Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers

Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning often remains static, relying on a fixed representation from a single LLM layer across all denoising timesteps and DiT blocks. This motivates a central question for DiT text conditioning: when multi-layer LLM features are available, how should hierarchical semantics be injected into the dynamic generation process? To investigate this question, we introduce Semantic Routing, a lightweight multi-layer conditioning interface that forms text features through normalized convex fusion of LLM hidden states and compares static, time-wise, depth-wise, and joint routing strategies under matched training and evaluation settings. Across text--image alignment and compositional benchmarks, depth-wise routing emerges as the most reliable strategy among the studied variants, with particularly strong gains on advanced prompt-following tasks (e.g., +9.97 on GenAI-Bench Counting over the penultimate-layer baseline). In contrast, purely time-wise routing can degrade visual fidelity; our analysis suggests a mismatch between nominal timesteps and the effective inference trajectory, making time-conditioned semantic injection poorly timed. These results indicate that DiT depth is an effective axis for improving text--image alignment through dynamic LLM feature injection, while robust time-dependent conditioning remains an open challenge.