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@@ -4,4 +4,50 @@ library_name: transformers.js
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  https://huggingface.co/caidas/swin2SR-classical-sr-x2-64 with ONNX weights to be compatible with Transformers.js.
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  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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
 
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  https://huggingface.co/caidas/swin2SR-classical-sr-x2-64 with ONNX weights to be compatible with Transformers.js.
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+ ## Usage (Transformers.js)
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+
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+ If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
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+ ```bash
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+ npm i @xenova/transformers
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+ ```
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+
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+ **Example:** Upscale an image with `Xenova/swin2SR-classical-sr-x2-64`.
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+ ```js
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+ import { pipeline } from '@xenova/transformers';
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+
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+ // Create image-to-image pipeline
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+ const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64', {
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+ // quantized: false, // Uncomment this line to use the quantized version
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+ });
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+
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+ // Upscale an image
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+ const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
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+ const output = await upscaler(url);
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+ // RawImage {
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+ // data: Uint8Array(786432) [ ... ],
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+ // width: 512,
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+ // height: 512,
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+ // channels: 3
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+ // }
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+
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+ // (Optional) Save the upscaled image
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+ output.save('upscaled.png');
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+ ```
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+
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+ <details>
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+ <summary>See example output</summary>
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+
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+ Input image:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/eqLyvsErNQvXAFDD2MylF.png)
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+
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+
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+ Output image:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/-SpyZeojGA9LIKkO-_cyY.png)
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+
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+ </details>
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+
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+ ---
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+
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  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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).