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About this paper
Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon
The authors explain diffusion models as ways to learn how probability distributions change over time. Forward corruption defines a path from data to noise, and learned reverse processes generate samples along it. They connect variational, score-based, and flow-based formulations through shared density evolution, while noting practical models and samplers can differ.
Key insights
- The "conditioning trick" serves as a fundamental, common strategy across all diffusion model perspectives for rendering intractable marginal objectives tractable through conditional regression targets.
- The Fokker-Planck equation universally governs the continuous-time evolution of probability densities in diffusion models, unifying both stochastic and deterministic dynamics.
- Sampling from diffusion models is fundamentally recast as the problem of numerically solving a continuous differential equation, informing both the challenges and systematic solutions for acceleration.
Summary by alphaXiv.
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