Latent Consistency Model (LCM) extends the Consistency Model to the latent
space and leverages the guided consistency distillation technique to achieve
impressive performance in accelerating text-to-image synthesis. However, we
observed that LCM struggles to generate images with both clarity and detailed
intricacy. To address this limitation, we initially delve into and elucidate
the underlying causes. Our investigation identifies that the primary issue
stems from errors in three distinct areas. Consequently, we introduce
Trajectory Consistency Distillation (TCD), which encompasses trajectory
consistency function and strategic stochastic sampling. The trajectory
consistency function diminishes the distillation errors by broadening the scope
of the self-consistency boundary condition and endowing the TCD with the
ability to accurately trace the entire trajectory of the Probability Flow ODE.
Additionally, strategic stochastic sampling is specifically designed to
circumvent the accumulated errors inherent in multi-step consistency sampling,
which is meticulously tailored to complement the TCD model. Experiments
demonstrate that TCD not only significantly enhances image quality at low NFEs
but also yields more detailed results compared to the teacher model at high
NFEs.

通过增加 Trajectory Consistency Distillation（TCD）的一组函数和策略性的随机采样的设计，可以显著提高图像生成质量，同时减少由多步一致性采样中积累的误差所致的细节不足，并且在较高的 NFEs 下相较于教师模型更加详细。