metadata
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. 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
- Code Repository: https://github.com/prs-eth/thera
- Demo: 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.
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:
./super_resolve.py IN_FILE OUT_FILE --scale 3.14 --checkpoint thera-rdn-plus.pkl
License
Apache-2.0