LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation
It addresses the challenge of instance-level customization in text-to-image generation, which is incremental as it builds on existing diffusion models.
The paper tackles the problem of personalized, controllable generation of multiple instances' appearance and position in images using a tuning-free diffusion model, achieving state-of-the-art performance on COCO and OpenImages datasets.
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.