端到端语义通信通过深度学习模型提取数据的语义特征，实现了以意图为导向的通信过程，显著提升了传输效率。然而，现有的基于判别式模型的语义通信范式采用符号级率—失真优化，仅根据接收信号进行最大似然估计，难以满足用户的感知需求。为保障传输数据的视觉质量，生成式视觉语义通信范式应运而生，该范式采用率—失真—感知优化框架，通过最大后验估计实现数据传输与人类感知的对齐。扩散模型在可控视觉生成方面的优势促使其成为生成式视觉语义通信的重要工具。然而，现有研究缺乏对扩散模型赋能语义通信技术路径的系统梳理。为此，该文将通信过程建模为数学逆问题，阐述了扩散模型通过后验采样求解语义压缩与传输问题的一般方法论，表明了基于扩散模型的解码器能实现数据的高效压缩并在恶劣的信道环境下保持传输韧性，进而对扩散模型在生成式视觉语义通信领域的未来研究进行展望。

Significance: End-to-end semantic communication leverages deep learning models to extract semantic features from data, enabling intent-driven communication processes that significantly enhance transmission efficiency. However, existing semantic communication paradigms based on discriminative models employ symbol-level rate-distortion optimization and perform maximum likelihood estimation solely based on received signals, failing to satisfy the perceptual requirements of users. To ensure the visual quality of transmitted data, a generative visual semantic communication paradigm has emerged, which adopts a rate-distortion-perception optimization framework to achieve alignment between data transmission and human perception through maximum a posteriori estimation. Diffusion models are advantageous for controlling visual generation and have thus become essential tools for this generative paradigm. Nevertheless, systematic organization of the technical roadmaps for empowering semantic communication using diffusion models is lacking in current research. Progress: This study addresses this gap by modeling the communication process as a mathematical inverse problem and elucidating the general methodology by which diffusion models solve data compression and transmission challenges through posterior sampling. The fundamental concepts, mathematical formulations, and sampling strategies underpinning diffusion models are systematically introduced. In addition, the general methods and key technologies employed for diffusion model-enabled generative compression and transmission are comprehensively reviewed from an inverse problem-solving perspective. Moreover, the performance metrics commonly used for objective assessment of the visual quality of transmitted data are summarized to provide a comprehensive evaluation framework. The core methodology demonstrates that generalized communication processes can be effectively modeled as inverse problems. The approach involves inferring the source data distribution using maximum a posteriori estimation based on channel measurements and forward operators composed of various signal processing operations. Through diffusion posterior sampling, diffusion models solve these communication inverse problems via a three-step process: first, pre-training diffusion models from large-scale datasets are used to obtain diffusion priors; second, joint source-channel codecs are used to mitigate channel distortions in visual data transmission and construct proximal regularization terms; finally, measurement regularization terms are constructed based on channel measurements. By integrating these regularization terms for posterior estimation and distribution sampling, diffusion models can implicitly reconstruct source data through gradient descent, effectively overcoming transmission challenges caused by strong channel noise, nonlinear operators, and time-varying channel conditions. Conclusions and Prospects: The analysis reveals that compared to visual semantic communication approaches based on discriminative deep learning models, the generative visual semantic communication paradigm based on diffusion models can significantly improve transmission efficiency and resilience while ensuring perceptual quality and semantic consistency of visual information. This advancement represents a fundamental shift toward communication systems that prioritize human perceptual requirements alongside traditional distortion metrics. Open issues, including image realism modeling and acceleration of diffusion model sampling, are discussed. The report highlights the effectiveness of conditional diffusion models for enabling existing semantic communication architectures to recover sources at the receiver based on minimal tokens and highly degraded measurements, offering an intelligent and concise design philosophy for future generative visual semantic communication systems.