Update model card: add paper link, pipeline tag, and fix license

README.md

@@ -1,14 +1,68 @@
----
-license: apache-2.0
-library_name: pytorch
-tags:
-- pytorch
-- computer-vision
-- image-super-resolution
----
-
-# LPNSR
-
-These are the official model weights (`.pth` files) for the [LPNSR project](https://github.com/Faze-Hsw/LPNSR) hosted on GitHub.
-
-To use these weights, clone the repository above and place the `.pth` files as specified in the repo instructions.
+---
+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={[]},
+  journal={arXiv preprint arXiv:2603.21045},
+  year={2026}
+}
+```
+
+## 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).