You can deploy this model and start earning money today!

runes/
birefnet-lite-webgpu

Name: birefnet-lite-webgpu
Availability: InStock

onnx

background-removal

matting

dichotomous-image-segmentation

webgpu

onnxruntime-web

image-segmentation

ZhengPeng7/BiRefNet_lite

quantized

mit

Views

No views yet

BiRefNet_lite — WebGPU-ready ONNX (fp16)

BiRefNet (lite / Swin-Tiny variant), rebuilt so it actually runs on onnxruntime-web's WebGPU EP in the browser. The stock ONNX exports (e.g. onnx-community/BiRefNet_lite-ONNX) fail there three ways (see microsoft/onnxruntime#21968):

The decoder contains Concat nodes with up to 1024 inputs and Split nodes with 32 outputs, exceeding WebGPU's storage-buffers-per-shader limit.
The variadic Sum op has no WebGPU kernel; its CPU fallback sits exactly where the unrolled deformable convolutions materialize ~784 MB tensors, which walks the 4 GB wasm heap into std::bad_alloc.
The fp16 export defeats onnxruntime's in-browser constant folding (no CPU fp16 kernels), leaving hundreds of CPU-only ConstantOfShape islands.

This artifact is the same weights after offline graph surgery:

ONNX Runtime BASIC-level offline optimization (constant fold: ~16k → ~4k nodes, eliminates every CPU-only shape op);
wide Concat/Split fanouts rewritten into cascades capped at 6, so any shader needs at most 7 storage buffers (safe even at the WebGPU minimum limit of 8);
variadic Sum rewritten into Add chains;
float16 conversion with keep_io_types — graph I/O stays float32, so you feed and read plain Float32Arrays.

Verified bit-exact against the source graph at fp32; after fp16, max sigmoid-space deviation is ~2e-3. Measured ~700 ms/frame warm at 1024×1024 on an RTX 4090 (Chrome, onnxruntime-web 1.27, ort.webgpu.min.mjs — use the JSEP build, not the .bundle native-EP build).

I/O

	name	shape	dtype
input	`input_image`	`[1, 3, 1024, 1024]`	float32, RGB, ImageNet mean/std, squish-resized
output	`output_image`	`[1, 1, 1024, 1024]`	float32 logits — apply sigmoid

Usage (onnxruntime-web)

const ort = (await import('https://cdn.jsdelivr.net/npm/onnxruntime-web@1.27.0/dist/ort.webgpu.min.mjs')).default;
ort.env.wasm.wasmPaths = 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.27.0/dist/';
const sess = await ort.InferenceSession.create(modelArrayBuffer, { executionProviders: ['webgpu'] });
const out = await sess.run({ input_image: new ort.Tensor('float32', chw, [1, 3, 1024, 1024]) });
// sigmoid(out.output_image.data[i]) = foreground alpha

Rebuilt by studio/tools/export-birefnet.py from the Lowkey Studio project; the same script converts the other BiRefNet variants (general / HR / matting / dynamic).

Credits: ZhengPeng7/BiRefNet (MIT) for the model; onnx-community for the source ONNX export.

runes/birefnet-lite-webgpu

BiRefNet_lite — WebGPU-ready ONNX (fp16)

I/O

Usage (onnxruntime-web)

runes/
birefnet-lite-webgpu