Consistent Story Generation: Unlocking the Potential of Zigzag Sampling
Authors: Mingxiao Li
Mang Ning
Marie-Francine Moens
Venue: NeurIPS 2025 poster ()
Keywords: story generation
diffusion model
generative model
Primary area: applications

Abstract: Text-to-image generation models have made significant progress in producing high-quality images from textual descriptions, yet they continue to struggle with maintaining subject consistency across multiple images, a fundamental requirement for visual storytelling. Existing methods attempt to address this by either fine-tuning models on large-scale story visualization datasets, which is resource-intensive, or
by using training-free techniques that share information across generations, which still yield limited success. In this paper, we introduce a novel training-free sampling strategy called Zigzag Sampling with Asymmetric Prompts and Visual Sharing to enhance subject consistency in visual story generation. Our approach proposes a zigzag sampling mechanism that alternates between asymmetric prompting to
retain subject characteristics, while a visual sharing module transfers visual cues across generated images to enforce consistency. Experimental results, based on both quantitative metrics and qualitative evaluations, demonstrate that our method significantly outperforms previous approaches in generating coherent and consistent visual stories. The code is available at https://github.com/Mingxiao-Li/Asymmetry-Zigzag-StoryDiffusion.

Consistent Story Generation: Unlocking the Potential of Zigzag Sampling
Mingxiao Li KU Leuven mingxiao. kuleuven.be
Mang Ning Utrecht University m.ning@uu.nl
Marie-Francine Moens
KU Leuven sien.moens kuleuven.be
Abstract
Text-to-image generation models have made significant progress in producing high-quality images from textual descriptions, yet they continue to struggle with maintaining subject consistency across multiple images, fundamental requirement for visual storytelling. Existing methods attempt to address this by either fine-tuning models on large-scale story visualization datasets, which is resource- -entrcceinttesive, by using training- training-free free techniques that share information across generations, which still yield limited success. this paper, we introduce novel training-frs -ree sampling strategy called Zigzag Sampling with Asymmetric Prompts and Visual Sharing to enhance subject consistency in visual story generation. Our approach proposes zigzag sampling mechanism that alternates between asymmetric prompting to retain subject characteristics, while visual sharing module transfers visual cues across generated images to enforce consistency. Experimental results, based on both quantitative metrics and qualitative evaluations, demonstrate that our method significantly outperforms previous approaches in generating coherent and consistent visual stories. The code is available at https: ////t/tttu//tt com/Mingxiao-Li/ Asymmetry- Eeyymettzy-Zigzag-SSe Saagaag-StoryDiffon
Introduction
In recent years, breakthroughs in visual generation techniques have fundamentally transformed the way visual content created. State-of-the-arr image [ and video generation models [0]
now enable users to produce highly diverse visual outputs with remarkable realism and flexibilityy
These models can be guided by various forms control, including bounding boxes [[] object
motion trajectories [13 14], image prompts (5) 4 and even brain signal inputs [15 significantly
expanding the creative possibilities both professional and everyday settings.
Despite these impressive advances, challenges remain, particularly in visual storytelling tasks, where multiple pieces of visual content must consistently preserve the identity and key characteristics of subjects across scenes, three examples are presented in Figure [ A dominant approach to this problem is personalization, which involves learning subject-specific embedding [1822]] While effective, this method has notable limitations: the need for per-subject fine-tuning makes it resource- intensive and difficult to scale, and often leads to overfitting, which can degrade the model's general understanding of text and reduce its generative diversity. To address these limitations, past research has explored tuning- fiieg-ffree methods 244 which encode subject information using a unified image encoder and leverage large-scale pretraining. These methods eliminate the need for per- subject optimization and offer greater scalability. However, they rely on high-quality, large-scale personalization datasets that are costly curate and demand substantial computational resources for effective training.
More recently, line of training-free methods has emerged, targeting the specific challenge of subject-consistent visual story generation without fine-tuning or extensive subject-specific data. The
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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"elimbing tree."
"playing with yarn ball."
"sitting, on shelf""
"slooping in box."
"wearing a fluffy collar.
Subject Description: "A typee-realistic digital painting of
"dressed formal evening gown"
"having picnic park."
kittchen."
"reading
"shopping at
Subject Description photo of woran with strigght freckles across the nose."
"playing tricks quiet village."
"sitting by fireplace with pipe."
"tending the plants in forest."
"truveling with mushroom cart."
"working in hidden underground garden."
Subject Descripsion: "A charming sketch of friendly gnome."
Figure 1: Visual Story Telling requires maintaining subject consistency across sequence of generated images while ensuring that each image faithfully reflects the corresponding prompt. (Images generated using the FLUX model with our proposed method.)
StoryDiffusion model [27] introduces consistent attention by constructing image-level interactions through the random sharing of visual features across different frames. In contrast, the ConsistentStory model (28 aims to preserve subject identity by establishing visual interactions between images more selectively. However, while both methods show promise in maintaining subject consistency, they suffer from notable drawbacks: a reduced ability to follow textual prompts and diminished generative diversity. These issues arise from their direct interference with the diffusion model's generative process. Departing from these strategies, the One-Prompp One-Story model [29) proposes different approach. First, it fuses all the prompts from story into a single extended prompt. Then, it generates each image by reweighting the prompt embeddings to reflect the desired focus for each scene. In addition, improves sot-to--maae attention by reinforeing subject-relevant information within the prompt itself. Although this model achieves good prompt fidelity, it struggles maintain subject consistency, particularly when the prompt offers limited detail about the subject.
The above approaches show that directly shar- ing visual features during the generative process tends to weaken the model's 's ability to follow tex- tual prompts, while relying solely on prompts to maintain subject consistency proves insuffi- cient. To address both issues, we propose novel method: Asymmetry Zigzag Sampling (AZS). Unlike prior methods that inject subject- specific information into the intermediate rep- resentations of each generated image, our ap- proach focuses on incorporating subject infor- mation directly into the latent representations of each scene. This distinction allows for more effective control over subject consistency while preserving alignment with textual prompts. As illustrated in Figure 2 our method strategically leverages latent-level visual sharing to improve narrative coherence in multi-scene generation.
Story Diffiusion, Consistory...
Inject Semantics
Intermediate representation
Diffusion
Network
Latent representation
Zig
Our Method
Inject Semantics to through Zigzag Sampling
Zag
Generation
Figure 2:
An illustration of Zigzag Sampling, along with a comparison to previous methods in how semantic information is incorporated during image generation.
As shown in Figure our method decomposes
each generation step into three sub-steps zig, zag, and generation. It proposes the combination

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of two key components: Zig Visual Sharing (ZVS) and Asymmetric Prompt Zigzag Inference (APZT). In the ZVS module, subject-speeific visual information that extracted using subject-only prompt is injected into the self-attention layers during the generation of each scene. This allows the model to encode subject identity directly into the latent representations without disrupting overall scene composition. To further balance subject consistency with textual fidelity, APZI introduces an asymmetric prompt schedule: the zig and generation steps leverage the one-prompt technique proposed in [29], while the zag step employs null prompt. This asymmetry prevents overfitting to textual descriptions and provides the model with a dedicated phase to integrate subject information. Together, ZVS and APZI enable effective subject conditioning in the latent space while preserving the model's 's ability to follow textual prompts, resulting in more coherent and consistent multiscene image generation.
We evaluate our method on two representative text-to-image architectures: the U-Net-based SDXL. model 30 and the transformer-hased FLUX model [31] Experimental results demonstrate that our approach significantly improves subject consistency in generated images while maintaining strong alignment with textual prompts, outperforming existing baselines. In summary, our main contributions are:
We propose a novel asymmetric zigzag sampling algorithm that injects visual semantics during the zig step to enforce subject consistency, uses a null prompt the zag step to refine the latent space, and applies only text guidance in the generation step to preserve narrative coherence.
We conduct extensive experiments and benchmark our approach against range of existing methods to demonstrate its superiority.
We validate the generalizability of our method by applying it to two widely used text-to- image architectures.
2
Related Work
2.1 Diffusion Models
Diffusion models are a class of generative models that have demonstrated remarkable success in synthesizing high-quality images []] 30 videos 9 7 5 6. and 3D content (32 These models consist of a forward (noising) process and a reverse (denoising) process. In the forward process, a clean image is progressively corrupted by the addition of Gaussian noise over several steps. During the reverse process, a neural network learns to gradually denoise a random Gaussian sample, ultimately reconstructing a coherent image. The multi-step nature of this inference pipeline offers significant flexibility for controllable or guided generation tasks, such as layout-constrained image synthesis 12 35
notion-controllet video generation 1 14, and subject-consisten Despite their strong performance, diffusion models remain an active area of research, with ongoing efforts to expand their generative capabilities and improve efficiency. For instance, recent works [37 38 have proposed accelerated sampling strategies that reduce the number of required inference steps to fewer than 50. Other studies address issues such as exposure bias (39 40), [0], leading to more robust and higher-fidelity outputs. In general, diffusion models continue to evolve rapidly, and innovations in architecture, training strategies, and sampling techniques push the boundaries of generative modeling.
36
image personalization 19
18
2.2 Training Based Consistent Text to Image Generation
Since the release of powerful open-source text-to-image diffusion models, generating images featuring consistent subjects whether for image personalization or visual storytelling has become an increasingly active area of research. Early approaches [I8] 19
41 23 22 primarily rely on test-time tuning techniques. These methods typically fine-tune either the entire diffusion network or specific components, learning a unique subject embedding or a small auxiliary network from a few reference images. Although these methods achieve high-quality results in subject-driven image generation, their reliance on test-time optimization poses significant scalability challenges. To address has explored encoder-based strategies. These methods utilize image encoders to extract subject-specific features from reference images and inject
this limitation, growing body of work 42 25
3

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the resulting embeddings into the diffusion model to guide subject- consssttcnn generation. By avoiding per-subject fine-tuning, such approaches improve efficiency and scalability in real-world applications.
2.3 Training-Frin Free Consistent Text to Image Generation
In parallel with training-basse baaiin approaches, training-free methods for consistent text-to-image genera- tion have also received significant attention in the visual generation community. popular direction involves leveraging the attention mechanism to identify subject-relevant visual features, which are then reused to guide subsequent image generation. Initially, such techniques were explored in tasks like appearance transfer [48], image editing 49 [50], and image translation [S]]. More recently, ConsisStory (28] adapted this idea to visual storytelling. Specifically, it uses cross- coss-attention scores be- tween text and image tokens to identify subject-relevant visual features in one image, and then injects these features into the generation of subsequent images to maintain subject consistency. In contrast to attention- based guidance, the current state-of-the offt-oo--he-arrr art method, 1 Prrompt Story consistency by composing a unified prompt that combines all scene descriptions. During generation, the model adjusts the weighting of each sub-prompt depending on the scene. Our approach differs from both strategies by introducing asymmetric guidance within the zigzag sampling framework. This asymmetric design strengthens subject consistency across generated images while preserving the model's s ability to follow textual prompts faithfully.
(29). maintains subject
3 Method
3.1 Preliminary
Latent Diffusion Model. The Latent Diffusion Model (LDM) comprises an autoencoder--consisiing of an encoder & and a decoder DD-that maps between pixel space and latent space. A diffusion model ca. parameterized by 0, is trained to model the noise in the latent space. For text-conditioned generation, frozen text encoder Tę is used embed the input text P into a dense representation The diffusion model is trained using the following loss function:
LLDM Ee (..))ee-((..)))eie-ioooomi Uniifoomm11))1111 [||e (
During training, the diffusion process involves predicting noise at randomly sampled time step t, drawn from a uniform distribution. To effectively model this denoising process- esspcialyyy in the context of text-to-image generation attention mechanisms play a central role in the architecture of diffusion models. In UNet-based models such as SDXL (30, cross-attention layers facilitate the integration of textual information by aligning latent visual features with text embeddings. This allows the model to generate images that are semantically consistent with the input text. Simultaneously, self-attention layers help capture spatial and semantic relationships within the visual latent space, FLUX
enabling more coherent and detailed image synthesis. In contrast, the transformer- model (1 adopts different strategy. It uses a unified self-attention mechanism with modality- specific projection layers for visual and textual inputs. This design allows FLUX to fuse semantic information across modalities without relying on explicit cross-attention, leveraging the transformer's strength in modeling complex dependencies. Our method is seamlessly integrated in the UNet-based model SDXL and the transformer-bact based model FLUX.
3.2 Asymmetry Zigzag Sampling.
To address the challenge of balancing subject consistency and text fidelity in story-based image generation. we propose a novel Asymmetric Zigzag Sampling strategy that leverages the strengths of both diffusion-based generation and semantic conditioning. Prior work has introduced zigzag sampling-al method that decomposes each diffusion denoising step into three sub-steps: zig, zag, and generation- to improve the performance of generative models. Inspired by the distinct functional roles identified in recent work [52), where the zig step facilitates exploration, the zag step enables refinement, and the generation step produces the final output, we introduce asymmetry into this process to more precisely regulate the flow of semantic information across steps. our design, identity visual semantics are injected exclusively during the zig (exploration) step to establish strong identity grounding early in the denoising trajectory. The zag step then adjusts the latent space without further visual input, helping to avoid overfitting to the identity. Finally, the generation step focuses

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Subject tolcen
Visio Attention
Visum Aftentonn Layer
Text Vision Anersoo Layer
od TM 00
6bb
I Atrention
prompt
00.1.0033
0,502000]
Viston
I Top
Cacho
Atcation
DB
000
000 00 00""00
Layer
(b)
Subject Semantic Contral (SSC)
Time stop:
Time slep: I-1
SSC
One Prumpl
Cross Attention
step
Teai-Visuo Amention
Attention
(SDXL)
(FLUX)
Layer
No SSC Null Prompo
DB
DDO
000 00
Prumpt token
Cachod
Zag-step
Visien Allection Layer
00
DD0
tokens
No SSC One Prompt
Solf Attention
DOO
O00
Generation-step
Diflusion
Visual takens prompt
Cached ID
(d)
Figure 3: Overview of our proposed pipeline. (a) Identity-guided diffusion inference, where identity prompts used to cache identity-related visual tokens. (b) Visual token selection module, which leverages attention scores to identify the most relevant tokens for the subject. (c) Illustration of the asymmetric design applied zigzag sampling. (d) Integration of identity-aware visual information during the zigzag sampling process.
solely on prompt alignment, free from additional visual conditioning. This asymmetric configuration allows our method to achieve superior balance between subject consistency and prompt fidelity, particularly complex, multi-image story generation tasks. An overview of the proposed method illustrated in Figure with detailed step-by-step operations described below. Formally, let æe denote the noisy latent at timestep 6, and x;- the denoised latent. While the zig and generation steps follow the standard forward denoising trajectory, the zag step performs an inverse denoising operation, mapping I₁ to a prior latent #++1- The computation for each sub-step is governed by the noise schedule a₁ predefined by the underlying diffusion model.
(i + ((-a(-o &e
21-1 V
vadly
Var
Diffusson denoising step
Inverse diffusion denoising step
Building on the bidirectional nature of zigzag sampling, which enables more effective semantic control during generation, we propose asymmetric zigzag sampling. In our approach, we inject strong subject-specific semantic cues into the intermediate network representation during the zig step, then propagate this information into the noisy latent space through the zag step, while keeping the generation step unchanged. This asymmetric design allows to enhance subject consistency across generated images without compromising the model's ability accurately follow the textual prompt. We describe our asymmetric setup for zigzag sampling below, and summarize the proposed method in Algorithms D and 2 in the appendix.
Asymmetry Visual Guidance. As described above, we inject strong subject-specific cues into the latent representation during the zig step. To extract these cues, we first run the diffusion denoising process using an identity-focused prompt that describes only the visual characteristics of the subject. Following prior work (28), we compute text-image attention scores to identify subject-relevant visual tokens at each layer and timestep. These tokens serve as the basis for semantic injection, helping the model preserve subject identity across generated images. We denote the subject-related attention scores the text-image attention layer as:
Maubject [s] S2
1.
where and m denote the layer and timestep indices, respectively. Based on these scores, we cache the top-k key and value projections of visual tokens from the image self-attention layers. This caching process is illustrated in Figure (a) and the selected tokens are defined as:
m 1
J.m
J.m
J.m kky
J.m. ..m |value, 1 Vaaloe.2
J.
key
(eyy
kgy.2
value
vallue,
5

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During image generation for each prompt, these cached subject tokens are concatenated with the current visual tokens in the image self-attention layers each layer and timestep. This integration is formalized as follows:
K Concatenate 1. K(d))
Vila Concatenatee (Taalee Vil)
The query tokens remain unchanged. The updated key K1, value ViA1 and original query tokens are then used to perform standard self-attention, which updates the visual representation in the image attention layer. To avoid compromising the model's 's ability to follow text prompts, this semantic injection is applied only during the zig step. The zag and generation steps remain unchanged. This process is further illustrated in the Subject Semantic Control (SSC) module shown in Figure
Asymmetry Prompt Guidance. For text-based guidance, we have first adapted an approach proposed in [29]. which concatenates all scene-level prompts into a single sentence and dynamically reweights the contribution of each sub-sentence during the generation of the corresponding image. This allows the model to maintain narrative coherence across scenes while emphasizing the relevant textual content at each generation step. addition, we have integrated the
Contrastive Alignment (IPCA) technique [29] to further strengthen subject representation through the text prompt, reinforcing the model's ability to retain subject identity throughout the story. However, directly applying these text-guie rext-guidance strategies within the standard zigzag sampling framework has led to suboptimal results. We hypothesize that the strong text-driven supervision during the zag step conflicts with the subject-specifie information introduced through visual token injection, potentially overriding or distorting it. This interference degrades subject consistency and overall image quality. To address this issue, introduce novel asymmetric zigzag prompt guidance strategy. Specifically, apply the enhanced text-guidance mechanisms only during the zig and generation steps, where they align well with semantic injection and image refinement. During the zag step. we instead use null prompt to prevent conflicting and allow the subject-specific visual information to propagate unimpeded. This asymmetry helps preserve both textual relevance and subject consistency across the generated sequence.
4 Experiments
Comparison Methods. We integrate our method in two backbone architectures: the UNet-based SDXL 330 and the transformer-based FLUUX [31 models. For the SDXL backbone, we compare our approach against several state-of-the-aas baselines, including both training- based methods-The Cho- sen One [53]. PhotoMaker (43), and IP-Adapter [24]- and training- free Beathods-ConsiStorry (28). StoryDiffusion (27), and IPromptIStory [29]. For the FLUX model, as no prior methods have been demonstrated to support this architecture, we re-implement the most recent and competitive training- method, Promppt Story, on top of FLUX. We then use this as baseline for comparison against our method. This dual- -lla-foatooom evaluation demonstrates the adaptability and effectiveness of our approach across diverse diffusion architectures.
we evaluate all models along two key dimensions: prompt alignment and subject consistency. To assess prompt alignment, use the CLIP image and text encoders to compute the average CL.IP-coore [54] between each generated image and its corresponding prompt. This metric reflects how well the visual content of the generated image aligns with the intended textual description. For subject consistency, we adopt two complementary metrics from previous studies (9 DreamSim (55) and CLIP-I [54]. DreamSim measures perceptual similarity and has shown strong correlation with human judgment in evaluating visual coherence. CLIP-I computes the average cosine similarity between CLIP image embeddings. capturing the consistency of subject identity across different images. To ensure that subject consistency is not influenced by variations in background content, we follow prior evaluation protocols and apply CarveKit (56] to remove the background from each generated image. The removed regions are then filled with random noise, isolating the subject and allowing for more focused and accurate evaluation.
Evaluation Metrics. Following prior work 29
28

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Results
Quantitative Comparison. Table I presents quantitative comparison between our proposed method and previously discussed approaches. As shown, our Asymmetry ZigZag Sampling technique achieves the best overall performance across all evaluation metrics among training-free visual storytelling methods. When compared with thining-based methods, our approach outperforms both PhotoMaker and The Chosen One across all evaluation metrics. While the I--Adptter method demonstrates the highest performance in DreamSim and CLIP-I scores, our method achieves comparable CLIP-I score and significantly narrows the gap DreamSim performance between training-free and training- based methods reducing it by 63.27%. Although the IP-Adapter excels in subject consistency metrics (CLIP-I and DreamSim), performs considerably worse in text-alignment metrics such as CLIP-T. This discrepancy may be due to the IP- IP-Adapter's tendency to overemphasize the subject, often generating images with highly similar or repetitive layouts, as illustrated in the figure
Method
Base Model
Train-Free
CLIP-T1
CLIP-I1
DreamSim
Steps
Vanilla SDXL
0.9074
0.8165
0.5292
50
Vanilla FLUX
0.8977
0.8494
0.3888
28
The Chosen One
SDXL
X
0.7614
0.7831
0.4929
35
PhotoMaker
SDXL
X
0.8651
0.8465
0.3996
50
AP-Apetter
SDXL
X
0.8458
0.9429
0.1462
30
ConsiStory
SDXL
V
0.8769
0.8737
0.3188
50
StoryDiffusion
SDXL
/
0.8877
0.8755
0.3212
50
1rromptIStory
SDXL
/
0.8942
0.9117
0.199:
50
Ours
SDXL
0.8946
0.923
0.
50
IPrompt|Story
FLUX
>
0.8716
0.9118
0.1957
28
Ours
FLUX
0.8949
0.9216
0.1843
28
Table 1: Quantitative Comparison. We report quantitative results for different methods. For the SDXL architecture, the best-performing value highlighted in bold, while the second-best indicated with box. For the FLUX model, only the best result is highlighted in bold. The baseline models-vanilla SDXL and vanilla FLUX- are included as references but are excluded from the comparative ranking.
User Study. While automatic evaluation metrics offer a useful quantitative assessment, they can be biased due to their reliance on pretrained models. To further validate the effectiveness of our proposed method, we conducted a human evaluation study. We randomly selected 30 prompts from the benchmark dataset and generated corresponding image sequences using all competing methods. Twenty participants were invited for the user study. For each participant, a custom program randomly selected 20 out of the 30 prompts, and presented four resulting image sequences obtained with different methods for each selected prompt. Participants were asked to choose their preferred image sequence based three criteria: identity consistency, prompt alignment, and overall image quality. As shown in Table images generated by our method received the highest preference from participants, indicating strong alignment with human judgment. Further details of the user study protocol and interface can be found in the Appendix.
Method
IP-Adapter
ConsiStory
StoryDiffuuss
1Pormpt1Story
Ours
Percent (%)
5.15
19.18
16.25
24.50
35.02
Table 2: Human preference comparison among different methods.
Qualitative Comparison. Figures and present qualitative comparisons of our method against existing approaches. For the SDXL model, we compare our method with IP-Adapter [24), Consis- Tory (28). StoryDiffusion (27]. and (Prompt1Story 29 For the FLUX model, we evaluate our method against the training- state-of-the-arr [Prompt1Stor approach. As shown in Figure 4 IP-Adapter tends to overemphasize identity consistency at the expense of faithfully representing input text. Mcanwhile, ConsisTory StoryDiffusion, and 1Prompt1Story struggle to maintain subject

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performance. consistency across different images. In contrast, our method achieves deseriptions. Figure effectively preserving subject identity while accurately following the further underscores the superiority of our approach over the Prompt1 Story model in terms of both subject consistency and text alignment. Additionally, highlights the generalizability our method, which maintains strong performance across diverse network architectures.
castle courtyard
medieval
party
birthday
in flower garden
beneath rainoww valley
shadow
the
clouds
consistory
Ip pp-addapter
StoryDiffusion
Prrompp Story
A pixel art depiction of 16 years old girl
magical artwork of unicom with gleaming silver hom
Figure 4: Qualitative Results Using the SDXL Backbone. We compare method with four baselines: ConsisStory. IP-Adapter, StoryDiffusion, and IPromptiStory The identity prompt is shown the bottom, while individual image prompts displayed above each corresponding image. Our method demonstrates a strong balance between maintaining subject consistency and adhering textual prompts. In contrast, the baseline methods often struggle-ethhe failing preserve the subject's is identity or deviating from the given text descriptions.
Method
CLIP-TT
CLIP-I
DreamSim
Asymmetry
0.8946
0.923
0.1798
Zig-gen
0,8534
0.919
0.1801
Zig-zag
0.8944
0.8916
0.2121
All
0.8788
0.8833
0.2312
Table 3: Quantitative comparisons of dif-
Figure 5: Qualitative comparisons of different zigzag sampling designs.
ferent zigzag sampling designs.
Ablation study:Effect of Asymmetric Visual Injection. We conduct experiments evaluate the ef- fectiveness of our proposed asymmetric visual injection design by comparing it with three alternative visual injection strategies: (1) zig-gen symmetric sampling, where visual semantics are injected during

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in a city park with autumn leaves
in a flower garden
building sundcastle
shopping at boutique
drawing in sketchbook
Prroppp Story
hyper-realistic digital painting a 16 years old girl
Ours
at veterinarian's office
beach
barking squirrel
playing toy
in a city alley
iPrompt Story
of A black and white dog with yellow collar
Ours
We compare method with IPrompt|Story using the FLUX backbone. Across varying prompts, our method demonstrates superior ability to preserve subject identity, highlighting its robustness in maintaining consistency.
Figure 6: Qualitative Results:
the zig and generation steps but omitted in the zag step: (2) zig-zag zag symmetric sampling, which injects visual information in the zig and zag steps while excluding it during generation; and (3) fully symmet- ric sampling, where visual injection applied across all three steps-zig. zag, and generation. These comparisons allow us to isolate the impact of asymmetric conditioning on balancing subject identity consistency and text-image alignment. Table[]shows that fully symmetric sampling performs worst, likely due to over-conditioning, which introduces visual artifacts also observable in Figure While the zig-gen strategy achieves higher identity similarity than zig-zag zig-zag-likcly due to visual injection dur- ing generation it suffers from poor text alignment. Conversely, zig-zag provides better alignment at the expense of identity consistency. Our asymmetric visual conditioning design achieves the best over- all balance between these two competing objectives, i.e., identity preservation and accurate adherence to textual prompts. These results validate the effectiveness of the proposed asymmetric strategy.
Ablation Study: Effect of Asymmetric Prompt Conditioning. In our method, we employ prompt conditioning in the zig and generation steps, while using a null (empty) prompt in the zag step. This asymmetric design is motivated by the intuition that removing the prompt in the zag step helps prevent overfitting to the text condition and allows the model better retain the spbject-specifie information introduced during the zig step. To validate the effectiveness of this design, we compare symmetric sariant-whers prompt conditioning applied uniformly across all steps-with our asymmetric configuration. As shown in Table 4 the asymmetric design consistently achieves superior performance, with CLIP-T and CLIP-I scores improving from 0.9098/0 /0..//... /0.88//.884 0.923/0 0.923/0.8944 /0.896,, and DreamSim (lower is better) decreasing from 0.2251 to 0. 0789. These results confirm that the asymmetric prompt conditioning facilitates stronger
CLIP-T1
CLIP-It
DreamSim
Symmetry Asymmetry
0.9098
0.8841
0.2251
0.923
0.8946
0.1798
Table 4: Quantitative comparisons of symmetry (using prompt condition in zag step) and asymmetry (using null prompt conditionin zag step) design.

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semantic alignment with the textual prompt while preserving visual fidelity and preventing excessive prompt dependency, making a more effective and balanced design choice for our framework.
Ablation Study: The Influence of Hyper- parameter k In our visual injection module, we select the top-k visual tokens based on at- tention scores for injection during the sampling process. We evaluate the impact of varying k values (ranging from 0.2 to 0.8) on generation performance (Table The results show that in- creasing & generally improves image similarity but slightly reduces text alignment. Notably, value of k 0.2 achieves the best balance between these two objectives. Although different i values affect both metrics, the variations are minor, suggesting that our method is robust to the choice of k.
CLIP-TT
CLIP-I
DreamSim
0.2
0.8946
0.923
0.1798
0.4
0.8931
0.9245
0.1799
0.6
0.8921
0.9258
0.1801
0.8
0.8924
0.9287
0.1827
Table 5: The influence of hyperparameter & on the generation performance.
Inference Time Comparison. Our method can also be regarded as a form of test-time scaling in diffusion models. Compared with the standard diffusion inference process, our approach decomposes each inference step into three sub-steps- 7ig, zag, and ceneration-whie additional computational overhead and leads to longer inference time. To examine this effect, we evaluated the inference speed of our method against the regular diffusion process using both FLUX model. For SDXL with 50 sampling
inevitably introduces
UNet-based SDXL model and a transformer- steps, the baseline model required 7.44 seconds per image, while our method took 21.33 seconds. Similarly, for FLUX with 28 sampling steps, the baseline achieved 1.50 seconds per image, whereas our method required 5.50 seconds. These results indicate that our approach increases the inference time by approximately 2.9 on SDXL and 3.7 on FLUX. Despite the additional computational cost, the improved subject consistency across images in visual storytelling justifies the longer inference time as a reasonable trade-off for enhanced generative performance.
Other Applications. Similar the Peee-Pooot-On--Stteee approach 29 our method can be naturally extended to generate long stories containing an arbitrary number of images. This is achieved by applying a sliding-window strategy over the sequence of prompts, where each window of prompts is processed using our proposed method to ensure coherent subject and style consistency across adjacent images. By iteratively moving the window through the entire prompt sequence, our approach can maintain both local and global narrative consistency, enabling the generation of super-long visual stories. visualization of such an extended story is provided in the appendix, demonstrating the capability of our method to handle narratives of considerable length while preserving high-quality and consistent image generation.
Conclusion
We propose asymmetric zigzag sampling strategy to address the challenge of training- trining-ffree visual storytelling, achieving a strong balance between subject consistency and text text alignment. By leveraging the distinct roles of zig (exploration), zag (adjustment), and generation, method injects visual semantics only where needed, enabling coherent, identity-consistent outputs without retraining. This approach also highlights the potential of sampling strategies promising direction for future research in controllable image generation.
7 Limitations
While our proposed method demonstrates strong performance in maintaining subject consistency and adhering to textual descriptions in visual story generation, it is not without limitations. First, the use of zigzag sumpling-comprising three sub-steps per generation step introduces additional computational overhead, which may increase inference time compared to standard sampling strategies. Second, although our approach is compatible with both the SDXL and FL.UX architectures, our experiments indicate that it integrates more seamlessly with the FLUX model. In some cases, we observe occasional failures or reduced performance when applied to SDXL, suggesting that further architecture-speciin optimization could enhance robustness and generalizability. Nonetheless, these limitations do not detract from the overall effectiveness of our method and highlight promising directions for future improvement.
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A Acknowledgement
This work is funded by the CALCULUS project (European Research Council Advanced Grant H2020-ERC-2017-ADDG 788506) and the Flanders Al Research Program.
B Boarder Impacts
Visual generative techniques, particularly text-to-image (T2I) models, hold significant potential for producing coherent visual content across diverse scenarios, making them highly applicable to downstream tasks such storytelling and personalized content creation. One of the most challenging aspects in this domain is the consistent synthesis of characters across varying contexts- problem that existing methods continue struggle with, discussed in this paper. Our proposed approach addresses this challenge by effectively balancing subject consistency and prompt fidelity, allowing users generate coherent story sequences featuring the same character while closely adhering to their provided descriptions. In addition, our exploration of the unique structure of zigzag sampling introduces new perspective its utility in diffusion-based generation, offering valuable insights that may inspire future research into more controllable and semantically aware generative models.
The application of text-to-image models in visual storytelling, while creatively empowering, intro- duces significant ethical, privacy, and security risks. A major concern is the non-consensual creation of fictional narratives featuring real individuals, where realistic and consistent character generation enables the fabrication of defamatory, misleading, or harmful visual stories- such as fake memoirs, satirical comics, illustrated scenarios depicting private citizens or public figures in compromising situations-withuur their knowledge or consent. The very capability to maintain character coher- ence across scenes, which enhances narrative immersion, can be exploited produce persuasive, long-form synthetic content that blurs the line between fiction and reality, facilitating disinformation campaigns emotional manipulation. Furthermore, privacy is compromised when models trained on unconsented web-scraped data generate characters closely resembling real people, effectively creating digital doppelgängers embedded in fictional universes. These capabilities also pose security threats, as coherent Al-generated visual stories can be weaponized for influence operations, identity exploitation, or viral misinformation. undermining trust and personal autonomy. Without robust safeguards- succhurrds-uuhhaaee as provenance tracking, consent filters, and transparent content policies-Alldrivee Al--drien visual storytelling risks enabling targe-scale narrative abuse with profound societal consequences.
C Usage of LLMS
We only use large language models (LLMs) for writing assistance, such as correcting grammar, fixing typos, and improving clarity.
D Implementation Details
We implement our method on two open-source models: SDXL and FLUX. For SDXL, we use the mabilityaistaalleddifissso ddiffusioonxl-base-llaae version, and for FLUX, we adopt the foacc--frret-labsFLLL dev version. All baseline methods-inchee
IP-Adapter [24]. Consistory (28). StoryDiffusion [27], and PPrompt1Story 29 are reproduced using their official open-source implementations with
default hyperparameters. Since IP-Adapter is designed for image-conditional generation, we adapt it our setting by first generating identity image using SDXL with the given identity prompt. This generated image is then used as the conditioning input for IP-Adapter to produce images guided by different prompts.
For the implementation of our method on the SDXL model, we cache visual tokens only from the mid and upper layers across all steps. Accordingly, feature injection during the zig step is also limited to these layers. We use a classifier-free guidance scale of 5.5 for both the zig and generation steps, single NVIDIA A100 GPU.
and set it to 0 during the zag step. All experiments are conducted
For the FLUX model, which differs architecturally from SDXL by separating text-image and image- image interaction stages, we adopt different strategy. FLUX begins with several layers of text-image interaction, followed by layers of purely image-based interaction. To cache visual tokens, we first average the attention scores from the early text-image interaction layers to identify subject-relevant

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visual features. These selected tokens are then used for feature injection across all interaction layers. Experiments for FLUX are also run on a single NVIDIA A100 GPU.
Algorithm Identity Visual Token Cache (Subject token extraction top-k selection)
Require: identity prompt P.d. pretrained diffusion model cg, text encoder TG. time steps {m} (used for identity extraction), layers L, top-k ratio it
Ensure: cached key tokens T (Kaay) } and value tokens Ivalue
Compute text embedding Tia t Tc (P.)..
2: for each layer E L and timestep used for identity extraction do
3: Run demoising pass (or inference pass) with prompt Pia obtain intermediate visual tokens
(4,m).
4: Compute text-image attention scores St. = AttentionScores(vissie (visual tokens, Ta).
5: Identify top-k indices by score: Jé.m + TopKIndices($44.. ,:).
6: Extract corresponding key value projections:
f.. key
+
xeyy j E Jd."
f.
I.. value
< value.. :j JJ.m..
f.f.m
7: Store F. into Kke and value into I key
8: end for
9: return Ikey Ivalue
Algorithm Asymmetric Zigzag Sampling with Zig Visual Sharing (ZVS) & Asymmetric Prompt Zigzag Inference (APZI)
Require: target prompt P, identity token caches Ztey Ivalue (from Alg. diffusion model eg, text encoder Tę. full zigzag time schedule t T,.... .., and its noise coefficients at
Ensure: latent 2o (decoded to image by decoder D)
1: Compute full prompt embedding T (P) (can one-prompt fusion / reweighting in paper).
2: Initialize noisy latent Zy A' I).
3: for each diffusion step index = - 1,.,, do
4: Zig step (forward denoise + visual injection):
Compute standard denoising prediction + co( t, T) using prompt embedding T.
2. Perform denoising update (forward) to get intermediate latent 24-1
Inject identity visual tokens into self-attention layers: for each layer l used,
K +++ + Concat( e.m.
K").
V + (I f.m
vallue V²),
where K V are the current layer key/value projections and f. come from the caches. Keep queries unchanged. (Apply only in Zig.)
5: Zag step (inverse denoise - null prompt / no text guidance):
1. Use null prompt.
Perform inverse denoising mapping to propagate the injected identity into the noisy latent:
3 + InverseDenoise((r.1) -- + l, null prompt),
(No visual injection in Zag.)
6: Generation step (final denoise with text guidance):
1. Use full prompt embedding T compute e-prediction 3t-
2. Perform forward denoising to obtain Jinal (standard step).
3. Set 2t-1 + final and continue.
7: end for
8: Decode vn to image: I -- D(to)) and return T.
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E More Details about User Study
We conducted user study evaluate our method in comparison with four existing approaches: IP-Adapter [24]. Consistory Model [28]. Story Diffusion (27). and IPrompt1 Story [29]. All models were used generate images based on prompts from the ConsiStory+ benchmark, using the same random seeds as reported in their respective papers to ensure a fair comparison.
From the generated dataset, we randomly selected 30 prompts, each associated with 4 images. For each participant in the user study, the system randomly sampled 20 out of these 30 prompts to form evaluation Before starting the evaluation, were briefed three key criteria:
Identity Consistency: Measures whether the same character or subject appears consistently across all images for a given prompt.
Prompt Alignment: Assesses how well each image reflects the content and intent of the original text prompt.
Image Quality: Evaluates the overall visual quality, including clarity, detail, and aesthetic appeal.
To minimize potential bias, the presentation order the five methods was randomized for each question in the study interface. Figure presentedd the user interface of our user study system.
Human Evaluation
grog
gropt (h-
Mearls Sen atrrwer Wheter the webbed sere aerecs offireee list.
wut group jeure
Figure 7: A visualization of the user interface used in our user study system.
F More Visualizations
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veterinarian's office
city
playing with
barking squirrel
animation of black and white
yellow
reading fairy tale book
building sandcastle
on playground
shopping boutique
drawing in sketcibook
hyper-ealistic digital painting of 16 years old
with chestnut hair, slender frame, and soft brown
eyes
on balcony overlooking cibyscupe
sitting beside laptop and notebooks
on an office desk filled with
by morning
in kitchen
per-
realistie dightl
painting of A
red coffee mug shaped like ball.
Walcor
skatepark
green
climbing
digging trowel
yer-
realistic digital pointing of A
teenage boy
spiky black hair, slight build, and dark brown eyes
dressed in jacket
cory
wearing
wearing
snimation of hedgchog
writing runes in dusty spellbook
mixing potions
consulting crystal ball
casting spell by the light af full moon
filled with
mystical illustration of long, flowing beard
walking through the
enjoving
lounging cazy planting flowers garden
smoking pipe under tree
peaceful rustic hobbit with
large fest and
pixer
a scaside pice
in cozy library
in rose garden
on city street in the 1950s wearing a gardening spran
illustration
w an
with skender
x
straight red
hair. and
free
across
nose
Figure 8: More visualzations of results generated using FLUX model.
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heneath a rainbow valley
gallopinng through desse, enchanted forest
drinking from orystalline
standing in meadow of blooning flowers
among clouds
mn magical artwork of A unicorn with
gleaming silver
horn
creating magical trinkets travelers
sitting mushmom with
tinkering im
uarding the entrance
underntound
illusration of friendly gnome
blanketed fresh snow
in lush woodland
shading peaceful clearing
tall in an sutumn forest
casling sladkws
A realist landscape painting mighty oak tree with spreading banches
dancing the spring broeze
futlering around Invender bush
on the edge ofa garden gate
perched on ammouu
resing en vibunt doiry
watercolor of A delicate butterfly
reading from an ancient tome
standing by bubbling cauldron
surroundec
chanting in stone cincle
contaring elowing orb
enchanted flames
A captivating fantasy portrait of powerful sarceress in elegant robes
pixel depiction of An gentleman with distinguished gray neatly trimmed mustache, and gentle eyes
golf
discuseing gallery
tending to garden
onjoying
sesside
in library
the 1950s
wearing gardening apron
A pixar style illustration of woman with slender figure, straight red and freckles
across the nose
climbing the walls of deserted
sneaking through dark market at midnight
laughing flickering campfire
stealing shiny objects from merchant's stall
playing tricks on travllrs village
quirky mischievous gablin with sharp features
Figure 9: More visualzations of results generated using SDXL model.
15

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G Long Story Visualizations
An anime-style illustration of 16 years old girl with wavy chestnut hair, slender frame, and soft brown
surfing photographo large
walking dog beighbothood
wulking
wealing greas knitted
botasical
outdorr
riding noller countrysáe
auusement park
morstmaloos
nritng bench overlooking suncet
sketching studie
stayyng morea with magnifying glase
painting a munal city wall
participatng in commuitty clzanup
participating school play
performing in strect band
plaing chese in quict park
nidigg bicycle through
heiping library
hiking up steep mall
a dense fooest
on soohap
gazing
on Snowy mounnan building soowran
reading: 3 comnC book 18 loving room
flasblight
exploting un abundosed building
exploring underwater world an siit
fishing on calm riverbank
Bying kilc open field
playing video ganmen in noom
huilding a sandeastle on a sumry beach
rond tnyy witiin
atonding
climbing
dressdd
booksttrr,
bustiing
at camival, holding
at 200, observing animals
attending crntumeg party
learning cook in kitchen
candy
Figure 10: Long story generated using FLUX model
16

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Reviews:
[{"review_id": "WCjne4kMBP", "reviewer": "Reviewer_mtYN", "summary": "This paper tackles the challenge of training-free consistent text-to-image generation. The authors propose a new sampling strategy called Asymmetry Zigzag Sampling (AZS). This method modifies the standard diffusion denoising process by decomposing each step into three sub-steps: zig, zag, and generation. This asymmetric design aims to disentangle the tasks of maintaining subject identity and adhering to the narrative prompt, thereby achieving a better balance between the two. The authors validate their method on both unet and dit based diffusion models.", "questions": "1. My questions on the impact, design choices, writing and ablation experiments are listed in the weakness session.\n2. Some may claim this method introduces more computational overhead. I think it is acceptable as long as the strategy is applicable in general subject consistent generations. I am willing to raise my score as long as my concerns are cleared by the authors.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. [Better Performance] The method is benchmarked against a wide range of relevant and strong baselines, including both training-free and training-based techniques. The result surpasses its close baseline 1Prompt1Story in all three metrics.\n2. [Straight Forward Idea] Using test time scaling to strike the balance between text alignment and subject consistency is straight forward and effective\n\nWeakness:\n1. The idea of incorporating inversion process during sampling cannot be considered new, for example as in [1]. Actually, it can be considered as a special case of test time scaling [2], with a different guidance.\n2. [Incremental based on 1Prompt1Story] The main framework heavily relies on 1Prompt1Story, which lessens the impact of the paper itself. Can this sampling strategy be applied to other consistency generation frameworks?\n3. [Method clarity] The method part is hard to follow even after reading ConsiStory and 1Prompt1Story. I am not sure which designs of the previous work are adopted and which are not. I suggest the author to add details for the token caching, and unify the terms such as \"one prompt\", \"SSC\" etc., which are not described in the method section. More precise, step-by-step details would improve reproducibility.\n4. [Lacks ablation studies] The ablation is designed to show each sampling process is necessary, but is not enough to answer all design choice questions.\n    First I am curious how are the time step set in zig-gen and zig-zag, since they are 2-forward forward-backward, not aligned with conventional time steps.\n    Second, and alternative zag process with SSC or one prompt should be given, to prove zag could fight identity overfitting\n    Third, why adding gen step at last also improves clip-i?\n\n[1] Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection\n[2] Scaling Inference Time Compute for Diffusion Models", "was_revised": true, "final_justification": "All of my concerns are well addressed. I would like to raise my score to 4: Borderline accept considering the overall contribution is limited and current writing quality.", "rebuttal": "Thanks for raising all your concerns, below are our responses:  \n \n*W1. The idea of incorporating inversion process during sampling cannot be considered new, for example as in [1]. Actually, it can be considered as a special case of test time scaling [2], with a different guidance.*\n\n[Response].  \nWe appreciate you highlighting related works on zigzag sampling and test-time scaling. We agree that zigzag sampling, as a concept for decomposing denoising steps, has been explored (e.g., [1]). Our novelty lies not merely in incorporating an inversion process, but in the asymmetric design of our Zigzag Sampling with Asymmetric Prompts and Visual Sharing (AZS) and its specific application to the challenging problem of maintaining subject consistency across multiple images in visual storytelling while preserving text fidelity.\n\nWhile \"test-time scaling\" [2] refers to a broader category of techniques that adjust model behavior during inference, our AZS is a concrete sampling strategy that orchestrates the flow of specific semantic information (visual subject cues and textual prompts) in an asymmetric manner across the zig, zag, and generation sub-steps. Previous zigzag methods primarily focused on improving general generation quality or text following. In contrast, our key innovation is the strategic and asymmetric injection of subject-specific visual information only in the \"zig\" (exploration) step, using a null prompt in the \"zag\" (refinement) step to prevent overfitting, and leveraging the full textual prompt in the \"generation\" step for narrative coherence. This precise control over information flow within the zigzag framework is what distinguishes our method and allows us to achieve a superior balance between subject consistency and prompt fidelity, which is a fundamental requirement for visual storytelling that prior methods struggle with. We will elaborate on this distinction in the revised introduction and method sections.\n\n*W2. The main framework heavily relies on 1Prompt1Story, which lessens the impact of the paper itself. Can this sampling strategy be applied to other consistency generation frameworks?* \n\n[Response]    \nWe conducted an experiment using our asymmetric zigzag sampling technique without the one-prompt method.   \nclip-I: 0.9101, clip-T: 0.8876, DreamSim: 0.2113   \nThese results still outperform both the Consistory and StoryDiffusion models, demonstrating the generalizability of our proposed sampling strategy. Moreover, when combined with the one-prompt technique, it achieves the best performance, highlighting the complementarity between the two methods.\n\n\n*W3. The method part is hard to follow even after reading ConsiStory and 1Prompt1Story. I am not sure which designs of the previous work are adopted and which are not. I suggest the author to add details for the token caching, and unify the terms such as \"one prompt\", \"SSC\" etc., which are not described in the method section. More precise, step-by-step details would improve reproducibility.*\n\n[Response]    \nThanks for raising this issue and providing the suggestions.    \n**Delineation of Adopted vs. Novel Designs**: We will explicitly state which components are adapted from prior work (i.e., , the idea of using an identity-focused prompt to extract subject-relevant visual features from [27], and the prompt fusion/reweighting mechanism from [28] for text fidelity) and clearly highlight our novel contributions (Asymmetry Zigzag Sampling, including Zig Visual Sharing (ZVS) and Asymmetric Prompt Zigzag Inference (APZI)).    \n**Unifying Terminology**: We will standardize and clearly define all terms.    \n\"One Prompt\" refers to the concatenated prompt strategy introduced by 1Prompt1Story [28], which we integrate for maintaining narrative coherence across scenes.   \n\"SSC\" (Subject Semantic Control) refers to our proposed module (illustrated in Figure 3(d)) that performs the asymmetric visual guidance within the zigzag sampling process. Step-by-Step Details: We will provide a more precise, step-by-step algorithmic description ( with pseudocode) for the Asymmetric Zigzag Sampling process, detailing how ZVS and APZI are integrated into the zig, zag, and generation sub-steps. This will significantly improve reproducibility.        \nW4. The ablation is designed to show that each sampling process is necessary, but is not enough to answer all design choice questions. First I am curious how are the time step set in zig-gen and zig-zag, since they are 2-forward forward-backward, not aligned with conventional time steps. Second, and alternative zag process with SSC or one prompt should be given, to prove zag could fight identity overfitting Third, why adding gen step at last also improves clip-i?   \n\n*W4.1 how are the time step set in zig-gen and zig-zag?*.  \n\n[Response]    \nWe apologize for the confusion caused by the \"zig-gen\" and \"zig-zag\" terminology in Table 3. This refers to where our proposed visual semantic injection is applied within the zigzag sampling sub-steps, not a different type of zigzag sampling. The core zigzag sampling mechanism (forward denoising for zig/generation, inverse denoising for zag, as defined in line 175) remains consistent across all these ablation settings. The \"time step t\" in these equations refers to the standard diffusion timestep.   \n**\"Asymmetry\" (Our Method):** Visual injection (ZVS) only in zig step. Text guidance (APZI with full prompt) in zig and generation steps. Null prompt in zag step.   \n**\"Zig-gen\":** Visual injection (ZVS) in zig and generation steps. Text guidance (full prompt) in zig and generation steps. Null prompt in zag step.   \n**\"Zig-zag\":** Visual injection (ZVS) in zig and zag steps. Text guidance (full prompt) in zig and generation steps. Null prompt in zag step.   \n**\"All\" (Fully Symmetric):** Visual injection (ZVS) in zig, zag, and generation steps. Text guidance (full prompt) in zig and generation steps. Null prompt in zag step.    \nWe will clarify this distinction in the revised paper, making it explicit that these ablations examine the impact of where visual conditioning is applied within our established asymmetric zigzag framework, not variations of the zigzag process itself.\n\n*W4.2 alternative zag process with ssc or one prompt should be given ?*   \n\n[Response]   \nThis is a valuable suggestion. Our hypothesis is that using a null prompt in the zag step allows the subject-specific visual information injected in the zig step to propagate and \"settle\" into the latent representation without being immediately overwritten or overly constrained by new textual descriptions, thereby preventing overfitting to prompt specifics and improving generalizable subject consistency.   \nWe have conducted the following experiment:   \n**\"Asymmetry + Zag-Text\"**: Our \"Asymmetry\" method, but with the full textual prompt (+zag-text) (or even the \"one prompt\" / \"SSC\" guidance) also applied during the zag step, instead of a null prompt.   \nAsymmetry + zag-text         clip-I :0.9098     clip-T :0.8841       DreamSim :0.2251.  \nAsymmetry + zag-null          clip-I :0.923       clip-T :0.8946        DreamSim :0.1789.  \nThe results above show that adding text in zag step leads to significant decreases in subject consistent evaluation metrics including clip-Ii and DreamSim score.    \nThe ablation of zag with ssc is exactly the experiment named zig-zag  in table 3 ( see above explanation of methods in table 3).   \n   \n*W4.3 why adding gen step at last also improve clip-i ?*   \n\n[Response]    \nThere might be a slight misunderstanding here. In our \"Asymmetry\" method, the \"gen step\" (generation step) always uses the complete textual prompt for guidance. The improvement in CLIP-I (subject consistency) is not solely because of \"adding the gen step at last.\" Rather, it is due to the synergistic effect of the entire asymmetric zigzag sampling process:   \n**Zig Step (Exploration + Initial Identity Grounding)**: Strong subject-specific visual cues are injected, firmly establishing the subject's identity early on.   \n**Zag Step (Refinement + Propagation, Null Prompt)**: The latent space is refined without additional textual or visual interference. The null prompt here is critical; it allows the visually conditioned subject information from the zig step to propagate through the denoising process without being immediately pulled too strongly by scene-specific textual prompts, which could otherwise lead to \"identity overfitting\" or reduce scene diversity.   \n**Generation Step (Final Alignment, Full Text Prompt)**: This step then focuses solely on aligning the image with the specific textual prompt, leveraging the consistent subject information that has been preserved and propagated from the zig and zag steps. Because the earlier zig and zag steps have already done the heavy lifting of grounding the subject consistently and preparing the latent space, the generation step can effectively refine the image according to the narrative without losing subject identity.   \n\nTherefore, the improvement in CLIP-I is a result of this **carefully orchestrated asymmetric information flow.** The \"generation step\" is where the narrative is finally realized with the subject consistently maintained due to the preceding zig and zag steps. The comparison in Table 3 specifically highlights that applying visual conditioning during the \"generation\" step (as in \"Zig-gen\") leads to poorer text alignment, while our \"Asymmetry\" method avoids this by focusing the \"gen\" step on text alignment alone after subject conditioning in the \"zig\" step."}, {"review_id": "Icz2khiu07", "reviewer": "Reviewer_4vAP", "summary": "The paper introduces Asymmetry Zigzag Sampling (AZS), a novel training-free sampling strategy for enhancing subject consistency in visual story generation using text-to-image models. The authors address a key challenge in multi-scene generation: maintaining consistent subject identity across images while preserving alignment with textual prompts. The proposed method decomposes each generation step into three sub-steps (zig, zag, generation) and integrates two main components:\n1. Zig Visual Sharing (ZVS): Injects subject-specific visual cues during the \"zig\" step to enforce consistency.\n2. Asymmetric Prompt Zigzag Inference (APZI): Uses a full prompt in the zig/generation steps and a null prompt in the \"zag\" step to balance subject fidelity and text alignment.\nThe method is evaluated on SDXL and FLUX architectures, outperforming existing training-free and training-based baselines in both quantitative metrics (CLIP-T, CLIP-I, DreamSim) and human evaluations. Key contributions include the asymmetric sampling framework, robust cross-architecture performance, and insights into latent-space conditioning for controllable generation", "questions": "1. The method adds sub-steps per generation. Could the authors quantify the runtime overhead compared to baselines (e.g., steps/second)?\n2. What is the intuition behind using a null prompt here? Does empirical evidence support this over other designs (e.g., weakened prompt)? \n3. The top-k ablation (Table 4) shows minor metric variations. Are there failure cases where k significantly impacts output? \n4. The paper notes occasional SDXL failures. Are there architectural insights explaining FLUX’s better compatibility?", "limitations": "The paper adequately discusses limitations (e.g., computational overhead, SDXL compatibility) in Appendix A. Suggestions for improvement:\n1. Expand on potential misuse (e.g., deepfake stories) and mitigation strategies (e.g., watermarking).\n2. While training-free, could the method handle longer story sequences (>10 images)? A stress test would be valuable.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. Rigorous experiments validate the method’s superiority over baselines, including ablation studies and user evaluations. The dual-architecture (SDXL/FLUX) validation strengthens generalizability.\n2. The paper is well-structured, with clear explanations of the zigzag mechanism and visual/prompt asymmetry. Figures (e.g., Figure 2) effectively illustrate the pipeline.\n3.  Addresses a critical gap in training-free consistent story generation, offering a practical solution with lower computational costs than fine-tuning approaches.\n4. The asymmetric design is novel, distinguishing AZS from prior symmetric feature-sharing methods (e.g., StoryDiffusion, ConsiStory).\n\nWeaknesses\n1. While computational overhead is acknowledged, deeper analysis of inference time vs. baselines would strengthen practicality claims.\n2. The role of the \"zag\" step (inverse denoising) could be elaborated further—why is a null prompt optimal here?\n3. The zigzag concept builds on prior work (e.g., [51]); the paper could better delineate its unique adaptation for asymmetry.", "was_revised": true, "final_justification": "In their reviews and rebuttal, the authors have clearly outlined their planned textual revisions to enhance clarity. These improvements adequately address the concerns raised, and I therefore maintain my recommendation to accept the paper.", "rebuttal": "*W1.While computational overhead is acknowledged, deeper analysis of inference time vs. baselines would strengthen practicality claims.*   \n\n[Response]    \nInference time comparison:  (seconds  per image).  \nSDXL[50steps]  7.44s ,  Ours [50 steps] 21.33s.   \nFLUX[28steps]  1.50s ,  Ours  [28 steps]   5.5 s    \n\n*W2. The role of the \"zag\" step (inverse denoising) could be elaborated further—why is a null prompt optimal here?*.    \n\n[Response]   \nThe \"zig\" step is designed for exploration and initial injection of strong subject-specific visual information. The \"zag\" step, performing inverse denoising, acts as a refinement and propagation phase for the latent representation. Our intuition is that after injecting visual identity cues in the zig step, applying a strong textual prompt or further visual conditioning in the zag step would cause immediate overfitting to the scene's textual description or over-constrain the subject's appearance too early, potentially disrupting the delicate balance needed for flexible storytelling. A null prompt in the zag step allows the visually grounded subject information to propagate through the denoising process without being pulled strongly by any specific text or new visual cues. This enables the subject identity to \"settle\" into the latent representation in a more generalized manner, preparing it for the final \"generation\" step which then focuses purely on aligning with the detailed scene prompt. This prevents the model from overly focusing on the current scene's textual details for subject definition, thus promoting better consistency across different scenes while preserving diversity.   \nWe conduct an additional ablation study to empirically support the optimality of the null prompt in the zag step, as also suggested by the other reviewer.    \nAsymmetry + Zag-Text\": Our \"Asymmetry\" method, but with the full textual prompt also applied during the zag step, instead of a null prompt.    \nAsymmetry + zag-text:              clip-I  0.9098, clip-T 0.8841, DreamSim 0.2251    \nAsymmetry + zag-null (ours):     clip-I  0.923,   clip-T 0.8946, DreamSim 0.1789   \nThe results above show that adding text in zag step leads to significant decreases in subject consistent evaluation metrics including clip-I and DreamSim scores. This demonstrates that the null prompt is indeed optimal for preserving a consistent subject identity across the story. We will include these results and discussion in the revised paper.\n\n*W3. The zigzag concept builds on prior work (e.g., [51]); the paper could better delineate its unique adaptation for asymmetry.*    \n\n[Response]   \nWe agree. While [51] (Golden Noise for Diffusion Models) explores the functional roles of zig (exploration), zag (refinement), and generation steps in general, our work uniquely adapts and leverages this structure for asymmetric information flow aimed specifically at the subject consistency challenge in visual storytelling.\nWe will explicitly delineate our unique adaptation in the revised \"Related Work\" and \"Method\" sections:   \nPrior Work ([51]): Introduced the zigzag decomposition and identified the general roles of zig (exploration), zag (refinement), and generation.    \nOur Unique Adaptation: We capitalize on these identified roles by proposing a novel, asymmetric strategy for when and how to inject different types of semantic information:   \n1. **Zig (Exploration)**: Dedicated to initial, strong subject identity grounding via visual token injection (ZVS). This sets the consistent subject foundation.   \n2. **Zag (Refinement/Propagation)**: Utilizes a null prompt (APZI) to allow the subject identity to propagate and refine in the latent space without being constrained by scene-specific text or further visual conditioning, preventing overfitting.   \n3. **Generation (Final Alignment)**: Focuses solely on text alignment with the full narrative prompt, relying on the already-consistent subject from previous steps.    \n\n This precise, purposeful asymmetry of information injection (visual vs. textual, and null vs. full prompt) at specific stages of the zigzag process is our distinct contribution, directly addressing the core problem of balancing consistency and fidelity, which [51] did not explore.   \n\n*Q1. The method adds sub-steps per generation. Could the authors quantify the runtime overhead compared to baselines (e.g., steps/second)?*   \n\n[Response]   \nSee response to W1.\n\n*Q2. What is the intuition behind using a null prompt here? Does empirical evidence support this over other designs (e.g., weakened prompt)?*   \n\n[Response]   \nSee response to W2.\n\n*Q3. The top-k ablation (Table 4) shows minor metric variations. Are there failure cases where k significantly impacts output?*   \n\n[Response]   \nWhile Table 4 indicates that our method is robust to the choice of 'k' within the tested range (0.2 to 0.8) and variations are minor, we can elaborate on potential edge cases and the rationale for our optimal 'k' selection:\n\n**Extremely Low 'k' (e.g., <0.1):** If 'k' is too small, only a very limited number of visual tokens would be selected, potentially leading to insufficient subject information being injected. This could manifest as a degradation in subject consistency, as the model might not have enough cues to maintain the identity across different scenes.   \n\n**Extremely High 'k' (e.g., >0.9):** Conversely, if 'k' is too high, it would mean injecting a very large proportion of visual tokens, including those that might be irrelevant or background-related. This could lead to an \"over-conditioning\" effect, similar to what is observed in our \"fully symmetric\" sampling ablation, resulting in visual artifacts or reduced generative diversity. It could also force the model to overemphasize the visual input, potentially hindering its ability to follow the textual prompt faithfully (lower clip-T).   \n\n**Empirical Sweet Spot:** Our chosen value of k=0.2 represents an empirical sweet spot that achieves the best balance between subject consistency and text alignment. This suggests that selecting a moderate number of the most relevant subject tokens is key, rather than an exhaustive or minimal set. We will clarify this in the revised paper to provide more comprehensive insights into the implications of 'k' selection.   \n\nWe will present qualitative examples illustrating these potential subtle impacts for extreme k values in the Appendix to provide a more comprehensive understanding beyond just the quantitative metrics.   \n\n*Q4. The paper notes occasional SDXL failures. Are there architectural insights explaining FLUX’s better compatibility?*   \n\n[Response]\nThis is an insightful question, and the architectural differences between FLUX and SDXL likely contribute to FLUX's better compatibility with our method.   \n\n**FLUX's Architectural Advantages:** FLUX is a transformer-based model that utilizes a unified self-attention mechanism with modality-specific projection layers. This design allows FLUX to fuse semantic information across visual and textual modalities without relying on explicit cross-attention, leveraging the transformer's strength in modeling complex dependencies. In our method, this means that the newly injected subject visual tokens, text tokens, and current generated visual tokens are jointly considered within FLUX's unified attention layers. This simultaneous interaction likely enables FLUX to better balance subject consistency and prompt following, leading to improved performance.   \n\n**SDXL's Architectural Considerations:** In contrast, SDXL is a U-Net-based model that combines convolutional and transformer blocks. Its transformer blocks contain separate cross-attention layers for text information and self-attention layers for visual information flow between patches. When we inject subject visual tokens into SDXL's self-attention layers, the interaction between these subject tokens and the text tokens (handled by separate cross-attention layers) might be less integrated. This separation could make it more challenging for SDXL to optimally balance text alignment and subject consistency, as the model needs to reconcile information from distinct interaction pathways.   \n\n**General Model Scale and Performance:** Beyond architectural design, FLUX is generally a larger and more capable model for text-to-image generation compared to SDXL, which has been demonstrated in various experiments. The transformer architecture, in general, has proven more effective when scaling model size and dataset, contributing to FLUX's overall superior performance in text-to-image generation.\n\n*L1. Expand on potential misuse and mitigation strategies.*   \n      \n[Response]\nWe acknowledge the importance of discussing potential misuse, especially for generative models. We briefly touch upon the positive societal impacts in Appendix A. We will expand on the \"Broader Impacts\" section (Appendix A) to include:   \n\nPotential Misuse: Explicitly mention the risk of generating misleading or harmful content, such as deepfake stories or perpetuating stereotypes, given the enhanced consistency capabilities.\nMitigation Strategies: While our work focuses on foundational research, we can suggest general mitigation strategies applicable to text-to-image models.  These include: Ethical Guidelines for Use, Watermarking/Provenance,Responsible Deployment \n\n*L2. Could the method handle longer story sequences.*\n\n[Response].   \nWe propose using a sliding window technique for the prompt lists. This approach allows the model to maintain context and subject consistency across a large number of images by processing prompts in overlapping segments. We conducted experiments using this solution, and the results demonstrate that our method can effectively generate consistent subjects throughout long stories and also maintaining strong text alignment.    \nWe will include these detailed results and a discussion of the sliding window technique in the revised version of our paper."}, {"review_id": "HT3T5DWeB2", "reviewer": "Reviewer_yE2y", "summary": "The paper introduces a training-free asymmetric zigzag scheme in text-guided diffusion to preserve subject-specific visual features across different text prompts. The zigzag scheme is designed specifically to optimize both adherence to ID preservation while still giving the diffusion process sufficient freedom to adhere to the text prompt. The scheme consists of three stages: a) During the zig step, strong subject-specific semantic cues are injected. b) This is propagated into the noisy latent by the zag step, which performs inverse denoising to get closer to the final output. c) Finally, the generation step is responsible for aligning with the text prompt. The resulting visual quality outperforms all existing approaches, striking a good balance between subject consistency and text guiding.  While others might even be stronger in ID preservation they typically suffer from adhering less to the prompt. The method has been integrated into two diffusion backbones - SDXL and Flux.", "questions": "Please try to provide a few more exact details on the individual steps, \n- In which layers is the zigzag pattern applied?\n- How much longer does it take compared to simple text-guided diffusion?\n- How many subject-specific tokens are used?\n\nPlease discuss some of the noticeable ID deviations.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strength\n- The authors present a training-free technique that strikes a good balance between subject consistency and text guidance. \n- The method is thoroughly validated, clearly showing qualitative and quantitative improvements compared to other existing schemes. \n- The proposed zigzag scheme might be applicable to other applications as well. \n\nWeaknesses\n- The writing is not always as clear as it should be. \n     - Fig. 3 does not contain the labels zig,zag, and generation in the for sub-images. \n     - In Sec.3.2 the zig, zag and generation steps are defined multiple times, each time emphasizing a different aspect.\n     - The abbreviations used in the sketches and in the tables are not always introduced in the text or are just slightly different, e.g. Fig 3. \"SSC\", and particularly irritating in the ablation study (page 8)  \"asymmetric\" vs. \"all\"   or \"fully symmetric\", ... \n- The paper does not provide all necessary details for re-implementation, e.g. how many subject tokens are extracted and induced. How much longer does the final inference really take with the zigzag scheme. Making the publically available would be helpful.\n- The discussion on not-so-well-performing cases could be more detailed. In Fig 6. the faun loses its wings or changes from more female to male appearance. The rainbow, while consistently showing a white border sometimes loses its clouds or looks more plastic than paper. \n\nA smaller note: In Figure 4, none of the approaches is actually depicting a centaur (half human-half horse).", "was_revised": true, "final_justification": "In the reviews and the rebuttal the authors clearly presented how they will update the text to improve clarity. I therefore, still suggest accepting the paper.", "rebuttal": "*W1.1 Fig. 3 does not contain the labels zig,zag, and generation in the for sub-images.* \n\n[Response]   \nWe will update Figure 3(c) to explicitly label the \"zig,\" \"zag,\" and \"generation\" sub-steps in the timeline, making the diagram a much clearer illustration of our method as described in the text.   \n\n*W1.2 In Sec.3.2 the zig, zag and generation steps are defined multiple times, each time emphasizing a different aspect.*   \n\n[Response]   \nIn Sec.3.2 We first introduce the foundational zigzag sampling mechanism, outlining the general role each step plays (e.g., zig for exploration, zag for refinement, generation for final output). Following this, we explain our motivation for introducing an asymmetric information flow within this pipeline to better balance subject consistency and prompt following. We will revise our current writing to improve clarity.   \n\n*W1.3 Inconsistent Abbreviations*   \n\n[Response]   \nThank you for pointing out this oversight. Consistency is key for clarity. We will explicitly define \"SSC (Subject Semantic Control)\" upon its first mention and ensure it is used consistently throughout the paper and in Figure 3(d). \nThe different methods in Table 3 follows below definition (line 279-284):  \n\"Asymmetry\" (Our Method): Visual injection (ZVS) only in zig step. Text guidance (APZI with full prompt) in zig and generation steps. Null prompt in zag step.\n\"Zig-gen\": Visual injection (ZVS) in zig and generation steps. Text guidance (full prompt) in zig and generation steps. Null prompt in zag step.\n\"Zig-zag\": Visual injection (ZVS) in zig and zag steps. Text guidance (full prompt) in zig and generation steps. Null prompt in zag step.\n\"All\" (Fully Symmetric): Visual injection (ZVS) in zig, zag, and generation steps. Text guidance (full prompt) in zig and generation steps. Null prompt in zag step.\nWe will clarify this distinction in the revised paper, making it explicit that these ablations examine the impact of where visual conditioning is applied within our established asymmetric zigzag framework, not variations of the zigzag process itself.   \n\n*W2  Missing Implementation Details.*  \n\n[Response]   \nNumber of Subject Tokens (k): As shown in our ablation study (Table 4, page 9), we experimented with different values for the hyperparameter k. We found that setting k=0.2 (i.e., caching the top 20% of visual tokens based on attention scores) achieves the best balance between subject consistency and text alignment. We will highlight this specific value in our main Implementation Details section (Appendix C) to make it more prominent.   \n\nInference Time Overhead:\nInference time comparison: (seconds per image).\nSDXL[50steps] 7.44s , Ours [50 steps] 21.33s.\nFLUX[28steps] 1.50s , Ours [28 steps] 5.5 s.  \n\nMaking code publicly available: We will include a commitment in the paper to release our source code and detailed instructions upon publication to facilitate reproducibility.\n\n\n*W3 Discussion of Failure Cases.*   \n\n[Response]   \nThis is an excellent suggestion that will add valuable nuance to our paper. We will perform a more detailed analysis of these cases.\nWe will add a new subsection in the Appendix titled \"Analysis of Failure Modes and Limitations,\" which will be referenced from the main text. In this section, we will discuss:   \nThe Faun (Fig. 6): We will analyze how semantic changes in prompts (e.g., from an active \"dancing\" pose to a passive \"resting\" one) can sometimes lead to the model dropping secondary attributes (like the wings), which might not have been encoded as a core part of the subject's identity.   \nThe Rainbow (Fig. 6): We will discuss how our method successfully preserves the core identity (\"sticker of a vibrant rainbow\"), but can show minor inconsistencies in secondary elements (clouds) which is not specifically mentioned in the prompt. This is a known challenge in generative models, and we will frame our discussion in this context.   \nThis analysis will provide a more balanced perspective on the method's strengths and current boundaries.   \n\nMinor Note on Figure 4 (Centaur):   \nThank you for pointing this out. We acknowledge that the difficulty in generating specific composite subjects, such as centaurs, appears to be an inherent limitation of the underlying text-to-image base models themselves, as we observed consistent challenges even when using the vanilla base model without our proposed method. We will revise Figure 4 to include a more representative example where the base model performs adequately, and we will add a short discussion in the limitations section or an appendix to address these inherent shortcomings of the base models in generating certain complex or hybrid subjects. This will clarify that such generation failures are not a specific limitation of our method but rather a challenge inherited from the foundational models.   \n\n*Q1. In which layers is the zigzag pattern applied?*    \n\n[Response]   \nThis is detailed in Appendix C (line 344-353) and will be made clearer. For SDXL, visual token caching and injection are applied only in the mid and upper layers of the U-Net. For FLUX, we cache tokens from the early text-image interaction layers and inject them into all subsequent layers.   \n\n*Q2. How much longer does it take compared to simple text-guided diffusion?*   \n\n[Response]   \nSee above responses to W2.   \n\n*Q3. How many subject-specific tokens are used?*    \n\n[Response]   \nSee above responses to W2."}, {"review_id": "BZnuztB4Jw", "reviewer": "Reviewer_U3LM", "summary": "This paper proposes Asymmetric Zigzag Sampling (AZS), a training-free strategy to enhance subject consistency in visual story generation. By decomposing each diffusion step into \"zig-zag-generation\" sub-steps, AZS integrates visual sharing and asymmetric prompting: the zig step injects subject-specific features via attention-based token caching, the zag step refines latents with null prompts to avoid overfitting, and the generation step ensures text alignment. Experiments on SDXL (UNet) and FLUX (Transformer) show AZS outperforms baselines in subject consistency (CLIP-I, DreamSim) and text fidelity (CLIP-T), with 35.02% user preference for generated sequences. Ablations validate the asymmetric design’s effectiveness and hyperparameter robustness, demonstrating broad applicability across architectures.", "questions": "Mentioned in the weaknesses. If my concerns can be addressed, I will increase my rating.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. Innovative Method Design: The Asymmetric Zigzag Sampling (AZS) strategy is proposed, combining a zigzag sampling mechanism with a visual sharing module to enhance subject consistency in multi-image story generation without extensive fine-tuning. By decomposing the generation process into \"zig-zag-generation\" sub-steps, AZS balances subject consistency and text alignment via asymmetric prompting and visual feature transfer, offering a novel training-free approach for visual storytelling.\n2. Comprehensive Experimental Validation: Experiments on two architectures (SDXL and FLUX) compare against various training-based and training-free baselines. Quantitative results show AZS significantly outperforms state-of-the-art methods in subject consistency metrics (CLIP-I, DreamSim) while maintaining high text alignment (CLIP-T). User studies further confirm that AZS-generated sequences are preferred for identity consistency and overall quality.\n3. Ablation Studies and Generalization: Ablations validate the effectiveness of the asymmetric design (vs. symmetric sampling) and the robustness of hyperparameter k. The method’s success across UNet-based and Transformer-based architectures demonstrates strong generalization.\n\n**Weaknesses**\n1. Qualitative results fail to clearly demonstrate consistency advantages over baselines: For instance, in the image generated in Figure 4, the appearance and shape proportions of the sheep people also change significantly; The color and appearance of the horse have changed significantly. Annotate key subject traits to highlight consistent preservation and contrast with baselines’ inconsistencies.   If instability occurs, analyze causes (e.g., sampling noise) and propose mitigations.\n2. Computational overhead lacks quantitative analysis: Decomposing each generation step into three sub-steps introduces additional computation, but the paper only mentions \"computational cost\" without metrics (e.g., inference time per image). This limits evaluation of its applicability to real-time scenarios.\n3. Architectural adaptation analysis is insufficient: While AZS integrates more smoothly with FLUX than SDXL, the paper does not analyze why. For instance, can the feature injection strategy of SDXL be further optimized? This may limit the universality demonstration of the method on different models\n4. Typography issues in Figures 8 and 9: Uneven letter spacing in left-side text causes readability problems, requiring formatting corrections.", "was_revised": true, "final_justification": "The authors have clearly addressed the raised issues and resolved most of my concerns. However, given the notable increase in inference cost, I still maintain my original rating of 4 (Borderline accept).", "rebuttal": "*W1. Qualitative results fail to clearly demonstrate consistency advantages over baselines: For instance, in the image generated in Figure 4, the appearance and shape proportions of the sheep people also change significantly; The color and appearance of the horse have changed significantly. Annotate key subject traits to highlight consistent preservation and contrast with baselines’ inconsistencies. If instability occurs, analyze causes (e.g., sampling noise) and propose mitigations.*   \n\n[Response]   \nWe thank the reviewer for this critical observation. We agree that while our method significantly improves consistency, the inherent stochasticity of diffusion models can still lead to minor variations. The primary goal of our qualitative examples was to show a superior balance between subject consistency and prompt fidelity compared to baselines, which often fail dramatically on one of these two axes.   \nHowever, we recognize that the presentation of these results can be substantially improved. To make our method's advantages clearer, we will implement the reviewer's excellent suggestion.   \n**Annotated Figures:** We will revise Figure 4 (and Figures 6, 8, 9) to include annotations with call-out boxes and arrows. These annotations will explicitly highlight the key subject traits that are consistently preserved by our method (e.g., the satyr's specific horn shape and reed pipe, the centaur's bow and torso markings) and contrast them with the clear inconsistencies in the baseline results (e.g., changes in subject identity, failure to include key objects).   \n**Analysis of Instability:** We will add a sentence to the qualitative comparison section acknowledging that minor variations can still occur. We will attribute this to the exploration phase of the zigzag sampling, which is essential for generating diverse scene compositions, and frame it as a deliberate trade-off between rigid identity replication and creative diversity. This will better contextualize the results.    \n\n*W2. Computational overhead lacks quantitative analysis: Decomposing each generation step into three sub-steps introduces additional computation, but the paper only mentions \"computational cost\" without metrics (e.g., inference time per image). This limits evaluation of its applicability to real-time scenarios.*    \n\n[Response]   \n Inference time comparison:  (seconds  per image).  \nSDXL[50steps]  7.44s ,  Ours [50 steps] 21.33s.   \nFLUX[28steps]  1.50s ,  Ours  [28 steps]   5.5 s     \n\n*W3. Architectural adaptation analysis is insufficient: While AZS integrates more smoothly with FLUX than SDXL, the paper does not analyze why. For instance, can the feature injection strategy of SDXL be further optimized? This may limit the universality demonstration of the method on different models*   \n\n[Response]   \nThis is an insightful question, and the architectural differences between FLUX and SDXL likely contribute to FLUX's better compatibility with our method.   \n**FLUX's Architectural Advantages:** FLUX is a transformer-based model that utilizes a unified self-attention mechanism with modality-specific projection layers. This design allows FLUX to fuse semantic information across visual and textual modalities without relying on explicit cross-attention, leveraging the transformer's strength in modeling complex dependencies. In our method, this means that the newly injected subject visual tokens, text tokens, and current generated visual tokens are jointly considered within FLUX's unified attention layers. This simultaneous interaction likely enables FLUX to better balance subject consistency and prompt following, leading to improved performance.   \n**SDXL's Architectural Considerations:** In contrast, SDXL is a U-Net-based model that combines convolutional and transformer blocks. Its transformer blocks contain separate cross-attention layers for text information and self-attention layers for visual information flow between patches. When we inject subject visual tokens into SDXL's self-attention layers, the interaction between these subject tokens and the text tokens (handled by separate cross-attention layers) might be less integrated. This separation could make it more challenging for SDXL to optimally balance text following and subject consistency, as the model needs to reconcile information from distinct interaction pathways.   \n\nWe believe there is still space to further improve the feature injection strategy in SDXL, and this is a promising direction for future work. Based on our analysis, we have identified several potential optimization strategies:   \n\n**Prompt-Aware Dynamic Injection:** Currently, we cache a static set of top-k visual tokens representing the subject's identity. A more advanced strategy would be to dynamically select or weight which subject tokens to inject based on the current scene's prompt. For example, for a prompt like \"a dog running in a field,\" tokens corresponding to the dog's body and legs would be prioritized. For \"a close-up portrait of a dog,\" face and fur texture tokens would be more relevant. This would create a more direct and semantic link between the textual guidance and the visual injection.   \n**Spatio-Temporal Adaptive Injection:** Our current method injects features into the mid and upper layers of the U-Net during each zig step. This could be optimized by making the injection adaptive:   \n        - **Spatially (Layer-wise):** Inject high-level structural features (e.g., face shape) into deeper layers of the U-Net and more fine-grained texture details (e.g., fur pattern) into earlier layers.   \n        - **Temporally (Step-wise):** Apply stronger visual injection during the initial diffusion timesteps to firmly establish the subject's identity, and then gradually reduce its strength in later steps to give more control to the text prompt for refining details and scene composition.         \n**Cross-Attention Modulation:** A more sophisticated approach would be to create a more direct bridge between the visual identity and text guidance pathways. Instead of only injecting tokens into the self-attention layers, the cached subject features could be used to modulate the output of the cross-attention layers. This would guide the model to interpret the text prompt in the context of the desired subject's appearance, potentially resolving the architectural separation more effectively.   \nWe are excited by these possibilities and thank the reviewer for prompting this valuable discussion. We will add a paragraph outlining these future research directions to the conclusion or limitations section of our final manuscript to acknowledge this potential for improvement and guide future work in this area.\n\n*W4.Typography issues in Figures 8 and 9: Uneven letter spacing in left-side text causes readability problems, requiring formatting corrections.*   \n\n[Response]   \nWe thank the reviewer for spotting this formatting error. This was an oversight in the script used to generate the figures.\nWe will regenerate Figures 8 and 9 for the final manuscript, ensuring that all text is formatted with proper letter spacing and is fully legible."}]

Author rebuttal:

Meta-review: Paper presents the approach for consistent training-free multi-image generation. Reviewers unanimously give a paper a Borderline Accept. Initial set of comments focused on (1) lack of / incremental novelty of the approach (e.g., with respect to 1Prompt1Story [mtYN] and others [4vAP]); (2) lacking clarity in the approach description [mtYN, yE2y, U3LM]; (3) lacking ablations [mtYN, 4vAP], (3) lacking motivation for some design choices [4vAP, yE2y, U3LM], (5) lack of analysis of inference time [4vAP, U3LM], and (6) lack of convincing qualitative results that illustrate clear advantages over the baselines [U3LM]. 

Rebuttal has provided many needed clarifications and reviewers mostly found it compelling, resulting in the current consistent Borderline Accept scores. 

AC has read the comments, rebuttal and discussion that followed carefully, as well as  the paper itself. Similar to the reviewers AC finds the approach interesting, albeit limited in novelty. AC also agrees with [U3LM]  that qualitative results are a bit hit-and-miss, where some do show clear advantages, while others (Figure 6 bottom) hardly show an improvement and can even be considered inferior to 1Prompt1Story baseline. However, the overarching idea of asymmetric zig-zag sampling is compelling and could potential have broader use. On these grounds AC is leaning towards Acceptance.
Decision: Accept (poster)