  The revolution of artificial intelligence content generation has been rapidly
accelerated with the booming text-to-image (T2I) diffusion models. Within just
two years of development, it was unprecedentedly of high-quality, diversity,
and creativity that the state-of-the-art models could generate. However, a
prevalent limitation persists in the effective communication with these popular
T2I models, such as Stable Diffusion, using natural language descriptions. This
typically makes an engaging image hard to obtain without expertise in prompt
engineering with complex word compositions, magic tags, and annotations.
Inspired by the recently released DALLE3 - a T2I model directly built-in
ChatGPT that talks human language, we revisit the existing T2I systems
endeavoring to align human intent and introduce a new task - interactive text
to image (iT2I), where people can interact with LLM for interleaved
high-quality image generation/edit/refinement and question answering with
stronger images and text correspondences using natural language. In addressing
the iT2I problem, we present a simple approach that augments LLMs for iT2I with
prompting techniques and off-the-shelf T2I models. We evaluate our approach for
iT2I in a variety of common-used scenarios under different LLMs, e.g., ChatGPT,
LLAMA, Baichuan, and InternLM. We demonstrate that our approach could be a
convenient and low-cost way to introduce the iT2I ability for any existing LLMs
and any text-to-image models without any training while bringing little
degradation on LLMs' inherent capabilities in, e.g., question answering and
code generation. We hope this work could draw broader attention and provide
inspiration for boosting user experience in human-machine interactions
alongside the image quality of the next-generation T2I systems.
