Title: Consistent Story Generation: Unlocking the Potential of Zigzag Sampling
Venue: NeurIPS 2025
Accepted: yes
Keywords: story generation

diffusion model

generative model
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nMentioned in the weaknesses. If my concerns can be addressed, I will increase my rating.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\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).\n\nQUESTIONS:\nPlease 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-05"}