Image-to-Image
Diffusers
Safetensors
sar-to-eo
remote-sensing
synthetic-aperture-radar
flow-matching
lora
Instructions to use JeonghyeokDo/GeoSET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JeonghyeokDo/GeoSET with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-base-4B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JeonghyeokDo/GeoSET") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
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Download baselines/sar2opt/conddiff/README.md from JeonghyeokDo/GeoSET: direct link, hf CLI and curl.
- Browser
- Download file 2.19 kB
-
https://huggingface.co/JeonghyeokDo/GeoSET/resolve/main/baselines/sar2opt/conddiff/README.md
- Command line
-
hf download hf://JeonghyeokDo/GeoSET/baselines/sar2opt/conddiff/README.md
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curl -L -o README.md https://huggingface.co/JeonghyeokDo/GeoSET/resolve/main/baselines/sar2opt/conddiff/README.md
2.19 kB
| # CondDiff · SAR2Opt | |
| CondDiff (GRSL'23) retrained by the GeoSET authors on the SAR2Opt split. These are the weights behind | |
| the CondDiff row for SAR2Opt in Table 2 of the GeoSET paper | |
| ([arXiv:2609.37496](https://arxiv.org/abs/2609.37496)). | |
| - Method: Bai, Pu and Xu, "Conditional diffusion for SAR to optical image translation", GRSL 2023. | |
| - Implementation: [Coordi777/Conditional-Diffusion-for-SAR-to-Optical-Image-Translation](https://github.com/Coordi777/Conditional-Diffusion-for-SAR-to-Optical-Image-Translation) at commit `8326f54adf3a8d717792cb7abc7d3d2b7dfac565`. | |
| - Training and inference code: [`baselines/conddiff`](https://github.com/KAIST-VICLab/GeoSET/tree/main/baselines/conddiff) in the GeoSET repository. | |
| ## Files | |
| | File | Bytes | SHA-256 | Role | | |
| |---|---:|---|---| | |
| | `ema_final.pt` | 662,459,343 | `3075a059144ef972d3b0129fded5d4d94193e5406bb98e5b30e6afd3fbb64653` | UNet EMA (0.9999) weights at step 50,000 (512 × 512 model) | | |
| ## Results (GeoSET paper, Table 2) | |
| SAR2Opt test split, 627 pairs, centre 512 × 512 crop. | |
| | FID↓ | DISTS↓ | KID↓ | DINO↑ | LPIPS↓ | SSIM↑ | PSNR↑ | | |
| |---:|---:|---:|---:|---:|---:|---:| | |
| | 211.8 | 0.415 | 0.1379 | 0.343 | 0.686 | 0.248 | 12.48 | | |
| ## Inference | |
| ```bash | |
| git clone https://github.com/KAIST-VICLab/GeoSET && cd GeoSET | |
| export BASELINES_ROOT=/path/to/upstream_code DATA_ROOT=/path/to/data WORK_DIR=/path/to/work CKPT_ROOT=/path/to/checkpoints | |
| bash baselines/conddiff/fetch.sh | |
| python baselines/data/diff_data.py conddiff sar2opt | |
| hf download JeonghyeokDo/GeoSET --include "baselines/sar2opt/conddiff/*" --local-dir hf | |
| mkdir -p $CKPT_ROOT/conddiff/sar2opt && cp hf/baselines/sar2opt/conddiff/ema_final.pt $CKPT_ROOT/conddiff/sar2opt/ | |
| bash baselines/conddiff/test.sh sar2opt # writes $WORK_DIR/results/conddiff/sar2opt/<stem>.png | |
| python scripts/evaluate.py --dataset sar2opt --pred-dir $WORK_DIR/results/conddiff/sar2opt | |
| ``` | |
| ## Licence | |
| The CondDiff repository contains no licence file; it is derived from [openai/guided-diffusion](https://github.com/openai/guided-diffusion) (MIT). These weights were trained by the GeoSET authors with that code; the GeoSET repository distributes only its changes to the code. | |