Consistency distillation methods have demonstrated significant success in accelerating generative tasks of diffusion models. However, since previous consistency distillation methods use simple and straightforward strategies in selecting target timesteps, they usually struggle with blurs and detail losses in generated images. To address these limitations, we introduce Target-Driven Distillation (TDD), which (1) adopts a delicate selection strategy of target timesteps, increasing the training efficiency; (2) utilizes decoupled guidances during training, making TDD open to post-tuning on guidance scale during inference periods; (3) can be optionally equipped with non-equidistant sampling and x0 clipping, enabling a more flexible and accurate way for image sampling. Experiments verify that TDD achieves state-of-the-art performance in few-step generation, offering a better choice among consistency distillation models.

本研究解决了现有一致性蒸馏方法在生成图像时模糊及细节丢失的问题。提出的目标驱动蒸馏（TDD）采用精细的目标时间步选择策略，并在训练中使用解耦指导，这使得TDD能够在推理期间进行指导尺度的后调优。实验结果表明，TDD在少步生成任务中实现了最先进的性能，提供了一种一致性蒸馏模型的新选择。

目标驱动蒸馏：带有目标时间步选择和解耦指导的一致性蒸馏