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/e3diff/README.md from JeonghyeokDo/GeoSET: direct link, hf CLI and curl.
- Browser
- Download file 2.24 kB
-
https://huggingface.co/JeonghyeokDo/GeoSET/resolve/main/baselines/sar2opt/e3diff/README.md
- Command line
-
hf download hf://JeonghyeokDo/GeoSET/baselines/sar2opt/e3diff/README.md
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curl -L -o README.md https://huggingface.co/JeonghyeokDo/GeoSET/resolve/main/baselines/sar2opt/e3diff/README.md
2.24 kB
| # E3Diff · SAR2Opt | |
| E3Diff (GRSL'24) retrained by the GeoSET authors on the SAR2Opt split. These are the weights behind | |
| the E3Diff row for SAR2Opt in Table 2 of the GeoSET paper | |
| ([arXiv:2609.37496](https://arxiv.org/abs/2609.37496)). | |
| - Method: Qin, Zou, Li and Zhang, "Efficient end-to-end diffusion model for one-step SAR-to-optical translation", GRSL 2024. | |
| - Implementation: [DeepSARRS/E3Diff](https://github.com/DeepSARRS/E3Diff) at commit `38601093ab8f8e4b478144621f20890b100a3b74` (stage 2, initialised from the DDPM (SR3) stage-1 weights). | |
| - Training and inference code: [`baselines/e3diff`](https://github.com/KAIST-VICLab/GeoSET/tree/main/baselines/e3diff) in the GeoSET repository. | |
| ## Files | |
| | File | Bytes | SHA-256 | Role | | |
| |---|---:|---|---| | |
| | `gen.pth` | 768,426,944 | `565c45c53434666fa0f62dbdbfa772742ae8b72eb204b324b05953bdf246d623` | One-step generator UNet (E3Diff stage 2), state dict at iteration 310,000; 3-channel output, [SAR-PPB, SAR-canny, SAR] condition | | |
| ## Results (GeoSET paper, Table 2) | |
| SAR2Opt test split, 627 pairs, centre 512 × 512 crop. | |
| | FID↓ | DISTS↓ | KID↓ | DINO↑ | LPIPS↓ | SSIM↑ | PSNR↑ | | |
| |---:|---:|---:|---:|---:|---:|---:| | |
| | 104.7 | 0.232 | 0.0306 | 0.541 | 0.529 | 0.249 | 16.09 | | |
| ## 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/e3diff/fetch.sh | |
| # vision-aided-loss (CLIP discriminator, built by the model): see baselines/e3diff/README.md | |
| python baselines/data/diff_data.py e3diff sar2opt | |
| hf download JeonghyeokDo/GeoSET --include "baselines/sar2opt/e3diff/*" --local-dir hf | |
| mkdir -p $CKPT_ROOT/e3diff/sar2opt && cp hf/baselines/sar2opt/e3diff/gen.pth $CKPT_ROOT/e3diff/sar2opt/ | |
| bash baselines/e3diff/test.sh sar2opt # writes $WORK_DIR/results/e3diff/sar2opt/<stem>.png | |
| python scripts/evaluate.py --dataset sar2opt --pred-dir $WORK_DIR/results/e3diff/sar2opt | |
| ``` | |
| ## Licence | |
| The E3Diff repository contains no licence file (its bundled `SoftPool/` directory is MIT). These weights were trained by the GeoSET authors with that code; the GeoSET repository distributes only its changes to the code. | |