Generate Videos with the Seedance 2.0 Family

The Seedance 2.0 Family is ByteDance's current multimodal video stack on Replicate. Seedance 2.0 is the higher-quality path, while Seedance 2.0 Fast is the quicker variant for iteration. Both expand the earlier Seedance workflow from prompt-plus-image generation into a much more controllable system that can respond to reference images, reference videos, and reference audio while still producing a single coherent output video.

The biggest practical change is native audio generation. Instead of treating sound as a separate post-process layer, the Seedance 2.0 Family can generate dialogue, ambience, and effects together with the visual sequence. That makes these models a stronger fit for presenter clips, spoken product demos, dubbed creative, and sound-led short-form ads.

Reference images help hold onto character identity, wardrobe, style, and scene composition. Reference videos help transfer motion, pacing, or edit intent. Reference audio gives the model timing cues for lip-sync and beat matching. When combined carefully in the prompt, these references reduce guesswork and make outputs more repeatable.

If you need quicker iteration, Seedance 2.0 Fast uses the same input structure and multimodal controls while prioritizing speed over top-end quality. That makes it useful for prototyping, batch exploration, and high-volume content pipelines before switching back to Seedance 2.0 for final renders.

The Seedance 2.0 Family also introduces intelligent duration control. Sending duration as -1 lets the model choose a suitable output length instead of forcing a fixed clip length too early. This is useful when the prompt and reference material imply a natural beat that is hard to pre-size manually.

On framing, the schema now supports adaptive aspect ratio in addition to fixed ratios like 16:9, 9:16, and 1:1. Adaptive is useful when you want the model to infer the most appropriate layout from the inputs, especially when working with mixed reference media.

A good Seedance 2-family prompt usually does three things clearly: identify the primary subject, describe the intended motion or edit behavior, and tell the model how to interpret any attached references. The more explicit that mapping is, the more consistent the results tend to be.