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--- |
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license: apache-2.0 |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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widget: |
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- text: city neighborhood |
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output: |
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url: 7d3aebe3-a08d-4a31-b5ac-06408e0c835a.jpeg |
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- text: resort in hawaii |
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output: |
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url: a983aca6-9deb-41f5-8e8a-b7932cc83ff4.jpeg |
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- text: factory |
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output: |
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url: be2fb507-af99-4258-a90f-c0df2bbab3ce.jpeg |
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- text: university campus |
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output: |
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url: 12bbc23c-850a-484b-99a6-478c19417993.jpeg |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This is a StableDiffusion based model that synthesizes satellite images given text prompts. 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). |
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* Use it with 🧨 [diffusers](#examples) |
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* Use it with [stablediffusion](https://github.com/Stability-AI/stablediffusion) repository |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [stable-diffusion](https://huggingface.co./stabilityai/stable-diffusion-2-1-base) |
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## Examples |
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```python |
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from diffusers import StableDiffusionPipeline |
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pipe = StableDiffusionPipeline.from_pretrained("MVRL/GeoSynth") |
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pipe = pipe.to("cuda:0") |
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image = pipe( |
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"Satellite image features a city neighborhood", |
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).images[0] |
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image.save("generated_city.jpg") |
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``` |
