Subject-Consistent and Pose-Diverse Text-to-Image Generation
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.
687517c3257d4f043537043e
Zhanxin Gao
claimed_verified
2025-07-15T19:10:18.143Z
673ff52c2510c8c0f8446667
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/f6jFg9GRBSVp3Td4AHc9j.png
Zhanxin Gao
user
zxgao
687517c3257d4f043537043f
Beier Zhu
claimed_verified
2025-07-15T19:10:13.803Z
65b77f013426ed9b26be4e36
/avatars/8208c0df81ec15defa20a7ca09481575.svg
Beier Zhu
user
BeierZ
687517c3257d4f0435370440
Liang Yao
687517c3257d4f0435370441
Jian Yang
687517c3257d4f0435370442
Ying Tai
claimed_verified
2025-07-15T19:10:15.585Z
65734004769f3ee9bde1af10
/avatars/d6310ed861972fd691687d8f47413f33.svg
Ying Tai
user
yingtai
Subject-Consistent and Pose-Diverse Text-to-Image Generation
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.
CoDi, a two-stage text-to-image framework, maintains subject consistency while enhancing pose diversity through identity transport and refinement using diffusion.
subject-consistent generation text-to-image diffusion progressive nature denoising steps optimal transport identity transport identity refinement visual perception prompt fidelity