Improve model card with project page, code, demo links, and usage

README.md

@@ -13,6 +13,10 @@ This is a model from the paper [Thera: Aliasing-Free Arbitrary-Scale Super-Resol
 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.
@@ -20,6 +24,22 @@ for anti-aliasing when moving across scales.
 - **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