Image Segmentation
Transformers.js
ONNX
BiRefNet
swin
background-removal
dichotomous-image-segmentation
webgpu
onnxruntime-web
Instructions to use CardAnvilModels/BiRefNet-lite-1024-WebGPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use CardAnvilModels/BiRefNet-lite-1024-WebGPU with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'CardAnvilModels/BiRefNet-lite-1024-WebGPU'); - BiRefNet
How to use CardAnvilModels/BiRefNet-lite-1024-WebGPU with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("CardAnvilModels/BiRefNet-lite-1024-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("CardAnvilModels/BiRefNet-lite-1024-WebGPU") - Notebooks
- Google Colab
- Kaggle
File size: 1,977 Bytes
6c2cf70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ---
license: mit
library_name: transformers.js
base_model: ZhengPeng7/BiRefNet_lite
pipeline_tag: image-segmentation
tags:
- onnx
- birefnet
- background-removal
- dichotomous-image-segmentation
- webgpu
- transformers.js
- onnxruntime-web
---
# BiRefNet-lite 1024 (ONNX, WebGPU-runnable)
BiRefNet-lite at 1024 × 1024 as an fp16 ONNX export whose graph is patched to run on WebGPU in the browser; the weights are unchanged. [Card Anvil](https://github.com/Card-Anvil) uses it with [Transformers.js](https://github.com/huggingface/transformers.js) to select the main subject of card art.
These are unchanged copies of the files in [`jiabins0303/birefnet-lite-1024-webgpu`](https://huggingface.co/jiabins0303/birefnet-lite-1024-webgpu) at commit [`1ad01ce`](https://huggingface.co/jiabins0303/birefnet-lite-1024-webgpu/tree/1ad01cef0f4101a285c5a3e0bd7f15597d93403f), at the same paths, kept here so the app loads files that can't change under it.
| Files | What they are | Loaded by Card Anvil |
| --- | --- | --- |
| `config.json`, `preprocessor_config.json`, `onnx/model_fp16.onnx` | The model | Yes |
| `scripts/patch_split.py`, `scripts/patch_deform.py`, `scripts/verify_patch.py` | The scripts that rewrote the graph to run on WebGPU, and the check that the result matches the original | No, kept for later |
| `SOURCE_README.md` | The source repo's README: how and why the graph was patched, and how it was verified | No |
## Credits and licence
- Model: [BiRefNet](https://github.com/ZhengPeng7/BiRefNet) by Peng Zheng et al. ([`ZhengPeng7/BiRefNet_lite`](https://huggingface.co/ZhengPeng7/BiRefNet_lite)), from *Bilateral Reference for High-Resolution Dichotomous Image Segmentation* (CAAI AIR 2024).
- WebGPU-runnable export: [`jiabins0303/birefnet-lite-1024-webgpu`](https://huggingface.co/jiabins0303/birefnet-lite-1024-webgpu).
Licensed under the MIT License, as the original model is. See [`LICENSE`](LICENSE).
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