Instructions to use hvbrt/GeoSynth-OSM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hvbrt/GeoSynth-OSM with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("hvbrt/GeoSynth-OSM") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
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library_name: diffusers
base_model: stabilityai/stable-diffusion-2-1-base
license: apache-2.0
widget:
- src: osm_tile_18_42048_101323.jpeg
prompt: Satellite image features a city neighborhood
tags:
- controlnet
- stable-diffusion
- satellite-imagery
- OSM
pipeline_tag: image-to-image
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a ControlNet based model that synthesizes satellite images given OpenStreetMap Images. The base stable diffusion model used is [stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) (v2-1_512-ema-pruned.ckpt).
* Use it with 🧨 [diffusers](#examples)
* Use it with [controlnet](https://github.com/lllyasviel/ControlNet/tree/main?tab=readme-ov-file) repository
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [stable-diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)
- **Paper:** [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)
## Examples
```python
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from PIL import Image
img = Image.open("osm_tile_18_42048_101323.jpeg")
controlnet = ControlNetModel.from_pretrained("MVRL/GeoSynth-OSM")
pipe = StableDiffusionControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", controlnet=controlnet)
pipe = pipe.to("cuda:0")
# generate image
generator = torch.manual_seed(10345340)
image = pipe(
"Satellite image features a city neighborhood",
generator=generator,
image=img,
).images[0]
image.save("generated_city.jpg")
```
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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