Visualization for a new era: Impact and application of large language models and AIGC to traditional business models

Qianqian Yang, Ngai Cheong, Dejiang Wang, Shi Li, Oi Neng Lei
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Abstract

This paper focuses on the application and business value of large-scale language models, such as GPT and Ernie’s model. These models combined with AIGC tools like stable diffusion generate images with fixed styles, character traits, and continuous plots using randomized story scripts. As a result, it enhances the operational efficiency between or within industries widely, and it fully demonstrate their business value. On the technical side, this paper describes in detail of building a pipeline to generate cue words required for stable diffusion, in which using large-scale language models and story scripts. Subsequently, the limitations of text-to-image are summarized by comparing the traditional method and language model, i.e. comparing characteristics from traditional book production and images generated using language model’s cue words. This leads to a supervised multiround iterative LoRA modeling scheme that utilizes CLIP to achieve character IP fixation. To evaluate the impact of the application direction, we combine application scenarios and researches on application aspects regarding current AIGC industry structure, we found that the AIGC tool has several major aspects, mainly includes the aspects of basic big model, industry and scenario models, business and domain small models, AI infrastructure and AIGC supporting services. big model and AIGC techniques generate images with no specific rules and have less limitation. We call this ‘visualization’ in the new AI era. In this paper, we explore the possible impacts and economic values when changing from traditional domain to the new AI ear.
新时代的可视化:大型语言模型和 AIGC 对传统商业模式的影响和应用
本文重点介绍 GPT 和厄尼模型等大规模语言模型的应用和商业价值。这些模型与稳定扩散等 AIGC 工具相结合,利用随机故事脚本生成具有固定风格、人物特征和连续情节的图像。因此,它大大提高了行业间或行业内的运营效率,充分体现了其商业价值。在技术方面,本文详细介绍了如何利用大规模语言模型和故事脚本,建立一个生成稳定扩散所需的提示词的管道。随后,通过比较传统方法和语言模型,即比较传统图书制作的特征和使用语言模型提示词生成的图像,总结了文本到图像的局限性。由此产生了一种监督式多轮迭代 LoRA 建模方案,该方案利用 CLIP 实现字符 IP 固定。为了评估应用方向的影响,我们结合应用场景和对当前 AIGC 产业结构应用方面的研究,发现 AIGC 工具有几个主要方面,主要包括基础大模型、行业和场景模型、业务和领域小模型、人工智能基础架构和 AIGC 配套服务。我们称之为新人工智能时代的 "可视化"。在本文中,我们将探讨从传统领域向新人工智能领域转变时可能产生的影响和经济价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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