Instructions to use jessolmstead/BiRefNet_lite-ONNX-webgpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use jessolmstead/BiRefNet_lite-ONNX-webgpu with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("jessolmstead/BiRefNet_lite-ONNX-webgpu", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("jessolmstead/BiRefNet_lite-ONNX-webgpu") - Notebooks
- Google Colab
- Kaggle
Download model_fp16_webgpu.onnx from jessolmstead/BiRefNet_lite-ONNX-webgpu: direct link, hf CLI and curl.
- Browser
- Download file 114 MB
-
https://huggingface.co/jessolmstead/BiRefNet_lite-ONNX-webgpu/resolve/main/model_fp16_webgpu.onnx
- Command line
-
hf download hf://jessolmstead/BiRefNet_lite-ONNX-webgpu/model_fp16_webgpu.onnx
-
curl -L -o model_fp16_webgpu.onnx https://huggingface.co/jessolmstead/BiRefNet_lite-ONNX-webgpu/resolve/main/model_fp16_webgpu.onnx
114 MB
- Xet hash:
- 33cccb4665ec2a43cd45649c33f57df82ace8b69a6ecc3aeb434b50e578df317
- Size of remote file:
- 114 MB
- SHA256:
- 0a019d6ba73c9cedc9a251f8c9390b196ff6399acd281a2872692861abbd78c2
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