Phase 4: Vision Transformers · ~75 minutes · Python
Diffusion Transformers & Rectified Flow
The U-Net is not the secret of diffusion. Replace it with a transformer, swap the noise schedule for a straight-line flow, and suddenly you have SD3, FLUX, and every 2026 text-to-image model.
Hiring signal: Understanding of diffusion transformers & rectified flow internals
What you will learn
- Trace the evolution from U-Net DDPM (Lesson 10) to Diffusion Transformer (DiT), MMDiT (SD3), and single+double-stream DiT (FLUX)
- Explain rectified flow: why a straight-line trajectory between noise and data lets models sample in 20 steps instead of 1000
- Implement a tiny DiT block and a rectified-flow training loop, both under 100 lines
- Distinguish model variants (SD3, FLUX.1-dev, FLUX.1-schnell, Z-Image, Qwen-Image) by architecture, parameter count, and licensing
Introduction
Type: Learn + Build Languages: Python Prerequisites: Phase 4 Lesson 10 (Diffusion DDPM), Phase 4 Lesson 14 (ViT), Phase 7 Lesson 02 (Self-Attention) Time: ~75 minutes
Learning Objectives
- Trace the evolution from U-Net DDPM (Lesson 10) to Diffusion Transformer (DiT), MMDiT (SD3), and single+double-stream DiT (FLUX)
- Explain rectified flow: why a straight-line trajectory between noise and data lets models sample in 20 steps instead of 1000
- Implement a tiny DiT block and a rectified-flow training loop, both under 100 lines
- Distinguish model variants (SD3, FLUX.1-dev, FLUX.1-schnell, Z-Image, Qwen-Image) by architecture, parameter count, and licensing
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