baselines/sar2opt/conddiff/README.md

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).

• 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 at commit 8326f54adf3a8d717792cb7abc7d3d2b7dfac565.
• Training and inference code: 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

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 (MIT). These weights were trained by the GeoSET authors with that code; the GeoSET repository distributes only its changes to the code.