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

Strictly-ID-Preserved and Controllable Accessory Advertising Image  Generation

Existing pre-trained language models (PLMs) have demonstrated the
effectiveness of self-supervised learning for a broad range of natural language
processing (NLP) tasks. However, most of them are not explicitly aware of
domain-specific knowledge, which is essential for downstream tasks in many
domains, such as tasks in e-commerce scenarios. In this paper, we propose
K-PLUG, a knowledge-injected pre-trained language model based on the
encoder-decoder transformer that can be transferred to both natural language
understanding and generation tasks. We verify our method in a diverse range of
e-commerce scenarios that require domain-specific knowledge. Specifically, we
propose five knowledge-aware self-supervised pre-training objectives to
formulate the learning of domain-specific knowledge, including e-commerce
domain-specific knowledge-bases, aspects of product entities, categories of
product entities, and unique selling propositions of product entities. K-PLUG
achieves new state-of-the-art results on a suite of domain-specific NLP tasks,
including product knowledge base completion, abstractive product summarization,
and multi-turn dialogue, significantly outperforms baselines across the board,
which demonstrates that the proposed method effectively learns a diverse set of
domain-specific knowledge for both language understanding and generation tasks.

本研究提出了一种基于 encoder-decoder transformer 的知识注入预训练语言模型 K-PLUG，利用五种知识感知的自我监督预训练目标，包括电子商务领域特定知识库、产品实体的方面、产品实体的类别和产品实体的独特卖点，有效地为语言理解和生成任务学习了各种领域特定知识，并在包括产品知识库完成、摘要产品总结和多次对话等领域特定 NLP 任务中达到了新的最佳性能。