Wan-Streamer v0.2: Higher Resolution, Same Latency

Hugging Face Daily Papers Papers

Summary

Wan-Streamer v0.2 is a latency-preserving upgrade to an end-to-end audio-visual interaction model, increasing output resolution from 192x336 to 640x368 while maintaining ~200 ms model-side latency via a multi-GPU thinker-performer architecture.

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.
Original Article
View Cached Full Text

Cached at: 07/07/26, 02:41 AM

Paper page - Wan-Streamer v0.2: Higher Resolution, Same Latency

Source: https://huggingface.co/papers/2607.04443 Published on Jul 5

#1 Paper of the day Authors:

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

Abstract

Wan-Streamer v0.2 enhances audio-visual interaction by increasing visual resolution while maintaining low latency through optimized thinker-performer architecture with multi-GPU parallel processing.

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 forstreaming perception, the short language/stateTransformerpass that builds thegeneration cache, andfinal decoding. The performer becomes a multi-GPUUlysses-style context-parallel groupfor the expensive next-unit latent generation. Each performer rank writes incoming K/V into apre-sharded local cache. The long high-resolution latent video sequence is split across ranks fordenoisingand gathered throughUlysses communication, while the much shorteraudio latent sequenceis generated without sequence sharding. In this split, the thinker’slanguage/state computationreaches the performer only asK/V conditioning, so no separate language sequence has to be communicated inside the performer group. This concentrates additional hardware onvisual generationwhile preserving the compact thinker-performer boundary, keeping total remote interaction latency at approximately 550 ms when a 350 ms bidirectional network budget is included.

View arXiv pageView PDFProject pageAdd to collection

Get this paper in your agent:

hf papers read 2607\.04443

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2607.04443 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2607.04443 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2607.04443 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

Wallie V2

Product Hunt

Wallie V2 is an open-source AI streamer designed to feel alive, offering a more interactive streaming experience.