Complexity-Balanced Diffusion Splitting

Problem — This work addresses the inefficiency of monolithic architectures in continuous-time generative models, which struggle to manage diverse signal regimes effectively. The authors highlight the limitations of existing methods that rely on uniform capacity allocation, which can lead to suboptimal performance, particularly in complex data distributions. This paper is a preprint and has not undergone peer review.

Method — The core contribution is the Complexity-Balanced Splitting (CBS) framework, which allocates generative workload across multiple specialized sub-networks based on local complexity. CBS is grounded in function approximation theory and de Boor’s equidistribution principle, allowing for the partitioning of the diffusion timeline into segments with equal approximation burden. The authors propose two monitor functions to estimate local complexity: a spatial measure derived from the Dirichlet energy of the flow and a geometric measure based on the acceleration of sampling trajectories. A lightweight auxiliary model is employed to estimate these complexity profiles, eliminating the need for heuristic splits or costly search procedures. The framework is evaluated using various architectures, including SiT, JiT, and UNet.

Results — The evaluation demonstrates that CBS significantly enhances synthesis quality without increasing per-step inference costs. Specifically, on the SiT-XL architecture with Classifier-Free Guidance (CFG), CBS achieves a ~35% improvement in Fréchet Inception Distance (FID) compared to naive temporal partitioning. This improvement is consistent across different datasets and architectures, indicating the robustness of the CBS approach.

Limitations — The authors acknowledge that while CBS improves performance, it may still be sensitive to the choice of auxiliary model for complexity estimation. Additionally, the reliance on specific architectural designs (SiT, JiT, UNet) may limit the generalizability of the findings to other architectures not evaluated in this study. The paper does not address potential scalability issues when applying CBS to very large models or datasets.

Why it matters — The implications of this work are significant for the development of more efficient generative models, particularly in scenarios where computational resources are constrained. By optimizing capacity allocation based on local complexity, CBS paves the way for future research into adaptive generative modeling techniques that can dynamically adjust to varying data characteristics. This approach could lead to advancements in various applications, including image synthesis and video generation, as published in arXiv cs.CV.