---
library_name: diffusers
license: apache-2.0
pipeline_tag: image-to-image
---

# Model Card for Model ID

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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]

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- **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, UniPCMultistepScheduler
import torch
from PIL import Image

img = Image.open("osm_tile_18_42048_101323.jpeg")

controlnet = ControlNetModel.from_pretrained("MVRL/GeoSynth-OSM")

scheduler = UniPCMultistepScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base", subfolder="scheduler")

pipe = StableDiffusionControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, scheduler=scheduler)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()

# generate image
generator = torch.manual_seed(10345340)
image = pipe(
    "Satellite image features a city neighborhood",
    num_inference_steps=50,
    generator=generator,
    image=img,
    controlnet_conditioning_scale=1.0,
).images[0]

image.save("generated_city.jpg")
```

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## Citation [optional]

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