Streaming Video Generation

Streaming video generation is the technical pattern 张金涛 / Zhang Jintao distinguishes from ordinary Video Models in 快一点！再快一点！快到世界能实时生成｜和生数科技张金涛聊：Vidu S1、推理加速、实时交互视频. Instead of generating a finite clip and then playing it back, the model must generate each frame quickly enough for continuous playback.

The source makes three constraints central: generation speed must exceed playback speed, long sessions must avoid identity drift or visual collapse, and live input must trigger the right visual response. Vidu S1 is the source’s concrete case, but the pattern also affects future Real-Time Interactive Video Generation, companion characters, games, desktop assistants, and camera-aware interfaces.

Key Claims

• Streaming generation is not merely offline generation with lower latency; it changes the model, inference, and evaluation problem.
• Long-form consistency matters because small visual errors can accumulate during continuous generation.
• The model must balance speed, image quality, input responsiveness, and identity consistency.
• Serving architecture matters because frame generation, user input, scheduling, and GPU utilization become part of one live loop.

Connections

• Vidu S1, Vidu, and 生数科技 / Shengshu Technology — source product and company case.
• Real-Time Interactive Video Generation, Video Models, and Multimodal Intelligence — adjacent interaction and model categories.
• Inference Acceleration Stack, SAGE Attention, and TurboDiffusion — acceleration requirements.
• AI Inference Cost Structure, High-Throughput Inference Batching, Low-Latency Inference Chip, and MaaS Infrastructure — serving and cost context.