While pre-trained generative models offer numerous benefits for semantic communication, there are also potential drawbacks and limitations to consider. One limitation is the need for extensive training data to ensure the models' effectiveness across various tasks and datasets. Additionally, pre-trained models may not always capture the specific nuances or context of a particular communication task, leading to potential inaccuracies or misinterpretations. Another drawback is the computational complexity and resource requirements associated with training and deploying these models, which can be a barrier for some applications. Furthermore, pre-trained models may struggle with adapting to real-time or dynamic communication scenarios, limiting their flexibility in certain contexts.