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Swin2Sr

Swin2SR is an open-source Python and PyTorch implementation of a SwinV2 Transformer model for image super-resolution and restoration, including compressed-image upscaling and JPEG artifact reduction.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Swin2Sr — 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 Swin2SR when a workflow needs to upscale low-resolution compressed images or reduce JPEG artifacts, or when exploring those restoration tasks with a pretrained model.

Who it is for

  • Developers adding compressed-image enhancement to a computer-vision or image-processing workflow.
  • Researchers exploring transformer-based image super-resolution and restoration.
  • People who want to try the model through the linked Kaggle, Colab or Hugging Face demos.

Documented uses

  • Upscale low-resolution JPEG-compressed images.
  • Reduce JPEG compression artifacts.
  • Apply image super-resolution in an image-processing workflow.
  • Explore a pretrained transformer restoration model through a demo.
Before you choose

Requirements & limits

The repository is public and lists an Apache-2.0 license. Its README identifies an interactive Hugging Face Space demo that requires no login.

  • The basic inference instructions expect low-quality, low-resolution JPEG-compressed images.
  • The README’s example uses a scale factor of four and writes outputs to a results directory.
  • The repository supplies pretrained models and points to separate demos for interactive exploration.
  • The README identifies KAIR as the location of the training code.

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

Run compressed-image super-resolution on a folder

Process low-quality, low-resolution JPEG images with the repository’s pretrained Swin2SR model.

Before you start

  • The Swin2SR repository and its Python/PyTorch implementation.
  • Low-quality, low-resolution JPEG-compressed images placed in an inputs folder.
  • A pretrained model file; the README names the model path used in its example.

Steps

  1. Create an inputs folder and place the JPEG images in it.

  2. Choose the task and scale; the example uses compressed_sr and scale 4.

  3. Run the documented main_test_swin2sr.py command with the selected task, scale, model path, input folder and --save_img_only option.

  4. Find processed images in results/swin2sr_{TASK}_x{SCALE}.

Expected outcome

The model processes the input folder and saves super-resolved images in the corresponding results directory.

Guide limitations

  • The documented basic setup specifies low-quality, low-resolution JPEG-compressed inputs.
  • The README example uses scale 4 and a particular pretrained model file; it does not establish a universal runtime or deployment target.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
mv-lab/swin2sr · Official homepage title · retained in its source language
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