File size: 2,737 Bytes
6655bb0 33ada0e 6655bb0 33ada0e 6655bb0 33ada0e 6655bb0 33ada0e 6655bb0 33ada0e 6655bb0 33ada0e 6655bb0 33ada0e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | ---
library_name: none
license: mit
pipeline_tag: image-to-image
tags:
- image-super-resolution
- remote-sensing
- satellite-imagery
- cloud-microphysics
---
# 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×.
- **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)
## Checkpoint Files
- `cloudsr_seviri_to_viirs_model_50000.pth`
Single-stage checkpoint for SEVIRI to VIIRS super-resolution.
- `cloudsr_msg_to_mtg_model_50000.pth`
Single-stage checkpoint for MSG to MTG super-resolution.
## Usage
These are custom PyTorch checkpoints intended to be used with the local inference code in the main project repository.
### 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/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}
}
``` |