  How humans can efficiently and effectively acquire images has always been a
perennial question. A typical solution is text-to-image retrieval from an
existing database given the text query; however, the limited database typically
lacks creativity. By contrast, recent breakthroughs in text-to-image generation
have made it possible to produce fancy and diverse visual content, but it faces
challenges in synthesizing knowledge-intensive images. In this work, we rethink
the relationship between text-to-image generation and retrieval and propose a
unified framework in the context of Multimodal Large Language Models (MLLMs).
Specifically, we first explore the intrinsic discriminative abilities of MLLMs
and introduce a generative retrieval method to perform retrieval in a
training-free manner. Subsequently, we unify generation and retrieval in an
autoregressive generation way and propose an autonomous decision module to
choose the best-matched one between generated and retrieved images as the
response to the text query. Additionally, we construct a benchmark called
TIGeR-Bench, including creative and knowledge-intensive domains, to standardize
the evaluation of unified text-to-image generation and retrieval. Extensive
experimental results on TIGeR-Bench and two retrieval benchmarks, i.e.,
Flickr30K and MS-COCO, demonstrate the superiority and effectiveness of our
proposed method.
