无需训练，通过布局引导与迭代优化提升文本生成图像的准确性。
Compositional Image Synthesis with Inference-Time Scaling
- 用大语言模型生成显式布局，再注入图像生成流程。
- 通过视觉语言模型迭代重排候选图，使结果更贴合提示词。
- 适合需要精确控制物体数量、位置和属性的生成任务。
尽管现代文生图模型表现出色，但在组合性方面仍存在不足，常无法准确生成物体数量、属性和空间关系。为此，我们提出一种无需训练的框架，结合物体中心方法与自我精炼机制，在保持美学质量的同时提升布局忠实度。具体而言，利用大语言模型（LLMs）从输入提示中合成显式布局，并将这些布局注入图像生成过程；在此过程中，一个物体中心的视觉-语言模型（VLM）判别器对多个候选结果进行重排序，迭代选择最符合提示的输出。通过统一显式布局引导与基于自精炼的推理时缩放，该框架在场景对齐提示词方面优于近期文生图模型。代码已公开于 https://github.com/gcl-inha/ReFocus。
原文摘要 · Abstract (English)
Despite their impressive realism, modern text-to-image models still struggle with compositionality, often failing to render accurate object counts, attributes, and spatial relations. To address this challenge, we present a training-free framework that combines an object-centric approach with self-refinement to improve layout faithfulness while preserving aesthetic quality. Specifically, we leverage large language models (LLMs) to synthesize explicit layouts from input prompts, and we inject these layouts into the image generation process, where a object-centric vision-language model (VLM) judge reranks multiple candidates to select the most prompt-aligned outcome iteratively. By unifying explicit layout-grounding with self-refine-based inference-time scaling, our framework achieves stronger scene alignment with prompts compared to recent text-to-image models. The code are available at https://github.com/gcl-inha/ReFocus.
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