轨迹一致性蒸馏

摘要

潜在一致性模型（LCM）将一致性模型扩展到潜在空间，并利用引导一致性蒸馏技术，在加速文本到图像合成方面取得了令人印象深刻的性能。然而，我们观察到LCM在生成既清晰又详细复杂的图像方面存在困难。为了解决这一局限性，我们首先深入探讨并阐明潜在原因。我们的调查确定主要问题源于三个不同领域的错误。因此，我们引入了轨迹一致性蒸馏（TCD），其中包括轨迹一致性函数和策略性随机抽样。轨迹一致性函数通过扩大自一致性边界条件的范围，赋予TCD准确追踪概率流ODE整个轨迹的能力，从而减少蒸馏错误。此外，策略性随机抽样专门设计用于规避多步一致性抽样中积累的错误，精心定制以补充TCD模型。实验证明，TCD不仅显著提高了低NFE时图像质量，而且在高NFE时与教师模型相比产生了更详细的结果。

English

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.