  Despite the stunning ability to generate high-quality images by recent
text-to-image models, current approaches often struggle to effectively compose
objects with different attributes and relationships into a complex and coherent
scene. We propose T2I-CompBench, a comprehensive benchmark for open-world
compositional text-to-image generation, consisting of 6,000 compositional text
prompts from 3 categories (attribute binding, object relationships, and complex
compositions) and 6 sub-categories (color binding, shape binding, texture
binding, spatial relationships, non-spatial relationships, and complex
compositions). We further propose several evaluation metrics specifically
designed to evaluate compositional text-to-image generation and explore the
potential and limitations of multimodal LLMs for evaluation. We introduce a new
approach, Generative mOdel fine-tuning with Reward-driven Sample selection
(GORS), to boost the compositional text-to-image generation abilities of
pretrained text-to-image models. Extensive experiments and evaluations are
conducted to benchmark previous methods on T2I-CompBench, and to validate the
effectiveness of our proposed evaluation metrics and GORS approach. Project
page is available at https://karine-h.github.io/T2I-CompBench/.
