diffusion-models
4 free lessons tagged diffusion-models across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
EBMs as a Unifying Lens
Why LeCun treats energy as the common language of machine learning. This lesson shows how classification, generative models, self-supervised learning, JEPA, and diffusion all read as energy-based models, ties the contrastive-versus-regularized split back to self-supervised learning, and gives an honest account of where explicit EBMs help and where they do not.
Flow matching: straightening the path from noise to data
The reframing that took over frontier image generation: learn a velocity field that transports noise to data along direct paths. Conditional flow matching, rectified flow, why straight trajectories mean fewer sampling steps, and how diffusion becomes a special case.
Guidance and samplers: steering diffusion and making it fast
How raw denoisers become text-to-image systems: conditioning, classifier-free guidance and the CFG scale, latent diffusion, the sampler zoo from DDPM to DDIM and beyond, and the distillation techniques that cut a thousand steps down to a few.
Diffusion models: learning to create by learning to denoise
How diffusion models generate images and more: the forward process that destroys data with noise, the reverse process that learns to undo it, the surprisingly simple training objective, and the network backbones (U-Net, DiT) that make it work.