What is Stream-DiffVSR?

Stream-DiffVSR is an open-source diffusion-based AI model designed for low-latency, streaming video super-resolution, enabling real-time upscaling of video frames using only past information.

Is Stream-DiffVSR free to use?

Yes, it is completely free and open-source, with model weights available on Hugging Face and code on GitHub for self-hosted inference.

How fast is Stream-DiffVSR?

It achieves extremely low latency, processing 720p frames in just 0.328 seconds on an RTX 4090 GPU, making it suitable for real-time online applications.

Does Stream-DiffVSR require future frames like other diffusion VSR models?

No, it uses causal conditioning and auto-regressive design to operate solely on past frames, enabling true streaming without lookahead delays.

Stream-DiffVSR

Low-Latency Streaming Video Super-Resolution Powered by Auto-Regressive Diffusion

From Independent Research

Founder: Hau-Shiang Shiu et al.

Video & Animation

29 Dec 2025

10K+

Free

Pricing Model

Free

Starting Price

$0/Month

About This AI

Stream-DiffVSR is an open-source AI model for real-time online video super-resolution using a causally conditioned diffusion framework.

It processes low-resolution video streams frame-by-frame using only past frames, achieving high perceptual quality with ultra-low latency through a distilled 4-step denoiser, auto-regressive temporal guidance, and lightweight temporal decoder.

Image To Video Text To Video Video Editor Video Generator

Pricing

Pricing Model

Free

Starting Price

$0/Month

Key Features

Causal conditioning for true streaming with no future frame dependency
Ultra-low latency: 0.328 seconds per 720p frame on RTX 4090
Auto-regressive temporal guidance for motion-aligned details
Distilled 4-step diffusion denoiser for efficiency
Lightweight temporal processor module for enhanced coherence

Pros

First practical diffusion-based model for low-latency online VSR
Superior perceptual quality over traditional online methods
Over 130x faster than prior diffusion VSR approaches
Open-source with model weights and inference code on Hugging Face
Suitable for real-time applications like streaming and conferencing

Cons

Requires powerful GPU like RTX 4090 for optimal low-latency performance
Limited to 4x upscaling based on current pretrained model
No built-in web demo or hosted service
Early-stage project with potential for ongoing improvements

Stream-DiffVSR is a breakthrough for researchers and developers needing real-time diffusion-quality video upscaling in streaming scenarios, offering unmatched latency while maintaining strong visual fidelity.

FAQs

What is Stream-DiffVSR?
Stream-DiffVSR is an open-source diffusion-based AI model designed for low-latency, streaming video super-resolution, enabling real-time upscaling of video frames using only past information.
Is Stream-DiffVSR free to use?
Yes, it is completely free and open-source, with model weights available on Hugging Face and code on GitHub for self-hosted inference.
How fast is Stream-DiffVSR?
It achieves extremely low latency, processing 720p frames in just 0.328 seconds on an RTX 4090 GPU, making it suitable for real-time online applications.
Does Stream-DiffVSR require future frames like other diffusion VSR models?
No, it uses causal conditioning and auto-regressive design to operate solely on past frames, enabling true streaming without lookahead delays.