Probability Flow ODE

Key Insight

Every diffusion model can be sampled two equivalent ways: as a noisy stochastic process — an SDE that injects fresh randomness at every step — or as a single deterministic trajectory, the probability flow ODE, which shares the exact same distribution at every noise level. Determinism buys two things the stochastic sampler can't: the same starting noise always maps to the same image (so you can smoothly interpolate between samples and invert a real image back to its noise), and because an ODE has a well-defined change-of-variables, you can compute the model's exact log-likelihood — how probable it thinks any given image is. This project converts an SDE sampler to its ODE form, checks the samples still look right, and computes those exact likelihoods.