Instructions to use naddy24/birefnet-512-webgpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use naddy24/birefnet-512-webgpu with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("naddy24/birefnet-512-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("naddy24/birefnet-512-webgpu") - Notebooks
- Google Colab
- Kaggle
BiRefNet 512x512, patched to run on ONNX Runtime WebGPU
Identical outputs to onnx-community/BiRefNet_512x512-ONNX (verified bit-exact). Five ScatterND nodes in /bb/layers.3_1/ received zero-sized updates tensors (the 12x12 map divides exactly by the swin window at 512 input), which ORT-web's WebGPU backend refuses to run. Since a scatter of nothing is a no-op with static shapes, those nodes are replaced with Identity and 530 dead nodes pruned. fp16 only.
- Downloads last month
- 113
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for naddy24/birefnet-512-webgpu
Base model
ZhengPeng7/BiRefNet_512x512 Quantized
onnx-community/BiRefNet_512x512-ONNX