Add pipeline tag, paper link, and improve model card

README.md

@@ -1,6 +1,7 @@
 ---
-license: mit
 library_name: none
+license: mit
+pipeline_tag: image-to-image
 tags:
 - image-super-resolution
 - remote-sensing
@@ -8,11 +9,15 @@ tags:
 - cloud-microphysics
 ---
 
-# CloudSR Checkpoints
+# Recovering Cloud Microstructures with Cascaded Diffusion Inversion
+
+This repository contains the model checkpoints for **CloudSR**, a two-stage diffusion-based super-resolution framework to enhance the resolution of multi-spectral cloud microstructures by a factor of 4×. 
 
-Checkpoint files for the Cloud Microphysics Super-Resolution project.
+- **Paper:** [Recovering Cloud Microstructures with Cascaded Diffusion Inversion](https://huggingface.co/papers/2607.05637)
+- **Project Page:** [Project Website](https://hananshafi.github.io/superresolution-cloud-microphysics/)
+- **Code:** [GitHub Repository](https://github.com/hananshafi/superresolution-cloud-microphysics)
 
-## Files
+## Checkpoint Files
 
 - `cloudsr_seviri_to_viirs_model_50000.pth`
   Single-stage checkpoint for SEVIRI to VIIRS super-resolution.
@@ -24,11 +29,60 @@ Checkpoint files for the Cloud Microphysics Super-Resolution project.
 
 These are custom PyTorch checkpoints intended to be used with the local inference code in the main project repository.
 
-Example:
+### Setup
+
+First, clone the repository and install the dependencies:
+
+```bash
+git clone https://github.com/hananshafi/superresolution-cloud-microphysics.git
+cd superresolution-cloud-microphysics
+conda env create -f environment.yaml
+conda activate cloudsr
+```
+
+### Download Checkpoints
+
+You can download the checkpoints directly using `huggingface_hub`:
+
+```bash
+pip install huggingface_hub
+hf download hanangani/cloudsr-checkpoints cloudsr_seviri_to_viirs_model_50000.pth --repo-type model --local-dir ./checkpoints
+hf download hanangani/cloudsr-checkpoints cloudsr_msg_to_mtg_model_50000.pth --repo-type model --local-dir ./checkpoints
+```
+
+### SEVIRI to VIIRS Inference
+
+Run inference using the `inference_sr.py` script:
 
 ```bash
 python inference_sr.py \
-  -i /path/to/input \
-  -o /path/to/output \
-  --started_ckpt_path /path/to/cloudsr_seviri_to_viirs_model_50000.pth
+  -i /path/to/seviri_input \
+  -o /path/to/output_dir \
+  --num_steps 1 \
+  --sd_path /path/to/sd-turbo \
+  --started_ckpt_path ./checkpoints/cloudsr_seviri_to_viirs_model_50000.pth
+```
+
+### MSG to MTG Inference
+
+Run inference using the `inference_msg_to_mtg_sr.py` script:
+
+```bash
+python inference_msg_to_mtg_sr.py \
+  -i /path/to/msg_input \
+  -o /path/to/output_dir \
+  --num_steps 1 \
+  --sd_path /path/to/sd-turbo \
+  --started_ckpt_path ./checkpoints/cloudsr_msg_to_mtg_model_50000.pth
 ```
+
+## Citation
+
+```bibtex
+@inproceedings{gani2026recovering,
+  title     = {Recovering Cloud Microstructures with Cascaded Diffusion Inversion},
+  author    = {Gani, Hanan and Pulik, Guy and Rosenfeld, Daniel and Watson-Parris, Duncan and Khan, Salman},
+  booktitle = {ICLR 2026 Workshop on Machine Learning for Remote Sensing (ML4RS)},
+  year      = {2026}
+}
+```