Functional Cross-layer Experts Routing for One-step Diffusion Distillation

One-step distillation compresses diffusion trajectories into a single forward evaluation, yet how internal capacity should be structured within that remaining pass remains unexplored. In this setting, identical parameters must simultaneously preserve general generative priors and resolve prompt-specific details without iterative refinement. We formalize this challenge as functional allocation. Our analysis reveals that decoupling representations is beneficial only when expert interference outweighs shared transfer, favoring an always-active shared backbone augmented with conditionally routed residuals over standard isolated MoE architectures. We propose $\text{DeMoE}$, which pairs a persistent shared expert with conditionally routed expert slices across image FFNs, trained under a one-step distribution-matching objective with executed-route supervision. On one-step FLUX.2-4B generation, DeMoE achieves a GenEval score of 0.8220 with only 80.5% active image-FFN capacity—substantially outperforming dense one-step baselines and matching the 50-step teacher while surpassing it on complex composition and counting. Across COCO-10K, DeMoE establishes state-of-the-art fidelity and diversity among single-step models, demonstrating that structured internal allocation provides a superior compression paradigm for diffusion models.