CoDi: Subject-Consistent and Pose-Diverse Text-to-Image Generation
Authors: Zhanxin Gao
Beier Zhu
Liangyao
Jian Yang
Ying Tai
Venue: ICLR 2026 Poster (2026)
Keywords: Consistent Subject Generation; Text-to-Image; Diffusion
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: CoDi is a cutting-edge framework for subject-consistent generation, achieving strong ID consistency while preserving diverse poses.

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. The code is provided in the supplementary material.

Published as a conference paper at ICLR 2026
CODI: SUBJECT-CONSISTEN AND POSE-DIVERSE TEXT-TO-IMAGE GENERATION
Zhanxin Gao' Beier Zhu? Liang Yao"
Jian Yang' Ying Tai
Nanjing University, UUniversity of Science and Technology of China, VVipshop zxgaolsmai njju edu. on beier. zhu@ustc. edu. en yingtailnju.
ABSTRACT
to maintain consistent subject iden- tity across diverse scenes-remail challenge for text-to-image (T2I) models. Ex- isting 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 Cobsett-Cossssten and pose-Di verse 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 two-stage strategy: Identity Transport (TT) and I dentity Refinement (TR). 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 fur- ther refine subject details. Extensive qualitative and quantitative results subject consistency, pose diversity, and prompt fidelity demonstrate that CoDi i achieves both better visual perception and stronger performance across all metries. The code is provided in htttt:t///// http:///itthb. com/NJU PCALab/CoDi
Subject-consistere generation
INTRODUCTION
While text-to-image (T2I) (Ramesh et al. 2022 Saharia et al. 2022 Rombach et al. 2022 B Battmann et al, 2023) models excel in high-quality image generation Rombach et al.] [022] Mou et al. (022).
they struggle maintain subject consistency across multiple scenes. Subject-consistent generation (SCG) aims to synthesize images of the same subject across diverse contextual prompts with three key objectives: (1) ensuring subject consistency across generated instances, (2) promoting layout and pose diversity across different instances to avoid repetitive or overly similar compositions, and (3) maintaining prompt fidelity to accurately reflect the semantics of each prompt. The capability enables numerous practical applications including multi-scene narrative for visual storytelling, customizable character design for animation and gaming, and coherent illustration sequences for graphic novels.
Current SCG methods (Kopickko et al., 2024 Ye et al., 2023) primarily rely on training- intensive optimization (Avrahami al. 2024) or mapping networks (Ruiz et al. [0244] Gal al.) (023b) to bind subjects to latent representations. These approaches often require computationally expensive fine- tuning per subject or depend on domain-specific encoders, limiting scalability and generalizability. Training-free methods (Tewel et al.) [0244] Zhou et al. (024) have gained significant attention due to their climination parameter tuning, strong generalization capabilities, and broad compatibility
with diverse diffusion architectures. Current training-fre ffree Bethods-Consise
(Tewel al. 2024
and Diffusiinn (Zhou et al. 2024) enhance subject consistency by sharing self-attention keys and values across generated images. However, noted in their limitations (Tewel al. 2024 Hertz et al. 2024) and evident in Fig. I these methods often achieve high consistency at the cost of severely reduced layout and pose diversity, making it challenging to balance all three objectives.
To better balance the three objectives, we propose training-free ftaneetrk-suujjct- Consistent and pose-Di verse generation, dubbed CoDi-that achieves strong subject consistency while preserving diverse poses. Motivated by the progressive nature of diffusion models ( (Yue et al. 2024)-whii whhic
iidicctess corresponding author.

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Published as a conference paper at ICLR 2026
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Vanilla SDXL
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Pose Diversity
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StoryDiffusior Nertt
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Subject Consistency (DINO-y2
(a) A warm cafe sketch of skilled (b) A delicate porcelain miniature of barista espresso machine.
(c) Subject consistency pose diversity
thoughtful princess lace.
Figure 1: Comparison of subject-consistent generation methods: Vanilla SDXL. (Podell et al. 2023 ConsiStory (Tewel et al., 2024), StoryDiffusion (Zhou et al. 2024) and CoDi (ours). (a&b) Existing methods sacrifice pose diversity for subject consistency, e.g., ConsiStory produces similar poses in Figure (a); and the lower right with hands placed in front in Figure (b). In contrast, CoDi generates consistent subjects, while matching the pose diversity of Vanilla SDXL. (c) Subject consistency vs. pose diversity. Current methods struggle balance the two, whereas CoDi achieves both effectively.
shows that fwwefrrquency attributes like pose and layout are formed in early denoising steps, while high-frequency details such as facial features emerge later- our CoDi i adopts a two-stage strategy: Identity Transport (IT) and Identity Refinement (IR). During the early denoising steps, IT uses optimal transport to align each target image's 's features with the reference identity features. Intuitively, this resembles mosaicking: assembling the subject using visual pieces from the reference image, rearranged to match the target pose thus naturally preserving identity and keeping the original pose. In the later denoising steps, IR further refines subject consistency by guiding each target image to attend to the most salient identity attributes via cross-attention, As shown in Figure [ CoDi achieves superior visual results in both subject consistency and pose diversity, and quantitatively demonstrates advantages over existing methods in balancing this trade-off.
We evaluate our method on the existing T2I SCG benchmark ConsiStory+ (Liu et al. 2025). Com- pared to other training-free free approaches. both quantitative and qualitative results validate that our framework achieves better subject consistency while preserving richer layout and pose diversity. demonstrates superior trade-off among subject consistency, pose diversity, and prompt fidelity. Further analysis is also provided to demonstrate CoDi's 's advantages in pose diversity.
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RELATED WORK
To steer T2I generation with diffusion models (Rombach et al. 2022 Podell et al. 2023 Nan et al.
2025 Esser et al. 2024| Zhu et al.
2025a Wang al. 2025aj Hu al. 2023 Lin al. 2025a
Zhou et al. 2025
Lin et al. 2025b Wang el al.
2025b various methods have been proposed to
incorporate control signals such as depth maps. edge maps, and segmentation (Mei et al. 2025
Zhang et al. 2023 Yang et al. 2023 Lei et al. 2025 Chen et al. (025). Among them, subject consistency ka. identity preservation) has attracted growing attention, aiming to generate a set of images conditioned on specified subject. Existing subject-consistent generation (SCG) methods can be broadly categorized into two groups: training-baseed and training-free.
Training-based SCG. Training-based methods require either (1) fine-tuning on additional training data (Yang et al. 2024 Li et al. 2024 2019 Betker et al., 2023 Liu et al., 2024) or (2) test-time optimization using reference images (Roich et al. 2022 Gal et al., 2023b Kumari al. 2023 Xiao
et al. 2024). The first line of work, represented by MoryDALL-E (Maharana et al. 2022) and Make-A-
Story (Rahman al. 2023), incorporates additional modules to capture subject information. followed
by fine-tuning on large datasets to enable direct control over the subject given a reference image.
The second line of work, exemplified by DreamBooth (Ruiz al. 2023) and Textual Inversion (Gall
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et al. 2023a), optimizes model parameters token embeddings on the given test images to inject subject identity. Despite their success in maintaining subject consistency. training based methods suffer from high training costs significant test-time latency. In contrast, our CoDi is training- free and introduces only mild additional latency.
Training-free SCG. Training- free methods circumvent the need for iterative tuning of model pa- rameters. For instance, P Story (Liu et al. 2025) improves consistency by aligning prompt embeddings across generations. However, textual embedding control alone is insufficient to to enforce consistency, often resulting in subject drift. The current leading methods, ConsiStory (Tewel et al. 2024) and StoryDiffussi consistency by sharing self self-attention keys and values. However, as noted in their limitation discus- sions (Tewel al.] 2024. Hertz al.
(Zhou et al. 2024). adopt attention- based mechanisms to promote subject
2024), applying attention across set of images reduces pose diversity. To address this issue, our CcDi explicitly preserves diversity and promotes consistency by aligning early-stage features between the target and reference images via optimal transport.
METHOD
Our CoDi consists of two stages: Identity Transport (IT) and Identity Refinement (IR). Our IT operates in the early denoising stage to transport identity features from the reference image while preserving the pose and background of the target images. IR applied in later denoising stages to refine subject consistency in fine- ine-ggained details. This two-stage design is inspired by Yue et al. 2024), which reveals that low-frequency attributes such as pose and layout are determined early in the denoising timesteps, whereas high-frequency components like facial details emerge in later steps. We begin with the setup of subject-consistent generation (SCG), a review of attention- SCG methods and brief introduction of optimal transport.
3.1 PRELIMINARIES
Setup. SCG aims to synthesize a batch of images that share the same subject identity across diverse scenes. Formally, given set of N textual prompts [t,)_n where each prompt is composed of shared identity prompt ta and a unique attribute prompt an, ie., t, [tia a.). For instance, given t ="A hyper-realist.e
digital painting of fairy giggling in grove of enchanted crystals" and t2 ="A ryper-realistic digital painting of a fairy lost in a maze giant sunflowers", the identity prompt is tid ="a hyper-realistie
digital painting of fairy", and the attribute prompts are a = "giggling in a grove of enchanted crystals" and a2 = "lost in a maze of giant sunflowers". We refer to the image generated from the identity prompt bid as the reference image, denoted as Xia. The objective is to generate turget images (x, ] that depict visually consistent subject with Xid, while capturing the scene-specific attributes described in a,, See Figure 2 for a concrete example.
Review of cross-image attention SCG. The current leading training-free SCG methods, ConsiS- tory (Tewel et al. 2024) and StoryDiffusion (Zhou et al. 2024), adopt attention- strategies that extend the standard self-attention to cross- cross-image attention mechanism. Formally, let {X,, J. denote the features of the target images {x,, NN For generating i-th image, standard self-attention first projects X; to queries Qi keys K2, and values Vi, then compute
Qi
Z, = Attn Q2, K,, V.) = softmax
V1
Vd
where d is the feature dimension. Let @ denote matrix concatenation. We compute the concatenated keys and values as KI.N [K] ... t Ky] and VI.N = [V1 e @ V.). respectively. To enhance consistency, cross-image attention mechanism allows the feature of the i-th image, X,, to attend to the values Vi.N of other images using their corresponding keys KINN-
QiKTI Vd
Z: Attn(Qi. KIN, VL.) softmax
VENN
While both SCG methods adopt cross-image attention, they differ slightly in implementation: Con- siStory Tewel et al. 2024) limits attention to masked subject regions, whereas StoryDiffusion (Zhou et al. 2024) randomly samples tokens from all regions without subject constraints.
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Eid sbppeerccllistic digital painting of fairy. Up abyper-realistic digital painting of fairy gigeling in a grove of enchanted crystals.
(a) Extract Subject Masks
(b) Compute OT Plan
Mi
tid
Su Xu @ Ma
Diffusion
Tp
tn
M
X
5, X, @ Mn
(c) Identity Transport
(d) Identity Refinement
Su
Vnnua
Sia
Si
top-or selection
X,
X
AV
dx
X
Background
Concal
X
Multipiccttion
Softmax
W(0, (4
Early Danoising Steps
Late Donoising Steps
Pawss
Figure 2: Illustration of our CoDi. (a) Extract subject masks (Mi and M,,) 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 C and the probability masses a and (detailed in Sec. 3.2 (c) Identity transport (IT) operates in the early denoising steps transfer reference subject features to targe images in a pose-aware manner. (d) Identity refinement (IR) operates the late denoising steps to refine subject details using selective cross-image attention mechanism.
As discussed their limitations (Tewel et al. 2024 Hertz et al., 2024), attention based methods significantly reduce layout and pose diversity. We conjecture that attending to shared pool of keys and values entangles feature updates acroxs images, implicitly aligning spatial layouts and poses. To mitigate this, prior work (Tewel et al.) (2024) introduces components such as attention dropout and query blending. However, these additions increase computational overhead and still fail to recover pose diversity (as shown in Figure D. this paper, we draw inspiration from structural learning to simultaneously preserve subject consistency and pose diversity by transporting identity features to each target image via optimal transport.
Optimal transport. Optimal transport (OT) (Villani et al., 2008 Monge 1781 Zhu al. 2025b)
provides a framework for measuring the distance between two distributions. Specifically, given two sets of support features { {vm JMM and {u, J.V we define two discrete distributions P and Q as
M
P(x)
Σ amōlvn xx,, Q(x) Σ b, 8(, xx)
m=l
where 5()) denotes the Dirac function, and am. bn denote the associated probabilities that sum to respectively. Let a [d],..,,ay) where each entry C'mm, n) denotes the transport cost between Vm and un (lypically defined by their similarity), the OT distance between IP and defined as:
and b [b1,..., bN] Given cost matrix C' MxN
dor (P,, Q C)
min TT, C),, s.t. Tly a I =b 7'20
where T E IR M is the transport plan, with T(m, n) 2 0 representing the amount of mass moved from V. to un (.) denotes the Frobenius inner product, 1 M M -dimenssional all-one vector.
We slightly abuse the notations and N, which here do not refer image the number of target images.
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3.2 IDENTITY TRANSPORT
Our IT operates in the early denoising steps (e.g., g., the first 10 of 50 total steps) to independently transport identity features from the reference image Xid to each target image Xn for all [1, N]. Our IT begins by extracting subject features from masked regions.
Extract subject features. Masking out background regions offers two benefits for subject consistency: it reduces background interference and computational cost by focusing on the subject alone. We adopt similar strategy that of previous methods (Hertz al., 2023 Tewel et al. 2024), using image-texi text cross- aross-attention to extract subject masks. Specifically. let Xia denote the features of the reference image Xid generated from the identity prompt tid. When generating Xid. we average the cross-attention maps at the final denoising timestep for subject-related tokens (e.g., "fairy"), followed by applying Otsu's method (Otsu al. [975) to produce binary mask Mis. This mask highlights the subject-relevant regions, from which extract the subject features as:
S Xia @ Mi E R
where @ applies the binary mask to retain subject features, Sid denotes the number of ones in the binary mask Ma. and d is the feature dimension. Similarly, for each target image Xn. we extract subject features as Sr Xn @ Mn E Xd The process is visualized in Figure 2 (a).
Transport between Sia and S,, Given the subject feature pairs Sia = [s,a..., and S, ( s we first derive an optimal transport plan T that aligns the reference features set {sid)i=1 1.a with the target features {s,) )-= (See Figure 2 (b)). Using this plan T, we compose the target subject features by transporting features from the reference image. Intuitively, this process resembles mosaicking: rearranged to match the target pose. Since the visual pieces originate from the reference image, subject identity is naturally preserved. To solve the OT problem in Eq. (4) first define the cost matrix C and the associated probability masses a and b.
assemble the target subject using pieces from the reference image,
Definition of the cost matrix C. The cost matrix is typically defined based on the pairwise distances between features: smaller distances imply lower transport costs. For a pair s/ and s/ from final denoising step (where features contain minimal noise), the cost is defined as:
% s
C(i.j) = cosssssssss sy
(6)
|| 2| |
Definition of the probability masses and b. Intuitively, [a1...................... Geal represents the impor- tance weights of the subject features, where a larger a; indicates that feature 8/ is more relevant to the subject Lid- We reuse the average cross-attention maps for generating the subject-relevant mask the feature importance and apply softmax function to ensure the sum 2; equals to 1. The importance weights b for the target subject features Sn are derived analogously.
With the cost matrix C' and the probability masses a and b, we solve the OT plan Ta in Eq. using network simplex algorithm (Orlin 1997). With the derived Tn. the subject target features composed by reference subject features are computed as
S = T Sia.
To form the final representation X we combine SOT with the non-subject features (masked out by M2) from Xn. The representation then passed through the diffusion network produce the output. The IT process is illustrated in Figure 2 (c).
3.3 IDENTITY REFINEMENT
The motivation behind this stage is that the IT module performs a coarse transport between Sia and S., However, since the binary subject masks are imprecise and the foreground of target images evolves during denoisingnaae while our transport plan T,, remains fixed- fxed-further refinement of subject details becomes necessary.
Our IR operates in the later denoising steps (e.g., g., the last 40 of 50 total steps) to reinforce subject details in the target images. IR resembles cross-image attention- based SCG methods, except that
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anulysing sumples snder a micrsicape
wriling reseurcl paper
exprrinctting with chomicass
reaying barefoor stage
footing to dreow ...
hovering mid while whippartng o.
Vanilaa SDXL
ConsiStory
StoryDiilusion
1roompt1Story
CoDi (ours)
(a) A focused scene of scientist with lab coal.
(b) A soft dreamy illustration of gentle lullaby singer night. blue dress.
Figure 3: Qualitative comparison among Vanilla SDXL (Podell al. 2023). ConsiStory Tewel et al. 2024), StoryDiffusion (Zhou et al. 2024). and PPrompt (Liu et al. 2025). ConsiStory and StoryDiffusion generate similar poses across examples, while (PromptSStory preserves pose diversity but struggles with subject consistency. In contrast, our CoDi achieves both.
each target image attend only the most relevant reference features to avoid entangled feature update across target images. Specifically, to generate the n-th image, we first construct the concatenated keys and values Kn,id IK, Kia] and Vn,id [Vn Vial. respectively. The cross-image attention scores are compute as
QnK vd
An softmax
(8)
For each query, we retain only the top-a attention scores of the reference tokens (i.e., Kia). Specifi- cally, for each row A, of A, we define the top-a index set I, (see Appendix A for details) and zero out all other entries Kia:
Ag = ( Aij 0,
if E
A Σjez, Aij"
and A,
(9)
otherwise
The final cross-attention output is then computed as:
Attna (Qns Kn,id, Vn,ia AV.
(10)
This filtering mechanism ensures that only the most relevant identity features from the reference image contribute to the attention update. The TR process demonstrated in Figure (d).

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Table 1:
Quantitative comparison of subject consistency, pose diversity and prompt fidelity. Best results are marked in bold. 1 indicates higher is better, and indicates lower is better.
Subject Consistency
Pose
Prompt
Method
CLIP-I (t)
DINO-v2 (*)
DreamSim (4)
Diversity (t)
Fidelity (1)
Vanilla SDXL (Podell et al. 2023
0.8417
0.8010
0.3139
0.0772
0.9082
1Promptl Story Luu et a.. 2025
0.8627
0.8233
0.2959
0.0662
0.8814
ConsiStory Tewel el al. 2024
0.8751
0.8428
0.2336
0.0621
0,9148
StoryDiffusion Zhou ct al. 2024
0.8776
0.8471
0.2356
0.0605
0,9038
CoDi (ours)
0.8809
0.8514
0.2136
0.0758
0.904
EXPERIMENTS
4.1
SETUP
Benchmark, We evaluate our CoDi on the standard SCG benchmark, ConsiStory+ et al. 2025 which comprises nearly 200 prompt sets and supports the generation of over 1,100 images. Each prompt set includes a subject described in a specific style, with multiple frame-specific descriptions.
Baselines and implementation details. We compare CoDi with SoTA training- free SCG methods, including ConsiStory (Tewel et al., 2024), StoryDiffusion (Zhou et al., 2024) and iPrompt1Story (Liu et al.) (025). We reproduce all baselines using their official released code. All methods are implemented using the same backbone model, Stable Diffusion XL. 1.0 (Podell et al.) 2023), with an image resolution of 1024 1024, except for StoryDiffusion, which is evaluated at 768 x 768 due to its high memory consumption, following its original setting. To ensure fairness, identical noise seeds are enforced for all methods, ensuring that each prompt is initialized with the same random noise input. Hyperparameter a in Eq. (TO) selects the top 50% of reference features.
Evaluation metries. Our evaluation framework assesses the quality of generated images from three aspects: (1) subject consistency, (2) pose diversity, and (3) prompt fidelity. Subject consistency is evaluated by computing the average pairwise cosine similarity (or distance) between image embeddings within each target image set. We use three image encoders for this evaluation: CI.IP- I (Hessel et al. 202T), DINO-v2 (Oquab et al. 2023), and DreamSim (Fu al. [202)). To evaluate pose diversity, we extract 2D human joint coordinates using ViTPose's 's pose estimation model (Xu ctt al. 2022). eliminate global variations translation, rotation, and scale, we align poses using Procrustes analysis (Schönemann) 1966), inspired by standard practices in face alignment (Lin ct al. (022). The pose diversity score is then computed as the average Euclidean distance between corresponding keypoints across aligned image pairs. A higher score indicates greater pose diversity. For prompt fidelity, we use CLIP-Score (Hessel et al. 2021) to measure the cosine similarity between image and textual prompt embeddings. See Appendix for more details.
4.2 EXPERIMENTAL RESULTS
Qualitative comparison. As shown in Figure 3 our CoDi achieves superior visual quality in terms of pose diversity, subject consistency, and prompt fidelity. Our CoDi preserves the pose diversity of Vanilla SDXL (Podell et al.] (023) while overcoming its limitation in subject consistency. In comparison, ConsiStory Tewel al. 2024) and StoryDiffusion (Zhou al. 2024) achieve subject consistency at the cost of pose diversity. For example, in the scientist scenario, the man exhibits nearly identical body poses. Although IPromptI (Liu et al., (2025) maintains strong layout and pose diversity in both cases, its subject consistency remains limited.
Quantitative comparison. Table presents a quantitative comparison. (1) Across all three subject consistency metrics- CLIP-I (Hessel et al.] 2021), DINO-v2 (Oquab et al. 2023). and DreamSim (Fu (et al. 2023 our CoDi achieves the best performance, demonstrating superior identity preservation across instances. In particular, our method obtains the lowest DreamSim score (0.2136). indicating closer alignment with human perceptual similarity than competing methods. (2) In terms of pose diversity, CoDi achieves the highest score (0.0758). closely matching Vanilla SDXL (0.0772). This demonstrates its ability to preserve the inherent pose diversity of the diffusion model while maintaining subject consistency. (3) For prompt fidelity, CoD performs competitively-nran
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Happing sndowwater SWAOn
collecnigg glowngg jobyoa
decorating anderwater coves
basking munlle
play leg with coloyfil
dryfing through the Douue
Vanila SDXL
+ IT
IR
CoDi (+ IT IR)
A dreamy underwater illustration of beautiful mermaid with shimmering tail.
Figure 4: Main component analysis (qualitative) on identity transport (IT) and identity refinement (IR). enhances subject consistency the coarse-grained level and preserves pose diversity. IR enhances subject consistency in the fine- grained level reduces pose diversity. Their combination yields the best consistency and preserves diversity.
Table 2:
Main component analysis (quantitative) identity transport (IT) and identity refinement (IR). IT enhances subject consistency and preserves pose diversity. IR enhances subject consistency while reduces pose diversity. Their combination yields the best consistency and preserves diversity.
Subject Consistency DINO-v2 (t)
Pose
Prompt
IT
IR
CLIP-I (t)
DreamSim (L)
Diversity (t)
Fidelity (1)
0.8417
0.8010
0.3139
0.0772
0.9082
0.8576
0.8207
0.2707
0.0800
0.9090
0.8859
0.8618
0,2044
0.0675
0.8975
0.8809
0.8514
0.2136
0.0758
0.9041
second only to ConsiStory and comparable to Vanilla SDXL. These results demonstrate CoDi's ability to achieve subject consistency without compromising pose diversity or prompt alignment.
4.3 ABLATION STUDIES
Main component analysis. The contribution of each module (IT and IR) to subject consistency and pose diversity are evaluated through quantitative and qualitative ablations, shown Table[2ann Figure 4 Table shows that both IT and TR improve subject consistency, while IT also enhances pose diversity. However, using alone reduces pose diversity-for example, the score drops from 0.0772 to 0.0675 compared to the SDXL baseline. When both modules are applied, subject consistency further improves due to their synergistic effect, while pose diversity is preserved.
Figure visualizes the effect of each module. Compared to Vanilla SDXL, applying IT preserves the original pose and improves subject consistency, but some details, such as facial identity, remain suboptimal. In contrast, IR alone enhances fine- ane-ggained consistency, but produces nearly identical poses across images, substantially reducing diversity. As shown the bottom row, combining IT and TR improves both coarse and fine-grained consistency without compromising pose diversity.
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03000
0.800
0008
08175
03575
-0075
-0.70
DINO-0
08525
0 000
DINDag
-0.65
0,000
0.060
08100
0.500
00055
08475
0.447
0.050
0.500
03450
0.8400
Dioway
10 12 24 1s
18 20
30
30
40
50
60
70
Trincition point
Figure 5: Ablation studies on (a) stage transition point, and (b) the effect of
A magical winter Illustration with contrasting colors of brave girl and her fox companion wandering the snow, the fox leads her through shadows trees; quiet blankets
slowly tuunk of frosty pine; the snow catches the pale morning light: the girl tosses stick into the snow.
forest;
hille; the girl against the
Figure 6: Multi-subject generation. CoDi consistently preserves identities of multiple subjects while maintaining diverse poses and spatial layouts.
Study on stage transition point. Our CoDi adopts a two-stage strategy: identity transport (IT) in the early denoising steps and identity refinement (TR) in the later ones. By default, we set the stage transition point at step /. = 10 out of a total of 50 denoising steps (IT is applied when t. S 10, and IR afterward). In this study, we investigate how the choice of transition point affects generation quality. As shown in Figure (a), vary t from 2 to 30 and evaluate subject consistency (DINO-v2) and pose diversity. We find that our default choice 10 achieves favorable trade-off between consistency and diversity.
Effect of a. the IR stage, we select the top-a percent of reference features to inject into the target subject features. In this study, examine how varying affects subject-consistent generation. Specifically, we vary a from 30% to 70% and report subject consistency (DINO-v2) and pose diversity in Figure (b). We observe that increasing improves subject consistency but reduces pose diversity. Setting 50% provides a favorable trade-off.
4.4 MULTTI-UBJJEC GENERATION
Both IT and IR are independently applied to each subject and are easily extendable, enabling CoDi to naturally support multi-subject generation. For each subject, perform IT and extract its most salient features for IR, which effectively prevents feature interference across subjects and enhances subject consistency, As shown in Fig. 6 our CoDi preserves multi-subject consistency while maintaining their pose diversity.
4.5 DIFFERENT STYLE GENERATION
CcDi first transports the identity features from the reference image during the stage, and the IR stage, the diffusion model refines the subject with specific style. As shown in Fig, 7g CoDi generates images with consistent subject appearance and diverse styles.
4.6
USER STUDY
We conducted user study to compare our method with oftte-of-thearr approaches. A total of 30 prompt were randomly sampled, each consisting of four fixed-length prompts. Thirty-nine participants were asked to evaluate which method demonstrated the best overall performance of the generated images in terms of subject consistency, pose/layout diversity, and prompt fidelity. As

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retro-futuriste neon glow style strolling through moonlit forest
minimalist, geometric abstraction wearing a shimmering gown
whimsical, watercolor dreamscape style riding white horse
steampunk mechanical style playing with a pet horse secret garden
Identity Prompt: beautiful princess with kind smile
retro-futurisn futuristicc neon glow style ... jumping over puddle
retro, comic book pop art style wearing collar
minimalist, geometric abstraction sticking head out of the car
sleek depiction wearing a small sweater
Identity Prompt: puppy
Figure 7: CoDi can generate images with consistent subject appearance and different style.
Table 3: User study with 39 participants evaluating T2I SCG methods based human preference.
Method
ConsiStory
StoryDiffusion
PPrompt Story
CoDi (ours)
Percent (%) f
19.06
21.20
14.53
45.21
shown Table CoDi achieved the highest overall preference, surpassing the second-best method (StoryDiffusion) by 24.01%. %.
CONCLUSION
In this paper, propose CoDi, novel training-free framework that addresses the trade-off between subject consistency and pose diversity. CoDi comprises two key components: identity transport (IT) and identity refinement (IR). During early denoising steps, IT aligns features across instances by optimally transporting the identity subject's features, while preserving pose diversity. IR further refines subject consistency by aligning instance features with the salient attributes of the identity subject in the later denoising steps. The effectiveness our CoD is demonstrated by its state-of-the- art performance in achieving subject consistency and maintaining pose diversity.
ACKNOWLEDGMENTS
This work supported by the Gusu Innovation and Entrepreneur Leading Talents: No. ZXL2024362, Natural Science Foundation of Jiangsu Province: BK20241198, and Natural Sci- Foundation of China: No. 62406135.
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REFERENCES
Omri Avrahami, Amir Hertz, Yael Vinker, Moab Arar, Shlomi Fruchter, Ohad Fried, Daniel Cohen-Or, and Dani Lischinski. The chosen one: Consistent characters in text-to-image diffusion models. In ACM SIGGRAPH, 2024.
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, al. Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall--3 pdf. 2(3):8, 2023.
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In CVPR, 2023.
Lifeng Chen, Jiner Wang, Zihao Pan, Beier Zhu, Xiaofeng Yang, and Chi Zhang. Detail++: Training- free detail enhancer for -ox-ato- diffusion models, 2025. URL attps://arxlv.org]
abs/ 0ess/250.117
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024.
Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. In NeurIPS, 2023.
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. In ICLR, 2023a.
Rinon Gal, Moab Arar, Yuval Atzmon, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. Design- ing an encoder for fast personalization of text-to-image models. arXiv preprint arXiv:2302.12228, 2(3), 2023b.
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt- to-prompt image editing with cross attention control. In ICLR, 2023.
Amir Hertz, Andrey Voynov, Shlomi Fruchter, and Daniel Cohen-Or. Style aligned image generation via shared attention. In CVPR, 2024.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference- free evaluation metric for image captioning. In EMNLP, 2021.
Xiantao Hu, Bineng Zhong, Qihua Liang, Shengping Zhang, Ning Li, Xianxian Li, and Rongrong Ji. Transformer tracking via frequency fusion. TCSVT, 2023.
Dawid J Kopiczko. Tijmen Blankevoort, and Yuki M Asano. Vera: Vector-based random matrix adaptation. In ICLR, 2024.
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-conceptt customization of text-to-image diffusion. In CVPR, 2023.
Mingkun Lei, Xue Song, Beier Zhu, Hao Wang, and Chi Zhang, Stylestudio: Text-driven style transfer with selective control of style elements. In CVPR, 2025.
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. Storygan: A sequential conditional gan for story visualization. In CVPR, 2019.
Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, and Ying Shan. Photomaker: Customizing realistic human photos via stacked id embedding. In CVPR, 2024.
Chunze Lin, Beier Zhu, Quan Wang, Renjie Liao, Chen Qian, Jiwen Lu, and Jie Zhou. Structure- coherent deep feature learning for robust face alignment. IEEE Transactions on Image Processing, 30:5313-5326,, 2021.
11

---

Published as a conference paper at ICLR 2026
Kun- Yu Lin, Hongjun Wang, Weining Ren, and Kai Han. Panoptic captioning: An equivalence bridge for image and text. The Thirty-Ninth Annual Conference on Neural Information Processing Systems, 2025a.
Xiaotong Lin, Tianming Liang. Jian-Fang Hu, Kun- Yu Lin, Yulei Kang, Chunwei Tian, Jianhuang Lai, and Wei-Shi Zheng- Coopdiff: Anticipating 3d human-object interactions via contact-consistent decoupled diffusion. arXiv preprint arXiv:2508.07 162, 2025b.
Chang Liu, Haoning Wu, Yujie Zhong, Xiaoyun Zhang, Yanfeng Wang, and Weidi Xie. Intelligent grimm-open-ended visual storytelling via latent diffusion models. In CVPR. 2024.
Tao Liu, Kai Wang, Senmao Li, Joost van de Weijer, Fahad Shahbaz Khan, Shiqi Yang, Yaxing Wang, Jian Yang. and Ming-Ming Cheng. the-prompt-one-stoe Free-lunch consistent text-to-image generation using single prompt. In ICLR, 2025.
Adyasha Maharana, Darryl Hannan, and Mohit Bansal. Storydall-e: Adapting pretrained text-to- image transformers for story continuation. ECCV. Springer, 2022.
Kangfu Mei, Hossein Talebi, Mojtaba Ardakani, Vishal M Patel, Peyman Milanfar, and Mauricio Delbracio. The power of context: How multimodality improves image super-resolution. In CVPR, 2025.
Gaspard Monge. Mémoire sur la théorie des déblais et des remblais. Mem. Math. Phys. Acad. Royale Sci., pp. 666-704, 1781.
Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In AAAI, 2024.
Kepan Nan, Rui Xie, Penghao Zhou, Tiehan Fan, Zhenheng Yang, Zhijie Chen, Xiang Li, Jian Yang, and Ying Tai. Openvid-Im: A large-scale high-quality dataset for text-to-video generation. In ICLR, 2025.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprini arXiv:2304.07199, 2023.
James Orlin. A polynomial time primal network simplex algorithm for minimum cost flows. Mathematical Programming. 78:109-129, 1997.
Nobuyuki Otsu et al. A threshold selection method from gray-level histograms. Automatica, 11 (285-296):23 27, 1975.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for nigh-resolution image synthesis. arXiv preprint arXiv:2307.01952. 2023.
Tanzila Rahman, Hsin- Lee, Jian Ren, Sergey Tulyakov, Shweta Mahajan, and Leonid Sigal. Make-a-story: Visual memory conditioned consistent story generation. In CVPR, 2023.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.
Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on graphics (TOG), 42(1):1-13, 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björ Ommer. High- resolution image synthesis with latent diffusion models. In CVPR, 2022.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In CVPR, 2023.
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---

Published as a conference paper at ICLR 2026
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Wei Wei, Tingbo Hou, Yael Pritch, Neal Wadhwa, Michael Rubinstein, and Kfir Aberman, Hyperdreambooth: Hypereetoors for fast personalization of text-to-image models. In CVPR, 2024.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 2022.
Peter H Schönemann. generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1);1-10, 1966.
Yoad Tewel, Omri Kaduri, Rinon Gal, Yoni Kasten, Lior Wolf, Gal Chechik, and Yuval Atzmon. Training-free consistent text-to-image generation. SIGGRAPH, 2024.
Cédric Villani et al. Optimal transport: old and new, volume 338. Springer, 2008.
Ruoyu Wang, Ziyu Li, Beier Zhu, Liangyu Yuan, Hanwang Zhang, Xun Yang, Xiaojun Chang, and Chi Zhang. Parallel diffusion solver via residual dirichlet policy optimization, 2025a. URL
htttps://arxiv.. org/ abs 225.2222227996666
Ruoyu Wang, Beier Zhu, Junzhi Li, Liangyu Yuan, and Chi Zhang. Adaptive stochastic coefficients for accelerating diffusion sampling. In NeurlPS, 2025b.
Shaojin Wu, Mengqi Huang, Wenxu Wu, Yufeng Cheng, Fei Ding, and Qian He. Less-to-more general- ization: Unlocking more controllability by in-context generation. arXiv preprint arXiv:2504002160, 2025.
Guangxuan Xiao, Tianwei Yin, William T Freeman, Frédo Durand, and Song Han. Fastcomposer: Tuning-free multi-subject image generation with localized attention. IJCV, 2024.
Yufci Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao. Vitpose: Simple vision transformer baselines for human pose estimation. In NeurlPS, 2022.
Shuai Yang, Yuying Ge, Yang Li, Yukang Chen, Yixiao Ge, Ying Shan, and Yingcong Chen. Seed-story: Multimodal long story generation with large language model. arXiv preprint 207iv:2407.08683 2024.
Zhengyuan Yang. Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, et al. Reco: Region-controlled text-to-image generation. In CVPR, 2023.
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint 20070 2023.
Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric Chang, Hanwang Zhang, al. Exploring diffusion time-steps for unsupervised representation learning. In ICLR, 2024.
Lymin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. CVPR, 2023.
Jaaming Zhou, Ke Ye, Jiayi Liu, Teli Ma, Zifan Wang, Ronghe Qiu. Kun- Yu Lin, Zhilin Zhao, and Junwei Liang. Exploring the limits of vision-language-action manipulations in cross-task generalization. In The Thirty-Ninth Annual Conference on Neural Information Processing Systems, 2025.
Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng. and Qibin Hou. Storydiffusion: Consistent self-attention for oog-range image and video generation. In NewrlPS, 2024.
Beier Zhu, Ruoyu Wang, Tong Zhao, Hanwang Zhang, and Chi Zhang. Distilling parallel gradients for fast ode solvers of diffusion models. ICCV, 2025a.
Xingyu Zhu, Shuo Wang, Beier Zhu, Miaoge Li, Yunfan Li, Junfeng Fang, Zhicai Wang, Dongsheng Wang, and Hanwang Zhang. Dynamic multimodal prototype learning in vasion-language models. In ICCV, 2025b.
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APPENDIX. This appendix presents supplementary materials that extend the methodological details, experimental evaluations, and analytical discussions introduced in the main body of the paper.
A ADDITIONAL IMPLEMENTATION DETAILS
Extracting subject masks. We extract subject masks (Mid and M,,) by averaging the image-texi cross-attention maps over all layers at the final denoising timestep, focusing specifically on subject- related tokens. Let Qimg denote the keys of image features and sub the keys of the subject-related tokens. For each cross-attention layer the unnormalized attention weights are computed as:
Q Qimg KT sub Vd
Wi
(11)
where d is the feature dimension. We then average the attention weights across all L layers:
W
W
(12)
We apply Otsu's thresholding (Otsu al. 1975 obtain the binary subject mask M:
M Olsu(W).
(13)
Selection of the most salient identity features. Our IR refines target images using the most salient reference features, which are determined by the OT plan. Specifically, the saliency score of the i-th identity feature is computed as:
N Σ (T,(i)), 1 C(i,)))
(14)
The top-ar index set I; Eq. contains indices with the a highest saliency scores.
ConsiStory
0.12
DifryDiifisson
Pose Diversity
0.10
CoDi
0.08
0.06
0.2
0.3
0.4
0.5
0.6
0.7
0.8
T
Figure 8: Pose diversity scores across different confidence thresholds Our CoDi consistently outperforms other SCG methods by a clear margin under all settings.
B ADDITIONAL EVALUATION DETAILS
Unified evaluation protocol. We adopt unified evaluation protocol across all metrics. Specifically, for each target image set & with N generated images [x,] we compute the average pairwise evaluation score follows:
N
N
2
Σ j=n+1
f(xn;
(15)
N(N 1)
a=1
where f(..)) denotes the metric-specific similarity or distance function between two images, depend- ing on the evaluation objective. The final evaluation score is then obtained by averaging uy all
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target image sets
K
Uk-
(16)
K
k=1
Pose diversity score. We begin by extracting normalized 2D human keypoints and their confidence scores from each target image using ViTPose (Xu et al., (022). SoTA transformer-base model known for its high accuracy and robustness in human pose estimation. Each image x is represented by a set of H keypoint locations p and their confidences ,3.
p (?)" ..... (pk > E RR
B _K] IRK
(17)
where each keypoint p; (F P²) is normalized by the image width and height and 8r e [o, denotes its confidence score. To ensure robustness, we discard keypoints with confidence scores below a threshold 7. For pair of target images x; and Xj. we retain only the indices of keypoints that are valid both images. We then perform Procrustes method (Schönemann) 1966) remove global variations in translation, rotation, and scale by aligning P, to Р,. Specifically, we first compute the centroids of the keypoints which are denoted as μni and μtj. We then center both keypoint sets by subtracting their respective centroids and normalize their lg norm:
P: p4
Pj P4j
P
Pj
(18)
P: 1,ll2
[P H
Next, we compute the optimal rotation matrix using singular value decomposition (SVD):
E, V = SVD(p, Pj).
R= V
(19)
The resulting R is an orthogonal rotation matrix that minimizes the Frobenius norm between the aligned keypoint sets, ensuring the hest rigid alignment in the least-squares sense. The optimal scaling factor is given by:
PP, l2 p:
tr (E).
(20)
The aligned keypoints are then obtained by applying the computed scale, rotation, and translation:
p: P. M.-
(21)
The pose diversity score between a pair of images x; and x, computed the average Fuclidean distance between p. and Py- To analyze pose diversity under different confidence thresholds we compare the pose diversity scores of various methods across a range of r values. As shown in the Fig. g our CoDi demonstrates clear advantage over other SCG methods across all settings. We use 0.7 in our experiments to balance keypoint reliability and coverage.
C LIMITATIONS
A heroic nature illustration of centaur with the body of horse and the torso of warrior, drinking from crystal-clear stream: forging weapons in a woodland forge: practicing swordsmanship in a field.
Xid
Mid
X1
X2
X3
Figure 9: Limitations. Our method relies on the quality of cross-attention from the pre-trained diffusion model to accurately localize the subject.
Similar to prior subject-mask- methods such as ConsiStory Tewel et al. 2024), our CoDi framework relies cross-attention scores to extract subject masks and estimate image
We slightly abuse the notations K, which here do refer keys in transformer.
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Identity Prompt: mysiical and powerrull ilustration A wis long. flownng
swemsning
braning poiinss is dark care
mandemin Anagha mustical forest
sbuhiing calesial mape under the stary
oesttror de clrvle
UNO
UNO(face)
CoDi
Figure 10: CoDi shows superior results compared to SOTA training models.
token importance in the OT Plan. Occasionally, the tre-trained diffusion model assigns higher attention to background regions than to the subject, as shown in Fig. Fig.9] which hinders the effective transport of identity features X from the reference image Xid to target image X,,, resulting in subject inconsistency. fowever,such failures are rare in practice (under 5%) and can be solved by simply changing the seed.
D INFERENCE TIME AND MEMORY USAGE
Table 4: Inference Time and Memory Usage We report the inference time (in seconds) and peak GPU memory usage on single A6000 GPU for generating of five images from a prompt with resolution of 1024 x 1024. StoryDiffusion (Zhou et al. 2024) is excluded due to excessive
GPU memory consumption beyond the A6000's limit.
Method
Inference time (s)
GPU memory (GB)
Vanilla SDXI. (Podell al. 2023
77.67
35.58
PromptlSStrry (Liu
115.85
17.13
ConsiStory (Tweel 2024
113.88
46.60
CoDi (ours)
154.89
45.20
We measure the inference time and memory usage of different SCG methods on a single A6000 GPU, as shown in Table 4 We report the wall-clock time for generating a set of five images from prompt set (since the baseline method ConsiStory (Tewel al. 2024) performs cross-image attention across batch of images) at a resolution of 1024 x 1024. Based on Table 4 our method CoDi exhibits slightly higher inference time (154.89s) and comparable GPU memory usage (45.20GB) relative to ConsiStory. While PromptiStory (Liu et al. 2025) is the most memory-efficien compromises subject consistency. Note that exclude Story Diffusion (Zhou et al., (024) due to excessive GPU memory usage beyond the A6000's limit at 1024 x 1024 resolution (its original setting uses 768 x 768).
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poss vay paiionig
ailling a muplon watching city lights
art gollory adedring paltonigg
dicorating coam fally rights
Vanilla SDXL
Reference Image
CoDi [Cosine]
Identity Prompt: pixel portrait girl with wavy locks and brown eyes
CoDi (OT)
Figure 11: CoDi (Cosine) fails to transport the corresponding features when there is a significant difference between the reference and target images. In contrast, CoDi (OT) achieves overall structural alignment, therehy preserving pose diversity.
E COMPARISON WITH SOTA TRAINING METHOD
We compare CoDi with UNO (wu et al. 2025). as shown in Fig. 10 While UNO achieves subject consistency, its identical layout across outputs shows poor action prompt adherence and aesthetics. In contrast, CoDi demonstrates improved performance, highlighting the effectiveness our training- free approach, which shows competitive results compared to SOTA company- models.
F THE EFFECTIVENESS OPTIMAL TRANSPORT
To assess the effectiveness of OT, we compare it with simpler cosine similarity approach. CoDi (OT) achieves 0.0758, significantly outperforming CoDi (Cosine) at 0.0704 in pose diversity. As shown in Fig. 11 when there is large feature difference between the reference and target images, especially across styles, CoDi (Cosine) tends to transport the mismatched features, failing to preserve the reference image's 's pose. In contrast, CoDi (OT) effectively maintains the pose while enabling diverse variations, resulting in a failure to preserve the reference image's pose. In contrast, CoDi (OT) effectively preserves the pose of the reference image while maintaining diverse pose variations.
G GENERALIZATION TO DIT-BASED ARCHITECTURES
We adapt CoDi to DiT-based models (Flux), with the generation results shown in Fig. 13 demon- strating richer details, subject consistency, and pose diversity.
H USER STUDY DETAILS
We conducted a user study comparing our method with state-of-the-ar approaches, including Con- siStory, StoryDiffusion, and I Thirty prompt sets, each containing four fixed-length prompts, were randomly sampled. Thirty-nine participants evaluated which method achieved the best overall image quality in terms of subject consistency, pose/layout diversity, and prompt fidelity.
Participants were instructed to select the set that best satisfied three evaluation criteria: subject consistency, pose/layout diversity, and prompt fidelity. Fig. 14 illustrates these criteria at the start
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talking with passerby on the corner
Identity Prompt: A fashion illustration style of short- haired man wearing black suit and a red tie
walking down the street
handling paperwork office
4. feeling weary, he heads to the edge of rooftop to take break
5. walking back home at sunrise, reflecting on day events
Figure 12: CoDi can maintain both subject and clothing consistency while preserving pose diversity by adding the clothing description in the prompt and adjusting the subject mask threshold to include clothing in the foreground mask.
A sleek and fast depiction cheetah sharp eyes, sprinting across the savannah; stalking gazelle the grass; relaxing in the shade under a tree; marking territory with scent.
A 3D animation of black and white dog with yellow collar, wearing bandana; biting bone; wearing birthday hat: sitting by fireplace.
Figure 13: We have extended CoDi to the DiT-based architecture (Flux), with the generated results maintaining subject consistency while achieving diverse pose diversity.
of the questionnaire. To facilitate informed selections, representative example provided, accompanied by performance comparison and rationale, highlighting the reasoning behind choosing the optimal set.
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I ADDITIONAL RESULTS
We present additional qualitative comparisons in Fig. 15| along with more results generated by our CcDi in Fig. I6 These examples further demonstrate that our method achieves state-of-the-ar performance in subject consistency, pose diversity, and prompt fidelity. In contrast, existing SCG methods remain limited, often excelling in only one or of these aspects espicll-typicaly the expense of pose diversity or subject consistency.
Long story generation. As each target image x, relies solely on reference image Xid for subject identity, our CoDi enables extended visual storytelling. As demonstrated in F it maintains subject consistency across diverse prompt semanties, supporting the generation of varied layouts and poses. This makes CoDi effective for long-form generation, where both prompt fidelity and visual diversity are essential.
THE USE OF LARGE LANGUAGE MODELS (LLMs)
our work, large language models (LLMs) were employed primarily for general writing assistance. Specifically, we used LLMs to refine sentence expressions, check for grammatical errors, and convert tables into 18TEX format.
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Evaluation Criteria
The questionnaire includes 30 questions, each showing four image sets generated by different methods. For each question, please choose the set that best meets the following criteria (evaluated within each row of images):
1.
Subject Consistency: the subject should maintain consistent appearance the
Pose/layout Diversity: the subject should exhibit varied poses and spatial arrangements
across the row.
Prompt Fidelity: Alignment with given text descriptions.
Example
at science lab
reading book
eating ice cream
in a library
A
B
C
D
A puppy
Performance Comparison and Selection Rationale
Row A: Although achieves subject consistency, the poses and layouts nearly identical.
Row B: Performs well across subject consistency, pose/layout diversity, and prompt fidelity.
Row C: Shows poor subject consistency. Shows poor subject consistency.
Row D:
Overall, Row represents the best choice.
Figure 14: Questionnaire of user study. Evaluation Criteria outlines the standards for selecting image sets. Example illustrates a representative question for demonstration purposes. Performance Comparison and Selection Rationale demonstrates how the best choice determined based on the example. These sections visually convey the evaluation criteria and guide participants' selections.
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striin bul atriner antestiall
pulisity mandel suotthng AwA
troiding contorges NOW
cillediass
uith notiral doer
Vantis SOXL
DATZER
SALE
ConsiStory
EES
StoryDifiussin
1Srompt1Story
CoDi (ours)
cartby tornootto
A shimmering aqualic fresco cutious mommid with rainbow scales.
focused potter clav-sntesred
coceeee playing
garing
andorwaker caner
cnimaaly in Avor
ropl hall
cantis hnfcrry
Va SCXL
ConelStory
SioryDiflussant
1Stoy
OsDi (ours)
(c) dreamy underwater ilustration of besutffll momaid with shimmering tail.
A dreamy illustration of bezutiful princess.
Figure 15: Additional qualitative comparisons. Our CoDi achieves the best trade-off among subject consistency, pose diversity, and prompt fidelity.
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pastoral countryside peinting flour-dusted miler apron, stacking flour sacks; sifting golden graine: reading wind signe: restoring waterwheel mechanisms: lesting flour quality: bagging fresh products.
haunting painting seduclive weaving fog: resting an whispering delphins moonlight: weaving seaweed into magical charma; floating among glowing jellyfish the deep sea: playing en enchanted harp on rocky shore.
peaceful and rustic illustration of hobbit with feet and smile, enjoying feast at hame; lounging cazy fireplace; walking through the rolling hills; baking bread in stone oven, reading book in a suniit nook; harvesting vegetables from flekd.
A terrifying and wild illustration of a werewolf with glowing yellow eyes, snarling beside flickering campfine; resting with pack under moonlight; glaring through
wall; tracking footprints through
snow; standing stop moon;
crouching behind tail reeds by a lake.
graceful and elegant Illustration beautiful princess flowing gown, befriending dragon stop a cliff; contemplating her destiny in candielit chapel; feeding swans the lake; reading in the palace library; walking through royal garden; lighting lanterns during peace ceremony.
3D animation of happy hedgehog, dressed festive outfit; hiding inside boot: cozy dressed miniature jacket; nibbling on strawberry, wearing round glasses.
STE
O&O
Figure 16: Additional qualitative results generated by our CoDi demonstrate strong subject consis- tency and pose diversity.
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a posion
proparing ng magical
reading spe
medhating dew from
toating graveyard
guaing into cryatal
summaning shaddoos
hinding moonlight
drawing protective runses
speakkng apin through
carving
collecting bones under a bloodrrod moon
feediig teling black
wanceriing through fog: swamp
hiding shadows near a
sketching forbidden dlyphs
taming serpent
wassing spels with
conjuring ing Busiona to bravelers
levitating stooes
whispering curses andd
disquiking herseff with shifling shadows
creating pations with and
commanding vinwa entangle intrucions
feeding a famm with enchanted paper
teming fiwry elemennal ...
camoulliing harsel within tree bark
guarding cursed relic her ur
crattng talommre from falln stars
gazing Into minor of smoko
Sending cursed doll on sheff
draw power from an eciipso
hiing secrets glwing ars
collecting whispers vials
skatching omens in the ashes
etching moon phases
Saming hands
trapp na EmO glass
hiding memories
summoninn light
walking through with
Identity Prompt: A dark drawing of mysterious witch.
Figure 17: Long Story Generation. CoDi supports extended visual storytelling by generating diverse scene compositions while consistently preserving subject identity throughout the sequence.
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Reviews:
[{"review_id": "0M9uIzEuJY", "reviewer": "Reviewer_tP1C", "summary": "This study introduces a novel training-free framework for subject-consistent image generation. The framework aims to address the limitation of existing methods that sacrifice pose and layout diversity to maintain consistency. Its core design employs a staged processing strategy: during the early generation phase, optimal transport is utilized for coarse-grained identity feature transfer to preserve subject consistency; in the later generation phase, fine-grained refinement is applied to the image features. Experimental results demonstrate that the proposed method outperforms existing mainstream approaches across three key metrics—subject consistency, pose diversity, and text alignment—achieving state-of-the-art performance on public benchmarks.", "questions": "1.How is the iterative generation performance? Can it maintain identity consistency?\n2.Why does the style of the generated images vary? Since style should also be correlated with features from certain layers of the U-Net, why do the other two models (ConsiStory and StoryDiffusion) not exhibit style changes (based on the observation from Figure 3 in the paper)? Could this limitation affect the model's practical applicability?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The key innovation is the explicit decoupling of identity alignment into coarse-grained transport in early steps and fine-grained refinement in later steps, which is a well-motivated approach based on the progressive nature of diffusion models.\n2.A significant strength is the superior balance it achieves. As claimed, the paper provides strong evidence that the method outperforms existing training-free baselines in subject consistency while preserving significantly greater pose diversity and text alignment, addressing a well-known trade-off in the field.", "weaknesses": "1.For long-story generation scenarios, it is crucial to maintain consistency in both character identity and their apparel. However, the results presented in the paper demonstrate that the method primarily ensures identity consistency, while the consistency of clothing remains inadequate. In my opinion, this limitation would significantly restrict the method's practicality in long-story applications.\n\n2.The method has a core reliance on binary masks derived from cross-attention maps to extract identity information from the reference image. However, recent and emerging generative models, such as SD3 and other DiT-based architectures, have moved away from using cross-attention mechanisms. This fundamental incompatibility means that the proposed facial extraction technique faces significant challenges in being adapted to these mainstream, state-of-the-art generative models, thereby limiting its generalizability and future relevance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YawiAeZ0f4", "reviewer": "Reviewer_rZBz", "summary": "To solve the trade-off between subject consistency and pose diversity in text-to-image subject-consistent generation (SCG), the paper proposes the CoDi framework. Inspired by diffusion models’ progressive nature, this paper includes two stages: Identity Transport and Identity Refinement.  Extensive experiments have demonstrated the effectiveness of the model.", "questions": "1. This work was conducted on the SDXLl, which is a relatively old base model. Could the proposed method in this paper work on DiT model (e.g., FLUX and SD 3.5)?\n2. In the experimental results shown in Figure 3(a) of this paper, the styles of the three scientists are not very consistent (and all are anime characters), and the portrait similarity is also lower compared to ConsiStory and StoryDiffusion. Will the proposed model in this paper affect the style?\n3. Could this method generate images with consistent subject appearance and different style?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1. This work proposes an effective method to improve pose diversity.\n2. This work is clearly expressed and easy to understand.\n3. This work introduces Optimal transport into Subject-consistent generation.", "weaknesses": "1. This model was tested on SDXL, but its effectiveness was not verified on the DiT architecture.\n2. The qualitative and quantitative experimental results of this work did not show significant improvement.\n3. The long description in lines L126-L131 seems informal in the main text.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zbag1GkM0W", "reviewer": "Reviewer_VX3M", "summary": "This work focuses on subject-consistent generation. It preserves pose diversity by leveraging Identity Transport in the early stage of denoising and promotes subject consistency through Identity Refinement in the late stage of denoising. Both qualitative and quantitative experiments demonstrate the effectiveness of the proposed method.", "questions": "Given that current state-of-the-art base models such as XVerse, UNO, Flux-Kontext and Nano Banana have already achieved the three key objectives outlined in the introduction with excellent performance, and further support unlimited multi-subject consistent generation as well as strong generalization capabilities, what is the core significance of this work?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "- The paper is well written and easy to follow.\n- The method proposed in the paper is training-free and can be directly applied during inference.\n- The paper includes comprehensive comparative experiments and ablation studies. The design of evaluation metrics also demonstrates certain insights, especially regarding \"pose diversity\".", "weaknesses": "- Optimal Transport (OT) essentially addresses the optimization problem of transforming one probability distribution into another with minimal cost. However, the problem in **IT** is to find the feature matching relationship between the reference image and the target image. Clearly, a straightforward ranking based on cosine similarity would be simpler and more efficient. In contrast, the \"globally optimal transport\" property of OT not only complicates the problem but may also introduce redundancies. To illustrate this with a simple though imperfect analogy: The IT task aims for direct and semantically consistent matches. For instance, it seeks to align the \"eye\" feature in the reference image with the \"eye\" feature in the target image, and the \"nose\" feature with the \"nose\" feature. Introducing OT could disrupt this consistency. Suppose the \"eye\" feature in the reference image has a higher correlation than other features. Under OT’s global optimization logic, this could lead to the \"eye\" feature being inappropriately \"transported\" to the \"nose\" region in the target image. This outcome violates the need for direct and semantically meaningful feature matching. In summary, I argue that the introduction of Optimal Transport in this work serves more as a mathematical formality rather than a practically useful component.\n- The design of Identity Refinement lacks innovation, as its feature fusion approach bears significant similarities to that of prior works.\n- SDXL is built on the U-Net architecture, which is considered outdated in the current field. Most state-of-the-art base models now adopt the DiT architecture, and the effectiveness of the proposed method when applied to DiT-based models remains unproven.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)