Though diffusion models excel in image generation, their step-by-step
denoising leads to slow generation speeds. Consistency training addresses this
issue with single-step sampling but often produces lower-quality generations
and requires high training costs. In this paper, we show that optimizing
consistency training loss minimizes the Wasserstein distance between target and
generated distributions. As timestep increases, the upper bound accumulates
previous consistency training losses. Therefore, larger batch sizes are needed
to reduce both current and accumulated losses. We propose Adversarial
Consistency Training (ACT), which directly minimizes the Jensen-Shannon (JS)
divergence between distributions at each timestep using a discriminator.
Theoretically, ACT enhances generation quality, and convergence. By
incorporating a discriminator into the consistency training framework, our
method achieves improved FID scores on CIFAR10 and ImageNet 64$\times$64,
retains zero-shot image inpainting capabilities, and uses less than $1/6$ of
the original batch size and fewer than $1/2$ of the model parameters and
training steps compared to the baseline method, this leads to a substantial
reduction in resource consumption.

通过在一步采样中最小化一致性训练损失来优化生成模型，提出了对抗一致性训练（ACT），通过在每一步骤使用鉴别器最小化分布间的 Jensen-Shannon 散度，从而提高了生成质量和收敛速度，这导致资源消耗大幅度减少，具有改进的 FID 分数。