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 (v2-1_512-ema-pruned.ckpt).

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Repository: stable-diffusion
Paper: Adding Conditional Control to Text-to-Image Diffusion Models

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")

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hvbrt/GeoSynth-OSM

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hvbrt/
GeoSynth-OSM