Wan-Streamer v0.2：更高分辨率，相同延迟

摘要

我们推出Wan-Streamer v0.2，它是原生流式端到端音视频交互模型的延迟保持升级版本。v0.2沿用了v0.1的建模框架，但将交互输出流从192×336提升至640×368，同时在25 FPS下保持约200毫秒的模型侧信号到信号延迟。更高分辨率的流支持场景锚定的中景智能体，使其在实时对话中姿态、视线、手部、附近物体及局部场景布局仍保持清晰可辨。为支持更大的视觉流而不增加用户可见延迟，v0.2保留了“思考者”作为单GPU低延迟路径，负责流式感知、构建生成缓存的短语言/状态Transformer通道以及最终解码。“执行者”则成为多GPU Ulysses风格的上下文并行组，用于计算昂贵的下一个单元潜变量。每个执行者秩将传入的K/V写入预分片的本地缓存。长高分辨率潜视频序列跨秩分片进行去噪，并通过Ulysses通信聚合，而短得多的音频潜序列则无需序列分片即可生成。在这种拆分中，思考者的语言/状态计算仅作为K/V条件传递给执行者，因此无需在执行者组内传输单独的语言序列。这使得额外硬件集中于视觉生成，同时保持紧凑的思考者-执行者边界，在包含350毫秒双向网络预算时，总远程交互延迟约为550毫秒。

English

We present Wan-Streamer v0.2, a latency-preserving upgrade of the native-streaming, end-to-end audio-visual interaction model. v0.2 keeps the v0.1 modeling formulation, but raises the interactive output stream from 192x336 to 640x368 while preserving approximately 200 ms model-side signal-to-signal latency at 25 FPS. The higher-resolution stream supports scene-grounded mid-shot agents whose posture, gaze, hands, nearby objects, and local scene layout remain legible during real-time conversation. To support the larger visual stream without adding user-visible delay, v0.2 keeps the thinker as a single-GPU low-latency path for streaming perception, the short language/state Transformer pass that builds the generation cache, and final decoding. The performer becomes a multi-GPU Ulysses-style context-parallel group for the expensive next-unit latent generation. Each performer rank writes incoming K/V into a pre-sharded local cache. The long high-resolution latent video sequence is split across ranks for denoising and gathered through Ulysses communication, while the much shorter audio latent sequence is generated without sequence sharding. In this split, the thinker's language/state computation reaches the performer only as K/V conditioning, so no separate language sequence has to be communicated inside the performer group. This concentrates additional hardware on visual generation while preserving the compact thinker-performer boundary, keeping total remote interaction latency at approximately 550 ms when a 350 ms bidirectional network budget is included.