How Diffusion Models Work
The core principle is surprisingly simple: take a real image, gradually add random noise until it becomes pure static, then train a neural network to REVERSE this process — learning to remove noise step by step until a clean image emerges.
The Anatomy of an Image Prompt
Model Selection Guide
Limitations and Ethics
- →Bias in training data — Models reproduce biases in their training images. Prompting "a CEO" disproportionately generates images of white men.
- →Copyright concerns — Generated images may closely resemble copyrighted works. Use commercially licensed models for business use.
- →Deepfake risk — Photorealistic generation enables misuse. Many platforms add watermarks or metadata.
- →Hands and text — Models still struggle with accurate hands (wrong number of fingers) and text rendering.
- →Consistency — Generating the same character across multiple images is difficult without specialized tools.
Test Your Understanding
Next Steps
You understand how image generation works and how to structure prompts. In the next workshop, you will master visual prompt engineering — creating specific, reproducible visual outputs for real projects.
Continue to the workshop: Visual Prompt Engineering for advanced image prompting techniques.
The Visual Prompt Formula
Style Control Techniques
Building Visual Consistency
Commercial Use Cases
Limitations and Workarounds
Test Your Understanding
Next Steps
You now have a complete visual prompting toolkit. In the next module, you will shift to a critical skill: detecting and mitigating AI failures — hallucinations, bias, and safety vulnerabilities.
Continue to AI Hallucinations and Bias Detection to learn to protect against AI failures.