SDiT: Semantic Region-Adaptive for Diffusion Transformers

2026-01-18

AI Summary

Diffusion Transformers for text-to-image generation suffer from high computational overhead due to the iterative denoising process and the quadratic complexity of global attention. This work observes that semantic regions within generated images exhibit markedly different convergence rates during denoising. Leveraging this insight, the authors propose a training-free, semantics-aware adaptive inference framework that dynamically allocates computational resources without altering the model architecture. The method employs Quickshift-based semantic segmentation to cluster image regions, followed by region-wise complexity assessment, selective update strategies, and boundary consistency optimization. Experiments demonstrate that this approach achieves up to 3.0× inference speedup while preserving perceptual and semantic quality nearly on par with full-attention inference.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterative nature of denoising and the quadratic cost of global attention. In this work, we observe that denoising dynamics are spatially non-uniform-background regions converge rapidly while edges and textured areas evolve much more actively. Building on this insight, we propose SDiT, a Semantic Region-Adaptive Diffusion Transformer that allocates computation according to regional complexity. SDiT introduces a training-free framework combining (1) semantic-aware clustering via fast Quickshift-based segmentation, (2) complexity-driven regional scheduling to selectively update informative areas, and (3) boundary-aware refinement to maintain spatial coherence. Without any model retraining or architectural modification, SDiT achieves up to 3.0x acceleration while preserving nearly identical perceptual and semantic quality to full-attention inference.

Problem

• Diffusion Transformers
• computational efficiency
• text-to-image synthesis
• global attention
• denoising dynamics

Innovation

• Diffusion Transformers
• region-adaptive computation
• semantic-aware clustering
• complexity-driven scheduling
• training-free acceleration