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范式的转变，这在无线系统接近理论容量极限时至关重要。生成式AI的出现催化了生成式语义通信，使接收端能够通过利用学习到的先验知识，从最少的语义线索中重建内容。在生成方法中，扩散模型因其卓越的生成质量、稳定的训练动态和严格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联系起来的系统性指导，迫使研究人员在分散的文献中摸索。本文首次提供了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，并系统性地回顾了三个技术支柱：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，将语义解码重新表述为后验推理，从而桥接语义通信与计算成像。通过分析以人为中心、以机器为中心和以智能体为中心的三种场景，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致的压缩。通过将生成式AI的创新与通信系统设计相结合，本文旨在将扩散模型确立为下一代无线网络及更远未来的基础组件。