---
license: apache-2.0
language:
- ru
- en
- zh
- as
- pl
- ii
- es
metrics:
- fastai
- code_eval
pipeline_tag: zero-shot-classification
library_name: transformers
---
# FELGUK Upscaler Model

![License](https://img.shields.io/badge/License-MIT-blue.svg)

## Overview

The **FELGUK Upscaler** is a state-of-the-art image upscaling model designed to enhance the resolution of images while preserving and even improving their quality. This model is particularly useful for tasks such as super-resolution, image restoration, and enhancing low-resolution images.

This repository contains the pre-trained weights and code to use the FELGUK Upscaler model via the Hugging Face `transformers` library.

## Model Details

| Attribute         | Description                                                                 |
|-------------------|-----------------------------------------------------------------------------|
| **Model Name**    | FELGUK Upscaler                                                             |
| **Architecture**  | Based on advanced deep learning techniques for image super-resolution.      |
| **Input**         | Low-resolution images (e.g., 128x128, 256x256)                              |
| **Output**        | High-resolution images (e.g., 512x512, 1024x1024)                           |
| **Training Data** | Trained on a diverse dataset of high-quality images.                        |
| **License**       | MIT                                                                         |

## Results

### Original Image
<img src="http://post-images.org/photo-page.php?photo=0wYasIUi" alt="Original Image">

### Resulting Image
<img src="http://post-images.org/photo-page.php?photo=piNJm97R" alt="Resulting Image">

## Installation

To use the FELGUK Upscaler model, you need to install the `transformers` library from Hugging Face. You can do this using pip:

```bash
pip install transformers
```
## Usage by transformers
```bash
from transformers import FelgukUpscaler, FelgukUpscalerProcessor
from PIL import Image
import requests

# Load the model and processor
model = FelgukUpscaler.from_pretrained("your-username/felguk-upscaler")
processor = FelgukUpscalerProcessor.from_pretrained("your-username/felguk-upscaler")

# Load an image from a URL or local file
url = "https://example.com/path/to/your/low-res-image.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# Preprocess the image
inputs = processor(image, return_tensors="pt")

# Upscale the image
with torch.no_grad():
    outputs = model(**inputs)

# Get the upscaled image
upscaled_image = outputs.upscaled_image

# Save or display the upscaled image
upscaled_image.save("upscaled_image.jpg")