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--- |
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library_name: diffusers |
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pipeline_tag: image-to-image |
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license: apache-2.0 |
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--- |
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# Model Card for StableNormal |
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This repository contains the weights of StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal |
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## Usage |
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See the Github repository: https://github.com/Stable-X/StableNormal regarding installation instructions. |
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The model can then be used as follows: |
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```python |
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import torch |
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from PIL import Image |
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# Load an image |
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input_image = Image.open("path/to/your/image.jpg") |
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# Create predictor instance |
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predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal_turbo", trust_repo=True, yoso_version='yoso-normal-v1-5') |
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# Generate normal map using alpha channel for masking |
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normal_map = predictor(rgba_image, data_type="object") # Will mask out background, if alpha channel is avalible, else use birefnet |
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normal_map = predictor(rgba_image, data_type="outdoor") # Will use Mask2Former to mask out sky and plants |
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normal_map = predictor(rgba_image, data_type="indoor") # Will not mask out |
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# Apply the model to the image |
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normal_image = predictor(input_image) |
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# Save or display the result |
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normal_image.save("output/normal_map.png") |
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``` |
