Standard continuous-time generative models rely on monolithic architectures that must navigate vastly different signal regimes, from isotropic noise to intricate data distributions. While scaling model capacity improves performance, deploying a massive network uniformly across the entire generative timeline is inherently inefficient. In this work, we propose Complexity-Balanced Splitting (CBS), a principled framework for temporal capacity allocation that distributes the generative workload across multiple specialized sub-networks. Grounded in function approximation theory and de Boor's equidistribution principle, CBS partitions the diffusion timeline into segments of equal approximation burden, allocating more representational capacity to regions where the generative dynamics are more difficult to model. To estimate this local complexity, we introduce two complementary and tractable monitor functions: a spatial measure based on the flow's Dirichlet energy, and a geometric measure based on the acceleration of the sampling trajectories. Using a lightweight auxiliary model to estimate these complexity profiles, our approach eliminates the need for heuristic temporal splits or computationally expensive search procedures. Extensive evaluation across multiple architectures (SiT, JiT, and UNet) and datasets demonstrates that CBS consistently improves synthesis quality without increasing per-step inference cost. In particular, CBS improves FID by ~35% on SiT-XL with CFG relative to naive temporal partitioning. Project page is available at https://noamissachar.github.io/CBS/.

翻译：标准连续时间生成模型依赖于整体化的架构，必须处理从各向同性噪声到复杂数据分布等截然不同的信号状态。虽然扩大模型容量能提升性能，但在整个生成时间线上均匀部署大规模网络本质上效率低下。本文提出复杂度均衡分裂（CBS）——一种基于原理的时间容量分配框架，通过多个专用子网络分布式承载生成工作负载。该方法建立在函数逼近理论与德布尔均衡分布原理之上，将扩散时间线划分为逼近负担相等的若干片段，将更多表征容量分配给生成动力学建模更困难的区域。为估计局部复杂度，我们提出两种互补且可计算的监测函数：基于流形狄利克雷能量的空间测度，以及基于采样轨迹加速度的几何测度。通过使用轻量级辅助模型估计这些复杂度分布，我们的方法无需启发式时间划分或计算昂贵的搜索过程。在多种架构（SiT、JiT和UNet）与数据集上的广泛评估表明，CBS能在不增加单步推理成本的前提下持续提升合成质量。特别地，相较于朴素时间划分方法，CBS在采用无分类器引导的SiT-XL上使FID改进约35%。项目页面见https://noamissachar.github.io/CBS/。