Image Segmentation
MLX
Safetensors
BiRefNet
moonbox
background-removal
dichotomous-image-segmentation
Instructions to use moonbox-dev/BiRefNet-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use moonbox-dev/BiRefNet-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download moonbox-dev/BiRefNet-MLX --local-dir BiRefNet-MLX
- BiRefNet
How to use moonbox-dev/BiRefNet-MLX with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("moonbox-dev/BiRefNet-MLX", 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("moonbox-dev/BiRefNet-MLX") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download 4bit/model.safetensors from moonbox-dev/BiRefNet-MLX: direct link, hf CLI and curl.
- Browser
- Download file 161 MB
-
https://huggingface.co/moonbox-dev/BiRefNet-MLX/resolve/main/4bit/model.safetensors
- Command line
-
hf download hf://moonbox-dev/BiRefNet-MLX/4bit/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/moonbox-dev/BiRefNet-MLX/resolve/main/4bit/model.safetensors
161 MB
- Xet hash:
- fdb4703539ac4780737c08fff59049ed4978c944d7609ff53df8b260522e9833
- Size of remote file:
- 161 MB
- SHA256:
- 2f9835d6d342417a503800283047794962507cb47483dd98af9799827d0e67f2
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