Latent Diffusion Super-Resolution - Sentinel 2 (LDSR-S2)

Description: This repository contains the code of the paper Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery with Latent Diffusion. This package contains the latent-diffusion model and weights to super-resolute the RGB-NIR bands of Sentinel-2. It can be embedded in the "opensr-utils" package in order to create a data pipeline for SR products.

🧪 Status: LDSR-S2 has exited the experimental phase as of v1.0.0
📌 For super-resolving 20m bands, check out SEN2SR, or use it alongside LDSR-S2 in the third notebook.

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Table of Contents

• 🚀 1. Interactive Notebooks - Google Colab
• 🛠️ 2. Install and Usage - Local
  • 🧱 2.1 Weights and Checkpoints
• 🖼️ 3. Super-Resolution Examples
  • 🛰️ 3.1 Example SR Image
  • 🔎 3.2 Demo File and Uncertainty Calculation
• 🗺️ 4. Inference on S2 Tiles: Data Pipeline
• 📝 5. Notes
  • 🎓 5.1 Training
  • 📚 5.2 Citation
  • 📈 5.3 Status

1. 🚀 Interactive Notebooks - Google Colab

Run LDSR-S2 directly in Google Colab within seconds! These notebooks let you fetch Sentinel-2 imagery, apply super-resolution, and save results — with or without going through the code. The no-code version even implements a user interface to select and process data.

Notebook Name	Description	Link
LDSR-S2 No-Code	🔘 No coding required — chose point on a map and download SR results as GeoTIFFs
LDSR-S2 Walkthrough	🧪 Code-level walkthrough with uncertainty estimation and advanced plotting
LDSR-S2 & SEN2SR	🔄 Use LDSR-S2 alongside SEN2SR to compare results on 10m + 20m bands

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2. 🛠️ Install and Usage - Local

pip install opensr-model

Minimal Example

# Get Config
from io import StringIO
import requests
from omegaconf import OmegaConf
config_url = "https://raw.githubusercontent.com/ESAOpenSR/opensr-model/refs/heads/main/opensr_model/configs/config_10m.yaml"
response = requests.get(config_url)
config = OmegaConf.load(StringIO(response.text))

# Get Model
import torch
device = "cuda" if torch.cuda.is_available() else "cpu" # set device
import opensr_model # import pachage
model = opensr_model.SRLatentDiffusion(config, device=device) # create model
model.load_pretrained(config.ckpt_version) # load checkpoint
sr = model.forward(torch.rand(1,4,128,128), sampling_steps=100) # run SR

2.1 🧱 Weights and Checkpoints

The model should load automatically with the model.load_pretrained command. Alternatively, the checkpoints can be found on HuggingFace.

3. 🖼️ Super-Resolution Examples

3.1 🛰️ Example SR Image

3.2 🔎 Demo File and Uncertainty Calculation

Run the demo.py file to see how the package works. It super-resolves an example tensor and saves the corresponding uncertainty map.

4. 🗺️ Inference on S2 Tiles: Data Pipeline

This repository itself exposes the raw model for tensor‑level inference. By default it performs 128→512 super‑resolution on 4‑channel tensors (B×4×128×128 → B×4×512×512). It does not handle geospatial IO, tiling, or metadata.

For real‑world workflows on Sentinel‑2 data, please use opensr-utils:

• Run inference over 4‑band GeoTIFFs (e.g., RGB‑NIR) or full Sentinel‑2 .SAFE folders (including .SAFE.zip).
• Handles tiling, overlap blending, stitching, and preserves CRS/transform.
• Works with every model (including this one), define LR‑space window_size=128, factor=4, overlap, and border trimming.
• Writes georeferenced outputs (e.g., GeoTIFF/COG), plus basic logs/metadata.

Minimal example (pseudocode; see opensr-utils for details):

import opensr_utils
sr_job = opensr_utils.large_file_processing(
    root="/path/to/S2_tile.SAFE",  # also supports GeoTIFFs and .SAFE.zip
    model=model,                   # your SRLatentDiffusion instance
    window_size=(128, 128),        # LR patch size → 512px SR at factor=4
    factor=4,                      # 10m → 2.5m
    overlap=12,                    # LR‑space overlap to reduce seams
    eliminate_border_px=2,         # trim LR‑space border per tile
    device=device,                 # "cuda"/"cpu"
    gpus=0,                        # single GPU id or list for multi‑GPU
)

Use the notebooks and demo.py for quick starts; for full‑tile processing and proper georeferencing, prefer opensr-utils.

5. 📝 Notes

5.1 🎓 Training

Training is not supported by default in this repository due to time and resource constraints. The provided models are implemented as PyTorch Lightning modules and ship ready for inference. If you want to train or fine‑tune them, you can extend the module(s) by adding the standard Lightning hooks (e.g., training_step, validation_step, configure_optimizers, and any callbacks or loggers you prefer). If you build a clean, reproducible training pipeline, please consider opening a Pull Request (PR). We’re happy to review community contributions.

5.2 📚 Citation

If you use this model in your work, please cite

@ARTICLE{ldsrs2,
  author={Donike, Simon and Aybar, Cesar and Gómez-Chova, Luis and Kalaitzis, Freddie},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
  title={Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion}, 
  year={2025},
  volume={18},
  number={},
  pages={6940-6952},
  doi={10.1109/JSTARS.2025.3542220}}

5.3 📈 Status

This repository has left the experimental stage with the publication of v1.0.0.