Semantic communications mark a paradigm shift from bit-accurate transmission toward meaning-centric communication, essential as wireless systems approach theoretical capacity limits. The emergence of generative AI has catalyzed generative semantic communications, where receivers reconstruct content from minimal semantic cues by leveraging learned priors. Among generative approaches, diffusion models stand out for their superior generation quality, stable training dynamics, and rigorous theoretical foundations. However, the field currently lacks systematic guidance connecting diffusion techniques to communication system design, forcing researchers to navigate disparate literatures. This article provides the first comprehensive tutorial on diffusion models for generative semantic communications. We present score-based diffusion foundations and systematically review three technical pillars: conditional diffusion for controllable generation, efficient diffusion for accelerated inference, and generalized diffusion for cross-domain adaptation. In addition, we introduce an inverse problem perspective that reformulates semantic decoding as posterior inference, bridging semantic communications with computational imaging. Through analysis of human-centric, machine-centric, and agent-centric scenarios, we illustrate how diffusion models enable extreme compression while maintaining semantic fidelity and robustness. By bridging generative AI innovations with communication system design, this article aims to establish diffusion models as foundational components of next-generation wireless networks and beyond.

翻译：语义通信标志着从比特精确传输向以意义为中心的通信的范式转变，这在无线系统接近理论容量极限时至关重要。生成式AI的涌现催生了生成式语义通信，其中接收器通过利用学习到的先验知识，从极少的语义线索中重构内容。在生成式方法中，扩散模型因其优越的生成质量、稳定的训练动态和严谨的理论基础而脱颖而出。然而，该领域目前缺乏将扩散技术与通信系统设计相联系的系统性指导，迫使研究者不得不查阅分散的文献。本文首次提供了关于面向生成式语义通信的扩散模型的全面教程。我们介绍了基于分数的扩散基础，并系统性地综述了三大技术支柱：用于可控生成的条件扩散、用于加速推理的高效扩散以及用于跨领域适应的广义扩散。此外，我们引入了一种逆问题视角，将语义解码重构为后验推断，从而将语义通信与计算成像相连接。通过对以人为中心、以机器为中心和以智能体为中心的场景分析，我们阐释了扩散模型如何在保持语义保真度和鲁棒性的同时实现极端压缩。通过桥接生成式AI创新与通信系统设计，本文旨在将扩散模型确立为下一代无线网络及其演进中的基础组件。