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---
base_model: caidas/swin2SR-classical-sr-x2-64
library_name: transformers.js
---
https://huggingface.co/caidas/swin2SR-classical-sr-x2-64 with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
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/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Upscale an image with `Xenova/swin2SR-classical-sr-x2-64`.
```js
import { pipeline } from '@huggingface/transformers';
// Create image-to-image pipeline
const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64', {
dtype: 'fp32', // Options: 'fp32', 'fp16', 'q8', 'q4'
});
// Upscale an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
const output = await upscaler(url);
// RawImage {
// data: Uint8Array(786432) [ ... ],
// width: 512,
// height: 512,
// channels: 3
// }
// (Optional) Save the upscaled image
output.save('upscaled.png');
```
<details>
<summary>See example output</summary>
Input image:
Output image:
</details>
---
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`). |