[2601.08011] TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.08011 (cs) [Submitted on 12 Jan 2026 (v1), last revised 1 Mar 2026 (this version, v4)] Title:TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models Authors:Xin Jin, Yichuan Zhong, Yapeng Tian View a PDF of the paper titled TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models, by Xin Jin and 2 other authors View PDF HTML (experimental) Abstract:Current text-conditioned diffusion editors handle single object replacement well but struggle when a new object and a new style must be introduced simultaneously. We present Twin-Prompt Attention Blend (TP-Blend), a lightweight training-free framework that receives two separate textual prompts, one specifying a blend object and the other defining a target style, and injects both into a single denoising trajectory. TP-Blend is driven by two complementary attention processors. Cross-Attention Object Fusion (CAOF) first averages head-wise attention to locate spatial tokens that respond strongly to either prompt, then solves an entropy-regularised optimal transport problem that reassigns complete multi-head feature vectors to those positions. CAOF updates feature vectors at the full combined dimensionality of all heads (e.g., 640 dimensions in SD-XL), preserving rich cross-head correlations while keeping memory low. Self-Attention Style Fusion (SASF) injects style at every self-attent...