Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
sar
synthetic-aperture-radar
remote-sensing
stable-diffusion
image-generation
synthetic-data
ship-detection
earth-observation
sentinel-1
Instructions to use sylviaHoch/SAR-StableDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sylviaHoch/SAR-StableDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sylviaHoch/SAR-StableDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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## Overview
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Fine-tuned **Stable Diffusion 1.5** backbone for generating synthetic **Sentinel-1 SAR** amplitude images from text prompts.
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Published alongside the paper **"Diffusion-Based SAR Training Data Synthesis Controlled by Spatial Annotations"** (Hochstuhl et al., 2026).
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The model generates single-channel SAR amplitude images for specific land cover types similar to Sentinel-1 GRD image products (in VH polarization).
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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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Fine-tuned **Stable Diffusion 1.5** backbone for generating synthetic **Sentinel-1 SAR** amplitude images from text prompts.
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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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The model generates single-channel SAR amplitude images for specific land cover types similar to Sentinel-1 GRD image products (in VH polarization).
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## License
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