## 4 Discussion
### AIGC Models  
Variational autoencoders (VAEs) provide a strong framework for learning compressed representations of incoming data and creating new samples. Their benefits include the capacity to capture data uncertainty, provide a continuous latent space for interpolation, and provide efficient data representation [19]. However, as compared to other models, VAEs may have limits in generating extremely realistic and varied samples, resulting in fuzzy or less detailed outputs. VAEs are especially useful in sectors where data structureand uncertainty are important, such as image generation [77], data compression, and anomaly detection. Generative adversarial networks (GANs) are excellent at producing realistic and varied samples, making them excellent for tasks requiring high visual quality. GANs provide versatility in data generation across domains. GANs, on the other hand, can be difficult to train and keep stable, requiring precise hyperparameter adjustment. Mode collapse, in which the generator fails to capture the whole data distribution, can also occur. Despite these problems, GANs are nevertheless quite effective in image synthesis [90; 92; 93], image-to-image translation [87; 88; 89], and providing synthetic training data [83; 94] for deep learning models. Diffusion Models are well-known for producing high-quality samples with fine features and realistic textures. They provide more control over sample quality by modifying the number of diffusion steps, and they are less prone to mode collapse than GANs. Training diffusion models, on the other hand, may be computationally expensive, and tuning hyperparameters is critical for their success. Diffusion Models excel in sectors requiring high-resolution image generation [49], creative image stylization, video generation [104; 106], and image inpainting, where the emphasis is on delivering aesthetically stunning and detailed outputs. Transformers, with their self-attention mechanism, efficiently capture long-term dependencies and have revolutionized natural language processing tasks. By creating coherent and contextually appropriate sequences, they excel at tasks like machine translation [45; 110], text generation [39; 116], and sentiment analysis [111; 121]. However, because of memory limits, transformers can struggle with very lengthy sequences, and training large-scale models can be computationally costly. Transformers excel in sectors where comprehending global context and producing high-quality language sequences are essential. Language models, which are frequently built on RNNs, have the benefit of producing cohesive and contextually appropriate sequences. They extract grammar, semantics, and style from training data, which makes them useful for applications like text generation [107], chatbots [33], and language understanding [108]. Language models, on the other hand, might be subject to training data biases and may output language that reflects such prejudices. Long sequences can also result in a lack of cohesion and meaningful context. Language models are useful in areas where creating natural language sequences is critical, such as conversation systems [34], text generation [107], and language modeling [109]. Normalizing flow models provide a versatile framework for producing samples and modeling complicated distributions. They are very good at density estimation and enable accurate likelihood calculation [21]. Normalizing flow models, on the other hand, can be computationally costly, especially when dealing with a high number of coupling layers or complicated data distributions. They discover efficiency in disciplines including generative modeling, density estimation, and simulation-based inference, where correct modeling of complicated data distributions is critical [76]. Hybrid models incorporating numerous deep learning architectures have been created to leverage on the strengths of distinct models in various areas.