  This paper proposes new framework of communication system leveraging
promising generation capabilities of multi-modal generative models. Regarding
nowadays smart applications, successful communication can be made by conveying
the perceptual meaning, which we set as text prompt. Text serves as a suitable
semantic representation of image data as it has evolved to instruct an image or
generate image through multi-modal techniques, by being interpreted in a manner
similar to human cognition. Utilizing text can also reduce the overload
compared to transmitting the intact data itself. The transmitter converts
objective image to text through multi-model generation process and the receiver
reconstructs the image using reverse process. Each word in the text sentence
has each syntactic role, responsible for particular piece of information the
text contains. For further efficiency in communication load, the transmitter
sequentially sends words in priority of carrying the most information until
reaches successful communication. Therefore, our primary focus is on the
promising design of a communication system based on image-to-text
transformation and the proposed schemes for sequentially transmitting word
tokens. Our work is expected to pave a new road of utilizing state-of-the-art
generative models to real communication systems
