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Running
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L4
Alexander Becker
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Make README more detailed
Browse files- README.md +34 -1
- requirements.txt +1 -1
README.md
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---
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---
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# Thera Arbitrary-Scale Super-Resolution
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This is an interactive demo for our paper "Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields
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" [(arXiV link)](https://arxiv.org/pdf/2311.17643) [(code link)](https://github.com/prs-eth/thera).
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## Run locally
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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:
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```bash
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> pip install --upgrade pip
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> pip install -r requirements.txt
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```
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Then, start the Gradio server like this:
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```bash
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> python app.py
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```
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The server should bind to port `7860` by default.
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## Useful XLA flags
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* Disable pre-allocation of entire VRAM: `XLA_PYTHON_CLIENT_PREALLOCATE=false`
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* Disable jitting for debugging: `JAX_DISABLE_JIT=1`
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## Citation
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If you found our work helpful, consider citing our paper 😊:
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```
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@article{becker2025thera,
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title={Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields},
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author={Becker, Alexander and Daudt, Rodrigo Caye and Narnhofer, Dominik and Peters, Torben and Metzger, Nando and Wegner, Jan Dirk and Schindler, Konrad},
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journal={arXiv preprint arXiv:2311.17643},
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year={2025}
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}
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```
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requirements.txt
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# gradio
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gradio_imageslider==0.0.20
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spaces
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# gradio
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gradio_imageslider==0.0.20
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spaces==4.44.1
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