Transformer-based Image Generation: How to Generate Realistic Faces with AI

Generating realistic faces using transformer-based models involves using techniques like conditional generative models and leveraging pre-trained transformer architectures for image generation. In this example, we'll use the BigGAN model, which is a conditional GAN based on the transformer architecture, to generate realistic faces using the PyTorch library.

First, make sure you have the required libraries installed:

pip install torch torchvision pytorch-pretrained-biggan
import torch
from torchvision.utils import save_image
from pytorch_pretrained_biggan import BigGAN, one_hot_from_names, truncated_noise_sample

# Load the pre-trained BigGAN model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = BigGAN.from_pretrained('biggan-deep-512').to(device)
model.eval()

# Function to generate realistic faces
def generate_faces(class_names, num_samples=5):
    with torch.no_grad():
        # Prepare the class labels (e.g., 'african elephant', 'zebra', etc.)
        class_vector = one_hot_from_names(class_names, batch_size=num_samples)
        class_vector = torch.from_numpy(class_vector).to(device)

        # Generate random noise vectors
        noise_vector = truncated_noise_sample(truncation=0.4, batch_size=num_samples).to(device)

        # Generate the faces
        generated_images = model(noise_vector, class_vector, truncation=0.4)
    
    # Save the generated images
    for i, image in enumerate(generated_images):
        save_image(image, f'generated_face_{i}.png')

if __name__ == "__main__":
    class_names = ['person', 'woman', 'man', 'elderly']
    num_samples = 5

    generate_faces(class_names, num_samples)
In this example, we use the BigGAN model, which is pre-trained on the ImageNet dataset and capable of generating high-resolution images. We provide a list of class names (e.g., 'person', 'woman', 'man', 'elderly'), and the generate_faces function uses the BigGAN model to produce corresponding realistic faces.
Keep in mind that generating realistic faces with AI models is an area of active research and development. While the BigGAN model can produce impressive results, the generated images might not always be perfect or entirely indistinguishable from real faces. Additionally, the generated images might not represent actual individuals but rather realistic-looking fictional faces.
For even better results, you might consider using more sophisticated models or fine-tuning the existing models on specific datasets relevant to your use case. Generating realistic faces requires a large amount of data and computational resources, and the results may still vary based on the quality and quantity of the training data and the hyperparameters used during the generation process.