TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts

TL;DR

This paper introduces TAG-MoE, a task-aware gating framework for unified generative models that reduces task interference by incorporating semantic intent into routing, leading to improved fidelity and specialized expert behaviors.

Contribution

The paper proposes a novel hierarchical semantic annotation scheme and predictive alignment regularization to make MoE gating task-aware, enhancing specialization and performance.

Findings

Outperforms dense baselines in fidelity and quality

Experts develop clear, semantically correlated specializations

Effectively mitigates task interference in unified models

Abstract

Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations.