README.md

metadata

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
• Project Page: Project Website
• Code: GitHub Repository

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:

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:

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:

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:

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

@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}
}