MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction

Monocular dense prediction has recently seen remarkablesuccess by repurposing pre-trained diffusion models. This opens a promis-ing yet challenging avenue for more efficient multi-task learning paradigm.However, existing multi-task diffusion methods often introduce parameter-heavy adapters, experts, or learnable task tokens, leading to computa-tional redundancy. In this paper, we reveal an inherent mechanism withinone-step diffusion models: the native, fixed timestep positional embed-ding can be repurposed as an endogenous task steering signal. Based onthis discovery, we propose Multi-task Unified eStimation via timestepEmbedding (MUSE), a parameter-free, single-model multi-tasking ap-proach for dense prediction. We interpret this mechanism via Mani-fold Decoupling, where discrete, fixed timestep values deterministicallysteer the generation process towards decoupled, task-specific manifolds inthe latent space. Extensive experiments across 10 datasets demonstratethat MUSE achieves highly competitive performance on both monoculardepth and normal estimation, and its efficacy generalizes across U-Netand DiT architectures. Our work offers a concise and efficient path to-ward generalist vision models by simply unlocking the latent potentialof existing generation infrastructure.