Build Your Own Image Generator
Train Vision Models from Scratch—Stop Renting, Start Owning
Create images from words. Own the visual AI your competitors rent.
This masterclass takes you inside the powerful models behind DALL-E and Stable Diffusion. You'll explore two distinct approaches to image generation—vision transformers and diffusion models—building each from scratch. Learn how transformers turn images into sequences of patches, and how diffusion models refine noise into coherent images. By the end, you'll have built models that classify images, add captions automatically, and generate high-resolution content from text prompts. You'll understand not just how to use these tools, but how they work—because you built them yourself.
Your Competitive Moat
Proven Transformation Results
Real outcomes from students who completed The LLM Sovereignty Stack™ and built their competitive moats
📈 Career Transformation
💰 Business Impact
What You'll Actually Build
Choose Your Path to Mastery
All modalities include the complete LLM Sovereignty Stack™. Choose based on your learning style and goals.
Self-Paced Mastery
- All 9 steps available immediately
- 50 hours of content + code
- Lifetime access to updates
- Community support
- Monthly live office hours
- 30-day money-back guarantee
9-Week Live Cohort
- Everything in Self-Paced
- Weekly live workshops (2 hrs)
- Direct instructor access
- Cohort accountability & networking
- 24-hour code review turnaround
- 1-on-1 kickoff & graduation calls
- Certificate + alumni network
Founder's Edition
- Everything in Live Cohort
- 6 monthly 1-on-1 coaching calls
- Fractional CTO advisory and implementation support
- Custom learning path for your business
- Same-day code reviews
- Architecture consulting for your product
- Your proprietary model built with you
- Investor pitch coaching
5-Day Immersive Bootcamp
Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.
Course Curriculum
11 transformative steps · 50 hours of hands-on content
Module 1: A Tale of Two Models
6 lessons · Shu-Ha-Ri cycle
- Executive Overview: The Business of Visual AI Generation
- Unimodal vs. Multimodal Models: Understanding the Landscape
- Practical Use Cases: Where Text-to-Image Creates Value
- Transformer-Based vs. Diffusion-Based Generation: Two Paths
- Challenges: The Pink Elephant Problem and Geometric Inconsistency
- Social, Environmental, and Ethical Considerations
Module 2: Build a Transformer
6 lessons · Shu-Ha-Ri cycle
- How the Attention Mechanism Works: A Visual Walkthrough
- Word Embedding and Positional Encoding
- Creating an Encoder-Decoder Transformer
- Coding the Attention Mechanism Step by Step
- Building a Language Translator: End-to-End Example
- Training and Using Your Translator
Module 3: Classify Images with Vision Transformers
6 lessons · Shu-Ha-Ri cycle
- How to Convert Images to Sequences of Patches
- The CIFAR-10 Dataset: Download and Visualization
- Dividing Images into Patches
- Modeling Patch Positions in an Image
- Multi-Head Self-Attention for Vision
- Building and Training a Complete Vision Transformer Classifier
Module 4: Add Captions to Images
6 lessons · Shu-Ha-Ri cycle
- How to Train a Transformer for Image Captioning
- The Flickr 8k Dataset: Images and Captions
- Building a Vocabulary of Tokens
- Creating a Vision Transformer as the Image Encoder
- The Decoder to Generate Text
- Training and Using Your Image Captioning Model
Module 5: Generate Images with Diffusion Models
7 lessons · Shu-Ha-Ri cycle
- How Diffusion Models Work: Forward and Reverse Processes
- Visualizing the Forward Diffusion Process
- Different Diffusion Schedules and Their Effects
- The Reverse Diffusion Process: Denoising
- Training a Denoising U-Net Model
- The DDPM Noise Scheduler
- Inference: Generating New Images
Module 6: Control What Images to Generate
6 lessons · Shu-Ha-Ri cycle
- Classifier-Free Guidance in Diffusion Models
- Time Step Embedding and Label Embedding
- The U-Net Architecture: Down Blocks and Up Blocks
- Building the Complete Denoising U-Net
- Training with Classifier-Free Guidance
- How the Guidance Parameter Affects Generated Images
Module 7: High-Resolution Image Generation
6 lessons · Shu-Ha-Ri cycle
- Incorporating Attention in the U-Net
- Denoising Diffusion Implicit Models (DDIM): Faster Sampling
- Image Interpolation in Diffusion Models
- Building a U-Net for High-Resolution Images
- Training on High-Resolution Data
- Transitioning Smoothly Between Images
Module 8: CLIP—Connecting Images and Text
6 lessons · Shu-Ha-Ri cycle
- How the CLIP Model Works
- Preparing Image-Caption Pairs for Training
- Creating Text and Image Encoders
- Building a Complete CLIP Model
- Training Your CLIP Model
- Using CLIP to Select Images Based on Text Descriptions
Module 9: Latent Diffusion Models
6 lessons · Shu-Ha-Ri cycle
- How Variational Autoencoders (VAEs) Work
- Combining Latent Diffusion with VAE
- Compressing and Reconstructing Images
- Text-to-Image Generation in Latent Space
- Guidance by CLIP: Steering Generation with Text
- Modifying Existing Images with Text Prompts
Module 10: A Deep Dive into Stable Diffusion
6 lessons · Shu-Ha-Ri cycle
- The Complete Stable Diffusion Architecture
- How Text Becomes Images: The Full Pipeline
- Text Embedding Interpolation
- Creating Text Embeddings with CLIP
- Image Generation in Latent Space
- Converting Latent Images to High-Resolution Output
Module 11: Transformer-Based Generation and Deepfake Detection
6 lessons · Shu-Ha-Ri cycle
- VQGAN: Converting Images to Sequences of Integers
- VQ-VAEs: Why We Need Discrete Representations
- A Minimal DALL-E Implementation
- From Text Prompt to Image Tokens
- Fine-Tuning ResNet-50 to Detect Fake Images
- Capstone: Your Complete Text-to-Image System
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
Intermediate Python skills and some knowledge of machine learning. We explain concepts visually and build everything step by step—no advanced math background required.
A modern laptop for development. GPU acceleration (local or cloud) is recommended for training but not required for understanding. We provide cloud compute options.
Yes. You'll build multiple working models: a vision transformer classifier, an image captioning model, and a complete text-to-image diffusion model. All running on your own machine.
APIs are black boxes. By building from scratch, you'll understand every component—enabling customization, fine-tuning for your domain, and the ability to build proprietary visual AI systems.
Yes. The final module includes fine-tuning models to detect deepfakes—increasingly important as visual AI becomes more prevalent.
Stop Renting AI. Start Owning It.
Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.
Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.
Self-paced · Lifetime access · 30-day guarantee
Start Your TransformationThis is not just education. This is technological sovereignty.