While latent diffusion models (LDMs) excel at creating imaginative images, they often lack precision in semantic fidelity and spatial control over where objects are generated. To address these deficiencies, we introduce the Box-it-to-Bind-it (B2B) module - a novel, training-free approach for improving spatial control and semantic accuracy in text-to-image (T2I) diffusion models. B2B targets three key challenges in T2I: catastrophic neglect, attribute binding, and layout guidance. The process encompasses two main steps: i) Object generation, which adjusts the latent encoding to guarantee object generation and directs it within specified bounding boxes, and ii) attribute binding, guaranteeing that generated objects adhere to their specified attributes in the prompt. B2B is designed as a compatible plug-and-play module for existing T2I models, markedly enhancing model performance in addressing the key challenges. We evaluate our technique using the established CompBench and TIFA score benchmarks, demonstrating significant performance improvements compared to existing methods. The source code will be made publicly available at https://github.com/nextaistudio/BoxIt2BindIt.

通过引入Box-it-to-Bind-it (B2B)模块来改进文本到图像扩散模型中的空间控制和语义准确性，针对文本到图像中的关键挑战（灾难性忽视、属性绑定和布局指导），通过两个主要步骤：目标生成，调整潜在编码以保证对象生成并将其定向到指定的边界框；属性绑定，确保生成的对象符合提示中指定的属性。B2B模块作为现有文本到图像模型的兼容插件，显著提高了模型性能。在CompBench和TIFA评分基准上对该技术进行评估，与现有方法相比，取得了显著的性能提升。源代码将在该网址公开提供。

框定它以进行统一布局控制和属性绑定：在T2I扩散模型中