Recent advancements in image synthesis are fueled by the advent of large-scale diffusion models. Yet, integrating realistic object visualizations seamlessly into new or existing backgrounds without extensive training remains a challenge. This paper introduces InsertDiffusion, a novel, training-free diffusion architecture that efficiently embeds objects into images while preserving their structural and identity characteristics. Our approach utilizes off-the-shelf generative models and eliminates the need for fine-tuning, making it ideal for rapid and adaptable visualizations in product design and marketing. We demonstrate superior performance over existing methods in terms of image realism and alignment with input conditions. By decomposing the generation task into independent steps, InsertDiffusion offers a scalable solution that extends the capabilities of diffusion models for practical applications, achieving high-quality visualizations that maintain the authenticity of the original objects.

该论文介绍了InsertDiffusion，一种新颖的、无需训练的扩散架构，可以高效地将对象嵌入图像并保持其结构和特征，通过将生成任务分解为独立步骤，该方法为产品设计和市场营销中的快速、可适应的可视化提供了可扩展的解决方案，并在图像逼真度和与输入条件对齐方面展现了优异性能。

InsertDiffusion: 无需训练的扩散架构实现对象的身份保持可视化