Overview of Key Generative AI Models
1. Overview of Key Generative AI Models
GAN – Generative Adversarial Network
- Structure: Two competing networks — a Generator and a Discriminator.
- Mechanism:
- Generator: Tries to create fake (but convincing) samples.
- Discriminator: Tries to distinguish real from fake samples.
- They train in a loop: each improving as they try to outwit each other.
- Use Case: Primarily image and video generation. Great for creating realistic photos, artistic styles, or synthetic data.
VAE – Variational Autoencoder
- Structure: Encoder–Decoder architecture.
- Encoder: Compresses data into a latent, abstract representation.
- Decoder: Reconstructs the input or generates variations from that latent space.
- Key Feature: Generates data probabilistically using distributions — good at creating variations with uncertainty baked in.
- Use Case: Art, design, style transfer, and conceptual generation.
Diffusion Models
- Structure: Probabilistic denoising process.
- Mechanism:
- Adds noise to data, then trains a model to reverse the noise step-by-step.
- Learns to reconstruct original or new images from noisy inputs.
- Use Case: High-quality image generation (e.g., DALL·E 2, Midjourney), photo restoration, or stylized generations.
2. Transformer-Based Architectures
Transformers
- Purpose: Designed for sequential data (e.g., text, speech).
- Innovation: Introduced self-attention — the ability to weigh and focus on important words/tokens regardless of position.
- Advantages:
- Captures long-range dependencies
- Highly parallelizable (unlike RNNs)
- State-of-the-art in NLP and now expanding to vision and multimodal AI
3. Types of Transformer Architectures in LLMs
| Model | Type | Strengths |
|---|---|---|
| GPT (Generative Pre-trained Transformer) | Decoder-only | Trained to predict the next token in a sequence, GPT models are optimized for fluent, coherent text generation. They're unidirectional (left-to-right), which makes them powerful for creative writing, code generation, and chatbot responses. Ideal for situations where generation is the goal. |
| BERT (Bidirectional Encoder Representations from Transformers) | Encoder-only | Reads input sequences in both directions (left and right context simultaneously), making it excellent for understanding tasks like sentiment analysis, entity recognition, and question answering. It's not a generative model but excels at comprehension and classification. |
| T5 (Text-to-Text Transfer Transformer) | Encoder-Decoder | Treats every NLP task as a text-to-text problem. For example, sentiment analysis becomes: "Classify: I loved this movie" → "positive". Extremely versatile, it supports translation, summarization, classification, question answering, and more. It's like a Swiss army knife for language tasks. |
| BART (Bidirectional and Auto-Regressive Transformer) | Encoder-Decoder | Combines the bidirectional understanding of BERT (via its encoder) with the generative fluency of GPT (via its decoder). It’s particularly well-suited for text summarization, translation, and creative text rewriting. Often used in content generation pipelines where understanding + output is required. |
- Key Distinction:
- Encoders = understand input
- Decoders = generate output
- Encoder-Decoders = do both!
4. Quick Summary Table
| Model | Architecture | Key Use | Notes |
|---|---|---|---|
| GAN | Generator + Discriminator | Image/video generation | Adversarial training dynamic |
| VAE | Encoder-Decoder | Data variation, art/design | Latent variable modeling |
| Diffusion | Denoising network | Creative image generation | Step-by-step reconstruction |
| GPT | Transformer (Decoder-only) | Text generation | Predicts next token |
| BERT | Transformer (Encoder-only) | Context understanding | Bidirectional attention |
| T5/BART | Transformer (Enc-Dec) | Summarization, translation, NLU+NLG | Highly versatile |
🌫️ Diffusion Models — In Plain Veer Terms
🧠 What are they?
A diffusion model learns to generate images (or other data) by starting with pure noise, then gradually removing that noise to create something meaningful — like a reverse chaos spell.
🔄 How They Work (Bitty Edition):
- Training phase:
- Take a real image
- Add noise to it — over and over — until it's completely unrecognizable
- Train a model to learn how to remove that noise in reverse steps
- Generation phase:
- Start from random noise
- Ask the model: “What would a less noisy version of this look like?”
- Repeat until you get a clear image
🎨 The result?
A brand-new, high-quality image that looks like it could’ve come from the training set, but didn’t.
💡 Real-World Examples
| Model | Uses Diffusion? | Description |
|---|---|---|
| DALL·E 2 | ✅ Yes | Text → image generation guided by CLIP & diffusion |
| Midjourney | ✅ Yes (custom variant) | Artistic text → image generator |
| Stable Diffusion | ✅ Yes | Open-source model that powers many indie text-to-image tools |
| Photo restoration tools | ✅ Often | Remove damage/noise from photos by reversing the visual “decay” |
🎭 Why It’s Different from GANs
| Feature | GAN | Diffusion |
|---|---|---|
| Learning style | Generator vs Discriminator (competition) | Gradual denoising (no adversary) |
| Training stability | Often unstable | More stable & scalable |
| Output quality | High, but sometimes weird artifacts | Usually higher quality & more controllable |
| Use cases | Deepfakes, style transfer | Art generation, restoration, creative design |
✨ Bitty’s Mental Image:
A GAN is like a con artist learning to fake paintings by fooling an art critic.
A Diffusion Model is like a fog sculptor who learns how to carve a statue by slowly clearing away the mist.
✨ Bitty's Closing Thought:
You don't need to memorize every architecture. Just understand their "personality types" — the Generator, the Interpreter, the Storyteller, and the Stylist — and choose based on what you're building. AI isn't magic; it's a toolbox. And you're the spellcaster.