Timestep distillation is an effective approach for improving the generation efficiency of diffusion models. The Consistency Model (CM), as a trajectory-based framework, demonstrates significant potential due to its strong theoretical foundation and high-quality few-step generation. Nevertheless, current continuous-time consistency distillation methods still rely heavily on training data and computational resources, hindering their deployment in resource-constrained scenarios and limiting their scalability to diverse domains. To address this issue, we propose Trajectory-Backward Consistency Model (TBCM), which eliminates the dependence on external training data by extracting latent representations directly from the teacher model's generation trajectory. Unlike conventional methods that require VAE encoding and large-scale datasets, our self-contained distillation paradigm significantly improves both efficiency and simplicity. Moreover, the trajectory-extracted samples naturally bridge the distribution gap between training and inference, thereby enabling more effective knowledge transfer. Empirically, TBCM achieves 6.52 FID and 28.08 CLIP scores on MJHQ-30k under one-step generation, while reducing training time by approximately 40% compared to Sana-Sprint and saving a substantial amount of GPU memory, demonstrating superior efficiency without sacrificing quality. We further reveal the diffusion-generation space discrepancy in continuous-time consistency distillation and analyze how sampling strategies affect distillation performance, offering insights for future distillation research. GitHub Link: https://github.com/hustvl/TBCM.

翻译：时间步蒸馏是提升扩散模型生成效率的有效方法。一致性模型（CM）作为一种基于轨迹的框架，凭借其坚实的理论基础和高质量少步生成能力展现出显著潜力。然而，当前连续时间一致性蒸馏方法仍严重依赖训练数据和计算资源，阻碍了其在资源受限场景下的部署，并限制了其向多样化领域的扩展。为解决这一问题，我们提出轨迹反向一致性模型（TBCM），通过直接从教师模型的生成轨迹中提取潜在表示，消除了对外部训练数据的依赖。与传统需要VAE编码和大规模数据集的方法不同，我们的自包含蒸馏范式显著提升了效率与简洁性。此外，轨迹提取的样本自然弥合了训练与推断之间的分布差距，从而实现更有效的知识迁移。实验表明，TBCM在MJHQ-30k数据集上单步生成达到6.52 FID和28.08 CLIP分数，同时相比Sana-Sprint减少约40%训练时间并节省大量GPU内存，在保持质量的同时展现出卓越效率。我们进一步揭示了连续时间一致性蒸馏中的扩散-生成空间差异，并分析了采样策略如何影响蒸馏性能，为未来蒸馏研究提供了见解。GitHub链接：https://github.com/hustvl/TBCM。