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Deep Learning For Vision

Deep Generative Models for Image Editing

Aim

To implement a deep generative model (e.g., StyleGAN or Variational Autoencoder) for image editing tasks such as style transfer or image inpainting, demonstrating how generative models can modify and create images.

Algorithm

1. Choose a Generative Model

  • Use models like StyleGAN, CycleGAN, or VAE depending on the task.

2. Data Preparation

  • Collect or use pre-trained models.

  • Prepare images for editing (resize, normalize).

3. Model Loading

  • Load a pre-trained model suitable for the task (for style transfer, CycleGAN is common).

4. Image Editing Process

  • For style transfer:

    • Input content image and style image.

    • Use the model to transfer style onto content image.

  • For inpainting:

    • Mask parts of an image.

    • Use the model to generate missing parts.

5. Generate Edited Image

  • Feed input (content+style or masked image) into the model.

  • Obtain the edited output.

Program (Example: Style Transfer with Pretrained CycleGAN)

python
import tensorflow as tf
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

# Load pretrained CycleGAN model for style transfer
# (use a compatible TensorFlow saved model or a library like tf-hub)
model = tf.keras.models.load_model('cycle_gan_style_transfer_model_path')

# Load content and style images
content_image = np.array(Image.open('content.jpg').resize((256, 256))) / 127.5 - 1
style_image = np.array(Image.open('style.jpg').resize((256, 256))) / 127.5 - 1

# Add batch dimension
content_input = np.expand_dims(content_image, axis=0)
style_input = np.expand_dims(style_image, axis=0)

# Generate stylized image
stylized = model.predict(content_input)

# Post-process image
stylized_img = ((stylized[0] + 1) * 127.5).astype(np.uint8)

# Show result
plt.imshow(stylized_img)
plt.axis('off')
plt.title('Style Transfer Result')
plt.show()

Output

  • An image where the style (color, texture) of style.jpg is transferred onto content.jpg.

  • Example: A photograph stylized with an artistic painting look.

Result

  • Demonstrates how deep generative models can be used for creative image editing.

  • Effective for artistic style transfer, face editing, inpainting, or domain adaptation.

  • The quality depends on the pretrained model and input data; high-quality models produce realistic, visually appealing edits.