Seedance 1.0 - A video generation model launched by ByteDance

What is Seedance 1.0?

Seedance 1.0 is a foundational video generation model developed by ByteDance's Seed team. The model supports text and image input, generates 1080p high-quality videos with seamless multi-camera switching, possesses native multi-camera storytelling capabilities, and can switch between long, medium, and close-up shots with stable subject movement and natural visuals. Seedance 1.0 supports various creative styles, such as realistic, animation, and film, and boasts fast generation speed and low cost. On the third-party benchmark Artificial Analysis, Seedance 1.0 ranked first in both text-based and image-based video generation tasks, demonstrating its powerful performance and advantages in the field of video generation.

Main features of Seedance 1.0

• Multi-camera storytelling abilityIt supports the generation of narrative videos containing multiple consecutive shots, and can switch between long, medium and close-up shots to ensure a high degree of consistency between the core subject, visual style and overall atmosphere.
• Smooth and stable motion performanceThe model can generate videos with large movements, maintaining a high level of stability and physical realism from subtle facial expressions to dynamic scenes.
• Creation in multiple stylesIt supports the generation of various video styles, including realistic, animation, film and television, and advertising.
• Precise semantic understanding and instruction complianceThe model can accurately parse complex natural language commands, stably control multi-subject interactions and multiple action combinations, and support a wide range of camera movement options.
• High-speed reasoning and low costBased on model structure optimization and inference acceleration, Seedance 1.0 supports video creation in a short time. For a 5-second 1080p resolution video generation task, the measured inference time is only 41.4 seconds (based on NVIDIA L20 test), which is significantly lower than other similar models.

Technical principles of Seedance 1.0

• Multi-source data processing and precise description modelBased on multi-stage screening and balancing, a large-scale and diverse video dataset was constructed, covering different themes, scenes, styles, and camera movements. A dense descriptive model that fuses dynamic and static features was trained and used to generate accurate video captions, serving as training data. The model focuses on action changes and camera movements in the video, emphasizing the properties and characteristics of key elements in the frame and scene information.
• Highly efficient pre-training frameworkThis project constructs a decoupled spatial and temporal Diffusion Transformer model. The spatial layer performs attention aggregation within a single frame, while the temporal layer focuses on attention computation across frames, improving training and inference efficiency. It supports interleaved sequences of visual and text tokens, extending to training on multi-camera videos and enhancing the model's multi-camera generation capabilities and multimodal understanding. Based on binary masks indicating which frames should adhere to generation control conditions, it achieves a unified framework for tasks such as text-to-image, text-to-video, and image-to-video.
• Post-training optimization and composite reward systemIn the fine-tuning phase, the dataset is trained with high-quality video-text to ensure that the generated videos perform better in terms of aesthetics and motion dynamics. A composite reward system is constructed, including a basic reward model, a motion reward model, and an aesthetic reward model. Based on the multi-dimensional reward model, the model's performance in text-to-image alignment, motion quality, and visual appeal is improved. By maximizing the reward values of multiple reward models and combining them with the RLHF (Reinforcement Learning from Human Feedback) algorithm, the overall performance of the model in text-generated video and image-generated video tasks is improved.
• Ultimate Reasoning AccelerationBased on an adversarial distillation mechanism guided by segmented trajectory consistency, score matching, and human preferences, a superior synergy between generation quality and speed is achieved with extremely low inference steps. A lightweight VAE decoder with refined channel structure achieves double the speedup in perceptual quality losslessness during video generation. Through system-level modifications such as fusion operator optimization, heterogeneous quantization sparsity strategy, adaptive hybrid parallelism, asynchronous offloading, and VAE parallel decomposition, an efficient inference path for long-sequence video generation is constructed, achieving a superior synergy between end-to-end throughput and memory efficiency.

Performance of Seedance 1.0

• On the third-party evaluation platform Artificial Analysis, Seedance 1.0 ranked first in both text-to-video (T2V) and image-to-video (I2V) tasks.
• In internal benchmark tests, Seedance 1.0 performed well across multiple core dimensions, including instruction compliance, motion quality, and aesthetic performance, compared to other models in the industry. In the T2V task, it achieved high scores in metrics such as instruction compliance, motion quality, and aesthetic performance.

Official examples of Seedance 1.0

• Native multi-camera storytelling capabilities:
  • PromptA girl plays the piano; multiple camera angles; cinematic quality (I2V).

• Enhanced motion generation effect:
  • PromptThe skier is skiing, kicking up a lot of snow as he turns, gradually accelerating down the slope, and the camera moves smoothly.

• supportHigh aesthetic appealCreation in multiple styles:

Seedance 1.0 project address

• Project official website:https://seed.bytedance.com/zh/seedance
• Technical Papers:https://lf3-static.bytednsdoc.com/obj/eden-cn/bdeh7uhpsuht/Seedance

Application scenarios of Seedance 1.0

• Film and television productionGenerates narrative videos with multiple camera cuts, supports complex narrative structures, and enhances the video's narrative ability and visual effects.
• Advertising and MarketingIt can quickly generate high-quality ad videos, supporting multiple styles and scenarios to meet the advertising needs of different brands and products.
• Game developmentGenerate cutscenes and dynamic scenes in the game to enhance its narrative and immersive experience.
• Education and TrainingGenerate educational videos and training materials to help students and employees better understand and master knowledge.
• News and MediaGenerate dynamic content for news reports and documentaries, enhancing their visual appeal.