  Impressive advances in text-to-image (T2I) generative models have yielded a
plethora of high performing models which are able to generate aesthetically
appealing, photorealistic images. Despite the progress, these models still
struggle to produce images that are consistent with the input prompt,
oftentimes failing to capture object quantities, relations and attributes
properly. Existing solutions to improve prompt-image consistency suffer from
the following challenges: (1) they oftentimes require model fine-tuning, (2)
they only focus on nearby prompt samples, and (3) they are affected by
unfavorable trade-offs among image quality, representation diversity, and
prompt-image consistency. In this paper, we address these challenges and
introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a
large language model (LLM) to improve prompt-image consistency in T2I models.
Our framework starts from a user prompt and iteratively generates revised
prompts with the goal of maximizing a consistency score. Our extensive
validation on two datasets, MSCOCO and PartiPrompts, shows that OPT2I can boost
the initial consistency score by up to 24.9% in terms of DSG score while
preserving the FID and increasing the recall between generated and real data.
Our work paves the way toward building more reliable and robust T2I systems by
harnessing the power of LLMs.
