Dynamic Differential Linear Attention: Enhancing Linear Diffusion Transformer for High-Quality Image Generation

TL;DR

This paper introduces Dynamic Differential Linear Attention (DyDiLA), a novel linear attention mechanism that improves the quality of high-fidelity image generation in diffusion transformers by reducing oversmoothing and enhancing expressiveness.

Contribution

The paper presents DyDiLA, a new linear attention formulation with dynamic projection, measure kernel, and token differential operator, significantly improving diffusion transformer performance.

Findings

DyDi-LiT outperforms state-of-the-art models in image generation quality.

DyDiLA effectively mitigates oversmoothing in linear diffusion transformers.

Extensive experiments validate the practical benefits of DyDiLA in high-fidelity image synthesis.

Abstract

Diffusion transformers (DiTs) have emerged as a powerful architecture for high-fidelity image generation, yet the quadratic cost of self-attention poses a major scalability bottleneck. To address this, linear attention mechanisms have been adopted to reduce computational cost; unfortunately, the resulting linear diffusion transformers (LiTs) models often come at the expense of generative performance, frequently producing over-smoothed attention weights that limit expressiveness. In this work, we introduce Dynamic Differential Linear Attention (DyDiLA), a novel linear attention formulation that enhances the effectiveness of LiTs by mitigating the oversmoothing issue and improving generation quality. Specifically, the novelty of DyDiLA lies in three key designs: (i) dynamic projection module, which facilitates the decoupling of token representations by learning with dynamically assigned knowledge; (ii) dynamic measure kernel, which provides a better similarity measurement to capture fine-grained semantic distinctions between tokens by dynamically assigning kernel functions for token processing; and (iii) token differential operator, which enables more robust query-to-key retrieval by calculating the differences between the tokens and their corresponding information redundancy produced by dynamic measure kernel. To capitalize on DyDiLA, we introduce a refined LiT, termed DyDi-LiT, that systematically incorporates our advancements. Extensive experiments show that DyDi-LiT consistently outperforms current state-of-the-art (SOTA) models across multiple metrics, underscoring its strong practical potential.