AlignedGen: Aligning Style Across Generated Images
Authors: Jiexuan Zhang
Yiheng Du
Qian Wang
Weiqi Li
Yu Gu
Jian Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion Model
Image Generation
Primary area: applications
TL;DR: A training-free style-aligned image generation method for Flux.

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.

Gli:nedGen: Aligning Style Across Generated Images
Jiexuan Zhang",
Yiheng Du" Qian Wang', Weiqi Li', Yu Gu', Jian Zhang 1,20
School of Electronic and Computer Engineering, Peking University
*Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University
lighthouse
cityscape
poae ort stylic
ENG
Figure 1: Generation results of AlignedGen. As training-free method, AlignedGen generates images with varying content while ensuring high style consistency.
Abstract
Diffusion- generative models struggle to maintain high style consistency across generated images via description. Although several style-aligned image generation methods have been proposed to address this issue, they exhibit subop- timal 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, novel training- style-aligned image generation method for DiT models to significantly enhance style consistency across generated images. Specifically, AlignedGen incorporates two key components achieve this: Shifted Position Embedding (ShiftPE) and Advanced Attention Sharing (AAS). ShifiPE 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 unleash the full potential DiT for style-aligned generation. Furthermore, to broaden the applicability of our method, present efficient query, key, and value feature extraction algorithm, enabling our method to seamlessly incorporate external images as style references. Extensive experimental results validate that method effectively enhances style consistency across generated images while maintaining favorable text controllability. Code: http://ggituub.com//eeeuaa//////
'qqual Contribution. ls: Corresponding author. zhangjian.sze @ww.ansszppuuuuuuuuuuuu edu.edu.en. This work was supported in part by National Natural Science Foundation of China (No. 62372016) and by Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology (No. (2241121200006))
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Text Control
Style Consistency
x
Text Control
X
Style Consistency
(a) Vanilla Flux
(b) StyleAligned
Text Control x
Style Consistency x
Text Control
Style Consistency
(c) IP-Adapter
(d) Ours AlignedGie
Figure 2: Generated results using different methods on Flux with the prompt "[Dog, Clock, Globe, Bicycle) in 3D realism style". The Vanilla Flux produces outputs with style discontinuities. The application of StyleAligned [66] on Flux leads to severe text controllability degradation. The results generated by IP-Adapter 50 perform suboptimally in text control and style consistency. Our method effectively enhances style consistency while maintaining alignment with the text prompt.
1 Introduction
empower users to create imaginative scenes from text. Early diffusion models commonly adopted the U-Net 37 as their backbone. Recently, diffusion models based on DIT, such as Flux M1, have emerged as mainstream architectures and are now the most widely used frameworks in this domain. However, all diffusion models struggle to maintain high style consistency across a series of generated images via prompts, as shown in Figure 2 (a). Such style consistency is crucial for many applications: from illustrating books and stories, through designing virtual assets, to creating graphic novels and synthetic data.
Large-scale text-to-image diffusion models 17 36 3
for maintaining style consistency across generated images primarily rely on fine-tuning of the diffusion model over one image or a set of images that share the same style. However, these fine-tuning-base new data that share the same style and retraining diffusion model for each target style substantially
Common approaches [10 39 9
methods suffer from significant limitations: collecting
increase temporal and computational costs. Alternative approaches [26] 34 [1 44
[45 [9] focus on
constructing style encoder combined with re- trained image-conditions
diffusion models. However, creating high-quality training datasets remains challenging due to defining "style" is challenging,
leading to suboptimal performance of these methods, Figure (c) illustrates this.
StyleAligned [16), a training-free approach for style-aligned image generation, has emerged to address these limitations. While promising, its reliance on the aging SDXL architecture inherently limits its output quality, often producing artifacts. Most critically, its core strategy is architecturally incompatible with DiT - the superior generative architecture for diffusion models. Designed explicitly for U-Net's self-attention, fails when applied to DIT, resulting in loss of text control and severe content collapse, as shown in Figure 2 (b). This reveals an unaddressed gap one that our work addresses: how to achieve robust, training- free style-aligned image generation for DìT models.
To address the limitations of existing methods, we propose AlignedGen, a novel framework to enhance style consistency across images generated by DiT-based diffsuion model like Flux. First, we conduct a critical investigation into a fundamental problem: the failure of attention sharing mechanisms in the DIT. We identify the root cause as an improper setup of Rotary Position Embedding (RoPE), which creates conflicting positional signals. To resolve this, we propose Shifted Position Embedding (ShifPPE), novel and elegant solution that assigns non-overlapping positional spaces to each image. This not only fixes generation collapse and restores text controllability but also offers crucial insight for all future attention sharing works on DìT. Building upon this breakthrough, we further develop Advanced Attention Sharing (AAS), suite of three specialized techniques Selective Shared Attention, Controllable Style Consistency Via Key Scaling, and Layer-Selective Application. These techniques are specifically designed for the DiT architecture, further unleashing its generative potential and leading to superior output quality. Finally, to extend our method's utility, we introduce an approximate query, key, value extraction algorithm, allowing it to condition external images

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as style references and expanding its application scope. Extensive experiments demonstrate that our method significantly outperforming prior work in both style consistency and faithfulness text prompts. In summary, our contributions are as follows:
(1) We present AlignedGen, the pioneering training-free freee style-aligned image generation framework designed for DiT. AlignedGen operates without any fine-tuning or extra modules, enabling the efficient utilization of large DiT models with billions of parameters.
O (2) We discover that conflicting position embeddings are the root cause of attention sharing failure in DiT. We introduce Shifted Position Embedding (ShiftPE), novel and effective solution that enables viable attention sharing in DiT architecture for the first time.
D (3) We propose Advanced Attention Sharing (AAS), a set of three specialized techniques engineered to unleash the full potential of DiT for style-aligned generation, markedly improving style fidelity and image coherence.
O (4) We devise an algorithm to extract approximate query, key, and value features from any external image. This allows our framework to support user-provided images as style references without retraining, significantly broadening its applicability.
Related Work
2.1 Attention Control in Diffusion Models
Recent years, diffusion-based generative models 17
30
40
36
33
g have made significant
advancements in image generation. This progress has been accompanied by a series of studies
47 14]. Hertz et al. [I5] examined the pivotal role of cross-attention in controlling image layout and content, introducing generation control method guided by attention maps. Plug-and and-Play 4 leverages spatial features and self-attention maps from the original image to guide text-conditioned image-to-image translation, effectively preserving the original image's spatial layout. Notably, these studies all rely on the design of self-attention and cross-attention mechanisms. With the advent of Transformer-bas diffusion models 32 8 [1, self-attention and cross-attention are integrated into single attention mechanism, presenting adaptability challenges for previous works based on separate self-attention and cross-attention designs.
focusing on attention mechanisms []5 43 28
25
29
22
48
23
2.2 Style Image Generation
Style image generation aims to produce images that embody specific artistic styles. Early studies
in this domain concentrated style transfer techniques [51 [2 the transfer of the content from one image to the style of another. Generative models 13
3 6
20
24 27
[52] which enable
17
33
enable widespread adoption for style image generation. Tuning-based methods [18
21
39
10
optimize model parameters or text embeddings to capture style of images, but suffer from training
overhead. Alternatively, dataset-driven approaches [34] 49 26 19 train diffusion models on curated datasets. yet face challenges in constructing high-quality style datasets. More pertinent to our approach is StyleAligned [16), which aligns the styles of generated images by sharing information between self-attention layers without any form of optimization or fine-tuning, In contrast to other style image generation methods, places greater emphasis on addressing the issue of inconsistent understanding of style descriptors by generative models during image generation.
3 Method
3.1 Preliminary
Diffusion Transformer (DIT). DIT 32 models enhance generative performance through Transformer-bast architectures, which process latent representations such as image tokens X E R xd and text tokens E IRMx where is the embedding dimension and N/M denote sequence lengths. In addition, these models typically encode positional information into tokens through Rotary Position Embedding (RoPE) [42, thereby embedding positional awareness into each token to achieve enhanced generation quality.
3

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Cacked Refertnae Image Query Key Valie
(Clock Oloke, Bisydlc]
Dog
Deaile Block
Single Block
abl:
Double Block
SiAAPE
Refertnce
Random
Flox Model
Irage
Random Notst
Torps Imaggs
Projecea from
Projocial Imagy Tikens
Coneat
Shifted Position Embedding
Prijerra from (M Trat Tokere
Projocual from
Image Tokere
(SBIRPE))
AddN
AdolN
Target
0,5
Refinenee Imags
2,5
MM-anietioo
Vanilla Share Attention
Selective Shared Attention (8SA)
Overview of the pipeline. ShiftPE and SSA are integrated into specific layers of Flux, replacing the MM-Attention.
Figure 3:
Multi-Modal (MenttionMMM-Attenttiion attention to replace the self-attention and cross-attention mechanisms commonly used in traditional diffusion models. Specifically, image tokens X and text tokens C are projected through a set of projection matrices to obtain the corresponding representations of query Q (img.tatt) key K (img and value V (imm.taa). This process is formulated as follows:
Some DiT models (e.g. Flux ( employ multi-modal
[Q, K, V Smng
w!Q.K.V) X, {Q,K, V) Sin Nxd.
img
{Q. K, V) tat
W (Q.K.)) C, [Q, K, V E Mxd,
txt
where W W(Q.K.r (img, txt) denote the projection matrices for the image and text tokens, d2 is the embedding dimension attention. The (img,tzt)) K (img.tazt) and (iig.tzt) concatenated along the
sequence length dimension obtain the complete Q R (M+N)xxd K (MM+N)x and
V E R (M++))x,
Q Concat( Qizt, Qimg), K Concat K tat, King), V Concat( V txt: V Ving).
(2)
After that, the final output is computed using the following formula:
QK'T dd
Attention (Q, K, V) Softmax
V.
3.2
Task Formulation
In this paper, objective is to generate
of images I {4,, In. that align with ..., Tv }, while maintaining consistent style across generated
input text prompts set T {Tı, ..., images. Notably, each prompt in the input text prompts set T incorporates shared style descriptor. To facilitate the achievement of this objective, we designate one image in the image set I as the reference image /res, while the remaining images defined target images faar, The style of all target images ptar are enforced to approximate the style of the reference image freʃ_ Furthermore, the query, key, and value representations of the reference image rref throughout the generation process are denoted as Qref, Kref, and yref, respectively. The query, key, and value representations of the target images ftar are denoted as Q Star. Ktar and Vtar
3.3 Vanilla Attention Sharing
Existing style-aligned image generation technique [6] based on U-Net (37] architecture and self- attention can be directly applied to DiT diffusion models. For clarity, we refer to this approach as

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Target Image
Reference Image
Target Image
Reference Image
Reference Image
Default RoPE
ShiftPE
Figure 4: Visualization of attention map. We visualize the attention map corresponding to the query feature indicated by the red box on the generated image /err_ When using RoPE, Iear over-attends to spatially aligned regions in reef, leading to high similarity between generated and reference images. ShiftPE introduces shifted positional indices for /re/, enabling more appropriate attention to semantically corresponding regions (e.g.. attention weights spread around the snowy area periphery).
Vanilla Attention Sharing. Specifically, for the query, the vanilla attention sharing aligns Qtar with Qre/ through Adaptíve Instance Normalization (AdaIN) to form the final query Q For the key, K is aligned with Kreff through AdaIN to produce the transformed key Ktar After which, K ktar is concatenated with Kref along the sequence length dimension to form the final key KF For the value, vanilla attention sharing directly concatenates Vier and Vref along the sequence length dimension to construct the ultimate value V This process is formulated as follows:
I (
AdaIN( y) =Vy
(z)(Z)
+ H(()).
Oz)))
Q AdaIN Q far Q R. (MM+N)xxd
(4)
K F Concat tor Kref) E (2(M+N))dd, where kitor (d tor Kref),
VE Concat
(VrcattVrar, (Vrar, Vre/) E R2(M+N)xxd
with uu-) and a(-) representing the mean and standard deviation. In the target image her generation process, the query. key, and value in MM-Attention are replaced by Q", K and V respectively, with the results computed according to Equation 3
3.4 The limitation of Vanilla Attention Sharing in DiT
Applying Vanilla Attention Sharing to DiT, while seemingly straightforward strategy, results in notable collapse in performance. As illustrated in Figure (b). this direct approach severely degrades text controllability and causes prominent content leakage from the reference image, leading to catastrophic failure mode where all generated images become mere copies of the reference. This raises critical question: Why does Vanilla Attention Sharing fail markedly within the DiT?
To answer this, we conduct a systematic investigation into the interplay between attention sharing mechanism and the DIT. Our investigation reveals that the root cause is not flaw in the attention sharing mechanism per se, but a subtle yet critical issue we term Positional Collision. While RoPE provides unique spatial coordinates (i, j) for each token within single image's 's h x w latent grid, the vanilla sharing scheme inadvertently assigns identical positional embeddings to tokens at the same coordinates (i.j) in both the reference and target images. This collision induces erroneous bias, forcing tokens to disproportionately attend to their spatial counterparts across images, irrespective of content or textual guidance, as illustrated in Figure (RoPE)) To resolve this, we introduce simple yet effective solution derived from this insight: Shifted Position Embedding (ShiftPE). The core idea is to ensure the reference and target images occupy distinct, non-overlapping coordinate spaces. We achieve this by virtually placing the reference image "next to" the target images, remapping its positional indices from (i.j) to (i.j w), visual depiction is provided in Figure (ShiftPH). As shown in Figure 4 (ShiftPE), ShiftPE resolves the positional collision, decoupling the spatial representations and thereby eliminating the content leakage. This frees the attention mechanism to focus on regions of high semantic and stylistic relevance, rather than being constrained by rigid spatial correspondence.
3.5 Advanced Attention Sharing
While ShiftPE effectively resolves the problem of positional collision in Vanilla Attention Sharing, unlocking state-of-the-art performance demands solution that works well with the specifies of
5

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Layer 19)
Layer [19, 38)
Layer 138, 57)
Layer [0, 38)
Layer (19,57)
Layer (0,
(phone. knight on horse, train passing village, tomato bowl) cartoon drawing style.
Figure 5: Visualization of results generated by applying method in different layers of Flux.
User-Providal
codding book in pali.
windnill
colorti bunerfy actling on Gloerr
Reference Image
Bout of cheuse
Geld.
Reference Image
susccilll sgcciee.
parioandee by reactation
Figure 6: Generalization from Arbitrary Style Reference. Our method successfully generate diverse images conditioned on external, user-provided style references.
the DiT architecture. The DiT components are not inherently designed for the attention sharing. Therefore, we introduce series of advanced modifications that enable smooth, effective cooperation with DiT's structure, significantly enhancing style consistency and generation quality.
Selective Shared Attention. Vanilla Attention Sharing shares the entire key and value, but is noteworthy that in MM-Attention, the key and value are derived from both image and text tokens. This implies that full attention sharing of key and value not only propagates information from reference image but also inadvertently shares information from its corresponding prompt, which is apparently unreasonable. To address this, we propose an intuitive yet effective solution: selectively sharing key and value derived from image tokens, shown in Figure This strategy not only effectively enhances the alignment between target images and their corresponding prompts but also reduces computational overhead. Finally, the Q K and V can be expressed follow:
Q Concat( Q
tart @imgg ((M+N))dd, where Q AdaIN(Q far Qingg):
K F Concat K
dar tata
King
KTn)) E ER (M++NN)xx, where Kaa AdaIN K far K Keef imgg
King:
(5)
F Concat( tzt kmgg
ytar Vtar
Vreff
E xd.
img
Controllable Style Consistency Via Key Scaling. In practical applications, controlling the strength of style consistency between generated images is often required. To achieve flexible control over style consistency, we introduce scaling factor A. Specifically, we scale K by the scaling factor and concatenate it with Ktar and K img to form the modified KF:
F
CoCCootntC tor Re tar txt: ingg
This design allows us to adjust the attention weights by varying X. As increases, the consistency of style between the generated images becomes stronger.
Layer-Selective Application. The experimental results show that applying our method across all AM-Attention modules can lead to unnatural generations and textual inconsistencies in the output.

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Metric
Ours
StyleAligned [16
IP-Atepter [50]
B-Lora 9
StyleShot [TI
CSGO 49
Stext f
0.282
0.277
0.278
0.259
0.276
0.280
Saty t
0.740
0.728
0.737
0.625
0.693
0.686
Sdioo" f
0.554
0.512
0.522
0.271
0.511
0.450
Table I: Quantitative comparison with other methods. We evaluate the generated image sets in terms of text alignment (Stext). style consistency (Ssty and Sdsno). The symbols f indicate higher values are better. Best result marked in bold, and the second-best results are marked in underline.
different layers in diffusion models capture and process distinct information. Following similar strategy. we achieve improved performance by applying our method only within a specific subset of layers ø. Specifically, setting á to [19, 57) (the Single Blocks of Flux) yields optimal results. As shown in Figure [ this approach not only enhances the naturalness of generated images but also maintains consistency with prompts and preserves stylistic coherence across image collections. Further experimental details can be found in Section 4.3
Inspired by prior studies
+
3.6 Scamlessly Adapt to Any Style Reference
core principle of our method is the injection style information via attention sharing, which is predicated on access to the Q. K, and V features from the style reference image. While these features are directly available if the reference is synthesized by the diffusion model itself, this assumption severely limits the method's 's applicability in real-world cases where To bridge this critical gap, we propose novel and simple feature extraction pipeline for arbitrary external images. This pipeline achieves this by directly leveraging the forward diffusion process. For any given timestep I, we first add a noise to the reference image's latent representation. This procedure yields noisy latent, which emulates the intermediate state during the standard generation process. By feeding this noisy latent into the diffusion model, we can query its intermediate layers to obtain the Q, K, and V features. We showcase qualitative results in Figure 6 and provide pseudocode in the Appendix A
provide their images.
4 Experiment
4.1 Implementation Detail
Settings. We apply our method the Flux. ①1 by replacing the MM-Altention in all Single blocks of Fllx. 1-dev with ShiftPE and AAS. For inference, we use the vanilla Rectified Flow sampler with 30 sampling steps and set the classifier-fres guidance scale to 3.5. Our evaluation dataset consists of 100 prompt sets from StyleAligned 16). These prompts cover wide range of generation targets and diverse set of style descriptions.
Metrics. (1) Text Alignment (Stent) To evaluate how well the images match the prompts, calculate the cosine similarity using CLIP (35) between the images and prompts. (2) Style Consis- tency (Ssty). To evaluate the style consistency across generated images, calculate the pairwise average cosine similarity of the CLIP embeddings within the set of generated images. (3) DINO assess style consistency by computing the pairwise average cosine similarity of DINO S 31 embeddings the generated image set. This choice is motivated by the fact that CLIP, trained with category labels, often rates images with similar content but different styles as highly similar, DINO trained in a self-supervised manner, better differentiates styles.
(Saino). Following 44 39 16).
4.2 Comparison
Competing Methods. We compare the proposed AlignedGen against five style-aligned image generation methods: StyleAligned [16), IP-Adapter (50) (we adopt the IP-Adapter architecture and weights designed by the Instant-X team 2 for I-ux, rather than utilizing the IP-Adapter
architecture based on SDXL or SD1.5), B-Lora 9 StyleShot [1]. CSGO 49
Quantitative Comparison. Table D presents the quantitative comparison results between our method and state-of-the-an style-aligned image generation approaches on the test set. Our method
7

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Image
StyloAligned
Adapter
StyleShet
(Persion cat playing with a ball of wool, man skiing down the hill, train of the station, bear eating honey) in cafe logo style.
(lighthouse, belloon, cat, cityscape) isometric illustration style.
(Beach umbrella, Rocket ship, Fox paper cutout Waterfall poper style.
(Rhino, Telescope, Stool, Pendo) style.
(paintbrush, sunflower field, rustic barn) minimal origami
Figure 7: Qualitative comparison between method and other approaches. Our method
generates images with superior style consistency and accurate alignment with text prompts.
Ours
StyleAligned 16
IP-Adapter (50)
B-Lora
StyleShot [1]]
CSGO (49
3.66
2.04
4.76
4.09
5.25
Table 2:
User Study. The results represent the average ranking outcomes for each method (lower is better). Our approach significantly outperformed other comparative methods in the user study.
achieves the highest scores in terms of Stert, Saty. and Saino metrics, demonstrating that it establishes the optimal balance between text-image alignment and style consistency among generated images.
Qualitative Comparison. Figure presents a qualitative comparison between method and other approaches. IP-Adapter struggles to capture style effectively. failing to maintain consistent style in generated images and introducing content leakage issues. B-Lora, due to training its LoRA with only a single image, results in highly challenging training, consequently leading to generated images that struggle capture the style of the reference image. StyleAligned demonstrates improved style consistency, but the visual quality of its generated images still falls short compared to our method. StyleShot and CSGO frequently exhibit issues of image duplication and visual artifacts. In contrast, our method produces images that not only exhibit superior style consistency across generated images but also perfectly align with the given prompts.
User Study. We conducted a user study to assess the results of our method compared to other methods. In each question, participants were asked rank the methods according the style consistency among their generated images and the alignment of these images with the prompts. In total, our study involved 40 participants, and Table [ the result. Our method significantly outperforms competing approaches, achieving an average ranking of 1.20, which demonstrates its superior performance.
4.3 Ablation Study
Effect of Shifted Position Embedding (ShiftPE). The experimental results in (k) and (m) of Table demonstrate the impact of ShiftPE. When default RoPE used, the Ssty and Saine metric rises to nearly 1, revealing severe content leakage problem. Besides, Figure illlustrates that default

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Type
#
PE
0-19
19-38
38-57
Sueza
Saty f
Saino
Original
0.284
0.658
0.313
(a)
ShiftPE
1.10
V
X
x
0.283
0.669
0.323
(b)
ShifIPE
1.10
V
0.283
0.697
0,439
Layer
ShifIPE
1.10
X
V
0,286
0.682
0.368
ShiftPE
1.10
X
0.274
0.697
0.420
(c)
ShiftPE
1.10
0.273 V
0.735
0,531
(f)
ShiftPE
0.90
X
4 0.286
0.694
0.435
(g)
ShifIPE
0.95
4
0.285
0.703
0.460
A
(h)
ShifIPE
1.00
0.286
0.711
0.485
ShiftPE
0.285
0.725
0.522
(
ShiftPE
1.15
X
0.274
0.755
0.561
w/o ShiftPR
(k)
RoPE
1.10
0.202
0.977
0.972
w/o SSA
(l)
ShiftPF
1.10
0.281
0.733
0.525
Ours
(m)
ShifIP
1.10
X
0.282
0.740
0.554
Table 3: Ablation study. The Layer and À denote the ablation study for Layer-Selective Application and scaling factor X, respectively. The "w/o ShiftPE" and "w/o SSA" represent the ablation study for the ShiftPE and the SSA module.
w/o SSA results
w/o Shif1PE results
Figure Visualization Ablation Study. The left part presents the result generated by our method. The upper-right image shows the result without the SSA, while the lower-right image displays the result without ShiftPE. The prompt used " "[Train, Airplane, Bicycle, Car in 3D realism style".
Reference Image
Figure 9: The impact of scaling factor. As À increases, the target image progressively aligns with the style of the reference image.
RoPE cause the four generated images to become nearly identical, whereas ShiftedPE maintain style consistency while ensuring alignment with the respective prompts.
Effect of Selective Shared Attention (SSA). Table (1) and (m) show results without and with the SSA. Omitting the SSA reduces Saty and Saino but does not improve Stext- Figure displays outputs under both configurations. Without the SSA, the second image mismatch its textual descriptions (airplane), and style similarity decreases.
Effect of SeyervSelective Application. We divided the blocks in 1-ux. into three groups: Group contains Double Blocks, Group 2 has the first 19 Single Blocks, and Group 3 includes the remaining 19 Single Blocks. The Layer part in Table shows quantitative results of applying our method in different blocks. Comparing settings (a), (b), and (c) shows that Group 2 blocks contain the richest style information. Comparing settings (b) and (d) reveals that using our method in Group blocks does not improve style consistency and even has negative effect, which is also confirmed by comparing settings (c) and (m). Comparing settings (d), (c), and (m) demonstrates that applying

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(dork rough charceal style, smudged cup, shoded charcoal style.
(landscape painting, povilions bridges, freehand lotus, panda] ink wash style.
Figure 10: Generation results of applying AlignedGen to other models. (Top) SD3, (Bottom) SD3.5.
(o) DreamBooth
(b) Depth-Control
Figure 11: Results from combining Ali with other controllable generation technologies.
method in the last groups achieves good balance between image-to-prome alignment and style consistency across images. Finally, we apply our method exclusively in the Single Blocks (Group and Group 3) enhance style consistency among generated images.
Effect of scaling factor. Table 3 part presents the quantitative results of our method for various values of X. Specifically, as À increases, the style consistency (Ssty. Sdino) improves, but this comes at the drop of Stezt- Our experiments indicate that, generally, setting the strength parameter À to 1.1 strikes favorable balance between style consistency and text alignment. Figure illustrates how the target image progressively aligns with the style of the reference image as À increases.
4.4 Generalization and Compatibility
Architectural Generalization. To demonstrate method is not tailored to Flux, test its performance on other DiT models, including SD3 and SD3.5. As shown in Figure 10 our method seamlessly integrates with these backbones. More results can be found in Appendix
Compatibility with Existing Tools. A key advantage of our method is its training-free, plug-and- play design. This allows for effortless composition with other generation techniques. As shown in Figure IT the generated results from combining our approach with other techniques demonstrate the flexibility of method in practical applications. More can be found in Appendix C
5 Conclusion
In this paper, we present AlignedGen, a training- free framework for style-aligned image generation with Diffusion Transformer (DiT). Our approach is built on critical discovery: the failure of vanilla attention sharing in DiT stems from conflicting position embeddings between reference and target images. We propose Shifted Position Embedding (ShiftPF) to overcome this limitation, unlocking the potential of attention sharing within DiT. Based on this foundation, we further introduce two key enhancements. First, Advanced Attention Sharing (AAS) techniques are specifically designed to refine the attention sharing mechanism for DiT architecture, markedly improving output quality. Second, the feature extraction pipeline broadens the framework's 's practical applicability, allowing users to provide external image style reference. We believe our findings position embedding and attention sharing could provide insight for future research in style-aligned image generation.
10

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References
[1] Flux. httpp//s/gggh.u...oo///cceeeeees
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A Feature Extraction Pipeline
Algorithm 1 Algorithm for Caching Q, K, V from a User-Provided Reference Image
Require: User-providee reference image I, number of inference timesteps T
Ensure: Cached attention Q K, pairs: cached qkv
1: Initialize random noise: noise (0
2: latent <- vae_eeeeeeee (
3: cached_gke <- {}
Initialize empty dictionary b Iterate over T timesteps in reverse A standard interpolation forward D e.g. prompt embeds
4: for T down to do & noise (1 &) latent
5: noise_ime mise__inptt-
6: Qr. Kr, Vi2 t- DiT D L, others)
7: pached_qkv[[ (Qt, K,, V,)
8: end for
9: return cached_gha
B Additional Compare With Image Editing Methods
Some zero-shot image editing methods can also be applied to style-aligned image generation tasks. We selected two Flux-based editing methods for comparison: RF-Solver [46] and RF-Edit (38). Figure[[2]preeen] qualitative comparison results between our method and editing methods. Image editing methods often exhibit insufficient editing strength when handling significant image edits such as modifying the main content of the image, leading to persistent retention of original content in the edited results. Table 4 presents the quantitative comparison results between method and these two methods. As can be observed, our method achieves significantly higher scores on Stent. Saty. and Saino compared these two baseline methods. It is worth noting that zero-shot editing methods, limited by the instability of editing processes, often require meticulous parameter adjustments achieve satisfactory results, which limits their practical applicability. In contrast, our method exhibits strong robustness and can typically achieve favorable outcomes in most scenarios without necessitating fine-tuning.
Reference Image
Ours
RF-Edit
RF-Solver
(robot, cupcake, woman playing bosketball, sunflower) in digital glitch art style.
A (football helmet, playmobil, truck, watch) in street art graffiti style.
A (smartphone, book, dinner table, glass of wine) in minimal digital art style.
(Magnifying glass, Gorilla, Airplane, Swing) pixel art style.
Figure 12: Qualitative comparison between our method and other zero-shot image editing methods.
22

---

Metric
Ours
RF-Solver [46
RF-Edit [38]
Steat 1
0.282
0.278
0.280
Saty Saino"
0.740
0.703
0.701
0.554
0.487
0.456
Table 4: Quantitative comparison with zero-shot image editing methods. Best result is marked in bold.
{ serene lake, bustling city street, vaze of sunflowers, woman with a parasol) in impressionism style
SD3
(an apple, cup, hat, tay bear) ail style
FB
blocky B-bit game coin, pixelcted tree) in style
(dragon, pomegranate, Peking Opera mask, fish) paper style
SD3.5
(a happy cloud, heart, tiny kawaii style
geometric frogmented portrait, still life with fruit, cubist guitar) cubism style
Generation results of applying AlignedGen to other MM-DiT models. The left side presents images generated by the original model, while the right side displays images generated after applying our method.
Figure 13:
C
More Visual Results
Figure 13 presents the generation results of applying AlignedGen to other MM-DiT architecture diffusion models. Figure 14 demonstrates the generation outcomes from combining AlignedGen with Dreambooth. Figure 15 Iluutratte the generation results achieved by integrating depth control with AlignedGen.
23

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Reference Image
Figure 14: Subject-driven image generation with AlignedGen. Each row shows style aligned image using the reference image on the left, applied on different personalized diffusion models, fine- tuned over the personalized content on top.
24

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THE FLTANKLADE TLUES
THE READ
BA SR, SPU ACIATON
THERFUMT
TRABURE
Figure 15: Depth Control with AlignedGenn
25


Reviews:
[{"review_id": "p9nVMnUm4R", "reviewer": "Reviewer_3iNL", "summary": "This 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.", "questions": "1. **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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "# 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.", "was_revised": true, "final_justification": "I have read the authors' rebuttal and decided to raise my score from 3 to 4.\n\nThe authors have convincingly demonstrated the non-incremental contribution of their method to the MM-DiT architecture with the introduction of ShiftPE and SSA. They also effectively addressed my concerns about robustness and generalizability by providing additional experiments under more challenging prompt settings. The new, training-free method to support user-provided reference images significantly improves the paper's practicality. Lastly, the authors clarified how high metric scores can sometimes indicate a degenerate failure mode, which enhances the paper's clarity.\n\n The rebuttal successfully resolves all my primary criticisms.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our responses as follows. We have summarized the reviewer's weaknesses and questions into four aspects, which we will address in turn.\n\n# Weakness 1\n\nWe argue that our method is not a simple incremental modification, but offers distinct academic value and deep insights into attention sharing strategies and the MM-DiT architecture.\n\nVanilla attention sharing methods employ identical position embedding for reference and target images. However, this practice in MM-DiT leads to content leakage and degraded text controllability, a fundamental challenge affecting all attention sharing methods in this architecture. To the best of our knowledge, our work is the first to address this issue by introducing ShiftPE and the SSA component, providing general insights for future attention sharing approaches.\n\nMoreover, beyond ShiftPE and SSA mentioned in Weakness 1, Flexible Strength Control (Section 3.4) and Delicate Attention Replacement (Section 3.5) also contribute significantly to the final performance, as shown in Table 2 and Figure 8 in paper. These contributions should not be overlooked.\n\n# Weakness 2 & Question 1\nWe constructed three types of challenging prompts using ChatGPT for more comprehensive testing—Long and Complex, Ambiguous Style, and Conflict Prompts—with 20 cases per type (4 prompts per case). The design principles are as follows:\n\n- **Long and Complex Prompts**: Intricate scenes with multiple objects, characters, and actions (e.g., *A high-altitude weather control station on a cliff with turbines, mid-air holograms, and engineers in pressure suits calibrating conduits during a storm, in low-poly style*).  \n- **Ambiguous Style**: Vague stylistic descriptions (e.g., *with a hazy echo*) instead of explicit style labels (e.g., *in oil painting style*).  \n- **Conflict Prompts**: Semantic mismatches between reference image and target image (e.g., reference: *bicycle in cloud library*; target: *fish walking in city*).\n\nQuantitative results are summarized below, where **Normal Test Dataset** refers to the test set used in the paper:\n|Type|$S_{text}\\uparrow$|$S_{sty}\\uparrow$|$S_{dino}\\uparrow$|\n|:-|:-:|:-:|:-:|\n|Normal Test Dataset|0.282|0.740|0.554|\n|Long and Complex|0.283|0.773|0.544|\n|Ambiguous Style|0.263|0.752|0.537|\n|Conflict|0.303|0.710|0.523|\n\nFor Ambiguous Style prompts, compared to the Normal Test Dataset, style consistency decreases slightly , while $S_{text}$ drops more noticeably—due to ambiguous style descriptions also impairing content clarity, leading to less coherent and focused outputs. For Long and Complex and Conflict Prompts, compared to the Normal Test Dataset, style consistency is somewhat reduced, yet performance remains strong, thanks to Flux’s robust generation capacity in handling complex or conflicting inputs.\n\n# Weakness 3 & Question 2\n\nOur method allows users to provide reference image as style input for generation.\n\nOur method inherently requires the Q, K and V features from the reference image during the generation process to produce style consistent images.  When the reference image is generated by Flux, we naturally have access to its intermediate Q, K and V.  For a user-specified reference image, we obtain approximate cached Q, K and V by applying noise at different timesteps \\( t \\) and feeding the noisy latent into the diffusion model.  \n\nThe algorithm proceeds as follows:\n\n```\nAlgorithm for user-provided reference image\nInput: user-provided reference image I, num of inference timesteps T\nOutput: Cached Q, K, V of I\nStep1 --> Initialize a random noise: noise ~ N(0, 1)\nStep2 --> latent = vae_encode(I)\nStep3 --> cached_qkv = {}\nStep4 --> for t = T, T-1, ..., 0 (T timesteps) do:\n              noise_input = t * noise + (1 - t) * latent\n              Q_t, K_t, V_t = Flux(noise_input, t, others(e.g., prompt embeds, guidance scale, ...))\n              cached_qkv[t] = (Q_t, K_t, V_t)\nStep5 --> return cached_qkv        \n```\n\nThe above algorithm extends our method to support user-provided images as style references. Moreover, our approach demonstrates strong performance. We conduct an evaluation on StyleCrafter's test set (20 distinct style images and 20 prompts, forming 400 test cases) and compare with StyleCrafter, StyleShot, and CSGO. The results are summarized in the table below:\n\n|Method|$S_{text}\\uparrow$|$S_{sty}\\uparrow$|$S_{dino}\\uparrow$|\n|:-|:-:|:-:|:-:|\n|Ours (user-provided reference image)|**0.306**|**0.701**|**0.558**|\n|StyleCrafter|0.292|0.562|0.451|\n|StyleShot|0.254|0.598|0.336|\n|CSGO|0.241|0.647|0.402|\n\nExperimental results show that our method significantly outperforms existing approaches in both text controllability ($S_{text}$) and style consistency ($S_{sty}$, $S_{dino}$). Moreover, the compared methods require large-scale training datasets and model training, and our method is training-free, demonstrating clear practical advantages.\n\n\n# Weakness 4 & Question 3\n\nWe clarify that $S_{{sty}}$ and $S_{{dino}}$ are not strictly \"higher is better\" for style similarity evaluation, but are meaningful only within reasonable ranges—typically $S_{{sty}} \\in [0.4, 0.8]$ and $S_{{dino}} \\in [0.3, 0.6]$ in our task. \nWithin these ranges, higher values could indicate more consistent stylization. \nValues beyond this range do not effectively reflect improved style similarity: e.g., scores approaching 1 indicate near-identical content, suggesting content leakage rather than enhanced stylization.\n\nThis is because neither metric is specifically designed for style assessment:\n- $S_{{sty}}$ measures semantic similarity and may yield high scores when the semantic content of two images are very similar, even under significant style differences (e.g., photo vs. cartoon).\n- $S_{{dino}}$, while more sensitive to texture and style due to self-supervised training, still captures structural and semantic information, not pure style.\n\nIn a word, although neither metric is purely designed for style similarity evaluation, both can effectively assess style consistency between images to a certain extent when used within their appropriate ranges."}, {"review_id": "z19Jx0gaFb", "reviewer": "Reviewer_6wGd", "summary": "This 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.", "questions": "Does 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength: \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.", "was_revised": true, "final_justification": "The paper is generally well-written paper with well-executed visual comparisons. The ablation study shows the effectiveness of the proposed SSA and ShiftPE strategy.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our responses as follows. We have summarized the reviewer's weaknesses and questions into four aspects, which we will address in turn.\n\n# Weakness 1\nWe thank the reviewer for the valuable feedback. \nWe agree that Figure 3, which illustrates the overall pipeline of our method, currently lacks sufficient integration with the main text. \nTo address this, in the revised manuscript, we will add:\n\n- A dedicated reference to Figure 3 in Section 3.3\n- incorporate explicit descriptions and discussions of the figure within the ShiftPE (lines 147–148) and SSA (lines 155–156) subsections. \n\nThese revisions will strengthen the connection between the figure and the methodological narrative, thereby improving clarity and readability.\n\n# Weakness 2\n\nWe thank the reviewer for the insightful comments. Due to an oversight on our part, We did not clearly articulate the motivations and theoretical analysis of the method. Below, we clarify the design rationale for each component:\n\n1. **Motivation of ShiftPE**: \nVanilla attention sharing methods employ identical position embedding for reference and target images. However, this practice in MM-DiT leads to content leakage and degraded text controllability, a fundamental challenge affecting all attention sharing methods in this architecture. As shown in Figure 4 of the paper (Default RoPE part), the attention heatmap reveals that using identical position embedding causes the model to attend only to spatially aligned regions. To mitigate this, we propose ShiftPE, which assigns non-overlapping shifted position embedding to reference and target images, effectively decoupling their spatial attention. This is validated in Figure 4 (ShiftPE part), where attention becomes more appropriate and disentangled. Moreover, ablation studies and visualizations in Table 2 and Figure 7 demonstrate the effectiveness of our approach.\n\n2. **Motivation of SSA**: In standard MM-Attention, Q, K and V are derived from image and text tokens. However, text tokens do not carry style information, and computing their projections for attention introduces redundant computation. To improve efficiency and focus on image-relevant features, SSA shares only the Q, K and V derived from image tokens, eliminating unnecessary computation while enhancing style consistency.\n\n3. **Motivation of Flexible Strength Control**: Practical applications often require fine-grained control over style consistency. To this end, we introduce a scaling factor $\\lambda$ that modulates the strength of style consistency, enabling flexible and user-controllable style-aligned image generation without retraining.\n\nWe will include these explanations in the revised manuscript to improve clarity.\n\n# Weaknesses 3 \n\nWe agree that discussing limitations in the main text enhances transparency and readability. In the revised manuscript, we will move the limitation section from the auxiliary material to the main body of the paper, placing it at the end of Section 5 (Conclusion) for better visibility.\n\n\n# Weaknesses 4 & Question 1\n\nOur method allows users to provide reference image as style input for generation.\n\nOur method inherently requires the Q, K and V features from the reference image during the generation process to produce style consistent images.  When the reference image is generated by Flux, we naturally have access to its intermediate Q, K and V.  For a user-specified reference image, we obtain approximate cached Q, K and V by applying noise at different timesteps \\( t \\) and feeding the noisy latent into the diffusion model.  \n\nThe algorithm proceeds as follows:\n\n```\nAlgorithm for user-provided reference image\nInput: user-provided reference image I, num of inference timesteps T\nOutput: Cached Q, K, V of I\nStep1 --> Initialize a random noise: noise ~ N(0, 1)\nStep2 --> latent = vae_encode(I)\nStep3 --> cached_qkv = {}\nStep4 --> for t = T, T-1, ..., 0 (T timesteps) do:\n              noise_input = t * noise + (1 - t) * latent\n              Q_t, K_t, V_t = Flux(noise_input, t, others(e.g., prompt embeds, guidance scale, ...))\n              cached_qkv[t] = (Q_t, K_t, V_t)\nStep5 --> return cached_qkv        \n```\n\nThe above algorithm extends our method to support user-provided images as style references. Moreover, our approach demonstrates strong performance. We conduct an evaluation on StyleCrafter's test set (20 distinct style images and 20 prompts, forming 400 test cases) and compare with StyleCrafter, StyleShot, and CSGO. The results are summarized in the table below:\n\n|Method|$S_{text}\\uparrow$|$S_{sty}\\uparrow$|$S_{dino}\\uparrow$|\n|:-|:-:|:-:|:-:|\n|Ours (user-provided reference image)|**0.306**|**0.701**|**0.558**|\n|StyleCrafter|0.292|0.562|0.451|\n|StyleShot|0.254|0.598|0.336|\n|CSGO|0.241|0.647|0.402|\n\nExperimental results show that our method significantly outperforms existing approaches in both text controllability ($S_{{text}}$) and style consistency ($S_{{sty}}$, $S_{{dino}}$). Moreover, the compared methods require large-scale training datasets and model training, and our method is training-free, demonstrating clear practical advantages."}, {"review_id": "VUJ6CAAc6R", "reviewer": "Reviewer_DDgf", "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.", "questions": "N/A", "limitations": "N/A", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Pros:\n1. The research problem is interesting and challenging. Solving this problem can surely help with style consistent image generation.\n2. The proposed method is training-free and enjoy good flexibility and seamless integration.\n3. The provided qualitative results are visually significant and looks promising.\n\nCons:\n1. 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.\n2. 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.", "was_revised": true, "final_justification": "This paper is interesting; however, as I stated in my review, understanding the failure cases and conflicts is critical to studying the model. Unfortunately, this is not possible during the rebuttal period, but I wish authors could include additional content in the paper in the future. I will keep my score for this time.", "rebuttal": "Thank you for the insightful comment. We provide our responses as follows.\n\n# Weakness 1\n\nWe agree that analyzing failure cases is crucial for deeper understanding and improvement. Due to limitations in this rebuttal (can't upload pdf file), we cannot show visual examples, but we observe that failures often involve content leakage—e.g., when the reference image is \"A bowl with apples\" and the target is \"A pencil\", the generated image may incorrectly include the bowl and apples alongside the pencil, indicating content leakage problem. This insight highlights the need for better disentanglement in future work.\n\nRegarding the reviewer’s example—target prompt: *a bike on top of ocean*, with ocean from the reference image—we have tested this scenario and found that AlignGen produces visually coherent results: a person riding a bicycle over the ocean, with the water texture and style well aligned to the reference. This demonstrates effective alignment under semantically challenging conditions.\n\nTo further evaluate robustness and uncover underlying mechanisms, we conducted more comprehensive testing using three challenging prompt types generated by ChatGPT (20 cases per type, 4 prompts per case):\n\n- **Long and Complex Prompts**: Intricate scenes with multiple objects, characters, and actions (e.g., *A high-altitude weather control station on a cliff with turbines, mid-air holograms, and engineers in pressure suits calibrating conduits during a storm, in low-poly style*).  \n- **Ambiguous Style**: Vague stylistic descriptions (e.g., *with a hazy echo*) instead of explicit style labels (e.g., *in oil painting style*).  \n- **Conflict Prompts**: Semantic mismatches between reference image and target image (e.g., reference: *bicycle in cloud library*; target: *fish walking in city*).\n\nQuantitative results are summarized below, where **Normal Test Dataset** refers to the test set used in the paper:\n|Type|$S_{text}\\uparrow$|$S_{sty}\\uparrow$|$S_{dino}\\uparrow$|\n|:-|:-:|:-:|:-:|\n|Normal Test Dataset|0.282|0.740|0.554|\n|Long and Complex|0.283|0.773|0.544|\n|Ambiguous Style|0.263|0.752|0.537|\n|Conflict|0.303|0.710|0.523|\n\nFor Ambiguous Style prompts, compared to the Normal Test Dataset, style consistency decreases slightly , while $S_{text}$ drops more noticeably—due to ambiguous style descriptions also impairing content clarity, leading to less coherent and focused outputs. For Long and Complex and Conflict Prompts, compared to the Normal Test Dataset, style consistency is somewhat reduced, yet performance remains strong, thanks to Flux’s robust generation capacity in handling complex or conflicting inputs.\n\nFurthermore, we will conduct a more in-depth theoretical analysis based on these results in future work to explore the underlying hidden mechanisms.\n\n# Weakness 2\n\nWe thank the reviewer for the suggestion. \n\nWe propose Flexible Strength Control in Section 3.4, which introduces a parameter $\\lambda$ to achieve a trade-off between prompt alignment and style fidelity. While we recommend setting $\\lambda = 1.1$ in the paper for good performance, in certain extreme cases, manual tuning of $\\lambda$ is still required to achieve optimal results. Developing an adaptive mechanism to automatically balance prompt alignment and style fidelity would indeed be highly desirable.\n\nIn the future, we will actively explore an automated mechanism to balance prompt alignment and style fidelity."}, {"review_id": "G2ES2Fj80L", "reviewer": "Reviewer_7Ugs", "summary": "The 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.", "questions": "1. 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.", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "Thanks for author's feedback, it addresses my part concerns, but the theoretical analysis is lack. I will still maintain my rating.", "rebuttal": "Thank you for the insightful comment. We provide our responses as follows.\n\n# Weakness 1 & Question 1\n\nWe argue that our work is not merely engineering-driven, but offers profound insights into style-aligned generation and other attention sharing approaches within the MM-DiT framework.\n\nVanilla attention sharing methods employ identical position embedding for reference and target images. However, this practice in MM-DiT leads to content leakage and degraded text controllability, a fundamental challenge affecting all attention sharing methods in this architecture. To the best of our knowledge, our work is the first to address this issue by introducing ShiftPE and the SSA component, providing general insights for future attention sharing approaches. \n\nFurthermore, our method is supported by solid data analysis. As shown in Figure 4 of the paper (Default RoPE part), the attention heatmap reveals that using identical position embedding for reference and target images causes the model to attend only to spatially aligned regions. ShiftPE, which assigns non-overlapping shifted position embedding to reference and target images, effectively decouples their spatial attention. This is validated in Figure 4 (ShiftPE part), where the attention becomes more appropriate and disentangled. Moreover, ablation studies and visualizations in Table 2 and Figure 7 demonstrate the effectiveness of our approach. We plan to conduct deeper theoretical analysis in future work.\n\n\n# Weakness 2 & Question 2\n\nAlthough our method is simple and effective, we did not claim that it is lightweight or efficient in the paper.\n\nBelow are the actual measured inference latency and GPU memory usage: \"Original\" denotes Flux generating two images directly, while \"Ours\" denotes our method generating both the reference and target images simultaneously (also two images in total), the experiment is conducted on a single H20 using bf16 precision:\n\n|Metric|Original|Ours|\n|:---|:--:|:--:|\n|Inference time (s)|45.63|66.67 (46.1%$\\uparrow$)|\n|GPU memory usage (MiB)|3310.86|3687.47 (11.3%$\\uparrow$)|\n\nAs shown in the table, both the inference latency and GPU memory overhead remain within reasonable bounds. More importantly, our approach requires no training, thus naturally avoiding the expensive training process. \n\n# Weakness 3 & Question 3\n\nFor [1], we conducted a detailed comparison using the official code and recommended configurations. The experimental results are as follows:\n\n|Method|$S_{text}\\uparrow$|$S_{sty}\\uparrow$|$S_{dino}\\uparrow$|\n|:---|:--:|:--:|:--:|\n|Ours|0.282|0.740|0.554|\n|[1]|0.235|0.897|0.845|\n\nAs can be observed, [1] barely performs meaningful image editing and fails to make substantial content changes given a reference image, resulting in a lower $S_{text}$ and higher $S_{sty}$ and $S_{dino}$ values. The reasonable range for $S_{sty}$ is 0.4–0.8 and for $S_{dino}$ is 0.3–0.6; values beyond these ranges indicate excessive similarity between the target and reference images, suggesting content leakage, which is further confirmed by the low $S_{text}$.\n\n[2] is a subject-driven image editing method that requires both a scene image and an object image as input, and thus is not applicable to style-aligned image generation tasks. \n\nWe will include the results of [1] to the Table 4, discuss and cite [2] in the related work section (Section 2) in the revised manuscript.\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."}]

Author rebuttal:

Meta-review: This paper tackles a timely problem: getting consistent style across images generated by the new MM-DiT models like Flux. The authors show that standard attention-sharing tricks don't work well here, causing a "content leakage" that degrades text control. Their solution is a training-free method called AlignedGen, which uses two clever fixes: a Shifted Position Embedding (ShiftPE) to stop the model from incorrectly matching up spatial locations, and Selective Shared Attention (SSA) to pass along only the necessary style information. They claim this solves the problem, giving you style consistency without breaking the text prompt.

The main strengths noted are that style alignment work well for the latest MM-DiT models like Flux. Reviewers agreed that the authors were the first to identify and convincingly solve the "content leakage" issue specific to these architectures (3iNL, 6wGd). Their solution, using ShiftPE and SSA, was noted as an elegant and effective fix (3iNL, 7Ugs). The method is training-free and plug-and-play, making it highly practical (3iNL, DDgf). 

The initial reviews raised several shared concerns, including whether the contribution was incremental (3iNL), the major practical limitation of not supporting user-provided reference images (3iNL, 6wGd), and a lack of analysis on robustness and computational overhead (3iNL, DDgf, 7Ugs). 

The authors' rebuttal addressed the most significant concerns. In terms of novelty, they focused on the unique "content leakage" problem specific to MM-DiT models. They delivered a new algorithm, with SOTA results, to support user-provided reference images, alongside the requested robustness and overhead analyses.

After this, only two minor, lingering weaknesses were noted. Reviewer 7Ugs, while largely satisfied, pointed out that the paper still "lacks robust theoretical grounding". Similarly, Reviewer DDgf noted that a thorough failure case analysis was still missing. Crucially, both reviewers who raised these points still recommended acceptance, viewing them as limitations on the paper's current scope rather than fundamental flaws.

Overall the AC finds the paper's solution to the timely and important problem of style alignment in new MM-DiT architectures to be a clear and valuable contribution. While minor concerns remain, the authors addressed the main concerns. The AC thus recommends acceptance.
Decision: Accept (poster)