Diffusion Media Generation

The chapters in this module, in reading order.

#	Chapter
00	Diffusion Models — The Five-Year-Old Version
01	Opening failure — the smart judge who still cannot paint
02	Forward process — teaching a clean image to disappear politely
03	Noise schedules — deciding how hard each corruption step should bite
04	Reverse process — learning the path from static back to structure
05	Training objective — what exactly the denoiser is asked to predict
06	DDPM sampling — the original thousand-tap sculpting loop
07	DDIM accelerated sampling — fewer steps, less randomness, faster drafts
08	Classifier-free guidance — steering with and without the prompt
09	Latent diffusion — doing the hard work in compressed space
10	Text-to-image architecture — Stable Diffusion as a whole factory
11	ControlNet image-to-image — extra rails for edges, depth, and pose
12	Consistency models distillation — learning the one-jump shortcut
13	Honest admission — what still breaks, surprises, and stays unsettled