In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial relation understanding and numeration failure) in complex natural scenes, which impedes the high-faithfulness text-to-image generation. Although recent efforts have been made to improve controllability by giving fine-grained guidance (e.g., sketch and scribbles), this issue has not been fundamentally tackled since users have to provide such guidance information manually. In this work, we strive to synthesize high-fidelity images that are semantically aligned with a given textual prompt without any guidance. Toward this end, we propose a coarse-to-fine paradigm to achieve layout planning and image generation. Concretely, we first generate the coarse-grained layout conditioned on a given textual prompt via in-context learning based on Large Language Models. Afterward, we propose a fine-grained object-interaction diffusion method to synthesize high-faithfulness images conditioned on the prompt and the automatically generated layout. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art models in terms of layout and image generation. Our code and settings are available at \url{https://layoutllm-t2i.github.io}.

翻译：在文本到图像生成领域，近期Stable Diffusion取得的显著进展使得生成丰富多样的新颖逼真图像成为可能。然而，当前模型在复杂自然场景中仍面临对齐问题（例如，空间关系理解错误和计数失败），这阻碍了高保真度的文本到图像生成。尽管近期通过提供细粒度指导（如草图与涂鸦）在提升可控性方面做出了努力，但由于用户需手动提供此类指导信息，该问题尚未得到根本解决。在本工作中，我们致力于在无需任何指导的情况下合成与给定文本提示语义对齐的高保真图像。为此，我们提出了一种从粗到细的范式，以实现布局规划与图像生成。具体而言，我们首先基于大语言模型的上下文学习，根据给定文本提示生成粗粒度布局。随后，我们提出一种细粒度物体交互扩散方法，在提示与自动生成的布局条件下合成高保真图像。大量实验表明，我们所提出的方法在布局与图像生成方面均优于当前最先进模型。我们的代码与设置可通过 \url{https://layoutllm-t2i.github.io} 获取。