# Stable Diffusion for Remote Sensing Image Generation

#### Author: Zhiqiang yuan @ AIR CAS,  [Send a Email](yuan_zhi_qiang@sina.cn)

A simple project for text-to-image remote sensing image generation,
and we will release the code of `using text to control regions for super-large RS image generation` later.
Also welcome to see the project of [image-condition fake sample generation](https://github.com/xiaoyuan1996/Controllable-Fake-Sample-Generation-for-RS) in [TGRS, 2023](https://ieeexplore.ieee.org/abstract/document/10105619/).

##  Environment configuration

Follow [original training repo](https://github.com/justinpinkney/stable-diffusion.git) .


## Pretrained weights

We used [RSITMD](https://github.com/xiaoyuan1996/AMFMN) as training data and fine-tuned stable diffusion for 10 epochs with 1 x A100 GPU.
When the batchsize is 4, the GPU memory consumption is about 40+ Gb during training, and about 20+ Gb during sampling.
The pretrain weights is realesed at [last-pruned.ckpt](https://github.com/xiaoyuan1996/AMFMN).

## Using
Download the pretrain weights to current dir, and run with:
```commandline
bash sample.sh
```
We will update the train code ASAP.

## Examples
**Caption:** Some boats drived in the sea.
![./assets/shows1.png](./assets/shows1.png)

**Caption:** A lot of cars parked in the airport.
![./assets/shows1.png](./assets/shows2.png)