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DiffusionSat (ICLR 2024)

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This is the official repository for the ICLR 2024 paper "DiffusionSat: A Generative Foundation Model For Satellite Imagery".

Authors: Samar Khanna ¹, Patrick Liu, Linqi (Alex) Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, Stefano Ermon.

Installation

Please refer to INSTALLATION.md.

Model checkpoint files

Model checkpoint files can be found at this link. Note that this is a temporary location, and checkpoint files may be moved to another location in the future (I will update the README in this case).
(While the files are on Google Drive, you can use gdown to download them).

Single Image Generation

This section covers image-generation using single-image DiffusionSat, without control signal inputs. The relevant jupyter notebook can be found in notebooks/single-image.ipynb.

The relevant model checkpoints can be found here:

Conditional Image Generation

The relevant model checkpoints can be found here:

The Jupyter notebook is:

Coming soon, stay tuned...

Training

These sections describe how to launch training using accelerate.

Single-Image Training

To train the (text, metadata) -> single_image model, use the following command:

./launch_scripts/launch_256_fmow_satlas_spacenet_img_txt_md.sh launch_accelerate_configs/single_gpu_accelerate_config.yaml

Here we provide an example config file to use with accelerate, but you can also configure your own file by running accelerate config and following the steps. This will save the config file in the cache location (eg: .cache/huggingface/accelerate/default_config.yaml), and you can simply copy over the .yaml file to launch_accelerate_configs/ or remove the --config_file argument from accelerate launch in the bash script.

Conditional (ControlNet) Training

To train the (text, target_metadata, conditioning_metadata, conditioning_images) -> single_image ControlNet model, use the following commands, detailed below. As a quick summary, these scripts use a frozen single-image model (see above) as a prior to train a ControlNet (which could be a 3D ControlNet for temporal conditioning images). This ControlNet can then generate a new image for the desired input text and metadata prompt, conditioned on additional metadata and images.

Note that you will need to specify the shardlist .txt files in ./webdataset_shards. You will also need to provide the path to the single-image model checkpoint (by specifying this path in the UNET_PATH variable) that will remain frozen throughout training.

Texas Housing Super-resolution

./launch_scripts/launch_texas_md_controlnet.sh launch_accelerate_configs/single_gpu_accelerate_config.yaml

This task uses the Texas housing dataset from satellite-pixel-synthesis-pytorch. The task is: given a low-res and high-res image of a location at time T, and a low-res image of the same location at time T', generate a high-res image of the location at time T'.

fMoW-Sentinel -> fMoW-RGB Super-resolution

./launch_scripts/launch_fmow_md_superres.sh launch_accelerate_configs/single_gpu_accelerate_config.yaml

The task is: given a multi-spectral low-res image of a location (from fMoW-Sentinel), generate the corresponding high-res RGB image (from fMoW-RGB).

fMoW Temporal Generation

./launch_scripts/launch_fmow_temporal_md_controlnet.sh launch_accelerate_configs/single_gpu_accelerate_config.yaml

This model conditions on a temporal sequence of input RGB images from fMoW-RGB to generate a single new image at a desired timestamp T.

xBD Temporal Inpainting

./launch_scripts/launch_xbd_md_controlnet.sh launch_accelerate_configs/single_gpu_accelerate_config.yaml

The task is: given a past (or future) image of a location affected by a natural disaster, generate the future (or past) image after (or before) the natural disaster struck. We use the xBD dataset.

Datasets

The datasets we use are in webdataset format. You will need to prepare your datasets in this format to be able to train using the given code, or you can modify the data-loading to use your own custom dataset formats.

We have provided example shardlists in webdataset_shards. The training code will read the relevant file, and load data using the data paths in this file. The advantage of using webdataset is that your data does not need to only be on disk, and you can stream data from buckets in AWS S3 as well.

fMoW

Example format for each entry in the fMoW webdataset .tar file.

__key__: fmow-{cls_name}-{instance_id}  # eg: fmow-airport-airport_0
output.cls: label_idx  # eg: 32
input.npy: (h,w,c) numpy array
metadata.json: {'img_filename': ..., 'gsd': ..., 'cloud_cover': ..., 'timestamp': ..., 'country_code': ...}

Note that fMoW also requires a metadata .csv file.

Citation

If you find our project helpful, please cite our paper:

@inproceedings{
khanna2024diffusionsat,
title={DiffusionSat: A Generative Foundation Model for Satellite Imagery},
author={Samar Khanna and Patrick Liu and Linqi Zhou and Chenlin Meng and Robin Rombach and Marshall Burke and David B. Lobell and Stefano Ermon},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=I5webNFDgQ}
}