S2AR: Dual-Axis Routing for Continuous Attribute Control in Visual Autoregressive Models

Visual autoregressive models have emerged as a powerful and scalable paradigm for image synthesis. However, continuous attribute control within this framework remains underexplored. To address this gap, we present S2AR, a training-free framework for continuous attribute control. Specifically, we formulate attribute control as a dual-axis routing problem across spatial and scale dimensions. S2AR first identifies target visual tokens to construct a spatial localization map, and subsequently determines scale-wise routing weights based on structural sensitivity. Guided by these routing weights, a single control scalar jointly modulates cross-attention and global conditioning, thereby continuously steering attribute intensity. On our constructed Continuous Attribute Control Benchmark, S2AR consistently outperforms existing training-free and LoRA-based methods while faithfully preserving identity and background details.