SeedanceTwo is an AI video generation model developed by ByteDance. Its primary function is to convert text or image inputs into video content, with support for multi-shot storytelling and audio synchronization. The model accepts four types of inputs: image, video, audio, and text, allowing users to combine these modalities for controlled generation. For example, an uploaded image can serve as a reference for composition, character details, and visual style, while a reference video can be used to replicate camera movements, motion rhythms, and creative effects. The tool also supports video extension, which smoothly connects and extends shots into continuous scenes, and video editing, which enables targeted modifications such as swapping characters or removing elements without regenerating the entire clip. The model includes several core capabilities that have been updated in version 2.0. These include more accurate physics simulation, natural and fluid motion, improved instruction following, and better style consistency across shots. Common consistency issues, such as character appearance varying between scenes, product detail loss, blurred small text, scene jumps, and inconsistent camera styles, are addressed. The model can handle complex actions and continuous motion, and it also supports audio features such as natural voice timbre for dialogue and narration, as well as precise alignment between audio beats and on-screen actions. Typical use cases include generating creative transitions, advertisement spots, film clips, and other complex effects. The workflow generally involves uploading source material, such as text prompts, images, videos, or audio, and then using references from that material to guide the generation process. According to the website, the creative process is intended to function like directing, with the model autonomously completing storylines and creative details. The service offers subscription features that are listed on the site, including credits that refresh monthly, access to all premium models, no watermark on outputs, priority queue processing, and human support. Credits are noted as never expiring.