Abstract
Diffusion models in AI-generated content (AIGC) have revolutionized image generation with their ability to transform noise into structured data through a denoising process. Despite their prowess, these models grapple with high computational demands, inconsistent image quality, and challenges in producing high-resolution images. This paper discusses the performance of diffusion models in image generation, emphasizing the need for improved computational efficiency, generation quality, and contextual understanding. Future research is aimed at developing real-time generation technologies, addressing ethical and legal concerns, and enhancing data privacy measures. With these advancements, diffusion models are expected to achieve broader application, enhanced performance, and increased safety in image generation, expanding their utility in fields such as advertising, medical imaging, and art creation. The potential of diffusion models to meet the increasing demands for quality, content, and diversity in image applications makes them a pivotal area of study in visual technology and artificial intelligence.
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