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Recent advancements in text-to-image generation have revolutionized numerousfields, including art and cinema, by automating the generation of high-quality,context-aware images and video. However, the utility of these technologies isoften limited by the inadequacy of text prompts in guiding the generator toproduce artistically coherent and subject-relevant images. In this paper, Wedescribe the techniques that can be used to make Large Language Models (LLMs)act as Art Directors that enhance image and video generation. We describe ourunified system for this called "LaDi". We explore how LaDi integrates multipletechniques for augmenting the capabilities of text-to-image generators (T2Is)and text-to-video generators (T2Vs), with a focus on constrained decoding,intelligent prompting, fine-tuning, and retrieval. LaDi and these techniquesare being used today in apps and platforms developed by Plai Labs.

LLM as an Art Director (LaDi): Using LLMs to improve Text-to-Media Generators