Vidu: Vidu Q2 Turbo Image to Video API

Vidu Q2 Turbo Image-to-Video is an accelerated image-driven video generation model developed by Shengshu Technology for Q2, part of the Vidu Q2 series. Positioned with high-speed inference as the priority within the Q2 series, this model significantly reduces generation time through inference acceleration technology while maintaining acceptable image-to-video quality output, providing an ideal solution for users and platforms highly sensitive to generation speed and call costs. Using a user-provided static image as the input foundation, combined with optional text descriptions, it rapidly generates video content with basic dynamic effects. Key capabilities include: Extreme Speed Inference Priority: The Turbo version is designed with maximizing inference speed as its core objective, delivering the fastest image-to-video generation speed within the Q2 series, significantly reducing user wait times, particularly suitable for high-throughput, low-latency batch processing scenarios and real-time content generation needs. Basic Image Feature Preservation: Under the premise of high-speed inference, maintains the consistency of major visual features in the input image as much as possible, including subject appearance, scene composition, and overall color style, ensuring basic visual association between generated videos and source images. Image-Text Joint Input Support: Supports simultaneous input of images and text prompts, specifying motion direction, basic action content, and scene changes through text descriptions, providing a degree of generation content controllability under the speed-priority premise. Smooth Basic Motion Generation: Even in Turbo acceleration mode, generates motion effects with basic smoothness, meeting the fundamental quality requirements for dynamic videos in everyday content creation, suitable for application scenarios with relatively relaxed video quality requirements. Cost-Efficient Resource Consumption: The Turbo version is the most economical in inference resource consumption, suitable for large-scale concurrent calls, high-frequency batch processing, and commercial application scenarios with limited cost budgets, effectively reducing the per-generation cost of image-to-video production. Typical use cases include rapid social media content production, batch image animation processing, real-time content generation tool integration, low-cost short video content production, prototype validation, and rapid generation of creative drafts.