Title: AlignedGen: Aligning Style Across Generated Images
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Model

Image Generation
Abstract: Diffusion-based generative models struggle to maintain high style consistency across generated images via text description.
Although several style-aligned image generation methods have been proposed to address this issue, they exhibit suboptimal performance and are primarily built upon the U-Net architecture, limiting their compatibility with DiT diffusion models like Flux that has emerged as a predominant model in the field of image generation.
To address these limitations, we propose AlignedGen, a novel training-free style-aligned image generation method for DiT models to significantly enhance style consistency across generated images.
Specifically, AlignedGen incorporates two key components to achieve this: Shifted Position Embedding (ShiftPE) and Advanced Attention Sharing (AAS).
ShiftPE alleviates the text controllability degradation observed in prior methods when applied to DiT models through its non-overlapping position indices design, while AAS comprises three specialized techniques to unleash the full potential of DiT for style-aligned generation.
Furthermore, to broaden the applicability of our method, we present an efficient query, key, and value feature extraction algorithm, enabling our method to seamlessly incorporate external images as style references. 
Extensive experimental results validate that our method effectively enhances style consistency across generated images while maintaining favorable text controllability. Code: https://github.com/Jiexuanz/AlignedGen.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes ​​AlignedGen​​, a training-free, plug-and-play method to enhance style consistency in images generated by ​​Flux​​ (an MM-DiT diffusion model). It mainly consists of  Shifted Position Embedding (ShiftPE)​​ to address text controllability degradation by assigning non-overlapping positional indices to reference and target images, and Selective Shared Attention (SSA) Layer​​  to selectively shares image-derived keys/values from a reference image to guide target image generation, improving style alignment while preserving content diversity.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nAlignedGen adapts style-aligned generation for ​​MM-DiT architectures​​. ShiftPE​​ and ​​SSA​​ are elegant solutions to positional ambiguity and text controllability issues, validated through ablation studies.  AlignedGen isTraining-free and compatible with existing tools.\n\nWeaknesses\n1. AlignedGen  leans more toward an engineering paper, presenting ShiftPE and SSA but lacking robust theoretical grounding and in-depth analysis.\n2. No analysis of ​​computational overhead​​ from SSA/ShiftPE. While claimed as \"lightweight,\" metrics like inference latency or memory usage are absent.\n3. The image editing methods like [1-2] also achieve the style aligning via adjusting the attention or copy some features. It is encouraged to add some discussion between two ways.\n[1]. Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing.\n[2].PS-Diffusion: Photorealistic Subject-Driven Image Editing with Disentangled Control and Attention.\n\nQUESTIONS:\n1. AlignedGen  leans more toward an engineering paper, presenting ShiftPE and SSA but lacking robust theoretical grounding and in-depth analysis.\n\n2. No analysis of ​​computational overhead​​ from SSA/ShiftPE. While claimed as \"lightweight,\" metrics like inference latency or memory usage are absent.\n\n3. The image editing methods like [1-2] also achieve the style aligning via adjusting the attention or copy some features. It is encouraged to add some discussion between two ways.\n\n[1]. Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing.\n\n[2].PS-Diffusion: Photorealistic Subject-Driven Image Editing with Disentangled Control and Attention.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper proposes a training-free method to align style in generated images for Flux. The two core modules are shifted position embedding and selective shared attention. The shifted position embedding module ensures non-overlapping positional indices between reference and target image tokens, while the selective shared attention shares key and value derived from image tokens. The authors produce good images that have good text alignment and style alignment across different objects and good numerical results. Beside this, authors also offer ablation studies for a deep dive into their method.

STRENGTHS AND WEAKNESSES:
Pros:
1. The research problem is interesting and challenging. Solving this problem can surely help with style consistent image generation.
2. The proposed method is training-free and enjoy good flexibility and seamless integration.
3. The provided qualitative results are visually significant and looks promising.

Cons:
1. I would love to see some failure cases from AlignedGen. What if there are some natural conflicts between the prompted object and the desired style, how will AlignedGen handle that case? For example, a bike on top of ocean, where ocean is from the reference image. I believe some studies like this can further explore AlignedGen, not only its robustness, but possiblely some hidden mechanisms.
2. As for some extreme cases there should have an implicit tradeoff between prompt alignment and style fidelity. Some studies on it would greatly benefit this paper and offer more insights.

QUESTIONS:
N/A

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed a training free image generation method called AligenedGen, the most distinctive feature of this paper is its ability to generate multiple images while maintaining a high degree of stylistic consistency. The main contribution of the work is designed a new position embedding strategy called ShiftPE and a new attention strategy called SSA(Selective Share Attention). Massive quantative and qualitative experiments and comparisons are conducted to show the advantage over existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: \n\n1.The paper is generally well-written paper if ignoring some minor wiriting issues (it will be listed in the following weakness part). The overall organization and formulation are both excellent. It is easy for readers to catch the main idea of the work.\n\n2.The visual comparisons are well-executed, effectively demonstrating the advantages of this work.\n\n3.The ablation study shows the effectiveness of the proposed SSA and ShiftPE strategy.\n\n\nWeakness:\n\n1.About writing part. Figure 3 illustrates the complete pipeline of our proposed method, and is the one of the most important figure in the manuscript. However, it appears that the figure lacks sufficient in-text explanation and integration with the main discussion.\n\n2.About the explaination of sec 3.4 Advanced Attention Sharing. I think the main contribution ( or difference from exiting method in other words) of this paper consist of two aspects: (1) the complete pipeline illustrated in Figure 3. and (2) the sec 3.4 Advanced Attention Sharing part. However, sec 3.4 spains less than one page in length. It would be better to do more detailed explaination of the movtivation, or do more theoretical analysis of the proposed strategy.\n\n3.About the limitation. The paper discussed the limitation, but it is listed in the Auxiliary Material part. I think it would be better to place it in  main body of the paper.\n\n4.Sitll about the limitation. The proposed method forced the refference image to be an generate image since it needs intermediate QKV of the Flux model by input the text prompt. This requirement severely restricts the applicability. In most case, users always offer an image for sytle reference.\n\nQUESTIONS:\nDoes SSA and ShiftPE still effective when they are pluged into an convention image sytle translation task. I mean the style reference is an ordinary image instead of an generated image, QKV of the Flux model (left part of Figure 3.) is not available.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AlignedGen, a training-free and plug-and-play method for improving style consistency across images generated by MM-DiT–based diffusion models, particularly Flux. Addressing the limitation that prior style alignment methods suffer from poor compatibility or text controllability in these architectures, AlignedGen proposes two main components: Shifted Position Embedding (ShiftPE), designed to mitigate text controllability loss via non-overlapping positional indices; and Selective Shared Attention (SSA), which selectively shares attention representations to balance style alignment and prompt fidelity. The method is demonstrated to be compatible with other controlled generation technologies and is validated, through both quantitative and qualitative experiments, to achieve stronger style alignment while maintaining accurate text-image correspondence.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n- **Core Technical Contributions**: The ShiftPE component is convincingly motivated and technically justified, addressing the “content leakage” issue that arises with naïve position index sharing, as visualized in Figure 4. SSA provides a targeted mechanism for selective sharing of attention, enabling balanced style and content control, with further flexible control via the scaling factor λ (discussed and illustrated in Figure 8).\n- **Generalizability and Practicality**: Demonstrated applicability to other MM-DiT models (SD3, SD3.5 in Figure 9), plug-and-play integration with other controllable gen technologies (Figure 10), and no need for additional training makes the approach appealing for practical adoption.\n\n# Weaknesses\n- **Novelty and Incrementality**: While the paper adapts and advances previous attention-sharing mechanisms (notably StyleAligned) to the MM-DiT regime, the approach is arguably evolutionary rather than fundamentally new. ShiftPE and SSA are logical extensions, and readers may perceive the contributions as incremental tweaks tailored to the newer architecture. The conceptual leap beyond StyleAligned relies mainly on diagnosing the limitations with position encoding and proposing Split/Shifted indices, rather than inventing a new principle for style alignment. The connection to prior shared attention works could be more rigorously framed (Related Work, Section 2.2).\n- **Ablation and Diagnostic Limitation**: While ablations (Table 2) cover architectural/module placement and scaling parameters, there’s less analysis of the method’s behavior under more challenging or failure-prone settings (e.g., very long prompts, highly ambiguous style descriptors, conflicting or low-quality prompts). There could be exploration of degradation modes.\n- **Broader Applicability and Reference Images**: The method currently relies on generating all images from text prompts only, with no direct support for user-specified reference images for style. This is mentioned in the Limitations, but future work in this key use-case would have made the framing stronger.\n- **Understanding of Results Tables**: In Table 2, the style consistency metrics (S_sty, S_dino) reach close to 1 in the absence of ShiftPE, which, as clarified in the text, indicates degenerate generation. This is counterintuitive, and more explicit explanation in the table caption or main text could clarify that higher is not always better when metric gaming is possible.\n\nQUESTIONS:\n1. **Failure Modes and Robustness**: Can you comment on how AlignedGen performs under more challenging settings, such as prompts that are ambiguous, contradictory, or extremely long? Are there prompt phrasing or style descriptor situations under which style alignment or text controllability degrade?\n2. **Support for Reference Images**: Many use-cases benefit from \"style transfer\" from an explicit user-provided reference image. Can you elaborate (with technical obstacles) on how difficult it would be to adapt AlignedGen to this scenario?\n3. **Metric Interpretation**: Table 2 shows degenerate metric values when ShiftPE is not used. Could you explain for readers precisely why higher is worse in these degenerate outlier cases and whether S_sty or S_dino are always reliable indicators?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}