Diffusion Models
Forward noising, denoising objectives, samplers, classifier-free guidance and latent diffusion.
7concepts
82flashcards
53minutes of reading
- 01 Classifier-Free Guidance The extrapolation trick that made text-to-image work, why it is not sampling from any distribution the model learned, and the fidelity-diversity tradeoff it exposes as a single tunable number.
- 02 Forward Diffusion and Noise Schedules The fixed corruption process that makes diffusion training possible, why the closed form in alpha-bar removes the need to simulate it, and how the schedule silently decides which frequencies the model learns.
- 03 Latent Diffusion and the Autoencoder Bottleneck Why running diffusion in a compressed latent space cuts cost by more than an order of magnitude, what the autoencoder throws away permanently, and the failure modes that belong to the VAE rather than the diffusion model.
- 04 Consistency Models and Few-Step Sampling How consistency models learn a direct map from any point on a probability-flow trajectory to its origin, the difference between distilling that map from a diffusion teacher and training it from scratch, and how latent and adversarial variants reach one to four steps.
- 05 Denoising Parameterisations: Epsilon, x-zero and v Three algebraically equivalent things a diffusion network can predict, why they train to completely different models, and how the choice interacts with the noise schedule and with distillation.
- 06 Diffusion Transformers (DiT) Why replacing the diffusion U-Net with a plain vision transformer over latent patches made image quality a predictable function of forward-pass compute, and how adaLN-Zero conditioning and the MM-DiT joint-attention block made it work.
- 07 Samplers: DDPM, DDIM and Higher-Order Solvers Why sampling is numerical integration of an ODE or SDE rather than a fixed algorithm, how DDIM makes the process deterministic and skippable, and what higher-order solvers buy at the cost of stability.