Chuang-Chieh Lin¹, Yung-Shen Huang², Shih-Yeh Chen^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.072890 - 09 December 2025

Abstract With the rapid development of generative artificial intelligence (GenAI), the task of story visualization, which transforms natural language narratives into coherent and consistent image sequences, has attracted growing research attention. However, existing methods still face limitations in balancing multi-frame character consistency and generation efficiency, which restricts their feasibility for large-scale practical applications. To address this issue, this study proposes a modular cloud-based distributed system built on Stable Diffusion. By separating the character generation and story generation processes, and integrating multi-feature control techniques, a caching mechanism, and an asynchronous task queue architecture, the system enhances generation… More >

Yujie Wu¹, Xing Deng^1,*, Haijian Shao¹, Ke Cheng¹, Ming Zhang¹, Yingtao Jiang², Fei Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2709-2778, 2025, DOI:10.32604/cmes.2025.066647 - 30 September 2025

Abstract The application of generative artificial intelligence (AI) is bringing about notable changes in anime creation. This paper surveys recent advancements and applications of diffusion and language models in anime generation, focusing on their demonstrated potential to enhance production efficiency through automation and personalization. Despite these benefits, it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models. We conduct an in-depth survey of cutting-edge generative AI technologies, encompassing models such as Stable Diffusion and GPT, and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics. Review of the surveyed literature… More >

Sabina Rakhmetulayeva^1,*, Zhandos Zhanabekov², Aigerim Bolshibayeva³

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4503-4523, 2024, DOI:10.32604/cmc.2024.057974 - 19 December 2024

Abstract The applications of machine learning (ML) in the medical domain are often hindered by the limited availability of high-quality data. To address this challenge, we explore the synthetic generation of echocardiography images (echoCG) using state-of-the-art generative models. We conduct a comprehensive evaluation of three prominent methods: Cycle-consistent generative adversarial network (CycleGAN), Contrastive Unpaired Translation (CUT), and Stable Diffusion 1.5 with Low-Rank Adaptation (LoRA). Our research presents the data generation methodology, image samples, and evaluation strategy, followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of More > Graphic Abstract

Hong Zhang^1,*, Haijian Shao²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2061-2102, 2024, DOI:10.32604/cmes.2023.030649 - 15 December 2023

Abstract OpenAI and ChatGPT, as state-of-the-art language models driven by cutting-edge artificial intelligence technology, have gained widespread adoption across diverse industries. In the realm of computer vision, these models have been employed for intricate tasks including object recognition, image generation, and image processing, leveraging their advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have found utility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactive player experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providing invaluable insights and support to healthcare professionals in… More > Graphic Abstract