Core Concepts
Trajectory Consistency Distillation (TCD) improves image quality and speed by reducing errors in consistency models.
Abstract
Abstract:
Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages guided consistency distillation for text-to-image synthesis acceleration.
TCD addresses limitations of LCM by introducing trajectory consistency function and strategic stochastic sampling.
Introduction:
Score-based generative models (SGMs) like Diffusion Models have shown proficiency in various domains.
Consistency Models (CMs) aim to generate high-quality data with single-step or few-step sampling without adversarial training.
Elucidating Errors in Consistency Models:
Errors in multistep sampling include distillation errors, estimation errors, and accumulated discretisation errors.
Trajectory Consistency Distillation:
TCD introduces trajectory consistency function and strategic stochastic sampling to enhance image quality and speed.
Experiments:
TCD outperforms LCM and numerical methods, generating high-quality images in fewer steps.
Impact Statements:
TCD advancements can reduce inference costs but may amplify negative effects like disinformation dissemination.
Stats
TCD는 이미지 품질을 향상시키고 속도를 높이는데 효과적입니다.
TCD는 LCM과 수치적 방법을 능가하여 적은 단계에서 고품질 이미지를 생성합니다.
Quotes
"TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs."
"TCD outperforms LCM across all sampling steps and exhibits superior performance compared to numerical methods of the teacher model."