Granularity-Aligned Selective Conditioning for Image Style Transfer

Despite recent advances in reference-based image style transfer, existing methods still struggle to transfer global appearance and local stylistic details while preserving content structure. We introduce Granularity-Aligned Selective Conditioning (GASC), a framework that aligns style granularity with the distinct conditioning interfaces in a pretrained Diffusion–Mamba denoiser. GASC assigns shared image-level style conditioning to state-transition dynamics and token-varying style carriers to input writes. To construct these conditions, per-head orthogonal residualization reduces content interference in reference features, while content-conditioned carrier retrieval captures local style information. A separate content stream anchors spatial structure throughout denoising. Together, these designs coordinate global and local style conditioning with content preservation. Extensive experiments demonstrate that GASC outperforms existing methods in overall stylization quality while maintaining competitive content preservation.