---
title: FlashVSR+ Video Super-Resolution
emoji: ⚡
colorFrom: red
colorTo: pink
sdk: gradio
sdk_version: 6.30.0
app_file: app.py
python_version: "3.12"
startup_duration_timeout: 1h
short_description: One-step video super-resolution at up to 4K
---

**FlashVSR+** — *Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching* — super-resolves videos in a single diffusion step. This Space runs the official **turbo** preset (hybrid sliding attention + Triton block-sparse stream attention + TinyDecoder) from the [FlashVSR_Plus](https://github.com/OpenImagingLab/FlashVSR_Plus) codebase with the [ruchangyao/FlashVSR_plus](https://huggingface.co/ruchangyao/FlashVSR_plus) weights.

## How it works

1. Upload a video (any resolution/FPS; audio is preserved when present).
2. Pick the **output resolution** — the input is center-cropped and resized to this size, then super-resolved in place. Both output sides must be multiples of 128.
3. Optionally lower *temporal compression* to 2 or 1 for large-motion content (slower), or enable *color correction* to match low-frequency colors to the input.
4. Press **Super-resolve**.

The requested height/width **is** the output resolution; FPS and audio of the input are kept.

## Technical notes

- Inference runs one stream chunk (4 latent frames) at a time with `--decode-every 28` incremental decoding, bounding RAM so 4K output resolutions fit the ZeroGPU card.
- Attention uses the project's vendored Triton block-sparse kernel (`flashvsr_attn`), plus the hybrid sliding attention of the turbo preset.
- The example inputs are the videos shipped in the official repository (Apache-2.0).

## Citation

```bibtex
@misc{yao2026flashvsrefficientvideosuperresolution,
  title={FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching},
  author={Yao, Ruchang and others},
  year={2026},
}
```