https://huggingface.co./hustvl/vitmatte-base-composition-1k with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @xenova/transformers
Example: Perform image matting with a VitMatteForImageMatting
model.
import { AutoProcessor, VitMatteForImageMatting, RawImage } from '@xenova/transformers';
// Load processor and model
const processor = await AutoProcessor.from_pretrained('Xenova/vitmatte-base-composition-1k');
const model = await VitMatteForImageMatting.from_pretrained('Xenova/vitmatte-base-composition-1k', { quantized: false });
// Load image and trimap
const image = await RawImage.fromURL('https://huggingface.co./datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_image.png');
const trimap = await RawImage.fromURL('https://huggingface.co./datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_trimap.png');
// Prepare image + trimap for the model
const inputs = await processor(image, trimap);
// Predict alpha matte
const { alphas } = await model(inputs);
// Tensor {
// dims: [ 1, 1, 640, 960 ],
// type: 'float32',
// size: 614400,
// data: Float32Array(614400) [ 0.997240424156189, 0.9971460103988647, ... ]
// }
You can visualize the alpha matte as follows:
import { Tensor, cat } from '@xenova/transformers';
// Visualize predicted alpha matte
const imageTensor = new Tensor(
'uint8',
new Uint8Array(image.data),
[image.height, image.width, image.channels]
).transpose(2, 0, 1);
// Convert float (0-1) alpha matte to uint8 (0-255)
const alphaChannel = alphas
.squeeze(0)
.mul_(255)
.clamp_(0, 255)
.round_()
.to('uint8');
// Concatenate original image with predicted alpha
const imageData = cat([imageTensor, alphaChannel], 0);
// Save output image
const outputImage = RawImage.fromTensor(imageData);
outputImage.save('output.png');
Example inputs:
Example output:
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx
).
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