Strictly-ID-Preserved and Controllable Accessory Advertising Image
  Generation
Youze Xue Binghui Chen Yifeng Geng Xuansong Xie Jiansheng Chen Hongbing Ma
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.