Seedance 1.0: Exploring the Boundaries of Video Generation Models

TL;DR

Seedance 1.0 is a high-performance, efficient video generation model that advances the field by integrating diverse data, innovative architecture, and optimized training to produce high-quality, coherent videos rapidly.

Contribution

It introduces a novel architecture and training paradigm supporting multi-shot, text-to-video, and image-to-video tasks with significant speed and quality improvements.

Findings

Achieves ~10x inference speedup through distillation and system optimization.

Generates 5-second 1080p videos in 41.4 seconds with high quality.

Outperforms state-of-the-art models in spatiotemporal fluidity and coherence.

Abstract

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.