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

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:

pip install transformers

Usage by transformers

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")