CoDi: Subject-Consistent and Pose-Diverse Text-to-Image Generation

Zhanxin Gao1, Beier Zhu2, Liang Yao3, Jian Yang1, Ying Tai1✉,
1Nanjing University, 2Nanyang Technological University 3Vipshop
arXiv Video Code
Teaser Image

Comparison of subject-consistent generation methods: Vanilla SDXL, ConsiStory, StoryDiffusion and CoDi(ours). (a&b) Existing methods sacrifice pose diversity for subject consistency, e.g., ConsiStory produces similar poses: the subject faces the lower left in (a); and the lower right with hands placed in front in (b). In contrast, CoDi generates consistent subjects, while matching the pose diversity of Vanilla SDXL. (c) We report two metrics to assess pose quality: 1) fidelity, measuring the distance to the pose of SDXL, and 2) diversity. Our CoDi attains the best performance on both metrics.

Video

Abstract

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.

Model Architecture

architecture

Illustration of our CoDi. (a) Extract subject masks (Mid and Mn) by averaging the image-text cross-attention at the final denoising timestep for subject-related tokens (e.g., "fairy"). (b) Compute the OT plan Tn using the cost matrix and the probability masses. (c) Identity transport (IT) operates in the early denoising steps to transfer reference subject features to target images in a pose-aware manner. (d) Identity refinement (IR) operates in the late denoising steps to refine subject details using selective cross-image attention mechanism.

Visualization

Qualitative Comparison

More Qualitative Examples Generated by CoDi

qr

Long Story Generation

lsg

BibTeX

        @article{gao2025codi,
          title={Subject-Consistent and Pose-Diverse Text-to-Image Generation},
          author={Gao, Zhanxin and Zhu, Beier and Yao, Liang and Yang, Jian and Tai, Ying},
          journal={arXiv preprint arXiv:2507.08396},
          year={2025}
          }