Types of Generative AI Models

The Generative Model Landscape

There are several fundamentally different approaches to building generative AI systems. Each has unique strengths, weaknesses, and ideal use cases.

📊 Overview of Model Types

Model Type	Best For	Examples	Difficulty
Transformers	Text, Code	GPT-4, BERT, T5	⭐⭐⭐
GANs	Images, Art	StyleGAN, ProGAN	⭐⭐⭐⭐
VAEs	Smooth latent space	β-VAE, CVAE	⭐⭐⭐
Diffusion Models	High-quality images	Stable Diffusion, DALL-E 2	⭐⭐⭐⭐
Autoregressive	Sequential data	PixelCNN, WaveNet	⭐⭐⭐
Flow-based	Exact likelihood	Glow, RealNVP	⭐⭐⭐⭐

1️⃣ Transformers

🎯 Best for: Text, Code, Sequential Data

Core Idea: Use attention mechanisms to process sequences and understand relationships between all parts of the input.

How They Work

Transformers process all tokens in parallel using self-attention to understand context:

# Simplified Transformer concept
class Transformer:
    def forward(self, input_tokens):
        # 1. Convert tokens to embeddings
        embeddings = self.embed(input_tokens)
        
        # 2. Add positional information
        embeddings += positional_encoding
        
        # 3. Self-attention: every word attends to every other word
        for layer in self.layers:
            # Q, K, V matrices
            Q = layer.query(embeddings)
            K = layer.key(embeddings)
            V = layer.value(embeddings)
            
            # Attention scores
            attention = softmax(Q @ K.T / sqrt(d_k))
            output = attention @ V
            
            # Feed-forward
            output = layer.ffn(output)
        
        return output

Key Features

✅ Excellent for long-range dependencies
✅ Highly parallelizable (fast training)
✅ State-of-the-art for language tasks
✅ Transfer learning friendly
❌ Quadratic memory complexity
❌ Requires massive compute

Famous Models

GPT-4

OpenAI's latest, multimodal

~1.76 trillion parameters

Claude

Anthropic's constitutional AI

200B parameters

LLaMA 2

Meta's open-source LLM

7B - 70B parameters

BERT

Bidirectional encoder

110M - 340M parameters

2️⃣ GANs (Generative Adversarial Networks)

🎯 Best for: Photorealistic Images, Art, Face Generation

Core Idea: Two neural networks compete - a Generator creates fake data, a Discriminator tries to detect fakes. They improve together.

How They Work

# GAN Training Loop
class GAN:
    def __init__(self):
        self.generator = Generator()
        self.discriminator = Discriminator()
    
    def train_step(self, real_images):
        # 1. Train Discriminator
        # Generate fake images
        noise = torch.randn(batch_size, latent_dim)
        fake_images = self.generator(noise)
        
        # Discriminator tries to tell real from fake
        real_pred = self.discriminator(real_images)
        fake_pred = self.discriminator(fake_images.detach())
        
        d_loss = -torch.mean(torch.log(real_pred) + torch.log(1 - fake_pred))
        d_loss.backward()
        
        # 2. Train Generator
        # Try to fool discriminator
        fake_images = self.generator(noise)
        fake_pred = self.discriminator(fake_images)
        
        g_loss = -torch.mean(torch.log(fake_pred))
        g_loss.backward()

# Generator: Random noise → Realistic image
# Discriminator: Image → Real or Fake?

Key Features

✅ Can generate very realistic images
✅ Good for high-resolution outputs
✅ No explicit likelihood needed
❌ Training instability (mode collapse)
❌ Difficult hyperparameter tuning
❌ Hard to evaluate quality

GAN Variants

DCGAN

Deep Convolutional GAN - first stable architecture

StyleGAN

Control different levels of image style - SOTA for faces

CycleGAN

Image-to-image translation without paired data

BigGAN

Large-scale GAN for high-resolution diverse images

3️⃣ VAEs (Variational Autoencoders)

🎯 Best for: Latent Space Exploration, Interpolation

Core Idea: Encode data into a compressed latent space, then decode back. Learn smooth, continuous representations.

How They Work

# VAE Architecture
class VAE(nn.Module):
    def __init__(self):
        self.encoder = Encoder()  # Image → μ, σ
        self.decoder = Decoder()  # z → Image
    
    def forward(self, x):
        # 1. Encode to latent distribution
        mu, log_var = self.encoder(x)
        
        # 2. Reparameterization trick
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        z = mu + eps * std  # Sample from N(μ, σ²)
        
        # 3. Decode back to image
        reconstruction = self.decoder(z)
        
        # Loss: reconstruction + KL divergence
        recon_loss = F.mse_loss(reconstruction, x)
        kl_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
        
        return reconstruction, recon_loss + kl_loss

# To generate new images:
z = torch.randn(batch_size, latent_dim)  # Random point in latent space
new_image = vae.decoder(z)

Key Features

✅ Smooth, continuous latent space
✅ Stable training (easier than GANs)
✅ Principled probabilistic framework
✅ Good for interpolation
❌ Often generates blurry images
❌ Posterior collapse issues

Use Cases

🎨 Image generation with smooth transitions
🔄 Data compression
🧬 Molecule design (drug discovery)
🎭 Face attribute manipulation

4️⃣ Diffusion Models

🎯 Best for: High-Quality Images, Text-to-Image

Core Idea: Gradually add noise to data, then learn to reverse the process. Generate by starting with noise and denoising.

How They Work

# Diffusion Process
class DiffusionModel:
    def forward_process(self, x0, t):
        """Add noise gradually: x0 → x1 → x2 → ... → xT (pure noise)"""
        noise = torch.randn_like(x0)
        alpha_t = self.noise_schedule[t]
        
        # Add noise according to schedule
        xt = torch.sqrt(alpha_t) * x0 + torch.sqrt(1 - alpha_t) * noise
        return xt, noise
    
    def reverse_process(self, xt, t, text_embedding):
        """Denoise step: predict and remove noise"""
        # U-Net predicts noise
        predicted_noise = self.unet(xt, t, text_embedding)
        
        # Remove predicted noise
        alpha_t = self.noise_schedule[t]
        x_prev = (xt - sqrt(1 - alpha_t) * predicted_noise) / sqrt(alpha_t)
        
        return x_prev
    
    def generate(self, text_prompt):
        """Generate image from text"""
        # Start with pure noise
        x = torch.randn(1, 3, 512, 512)
        text_emb = self.clip_encoder(text_prompt)
        
        # Denoise iteratively (50-1000 steps)
        for t in reversed(range(self.num_steps)):
            x = self.reverse_process(x, t, text_emb)
        
        return x  # Clean image!

Key Features

✅ State-of-the-art image quality
✅ Stable training
✅ Excellent text-to-image capabilities
✅ Good diversity
❌ Slow generation (many steps)
❌ High computational cost

Famous Models

Stable Diffusion

Open-source, runs locally

DALL-E 2

OpenAI's text-to-image

Midjourney

Artistic, stylized images

Imagen

Google's photorealistic model

5️⃣ Autoregressive Models

🎯 Best for: Sequential Generation, Audio, High-fidelity

Core Idea: Generate one element at a time, conditioning on all previous elements.

How They Work

# Autoregressive Generation
def generate_autoregressive(model, seq_length):
    """Generate pixel by pixel (or token by token)"""
    image = torch.zeros(1, 3, 256, 256)
    
    # Generate one pixel at a time
    for i in range(256):
        for j in range(256):
            # Condition on all previous pixels
            prev_pixels = image[:, :, :i, :j]
            
            # Predict current pixel
            pixel_probs = model(prev_pixels)
            pixel = sample(pixel_probs)
            
            image[:, :, i, j] = pixel
    
    return image

# P(image) = P(pixel_1) * P(pixel_2 | pixel_1) * P(pixel_3 | pixel_1, pixel_2) * ...

Key Features

✅ Exact likelihood computation
✅ High-quality, detailed outputs
✅ Works well for audio (WaveNet)
❌ Very slow generation (sequential)
❌ Can't be parallelized

Examples

PixelCNN: Image generation pixel-by-pixel
WaveNet: Audio generation sample-by-sample
VideoGPT: Video generation frame-by-frame
PixelSNAIL: Improved PixelCNN with self-attention

6️⃣ Flow-based Models

🎯 Best for: Exact Likelihood, Invertible Transformations

Core Idea: Learn invertible transformations between simple and complex distributions.

How They Work

# Flow-based Model
class NormalizingFlow:
    def forward(self, x):
        """Data → Latent (exact)"""
        z = x
        log_det = 0
        
        for flow_layer in self.layers:
            z, layer_log_det = flow_layer(z)
            log_det += layer_log_det
        
        return z, log_det
    
    def inverse(self, z):
        """Latent → Data (exact)"""
        x = z
        
        for flow_layer in reversed(self.layers):
            x = flow_layer.inverse(x)
        
        return x
    
    def generate(self):
        """Sample from simple distribution, transform to complex"""
        z = torch.randn(batch_size, latent_dim)  # Simple Gaussian
        x = self.inverse(z)  # Complex data distribution
        return x

Key Features

✅ Exact likelihood computation
✅ Exact inference (both directions)
✅ Stable training
❌ Architecture constraints (must be invertible)
❌ Can be computationally expensive

Popular Models

Glow: Generative flow for high-resolution images
RealNVP: Real-valued non-volume preserving transformations
NICE: Non-linear independent components estimation

🔄 Comparison Matrix

Feature	Transformers	GANs	VAEs	Diffusion
Quality	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐
Training Stability	⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Generation Speed	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐
Diversity	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Controllability	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐

🎯 Which Model Should You Choose?

Decision Guide

For Text Generation:

→ Transformers (GPT, T5, LLaMA)

Best quality, most flexible, industry standard

For Image Generation:

→ Diffusion Models if quality is priority

→ GANs if speed is critical

→ VAEs if you need smooth interpolation

For Audio/Music:

→ Autoregressive (WaveNet) for quality

→ Diffusion for music composition

For Research/Custom Applications:

→ Flow-based if you need exact likelihoods

→ VAEs for interpretable latent spaces

🚀 Emerging Hybrid Approaches

                Modern systems often combine multiple approaches:
                DALL-E 2: Diffusion + CLIP (Transformer)
Stable Diffusion: Diffusion + VAE latent space
VQ-GAN: GAN + Transformer
Parti: Pure Transformer for images

            

🎯 Key Takeaways

Transformers dominate text generation - GPT-4, Claude, etc.
Diffusion Models are current SOTA for images - Stable Diffusion, DALL-E 2
GANs still useful for fast generation and specific domains
VAEs excel at latent space manipulation and interpolation
Choose based on your use case: quality vs speed vs controllability
Hybrid approaches combining multiple techniques are increasingly common