Story-Iter: A Training-free Iterative Paradigm for Long Story Visualization
Authors: Jiawei Mao
Xiaoke Huang
Yunfei Xie
Yuanqi Chang
Mude Hui
Bingjie Xu
Zeyu Zheng
Zirui Wang
Cihang Xie
Yuyin Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Subject-Consistent Image Generation
Diffusion Model
Story Visualization
Primary area: generative models

Abstract: This paper introduces **Story-Iter**, a new training-free iterative paradigm to enhance long-story generation. Unlike existing methods that rely on fixed reference images to construct a complete story, our approach features a novel external **iterative paradigm**, extending beyond the internal iterative denoising steps of diffusion models, to continuously refine each generated image by incorporating all reference images from the previous round. To achieve this, we propose a plug-and-play, training-free **g**lobal **r**eference **c**ross-**a**ttention (**GRCA**) module, modeling all reference frames with global embeddings, ensuring semantic consistency in long sequences. By progressively incorporating holistic visual context and text constraints, our iterative paradigm enables precise generation with fine-grained interactions, optimizing the story visualization step-by-step. Extensive experiments in the official story visualization dataset and our long story benchmark demonstrate that Story-Iter's state-of-the-art performance in long-story visualization (up to 100 frames) excels in both semantic consistency and fine-grained interactions.

Published as a conference paper at ICLR 2026
STORY-ITER:
A TRAINING-FREE ITERATIVE
PARADIGM FOR LONG STORY VISUALIZATION
Jiawei Mao
Xiaoke Huang 1+
Yunfei Xie
Yuanqi Chang'
Mude Hui
Bingjie Xu
Zeyu Zheng'
Zirui Wang
Cihang Xie
Yuyin Zhou
1 UC Santa Cruz
Rice University
Sinnaapore Institute of Technology Apple
*UC Berkeley
Project Page: https: htmps:///gie
snowmann stretshed Its arms
snowman find bird forest in the snow
snowman and children singlng together
snowman build snow castle snow
snowman SOW fox In SnOW
snowman greeted .... the fox
snowman sleeping Icc
Initialization
Story Iter
ith iteration
Lth iteration
StoryDiffusion
StoryGen
Figure I: A long story of "noowmnn" visualized by our Story Iter from different iterations, compared with those visualized by previous StoryDiffusion Zhou et al. (2024) and StoryGen Liu et al. (2024). Notable differences are highlighted in green and red. Zoom in for a better view.
ABSTRACT
This paper introduces Story-Iter, new training-free iterative paradigm to en- hance long-story generation. Unlike existing methods that rely fixed reference images to construct a complete story, our approach features novel external it- erative paradigm, extending beyond the internal iterative denoising steps of dif- fusion models, to continuously refine each generated image by incorporating all reference images from the previous round. To achieve this, we propose a plug- and-play, training-free global reference cross-attention (GRCA) module, model- ing all reference frames with global embeddings, ensuring semantic consistency in long sequences. By progressively incorporating holistic visual context and text constraints, our iterative paradigm enables precise generation with fine-grained in-
"Equal contribution.

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Published as a conference paper at ICLR 2026
reference
reference
update itenation
Diffusion Model
referrence
results
biffusion Model
biffusion Model
results
reference
Diffusion Model
update 1-1th Iteration
(A) Auto Regressive Paradigm
Reference -Image
Paradigm
(C)
Iterative Paradigm
Figure 2: Comparison of paradigms for long story visualization: (A) Auto-Regressive (AR): gen- erates frames sequentially referencing previous finite frames (e.g. the previous three frames); (B) Reference-Imad Image (RI): employs fixed reference images (e.g. the beginning four frames) as reference images; (C) Iterative Paradigm: leverages all frames from the previous iteration as reference images.
teractions, optimizing the story visualization step-by-step. Extensive experiments in the official story visualization dataset and our long story benchmark demon- strate that Story-Iter's statc-of-the-ar performance in long-story visualization (up to 100 frames) excels in both semantic consistency and fine-grained interactions.
INTRODUCTION
Story visualization aims to generate a sequence of coherent images from text prompts, reflecting the narrative's progression and enabling users, without artistic background, to visually present their stories (Li et al. 2019 Maharana & Bansal 2021; Chen et al. 2022). Recent advancements in text-to-image models, particularly diffusion models, have significantly improved the quality of generated visuals, producing high-quality, creative, and aesthetically pleasing images (Saharia et al.) a 2023). These models greatly outperform earlier approaches, such as generative adversarial networks (Brock 2018) in terms of image quality.
2022 Rombach al. 2022 Kang al.)
However, story visualization remains challenging, particularly in maintaining semantic consistency and synthesizing interactions as the story length increases. Two main paradigms have emerged in this domain. The Auto-Regressive Paradigm (Fig. 2 (A)), which generates frames sequentially referencing previous finite frames (Pan et al. 2024 Liu et al. 2024), often struggles with semantic consistency due to error accumulation and inability to reference future frames. Although tech- niques like Consistent cies, their reliance on intermediate denoising features results in high memory consumption, limiting scalability for longer stories. To address these challenges, Zhou et al. (2024) further proposes the Reference-Image Paradigm (Fig. (Fig.2((B) (B)), which employs fixed reference images to guide the visual- ization. However, while using only the initial frames as reference images alleviates scalability issues, it fails to provide the global semantic coherence necessary for long-story visualization, resulting in error propagation from the reference images to subsequent frames. As such, both paradigms expe- rience quality degradation when visualizing long stories. Additionally, they inherit the limitations from Stable Diffusion Model (SDM) (Rombach et al. 2022), particularly in generating fine- mne-grained interactions in the story (Fig. I..
(CSA) (Zhou et al. 2024) can help mitigate these inconsisten-
To address the limitations in existing story generation methods, we present Story-Iter, new It- erative Paradigm. Unlike existing methods that use multiple fixed reference frames but do not refine them over time or incorporate holistic visual context (Fig. 2 A&B, e.g. StoryGen (Liu al., 2024) and StoryDiffusion (Zhou et al. 2024)), our approach continuously refines each generated story frame, utilizing the full-length reference images generated from the previous iteration. Here, iteration refers to the external round beyond the internal denoising steps of diffusion models.
The proposed iterative paradigm offers two key advantages. 1) It progressively approximates the distribution of reference image global embeddings across iterations, ultimately ensuring visual co- herence (see Fig. 4. 2) By iteratively engaging the full-length story frames and text prompts, optimizes fine- ine-grained control based both global and local semantic contexts. Shown in Fig, [ Story-Iter enhances both visual consistency and fine control across iterations, resulting in more coherent and higher-quality visualizations. For example, the image depicting complex character
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interactions, such "snowman fox" demonstrates substantial improvement iterations
compared to previous methods (Liu et al. 2024 Zhou et al. 2024).
0.000
story1
UITE may
story2
00055
story3
Divergence
0.050
story4
0.045
0.6
0.040
00035
00030
00025
iteration
Attention maps for each generated image in our Global Reference Zoom in for better view.
Figure 3:
Figure 4: Distribution among the global embed- dings full-length reference images as itera- tions progress.
Specifically, during initialization, only text prompts of the story are utilized generate the full story frames without any reference. In the subsequent iterations, the global embeddings of the full- length story frames from the previous iteration the reference, along with the text embeddings, collaboratively guide story generation. To implement the iterative paradigm, we introduce a plug- and-play Global Reference Cross-Attention (GRCA) for long story visualization. Some subject- consistent generation methods, such as the decoupled cross-attention in IP-Adapter (Ye et al.) 2023), also contribute story visualization. However, they are designed to maintain semantic consistency in individual images, whereas story visualization requires connections across all frames, making them not directly applicable. Different from the IP-Adapter, the global embeddings of the full- length reference images act keys and values in GRCA. Unlike the intermediate denoising features used in CSA in StoryDiffusion (Zhou et al.] 2024). the global embeddings in GRCA allow more image frames be referenced to maintain global semantic consistency. The processes of computing the token similarity matrix and merging tokens in GRCA adaptively aggregate visual features from reference images similar the currently generated image (Fig. 33. This ultimately ensures the semantic consistency in the iterative paradigm and reduces the influence from noisy references. Additionally, strike balance between visual consistency and text controllability, we introduce
linear weighting strategy to fuse both modalities.
Extensive experiments demonstrate that Story-Iter consistently outperforms existing methods for visualizing both regular-length and long stories (up to 100 frames). Specifically, in the context of regular-length story visualization using the StorySalon benchmark (Liu al., (2024), Story-Iter exceeds the baseline model, StoryGen (Liu et al. 2024), achieving 9.4% improvement in aver- age Sharacter-Charactee Similarity (aCCS) (Cheng et al. 2024b) and a 21.71 reduction in average Fréchet Inception Distance (aFID) (Cheng et al. 2024b). For long story visualization. Story-Iter also demonstrates solid advancements, achieving gains of 3.4% in aCCS and 8. 14 in aFID compared to Story Diffuuifusion (Zhou et al. 2024). demonstrating the superior generative quality of Story-Iter, par- ticularly in semantic consistency and fine- mne-grained interactions.
Our contributions are summarized follows:
A new long story benchmark for evaluating long story visualization for up to 100 frames.
A new iterative paradigm that enhances story consistency by continuously updating ref- erence images in each external iteration, beyond the diffusion model's internal denoising steps.
A new global attention mechanism GRCA that enables modeling all frames as reference images, thus ensuring semantic consistency in long sequences.

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Published as a conference paper at ICLR 2026
State-of-the-art story visualization performance various benchmarks, especially in long-story scenarios.
2 RELATED WORK
Story Visualization Story visualization (Chen et al.
2022 Li 2022 Tao et al. 2025; Papadim-
itriou et al. 2024; Bugliarello et al. 2024 Ahn et al., 2023 Rahman et al. [0033 Tsakas et al.
2023 Wu al.) 2024) has evolved from GAN-based approaches like Story GAN (Li et al. 2019
to more advanced techniques. Recent developments leverage diffusion models (Shen et al. 2024
Tao al. 2024) and combine them with the auto-regressive paradigm, as seen in AR-LDM (Pan
et al. 2024) and StoryGen (Liu et al. 2024).. StoryDiffusion (Zhou et al.) 2024), on the other hand, uses reference-image paradigm with fixed reference images. DreamStory (He al., (2024) Sto- ryGPT (Shen & Elhoseiny] 2023). and MovieDreamer (Zhao et al. 2024) introduce LLM LLM-generated guidance for story visualization. There also attempts on interactive story visualization (Cheng ct al. 2024a b; Gong et al.] 2023: Yang et al. (024).. However, challenges remain in maintaining semantic consistency for the whole story and avoiding eπror accumulation, especially for longer narratives (Wang et al., 2023 Tewel et al., 2024 Zhou et al. 2024 Liu et al.) 2024). Unlike Story- Diffusion (Zhou al.) 2024). which uses fixed reference frames but does not refine them or incor- porate holistic visual context, paradigm continuously refines each generated frame, utilizing the full-length story frames generated from the previous external iteration.
3 METHOD
3.1
INITIALIZATION
To build the initialization for iteration, we only employ text prompt T. for the kςs image in the story to guide the pretrained Stable Diffusion Model (Rombach et al. 2022) SDM(z, T: in generating the initial images, where the random noise. All generated images from the initial step will be stored reference images for the first iteration. We denote Thus, the initially generated story frames can be represented as:
the initialization of Story-Iter.
zk i-0 2DM(,, Tk), k E [1, B),
= |
B-1 2
2I..B B
where B denotes the story length. Compared to subject-consistert image generation methods (Ye)
et al., 2023) that introduce reference image guidance, Story-Iter initialized only by text prompts to outline the story faithfully.
3.2 ITERATIVE PARADIGM
In this subsection, we describe how each image is updated within an external iteration beyond the internal denoising steps of diffusion models (see Fig. 5). Formally, for the is iteration, we use full-length B story frames from the (i 1)th iteration "|... (i E [1, L), refers to total iteration number) as the reference images to refine story visualization. With SDMGRCA (z, Th. ( repre- senting the whole denoising process with our Global Reference Cross-Attention in pretrained Stable Diffusion Model (Sec. 3.3), the kig story frame generated from the ish iteration can be expressed as:
1 = SDMGRCA (2, Tk, -i-..))) & E [1, B). ri... B |xj, xby 1x BB-1 xxph
As the iterations progress, the distribution of reference image global embeddings gradually con- verges, thereby assisting Story-Iter in continuously enhancing the semantic consistency of the global view. As shown in Fig. 4 Story-Iter does not enforce consistency in single step. In- stead, through successive external iterations, it progressively refines GRCA by aligning the current frame's features with semantically relevant content from reference frames. Fine-graine interactions (limitation for SDM) are also optimized as Story-Iter repeatedly engages text constraints T₃ in the iterative paradigm.
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Story-Iter updating reforence images
Section
Initialization
Section 3.1
Reploce Mih
Refarence Images
Stadle Diftusion Model
Reference Images
Current Results
danosing
coporte
#1 1...0
Text Enoodentt
encuted beie Baalarlyy espiande
Story
Story
Itorative Paradigm
Section 3.2
Global Reference Cross.Attention
Soction 3.3
Reference images
Image Emcodent
Rss image
visualization
Overview
danoliing sleps
Gibbal Growdiing
Gioel Growdinng
Query
(de)
Liwer
Gress
Giri bant down. picked Flower
Tout Encoderist
Current Results
(b) Iratastiation
Insertion Location
Figure 5: Overview of Story-Iter. The full-length story frames are generated with only text prompts during initialization (Sec. [.1]. In the subsequent external iterations, the story frames are generated referencing the full-length story frames from the previous iteration (Sec. 3.2). The Global Reference Cross-Attention (GRCA) adaptively aggregates features from full-length reference images to ensure
semantic consistency (Sec. 3.3)
3.3 GLOBAL REFERENCE CROSS-ATTENTE
We propose plug-and-play augmentation module to equip Stable Diffusson models, called Global Reference Cross-Attention (GRCA). We utilize pre-trained CLIP (Radford et al. (021] as the image encoder and project the global embedding (termed c) for each reference image into few tokens. This global embedding enables guidance of structural and visual similarity to reference images dur- ing the denoising process. Furthermore, with fewer tokens, GRCA can incorporate more reference images.
Given full-length B images generated from the (i ( 1)t, iteration 2i...B R Bxhxxxx (h: height, wt width), we define Attention(Q.. K, V) to indicate the attention calculation, where Q, K, and V represent the query, key, and value respectively. GRCA of the kns image in the én iteration:
c = CLIP(ri-!n)
d..BERR.
a = G...We. We E Rdx(nxe),
č... flatten(C)....))
ai.. e (BxxNx
Q We Kj = W... =z= č We: GRCA(T,, xf-.BB) = Attention(Qi, K; V.))
The dimension of global embedding of and the projected dimension denoted by d and e, respec- tively. n indicates the length of reference tokens, n 4 if not specified. flatten(.) represents flatten operation on vectors. W, is the projection matrix transforming the global embeddings into reference tokens. W is the weight matrix of the intermediate denoising feature 1 in SDM. W; and GRCA directly
W, are the weight matrices of the reference tokens. The proposed reuses the cross-attention weights from IP-Atteeer (Ye et al.) 2023) in subject-consistett single- image generation without training. Finally, we merge the outputs from GRCA with the outputs from cross-attention texts, to guide the hes story frame generation. With corresponding text prompt Tş and the full-length reference images x the intermediate denoising feature I is updated as:
B.
1 = Attention (Tè, Tr, Tx) + +. A.GRAA 2i...))
(4)
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noran Mtcchan reall wakiee
Ayyangg vommtn Laing bhilddingg
can whde brewning her
Permer feeding chickena
Jortll Journeg in picup
Biin the phone
thes agn the formet
Ground Truth
AR LDM
StoryGen
Affected by Reference Image Flaws
Subject Inconsistency
Interaction Missing
StoryDiffusion
Without Uniformity Flaws
Subject Consistency
Correct Interaction
Story Iter
Figure 6: Qualitative comparisons for regular-length story visualization. Zoom in for better view.
where the weight factor A; of the izs iteration is adjusted as A; = Å1 + x (i 1) with fixed. A increases linearly with lower bound λ₁ to balance visual consistency and text alignment in the iterative paradigm. Leveraging pretrained weights, GRCA adaptively re-weights attention at each iteration, thereby mitigating the accumulation of noise from irrelevant historical states and instead prioritizing information most pertinent to the current frame (see Fig. B). The global embedding undergoes progressive updates, serving as soft forgetting mechanism that attenuates contextual drift and naturally suppresses obsolete or extraneous information throughout the iterative refinement process. We demonstrate Pseudo-code of Story-Iter in Algo. |
EXPERIMENTS
4.1 EXPERIMENTAL SETTING
We use the StorySalon dataset (Liu et al. 2024) to benchmark performance for regular-length story visualization. Compared to the closed Flintstones (Gupta al. 2018)) and Pororo (Kim al.) (017) benchmarks, the open-ended StorySalon benchmark provides an evaluation of more diverse scenar- ios. For long story visualization, we curate multiple long stories using GPT-4o (OpenAl 2024). To evaluate the efficacy of Story-Iter, we report CLIP text-image similarity (CLIP-T) (Radford et al.) 2021), average Fréchet Inception Distance (aFID) (Cheng et al., 2024b). and Character- Charracter Similarity (aCCS) (Cheng et al. 2024b). CLIP-T is to measure image-text alignment, and both aFID and aCCS are used to evaluate semantic consistency among generated images. We use 10 iterations and the fixed random seed for Story- -ttr in experiments by default. For story visualization within 100 frames, iterations are sufficient to maintain the consistency of the story sequence. Iteration number ablation in Sec. F Human evaluation and diversity be found in Sec. Cladd Sec. H.3

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IP Ad-Aadpter
Dronh
StoryGen
inging
"Oocing
StoryDiffusion
perforate
ConsiStory
Story-Ire Iter
Figure 7: Qualitative comparisons for long story visualization. Story-Iter shows advantages in gen-
erating semantic consistency (consistency and differences highlighted in green and red boxes) and character interactions (correct and incorrect interactions are highlighted green and red cir- cles). For the complete story, refer to the Fig. 24 Table Quantitative comparison for story visualization.
Table 2: Quantitative comparison for long
regutar- stories.
Method
CLIP-T TaCCS TaFID
Method
CLIP-T "aCCS faFID
SDM (Rombach al
2022
0.323
0,662
23.10
AR-LDM (Pan et 2024
0,216
0.673
133.62
PrompE-DMM (Rombach
2022
0.289
0.699
StoryGen Liu 2024
0.223
0.740
126.13
Finetuned-SDDX [Kombsch
2023
0.309
0.639
23.05
IP-Adapter
0,274
0.751
93.70
AR-LDM (Pan
0,239
0.684
42.55
Story-Iter (Ous)
0.307
0.754
98.51
StoryGen (1.0 e1
217/4
0.255
0.724
36.34
AP-Atapter
Ye (Yr
el
0,241
0.737
25.18
FLUX. I. Kontext
Batifol et al. 2025
0.290
0.783
78.03
Story-lier (Ours)
0.305
0,760
16.52
Nano Banana
0,310
0.791
106.
PPAAdspierLL Ye 2028
0,244
0.758
14.70
IP-AdapterXI. et Ye al.
2023
0.297
0.787
88.69
StoryDiflusion Z200 et al. 2.3.22200 2024
0.311
0,765
14.84
DALL-E BBeker al 2025
0.301
0.674
251.71
Story-IterXL (Ours)
0.310
0.818
14.63
SEED-LLaMA
88.96
ConsiStory (Tewel al 2024
0,316
0.761
108.83
Iprompti story [LU et
2025
0.316
0.790
99,25
Siffuuiflusion Zhou
0,315
0.768
102.44
Story IeerXL. (Ours)
0.318
0.802
94.30
4.2 REGULAR-LENGTI STORY VISUALIZATION
Based the standard setup StorySalon dataset (Liu et al. 2024). ) compare with the following baselines: StoryDiffusion (Zhou et al.
2024), StoryGen (Liu et al. 2024). AR-LDM (Pan et al.)
2024 SDM (Rombach | 2022), Prompt-SDM (Rombach al. 2022 Finetuned-SDM (fine-
2022). and IP-Adapter (individual image generation) (Ye) al. 2023). Autostudio (Cheng al. 2024a) requires the additional bounding boxes as prompt, thus it is not included in comparison. For Prompt-SDM, we use prompts of "cartoon-style images". For StoryDiffusion, we follow the official setup by using the initial four generated images as the reference. To adhere to copyright restrictions and ensure fair comparisons, we exclusively utilize text prompts from the open-source subset the StorySalon test set for evaluation. This subset comprises 6,026 prompts, with an average of 14 frames per story and the longest story up to 44 frames.
tuned on StorySalon) (Rombach et al.
Quantitative Evaluation. CLIP-T results in Tab. show that Story-Iter and StoryDiffusion (Zhou ct al. 2024) visualize content more aligned to the text prompt than previous story visualization models (AR-LDM (Pan et al. 2024) and StoryGen (Liu al. 2024)). Since the story visualization
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@ woman weamin space suis photographing with olian @ A woman weoring space suits and dlien on icebound womsn weaning space suits shoking hand with clien
D1, Arl (0..0.5)
Figure 8: Ablation study of the weighting strategies in the iterative paradigm: linear weighting strategy \; = t1 +xx (i-1) 1) for the in iteration or fixed weighting strategy (e.g. X = 0.3, X = 0.5). Zoom in for better view. See Sec. F for results with more iterations.
framework introduces additional image conditioning, it inevitably affects text alignment compared to SDM, which is conditioned only on text. Meanwhile, since neither Story-Iter nor most baselines trained on the StorySalon dataset, we introduce aFID and aCCS metries for fair evaluation of the character consistency among generated story images. aFID and aCCS in Tab. [ illustrate that Story-Iter achieves higher semantic consistency of the generated images compared to most baselines.
Qualitative Evaluation. In Fig. 6 we provide the qualitative comparison results of the open-ended story visualization. Although AR-LDM (Pan et al.] 2024) and StoryGen (Liu al. 2024) generate coherent image sequences based on story prompts, the quality of the generated images degrades when story length increases due to the error accumulation issue of the AR paradigm. Results of StfryDiffusion (Zhou et al.) (024) and Story-Iter show satisfactory story visualization performance. However, StoryDiffusion cannot maintain consistency between certain subjects due to lacking global story comprehension (e.g., "cat" in Fig. 6) Additionally, since StoryDiffusion requires the first few generated images as references, the visualization results are affected by the reference image flaws (e.g., "closed-eye issue" in Fig. C). In comparison, Story-Iter outperforms in regular-lenghh story visualization by generating coherent image sequences. These findings highlight the effectiveness of GRCA, it incorporates all reference images to ensure semantic consistency.
4.3 LONG STORY VISCALIZATION
To better evaluate generative quality for long story visualization (i.e., up 100 frames (Fig. 32 and Fig. 33)), we compare to existing story visualization methods, reference-image based image editing models, and seference-guider image generation methods. We use GPT-4o generate 20 long story cases of 10 50-sentence and 10 100-sentence narratives.
Quantitative Evaluation. The quantitative results in Tab. 2 show that Story-Iter significantly im- proves the semantic consistency and the generative coherence for fine-grained interactions for long story visualization compared existing models (Pan et 2024 Liu al.] 2024 Betker et al.) 2023 Ge et al. 2023] Tewel et al.) 2024 Liu et al., 2025 A key difference between our Story- Iter and StoryDiffusion (Zhou al. (024)) lies in the paradigm: by refining frame generation with global (visual) and local (text) semantic context in the iteration paradigm, Story-Iter achieves higher consistency compared to StoryDiffusion which relies on the fixed reference images. IP-Adapter (Ye) et. 2023) employs the same single reference image that leads to less aFID. In contrast, referencing full-length frames in our paradigm, our method ensures both visual consistency and text alignment. Compared with Ster, reference-ing immge editing models suffer from overly strong identity con- straints and their inability to handle newly introduced characters, which makes them unsuitable for long-story visualization. See Sec. for more comparisons with reference-image editing models.
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Table Comparison of computational efficiency across different Story-Iter variants for 100 frames x story visualization.
Method
Diffusion Steps
External Iterations
FLOPs
VRAM
Time
CLIP-T
aCCS
aFID
CooryDiffusion Zhou 2024
PELOPs
40GB
0.315
0.768
102.44
Story-IteXLL
PELOPS
19GB
250 män
0.318
0.802
94.30
Story-heeXL-PPo
3 PFLOPs
19GB
20 min
0.309
0.788
109.13
Toreut
Salonn
Figure 9: Pose-conditioned generation results of Story-Iter Itet-ContoolNett. Given pose maps, the model produces frames that closely match the target poses while maintaining global story coherence.
Qualitative Evaluation. Fig. shows the visualization results for long stories, indicating that Story-Iter generate high-quality, thematically consistent long image sequences hased the text prompts. The results of the IP-Adapter (Ye et al.) (023) exhibit significant missing or incor- rect interactions due limited text controllability. Our method outperforms IP-Adapter in global context visualization, benefiting from the proposed iterative paradigm and referencing full-length story-frames, in contrast single-image guided generation. StoryGen (Liu al. 2024) suffers from more serious error accumulation issue from the AR paradigm during extended storytelling. With- out modeling the holistic visual context, StoryDiffusion (Zhou et al., 2024) and ConsiStory (Tewel et al. 2024) fail to generate faithful character interactions and maintain consistency throughout the story. Our Story-Iter refers to the full-length story frames and individual text prompts in external iterations, thus achieving more accurate character interactions and maintaining subject consistency.
4.4 VARIANTS
-tory-Iter-Fast. To improve the computational efficiency of Story-Iter for long-story visualization, we develop a fast variant built upon the SDXL-LCM backbone (Luo et al., 2023). This version reduces the diffusion sampling steps from 50 to only 4, substantially accelerating the iterative gen- eration process while maintaining story-level consistency comparable to the original Story-Iter (see Tab. and Sec. H.2
Story -er-IteerCooooNeee We introduce a new variant, Story-Iter-Con Cee which integrates a pose- conditioned ControlNet (Zhang al.) into the Story-Iter framework. This variant enables explicit control over character pose and spatial layout while fully preserving the global story consistency and text-image alignment achieved by Story-Iter. As shown Fig. g by using pose maps (e.g., OpenPose skeletons) control signals, Story-Iter-Controlly enhances local-frame diversity with- out compromising global consistency or text-image alignment.
4.5 ABLATION STUDY
Global Reference Cross-Attention. We ablate the effect global semantics modeling by GRCA for long story visualization. Specifically, for each image visualization in the sequence, we only use the single reference image at the corresponding index during the iteration as guidance. By establishing global comprehension of the story for the diffusion model, Story-Iter maintains the semantic consistency in the generated image sequence (Tab. 4 and Fig. 10 For further discussion
on GRCA, please refer to the Sec. Sec.A.5) A.5
Iterative Paradigm. We conduct ablation experiments to evaluate the effect of the proposed iterative paradigm for long story visualization. As shown in Tab, 4nd Fig. 8 the iterative paradigm improves

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Table 4: Ablation of the design choices of Story-Iter.
Hugimage and
Setting
CLIP-T 1
aCCS |
aFID)
pagnregek (ed planes Stoppell
Ore da.. bengerr
w/o Initialization
0.302
0.788
90.30
w/o GRCA
0.319
0.740
97.86
w/f lteration Paradigm
0.322
0.757
105.17
Ours
0,318
0.802
94,30
Suylay fogether
aageongl Pela bo bol.
Table 5: Effect of different weighting strate- gies in the Story-Iter. [λ1, Az. denotes a lin- car weighting strategy with lower bound of
A and an upper term of λz in 10 iterations.
Robroor
fixed weighting stralegy
CLIP-T f
aCCS 1
aFID
=
0,320
0.760
101.55
0
0,261
0.753
81.72
lineur weighting sirutegy
CLIP-T f
aCCS 1
aFID
Au. Az. 0.52 0.5]
0.320
0.781
101 44
Tait Part plar the
AL, (0.3.0.4
0,318
0.790
Ar. 00.3.0.5
0.318
0.802
94.30
Figure 10: Qualitative ablation studies of initial-
Ar, 0..3.066
0,309
0.811
90.92
AL.AZ] [0.4.0.5]
0,310
0.808
95.73
ization and GRCA. Zoom in for a better view.
generation quality for fine-grained interactions and semantic consistency. This is mainly because the iterative paradigm offers a global view of the entire story, thus reducing error accumulation and alleviating the propagation of the reference image flaws.
In Tab, Tab.5)and and Fig. 8, we validate our linear weighting strategy by comparing it with the fixed weight- ing strategy. While smaller fixed weight factor enhances early text-image alignment, it limits consistency in later stages. Conversely, a larger fixed weight factor enforces excessive consistency across iterations, reducing flexibility. Thus, the fixed weighting strategy proves to be suboptimal. Based on the ablation results in Tab. 5 we adopt linear weighting strategy: [Yı Az] [0.3, 0.5] in 10 iterations. This enables greater flexibility within the iterative paradigm.
Initialization. To ablate the effect of the initialization, of the characters as reference images (i.e., w/o initialization). Adopting the same character as the reference image for the story sequence resulted in high consistency of all story visualization results, triggering a better aFID. However, Tab. 4 shows that when removing the initialization, there is a significant decrease in the image-text alignment of Story-Iter in terms of CLIP-T. Fig, iOllusttrttss that without initialization, the diffusion model fails to generate required objects according to texts.
sequence of images consisting
5 CONCLUSIONS AND DISCUSSIONS
We introduce Story-Iter, a training-free iterative paradigm for long story visualization. By using full-length generated images from the previous external iteration as reference images, our frame- work maintains semantic consistency and enhances generative quality for fine-grained interactions throughout the story, effectively reducing error accumulation and avoiding the propagation of flaws. Extensive experiments demonstrate that Story-Iter outperforms existing methods on the regular- length story visualization dataset, and shows strong results in long story visualization. These indi- cate the potential of the proposed iterative paradigm to advance long story visualization.
6 ETHICS STATEMENT
All authors of this work have read and commit adhering the ICLR Code of Ethics.
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7 REPRODUCIBILITY
To ensure reproducibility, we provide pseudocode in Algo. 1 and implementation details in Sec. A.4 The full code can be found in the Supplementary Material.
REFERENCES
Dacchul Ahn, Daneul Kim, Gwangmo Song. Seung Hwan Kim, Honglak Lee, Dongyeop Kang, and Jonghyun Choi. Story visualization by online text augmentation with context memory. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 3125-3135, 2023.
Omri Avrahami, Amir Hertz, Yael Vinker, Moah Arar, Shlomi Fruchter, Ohad Fried, Daniel Cohen- Or, and Dani Lischinski. The chosen Consistent characters in text-to-image diffusion models. In ACM SIGGRAPH 2024 Conference Papers, pp. 1-12, 2024.
Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dock- horn, Jack English, Zion English, Patrick Esser, Sumith Kulal, et al. Flux. 1 kontext: Flow match- ing for in-context image generation and editing in latent space. arXiv e-prints, pp. arXiv-2506, 2025.
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang. Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://edn. openai. com/papers/dall-e-. pdf. 2(3):8, 2023.
Andrew Brock. Large scale gan training for high fidelity natural image synthesis. arXiv preprint 1rXIv:1809.11096, 2018.
Emanuele Bugliarello, H Hernan Moraldo, Ruben Villegas, Mohammad Babacizadeh, Moham- mad Taghi Saffar, Han Zhang. Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, and Paul Voigtlaender. Storybench: multifaceted benchmark for continuous story visualization. Advances in Neural Information Processing Systems, 36, 2024.
Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Mas- actrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22560-22570, 2023.
Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, and Nanyun Peng. Character-centric story visualization via visual planning and token alignment. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 8259-8272, 2022.
Junhao Cheng, Xi Lu, Hanhui Li, Khun Loun 7ai, Baiqiao Yin, Yuhao Cheng, Yiqiang Yan, and Xi- aodan Liang. Autostudio: Crafting consistent subjects in multi-turn interactive image generation. arXiv preprint arXiv:2406.01388 2024a.
Junhao Cheng, Baiqiao Yin, Kaixin Cai, Minbin Huang, Hanhui Li, Yuxin He, Xi Lu, Yue Li, Yifei Li, Yuhao Cheng, et al. Theatergen: Character management with Ilm for consistent multi-turn image generation. arXiv preprint 0tti::2044.1919999 2024b.
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. arXiv preprint
2022.
Yuying Ge, Sijie Zhao, Ziyun Zeng, Yixiao Ge, Chen Li, Xintao Wang, and Ying Shan. Making llama see and draw with seed tokenizer. arXiv preprint arXiv:2310.012/18, 2023.
Zhengyang Geng, Mingyang Deng, Xingjian Bai, Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. arXiv preprint arXiv:2505./344 2025.
Yuan Gong. Youxin Pang, Xiaodong Cun, Menghan Xia, Yingqing He, Haoxin Chen, Longyue Wang, Yong Zhang. Xintao Wang, Ying Shan, et al. Talecrafter: Interactive story visualization with multiple characters. arXiv preprint Xr3iv:2305. 18247, 2023.

---

Published as a conference paper at ICLR 2026
Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha Kembhavi. Imagine this! scripts to compositions to videos. In Proceedings of the European conference on computer vision (ECCV), pp. 598 613, 2018.
Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang. Svdiff: Compact parameter space for diffusion fine-tuning. In Proceedings of the IEEE/CVE Interna- tional Conference on Computer Vision, pp. 7323-7334, 2023.
Huiguo He, Huan Yang. Zixi Tuo, Yuan Zhou, Qiuyue Wang, Yuhang Zhang, Zeyu Liu, Wenhao Huang, Hongyang Chao, and Jian Yin. Dreamstory: Open-domain story visualization by 11m- guided multi-subject consistent diffusion. arXiv preprint arXiv:2407.12849 2024.
Minguk Kang. Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10124-10134, 2023.
Bo-Kyeong Kim, Hyoung-Koy Kyu Song, Thibault Castells, and Shinkook Choi. On architectural com- pression of text-to-image diffusion models. 2023a.
Bo-Kyeong Kim, (lyoung-Kyu Song, Thibault Castells, and Shinkook Choi. Bk-sdm: Architec- turally compressed stable diffusion for efficient text-to-image generation. In Workshop on Effi- cient Systems for Foundation Models@ ICML2023, 2023b.
Kyung-Min Kim, Min-Oh Heo, Seong-Ho Choi, and Byoung- Tak Zhang. Deepstory: Video story qa by deep embedded memory networks. arXiv preprint trXiv::707.0088:: 2017.
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 1931-1941, 2023.
Bowen Li. Word-level fine-grained story visualization. In European Conference on Computer Vision, pp. 347-362. Springer, 2022.
Dongxu Li, Junnan Li, and Steven Hoi. Blip-diffusion: Pre-trained subject representation for con- trollable text-to-image generation and editing. Advances in Neural Information Processing Sys- tems, 36, 2024a.
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. Storygan: A sequential conditional gan for story visualization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6329- 6338, 2019.
Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, and Ying Shan. Pho- tomaker: Customizing realistic human photos via stacked id embedding. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 8640-8650, 2024b.
Chang Liu, Haoning Wu, Yujie Zhong, Xiaoyun Zhang, Yanfeng Wang, and Weidi Xie. Intelli- gent grimm-open-ended visual storytelling via latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6190-6200. 2024.
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for 2023.
open-set object detection. arXiv preprint arXiv:2303.054
Tao Liu, Kai Wang, Senmao Li, Joost van de Weijer, Fhad Khan, Shiqi Yang. Yaxing Wang, Jian Yang, and Mingming Cheng. One-prompt-one-stoon Free-lunch consistent text-to-image gener- ation using a single prompt. In The Thirteenth International Conference on Learning Represen- tations, 2025, URL
net/
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthe- sizing high-resolution images with few-step inference. arXiv preprint arxiv:2310.04378, 2023.
Adyasha Maharana and Mohit Bansal. Integrating visuospatial, linguistic and commonsense struc- ture into story visualization. arXiv preprint arXiv:2 / 1..i::2/100.0000 10834 2021.
12

---

Published as a conference paper at ICLR 2026
OpenAl. GPT-4o system card, 2024.
URL bttps://openai.coommmmm
gpt - 11-40----ssss system-card/
Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue, and Wenhu Chen. Synthesizing coherent story with auto-regressive latent diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2920-29322 2024.
Christos Papadimitriou, Giorgos Filandrianos, Maria Lymperaiou, and Giorgos Stamou. Masked generative story transformer with character guidance and caption augmentation. arXiv preprint arXiv:2403.08502, 2024.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. PMLR, 2021.
Tanzila Rahman, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Shweta Mahajan, and Leonid Sigal. Make-a-story: Visual memory conditioned consistent story generation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp- 2402-2502, 2023.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning -xt-tt--mage diffusion models for subject-driven generation. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 22500- 22510, 2023.
Simo Ryu. Low-rank adaptation for fast text-to-image diffusion fine-tuning, Low-rank adaptation fast text-to- toittto-mmae diffusion fine-tuning. 2023.
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. Advances in neural informa- tion processing systems, 11334499-64994 2022.
Fei Shen, Hu Ye, Sibo Liu, Jun Zhang, Cong Wang, Xiao Han, and Wei Yang. Boosting con- sistency in story visualization with rich-contextual conditional diffusion models. arXiv preprint arXiv:2407.0224 02402, 2024.
Xiaoqian Shen and Mohamed Elhoseiny. Storygpt-v: Large language models consistent story visualizers, 2023, URL https: /ttlss///axxvii
www.aiiwwgaaaaaaaaaaaaaaaooeaeee htgg/bbs/2312 02252
Jiaming Song. Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502. 2020.
Ming Tao, Bing-Kun Bao, Hao Tang, Yaowei Wang, and Changsheng Xu. Storyimager: A uni- fied and efficient framework for coherent story visualization and completion. arXiv preprint arXiw:2404.05999, 2024.
Ming Tao, Kun Bao, Hao Tang, Yaowei Wang, and Changsheng Xu. Storyimager: A unified and efficient framework for coherent story visualization and completion. In European Conference on Computer Vision, pp. 479-495. Springer, 2025.
Yoad Tewel, Omri Kaduri, Rinon Gal, Yoni Kasten, Lior Wolf, Gal Chechik, and Yuval Atzmon. Training-free consistent text-to-image generation. ACM Trunsactions on Graphics (TOG), 43(4); 1-18, 2024.
Nikolaos Tsakas, Maria Lymperaiou, Giorgos Filandrianos, and Giorgos Stamou. An impartial transformer for story visualization. arXiv preprint -XXv:2301.03563 2023.
13

---

Published as a conference paper at ICLR 2026
Wen Wang, Canyu Zhao, Hao Chen, Zhekai Chen, Kecheng Zheng, and Chunhua Shen. Au- tostory: Generating diverse storytelling images with minimal human effort. arXiv preprint arXfv:2311.11243, 2023.
Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo. Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15943-15953, 2023.
Jianzong Wu, Chao Tang, Jingbo Wang, Yanhong Zeng, Xiangtai Li, and Yunhai Tong. Diffsensei: Bridging multi-modal Ilms and diffusion models for customized manga generation. arXiv preprint arXiv:2412.07589, 2024.
Guangxuan Xiao, Tianwei Yin, William T Freeman, Frédo Durand, and Song Han. Fastcomposer: Tuning-free multi-subject image generation with localized attention. International Journal of Computer Vision, pp. 1-20, 2024.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36, 2024.
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 1.Xiv:2407.08683. 2024.
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 Siti::2300.062211 2023.
Ge Yuan, Xiaodong Cun, Yong Zhang, Maomao Li, Chenyang Qi, Xintao Wang, Ying Shan, and Huicheng Zheng. Inserting anybody in diffusion models via celeb basis. arXiv preprint arXiv:2306.0009 00926, 2023.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models.
Canyu Zhao, Mingyu Liu, Wen Wang. Jianlong Yuan, Hao Chen, Bo Zhang, and Chunhua Shen. Moviedreamer: Hierarchical generation for coherent long visual sequence. arXiv preprint arXiv:2407.16655, 2024.
Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng, and Qibin Hou. Storydiffusion: Con- sistent self-attention for long-range image and video generation. NeurIPS 2024, 2024.
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TECHNICAL APPENDICES
A PARADIGMS
Existing story visualization methods usually employ the Auto-Regressive (AR) or Reference-Image (RI) paradigms. In this work, we propose novel iterative paradigm for story visualization. Next, we will discuss different story visualization paradigms in detail.
Heration
Frame Promed
Diffusion
Frame Image
Frame Pronpt
Model
Freme Generated Image
saving
Cemerated
Frams 8-2
Diflusion Model
Frame In Generated Image
Prompt
Frame 2
Images.
Generated Image
Frams 2 Prompi
Difuuson Model
Generated Image
Reference Images
Denerated Image
Frams Promge
Cenerated Image
Frame 5
Frams Cenerated Image
Frane 1-2 Generated Image
Framre Cenerated Image
Frane k+! Genertte Image
Diflusion Model
i-1 Beration
Frarre Generated Image
Gonerated Inags
PT Frams
Diffusion
Frame Image
Cenerated Image
Diflusion Model
Images
Generated
Fame Fei Phomps
Frme & Prompi
Frams Prompti
Auto-Regressive Paradigm
(B) Reference-Image Paradigm
(C) Iterative Paradigm
Figure 11: Different paradigms for story visualization. Zoom in for better view.
A.1 EUTO-REGRESSIVE PARADIGM
Setting. As shown in Fig. 11 AR paradigm- ous frames and the corresponding text prompt of the current frame to guide current image generation. This helps the methods maintain semantic consistency between consecutive frames.
paradigm-based based methods typically use limited number of previ-
Discussion. However, the AR paradigm cannot consider future frames when synthesizing the current image, which makes the AR paradigm only maintain semantic consistency in neighboring frames but not throughout the story. Besides, the AR paradigm easily suffers from error accumulation. Therefore, the image quality of the AR paradigm gets worse as the length of the story increases.
A.2 REFERENCE- PARADIGM
Setting. RI paradigm barddigm-based methods employ the beginning visualized frames as reference images to guide the visualization of the rest of the story when performing long story visualization (see Fig. TTI. Bootstrapping based on fixed reference images helps the methods to effectively maintain identity consistency in long story visualizations.
Discussion. However, such setup ignores the consistency of emerging characters in the story, and all visualizations are affected by flaws in the reference images, Both issues affect the quality of long story visualizations with the RI paradigm.
A.3 ITERATIVE PARADIGM
Setting. To address the aforementioned limitations, we propose an iterative paradigm in Story- (Fig. IT). We constantly consider all generated images in the previous iteration with iterative mechanism and model on the global embeddings. Specifically, when generating for the kts image, propose implement Global Reference Cross-Attention (GRCA) on global embeddings from all generated images in the previous iteration.
Discussion. By using all generated images from the previous iteration as reference images to guide the current generation, we effectively maintain semantic consistency throughout the story. More- over, all the generated images as references are updated through each iteration. We illustrate in Fig. 12 how different reference images contribute to the generation of each image within the itera- tive paradigm. Taken together, the iterative paradigm effectively avoids the influence defects in some reference images. We demonstrate the procedure of Story-Iter in Algo.
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Algorithm Pseudo-Code of Story-Iter.
diffusion model:0, iteration epochs:L, starting weight factor:
ending weight factor: in iteration jen diffusion step kss
intermediate denoising features: story length:B, diffusion steps :J, decoder:
# Initialize fejr 4 (O,, I), (1 B), 17,,, LI, (7, J)
Initialize Story-Iter iteration
for j in reversed (range (0, J)):
# Init (0, I) if js1 else z=0
(1/s-(t (a,)) * x (0:5)(11000 (1-a,) +0 i Tk) /sert (1-03)) ta Reconcat ([4), ..., ................................ ...,,))),, =
# Insert GRCA to 0 and initialize weighting factor list Anst Ans==Iissacee (A,, Lt)
# Story-Iter Iteration for 1 in enumerate (Aua):
for j in reversed (range (0, J)):
(55-1=(1/qpp) (ay)) ( (1-ay) +0 (1g R, Reconcat (iæi,...,t) .............. 2b))) =t=D (12
R) / (1-0,) ) +a
Table GRCA vs. CSA.
reference
Setting
CLIP-T f
aCCS f
aFID 1
GRCA
0.322 0.315
0.757
105.17
CSA
0.768
102.44
Setting
FLOPs
Time 4
VRAM 1
GRCA
44.09T
15S
19GB
CSA
22.32P
19S
40GB
Story Iter
map
Story IterXL
StoryDiifusion
FLOPs (log(G))
ottn
map
3.21
# reference images
Figure 12: Attention maps for each reference im- age in the ith iteration of Story-Iter for each image generation.
Figure 13: Computational feasibility with scaling reference images.
A.4 IMPLEMENTATION DETAILS
To ensure fair comparison, we used the weights of IP-Adapter (Ye et al.) 2023) and IP- AdapterXL (Ye et al. 2023), respectively, resulting two models: Story-Iter and Story-IterXL. We utilized DDIM (Song et al. 2020) for 50-step sampling with Classifier-Free Guidance score set to 7.5. For the hyperparameters in our iterative paradigm, we set the number of story iterations to 10 by default. The weight factor À is set 0.3 for the initial iteration and 0.5 for the final itera- tion, with linearly interpolated values for the intermediate iterations by our linear weighting strategy. Story-Iter is implemented in the NVIDIA RTX A6000.
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A valoy was dloandoned
brew memmbrr Fund time 1900 Kold.as matppe
1400 walk the
1900 welkowg to fine ship
lubrry Gruise shipp.
eloordouce 9aey.
on His Gabin.
cakan Shil..
hall.
GRCA
Valoy vias dlondonedd a loway prvise ships
Srew memberr find tire 19D0 holding ng Wotpect oboandonce 2ale
1900 walk ug the 1900 walkinn to the ship
Hie Cabin.
cabi un sille.
hall.
Figure 14: Qualitative comparison of GRCA and CSA.
A.5 GRCA VS CSA
We investigate GRCA and CSA in Tab. 6 and Fig. using the outputs of the first iteration from Story-Iter and StoryDiffusion, respectively. Though GRCA generates less visual consistency during the first iteration than CSA in terms of aCCS and aFID in Tab. [ GRCA's global comprehension improves the consistency of multiple characters throughout stories shown in Fig. [4] For example, GRCA effectively preserves the consistency of emerging characters (e.g. "the churacter 1900"), while CSA fails.
Establishing global story comprehension is fundamental to long-story visualization. Although the CSA can establish connections among all generated images during the generation process, its scal- ability for reference image scaling is limited, particularly for long stories, due to the high memory consumption of latent denoising representations. In Fig. 13] we analyze reference scaling of GRCA in single iteration and CSA in StoryDiffusion, without adopting the paradigm. FLOPs are calcu- lated within the diffusion UNet. As shown in Fig. 13] as the number of reference images increases, Story Diffusion experiences a significant rise in computation in terms of FLOPs, eventually becom- ing computationally prohibitive and can't I maintain global story comprehension of long narratives. While Story-Iter and Story-IterXL are slightly affected. This lays the foundation for GRCA to pre- serve global story semantics in long story visualizations.
Though our focus is on addressing the fundamental modeling challenges in long story visualization, rather than optimizing for computational efficiency, we still discuss the computational cost of 50 diffusion steps for 1024-resolution image generation with 100 reference frames in Tab. 6 Since the Story-Iter paradigm involves multiple iterations, generating a full 100-frame story incurs high computational complexity (100 frames of 1024-resolution story visualization externally iterates one round at a cost of 4.30 PFLOPs/25 minutes). With that said, it is still easily scalable across a wide range of hardware devices since the generation update of single frame, the computational cost of 50 diffusion steps is only 44.09 TFLOPs and takes 15 seconds with 100 reference frames. The required VRAM is just 19 GB at 1024x 1024 resolution. In comparison, for 1024x 1024 resolution story visualization, StoryDiffusion generates 22.32 PFLOPS and occupies 3 minutes.. For each gen- erated image, VRAM consumption is 40 GB, and inference time is 19 seconds. Moreover, with the introduction of global embedding in GRCA, Story-Iter can be easily expanded to 200 frames of story visualization. The actual computational limit mainly depends on the VRAM, e.g., 100 fps story visualization VRAM is 19GB. 200 fps story visualization VRAM is 24GB. Additionally, with 200 reference frames, the image generation time of Story-Iter is 17 seconds. This indicates that in- creasing the number of frames has minimal impact on the memory consumption and inference time of Story-Iter. Also, we note that these costs can be reduced(-500x using MeanFlow (Geng et al.) 2025), distilled diffusion models (Kim et al.) 2023a b). or latent consistency models (Luo et al. 2023 y. As shown in Tab. II and Fig. 19 Story- Iter achieves high-quality results within just 10 itera- tions for 100-frame stories. Depending on the length and complexity of the given story, the number of external iterations in Story-Iter can be reduced accordingly, thereby further improving efficiency while still achieving high-quality story visualization. With these improvements, Story-Iter requires
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train (0.96)
train (0.95)
frisbes
bread (0.75)
bread
dog (0.94)
dog (0.96)
dog (0.96)
ee
dog (0.95)
dog 10. (3.93)
Figure 15: Calculation of aCCS and aFll)
only about 5 minutes per external iteration generate 100-frame story at 1024x1024 resolution. Therefore, efficiency is unlikely to pose a limitation for Story-Iter in long story visualization.
A.6 GRCA SETTING ABLATION
To systematically evaluate how different con- figurations of GRCA affect the generation qual- ity of Stery-Iter, conduct a series of ablation experiments by replacing GRCA with simpler global context aggregation settings (see Tab. (7). All variants are tested under the same iterative generation paradigm isolate the contribution of long-range dependency modeling.
Table Ablation study on GRCA settings.
Setting
CLIP-T
aCCS
aFID
Sliding Window
0.313
0.774
101.08
Random Reference
0.306
0.750
119.52
Mean-pooled Global Embedding
0.311
0.741
108.19
Story-Iter
0.318
0.802
94.30
Sliding Window. This variant aggregates only
the most recent 10 frames, capturing short-
range temporal dependencies but failing to model long-range narrative structure. When characters or objects reappear after long intervals, the model lacks sufficient global reference cues, leading to degraded long-story consistency.
Random Reference Set. Random sampling introduces unstable and conflicting contextual signals. Such inconsistency accumulates across iterations and is particularly harmful in multi-character nar- ratives, where mismatched global cues result in incoherent visual continuity.
Mean-pooled Global Embedding. Averaging all global features compresses scene-specie scenesspecific and character-speeific information into a single homogenized vector, causing significant loss of structural and semantic cues. As result, the model struggles to preserve cross-scene consistency.
These results demonstrate that long-range content-adaptive attention over the entire story sequence is essential, and simple aggregation methods cannot replace GRCA without significant quality degra- dation.
B EVALUATION METRIC
We use Grounding- -DNn (Liu al. 2023) extract object bounding boxes, using the name of each repeated object in the story the grounding text. For each repeated object, the first frame in which it appears is treated as the reference frame. aCCS (Cheng et al. 2024b) is defined as the average CLIP-I similarity between the reference crop and its corresponding crops in subsequent frames. aFID (Cheng et al. 2024b) is defined as the FID between the reference crop and the distribution of later crops of the same object. Because repeated objects appear across multiple frames and all reference crops are obtained from cox-iiondi generation, these metrics are stable and not overly sensitive detection noise (see Fig. 15
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Table Human evaluation comparison of subject-consistent image generation, regular-length story visualization, and long story visualization. The best is highlighted in red.
Image Generation
Model
Align.
Inter.
Cons. t
Pref. f
IP-Adapter YY et al. 2023
2.51
3.27
4.58
4.19
IP- PP-AdateeXLL
Ye et a
2026
2.66
3.36
4.72
4.26
PhotoMaker (Li ct 2024D
3.79
4.18
4.25
4.11
StoryDiffusion
Zhou
2024
4.15
4.28
4.50
4.48
Story-Iter
4.02
4.20
4.41
4.33
Story-IterXL.
4.20
4.35
4.58
4.54
Regular-Lengho Story Visualization
SDM (Rombach et al.
2022)
4.11
2.37
2.01
1.14
Prompt-SDM (Rombach
2022
4.03
3.49
1.99
1.26
Finetuned-SDM Rombach ct al. 2022
3.35
3.82
2.15
1.60
AR-LDM Pan et al
2024
3.08
3.64
2.90
2.05
StoryGen Liu al.
2024
3.72
4.17
3.83
3.39
StoryDiffusion Zhou
2024
3.96
4.48
4.52
4.37
Story-Iter
3.89
4.21
4.36
4.10
Story-IterXL
4.06
4.60
4.74
4.62
Long Story Visualization
AR-LDM [ann et al
2024
3.30
3.68
3,42
3.27
StoryGen Liu 2024
3.51
4.06
3.88
3.51
IP-Adapter
Ye al. 2025
3.79
4.27
4.30
4.06
IP-AdapterXI. et al.
2023
3.83
4.23
4.11
StoryDiffusion (Zhou et al.
2024
4.16
4.30
4.53
4.35
Story-Iter
3.97
4.15
4.42
4.29
Story-Iter
4.35
4.47
4.70
4.65
Table 9: Efficiency comparison with editing models for 100 frames 1024x story visualization.
Method
Diffusion Steps
External Iterations
PLOPS
VRAM
Time
CLAP-T
aCCS
aHH1)
FLUX. I. Kontest Batifol et al. 2025
371 PFLOPs
68GB
min
0.290
0.783
78.03
Story-IterII
10
43 PHLOPs
19G8
250
0.318
0,802
94.30
Story- Mary-lierXL-Fat
C HUMAN EVALUATION
Setting. To complement the evaluation metrics to accurately reflect the quality of the generated stories, we involve human evaluation to further compare Story-Iter and baselines. Referring to the setting in StoryGen (Liu al. 2024), invite participants rate various aspects: text-image alignment (Align.), character interaction (Inter.), content consistency (Cons.), and preference (Pref.) scale from 5. The higher the better.
Results. Tab. shows that our Story-Iter receives more preference from the participants. It is worth noting that although IP-Adapter receives higher scores for consistency in the subject-consistent im- age generation task, Story-Iter is more favored in text-image alignment and generating character interactions. For regular-length and long story visualization, Story-Iter more preferred compared to baselines in most evaluation aspects, especially visual consistency and capability to generate character interactions. This aligned with the quantitative measurement.
D COMPARISON WITH EDITING MODELS
In this section, we provide a further comparative analysis between Story-Iter and the latest reference- image based image editing models.
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Nano Banana
Story-Iter
FLUX FLUX.LKOOEE K.ontest
Story-tte
Figure 16: Overly rigid identity constraints for Reference-image based image editing models.
FLUX.L Kontest
Father and Seoprother are walking in the garden.
Einderella ng
Cinderclla dansing
Cinderella and Prince Prince singing to waiking in the garden. Cinderella the garden.
kittchen
bedroom.
Naao Bonana
Father and Stepmother are walking in the garden.
Cinderella coolingg kitchen.
+he Cinderella danzing Cinserella and Trime are wil kiin in the gardew. Cmderella the garden
Prince singing
Incdroom.
Stery-Iter
Father and Strommother are Walkinng in the garden.
Cinderella cooking the Cinderclla dancing
Linderella And Frince Frinc singling to Wie kking the gardon. Eindrrllla the gardow.
kitcher.
locdroom.
Figure 17: New character missing for Reference-image based image editing models.
Qualitative Evaluation. As shown Tab. 9
Fig. 16 and Fig. 17 although reference- imae based
editing models help maintain object consistency story visualization, they impose overly rigid identity constraints compared with Story-Iter. Models such as Flux. 1. Kontext (Batifol et al.) (025), and Nano-Banana are designed to preserve the appearance of the reference image strictly possi-
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ble. While this behavior is desirable for image editing, it introduces critical limitation in long-story visualization: the character remains nearly identical across frames, regardless of changes in action, pose, narrative context. This severely reduces intra-story diversity and leads to lower CLIP-T scores. Reference-image based editing models also struggle to handle new characters or objects that do not appear in the reference image. Long stories frequently introduce new entities, for which mod- els like Flux. 1. Kontext and Nano-Banana cannot maintain cross-frame consistency. This results in degraded CLIP-T performance and unstable behavior in multi-character scenarios. This limitation is reflected in the lower CLIP-T of when multiple characters are present. In contrast, Story-Iter designed for open-ended, multi- character narratives rather than single-image editing. It employs training-free, content-adaptiv GRCA mechanism that maintains consistency across diverse scenes and newly introduced entities, without relying a fixed reference image.
as well as its difficulty in maintaining consistency
Efficiency Comparison. Furthermore, Tab. 9 presents an additional efficiency comparison be- tween Story-Iter and the reference-image based editing models. As shown in Tab. 9 Story-Iter is substantially more efficient than reference-image based editing models such as Nano Banana and when generating long stories. In particular, Story-IterL requires only 43 PFLOPs and 19GB VRAM for a 100-frame sequence across 10 external iterations, while FLUX. S.UX.IKKome Konttext incurs 371 PFLOPs and 68GB VRAM even for a single pass, making the fliting-based models significantly more expensive despite lacking multi-iteration refinement. However, despite its effi- ciency advantage over editing models, Story-Iter still exhibits high inference time (250 minutes for 100 frames), due to the external iterations. To address this limitation, we introduce Story-Iter-Fast. a variant built upon the SDXL-LCCM backbone. By reducing diffusion sampling from 50 steps to only 4, this fast version improves efficiency dramatically -cchivvvilyaaiiiinnnnggn 12x speed-up (250 min to 20 min) and a 14x reduction in FLOPs (43 PFLOPs to PFLOPs)-whine maintaining compara- ble story-level consistency to the Story-Iter. This demonstrates that our iterative GRCA framework remains effective even under highly accelerated diffusion settings.
E CUBJECT-CONSISTEE GENERATION COMPARISON
Related Work. Subject consistency is critical for tasks such story visualization and video gener- ation. Recent advancements in subject-consistent image generation have focused on reducing com-
putation while maintaining consistency. Early approaches like Gal et al. (2022), ; Ruiz al. (2023)
require extensive fine-tuning, prompting more efficient methods (Ryul 2023 Han et al. 2023 Ku-
mari al. 2023 Yuan et al., 2003; Tewel al. 2024 Cao et al. 2023 Xiao et al.) 2024)) ). Notable
progress (Li et al. 2024b] Avrahami et al. [2224] Ye et al. 2023
Li et al. 2024a Wei et al., 2023]
Xu et al., 2024) includes P-Adapter (Ye al.) 2023) with its decoupled arosstattetiion module.
However, IP-Adapter is single image generation model, which cannot be directly applied to story
visualization that requires establishing connections for all frames.
In the evaluation phase, we employ GPT-4o (OpenAl (024)) according to the settings of StoryD- iffusion (Zhou et al., (224) to generate 20 character descriptions and 100 specific activity descrip- tions, respectively. We combine them 2000 test descriptions. to compare Story-Iter and subject- consistent image generation baselines, including IP-Adapter (Ye ct al. 2023), PhotoMaker (Li ct al.
2024b), and StoryDiffusion (Zhou al. 2024).
Quantitative Evaluation. For quantitative comparisons on subject-consistent image generation, we employ CLIP text-to-image similarity (CLIP-T) and image-image similarity (CLIP-I) to measure consistency between the character images and generated images. Tab. IO shows that Story-Iter achieves SoTA performance in terms of both quantitative metrics, which demonstrates Story-Iter's ability to generate subject-consistet image sequences based on text prompts or image prompts.
Qualitative Evaluation. Fig. 18 shows the qualitative comparison results. Story-Iter generates higher-quality images in subject-consistent and detailed interactions. In contrast, IP-Adapter fails to generate correctly, e.g., "paper", "whiteboard", and "chainsaw".. PhotoMaker cannot generate images consistently, e.g., maintaining details of the attire. Despite accurately generating content according to text prompts with visual consistency, StoryDiffusion suffers from visualizing complex details due to lacking global story comprehension. By incorporating global story view in our
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An male scientist wearning white and mask
construction worker wearning helmet and work clothes operates chainsaw
uses microscope look sample
writes paper explains concepts whiteboard
installs window
lays brick
IP- Pd-Aapter
PhotoMaker
StoryDiffusion
Story- -Iter
Controllobiiiy
Attire Cohesinn
Detail Consistency
Controlibbiiiy
Attire Coheeinn
Detail Consistency
IP-Atepter
-P-Adepter
X
PhatoMoker
X
PhotoMaker
X
X
StoryDiffusion
StoryDiffusion
Story-Iter
Story-Iter
Figure 18: Qualitative comparison of subject-consisent image generation methods.
Table 10:
Quantitative comparison in
Table 11: Quantitative comparison of multiple iterations.
subject-consistent image generation tasks.
Method
CLIP-T 1CLIP-I
Iteration
CLIP-T T faCCS faFID t
IP-Adapter Ye al. 2023
0.307
0.872
initialization
0.330
0.502 214.94
Story-Iter (Ours)
0.326
0.877
1eh iteration
0.322
0.757
105.17
IP-AdapterXL (Ye et al. 2023
0.312
0.879
PhotoMaker eL al.. 2024b
0.317
3zh iteration
0.325
0.770
110.86
StoryDiffusion (Zhou et al. 2024)
0.330
5eh iteration
0.319
0.783
100.81
Story- IterLL (Ours)
0.332
0.884
10 iteration
0.306
0.840
91.35
15th iteration
0.297
0.848 90.62
iterative paradigm, Iter can maintain visual consistency, especially in details throughout the story.
F MORE ITERATIONS
Setting. In this section, we compare results on different iterations in the iterative paradigm and investigate the impact of longer iterations on story visualization. Specifically, we study the visual- ization results in the initialization, 1st, 5th, 10th and 15th iterations, respectively.
shows that as iteration increases, Story-Iter achieves significant improvement in vi- sual consistency (aCCs and aFID) while text-image alignment (CLIP-T) drops slightly. This further demonstrates the contribution of the iterative paradigm to the semantic consistency of the over- all story. However, also note that further increase in iterations harms text-image alignment,
Results. Tab. I
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i A black german shepherd jumping out of black SUV on the road of an abandoned city blackman black jocket holding gasoline can near black SUV a abandoned tunnel blackman block jacket holding tube the abandoned barrack
I.2 initialization
Figure 19:
Story visualization results from different iterations by Story-Iter Accurate interactions are denoted in green, wrong or missing ones are in red.
with limited gain in visual content consistency. This indicates that while Global Reference Cross- Attention (GRCA) effectively improves the content consistency of the long story, the increasing weighting factor of GRCA during the iterations poses a challenge to aligning the text prompts.
Fig. 19 demonstrates a significant improvement in generative quality for fine-grained interactions as the iteration proceeds. The iterative paradigm effectively alleviates the diffusion model's limitations on complex interaction generation by continuously creating input channels for text prompts. But more iterations wouldn't improve the generation quality further. Therefore, based on the above results, fewer than 10 iterations are sufficient to achieve high-quality story visualization, with any further iterations yielding negligible improvements.
This trade-off between consistency and text-image alignment is analogous to autoregressive models, where increased sequence length may introduce compounding errors, yet also allows for stronger global coherence. This tradeoff does not indicate inefficiency in our iterative refinement process, rather highlights the importance of balancing local fidelity and global consistency. Importantly, this trade-off can be effectively mitigated. Also, as demonstrated in Tab. [1] adjusting the hyperpa- rameters based on the specific story length and content, as well as terminating the external iteration of Story-Iter Iter earlier, can offer a better trade-off. This opens promising directions for future work, where we plan to integrate content- aware and length-sensitiv hyperparameter scheduling, guided by automatic evaluations (c.g., GPT-based metrics), to further optimize both consistency and align- ment.
G LIMITATION
While Story-Iter demonstrates great potential in long-story visualization, there remains inherent trade-off between consistency and diversity. Although our initialization and linear weighting strat- egy help maintain global diversity throughout the story, certain local frames still exhibit diversity
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limitations. In future work, aim incorporate pose or layout conditional controls to further encourage diversity between consecutive frames.
At the same time, multiple external iterations for long story generation may affect the practical ef- ficiency of Story-Iter. However, this limitation can be fundamentally addressed through accelerated generation methods such MeanFlow (Geng al. 2025), which discuss in Sec. A.3
Additionally, we observe a trade-off between iterations and text -ixte alignment: longer iterations enhance global consistency but weaken alignment. This does not indicate inefficiency of the itera- tive refinement process, but rather reflects the balance between local fidelity and global consistency. Notably, this trade-off can be mitigated by tuning hyperparameters lier, as discussed in Sec. F For future work, we plan to introduce Qwen3-VL- scheduling, where Qwen3-VL evaluates story semantics during refinement and adaptively decides: whether an iteration should stop early, and set the next iteration's 's GRCCAtext-condiioon ing. This content-adane -ddativve controller will further optimize ceeiiittecc-alignnaan trade-off's without sacrificing Story-Iter's strong narrative coherence. In addition, plan to expand our benchmark and release a larger corpus to support more comprehensive and standardized evaluation for the com- munity.
terminating iterations ear- content-aware
weight-
H MORE VISUALIZATION RESULTS
In this section, we provide more visualization results from Story-Iter and the baselines.
H.1 VISUAL COMPARISON
We compare the long story visualization results representative work with AR-based, RI-based, and iterative paradigms, respectively. Specifically, Fig. [4 Fig. 20 Fig. 21
Fig. 23 and Fig. [2
show the generated results of the same "Robinson" story from the proposed Story-Iter (iterative), StoryGen (Liu et al.) 2024) (AR-based), IP-Adapter (Ye et al. 2023) (subject-consistent image gen-
eration). ConsiStory (Tewel et al. 2024) (RI-based), and StoryDiffusiin (Zhou et al. 2024) (RI-
based), respectively.
Results. Fig. 20 shows that the visualization quality from StoryGen constantly gets worse as the length of the story increases. The visualization results of the IP-Adapter in Fig. 21 show obvious interaction missing wrong interactions. In Fig. 22, StoryDiffusion maintains high visual quality throughout the story but suffers from some shortcomings in multi multi-character interactions. In contrast, our Story-Iter Iter effectively achieves high-quality story visualization and addresses the aforementioned limitations (see Fig. 24).
We also observe that, although Story-Iter employs global embeddings, it still preserves fine-grained consistency compared to methods relying heavily on local visual embeddings- as StoryDif- fusion, StoryGen, and ConsiStory. By summarizing an entire image into a compact representation, global embeddings enable computationally affordable processing while preserving subject relevance and semantic consistency across full-length story sequences. At the same time, method lever- ages latent denoising features that retain pixel-wise, fine-grained visual information, guided by the global embedding. This combination helps preserve important local details, such clothing and facial features, as illustrated in Fig. 24] In contrast, StoryDiffusion, StoryGen, and ConsiStory paradoxically yield inconsistent details, as they are substantially disrupted by irrelevant noise due to their excessive emphasis on local features.
H.2 STORY-ITER-FAST
We provide qualitative long-story visualization result of Story-Iter-Fas in Fig. 25 This result demonstrates that the proposed GRCA mechanism and iterative framework fully compatible with distilled and accelerated diffusion models; even under extremely efficient sampling regimes, Story-Iter-Fass Fast is able maintain strong global story coherence.
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Figure 20: Visualization results of StoryGen for the "Robinson" story. Zoom in for better view.
ope
Figure 21: Visualization results of IP-Adapter for the "Robinson" story. Zoom in for a better view.
H.3 DIFFERENT STYLE
We visualize scenes of the same long story in different styles in Fig. 26 Fig. 27 and Fig. 28
Meanwhile, we provide more long story visualization examples from Story-Iter different styles in Fig. 29 Fig. 30 and Fig. 31 The experiment results suggest that Story-Iter can be applied to different visual styles as well. Furthermore, these results demonstrate that Story-Iter consistently generates high-quality visual narratives across a variety of initialization innditions-inllddin dif- ferent styles, content types, story lengths, and random seeds highlliiggiggting its robustness and gen- eralizability.
H.4 LONGER STORY VISUALIZATION RESULTS
In Fig.
32 and Fig.
33
show the visualization results of the long story (up 100 frames).
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curddeyy Sotooaatio
Rodtacs J Meachou
Fercel wolii
Figure 22: Visualization results of StoryDiffusion for the "Robinson" story. 7oom in for better view.
Verve dereteo
Aoalss
Aww.
Trods
Figure 23: Visualization results of ConsiStory for the "Robinson" story. Zoom in for better view.
DECLARATION OF LLM TOOL USAGE
During the preparation of this manuscript, used OpenAl's GPT-4.1 model for minor language re- finement and smoothing of the writing. The LLM tool was not used for generating original content, conducting data analysis, or formulating core scientific ideas. All conceptual development, exper- imentation, and interpretation were conducted independently without reliance on LLM tools. The other points involving the use of LLMs have already been highlighted in the paper.
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wwthe Bart
2000
Figure 24: Visualization results of our Story-Iter for "Robinson" Zoom in for a better view.
Radilo
clics
Figure 25: Long story visualization results of Story-Iter-Fai for "oaal dog".
relen 1ES dr1.
Jin 100
Anarial of Allnl Ioics
1ol of Adt
/
Figure 26: Our comic style story visualization results for "Robinson". Zoom in for a better view.
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Figure 27: Our realistic style story visualization results for "Robinson" " Zoom in for a better view.
Figure 28: Our film style story visualization results for "Robinson" Zoom in for better view.
Land
Ailley
Fool
Figure 29: Our realistic style story visualization results for "loyal dog". Zoom in for a better view.
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Figure 30: Our monochrome style regular story visualization results in StorySalon (iu al. 2024) Zoom in for better view.
the
or
Figure 31: Our realistic style story visualization results for "The Prince and the Princess". Zoom in for a better view.
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Tarth pollily
Figure 32: Our long story visualization results for "Tortoise and the Hare race". Zoom in for better view.
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www.
rresume
Figure 33: Our long story visualization results for "Winnie the Pooh". Zoom in for a better view.
31


Reviews:
[{"review_id": "mbIC9HnGuB", "reviewer": "Reviewer_TFke", "summary": "This paper proposes Story-Iter, a training-free iterative paradigm for long story visualization that addresses the limitations of existing auto-regressive and reference-image paradigms by using full-length frames from the previous external iteration as references and integrating a Global Reference Cross-Attention (GRCA) module to model global semantic consistency.", "questions": "Please refer to the detailed points I raised in the \"Weakness\" section and respond to each numbered item in your rebuttal with clarifications.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel and Targeted Iterative Paradigm**: The proposed external iterative framework directly addresses the core limitations of existing AR and RI paradigms in long story visualization. By using full-length frames from the previous iteration as references (instead of fixed or limited frames), it effectively mitigates error accumulation and global consistency loss, a long-standing challenge in the field .\n\n2. **Efficient and Lightweight GRCA Module**: The Global Reference Cross-Attention (GRCA) module, based on CLIP global embeddings, enables global semantic modeling while significantly reducing computational costs. \n\n3. **Rigorous Experimental Validation**: Experiments on both regular-length (StorySalon) and long-story (100-frame) benchmarks show consistent SOTA performance: it outperforms StoryGen and surpasses StoryDiffusion for long stories. Human evaluations further confirm its superiority in character interaction and content consistency .\n\n4. **Practical Training-Free Design**: Story-Iter requires no retraining, leveraging pre-trained Stable Diffusion and CLIP weights. Its plug-and-play GRCA and linear weighting strategy ensure easy integration into existing pipelines, with minimal hyperparameter tuning .\n\n5. **Strong Generalization to Multi-Style Generation**: Beyond standard realistic style, it successfully generates long stories in comic, film, and monochrome styles, with CLIP-T scores remaining above 0.30. This demonstrates robust adaptability to diverse visual requirements .", "weaknesses": "1. **Computational Efficiency for Extremely Long Stories**: While more efficient than baselines, generating a 100-frame 1024×1024 story still incurs 4.30 PFLOPs per iteration. If the paper were to include experiments based on a distilled single-step diffusion model to demonstrate the universality of its method, it would more fully prove the superiority of its method.\n\n2. **Tradeoff Between Consistency and Text Alignment**: Longer iterations (≥10) slightly weaken text-image alignment (CLIP-T drops from 0.330 to 0.297), and the current linear weighting strategy lacks content-aware adaptability for stories with complex plot shifts .\n\n3. **Limited Diversity in Local Frames**: Despite global consistency, certain consecutive frames exhibit limited diversity (e.g., similar character poses). The framework lacks explicit controls (e.g., pose/layout constraints) to enhance frame-wise variation .", "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": "Sf5WKZIKNy", "reviewer": "Reviewer_A9PP", "summary": "This paper introduces Story-Iter, a novel, training-free iterative paradigm for long story visualization. The key problem addressed is maintaining semantic consistency and accurately rendering interactions in long image sequences generated from text prompts. Existing methods, primarily Auto-Regressive (AR) and Reference-Image (RI) paradigms, suffer from error accumulation or an inability to adapt to the full story context. \n\nStory-Iter proposes an external iterative loop that refines the entire story sequence in each pass. In iteration `i`, the generation of each frame is conditioned on the text prompt *and* all generated frames from the previous iteration `i-1`. This is enabled by a plug-and-play Global Reference Cross-Attention (GRCA) module, which uses the global CLIP embeddings of all previous frames as context. The influence of this visual context is controlled by a linearly increasing weight $\\\\lambda_i$ across iterations, balancing text alignment and visual consistency. The authors demonstrate through extensive experiments on both regular-length (StorySalon) and a new long-story benchmark that Story-Iter achieves state-of-the-art performance in generating coherent and high-quality story visualizations, particularly for sequences up to 100 frames.", "questions": "1.  **Computational Cost:** Could you please add a table to the main paper that explicitly compares the end-to-end inference time (or total GFLOPs) and peak VRAM usage of Story-Iter (10 iterations) against StoryDiffusion and StoryGen for generating a 100-frame story? This would provide a clearer picture of the quality-compute trade-off.\n\n2.  **Dynamics of Iteration:** The linearly increasing weight $\\\\lambda_i$ is crucial for balancing text-alignment and consistency. Is there a risk that in later iterations, the strong visual conditioning from `GRCA` overpowers the text prompt $T_k$, leading to a failure to generate new objects or actions described in the text? The slight drop in CLIP-T in Table 8 suggests this might be the case. How does the model handle the introduction of entirely new characters or settings late in the story across iterations?\n\n3.  **Metric Definition:** For reproducibility and clarity, could you please add formal definitions of `aCCS` and `aFID` to the appendix? Specifically, how are character bounding boxes obtained for `aCCS` calculation, and is it robust to detection failures? For `aFID`, is the distance computed between all pairs of images, consecutive images, or against a reference set?\n\n4.  **Sensitivity to Hyperparameters:** The linear schedule for $\\\\lambda_i$ is defined by $\\\\lambda_1$ and $\\\\lambda_L$. How sensitive is the final result to these start/end points? Does the optimal schedule depend on the story length $B$ or the diversity of the content within the story? For example, would a story with many scene changes require a different schedule than one with a fixed background?\n\n5.  **Necessity of Full Context:** The GRCA module attends to all $B$ frames from the previous iteration. Have you investigated whether this is necessary? Could a similar level of performance be achieved with a more efficient context, such as a fixed-size window of $k$ surrounding frames, or a stochastically sampled subset of frames? This could be a path to mitigating the high computational cost.\n\n6.  **Potential for Training:** The training-free aspect is a great feature for accessibility. However, have you considered the possibility that fine-tuning the IP-Adapter weights (or a dedicated GRCA module) on the task of iterative story refinement could lead to even better performance or, more importantly, reduce the number of required iterations, thus improving efficiency?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1.  **Novel and Effective Paradigm:** The core strength of the paper is the proposal of an iterative refinement paradigm for story visualization. This is a genuinely new approach in this domain that effectively addresses the key challenge of long-range consistency by allowing the model to gain a global view of the entire story and refine it over multiple passes. It elegantly sidesteps the error accumulation of AR models and the rigidity of RI models.\n\n2.  **State-of-the-Art Performance:** The method achieves impressive empirical results, outperforming strong recent baselines like StoryDiffusion and StoryGen on benchmarks for both regular and long-story generation. The qualitative results, especially for long stories (up to 100 frames), are particularly compelling and clearly demonstrate superior character consistency and interaction modeling.\n\n3.  **Excellent Presentation and Clarity:** The paper is extremely well-written, with clear explanations and high-quality figures that make the concepts easy to understand. The comprehensive appendix and extensive visual examples further bolster the quality of the submission.\n\n4.  **Practicality and Accessibility:** By designing Story-Iter as a training-free, plug-and-play module that leverages pre-trained IP-Adapter weights, the authors have made their method easy to implement and use. This significantly lowers the barrier to entry for other researchers to verify, use, and extend this work.", "weaknesses": "1.  **Prohibitive Computational Cost:** The most significant weakness is the method's computational expense. An $L$-iteration process results in an $L$-fold increase in generation time compared to single-pass methods. The default of 10 iterations makes the method an order of magnitude slower than its competitors. This is a major practical limitation that is understated in the main paper and largely relegated to the appendix. While the authors suggest using acceleration techniques, no experiments are presented to show this is feasible without sacrificing quality. This trade-off between quality and compute needs to be a more central part of the discussion.\n\n2.  **Limited Technical Novelty of GRCA:** The GRCA module is a direct reuse of the cross-attention mechanism and weights from IP-Adapter. While its application is novel, the module itself does not introduce a new architectural or algorithmic concept. The paper's contribution lies almost entirely in the iterative paradigm, not in the attention mechanism itself. This should be stated more explicitly to manage expectations about the technical depth of the proposed module.\n\n3.  **Insufficient Justification of the Iterative Process:** The paper provides an intuitive but superficial explanation for why the iterative process works. The argument for convergence is based on a t-SNE visualization (Fig. 4), which is insufficient. A more formal discussion on the dynamics of this process would be beneficial. For example, what prevents the process from \"over-fitting\" to the initial generation and losing diversity, or collapsing to a mode where all images are nearly identical? The linear weighting $\\\\lambda_i$ is a heuristic to prevent this, but its behavior is not deeply analyzed.\n\n4.  **Incomplete Ablation Studies:** The ablation study, while good, could be more comprehensive. The `w/o GRCA` ablation replaces global attention with a per-frame self-refinement, which is a very different task. A more informative ablation would be to compare GRCA against simpler aggregation strategies within the same iterative paradigm. For instance:\n    *   What if only a sliding window of $k$ previous frames is used as reference?\n    *   What if only a random subset of frames is used?\n    *   What if the global embeddings `c_1...B` are simply mean-pooled into a single context vector?\n    This would help to truly justify the need for attending to *all* frames in the sequence.", "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": "tZoqC67A5a", "reviewer": "Reviewer_LXWx", "summary": "This paper introduces Story-Iter, a training-free iterative paradigm for long story visualization. Unlike existing autoregressive or fixed-reference methods, Story-Iter progressively refines each frame across external iterations by referencing all frames from the previous round. The key contribution is a Global Reference Cross-Attention (GRCA) module that uses compact global embeddings instead of high-dimensional latent features, enabling scalable processing of sequences up to 100 frames. A linear weighting strategy balances visual consistency and text alignment across iterations. Experiments show state-of-the-art results: over StoryGen and StoryDiffusion, with showing some good fine-grained interaction generation and detail preservation.", "questions": "Written in the weakness.I know that (6) and (7) are difficult to answer during rebuttal period. Please focus on answering questions of (1) to (5). I will adjust my score after checking on the rebuttal.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) Novel iterative paradigm: External iterations that refine all frames by referencing the complete previous sequence, effectively addressing error accumulation in autoregressive methods and fixed-reference limitations\n2) Scalable GRCA module: Uses compact global embeddings rather than high-dimensional latent features, enabling 100+ frame stories with manageable memory (19GB vs 40GB for StoryDiffusion)\n3) Strong empirical validation: Consistent improvements across multiple metrics and benchmarks, with comprehensive ablations, human evaluation, and comparisons to diverse baselines\n4) Training-free and practical: Plug-and-play approach reusing IP-Adapter weights, no dataset-specific training required\n5) Thorough experimental design: Addresses both regular-length (StorySalon) and long stories (up to 100 frames), with qualitative evidence of improved fine-grained interactions", "weaknesses": "1) Base model (IP-Adapter) outdated. Even though we are in the era of Flux/SD3.5, use of IP-Adapter-based models with SD 1.5 (I am not sure whether it is 1.5 - I checked the code and you only provided StoryIter-XL version of the code) or SD-XL based implementation feels a bit outdated. I guess you used it to implement based on IP-Adapter, but when we consider Flux.1.Kontext or Nano-banana-like reference-image based image editing models, I think these can also achieve good result in generating good result on such scenarios, so, I feel that current paper is outdated.\n> I suggest adding the SOTA editing models (Flux.1.Kontext / Nano-Banana) into comparison. Since these model show good consistency, I think they can serve as powerful baselines. If your model performs somewhat close to these baselines, I think it will demonstrate the superiority of your method.\n2) Question on (1) leads to questionable motivation. If we can achieve good result with Flux.1.Kontext or Nano-banana with good consistent result, why should we use this model to achieve good consistency? I see that the point of using Story-Iter framework is to achieve good quality of consistency with text alignment in iterative generation scenario, but if the SOTA models show good quality, I  cannot understand this paper's motivation.\n> I want the motivation of the paper further strengthened. \n3) Question on (1) and (2) leads to the question on the work's high computational cost: 10 external iterations for 100 frames requires 4.30 PFLOPs/25 minutes per iteration; total generation time is substantial despite claims of reducibility. Compared to those SOTA models, is it more efficient?\n> How does performance scale with 3-5 iterations versus 10? What is the minimum number of iterations needed for acceptable quality-efficiency trade-off? Also I think adding comparison on inference FLOPs with those SOTA editing models is necessary.\n4) Text-alignment degradation: Table 8 shows CLIP-T drops from 0.330 to 0.297 as iterations increase (from 1 to 15), indicating the method overfits to visual consistency at the expense of text fidelity\n> What causes the text-alignment degradation (CLIP-T drop) with more iterations? Can this be addressed with alternative weighting strategies or architectures?\n5) Limited diversity: Authors acknowledge \"local frames still exhibit diversity limitations\"; the iterative refinement may overly homogenize the visual sequence\n> How does the method handle significant content changes across frames (scene transitions, new character introductions, location changes) with diverse scenarios? When you look at the result of Story-Iter, they look so-alike in terms of colors and backgrounds. We can say it is consistent, but it means less diverse, so I want some more explanations on how it demonstrates on complex/dynamic changing scenarios.\n6) Incremental technical contribution: GRCA is essentially IP-Adapter's cross-attention adapted with global embeddings; the iterative paradigm, while effective, is a relatively straightforward extension. Moreover, doing the explicitly set iterations (10 for the paper) is somewhat naive.\n> Even though the iterating over the result itself is somewhat interesting, I think there should be some way to stop at appropriate iteration if the desired quality is achieved.\n7) Limited long-story evaluation: Custom benchmark has only 20 cases (10×50, 10×100 frames) generated by GPT-4o, which is insufficient for robust evaluation and may introduce generation biases\n> It would be really good if we can evaluate on excessive dataset constructed since doing only on 20 cases can be a bit small.", "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)