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README.md

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+---
+license: mit
+datasets:
+  - doem1997/openearthmap-sar
+language:
+  - en
+pipeline_tag: image-to-image
+library_name: diffusers
+tags:
+  - remote-sensing
+---
+
+# CUT-OpenEarthMap-SAR
+
+CUT (Contrastive Unpaired Translation) models for SAR ↔ optical image translation. Trained on OpenEarthMap data with anti-aliased ResNet generators.
+
+## Model variants
+
+| Model | Direction | Epoch |
+|-------|-----------|-------|
+| `opt2sar` | Optical → SAR | 20 |
+| `sar2opt` | SAR → Optical | 15 |
+| `seman2opt` | Semantic → Optical | 25 |
+| `seman2opt_pesudo` | Semantic (pseudo) → Optical | 195 |
+| `seman2sar` | Semantic → SAR | 25 |
+| `seman2sar_pesudo` | Semantic (pseudo) → SAR | 200 |
+
+## Usage
+
+Use with [pytorch-image-translation-models](https://github.com/Bili-Sakura/pytorch-image-translation-models):
+
+```python
+from examples.community.openearthmap_sar import load_openearthmap_sar_pipeline
+
+pipeline = load_openearthmap_sar_pipeline(
+    checkpoint_dir="BiliSakura/CUT-OpenEarthMap-SAR",  # or local path
+    model_name="sar2opt",
+    device="cuda",
+)
+output = pipeline(source_image=pil_image, output_type="pil")
+```
+
+**CLI:**
+
+```bash
+python -m examples.community.openearthmap_sar \
+    --checkpoint-dir BiliSakura/CUT-OpenEarthMap-SAR \
+    --model sar2opt \
+    --input sar.png \
+    --output out.png
+```
+
+> Pass `source_image` as `PIL.Image`. The generator uses anti-aliased down/upsampling to match the original CUT training.
+
+## Repository layout
+
+```
+{model_name}/
+  generator/
+    config.json
+    diffusion_pytorch_model.safetensors
+```
+
+## Architecture
+
+| Parameter | Value |
+|-----------|-------|
+| `in_channels` | 3 |
+| `out_channels` | 3 |
+| `base_filters` | 64 |
+| `n_blocks` | 9 |
+| `norm` | InstanceNorm |
+
+## Citation
+
+**CUT (architecture):**
+
+```bibtex
+@inproceedings{park2020cut,
+  title={Contrastive Learning for Unpaired Image-to-Image Translation},
+  author={Park, Taesung and Efros, Alexei A and Zhang, Richard and Zhu, Jun-Yan},
+  booktitle={ECCV},
+  year={2020}
+}
+```
+
+**OpenEarthMap-SAR (dataset & baseline):**
+
+```bibtex
+@ARTICLE{11303033,
+  author={Xia, Junshi and Chen, Hongruixuan and Broni-Bediako, Clifford and Wei, Yimin and Song, Jian and Yokoya, Naoto},
+  journal={IEEE Geoscience and Remote Sensing Magazine},
+  title={OpenEarthMap-SAR: A benchmark synthetic aperture radar dataset for global high-resolution land cover mapping [Software and Data Sets]},
+  year={2025},
+  volume={13},
+  number={4},
+  pages={476-487},
+  keywords={Translation;Semantic segmentation;Source coding;Urban planning;Land surface;Geoscience and remote sensing;Benchmark testing;Software;Sustainable development;Synthetic aperture radar},
+  doi={10.1109/MGRS.2025.3599512}
+}
+```
+
+## Credits
+
+Models trained on the [OpenEarthMap-SAR](https://github.com/cliffbb/OpenEarthMap-SAR) benchmark dataset. Thanks to the authors for the dataset and CUT baseline.

opt2sar/generator/config.json

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+{
+  "_class_name": "OpenEarthMapSARGenerator",
+  "in_channels": 3,
+  "out_channels": 3,
+  "base_filters": 64,
+  "n_blocks": 9,
+  "use_dropout": false,
+  "padding_type": "reflect",
+  "no_antialias": false,
+  "no_antialias_up": false
+}

opt2sar/generator/diffusion_pytorch_model.safetensors

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sar2opt/generator/config.json

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+{
+  "_class_name": "OpenEarthMapSARGenerator",
+  "in_channels": 3,
+  "out_channels": 3,
+  "base_filters": 64,
+  "n_blocks": 9,
+  "use_dropout": false,
+  "padding_type": "reflect",
+  "no_antialias": false,
+  "no_antialias_up": false
+}

sar2opt/generator/diffusion_pytorch_model.safetensors

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seman2opt/generator/config.json

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+{
+  "_class_name": "OpenEarthMapSARGenerator",
+  "in_channels": 3,
+  "out_channels": 3,
+  "base_filters": 64,
+  "n_blocks": 9,
+  "use_dropout": false,
+  "padding_type": "reflect",
+  "no_antialias": false,
+  "no_antialias_up": false
+}

seman2opt/generator/diffusion_pytorch_model.safetensors

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seman2opt_pesudo/generator/config.json

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+{
+  "_class_name": "OpenEarthMapSARGenerator",
+  "in_channels": 3,
+  "out_channels": 3,
+  "base_filters": 64,
+  "n_blocks": 9,
+  "use_dropout": false,
+  "padding_type": "reflect",
+  "no_antialias": false,
+  "no_antialias_up": false
+}

seman2opt_pesudo/generator/diffusion_pytorch_model.safetensors

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seman2sar/generator/config.json

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+  "_class_name": "OpenEarthMapSARGenerator",
+  "in_channels": 3,
+  "out_channels": 3,
+  "base_filters": 64,
+  "n_blocks": 9,
+  "use_dropout": false,
+  "padding_type": "reflect",
+  "no_antialias": false,
+  "no_antialias_up": false
+}

seman2sar/generator/diffusion_pytorch_model.safetensors

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seman2sar_pesudo/generator/config.json

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+{
+  "_class_name": "OpenEarthMapSARGenerator",
+  "in_channels": 3,
+  "out_channels": 3,
+  "base_filters": 64,
+  "n_blocks": 9,
+  "use_dropout": false,
+  "padding_type": "reflect",
+  "no_antialias": false,
+  "no_antialias_up": false
+}

seman2sar_pesudo/generator/diffusion_pytorch_model.safetensors

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