Diffusion image generation: cheatsheet

Diffusion in one row

Phase	What happens
Training	add noise to images, denoiser learns to predict the noise
Inference	start from pure random noise, iteratively denoise to an image, conditioned (typically) on a text prompt
Core component	the denoiser network (architecture choice = this lesson’s topic)

U-Net vs DiT (the central contrast)

Aspect	U-Net (original)	DiT (modern)
Architecture	convolutional + skip connections	transformer over patches
Receptive field	local (per conv); global needs many layers	global (attention) at every layer
Scaling behavior	unclear / less predictable	transformer scaling laws apply
Best at	small/efficient deployments	frontier scale + global composition
Shared infra with text transformers	no	yes (architectural unification)

What DiT buys

Benefit	What it enables
Scaling laws transfer	predictable quality from more parameters/data/compute
Better global structure	composition, coherence, lighting across the image
Architectural unification	same training stack, hardware kernels, infra investment as text/MM transformers

Modern systems on DiT

System	Org	Notes
Stable Diffusion 3	Stability AI	adopted DiT (uses MM-DiT for text+image conditioning)
Flux	Black Forest Labs	DiT-family backbone
Sora	OpenAI	DiT extended to video (Phase 3 lesson 6)

Text conditioning evolution

Approach	How text fuses with image
Cross-attention (SD 1.x/2.x)	text vectors “on the side”; image features attend to them
MM-DiT (SD 3 era)	text + image patch tokens in one transformer, attending to each other in every block

MM-DiT recapitulates the native-multimodal pattern (L3) on the generative side.

Tradeoffs and mitigations

Tradeoff	Mitigation
Expensive per step at small scale	use U-Net there; DiT wins at scale
Quadratic attention at high resolution	latent diffusion (operate in compressed latent space)
Many denoising steps at inference	flow-matching / rectified-flow variants reduce step count

Scope of this lesson

IN scope (this lesson)	OUT of scope (separate conversations)
Architecture / technique	Use-case policy (when synthetic images are appropriate)
Evaluation (FID, scaling curves, human pref)	Provenance / watermarking (C2PA, SynthID)
MM-DiT conditioning, latent diffusion	Sector-specific policies (journalism / political / legal / medical)
Tradeoffs, modern systems landscape	Training-data licensing (scraped-image IP claims)
	Likeness / consent for real people