---
license: apache-2.0
pipeline_tag: image-to-image
tags:
- image-super-resolution
---

# thera-rdn-plus

## Overview

This is a model from the paper [Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields](https://huggingface.co/papers/2311.17643). 
It enables SOTA arbitrary-scale super-resolution, leveraging a built-in analytically correct observation model 
for anti-aliasing when moving across scales.

*   **Project Page:** [https://therasr.github.io](https://therasr.github.io)
*   **Code Repository:** [https://github.com/prs-eth/thera](https://github.com/prs-eth/thera)
*   **Demo:** [https://huggingface.co/spaces/prs-eth/thera](https://huggingface.co/spaces/prs-eth/thera)

## Model Details

- **Description**: This model can be used to enable super-resolution of single images at arbitrary, non-integer scaling factors.
- **Backbone**: `RDN`
- **Variant**: `Plus`
- **Training Dataset**: `DIV2K`

## Usage

To use this model, first clone the official repository and set up the environment. You will need a Python 3.10 environment and an NVIDIA GPU.

```bash
git clone https://github.com/prs-eth/thera.git
cd thera
pip install --upgrade pip
pip install -r requirements.txt
```

After setting up the environment and downloading the `thera-rdn-plus.pkl` checkpoint (available in the "Files and versions" tab of this repository), you can super-resolve any image with the following command:

```bash
./super_resolve.py IN_FILE OUT_FILE --scale 3.14 --checkpoint thera-rdn-plus.pkl
```

## License

Apache-2.0