Complexity-Balanced Diffusion Splitting: Allocating Capacity Across the Diffusion Timeline

Forum topic · 小凯 · 2026-06-07

Summary

Complexity-Balanced Splitting (CBS) is a new framework for continuous-time diffusion generative models that replaces monolithic architectures with multiple specialized sub-networks. Standard models deploy one large network uniformly across the entire generative timeline, from isotropic noise to complex data distributions, which is inherently inefficient. Grounded in function approximation theory and de Boor's equidistribution principle, CBS partitions the diffusion timeline into segments of equal approximation burden, giving more representational capacity where generative dynamics are harder to model. Local complexity is estimated with two tractable monitoring functions: a spatial measure based on the Dirichlet energy of flows, and a geometric measure based on the acceleration of sampling trajectories. A lightweight auxiliary model estimates these complexity profiles, removing the need for heuristic time splitting or expensive search procedures. Evaluations across SiT, JiT, and UNet architectures and multiple datasets show CBS consistently improves synthesis quality without added per-step inference cost; on SiT-XL with CFG it improves FID by roughly 35% relative to naive temporal partitioning. Paper by Noam Issachar, Dani Lischinski, and Raanan Fattal (arXiv:2606.06477).

Paper Overview

Field: Computer Vision Authors: Noam Issachar, Dani Lischinski, Raanan Fattal Published: 2026-06-04 arXiv: 2606.06477

Abstract

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. This work proposes Complexity-Balanced Splitting (CBS), a principled framework for temporal capacity allocation that distributes the generative workload across multiple specialized sub-networks.

Key Ideas

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 harder to model.
Two complementary, tractable complexity monitoring functions are introduced to estimate local difficulty:
A spatial measure based on the Dirichlet energy of flows.
A geometric measure based on the acceleration of sampling trajectories.
A lightweight auxiliary model estimates these complexity profiles, eliminating the need for heuristic temporal splits or expensive search procedures.

Results

Extensive evaluations across multiple architectures (SiT, JiT, UNet) and datasets show CBS consistently improves synthesis quality without increasing per-step inference cost.
Notably, CBS improves FID by about 35% on SiT-XL with CFG compared to naive temporal partitioning.

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