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.
核心贡献 · Key contributions
多源数据策展与精准视频描述，支持跨多样化场景的全面学习。 Multi-source data curation with precision video captioning enables comprehensive learning across diverse scenarios.
高效架构采用解耦时空层与多模态 RoPE，原生支持多镜头生成及文生视频/图生视频统一任务。 Efficient architecture with decoupled spatial-temporal layers and MM-RoPE supports multi-shot and unified T2V/I2V tasks.
通过细粒度监督微调和视频专用 RLHF（多维奖励）的后训练优化，提升运动、美学和对齐能力。 Post-training optimization via fine-grained SFT and video-specific RLHF with multi-dimensional rewards improves motion, aesthetics, and alignment.
多阶段蒸馏与系统优化实现约 10 倍推理加速，且质量无损。 Multi-stage distillation and system optimizations achieve ~10x inference speedup with no quality degradation.
统一模型在 Artificial Analysis 的文生视频和图生视频榜单均居首位，领先竞品超 100 Elo 分。 Unified model tops both text-to-video and image-to-video leaderboards on Artificial Analysis, outperforming competitors by over 100 Elo points.
原生多镜头叙事保持主体一致性及电影化转场，增强创作可控性。 Native multi-shot narrative with consistent subject representation and cinematic transitions enhances creative control.
局限 · Limitations
评估依赖内部基准 SeedVideoBench-1.0 和公开竞技场，更广泛的泛化性尚未充分验证。 Evaluation relies on internal benchmark SeedVideoBench-1.0 and public arena; broader generalization not fully validated.
在超长视频或超出训练分布的复杂多镜头场景下，模型性能可能下降。 Model performance may degrade on extremely long videos or complex multi-shot scenarios beyond training distribution.
推理加速技术可能在高运动或精细纹理区域引入细微伪影。 Inference acceleration techniques may introduce subtle artifacts in high-motion or fine-texture regions.
双语支持仅限于中文和英文，其他语言未评估。 Bilingual support is limited to Chinese and English; other languages not evaluated.
尽管有优化，训练和推理所需的算力资源仍然可观。 Computational resources required for training and inference remain substantial despite optimizations.