng
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Yan Zeng
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Heng Zhang
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Yang Zhao
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Xiaozheng Zheng
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Peihao Zhu
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Jiaxin Zou
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Feilong Zuo
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Notable breakthroughs in diffusion modeling have propelled rapid improvements
in video generation, yet current foundational model still face critical
challenges in simultaneously balancing prompt following, motion plausibility,
and visual quality. In this report, we introduce Seedance 1.0, a
high-performance and inference-efficient video foundation generation model that
integrates several core technical improvements: (i) multi-source data curation
augmented with precision and meaningful video captioning, enabling
comprehensive learning across diverse scenarios; (ii) an efficient architecture
design with proposed training paradigm, which allows for natively supporting
multi-shot generation and jointly learning of both text-to-video and
image-to-video tasks. (iii) carefully-optimized post-training approaches
leveraging fine-grained supervised fine-tuning, and video-specific RLHF with
multi-dimensional reward mechanisms for comprehensive performance improvements;
(iv) excellent model acceleration achieving ~10x inference speedup through
multi-stage distillation strategies and system-level optimizations. Seedance
1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds
(NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance
1.0 stands out with high-quality and fast video generation having superior
spatiotemporal fluidity with structural stability, precise instruction
adherence in complex multi-subject contexts, native multi-shot narrative
coherence with consistent subject representation.
Seedance 1.0 offers high-performance video generation by integrating advanced data curation, efficient architecture, post-training optimization, and model acceleration, resulting in superior quality and speed.
diffusion modeling multi-source data curation precision and meaningful video captioning efficient architecture training paradigm multi-shot generation text-to-video image-to-video fine-grained supervised fine-tuning video-specific RLHF multi-dimensional reward mechanisms multi-stage distillation strategies model acceleration spatiotemporal fluidity structural stability instruction adherence multi-shot narrative coherence consistent subject representation