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Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping

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arxiv 2402.19159 v2 pith:2CJTXKFU submitted 2024-02-29 cs.CV

Jianbin Zheng , Minghui Hu , Zhongyi Fan , Chaoyue Wang , Changxing Ding , Dacheng Tao , Tat-Jen Cham This is my paper · ORCID
classification cs.CV
keywords consistencytrajectorydistillationfunctionlatentmodelsamplingstochastic
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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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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Truncated Jump Sampling stops the ODE early and outputs the algebraically decoded x0 estimate, reducing NFEs by 20-70% across six model families with no retraining.

  3. Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning

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    CACFM applies RL to adaptively select critical regions in probability flow ODE trajectories for consistency distillation, yielding SOTA few-step results on FLUX and SDXL.

  4. Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

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    Q-Sched's quantization-aware scheduler with a reference-free JAQ loss lets 2-8 step quantized diffusion models reach lower FID than full-precision baselines.

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