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| 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} | |
| } | |
| ``` | |