A Multidimensional Analysis and Research on Breakthrough Optimization of Mainstream AIGC Generation Models

DOI

10.2991/978-94-6463-823-3_16How to use a DOI?

Keywords

Artificial intelligence; Generative Adversarial Network; Variational Auto Encoder; Wasserstein GAN; Auto-Regressive Models; Diffusion Models

Abstract

Artificial Intelligence Generated Content (AIGC) originated in the mid-20th century and has evolved from rule-based systems to deep learning. While AIGC technology has penetrated diverse applications, it faces critical bottlenecks in meeting practical demands such as high-resolution generation, cross-modal adaptation, and edge deployment. This essay systematically reviews the evolutionary trajectory of mainstream generative models, revealing four core challenges: Generative Adversarial Network (GAN) suffers from mode collapse and high computational dependency; Variational Auto Encoder (VAE) exhibits deficiencies in detail restoration and latent space disentanglement; Auto-Regressive Models are constrained by inefficiency and long-range modeling limitations; Diffusion Models grapple with computational costs and semantic consistency issues. To address these challenges, this study proposes a multi-dimensional improvement framework: enhancing GAN stability through Wasserstein GAN with Gradient Penalty (WGAN-GP) and progressive knowledge distillation; optimizing VAE representation via Inverse Auto-Regressive Flow (IAF) and adversarial training; breaking sequence generation efficiency barriers with sparse-attention non-auto regressive Transformers; and designing fractional latent Diffusion Models to achieve breakthroughs in complexity and semantic coherence. Experimental results demonstrate that the proposed improvements not only significantly enhance image resolution but also reduce GPU memory usage by 47%, enabling real-time generation on resource-constrained devices. These breakthroughs provide a scalable technical foundation for the deep integration of AIGC in digital twins, industrial design, and related fields.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tianwei Yang
PY  - 2025
DA  - 2025/08/31
TI  - A Multidimensional Analysis and Research on Breakthrough Optimization of Mainstream AIGC Generation Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 172
EP  - 185
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_16
DO  - 10.2991/978-94-6463-823-3_16
ID  - Yang2025
ER  -