Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping
arxiv(2024)
摘要
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. Consequently, we introduce Trajectory Consistency Distillation
(TCD), which encompasses trajectory consistency function and strategic
stochastic sampling. The trajectory consistency function diminishes the
parameterisation and distillation errors by broadening the scope of the
self-consistency boundary condition with trajectory mapping and endowing the
TCD with the ability to accurately trace the entire trajectory of the
Probability Flow ODE in semi-linear form with an Exponential Integrator.
Additionally, strategic stochastic sampling provides explicit control of
stochastic and circumvents the accumulated errors inherent in multi-step
consistency sampling. 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.
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