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---
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
## 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") |