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 \url{https://chengyou-jia.github.io/ChatGen-Home}

翻译：尽管文本到图像（T2I）生成模型已取得显著进展，但在实际应用场景中，用户仍常面临反复试错的挑战。这一挑战源于繁琐步骤的复杂性与不确定性，例如构思合适的提示词、选择恰当的模型以及配置特定参数，导致用户不得不进行大量人工尝试以获得期望的图像。本文提出自动T2I生成方法，旨在自动化这些繁琐步骤，使用户能够以自由聊天的形式简单描述需求。为系统研究该问题，我们首先引入了ChatGenBench——一个专为自动T2I设计的新型基准测试集。该数据集包含高质量配对数据与多样化的自由式输入，支持对自动T2I模型所有步骤的全面评估。此外，基于将自动T2I视为复杂多步推理任务的认知，我们提出了ChatGen-Evo——一种多阶段进化策略，通过渐进式训练使模型逐步掌握关键自动化技能。通过对步骤准确性和图像质量的广泛评估，ChatGen-Evo在各项基线模型上均实现了显著性能提升。我们的评估还揭示了推动自动T2I发展的宝贵洞见。所有数据、代码与模型均发布于 \url{https://chengyou-jia.github.io/ChatGen-Home}