| 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](https://huggingface.co/papers/2311.17643). | |
| 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](https://therasr.github.io) | |
| * **Code Repository:** [https://github.com/prs-eth/thera](https://github.com/prs-eth/thera) | |
| * **Demo:** [https://huggingface.co/spaces/prs-eth/thera](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. | |
| ```bash | |
| 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: | |
| ```bash | |
| ./super_resolve.py IN_FILE OUT_FILE --scale 3.14 --checkpoint thera-rdn-plus.pkl | |
| ``` | |
| ## License | |
| Apache-2.0 |