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title: Thera Arbitrary-Scale Super-Resolution | |
emoji: π₯ | |
colorFrom: red | |
colorTo: green | |
sdk: gradio | |
sdk_version: 4.44.1 | |
app_file: app.py | |
pinned: false | |
# Thera Arbitrary-Scale Super-Resolution | |
This is an interactive demo for our paper "Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields | |
" [(arXiV link)](https://arxiv.org/pdf/2311.17643) [(code link)](https://github.com/prs-eth/thera). | |
## Run locally | |
If you want to run the demo locally, you need a Python 3.10 environment (e.g., installed via conda) on Linux as well as an NVIDIA GPU. Then install packages via pip: | |
```bash | |
> pip install --upgrade pip | |
> pip install -r requirements.txt | |
``` | |
Then, start the Gradio server like this: | |
```bash | |
> python app.py | |
``` | |
The server should bind to port `7860` by default. | |
## Useful XLA flags | |
* Disable pre-allocation of entire VRAM: `XLA_PYTHON_CLIENT_PREALLOCATE=false` | |
* Disable jitting for debugging: `JAX_DISABLE_JIT=1` | |
## Citation | |
If you found our work helpful, consider citing our paper π: | |
``` | |
@article{becker2025thera, | |
title={Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields}, | |
author={Becker, Alexander and Daudt, Rodrigo Caye and Narnhofer, Dominik and Peters, Torben and Metzger, Nando and Wegner, Jan Dirk and Schindler, Konrad}, | |
journal={arXiv preprint arXiv:2311.17643}, | |
year={2025} | |
} | |
``` |