Recently, has revealed ProT-Diff, a novel modularized deep generative model, for de novo Anti-Microbial Peptides (AMPs) generation, AMPs are the current hope for overcoming the antimicrobial resistance world-wide problem. ProT-Diff is a sandwich model, embedding a continuous diffusion model between the encoder and the decoder of the transformer-based protein language model (PLM) ProtT5-XL-UniRef5023. This technique maximized the efficacy of the pre-trained PLM and minimized the needed computational power. In one experiment they trained the diffusion model with a dataset of AMPs and non-AMPs, pre-trained it on a peptide dataset, UniProtKB, to learn a general grammar of protein sequences, and then fine-tuned the pre-trained model on the specific AMP dataset to learn specific details from AMPs; this full training took less than forty hours. The model was evaluated in-silico, in-vitro, and in-vivo. The model has not only produced replicated sequences but has shown uniqueness in the produced AMPs; the sequences differed in a range of 20-100 %. Moreover, one of the produced AMPs, AMP_2, shown effectiveness against various anti-microbial-resistant bacteria, having low Minimum Inhibitory Concentration (MIC) value, and a safe toxicity profile (tested for hemolytic toxicity and cytotoxicity).