Use-case: Image generation, super-resolution

Generative Adversarial Networks (GANs) have revolutionized image generation and super-resolution by enabling models to create high-quality, realistic images from random noise or low-resolution inputs.

In image generation, GANs learn to produce entirely new images that mimic the training data distribution, making them useful for creative tasks like art, gaming, and virtual environments.

In super-resolution, GANs enhance low-resolution images by generating detailed high-resolution versions, improving clarity and sharpness beyond traditional interpolation methods.

Example: Using a GAN-based model for image Super-Resolution Code ESRGAN (Using torch.hub)

import torch
from torchvision.transforms import ToTensor, ToPILImage
from PIL import Image

# Load the pre-trained ESRGAN model from torch.hub
model = torch.hub.load('xinntao/ESRGAN', 'esrgan', pretrained=True)
model.eval()  # Set model to evaluation mode

# Load low-resolution image
lr_image = Image.open("low_res_image.jpg").convert("RGB")

# Transform image to tensor and add batch dimension
lr_tensor = ToTensor()(lr_image).unsqueeze(0)

# Run the model to get high-resolution output
with torch.no_grad():
    sr_tensor = model(lr_tensor)

# Remove batch dimension and convert tensor back to image
sr_image = ToPILImage()(sr_tensor.squeeze(0).clamp(0, 1))

# Save super-resolved image
sr_image.save("super_res_image.jpg")

print("Super-resolution completed. Image saved as super_res_image.jpg")