ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
Authors: name: Chengyou Jia, name: Changliang Xia, name: Zhuohang Dang, name: Weijia Wu, name: Hangwei Qian, name: Minnan Luo

Despite the significant advancements in text-to-image (T2I) generative
models, users often face a trial-and-error challenge in practical scenarios.
This challenge arises from the complexity and uncertainty of tedious steps such
as crafting suitable prompts, selecting appropriate models, and configuring
specific arguments, making users resort to labor-intensive attempts for desired
images. This paper proposes Automatic T2I generation, which aims to automate
these tedious steps, allowing users to simply describe their needs in a
freestyle chatting way. To systematically study this problem, we first
introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It
features high-quality paired data with diverse freestyle inputs, enabling
comprehensive evaluation of automatic T2I models across all steps.
Additionally, recognizing Automatic T2I as a complex multi-step reasoning task,
we propose ChatGen-Evo, a multi-stage evolution strategy that progressively
equips models with essential automation skills. Through extensive evaluation
across step-wise accuracy and image quality, ChatGen-Evo significantly enhances
performance over various baselines. Our evaluation also uncovers valuable
insights for advancing automatic T2I. All our data, code, and models will be
available in https://chengyou-jia.github.io/ChatGen-Home