README.md

# Felguk-upscaler-all

Felguk-upscaler-all is a powerful image upscaling tool built on top of the Hugging Face Transformers library. It leverages state-of-the-art models to enhance the resolution and quality of images, making it ideal for various applications such as photo editing, medical imaging, and more.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)

## Installation

To get started with Felguk-upscaler-all, you need to have Python 3.6 or higher installed on your system. You can install the required dependencies using pip:

```bash
pip install transformers
pip install torch  # Ensure you have PyTorch installed
```
## Usage
To use this model in transformers
**Example**
```bash
from transformers import AutoModelForImageSuperResolution, AutoFeatureExtractor
from PIL import Image
import requests

# Load the feature extractor and model
feature_extractor = AutoFeatureExtractor.from_pretrained("Felguk/upscaler-all")
model = AutoModelForImageSuperResolution.from_pretrained("Felguk/upscaler-all")

# Load an image from URL
url = "http://example.com/sample-image.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# Preprocess the image and run inference
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)

# Post-process the output to get the upscaled image
upscaled_image = feature_extractor.post_process(outputs, size=(256, 256))[0]
upscaled_image.show()
```