# LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation Source: [LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:41:42.944Z Authors: Fan Deng, Yaguang Wu, Xinyang Yu, Xiangjun Huang, Jian Yang, Guangyu Yan, Qiang Xu Published: 2024-11-22T08:44:39.000Z ## Abstract Recently, text-to-image models based on diffusion have achieved remarkable success in generating high-quality images. However, the challenge of personalized, controllable generation of instances within these images remains an area in need of further development. In this paper, we present LocRef-Diffusion, a novel, tuning-free model capable of personalized customization of multiple instances' appearance and position within an image. To enhance the precision of instance placement, we introduce a Layout-net, which controls instance generation locations by leveraging both explicit instance layout information and an instance region cross-attention module. To improve the appearance fidelity to reference images, we employ an appearance-net that extracts instance appearance features and integrates them into the diffusion model through cross-attention mechanisms. We conducted extensive experiments on the COCO and OpenImages datasets, and the results demonstrate that our proposed method achieves state-of-the-art performance in layout and appearance guided generation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()