Diffusion models generate high-quality images but require dozens of forward
passes. We introduce Distribution Matching Distillation (DMD), a procedure to
transform a diffusion model into a one-step image generator with minimal impact
on image quality. We enforce the one-step image generator match the diffusion
model at distribution level, by minimizing an approximate KL divergence whose
gradient can be expressed as the difference between 2 score functions, one of
the target distribution and the other of the synthetic distribution being
produced by our one-step generator. The score functions are parameterized as
two diffusion models trained separately on each distribution. Combined with a
simple regression loss matching the large-scale structure of the multi-step
diffusion outputs, our method outperforms all published few-step diffusion
approaches, reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot
COCO-30k, comparable to Stable Diffusion but orders of magnitude faster.
Utilizing FP16 inference, our model can generate images at 20 FPS on modern
hardware.

通过实施分布匹配蒸馏 (Distribution Matching Distillation) 以及多步扩散输出的大规模结构简单回归损失相匹配的方法，我们将扩散模型转化为一步图像生成器，以显著降低对图像质量的影响，使其在 ImageNet 64x64 上达到 2.62 FID，在无监督 COCO-30k 上达到 11.49 FID，并可通过 FP16 推理在现代硬件上以 20 FPS 生成图像。