OpenImagingLab/FlashVSR
[CVPR 2026] Towards Real-Time Diffusion-Based Streaming Video Super-Resolution — An efficient one-step diffusion framework for streaming VSR with locality-constrained sparse attention and a tiny conditional decoder. observed · 2026-08-28
Health v2 · maintenance only
61/100
- Activity 99
- Release rhythm 35
- Longevity 23
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 323
- days_rel: n/a
- days_push: 10
- n_releases_24m: 0
Adoption not part of the score
1799 stars · 145 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
FlashVSR is a one-step diffusion-based streaming video super-resolution framework that runs at ~17 FPS for 768x1408 video on a single A100 GPU. It combines a three-stage distillation pipeline, locality-constrained sparse attention, and a tiny conditional decoder, with pretrained model weights and a VSR-120K training dataset.
Use cases
- upscale low-resolution videos in real time
- restore and enhance blurry or degraded video streams
- run diffusion-based video super-resolution efficiently on a single GPU
- super-resolve ultra-high-resolution videos
- integrate video super-resolution into ComfyUI workflows
- train video super-resolution models on a large-scale dataset
When to choose
- you need real-time or streaming video super-resolution rather than offline batch processing
- you want diffusion-quality restoration with far lower latency than multi-step diffusion VSR models
- you need to scale super-resolution to ultra-high resolutions without artifacts
- you want pretrained weights and a ComfyUI integration for quick use
When to avoid
- you only need simple classical upscaling (e.g., bicubic or ESRGAN) with minimal GPU cost
- you lack a CUDA-capable GPU, since the framework targets A100-class hardware for real-time speeds
- you need image-only super-resolution without video/temporal handling
- you require a permissively simple setup without diffusion model dependencies
Facets
library · maturity active
video-processing image-processing machine-learning deep-learning llm-inference computer-vision image-processing deep-learning artificial-intelligence python cross-platform diffusion-models video-super-resolution video-restoration one-step-diffusion streaming-inference sparse-attention cvpr-2026 comfyui video gpu linux
2 sources
- readme: https://github.com/OpenImagingLab/FlashVSR · fetched 2026-08-28 · 4deda4f1d145
- homepage: https://zhuang2002.github.io/FlashVSR/ · fetched 2026-08-29 · 99c49cddf9ee
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| OpenImagingLab/FlashVSR | main | 61 |
For agents
markdown · JSON · MCP: product_card(name="OpenImagingLab/FlashVSR")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem