Video Upscaling - FlashVSR-v.1.1

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Real-Time Diffusion-Based Streaming Video Super-Resolution

Upscale and sharpen low-resolution videos in near real time. FlashVSR-v1.1 is the first diffusion-based one-step streaming video super-resolution framework, delivering state-of-the-art quality at up to ~17 FPS on a single A100 GPU with v1.1 adding enhanced stability and fidelity over the original release.

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What it does

FlashVSR-v1.1 is a diffusion-based video super-resolution (VSR) model that takes low-resolution video and reconstructs it at significantly higher resolution (optimized for 4× upscaling). What sets FlashVSR apart from other diffusion-based VSR tools is its speed: instead of running many denoising passes per frame, it produces results in a single step and processes frames in a streaming fashion. This makes diffusion-quality video super-resolution practical at scale, even at ultra-high output resolutions.

Problem it solves

Low-resolution video sources – Upscales legacy, web-compressed, or low-bitrate footage to sharp, high-resolution output (4× recommended)
Slow diffusion VSR pipelines – Most diffusion-based super-resolution tools are far too slow for practical use on video. FlashVSR completes super-resolution in a single inference step, achieving up to ~12× speedup over prior one-step diffusion VSR models
Streaming / real-time use cases – Built as a streaming framework, enabling near real-time processing rather than batch-only workflows
Ultra-high resolutions – Scales reliably to very large output resolutions where competing tools tend to break down or introduce artifacts
Stability across aspect ratios and content types – v1.1 specifically targets improved stability and fidelity over the initial v1 release

Input/Output

Input:
- A low-resolution video (optimized for 4× super-resolution input)
Output: A high-resolution MP4 video file
- Sharp, detailed reconstruction with preserved fine structures and textures
- Temporally coherent motion across frames, processed in a streaming pass

Accuracy & Speed

Speed & Scale


Processing speed	~17 FPS for 768 × 1408 video on a single NVIDIA A100 GPU
Speedup vs. prior work	Up to ~12× faster than previous one-step diffusion VSR models
Infrastructure needed	Single GPU; optimized for NVIDIA A100 / A800 (Ampere). Runs on H200 (Hopper) with reduced acceleration. Other GPUs (RTX 40/50, H800) currently unverified
Output quality	State-of-the-art among diffusion-based VSR models

Technical Details


Architecture	Diffusion-based one-step streaming VSR framework
Approach	Three-stage distillation pipeline that enables streaming super-resolution in a single inference pass
Attention	Locality-Constrained Sparse Attention (LCSA) — cuts redundant computation and bridges the train–test resolution gap
Decoder	Tiny Conditional Decoder for fast, high-quality reconstruction
Training Data	VSR-120K — purpose-built dataset of 120k videos and 180k images
Backend	Requires Block-Sparse Attention library for full LCSA acceleration
Recommended setting	4× video super-resolution
Developer	Junhao Zhuang et al. (Tsinghua University / OpenImagingLab)

Model Variants

FlashVSR is available in two released versions: