While diffusion models can learn complex distributions, sampling requires a
computationally expensive iterative process. Existing distillation methods
enable efficient sampling, but have notable limitations, such as performance
degradation with very few sampling steps, reliance on training data access, or
mode-seeking optimization that may fail to capture the full distribution. We
propose EM Distillation (EMD), a maximum likelihood-based approach that
distills a diffusion model to a one-step generator model with minimal loss of
perceptual quality. Our approach is derived through the lens of
Expectation-Maximization (EM), where the generator parameters are updated using
samples from the joint distribution of the diffusion teacher prior and inferred
generator latents. We develop a reparametrized sampling scheme and a noise
cancellation technique that together stabilizes the distillation process. We
further reveal an interesting connection of our method with existing methods
that minimize mode-seeking KL. EMD outperforms existing one-step generative
methods in terms of FID scores on ImageNet-64 and ImageNet-128, and compares
favorably with prior work on distilling text-to-image diffusion models.

通过 EM Distillation 方法，将扩散模型精简至一步生成器模型，以最小损失的感知质量学习最大似然，提高了在 ImageNet-64 和 ImageNet-128 上的 FID 评分，并优于先前在文本到图像扩散模型提取方面的工作。