Stream-T1: Test-Time Scaling for Streaming Video Generation
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| Authors | Yijing Tu et al. |
| Year | 2026 |
| HF Upvotes | 98 |
| arXiv | 2605.04461 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video generation methods based on diffusion models suffer from exorbitant candidate exploration costs and lack temporal guidance. To address these structural bottlenecks, we propose shifting the focus to streaming video generation. We identify that its chunk-level synthesis and few denoising steps are intrinsically suited for TTS, significantly lowering computational overhead while enabling fine-grained temporal control. Driven by this insight, we introduced Stream-T1, a pioneering comprehensive TTS framework exclusively tailored for streaming video generation. Specifically, Stream-T1 is composed of three units: (1) Stream -Scaled Noise Propagation, which actively refines the initial latent noise of the generating chunk using historically proven, high-quality previous chunk noise, effectively establishes temporal dependency and utilizing the historical Gaussian prior to guide the current generation; (2) Stream -Scaled Reward Pruning, which comprehensively evaluates generated candidates to strike an optimal balance between local spatial aesthetics and global temporal coherence by integrating immediate short-term assessments with sliding-window-based long-term evaluations; (3) Stream-Scaled Memory Sinking, which dynamically routes the context evicted from KV-cache into distinct updating pathways guided by the reward feedback, ensuring that previously generated visual information effectively anchors and guides the subsequent video stream. Evaluated on both 5s and 30s comprehensive video benchmarks, Stream-T1 demonstrates profound superiority, significantly improving temporal consistency, motion smoothness, and frame-level visual quality.
Engineering Breakdown
Plain English
This paper introduces Stream-T1, a test-time scaling framework that improves video generation quality without expensive retraining by applying computational scaling at inference time. The key innovation is shifting from traditional diffusion-based video generation to streaming video generation, which naturally breaks the problem into smaller chunks with fewer denoising steps, dramatically reducing the computational overhead while allowing better temporal control between video frames.
Key Engineering Insight
Streaming video generation's chunk-level synthesis is fundamentally better suited for test-time scaling than traditional full-video diffusion approaches—you get fine-grained temporal control and lower per-token compute costs by working on smaller temporal windows, not massive full videos.
Why It Matters for Engineers
Video generation at scale is expensive; most teams can't afford to retrain models constantly. This approach lets you improve output quality by spending more compute at inference time instead, which is operationally cheaper and easier to control. The temporal control aspect matters for production use cases where you need consistent frame-to-frame coherence without expensive multi-pass generation.
Research Context
Prior test-time scaling for video relied on expensive candidate exploration over full videos with many denoising steps. Stream-T1 advances this by recognizing that streaming architectures (which generate video sequentially in chunks) are inherently more efficient for scaling. This bridges the gap between research-grade quality improvements and practical deployment constraints that production systems actually face.
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