Model overview
Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving **efficiency, scalability, and real-time performance**. To this end, we propose **FlashVSR**, the first diffusion-based one-step streaming framework towards real-time VSR. **FlashVSR runs at ∼17 FPS for 768 × 1408 videos on a single A100 GPU** by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that cuts redundant computation while bridging the train–test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct **VSR-120K**, a new dataset with 120k videos and 180k images. Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves **state-of-the-art performance with up to ∼12× speedup** over prior one-step diffusion VSR models. First of all, huge thanks to the community for the fast adoption, feedback, and contributions to FlashVSR! 🙌 During community testing, we noticed that some third-party implementations of FlashVSR (e.g. early ComfyUI versions) do **not include our Locality-Constrained Sparse Attention (LCSA)** module and instead fall back to **dense attention**. This may lead to **noticeable quality degradation**, especially at higher resolutions. Community discussion: https://github.com/kijai/ComfyUI-WanVideoWrapper/issues/1441 ✅ The **official FlashVSR pipeline (this repository)**: - **Better preserves fine structures and details** - **Effectively avoids texture aliasing and visual artifacts**
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。