Seedance 1.0: Exploring the Boundaries of Video Generation Models

摘要

Notable advances in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still confront critical challenges in synergistically 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 pre-training paradigm that enables multiple features or functions such as interleaved multimodal positional encoding, native multi-shot generation capacity, and multi-task modeling; (iii) carefully-designed post-training optimization leveraging fine-grained supervised fine-tuning, video-specific RLHF with multi-dimensional reward mechanisms for considerable performance improvements; (iv) excellent model acceleration achieving 10× 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. Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation with superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation, and ultra-fast inference.

作者

Seed Vision Team

期刊/会议

arXiv