The explosive popularity of diffusion models[ 1][ 2][ 3 ] has provided a huge stage for further development in generative-text modelling. As prompt based models are very nuanced, such that a carefully generated prompt can produce truely breath taking images, on the contrary producing powerful or even meaningful prompt is a hit or a miss. To lavish on this we have introduced a large scale derived and synthesized dataset built with on real prompts and indexed with popular image-text datasets like MS-COCO[4 ], Flickr[ 5], etc. We have also introduced staging for these sentences that sequentially reduce the context and increase the complexity, that will further strengthen the output because of the complex annotations that are being created. MTTN consists of over 2.4M sentences that are divided over 5 stages creating a combination amounting to over 12M pairs, along with a vocab size of consisting more than 300 thousands unique words that creates an abundance of variations. The original 2.4M million pairs are broken down in such a manner that it produces a true scenario of internet lingo that is used globally thereby heightening the robustness of the dataset, and any model trained on it.