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Sparkvsr

Sparkvsr is an Apache-2.0 Python model and research implementation for interactive video super-resolution and restoration through sparse keyframe propagation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Sparkvsr — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Sparkvsr when a video enhancement workflow benefits from sparse frame-level guidance and self-hosted Python tooling rather than an opaque, fixed restoration pass.

Who it is for

  • Video editors and VFX practitioners exploring controllable restoration workflows.
  • Researchers working on video super-resolution or keyframe-conditioned video processing.
  • Developers integrating self-hosted enhancement into Python-based video pipelines.

Documented uses

  • Enhance low-resolution video using selected high-resolution keyframes.
  • Propagate keyframe-based visual information across a video sequence.
  • Explore old-film restoration and video style-transfer processing.
  • Train or run a video-restoration model from a public implementation.
Before you choose

Requirements & limits

The repository is public and lists an Apache-2.0 license. It documents self-hosted installation with Python, PyTorch, Diffusers and other requirements; no service pricing or account requirement is documented.

  • Keyframes can be manually specified, extracted from codec I-frames, or randomly sampled.
  • The project includes inference code, training code and pretrained models.
  • The documented installation uses Conda and a CUDA 12.4 PyTorch wheel example.
  • The repository describes ComfyUI support as released.
  • The project is a model and implementation rather than a hosted video-processing service.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Sparkvsr environment

Prepare the documented Python environment and install the repository dependencies.

Before you start

  • A self-hosted environment with Conda.
  • Python 3.10 or later.
  • A platform and CUDA setup compatible with the selected PyTorch build.

Steps

  1. Clone the Sparkvsr repository and change into its directory.

  2. Create and activate a Conda environment using Python 3.10.

  3. Install the documented PyTorch 2.5.0, torchvision 0.20.0 and torchaudio 2.5.0 CUDA 12.4 wheels.

  4. Install the remaining packages from requirements.txt.

  5. Adjust the PyTorch installation for the platform, CUDA version or desired PyTorch version when necessary.

Expected outcome

The repository dependencies are installed in an activated Sparkvsr environment.

Guide limitations

  • The repository notes that the example PyTorch installation may require platform- and CUDA-specific changes.
  • This setup recipe does not document a complete inference run or output-validation procedure.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
4 October 2026
Original website title
taco-group/SparkVSR · Official homepage title · retained in its source language
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