Phase 2: Object Detection & Segmentation · ~75 minutes · Python

Stable Diffusion — Architecture & Fine-Tuning

Stable Diffusion is a DDPM that runs in the latent space of a pretrained VAE, conditioned on text via cross-attention, sampled with a fast deterministic ODE solver, and steered by classifier-free guidance.

Hiring signal: Understanding of stable diffusion — architecture & fine-tuning internals

What you will learn

Trace the five pieces of a Stable Diffusion pipeline: VAE, text encoder, U-Net, scheduler, safety checker — and what each of them actually does
Explain latent diffusion and why training in a 4x64x64 latent space (instead of a 3x512x512 image) reduces compute by 48x without quality loss
Use `diffusers` to generate images, run image-to-image, inpainting, and ControlNet-guided generation
Fine-tune Stable Diffusion with LoRA on a small custom dataset and load the LoRA adapter at inference

Introduction

Type: Learn + Use Languages: Python Prerequisites: Phase 4 Lesson 10 (Diffusion), Phase 7 Lesson 02 (Self-Attention) Time: ~75 minutes

Learning Objectives

Trace the five pieces of a Stable Diffusion pipeline: VAE, text encoder, U-Net, scheduler, safety checker — and what each of them actually does
Explain latent diffusion and why training in a 4x64x64 latent space (instead of a 3x512x512 image) reduces compute by 48x without quality loss
Use diffusers to generate images, run image-to-image, inpainting, and ControlNet-guided generation
Fine-tune Stable Diffusion with LoRA on a small custom dataset and load the LoRA adapter at inference

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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