Short Review
Accelerating Large-Scale Diffusion Models with Score-Regularized Consistency Distillation
This groundbreaking research addresses the critical challenge of scaling continuous-time consistency distillation (sCM) to large-scale text-to-image and text-to-video diffusion models. The authors tackle infrastructure hurdles in Jacobian-vector product (JVP) computation and inherent quality limitations of sCM. They introduce a novel FlashAttention-2 JVP kernel, enabling sCM training on models exceeding 10 billion parameters. Furthermore, they propose the score-regularized continuous-time consistency model (rCM), which integrates score distillation to overcome sCM's fine-detail degradation and mode-covering bias. This innovative approach significantly accelerates diffusion sampling, achieving high-fidelity results with remarkable efficiency.
Critical Evaluation of rCM for Diffusion Model Acceleration
Strengths
A significant strength lies in the development of a parallelism-compatible FlashAttention-2 JVP kernel, which is crucial for scaling consistency distillation to massive models. This technical innovation effectively resolves a major infrastructure bottleneck. The proposed rCM framework is theoretically grounded, combining the strengths of forward-divergence consistency with reverse-divergence score distillation to enhance visual quality while preserving generation diversity.
The empirical results are compelling, demonstrating that rCM matches or surpasses state-of-the-art distillation methods like DMD2 on large-scale models (up to 14 billion parameters) and complex 5-second video tasks. It achieves impressive sampling acceleration, generating high-fidelity samples in just 1-4 steps, leading to 15x-50x faster inference. Crucially, rCM achieves this without requiring extensive GAN tuning or hyperparameter searches, highlighting its practical robustness and efficiency.
Weaknesses
The paper effectively highlights the inherent limitations of pure sCM distillation, particularly its tendency towards fine-detail degradation and error accumulation. This "mode-covering" bias of sCM's forward-divergence objective leads to blurred textures and temporal artifacts in high-dimensional generation tasks. While rCM successfully mitigates these issues, the initial challenges with sCM underscore the complexity of applying consistency models directly to large-scale, high-fidelity generation without significant modifications.
Implications
This work has profound implications for the future of diffusion model acceleration and high-fidelity content generation. By providing a practical and theoretically sound framework, rCM paves the way for more efficient deployment of large text-to-image and text-to-video models in real-world applications. The ability to generate high-quality outputs with significantly fewer sampling steps could revolutionize creative industries, research, and various AI-powered content creation platforms. It also opens new avenues for exploring hybrid distillation strategies.
Conclusion
This research represents a significant advancement in the field of diffusion model distillation, offering a robust solution to accelerate large-scale generative models. The introduction of rCM, coupled with the FlashAttention-2 JVP kernel, provides a powerful and practical framework. Its demonstrated ability to achieve superior quality and diversity with remarkable efficiency positions it as a key development for advancing high-fidelity content generation and making complex diffusion models more accessible and deployable.
