Box It to Bind It: Unified Layout Control and Attribute Binding in T2I
  Diffusion Models
Ashkan Taghipour Morteza Ghahremani Mohammed Bennamoun Aref Miri Rekavandi Hamid Laga Farid Boussaid
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.