---
title: Stream-DiffVSR
emoji: 🎞️
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 6.26.0
app_file: app.py
short_description: Low-latency streamable 4x video super-resolution
python_version: "3.12"
startup_duration_timeout: 30m
---

# Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion

Gradio demo for [Jamichsu/Stream-DiffVSR](https://huggingface.co/Jamichsu/Stream-DiffVSR)
([paper](https://huggingface.co/papers/2512.23709),
[code](https://github.com/jamichss/Stream-DiffVSR),
[project page](https://jamichss.github.io/stream-diffvsr-project-page/)).

Stream-DiffVSR is a causally conditioned diffusion framework for online 4× video
super-resolution. It combines a four-step distilled denoiser, an Auto-regressive
Temporal Guidance (ARTG) module that injects motion-aligned cues during latent
denoising, and a lightweight temporal-aware decoder with a Temporal Processor
Module (TPM). Every frame is restored from **past frames only**, so there is no
look-ahead latency.

The demo mirrors the authors' reference `inference.py`: the official
`StreamDiffVSRPipeline`, `TemporalAutoencoderTiny` and custom DDIM scheduler from
the GitHub repo, RAFT-large optical flow for the auto-regressive warping, empty
prompt, `guidance_scale=0`, 4 denoising steps.

## Examples

The bundled example clips were produced by 4× bicubic downsampling of videos from
[`linoyts/repo-to-space-example-videos`](https://huggingface.co/datasets/linoyts/repo-to-space-example-videos).

## Notes

- Input is downscaled so its longest side is at most `max_input_size` (default 320 px)
  before the 4× upscale, and the clip is truncated to `num_frames` (default 30) to fit
  in a ZeroGPU window.
- TensorRT acceleration from the original repo is not used here.

```bibtex
@article{shiu2025stream,
  title={Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion},
  author={Shiu, Hau-Shiang and Lin, Chin-Yang and Wang, Zhixiang and Hsiao, Chi-Wei and Yu, Po-Fan and Chen, Yu-Chih and Liu, Yu-Lun},
  journal={arXiv preprint arXiv:2512.23709},
  year={2025}
}
```