Complexity-Balanced Diffusion Splitting
Summary
Complexity-Balanced Splitting (CBS) partitions the diffusion timeline into segments of equal approximation burden using local complexity measures, improving synthesis quality by ~35% in FID without increasing inference cost.
View Cached Full Text
Cached at: 06/05/26, 06:06 AM
Paper page - Complexity-Balanced Diffusion Splitting
Source: https://huggingface.co/papers/2606.06477
Abstract
Complexity-Balanced Splitting (CBS) allocates generative capacity across specialized sub-networks by partitioning the diffusion timeline based on local complexity measures, improving synthesis quality without increasing inference cost.
Standard continuous-timegenerative modelsrely 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 fortemporal capacity allocationthat distributes the generative workload across multiple specialized sub-networks. Grounded infunction approximation theoryandde Boor’s equidistribution principle, CBS partitions thediffusion timelineinto 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’sDirichlet energy, and a geometric measure based on the acceleration of thesampling trajectories. Using a lightweight auxiliary model to estimate thesecomplexity 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 improvesFIDby ~35% on SiT-XL with CFG relative to naive temporal partitioning. Project page is available at https://noamissachar.github.io/CBS/.
View arXiv pageView PDFProject pageGitHub2Add to collection
Get this paper in your agent:
hf papers read 2606\.06477
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2606.06477 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2606.06477 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2606.06477 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
Backbone-Equated Diffusion OOD via Sparse Internal Snapshots
This paper introduces a protocol for fair comparison of diffusion-based OOD detectors and proposes Canonical Feature Snapshots (CFS), which leverage sparse internal activations for efficient detection.
Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
This paper introduces Temporal-Spatial Parallel Decoding (TSPD) and Confidence Extrapolation (CE) to accelerate inference in diffusion-based large language models by dynamically deciding when tokens have converged and forecasting logit trends, reducing unnecessary denoising steps while preserving output quality.
Elucidating the SNR-t Bias of Diffusion Probabilistic Models
This paper identifies a Signal-to-Noise Ratio timestep (SNR-t) bias in diffusion probabilistic models during inference, where SNR-timestep alignment from training is disrupted at inference time. The authors propose a differential correction method that decomposes samples into frequency components and corrects each separately, improving generation quality across models like IDDPM, ADM, DDIM, EDM, and FLUX with minimal computational overhead.
Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
This paper introduces Continuous-Time Distribution Matching (CDM), a method for few-step diffusion distillation that migrates from discrete to continuous optimization to improve visual fidelity and preserve fine details.
Dynamic Chunking for Diffusion Language Models
This paper introduces Dynamic Chunking for Diffusion Language Models (DCDM), which replaces fixed positional blocks in block discrete diffusion with content-defined semantic chunks using a differentiable Chunking Attention mechanism, achieving consistent improvements across scales up to 1.5B parameters.