## 1 Introduction
### Research Questions  
The motivation for this research stems from the growing importance of generative AI systems and the need for a deeper understanding of their fundamental aspects. By investigating the requirements necessary for implementing generative AI systems, exploring the different types of generative AI models, identifying specific input and output formats, and discussing commonly used evaluation metrics, we aim to provide researchers, developers, and practitioners with comprehensive insights and guidance in the field. The goal is to enhance the implementation, selection, customization, and evaluation of generative AI models for various applications, ultimately fostering advancements in generative AI.  
* What are the requirements necessary for implementing generative AI systems?
* To address this question, we present three distinct categories of requirements for AIGC: hardware, software, and user experience requirements.
* What are the different types of generative AI models described in the literature?
* To explore this, we present a taxonomy of AIGC models based on their architecture, including VAEs, GANs, diffusion models, transformers, language models, normalizing flow models, and hybrid models.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attribute** & **Web 1.0** & **Web 2.0** & **Web 3.0** \\ \hline Communication & Basic email and discussion forums. & Social media platforms and instant messaging. & Advanced semantic communication and real-time collaboration. Seamless communication across platforms and devices. \\ \hline Innovation & Limited innovation, focus on information. & Rapid innovation and collaboration. & Emphasis on AI, automation, and emerging technologies. \\ \hline Computation resources & Limited computational and processing power on user devices. Focus on server-side computation. & Increased computational power on user devices. Focus on client-side computing. & Distributed computation and edge computing. \\ \hline Storage capacity & Limited storage capacity on servers. & Increased cloud storage and scalable data storage solutions. & Expanded storage capacity. Distributed and scalable storage solutions. \\ \hline Data access & Passive data consumption. & Active participation and data sharing. & Intelligent data access and personalized recommendations. \\ \hline Examples & Early websites, static informational pages. & Social media platforms (Facebook, Twitter), blogging platforms. & Virtual collaborative assistants, AI-powered applications, blockchain platforms. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._* _What specific input and output formats are used for different prescribed tasks in generative AI systems?_
* _We provide a comprehensive classification of input and output formats for AIGC tasks, along with specific tasks and the corresponding models used in the literature, presented in a tabular format._
* _What evaluation metrics are commonly employed to validate the output generated by generative AI models?_
* _We propose a classification system based on the output types of generative AI and discuss commonly used evaluation metrics in the field._