Subject-Consistent and Pose-Diverse Text-to-Image Generation
Authors: name: Zhanxin Gao, name: Beier Zhu, name: Liang Yao, name: Jian Yang, name: Ying Tai

Subject-consistent generation (SCG)-aiming to maintain a consistent subject
identity across diverse scenes-remains a challenge for text-to-image (T2I)
models. Existing training-free SCG methods often achieve consistency at the
cost of layout and pose diversity, hindering expressive visual storytelling. To
address the limitation, we propose subject-Consistent and pose-Diverse T2I
framework, dubbed as CoDi, that enables consistent subject generation with
diverse pose and layout. Motivated by the progressive nature of diffusion,
where coarse structures emerge early and fine details are refined later, CoDi
adopts a two-stage strategy: Identity Transport (IT) and Identity Refinement
(IR). IT operates in the early denoising steps, using optimal transport to
transfer identity features to each target image in a pose-aware manner. This
promotes subject consistency while preserving pose diversity. IR is applied in
the later denoising steps, selecting the most salient identity features to
further refine subject details. Extensive qualitative and quantitative results
on subject consistency, pose diversity, and prompt fidelity demonstrate that
CoDi achieves both better visual perception and stronger performance across all
metrics. The code is provided in https://github.com/NJU-PCALab/CoDi.