File size: 1,540 Bytes
a75fe9c 8e3d003 a75fe9c 6b76cab a75fe9c 6b76cab a75fe9c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
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 |