Why Diffusion Models Are Designed This Way: From Score Matching to Inference Bottlenecks
Gauri_the_great · x · 2026-07-06
The author dives deep into diffusion models, focusing not on Flow Matching itself, but on understanding why score-based diffusion models are designed the way they are and where they face limitations. These models learn the Stein score (the gradient of the log probability density) via denoising score matching, and recover samples during inference by numerically integrating reverse-time SDEs or probability flow ODEs. This framework underpins image models like Stable Diffusion and speech systems like Grad-TTS and NaturalSpeech 2.
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