The Art of Transformation
Imagine being able to breathe life into a simple sketch, transforming rough pencil lines into a vivid, realistic portrait.
Artists, forensic investigators, and digital creators all share a common challenge—bridging the gap between sketched concepts and photorealistic renderings. With CycleGAN and a custom UNet-based architecture, we bring AI-powered transformation to face sketch conversion.
The Science Behind the Art
Traditional GANs (Generative Adversarial Networks) often struggle with cycle consistency when learning mappings between two domains. CycleGAN, however, enables bidirectional mapping between face sketches and realistic portraits without needing perfectly paired training images.
To enhance performance, we implemented:
- Custom-Modified UNet: Improving encoder-decoder pathways for sharper details.
- Local Self-Attention: Enhancing texture details in fine regions like eyes & hair.
- Buffered Past Images: Reducing mode collapse and improving training stability.
How It Works
At its core, CycleGAN consists of two generators and two discriminators:
- Generator A → B: Converts sketches to portraits.
- Generator B → A: Converts portraits back to sketches.
- Cycle Consistency Loss: Ensures that a converted portrait can be reconstructed back into its original sketch.
Step 1: Data Preparation
We prepare unpaired datasets containing sketches and real faces.
from dataset_loader import load_data
# Load sketches and portrait datasets
sketches, portraits = load_data("data/sketches", "data/portraits")
# Visualize samples
show_images(sketches[:5], title="Sample Sketches")
show_images(portraits[:5], title="Sample Portraits")Step 2: Training the CycleGAN
Our custom UNet architecture enhances the generator’s performance.
from cyclegan import CycleGAN
# Initialize CycleGAN model with custom UNet
model = CycleGAN(input_shape=(256, 256, 3), use_custom_unet=True)
# Train model
model.train(sketches, portraits, epochs=100, batch_size=16)By incorporating local self-attention layers, our generator learns to focus on critical face regions, improving texture quality and fine-grained details.
Step 3: Generating Realistic Portraits
After training, we convert a new sketch into a realistic face.
from cyclegan import generate_image
# Load trained model
model.load_weights("cyclegan_weights.h5")
# Convert sketch to portrait
portrait = generate_image(model, input_sketch="test_sketch.png")
# Show result
display_image(portrait, title="Generated Portrait")The result? A lifelike face, retaining the unique structure of the original sketch.