CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model

Paper (ArXiv)

TODO

• Release inference code.
• Release pretrained models.
• Release Gradio UI.
• Release training code

Environment

conda env create -f environment.yaml
conda activate csrldm

You can download pre-trained models last.ckpt and put it to ./ckpt/ folder.

Testing

You can run the code to start the gradio interface by:

python src/test/test.py

The demonstration effects of the project are as follows:

You can also use the following code to generate images more quickly

python src/test/inference.py

Some of the results are shown below：

Acknowledgments:

This repo is built upon ControlNet and Uni-ControlNet. Some of the functional implementations of remote sensing imagery refer to: GeoSeg,Txt2Img-MHN and SGCN. Sincere thanks to their excellent work!

Citation

@article{tang2024crs,
  title={Crs-diff: Controllable remote sensing image generation with diffusion model},
  author={Tang, Datao and Cao, Xiangyong and Hou, Xingsong and Jiang, Zhongyuan and Liu, Junmin and Meng, Deyu},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2024},
  publisher={IEEE}
}