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The increased interest in diffusion models has opened up opportunities foradvancements in generative text modeling. These models can produce impressiveimages when given a well-crafted prompt, but creating a powerful or meaningfulprompt can be hit-or-miss. To address this, we have created a large-scaledataset that is derived and synthesized from real prompts and indexed withpopular image-text datasets such as MS-COCO and Flickr. We have alsoimplemented stages that gradually reduce context and increase complexity, whichwill further enhance the output due to the complex annotations created. Thedataset, called MTTN, includes over 2.4 million sentences divided into 5stages, resulting in a total of over 12 million pairs, and a vocabulary of over300,000 unique words, providing ample variation. The original 2.4 million pairsare designed to reflect the way language is used on the internet globally,making the dataset more robust for any model trained on it.

MTTN: Multi-Pair Text to Text Narratives for Prompt Generation