Customized generative text-to-image models have the ability to produce images that closely resemble a given subject. However, in the context of generating advertising images for e-commerce scenarios, it is crucial that the generated subject's identity aligns perfectly with the product being advertised. In order to address the need for strictly-ID preserved advertising image generation, we have developed a Control-Net based customized image generation pipeline and have taken earring model advertising as an example. Our approach facilitates a seamless interaction between the earrings and the model's face, while ensuring that the identity of the earrings remains intact. Furthermore, to achieve a diverse and controllable display, we have proposed a multi-branch cross-attention architecture, which allows for control over the scale, pose, and appearance of the model, going beyond the limitations of text prompts. Our method manages to achieve fine-grained control of the generated model's face, resulting in controllable and captivating advertising effects.

经过开发Control-Net的定制图像生成流程以及提出多分支交叉注意力架构，我们成功实现了面向电子商务广告场景的广告图像生成，确保生成的主题与所宣传的产品完美匹配，并实现了对生成模型面部的精细控制，展现了可控且引人注目的广告效果。

严格保留ID和可控辅助广告图像生成