Systematically master the core technologies of modern AI image generation from fundamentals
Series Overview
This series is a practical educational content consisting of 5 chapters that progressively teaches the theory and implementation of generative models from the basics.
Generative Models are deep learning models that learn the probability distribution of data and generate new data. These technologies, including learning latent space representations with Variational Autoencoders (VAE), adversarial learning with Generative Adversarial Networks (GAN), and gradual denoising processes with Diffusion Models, form the core of creative AI applications such as image generation, speech synthesis, and video generation. You will understand and be able to implement the foundational technologies behind text-to-image generation systems like DALL-E, Stable Diffusion, and Midjourney. We provide systematic knowledge from probabilistic generative model fundamentals to cutting-edge Diffusion Models.
Features:
- ✅ From Theory to Implementation: Systematic learning from probabilistic foundations to the latest Stable Diffusion
- ✅ Implementation-Focused: 35+ executable PyTorch code examples with practical techniques
- ✅ Intuitive Understanding: Understand operating principles through visualization of generation processes and latent space exploration
- ✅ Latest Technology Compliance: Implementation using Hugging Face Diffusers and Stable Diffusion
- ✅ Practical Applications: Application to practical tasks such as image generation, text-to-image, and speech synthesis
Total Study Time: 120-150 minutes (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (completely new to generative models):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Duration: 120-150 minutes
For Intermediate Learners (with autoencoder experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 90-110 minutes
For Specific Topic Enhancement:
- VAE Theory: Chapter 2 (focused study)
- GAN Implementation: Chapter 3 (focused study)
- Diffusion/Stable Diffusion: Chapter 4 (focused study)
- Duration: 25-30 minutes/chapter
Chapter Details
Chapter 1: Generative Model Fundamentals
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 7
Learning Content
- Discriminative Models vs Generative Models - P(y|x) vs P(x), differences in objectives and applications
- Probability Distribution Modeling - Likelihood maximization, KL divergence
- Latent Variable Models - Latent space, low-dimensional data representations
- Sampling Methods - Monte Carlo methods, MCMC, importance sampling
- Evaluation Metrics - Inception Score, FID, quantitative evaluation of generation quality
Learning Objectives
- ✅ Understand fundamental concepts of generative models
- ✅ Explain probability distribution modeling techniques
- ✅ Understand the role of latent variable models
- ✅ Implement sampling methods
- ✅ Quantitatively evaluate generation quality
Chapter 2: VAE (Variational Autoencoder)
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Autoencoder Review - Encoder-Decoder, reconstruction error
- Variational Inference Fundamentals - ELBO, variational lower bound, evidence lower bound
- Reparameterization Trick - Gradient propagation, making sampling differentiable
- KL Divergence - Regularization term, distribution similarity
- VAE Implementation and Visualization - PyTorch implementation, latent space exploration
Learning Objectives
- ✅ Understand the principles of variational inference
- ✅ Explain the derivation of ELBO
- ✅ Understand the necessity of the Reparameterization Trick
- ✅ Explain the role of KL divergence
- ✅ Implement VAE in PyTorch
Chapter 3: GAN (Generative Adversarial Network)
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- GAN Principles - Generator and Discriminator, adversarial learning
- Minimax Game - Nash equilibrium, objective function
- DCGAN - Convolutional GAN, stable training techniques
- StyleGAN - Style-based generation, AdaIN, high-quality image generation
- Training Stabilization - Mode collapse countermeasures, Spectral Normalization
Learning Objectives
- ✅ Understand GAN's adversarial learning
- ✅ Explain the roles of Generator and Discriminator
- ✅ Understand DCGAN design principles
- ✅ Explain StyleGAN mechanisms
- ✅ Implement GAN training stabilization techniques
Chapter 4: Diffusion Models
Difficulty: Advanced
Reading Time: 30-35 minutes
Code Examples: 7
Learning Content
- Diffusion Process Fundamentals - Forward process, Reverse process
- DDPM (Denoising Diffusion Probabilistic Models) - Noise removal, iterative generation
- Score-based Models - Score function, Langevin Dynamics
- Stable Diffusion - Latent Diffusion, Text-to-Image
- Fast Sampling - DDIM, Classifier-free Guidance
Learning Objectives
- ✅ Understand the principles of Diffusion Process
- ✅ Explain DDPM training and generation methods
- ✅ Understand Score-based Models concepts
- ✅ Explain Stable Diffusion mechanisms
- ✅ Generate images using the Diffusers library
Chapter 5: Generative Model Applications
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 5
Learning Content
- High-Quality Image Generation - DALL-E 2, Midjourney, Imagen
- Text-to-Image Generation - CLIP guidance, prompt engineering
- Image Editing - Inpainting, Style Transfer, Image-to-Image
- Speech Synthesis - WaveGAN, Diffusion-based TTS
- Video and 3D Generation - Gen-2, NeRF, DreamFusion
Learning Objectives
- ✅ Understand latest image generation systems
- ✅ Explain Text-to-Image mechanisms
- ✅ Implement image editing techniques
- ✅ Understand applications to speech synthesis
- ✅ Grasp latest trends in video and 3D generation
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain theoretical foundations of generative models
- ✅ Understand mechanisms of VAE, GAN, and Diffusion Models
- ✅ Explain strengths, weaknesses, and selection criteria for each model
- ✅ Understand the significance of latent space representations
- ✅ Explain operating principles of latest generative AI systems
Practical Skills (Doing)
- ✅ Implement VAE, GAN, and Diffusion models in PyTorch
- ✅ Generate high-quality images with Stable Diffusion
- ✅ Implement Text-to-Image generation
- ✅ Quantitatively evaluate generation quality
- ✅ Design effective prompts
Application Ability (Applying)
- ✅ Select appropriate generative models according to tasks
- ✅ Apply generative models to practical work
- ✅ Build image generation and editing systems
- ✅ Understand and utilize latest generative AI technologies
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Fundamentals: Variables, functions, classes, loops, conditional statements
- ✅ NumPy Fundamentals: Array operations, broadcasting, basic mathematical functions
- ✅ Deep Learning Fundamentals: Neural networks, backpropagation, gradient descent
- ✅ PyTorch Fundamentals: Tensor operations, nn.Module, Dataset and DataLoader
- ✅ Probability and Statistics Fundamentals: Probability distributions, expectation, variance, normal distribution
- ✅ CNN Fundamentals: Convolutional layers, pooling layers, image processing
Recommended (Nice to Have)
- 💡 Autoencoders: Encoder-Decoder, latent representations
- 💡 Variational Inference: ELBO, KL divergence
- 💡 Optimization Algorithms: Adam, learning rate scheduling
- 💡 Transformer Fundamentals: Attention mechanism (for understanding Text-to-Image)
- 💡 GPU Environment: Basic understanding of CUDA
Recommended Prior Learning:
Technologies and Tools
Main Libraries
- PyTorch 2.0+ - Deep learning framework
- torchvision 0.15+ - Image processing and datasets
- diffusers 0.20+ - Hugging Face Diffusers library
- transformers 4.30+ - CLIP, text encoders
- NumPy 1.24+ - Numerical computation
- Matplotlib 3.7+ - Visualization
- Pillow 10.0+ - Image processing
- scipy 1.11+ - Scientific computing, evaluation metrics
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - GPU environment (available for free)
- CUDA 11.8+ / cuDNN - GPU acceleration (recommended)
Datasets
- MNIST - Handwritten digit dataset
- CelebA - Face image dataset
- ImageNet - Large-scale image dataset
- COCO - Images and captions (Text-to-Image)
Let's Get Started!
Are you ready? Start with Chapter 1 and master generative model technologies!
Chapter 1: Generative Model Fundamentals →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Deep Dive Learning
- 📚 ControlNet: Conditional image generation, spatial control
- 📚 LoRA and DreamBooth: Model customization, fine-tuning
- 📚 3D Generation: NeRF, 3D Gaussian Splatting, DreamFusion
- 📚 Video Generation: Gen-2, Pika, Sora
Related Series
- 🎯 - Image recognition, object detection
- 🎯 - CLIP, DALL-E, Vision-Language Models
- 🎯 - Practical generative AI applications
Practical Projects
- 🚀 Avatar Generation System - Face generation with StyleGAN
- 🚀 Text-to-Image App - Image generation using Stable Diffusion
- 🚀 Image Editing Tool - Inpainting, Style Transfer
- 🚀 AI Art Generator - Prompt-based creative support
Update History
- 2025-10-21: v1.0 Initial release
Your generative model learning journey begins here!