#latent-space
Operating in learned compressed spaces.
9 chapters, oldest era first: one idea traced across the books.
- From Noise to Image 2006–2013 Ch. 1: The Generative Problem What modeling p(x) means, density versus sampling, and why image generation stalled until deep learning's revival.
- From Noise to Image 2013–2015 Ch. 2: Variational Autoencoders The ELBO, the reparameterization trick, and the first neural networks that could imagine — blurrily.
- From Noise to Image 2016–2017 Ch. 5: Pixels One at a Time Autoregressive factorization, PixelRNN/PixelCNN, and VQ-VAE's discrete codebooks.
- From Noise to Image 2017–2020 Ch. 6: The GAN Golden Age ProGAN to StyleGAN2: style modulation, latent arithmetic, and photorealistic faces of people who do not exist.
- From Frames to Worlds 2018–2019 Ch. 3: Predicting the Next Frame World-model prehistory: learn a latent simulator, then act inside it.
- From Noise to Image 2020–2021 Ch. 9: Scores, SDEs, and Faster Sampling The SDE/ODE unification, probability flow, and DDIM's deterministic shortcut.
- From Frames to Worlds 2020–2023 Ch. 17: The Third Dimension NeRF, 3D from 2D priors, and Gaussian splatting — where 3D meets video and worlds.
- From Noise to Image 2021–2022 Ch. 13: Latent Diffusion and Stable Diffusion Perceptual compression, cross-attention conditioning, and the open-weights detonation of August 2022.
- From Frames to Worlds 2023 Ch. 6: Latent Video Diffusion Temporal layers bolted onto image LDMs, AnimateDiff, and Stable Video Diffusion's open weights.