Text-to-Image
Diffusers
Safetensors
English
sar
synthetic-aperture-radar
remote-sensing
stable-diffusion
controlnet
image-generation
synthetic-data
ship-detection
earth-observation
sentinel-1
Instructions to use sylviaHoch/SAR-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sylviaHoch/SAR-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("sylviaHoch/SAR-ControlNet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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## Overview
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**ControlNet** for spatially controlled synthesis of **Sentinel-1 SAR** images.
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Published alongside the paper **"Diffusion-Based SAR Training Data Synthesis Controlled by Spatial Annotations"** (GCPR 2026).
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This model must be used together with the corresponding Stable Diffusion backbone:
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[`sylviaHoch/SAR-StableDiffusion`](https://huggingface.co/sylviaHoch/SAR-StableDiffusion)
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## Citation
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If you use this model, please cite:
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## License
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## Overview
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**ControlNet** for spatially controlled synthesis of **Sentinel-1 SAR** images.
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Published alongside the upcoming paper **"Diffusion-Based SAR Training Data Synthesis Controlled by Spatial Annotations"** (Hochstuhl et al., 2026; accepted for GCPR conference 2026).
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This model must be used together with the corresponding Stable Diffusion backbone:
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[`sylviaHoch/SAR-StableDiffusion`](https://huggingface.co/sylviaHoch/SAR-StableDiffusion)
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```
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## License
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