Seedance 2.0

Seedance 2.0 accepts text, images, audio, and video clips as simultaneous inputs for a single generation. This allows users to anchor specific visual elements with a reference photo, define motion and narrative with text, and synchronize pacing with an audio track — all blended by the model into a unified output. This multi-signal approach provides substantially more creative control than text-only prompting.

The platform is built on ByteDance's diffusion transformer framework, which applies transformer-based attention mechanisms to the video diffusion process. This architecture is specifically optimized for maintaining temporal coherence — ensuring consistent lighting, object persistence, and smooth motion across all frames of the generated clip. The result is noticeably fewer visual artifacts compared to earlier convolutional or GAN-based video generation approaches.

Seedance 2.0 supports output resolutions up to 2K, producing clips that are sharp enough for direct publishing to social media platforms and digital marketing channels without requiring post-generation upscaling. Higher resolution generation consumes more credits, giving users the flexibility to choose between quality and cost depending on the intended use of the content.

Users can influence motion dynamics at the frame level through their input prompts and reference materials, guiding camera movement, subject motion, and scene transitions. This goes beyond simple prompt-based direction by allowing reference videos to define motion patterns that the model replicates or adapts in the generated output. The result is more predictable and intentional movement within clips.

The combined-input guidance system specifically addresses scene layout and compositional control by letting users define spatial relationships, style consistency, and visual hierarchy through reference inputs. For example, a user can supply a background image, a foreground subject reference, and a text prompt describing their interaction to generate a composed scene. This feature is particularly valuable for branded content where visual standards must be maintained across multiple generated clips.