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.
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.
