Week 1: Advanced GAN Architectures
Goal: Understand progressive training and high-resolution generation
Study:
- Progressive GAN paper and architecture
- Multi-scale training concepts
- Gradient penalty vs weight clipping
Implementation:
- Code Progressive GAN from scratch
- Train on CelebA dataset (64x64 → 256x256)
Resources:
- “Progressive Growing of GANs” paper
- CelebA dataset
Week 2: StyleGAN Fundamentals
Goal: Master style-based generation and latent space control
Study:
- StyleGAN architecture and mapping network
- AdaIN (Adaptive Instance Normalization)
- Style mixing and truncation tricks
Implementation:
- Implement StyleGAN generator
- Experiment with style mixing
- Create latent space interpolations
Resources:
- “Analyzing and Improving StyleGAN” paper
- Pre-trained StyleGAN weights for comparison
Week 3: Conditional Generation & Control
Goal: Advanced conditioning techniques beyond basic CGAN
Study:
- Class-conditional GANs (BigGAN concepts)
- Feature matching loss
- Spectral normalization
Implementation:
- Enhance your CGAN with spectral normalization
- Add feature matching loss
- Train on CIFAR-10 with class conditioning
Resources:
- “Large Scale GAN Training” (BigGAN) paper
- CIFAR-10 dataset
Week 4: Image-to-Image Translation
Goal: Learn paired and unpaired translation
Study:
- Pix2Pix architecture and L1 loss
- CycleGAN and cycle consistency
- Least squares GAN loss
Implementation:
- Build Pix2Pix for edges→photos
- Implement CycleGAN for style transfer
- Compare different loss functions
Resources:
- “Image-to-Image Translation” (Pix2Pix) paper
- “Unpaired Image Translation” (CycleGAN) paper
- Facades/Maps datasets
Week 5: Diffusion Models Introduction
Goal: Understand diffusion process and denoising
Study:
- Forward/reverse diffusion process
- Denoising diffusion probabilistic models (DDPM)
- Noise scheduling and sampling
Implementation:
- Code basic DDPM from scratch
- Train on MNIST/CIFAR-10
- Implement different noise schedules
Resources:
- “Denoising Diffusion Probabilistic Models” paper
- DDPM GitHub implementations for reference
Week 6: Advanced Diffusion Techniques
Goal: Faster sampling and conditioning
Study:
- DDIM (deterministic sampling)
- Classifier guidance
- Classifier-free guidance
Implementation:
- Add DDIM sampling to your DDPM
- Implement conditional diffusion with guidance
- Compare sampling quality vs speed
Resources:
- “DDIM” and “Classifier-Free Guidance” papers
Week 7: Text-to-Image Generation
Goal: Multimodal generation basics
Study:
- CLIP embeddings for conditioning
- Cross-attention mechanisms
- Stable Diffusion architecture overview
Implementation:
- Use CLIP embeddings to condition your diffusion model
- Build simple text-to-image pipeline
- Experiment with different text encoders
Resources:
- CLIP paper and pre-trained models
- Hugging Face diffusers library for reference
Week 8: Model Optimization & Deployment
Goal: Make models practical and efficient
Study:
- Model distillation for faster generation
- Quantization techniques
- Inference optimization
Implementation:
- Optimize your best model for speed
- Create web demo or API
- Document your implementations
Project: Create portfolio showcasing all 8 weeks of work
Weekly Structure
- Monday-Tuesday: Study papers and theory
- Wednesday-Friday: Implementation and coding
- Saturday: Experimentation and hyperparameter tuning
- Sunday: Documentation and next week prep
Success Metrics
- Working implementation each week
- Visual quality improvements over time
- Understanding of trade-offs between different approaches
- Final portfolio with 4-5 different generation methods