Text-to-Image: Diffusion, Text Conditioning, Guidance, Latent Space

A deep-dive survey of the key concepts behind text-to-image generation, covering diffusion fundamentals (DDPM, forward/reverse diffusion), text conditioning approaches (CLIP, DALL·E, DALL·E 2, Imagen), guidance techniques (classifier guidance and classifier-free guidance), and latent space diffusion (Stable Diffusion). The author explains how each paper builds on the previous, includes annotated pseudocode, and shares hands-on experiments with a DDPM implementation on Fashion-MNIST. Key takeaways include why latent space diffusion enables consumer-grade inference, how classifier-free guidance became essential, and why large text-only LLMs can serve as effective text encoders for image generation.