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
- Xet hash:
- 01ce487e4c2cd859213e02d4352498c61fbdafa7f77e7e5bb1f78b39f1815a7f
- Size of remote file:
- 1.45 GB
- SHA256:
- 1e89f9a3711fdde52116811a065d1b9a0e17bacf10aa6ca3d0ce3e573f227c72
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