Recent advances in diffusion models can generate high-quality and stunning
images from text. However, multi-turn image generation, which is of high demand
in real-world scenarios, still faces challenges in maintaining semantic
consistency between images and texts, as well as contextual consistency of the
same subject across multiple interactive turns. To address this issue, we
introduce TheaterGen, a training-free framework that integrates large language
models (LLMs) and text-to-image (T2I) models to provide the capability of
multi-turn image generation. Within this framework, LLMs, acting as a
"Screenwriter", engage in multi-turn interaction, generating and managing a
standardized prompt book that encompasses prompts and layout designs for each
character in the target image. Based on these, Theatergen generate a list of
character images and extract guidance information, akin to the "Rehearsal".
Subsequently, through incorporating the prompt book and guidance information
into the reverse denoising process of T2I diffusion models, Theatergen generate
the final image, as conducting the "Final Performance". With the effective
management of prompt books and character images, TheaterGen significantly
improves semantic and contextual consistency in synthesized images.
Furthermore, we introduce a dedicated benchmark, CMIGBench (Consistent
Multi-turn Image Generation Benchmark) with 8000 multi-turn instructions.
Different from previous multi-turn benchmarks, CMIGBench does not define
characters in advance. Both the tasks of story generation and multi-turn
editing are included on CMIGBench for comprehensive evaluation. Extensive
experimental results show that TheaterGen outperforms state-of-the-art methods
significantly. It raises the performance bar of the cutting-edge Mini DALLE 3
model by 21% in average character-character similarity and 19% in average
text-image similarity.

TheaterGen 是一个无需训练的框架，通过将大型语言模型（LLMs）和文本到图像（T2I）模型集成，实现了多回合图像生成。该框架通过 LLMs 生成和管理一个标准化的提示书，从而提高合成图像的语义一致性和上下文一致性。通过将提示书和指导信息结合到 T2I 扩散模型的反降噪过程中，TheaterGen 生成最终的图像，从而大大提升了合成图像的质量。