MVRL
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GeoSynth-OSM

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 library_name: diffusers
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 # Model Card for Model ID
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-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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@@ -179,12 +63,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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@@ -195,6 +73,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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 ---
 library_name: diffusers
+license: apache-2.0
+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, 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")
+```
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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