| --- |
| title: Thera Arbitrary-Scale Super-Resolution |
| emoji: 🔥 |
| 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 😊: |
|
|
| ``` |
| @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} |
| } |
| ``` |
