---
library_name: pytorch
license: mit
pipeline_tag: image-to-image
tags:
- pytorch
- computer-vision
- image-super-resolution
- diffusion
---

# LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction

This repository contains the official model weights for **LPNSR**, a prior-enhanced efficient diffusion framework for image super-resolution (SR).

- **Paper:** [LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction](https://huggingface.co/papers/2603.21045)
- **GitHub Repository:** [Faze-Hsw/LPNSR](https://github.com/Faze-Hsw/LPNSR)

## Introduction

LPNSR addresses the efficiency-quality trade-off in diffusion-based SR. While state-of-the-art frameworks like ResShift achieve efficient 4-step inference, they can suffer from performance degradation due to unconstrained random noise. LPNSR addresses this by:
- Replacing random Gaussian noise with an **LR-guided multi-input-aware noise predictor**, embedding structural priors into the reverse process.
- Mitigating initialization bias using a **high-quality pre-upsampling network** to optimize the diffusion starting point.
- Maintaining a compact 4-step sampling trajectory for high-quality, real-world super-resolution.

## Features

- **Efficient Sampling**: Only 4 sampling steps required for high-quality super-resolution.
- **Noise Predictor**: Learns to predict optimal noise maps for partial diffusion initialization.
- **Real-world SR**: Designed to handle complex real-world degradations.
- **SwinIR Integration**: Optional SwinIR refinement for enhanced details.

## Quick Start

To use these weights, clone the [official repository](https://github.com/Faze-Hsw/LPNSR) and follow the environment setup instructions.

### Inference

Once the environment is set up and weights are placed in the `pretrained/` folder, run:

```bash
python LPNSR/inference.py -i [image folder/image path] -o [output folder]
```

### Online Demo

You can also launch a local Gradio web interface:

```bash
python LPNSR/app.py
```

## Citation

If you find this work useful, please cite:

```bibtex
@article{lpnsr2026,
  title={LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction},
  author={Huang, Shuwei and Liu, Shizhuo and Wei, Zijun},
  journal={arXiv preprint arXiv:2603.21045},
  year={2026},
  eprint={2603.21045},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```

## Acknowledgement

This project is based on [ResShift](https://github.com/zsyOAOA/ResShift), [BasicSR](https://github.com/XPixelGroup/BasicSR), [SwinIR](https://github.com/JingyunLiang/SwinIR), and [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN).