Vidu: Vidu Q2 Turbo Start-End to Video API

Vidu Q2 Turbo Start-End to Video is the extreme-speed start-end frame-driven video generation model of the Q2 series developed by Shengshu Technology. As the speed-maximized version for start-end frame video generation in the Q2 series, the Turbo version takes maximizing generation speed as its core design objective, compressing generation waiting time to the lowest level in the Q2 series through deep inference acceleration optimization, providing an efficient start-end frame video generation solution for users, platforms, and high-throughput application scenarios highly sensitive to generation timeliness and call costs. The model accepts simultaneously provided starting frame and ending frame images, combined with optional text descriptions, generating transition video content between the two frames at maximum speed. Key capabilities include: Extreme-Speed Start-End Frame Transition Generation: Using the starting and ending frames as dual visual anchor points, outputs transition video content between the two frames at the fastest generation speed in the Q2 series under a deep inference acceleration framework. The Turbo version places inference speed at the highest priority, significantly improving user experience and platform throughput by drastically compressing generation waiting time, particularly suitable for real-time content generation scenarios with strict requirements for response latency. Efficient Visual Anchor Point Constraint Processing: Under the Turbo extreme-speed inference framework, quickly identifies and processes the main visual features of the starting and ending frames as constraints, completing the generation of transition videos with maximum efficiency while ensuring basic visual anchor point compliance, ensuring the output video visually transitions reasonably from the starting frame to the ending frame. Rapid Motion Trajectory Inference: Under the speed-priority inference framework, quickly infers motion trajectories between starting and ending frames, generating transition video content with basic motion rationality and fluidity, retaining a motion quality level that meets basic application needs under the premise of extreme-speed generation. Text-Assisted Guidance Support: Supports optional text prompt input, using text descriptions to provide basic guidance on transition content, motion style, and scene atmosphere between start and end frames, providing basic-level text controllability within the speed-priority generation framework. Lowest Resource Consumption and Cost Advantage: The Turbo version has the lowest inference resource consumption and per-generation cost in the Q2 series, suitable for large-scale concurrent calls, high-frequency batch start-end frame animation processing, and commercial application scenarios with limited cost budgets, representing the most economical choice among Q2 series start-end frame video generation options, effectively reducing the overall cost of large-batch video content production. Typical use cases include large-batch start-end frame rapid animation processing, high-throughput video content automated production pipelines, latency-sensitive real-time video content generation, low-cost video transition content rapid generation, high-frequency rapid creative video prototype validation, small-to-medium content creation projects with limited cost budgets, and content factory-type application scenarios requiring rapid output of large volumes of start-end frame transition videos.