metadata
license: apache-2.0
tags:
- image-super-resolution
- image-to-image
- mlx
- apple-silicon
- super-resolution
- neural-heat-fields
base_model:
- prs-eth/thera-rdn-air
- prs-eth/thera-rdn-pro
library_name: mlx
language:
- en
pipeline_tag: image-to-image
✨ thera-mlx
Aliasing-Free Arbitrary-Scale Super-Resolution for Apple Silicon
An MLX port of Thera — running natively on M1/M2/M3/M4
128×128 → 512×512 in ~1.5 seconds on M1 · Arbitrary scale factors · Web UI + CLI · Video support
Model Description
thera-mlx contains MLX-format weights for the Thera super-resolution model, converted from the original JAX/Flax checkpoints released by PRS-ETH (ETH Zurich).
Thera uses neural heat fields to perform aliasing-free image super-resolution at any scale factor — not just 2× or 4×. This repo provides two variants:
| File | Model | Speed | Quality |
|---|---|---|---|
weights-air.safetensors |
Air | ~1.5s for 128→512px on M1 | Great |
weights-pro.safetensors |
Pro (+ SwinIR refinement) | ~5s for 128→512px on M1 | Best |
Usage
git clone https://github.com/madeleinelmuller/thera-mlx
cd thera-mlx
pip install -r requirements.txt
# Download weights (pulls from this repo automatically)
python run.py convert --model air
python run.py convert --model pro
# Launch web UI
python run.py
# Or use CLI
python run.py run input.png output.png --scale 4 --model air
See the full GitHub repository for complete documentation.
Original Weights
The weights in this repository are converted from:
Citation
If you use this in your work, please cite the original Thera paper:
@article{becker2023thera,
title = {Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields},
author = {Becker, Alexander and Caye Daudt, Rodrigo and Narnhofer, Dominik and
Peters, Torben and Metzger, Nando and Wegner, Jan Dirk and Schindler, Konrad},
journal = {arXiv preprint arXiv:2311.17643},
year = {2023}
}
Original resources:
- Paper: arxiv.org/abs/2311.17643
- Project page: therasr.github.io
- Original HF weights: huggingface.co/prs-eth