64 lines
2.5 KiB
Markdown
64 lines
2.5 KiB
Markdown
# Stable Diffusion for Remote Sensing Image Generation
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#### Author: Zhiqiang yuan @ AIR CAS, [Send a Email](yuan_zhi_qiang@sina.cn)
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#### Welcome :+1:_<big>`Fork and Star`</big>_:+1:, then we'll let you know when we update
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#### -------------------------------------------------------------------------------------
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A simple project for `text-to-image remote sensing image generation`,
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and we will release the code of `using multiple text to control regions for super-large RS image generation` later.
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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/).
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## Environment configuration
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Follow and thanks [original training repo](https://github.com/justinpinkney/stable-diffusion.git) .
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## Pretrained weights
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We used RS image-text dataset [RSITMD](https://github.com/xiaoyuan1996/AMFMN) as training data and fine-tuned stable diffusion for 10 epochs with 1 x A100 GPU.
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When the batchsize is 4, the GPU memory consumption is about 40+ Gb during training, and about 20+ Gb during sampling.
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The pretrain weights is realesed at [last-pruned.ckpt](https://drive.google.com/drive/folders/10vK3eNpIw7H3lxxZbB7NF2IZktGt95As?usp=sharing).
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## Using
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### Samling
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Download the pretrain weights `last-pruned.ckpt` to current dir, and run with:
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```commandline
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python scripts/txt2img.py \
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--prompt 'Some boats drived in the sea' \
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--outdir 'outputs/RS' \
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--H 512 --W 512 \
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--n_samples 4 \
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--config 'configs/stable-diffusion/RSITMD.yaml' \
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--ckpt './last-pruned.ckpt'
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```
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### Traing
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Put images of [RSITMD](https://github.com/xiaoyuan1996/AMFMN) in `data/RSITMD/images`, and run with:
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```commandline
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python main.py \
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-t \
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--base configs/lammbda/RSITMD.yaml \
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--gpus 1 \
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--scale_lr False \
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--num_nodes 1 \
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--check_val_every_n_epoch 10 \
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--finetune_from './last-pruned.ckpt'
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```
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## Examples
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**Caption:** `Some boats drived in the sea.`
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**Caption:** `A lot of cars parked in the airport.`
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**Caption:** `A large number of vehicles are parked in the parking lot, next to the bare desert.`
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**Caption:** `There is a church in a dark green forest with two table tennis courts next to it.`
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