Felguk-upscaler

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

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:

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")