Large Scale Diffusion Distillation via Score-Regularized Continuous-Time
  Consistency
Authors: name: Kaiwen Zheng, name: Yuji Wang, name: Qianli Ma, name: Huayu Chen, name: Jintao Zhang, name: Yogesh Balaji, name: Jianfei Chen, name: Ming-Yu Liu, name: Jun Zhu, name: Qinsheng Zhang

This work represents the first effort to scale up continuous-time consistency
distillation to general application-level image and video diffusion models.
Although continuous-time consistency model (sCM) is theoretically principled
and empirically powerful for accelerating academic-scale diffusion, its
applicability to large-scale text-to-image and video tasks remains unclear due
to infrastructure challenges in Jacobian-vector product (JVP) computation and
the limitations of standard evaluation benchmarks. We first develop a
parallelism-compatible FlashAttention-2 JVP kernel, enabling sCM training on
models with over 10 billion parameters and high-dimensional video tasks. Our
investigation reveals fundamental quality limitations of sCM in fine-detail
generation, which we attribute to error accumulation and the "mode-covering"
nature of its forward-divergence objective. To remedy this, we propose the
score-regularized continuous-time consistency model (rCM), which incorporates
score distillation as a long-skip regularizer. This integration complements sCM
with the "mode-seeking" reverse divergence, effectively improving visual
quality while maintaining high generation diversity. Validated on large-scale
models (Cosmos-Predict2, Wan2.1) up to 14B parameters and 5-second videos, rCM
matches or surpasses the state-of-the-art distillation method DMD2 on quality
metrics while offering notable advantages in diversity, all without GAN tuning
or extensive hyperparameter searches. The distilled models generate
high-fidelity samples in only 1sim4 steps, accelerating diffusion sampling
by 15timessim50times. These results position rCM as a practical and
theoretically grounded framework for advancing large-scale diffusion
distillation.