Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape
Authors: Ruichen Chen
Keith G. Mills
Liyao Jiang
Chao Gao
Di Niu
Venue: NeurIPS 2025 poster ()
Keywords: Sparse Attention
Diffusion Transformer
Image and Video Generation
Primary area: deep_learning
TL;DR: We propose a very sparse attention mechanism for diffusion models on T2V and T2I tasks through re-use of softmax statistics.

Abstract: Diffusion Transformers (DiT) have become the de-facto model for generating high-quality visual content like videos and images. A huge bottleneck is the attention mechanism where complexity scales quadratically with resolution and video length. One logical way to lessen this burden is sparse attention, where only a subset of tokens or patches are included in the calculation. However, existing techniques fail to preserve visual quality at extremely high sparsity levels and might even incur non-negligible compute overheads. To address this concern, we propose Re-ttention, which implements very high sparse attention for visual generation models by leveraging the temporal redundancy of Diffusion Models to overcome the probabilistic normalization shift within the attention mechanism. Specifically, Re-ttention reshapes attention scores based on the prior softmax distribution history in order to preserve the visual quality of the full quadratic attention at very high sparsity levels. Experimental results on T2V/T2I models such as CogVideoX and the PixArt DiTs demonstrate that Re-ttention requires as few as 3.1% of the tokens during inference, outperforming contemporary methods like FastDiTAttn, Sparse VideoGen and MInference.

Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape
Ruichen Chen
Keith G. Mills Division of CSE Louisiana State University keith mailss@ssu.en edu
ECE Department University of Alberta ruicheni Qualberta.
Liyao Jiang
Chao Gao Huawei Technologies Edmonton, Alberta, Canada chao. gao4@huawei. com
Di Niu ECE Department University of Alberta dniu@ualbers barta. ca
ECE Department
University of Alberta
HlyaoQuulberraa
Abstract
Diffusion Transformers (DiT) have become the de-facto model for generating high- quality visual content like videos and images. A huge bottleneck the attention mechanism where complexity scales quadratically with resolution and video length. One logical way to lessen this burden is sparse attention, where only subset tokens patches are included in the calculation. However, existing techniques fail to preserve visual quality at extremely high sparsity levels and might even incur nog-negligible compute overheads. To address this concern, Re-ttention, which implements very high sparse attention for visual generation models by leveraging the temporal redundancy of Diffusion Models to overcome the probabilistic normalization shift within the attention mechanism. Specifically, Re-ttention reshapes attention scores based the prior softmax distribution history in order to preserve the visual quality of the full quadratic attention at very high sparsity levels. Experimental results on T2V/T2I models such Cog VideoX and the Pix DiTs demonstrate that Re-ttention requires as few as 3.1 % of the tokens during inference, outperforming contemporary methods like FastDiTAttn, Sparse VideoGen and MInference.
propose
Introduction
Diffusion Transformers (DiT) 63
3
2
23
10
combine the attention 38 mechanism with the
generate high-quality visual content such videos [47] However, a key bottleneck to generating longer videos and higher resolution content is the global properties of the self-attention module, whose compute cost scales quadratically with sequence size, i.c., resolution and video length.
iterative denoising of Diffusion Models 35
54 and images 20 44 41 31
Sparse attention techniques [6] aim to lower the computational burden by reducing the number of sequence tokens/patches that the attention mechanism considers during inference. Contemporary techniques like MInference (18) and Sparge Attention [51], well as XAttention [46] achieve ~50% sparsity (i.e., reducing only 50% of the attention computations) by relying on downsampling the attention map anti-diagonal scoring, respectively. In parallel, several recent methods 42 48 50
have been proposed specifically for DiTs, increasing the attention sparsity to 70% by exploiting the inherent characteristics of diffusion process such the progressively denoising structure and the spatial/temporal locality of attention.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Full-Attention
SVG (87.5%)
Minference (87.5%)
DiTFastAttn (96.9%)
Ours: Re-ttention
(96.9%)
Figure I: Visual comparison using CogVideoX [47] T2V model. Columns correspond to different frames. Rows correspond to different sparse attention methods (sparsity degree in paranthesis; higher is better). Prompt: "a colorful butterfly perching on a bud"". More examples in the Appendix.
While these methods reduce more than half of the attention computation, their effectiveness re- mains limited for the growing computational demands of high-resolution image and video generation. Previous researches like LongFormer [1] and BigBird [49 can achieve >95% sparse attention. How- ever, their reliance on retraining and fine-tuning introduces significant computation burdens, limiting their applicability to modern large-scale, pretrained generative models. Thus, the development of sparse attention techniques that achieve >95% sparsity with minimal visual quality loss remains an open challenge.
In this paper, we propose an effective method to statistically reshape the attention distribution distorted by the deployment of sparse attention, which we call Re-ttention. Re-ttention overcomes the high sparsity challenge faced by the training-free sparse attention method. Moreover, is simple to implement and incurs negligible overhead compared to standard sparse attention at the same sparsity level. Figure [ provides sample content from our technique compared to other sparse attention methods. Our detailed contributions are as follows:
1. We relate the failure to achieve degradation-frrs free >95% sparsity without training from scratch the distributional shift attention scores caused by the reduced softmax denominator term, i.e., the row-wise sum of the exponentials of involved elements. We design experiment to illustrate the importance preserving this term and the impact on visual generation.
2. We discover the softmax distribution redundancy among neighboring denoising steps. Al- though the actual value of denominator changes unpredictably, the ratio between the sparse and full denominator is relatively stable.
3. We propose that the attention scores shifted by sparse attention are viable to be recovered by approximating the real softmax denominator.
4. The recovered attention scores deviate from valid probability distribution, as their sum is less than one, violating the normalization property softmax. We leverage the redundancy among neighboring denoising steps to compensate the missing probability with residual.

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We apply Re-ttention to T2V models such as CogVideoX A4 in order to outperform contemporary methods like FastDiTAttn 48 Sparse VideoGen 42 and MInference [18 on relevant tasks such
as VBench [16]. Furthermore, we apply Re-ttention to T2I models like the PixArt series series
3
2 and
others [3] to maximize performance on Human Preference Score v2 (HPSv2) [1 and GenEval
12
while achieving high sparsity of 96.9%.
2 Related Work
Diffusion Models (DM) [4] dominate visual generation tasks, Early DMs [35
34 use convolutional
U-Net structures 33 as their backbones. Later, Diffusion Transformers (DiT) [33
5
adopt the
attention-based [38] of Vision Transformers (ViT) to increase scalability and visual generation
quality. In addition being the favored backbone structure for text-to-image (T2I) DMs 3
23 10
20 the DiT structure is extensible to video generation [40] 19] as well. Specifically, Latte 33
proposes a 2D+1D attention block for video generation, which performs spatial and temporal attention separately. Subsequent works like CogVidecs VidcoX [47] and OpenSora (24] adopt a 3D attention structure which processes the spatial and temporal dimensions simultaneously, yielding improved generation quality. However, this enhancement comes at the cost of significantly increased computation due to the quadratic complexity of attention, highlighting the pressing need for more efficient and sparse attention, which we explore in this work.
Sparse Attention denotes a class of techniques that aim to alleviate the hardware cost of the attention mechanism by omitting computation for unnecessary query-key pairs. Specifically, it is well documented that the attention mechanism produces sparse results [8] 28,, yet suffers from a burdensome quadratic complexity and wasted computation by default. LongFormer D proposes sliding window attention that restricts attention to a local region. BigBird [49] and Mistal-7B [17] extend this idea to fine-grained attention masks, while SwinFormer (29) use local attention for efficient ViT design. Although these methods can reduce the attention computation by factor or 8x or more, they often necessitate training or fine-tuning the model, thus restricting the scope of deployment.
There are also training- free sparse attention methods. MInference [18 downsamples the attention probability matrix (QK") into blocks then dynamically select the top-k blocks to perform sparse attention. Subsequent research like FlexPrefill (21). Sparge Attention [51] and XAttention [46] rely on the block selection idea and propose dynamic block sorting algorithms. Further methods like StreamingL.LM 43 DiTFastAttn 48 and Sparse VideoGinn (42) identify the special attention patterns in L.LM and DiT and propose efficient attention masking based on those patterns. However, the sparsity achievable by these methods is limited < 70%, which is much higher compared prior works that require re-training or fine-tuning, We aim to address this gap and provide a training-free sparse attention method that can achieve 95% sparsity on visual generation tasks.
Caching technique used in computer systems to temporarily store data or computations, thereby reducing redundant processing and improving overall efficiency. In DiT, the lengthy denoising process makes well-suited for the application of caching techniques. Recent methods 27 [3 re-use the attention outputs or the intermediate features at different denoising timesteps to skip the attention computation. Methods like DiTFastAttn 48 50 leverage the caching mechanism improve the visual quality.
3 Background: The Attention Mechanism and Sparsity
The attention mechanism (8] is the foundation of transformer architectures like DiTss Let X E Rxxd be an input token/patch sequence. where T' the sequence length, dependent on the input size (c.g.. image resolution or video length) and d is the embedding dimension, a hyperparameter of the transformer model. The attention mechanism contains heads such that d₁ = f.: d, E Zt. We first map X into three representations, Query (Q), Key (K) and Value (V) of identical size xd then compute the attention as follows,
QKT
Attention (qntionn(,AAr (QKK V) Softmax
VV. Vdh
(1)
3

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Full Attention
Sparse Attention
Re-ttention Ultra-Sparse Content Generation
0.3
0.2 0.2 02 0.1
0.6
0.4
0.
0.0
0.0 0.8
0.4
0.0
0.0
oo
0.3
0.2
n.0
o.
0.0
0.0
0.0
0.2
0.
0.1
0.3
0.2
0.2
0.5
High performance
Compute-frende -otdndyy Limitedinacourate
V Compute-friendl Denominator Ratio Re-use
Accurate
High performance
Expensive
Residual Reshdual'Caching
Figure 2: Illusion of attention map A computed by full attention, contemporary sparse attention (window-based) and our proposed Re-ttention. Sparse attention shifts the distribution of attention scores, resulting in degraded performance sparsity increases. In contrast, Re-ttention re-uses the denominator ratio cached from the previous denoising steps to scale the sparse attention score to the full attention level. Then, apply residual caching accurately restore the full attention scores.
We can decompose this mechanism into several intermittent matrices, specifically the product of Q and K before (A"t) and after (A) the softmax operation:
A QKT
E BAXTXT
= Softmax(A4Pm)) E [0, 1)
(3)
vd,
The computation of these matrices is very expensive [1] To make matter worse, their size scales quadratically with T, which depends the image/video resolution and video length. However, the softmax operation is computed row-wise and produces a probability distribution Σ A.s.
which empirically produces sparse A in practice 8 28
Therefore, one way to alleviate this computational burden is to use a sparse attention mechanism. The key idea is omit less relevant values of A that are likely to be 0 or close to 0 in A, from the softmax computation altogether. Formally, we express the sparse attention calculation using a mask M (0, 1 where 1 means index of Are will be included in the softmax, while the rest are excluded. The indexes of the included values in A form a set S =U U Sg,i where Sk.; ((k, i, Ma,aj 1,0 < < T).
Given an arbitrary element the pre-softmax matrix Age the normal and sparse softmax computa- tion are given by
exp(4 k.ij
Ag.újj exp( (
if E Sp.5.
(4)
Akii
Eiesu, exp(A
exp(e k.L.
otherwise,
respectively. Ultimately, the mask matrix M determines the potential amount of computational
savings. M can be computed statically (7] prior to inference dynamically [18 21 51 46) at runtime. Static techniques make more assumptions about the sparse regions of A while dynamic techniques impose additional inference overhead to compute M.
Regardless of technique, we can quantify the attention sparsity as percentage, e.g., 10%, 50%, 90%, etc., simply by computing the ratio of values in M that are as follows:
|
Sparsity (1
100%,
(6)
hT2
where higher value for sparsity corresponds lower computational burden. Therefore, sparse M corresponds to an overall sparse attention. However, high sparsification can cause significant shifts in the softmax calculation statistics (43) and lead detrimental performance. As will next show, our proposed method, Re-ttention, aims to identify these statistical issues and address them.
4 Proposed Method: Re-ttention
In this section we form hypothesis regarding how distributional shift softmax statistics prevents current frree sparse attention methods from satisfactorily operating at high sparsity, e.g..

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95%. We then elaborate on our proposed Re-ttention technique, which overcomes this burden by re-using and caching softmax statistics at high sparsity. Figure high-level overview of our proposed technique in comparison to full and sparse attention.
4.1
Importance of the Softmax Denominator
As preliminary investigation, gauge the perfor- mance of several existing sparse attention techniques [18]
0.5
48
[2).
We consider the GenEval [12 benchmark and
Quality (GenEval) 1
evaluate performance across spectrum of sparsity val- ues, i.e., starting at the highest sparsity these techniques consider in their original manuscript and then further increasing the sparsity.
0.2
Minference
0.1
DiTTastAltn
Figure iillustrates our findings. We observe that exist- ing approaches suffer monotonic performance drop when the sparsity is further increased beyond their pro- posed value (denoted with *). Per Eq. [5] higher value of sparsity corresponds the inclusion of fewer tokens in the softmax denominator as Sk. shrinks. Thus, the further removal of tokens, i.c, increasing sparsity closer 100%. has larger impact on the overall denominator value 43.. This phenomenon, introduces a detrimental distribution shift in the overall attention scores.
Ours
0.0
87.5%
93.75%
Sparsity
Figure 3: Quality-sparsity comparison of Re-ttention, Sparse VideoGen (SVG), MInference and DiTFastAttn. * denotes the sparsity level that prior methods oper- ate under non-degraded conditions.
We design toy experiment to test this hypothesis and showcase the significance of the softmax denominator term. Specifically, define a post-softmax masking operation as
A' A o M,
where the output of the original full softmax attention via Eq. 4 denotes element- multiplication and A' is masked attention. We emphasize that Equation is not proper sparse attention calculation and does not entail speedup. However, mimics the output of sparse attention M still zeroes out the same indices of A, yet preserves the denominator of the full softmax.
Pre-Sofimax
We calculate M using sliding window attention [1. We then generate visual content using both the formal sparse attention from Equation 5 and our post-softmax Equation 7
Post-Softinax
for comparison. Figure provides comparison, though provide additional examples and prompts in the sup-
plementary due to space constraints. We observe how the post-sofftaa attention preserves the guitar-playin panda, chair and background while the pre-softmax attention cre- ates noisy frame with jumbled contents where the panda appears to eat the guitar. Thus, these visual results validate our assumption regarding the importance of maintaining the softmax denominator. The challenge now becomes how preserve this information in efficient sparse attention setup.
Figure 4: Visual comparison of pre- Softmax and nost-Softmax masking on Cog VideoX-28 -2B with 66% sparsity, us- ing sliding-windon window attention [.].
4.2 Leveraging Denoising Properties for Statistical Reshape
One way to mitigate the distribution shift is to maintain the softmax denominator from the full attention calculation. We achieve this by exploiting the sequential nature the DM denoising process
and taking inspiration from DiT caching 4 27
53 and redundancy [77 methods.
Denominator Approximation. Figure 5 tracks the magnitude of the softmax denominator for single token in the 9th head of the 12th DiT block in PixArt-a Specifically, calculate the
5

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denominator using both the full and sparse attention (with 87.5% sparsity) as well as the ratio between these statisties.
Erese, exp(AA...))
(8)
Eim1 exp(AA.
This yields an insightful observation: While the actual value of the denom- inators may change unpredictably and non-monotonically over the denoising process, given a statically computed mask M, the ratio p follows pre- dictable trend. Therefore, we propose a simple method to recover the atten- tion distribution by modifying the out- put of the sparse softmax operation Eq. follows:
600
1.0
Full Denom.
Sparse Denom.
Denominator
400
Ratio
0.6 9
0.4
200
0.0
Step
Figure 5: Plotting softmax denominators for full and sparse attention as well as the ratio P per Eq. across 20 steps.
Aksis pAkKi..
(9)
where we cache < p S from previous step. Multiplying the Softmax output of sparse attention by approximates the full attention value, mitigating the distribution shift. In practice, since is not constant per Fig. 5 empirically find that slightly increasing the cached p after each denoising step achieves better performance. Thus, we parameterize P: Pt + À after each denoising step, where À is ramp-up hyperparameter.
Residual Caching. Although we modify the softmax to make the sparse attention more closely match that of full attention, Equation S does not yield proper probability distributions as Σj= A.:. < 1. Practically, this reduces the magnitude of overall attention outputs per Equation which can negatively impact performance. To address this issue, we first define the residual R as the difference between the full attention computed via Equation 4 and our p-reshaped attention via
Equations 5 and 9
R FullAttention(
ReshapeAttention((2 K,
(10)
We can later add R our sparse attention output. In fact, R is mathematically equivalent to the attention output of the masked tokens at the caching timestep.
Overall, the idea behind Re-ttention is to compute sparse attention for important regions, while re-using previously cached statistics from previous steps in less important regions of the attention map. This involves caching necessary statisties from the full attention in some steps, which is common in DiT sparse attention methods [8]] 55)
5 Experimental Setup and Results
We evaluate Re-ttention on both the text-to-video (T2V) and text-to-image (T2I) tasks using a number of DiT models, such as Video (2B) 47). PixArt-o/E (0.6B) 3 2 and Hunyuan-DiT (1.6B) 23 We generate 720 x 480 resolution, second videos at 8 fps and 1024 x 1024 pixel images throughout this paper. We compare to several existing sparse attention methods for visual content in the literature like Sparse VideoGen (SVG) [42], MInference (18] and DitFastAttn 48 50 to demonstrate both qualitative and quantitative performance gains and computational cost savings.
Implementation Details. Specifically, we use the HuggingFace Diffusers library 39 to instantiate the base DiT models and consider the default values for inference parameters like the classifier-fre free guidance (CFG) scale and number of denoising steps 50 for Cog VideooX/Huyyua and 20 for
PixArt DiTs. Following prior literature on DiT acceleration [42 53 22 27 we apply the full
attention during the first 5, 10 or 15 steps for the Pix DiTs, Hunyuan and Cog VidexX models, respectively, and then apply the sparse attention mechanism for the remainder of the denoising process. Further, set caching period of 5 steps for DiTFastAttn and Re-ttenion, where

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Table 1: Quantitative evaluation results for T2V model CogVideoX-2B 47 on VBench [16 and
other metrics. Arrows indicate if a higher or lower value of a metric is preferred. Best and second-best results in bold and daliies respectively.
Attention
Sparsity
PSNR 1
SSIM
LPIPS +
ImageQual
SubConsist t
Full-Attention
0.0%
Reference
Reference
Reference
65.72%
94.97%
SVG
87.5%
14.48
0.548
0.501
54.48%
89.26%
SVG
96.9%
10.50
0,418
0.898
51.82%
96.73%
MInference
87.5%
14.99
0.558
0.480
53.78%
83.71%
MInference
96.9%
9.25
0.325
0.818
34.36%
75.84%
DiTFastAttn
96.9%
27.93
0,865
0.098
64.86%
94.32%
Re-ttention 96.9%
27.96
0.894
0.059
64.87 %
94.80%
perform full attention cache required statistics. For fair comparison, we apply this caching to SVG and MInference as well: SVG and MInference will perform full attention at the same timesteps as DiTFastAttn and Re-ttention. To perform T2I using SVG, we treat the image as video containing single frame. We provide further baseline experimental details in the supplementary.
The rest of this section is organized follows: We enumerate T2V and T21 evaluation setup and results in Sections 5.1 and 5.2 respectively. Next, we provide ablation studies in Section 5.3
5.1 Text-to-Vide Evaluation
We perform quantitative T2V evaluation using the Animal and Architecture categories of VBench [6]. which consist of 100 videos each. For video quality, we use VBench score to evaluate standalone video quality. Specifically, we follow previous literature [42] and report the Image Quality and Subject Consistency metrics in VBench. Additionally,
compute the Peak Signal-to-Noise Ratio (PSNR) [1S] Structural Similarity Index Measure (SSIM) [32] and Learned Perceptual Image Patch Simularity (LPIPS) [52] These metrics evaluate the similarity and quality of videos generated by sparse attention methods relative to those generated using the full attention mechanism. We evaluate all methods at 96.9% sparsity to provide apples-to-apee to-apples performance investigation. However, some methods exhibit substantial degradation at this level and produce very noisy/black frames, so additionally report results at less aggressive setting of 87.5% sparsity.
Table presents our findings. Results demonstrate that Re-ttention consistently outperforms other baselines in terms of video quality and similarity metrics. Notably, Re-ttention not only outperforms both SVG and MInference at the strict sparsity of 96.9%, but also at 87.5% sparsity. The one exception is SVG at 96.9% sparsity, which achieves the highest SubConsist performance. However, this result is an outlier, as it even exceeds the SubConsist performance of full attention significantly while SVG substantially underperforms Re-ttention also outperforms DiTFastAttn, which also involves caching additional statistics at the high sparsity level of 96.9%. Therefore, overall, these results demonstrate the robustness, competitive performance of Re-ttention at > 95% sparsity T2V applications.
all other metries at this sparsity level. Furthermore,
We provide some sample frames from videos generated by Re-ttention, baseline sparse attention methods and full attention. Specifically, recall Figure in the introduction. The video generated by Re-ttention shows the best clarity and temporal consistency across frames for the main subject, and has no artifacts in the background. Moreover, the video generated by Re-ttention is most similar to the reference video generated with full-attention. In contrast, the video generated by DtTFastAttn has noisy texture artifacts both in the background and the subject. For SVG and MInference, the subject is inconsistent and deformed despite using much lower sparsity. We provide more T2V visual comparisons in the supplementary materials.
5.2 Text-to-Image Results
We evaluate T2I performance on a comprehensive set benchmark metrics: GenEval [12], HPSv2 [1], and MS-COCO 2014 [25]. GenEval consists of 553 unique prompts. For each prompt, the DiT generates 4 images. HPSv2 consists of four image categories: Animation, Concept-art, Painting and Photos. Each category consists of 800 images for 3.2k generations in total. Finally, generate 10k
7

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Table Quantitative evaluation results for PixArt-a B]. PixArt-E [ and Hunyuan-Dif 23 across
the GenEval [[2]. HPSv2 4T, and MS-COCO 2014 25
benchmarks. Best and second best results
bold and italics, respectively.
Model
Atlention
Sparsity t
GenEval
HPSv21
LPIPS &
IR f
CLIP
Full-Attention
0.0%
0.480
30.79
Reference
0.864
31.28
SVG
75.0%
0.368
25.24
0.655
-0.141
29.43
MInference
75.0%
0.433
28.04
0.458
0.549
30.93
PixArt-a
DiTFastAun
93.8%
0.431
27.26
0,506
0.688
30.72
DiTFastAttn
96.9%
0.364
26.71
0.590
0.314
29.63
Re-ttention
93.8%
0.456
28.29
0.354
0.688
31.21
Re-ttention
96.9%
0.448
27.57
0.372
0.646
31.20
Full-Attention
0.0%
0.544
30.70
Reference
0.953
31.54
SVG
75.0%
0.172
18,48
0.742
-1.315
26.09
MInference
75.0%
0.429
27.09
0.536
0.457
30.76
PixArt-E
DiTFastAttn
93.8%
0.411
27.64
0.591
0.507
30.08
DiTFastAttn
96.9%
0,233
22.79
0.734
-0.600
27.37
Re-tiention
93.8%
0.513
28.37
0,417
0.808
31.59
Re-ttention
96.9%
0.512
27.72
0.435
0.784
31.59
Full-Attention
0.0%
0.610
30.41
Reference
1.027
31.77
SVG
75.0%
0.317
24.73
0.854
-0.574
27.92
MInference
75.0%
0.450
23.94
0.720
-0.063
30.15
Hunyuan
DiTFastAttn
93.8%
0.024
14.77
0.896
-2.074
22.47
DiTFastAttn
96.9%
0,002
12.28
0.923
-2.237
22.10
Re-ttention
93.8%
0.585
29.03
0.598
0.911
31.63
Re-ttention
96.9%
0.590
28.89
0.606
0.923
31.65
Full- Attention
SVG (75%)
Minference (75%)
DiTFastAttn (93.8%)
DiTFastAttn (96.9%)
Re-ttention (93.8%)
Re-ttention
(96.9%)
Prompt: "A cat on city street with people."
Prompt: teddy wearing white shirt and green apron"
Prompt: "A view of Big Ben from over the water, during the day."
Figure 6: Visual comparison MS-COCO 2014 25 prompts using PixArt-a (row 1), PixArt-E (row 2), and Hunyuan (row 3). We show images generated by Re-ttention (our method) and by other attention methods in different columns. We provide further examples in the appendix.
images using the MS-COCO 2014 validation set and measure the LPIPS score 52 ImageReward (IR) [45 and CLIP score 13 using the ViT-B/16 backbone.

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Table our results on the T2I task. Re-ttention outperforms all other sparse attention methods across models and metries, showing consistently better performance. Additionally, Re-ttention achieves this while operating under extremely high sparsity of 96.9%, which reduces the to- ken/patch sequence to less than one twentieth of its original size, whereas other baseline methods underperform at 75% sparsity, which only reduces sequence length down to one fourth. Additionally, our performance on the IR metric is consistently positive, feat that no other sparse attention method attains. Moreover, while DiTFastAttn attains similar T2V performance Re-ttention, it fails to generalize to the T2I task on Hunyuan in terms of GenEval and HPSv2 performance, even at reduced sparsity of 93.8%. In contrast, Re-ttention performance neither suffers at 93.8% nor 96.9% sparsity, underscoring the effectiveness our technique.
Next, we present the visual (qualitative) comparisons on PixArt-a [3] PixAn-- 2 and Hunyuan 23 T2I models in Figures 6 More visual comparisons can be found in the Appendix. Overall, Re- ttention generates images with better image quality than other sparse attention methods and has higher similarity to the reference images generated by full-attention, even when using an extreme sparsity of 96.9%. For PixArt-a: are well aligned to the prompts. Whereas the other methods often generate colored noise artifacts, distorted subjects, and lower quality images. For Hunyuan [3] we observe that the other sparse attention methods generate severely degraded images, while Re-ttention can generate images that are similar to images generated by full-attention.
3 and Pix [2]. Re-ttention generates clean, high-quality images that
5.3 Ablation Studies
Table 3: HPVs2 score under different ramp-up hyperparameter with 96.9% sparsity on PixArt- Σ 2.
Finally, we ablate the effect of the ramp-up hy- perparameter Ả on PixArt-E [2] overall per-
formance. Specifically, we evaluate on HPSv2 overall well as the 'animation' and "concept- art' categories. Table 3 reports our findings. These findings demonstrate the robustness of Re-ttention as is possible to forgo the À param- eter, yet it is better to select moderate value.
Anime 1 ConceptArt f
HPSv2
0
29.
26.94
27.46
0.01
28. 89
26.21
26.83
0.02
28. 28.88
16.
26.82
0.04
29.60
27.23
27.72
6 Conclusions and Future Work
We propose Re-ttention, a training-free sparse attention method for Diffusion Transformers, which achieves 96.9% sparsity without performance loss on DiTs like CogVideoX and Hunyuan. We attain these gains by identifying the distribution shift of attention scores incurred by sparse attention methods that prevents extreme sparsity (> 95%) without significant performance degradation and resolve this issue using a combination of caching and statistical re-use. We evaluate Re-ttention on T2V and T2I tasks, outperforming contemporary baselines like SVG, MInference and DiTFastAttn.
Potential future directions to expand Re-itention and address limitations should aim to repurpose our contributions in the application domain of I.IMs or autoregressive visual content generation models. These models rely on causally masked attention, meaning that our attention statistical reshape, which leverages the step-wise denoising process in diffusion models to reuse cached attention statistics from previous steps, must be handled differently in this setting where such sequential caching is unavailable. Also, Re-ttention implements sparse attention using static mask, though further investigation is merited to validate it in the context of dynamically-generteee sparse attention masks. Dynamically adapting the sparsity pattern based on attention statistics or token importance could further improve efficiency while preserving output quality, enabling Re-ttention to generalize across wider range of sequence modeling and generative tasks.

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A Appendix
We provide additional information about our work. Sections A. and A.2 provide statements about the broader impacts of our work and limitations, respectively. Further, provide additional details
on our methodology in Sec. A.3 and baselines in Sec. A.4 Section A.5 provides elaborates on our
pre post-softmax example from Figure Finally, Sections A.6 and A.7 provide additional T2V
and T2I results, respectively.
A.1 Societal Impacts
Re-ttention improves the efficiency of image and video generation by enabling extremely sparse atten- tion. This makes high-quality generative models more accessible and environmentally sustainable by reducing computational and energy demands. By lowering resource barriers, Re-ttention can benefit creators, educators, and researchers in low-resource settings. While any generative model carries a risk of misuse, Re-ttention does not introduce new risks beyond existing systems. Responsible deployment and continued dialogue ethical use remain important.
A.2 Limitation
We design Re-ttention around achieving high sparsity for the non-autoregressive self-attention mechanism utilized by visual generation DiTs, rather than the autoregressive, causally-masker attention of LLMs which may more often feature different attention patterns such as columns 43] Additionally. Re-ttention exploits the sequential nature of DMs and inspired by DiT caching techniques 4 27 53). Our method may not be readily generalizable to autoregressive LLMs, though modifications and expansions into this field are a potential future work. Furthermore, Re-ttention is currently designed for statically computed attention masks, which offer speedup advantages. Extending the approach to support dynamically computed masks to support fine-grained sparse attention presents promising direction for future work.
Although we did not implement a custom GPU kernel, we measured inference latency on typical GPUs and observed that Re-tention achieves comparable runtime to DiTFastAttn across all tested models. This demonstrates that our contributions do not impose significant computational overhead, confirming that Re-tention maintains both high sparsity and practical efficiency.
A.3 Explanation Re-ttention
In Section 4 we claim that the residual R in Re-ttention is mathematically equivalent to the attention output of the masked tokens at the caching timestep. We now further elaborate on this claim:
Recall the definition of in Eq. and the set S that contains the included values (by sparse attention) in A. Hence, the A can be decomposed into two parts:
Aes + Ags:
Aes = Ao 101 (k..))s
(11)
Ags = Ao A (k.id))s
where Les is the indicator matrix that is where (ki, (, S, and elsewhere. Conversely, lęs is where (, & S and 0 elsewhere.
At the caching timestep. have the ratio between the denominator of full and sparse attention according to Eq. g Because we compute full attention in the caching step, the ratio p is not an approximation but accurate value. Hence, we have:
ReshapeAAtenntion Q, K, K,V.p) PA AEs V
(12)
Therefore, the residual R in Eq.
10
R A-V. Ags V Ags V,
(13)
which mathematically equivalent to the attention output of the masked tokens at the caching timestep.
20

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pre-softmax
post-softmax
Figure 7: Visual comparison of pre-sofumax and post-softmax masking on Cog VideoX-28 with 66% sparsity. Prompt: "A panda, dressed in small, red jacket and tiny hat, sits a wooden stool in a serene bamboo forest. The panda's fluffy paws strum miniature acoustic guitar, producing soft, melodic tunes. Nearby, few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting gentle glow on the scene. The panda's 's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance".
pre-softmax
post-softmax
Figure 8: Visual comparison of pre-softmax and post-softmax masking on Cog VodeoXX222 2B with 66%
sparsity. Prompt: "A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's 's items, hinting at playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting".
A.4 Details of Baseline Implementation
We compare Re-ttention three different baseline methods: Sparse VideoGen (SVG) 42 MIn- ference [18] and DiTFastAttn 48]. We enumerate the experiment implementation details for Text-to- Video (T2V) and Text-to-Image (T2I) generation tasks, respectively.
Text-to-Video For DiTs, MInference classifies all attention heads into a block sparse format [18 to generate M. We use the SVG official implementation for CogVideox series to generate videos. As for Re-ttention and Attt we use sliding window attention, which restricts each token's attention to local neighborhood and will repeat the same mask each frame of the video.
Text-to-Image Since T2I generation lacks temporal dimension, apply only the spatial attention heads in SVG and adjust the window size to match the target sparsity. Re-ttention and DiTFastAttn use the same sliding window attention as SVG.
A.5 Additional Examples for Post-Softmax Masking Operation
Figure expands on Fig. by providing additional video frame comparisons and the lengthy textual prompt. Post-Softmax masking not only better preserves the objects (panda, stool, guitar, etc.), but also consistently maintains the main part of the video over time, while pre-softmax causes the
21

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large panda to vanish. This example further validates our assumption regarding the importance of maintaining the softmax denominator.
Further, F provides an additional comparison with a different prompt. In the pre-softmax video, the ship becomes increasingly distorted over time, whereas in the post-softmax video, it remains consistent throughout. Notably, the reduced texture detail in the post-Softmax output reveals an issue caused by denormalized attention cubabilitee--spccffillll information loss due to the sum of softmax probabilities being less than one, leading to a shrinkage effect in the features.
A.6 Visual Comparison for Video Generation
We show additional visual (qualitative) comparisons video generation using the Cog VideoX- 2B [47] model in Figures 9
TO 11 and For example, in Figure [0] Re-ttention has the best looking otter well as the food with the most similar shape the reference video. Besides, while other baseline methods have artifacts like blurry textures and distortions in the background, Re-ttention preserves background fidelity, closely matching the reference video. Those additional comparisons match the experiment result in the main paper: The videos generated by Re-ttention are the most similar the reference video generated by full-attention; also, it has the best clarity and consistency and no artifacts in the background.
A.7 Visual Comparison for Image Generation
We show additional visual (qualitative) comparisons image generation using the PixArt-a B) PixArt- (2). and Hunyuan [23] models in Figures 13] 14 and [5] respectively. The main object generated by the dynamic sparse attention baseline MInference deviates significantly from the full-attention reference, often resulting in unnatural or distorted appearances. For static baseline methods like SVG and DiTFastAttn, although the main objects in their images more similar to the full-attention reference images, there are artifacts in the background which degrade the image quality. In comparison, Re-ttention not only preserves the fidelity of the main object but also mitigates background artifacts, demonstrating superior performance in T2I generation and strong generalization across different DiT architectures.
22

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Full-Attention
SVG (87.5%)
MInference (87.5%)
DiTFastAttn
(96.9%)
Ours: Re-ttention
(96.9%)
Figure 9: T2V visual comparison using CogVideoX-BB 47 T2V model. Each row corresponds to video frames generated by different methods. Prompt: "a curious sloth hanging from a tree branch"..
Full-Attention
SVG (87.5%)
MInference (87.5%)
DiTFastAttn
(96.9%)
Ours: Re-ttention
(96.9%)
Figure 10: T2V visual comparison using - 47 T2V model. Each row corresponds to video frames generated by different methods. Prompt: "otter on branch while eating".
23

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Full-Attention
SVG (87.5%)
MInference (87.5%)
DiTFastAttn
(96.9%)
Ours: Re-ttention
(96.9%)
Figure 11: T2V visual comparison using CogVideoX-2B 47 T2V model. Each row corresponds to video frames generated by different methods. Prompt: "a church interior".
Full-Attention
SVG (87.5%)
MInference (87.5%)
DiTFastAttn
(96.9%)
Ours: Re-ttention
(96.9%)
Figure 12: T2V visual comparison using Cog CogVideoX-2B 47 T2V model. Each row corresponds to video frames generated by different methods. Prompt: "the georgian building".
24

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Full-
SVG (75%)
MInference (75%)
DiTFastAttn DiTFastAttn (96.9%)
Re-ttention (93.8%)
Re-ttention (96.9%)
Attention
(93.8%)
Prompt: "A cat on a city street with people."
Prompt: "A small dog sitting wooden chair."
Prompt: "A man with white shirt and lose tie with messed up hair."
Prompt: "A girl with blue hair is taking a self portrait."
Prompt:
"The water the boat is in is reflecting the sun."
Prompt: "A train engine carrying carts down a track."
Prompt: "A duck floating on top of body of water."
Prompt: "there is large book shelf in this living room"
Figure 13: T2I visual comparison MS-COCO 2014 [26] dataset using PixArt-a model. Each row corresponds to one prompt, we show images generated by Re-ttention (our method) and by other attention methods in different columns.
25

---

Full-
SVG (75%)
MInference (75%)
DiTFastAttn DiTFastAttn (96.9%)
Re-ttention (93.8%)
Re-ttention (96.9%)
Attention
(93.8%)
IXII
Prompt: "A black and brown dog sits on white rug with a toy donut in front him." "
FENU MIN L
Prompt: "a couple of zebras that running through some grass"
Prompt: "a teddy bear wearing white shirt and green apron"
Prompt: "A person walking across street in the rain."
Prompt: "an aerial view from a planes window clouds and a sunset"
Prompt: "A steak topped with an egg and peppers." "
Prompt: "Two asian people pose for a picture while sharing drinks table."
Prompt: "A pretty young lady holding black umbrella."
Figure 14: T2I visual comparison MS-COCO 2014 (26) dataset using PixArt- 2 model. Each
row corresponds to one prompt, we show images generated by Re-ttention (our method) and by other attention methods in different columns.
26

---

Full-
SVG
MInference (75%)
DiTFastAttn DiTFastAttn
Re-ttention (93.8%)
Re-ttention (96.9%)
Attention
(75%)
(93.8%)
(96.9%)
Prompt: "A very rusty car near some pretty flowers." TM
Prompt: "An adult polar bear is swimming in the icy water"
Prompt: "A man holding baseball bat during baseball game."
Prompt: "A girl with pale skin wearing hoodie holds up toothbrush."
Prompt: "A mountain area with rocks and grass, and a large ram standing in the grass."
Prompt: "A small clock sitting the middle a side walk." "'
Prompt: "A view of Big Ben from over the water, during the day."
Prompt: "Light breaks through cloudy day at the pier."
Figure 15: T2I visual comparison MS-COCO 2014 (26 dataset using Hunyuan DiT 23 model. Each row corresponds to one prompt, we show images generated by Re-ttention (our method) and by other attention methods in different columns.
27


Reviews:
[{"review_id": "JKQi9sBtXB", "reviewer": "Reviewer_wos9", "summary": "This paper introduces Re-ttention, a sparse attention mechanism that addresses the computational inefficiencies of DiT for visual generation. By leveraging the temporal redundancy in Diffusion Models, Re-ttention reshapes attention scores using prior softmax distribution history, achieving extremely high sparsity while preserving the visual quality. It outperforms state-of-the-art methods like FastDiTAttn and Sparse VideoGen in both quality and efficiency. Experimental results on T2V/T2I models such as CogVideoX and PixArt DiTs validate its effectiveness, offering a significant step forward in sparse attention for visual generation.", "questions": "see above", "limitations": "see above", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**  \n1. The paper is well-motivated, with abundant analyses and examples that clearly demonstrate the reasoning behind its technical designs.  \n2. It provides extensive experimental results across various models, effectively showcasing its robustness and effectiveness.  \n3. The figures are highly informative and significantly aid in understanding the core concepts of the paper.  \n\n**Weaknesses**  \n1. The paper introduces a hyperparameter, \\lambda. While the ablation study indicates that a non-zero value for \\lambda is beneficial, the performance difference between \\lambda = 0 and the optimal \\lambda = 0.04 is not substantial. A more thorough explanation of the rationale behind this parameter would enhance the paper.  \n2. Re-ttention relies on periodically performing full attention computations to refresh its cache. The paper follows a fixed schedule. However, a more in-depth analysis of the trade-offs here would be beneficial.", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns. I will maintain my borderline accept rating.", "rebuttal": "> **W1. The paper introduces a hyperparameter, $\\lambda$. While the ablation study indicates that a non-zero value for $\\lambda$ is beneficial, the performance difference between $\\lambda$ = 0 and the optimal $\\lambda$ = 0.04 is not substantial. A more thorough explanation of the rationale behind this parameter would enhance the paper.**\n\nThe rationale behind this parameter is that DiT tends to focus on global structures during the early denoising steps and shifts its attention to finer local details in the later steps. In other words, the attention in DiT progressively shifts from long-range tokens to local tokens over the denoising steps, thereby increasing the denominator ratio $\\rho$. It also matches our observation (see Figure 5) about the ratio $\\rho$. Therefore, we introduce a ramp-up hyperparameter $\\lambda$ to provide a better approximation of the parameter.\n\n> **W2. Re-ttention relies on periodically performing full attention computations to refresh its cache. The paper follows a fixed schedule. However, a more in-depth analysis of the trade-offs here would be beneficial.**\n\nHere we add the result on PixArt-$\\Sigma$ with 96.9% sparsity after changing the sampling schedule $T$ from 5 to 3 and 7. A smaller $T$ means more inferences in full attention and more accurate cached residual, thereby resulting in better performance. Our Re-ttention outperforms DiTFastAttn, regardless of $T$.  We choose $T$=5 to balance the trade-off between computational efficiency and generation performance, while maintaining image/video quality, making it a practical and generalizable choice across different models and tasks.\n\n| Attention     | T = 3 GenEval ↑ | T = 3 HPSv2 ↑ | T = 5 GenEval ↑ | T = 5 HPSv2 ↑ | T = 7 GenEval ↑ | T = 7 HPSv2 ↑ |\n|---------------|------------------|----------------|------------------|----------------|------------------|----------------|\n| DiTFastAttn   | 0.376            | 26.81          | 0.233            | 22.79          | 0.147            | 20.87          |\n| Re-ttention   | 0.544            | 29.26          | 0.512            | 27.72          | 0.479            | 26.16          |"}, {"review_id": "DMlh0QK70F", "reviewer": "Reviewer_Pwod", "summary": "This paper proposes Re-ttention, a method to accelerate pre-trained diffusion transformers based on ultra sparse attention computation. Previous methods like DiTFastAttn typically fail in the scenarios of highly sparse attention patterns. This method finds the source of such failures is on the denominator of softmax and takes this factor into consideration to address the problem. Experiments demonstrate its robustness and superiority over state-of-the-art baselines.", "questions": "* The acceleration is compared against naive full attention implementation or fused attention in PyTorch 2.0 or higher?\n\nAnd please refer to the weaknesses part above.", "limitations": "The authors have discussed the limitations in the supplement.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths:\n\n1. The method is well motivated and novel. The authors provide sufficient analysis to support the source of the focused issue, which is on the denominator of softmax. Successfully, after taking it into consideration, the problem is largely alleviated according to the experiments.\n2. The experiments are extensive. Almost all popular DiT-based models can be accelerated by the proposed method.\n3. The codes are attached as supplementary materials. I assume that they will be released if the paper is accepted.\n\n### Weaknesses:\n\n1. It seems that the method is a patched version of DiTFastAttn. Although effective, it does not introduced too much difference. For example, it still largely relies on attention residual proposed in DiTFastAttn.\n2. The logical flow can be improved. It would be better to illustrate what has been achieved by previous methods, like identifying the important regions and using cached residual, then analyze what is the drawbacks of them, and finally present the proposed method.\n3. The implementation has a large room of improvement. I have a glance of the codes. It seems that the authors adopt naive implementation to extract the attention score and compute the window attention, without using fused kernels like FlashAttention. I wonder if this will consume more GPU memory. And is there any solution to further enhance the efficiency using fused kernels or the api of flexattention in PyTorch?", "was_revised": true, "final_justification": "The paper presents some valid exploration in image generation with sparse attention. The authors' response clear most of my concerns. I lean towards the positive side.", "rebuttal": ">**W1. It seems that the method is a patched version of DiTFastAttn. Although effective, it does not introduced too much difference. For example, it still largely relies on attention residual proposed in DiTFastAttn.**\n\nOur method is not simply a patched version of DiTFastAttn, but rather addresses an important limiting factor in existing sparse attention schemes and has not appeared in any of the sparse attention acceleration techniques that the literature had considered. First, DiTFastAttn proposes to use a combination of attention acceleration techniques, including attention residual caching, attention sharing across timesteps, and CFG, etc. Their work focuses on deciding which technique should be used for each layer of DiT and for example, achieves only 87.5% sparsity (using ⅛ sequence length as window size in their paper) without FID degradation after a greedy search. Here we re-evaluate their method on GenEval T2I benchmark in Figure 3, which confirms that DiTFastAttn only achieves up to 87.5% sparsity without degrading generation quality. \n\nIn this paper, we point out that a key limitation of the existing sparse attention schemes is that the denominator of Softmax is decreased due to applying sparse attention, which “causes the attention score distribution to shift away from what would be expected in full attention” [1]. Motivated by this, our Re-ttention proposes statistical attention reshaping which aims to recover the original Softmax denominator before sparsity is applied by estimating and calibrating the denominator from step to step (see Sec. 4.2) through a cached $\\rho$ value plus ramp-up hyperparameter $\\lambda$. As a result, we achieve as high as 96.875% sparsity without degrading the T2I quality on GenEval (Figure 3), largely outperforming DiTFastAttn.   \n\nFurthermore, the cached attention residual in our paper is NOT the same as that in DiTFastAttn. There is a key difference. While existing methods like DiTFastAttn define the residual as the difference (subtraction) between full attention and sparse attention, our Re-ttention defines it as the attention contribution of masked tokens only (those outside the sparse attention window)—which is a critical distinction and completely different from DiTFastAttn and other previous residual caching techniques.\n\nFor example, consider a row of attention scores: $\\text{softmax}(QK) = [0.1, 0.4, 0.4, 0.1]$ in full attention. With residual caching for sparse attention in DiTFastAttn, we have the row of sparse attention scores: $\\text{softmax}(QK) = [0, 0.5, 0.5, 0]$, and residual $R =  [0.1, 0.4, 0.4, 0.1] - [0, 0.5, 0.5, 0] = [0.1, -0.1, -0.1, 0.1]$ is cached to the next step. However, in Re-ttention, the row of $\\text{softmax}(QK) = [0, 0.4, 0.4, 0]$ after rescaling the denominator with our statistical reshaping, and the cached residual is $R’ = [0.1, 0, 0, 0.1]$, which contains only the attention score of masked tokens outside of the sparsity window. The former caching scheme in existing literature relies on the assumption that R changes minimally over steps, which however is not necessarily true within the sparse attention window and is prone to contaminating the attention scores within the window if in the next step, e.g., $[0.1, 0.4, 0.4, 0.1]$ changes to $[0.1, 0.3, 0.5, 0.1]$. In contrast, our residual is mathematically equivalent to caching only the attention scores outside the sparse window, and in the meantime focuses on updating only the important attention scores within the window and approximating the full attention via statistical reshaping (through cached $\\rho$). A detailed explanation can be found in Appendix A.3. \n\nIn other words, Re-ttention proposes a holistic new method with two parts: 1) approximating the denominator via a reused $\\rho$ value to rescale the attention scores within the attention window, 2) caching the true residuals outside the window to represent the residual attention of masked tokens. Both parts are necessary and work together to yield a much closer approximation to original full attention, without contaminating attention values due to distribution shift.\n\nFinally, experimental results show that Re-ttention can achieve up to 96.9% sparsity with non-degraded generation quality and substantially outperforms DiTFastAttn in visual quality, demonstrated by the extensive T2I and T2V examples provided in Figures 1, 6, 9, 10, 11, 12, 13, 14, 15. In fact, more examples confirm the same observation, which are not listed due to space limit. This again confirms that there is a fundamental limitation of the caching scheme adopted by DiTFastAttn, which does not uphold its assumption, and the significance of our Re-ttention technique.\n\n>**W2. The logical flow can be improved. It would be better to illustrate what has been achieved by previous methods, like identifying the important regions and using cached residual, then analyze what is the drawbacks of them, and finally present the proposed method.**\n\nWe thank the reviewer for their suggestions. We will definitely aim to revise and better illustrate the approach of Re-ttention and existing methods, and then provide contrast of the contributions, such as the statistical reshaping method and the minimal performance degradation observed under ultra-sparse attention.\n\n>**W3. The implementation has a large room of improvement. I have a glance of the codes. It seems that the authors adopt naive implementation to extract the attention score and compute the window attention, without using fused kernels like FlashAttention. I wonder if this will consume more GPU memory. And is there any solution to further enhance the efficiency using fused kernels or the api of flexattention in PyTorch?**\n\nThe implementation we uploaded uses the original Pytorch implementation instead of specialized fused kernels, this work focuses on showing and verifying that the proposed statistical reshaping mechanism is critical and necessary to maintain generation quality for DiT under ultra-sparse attention on a wide range of T2I and T2V benchmarks, and its performance comparison to existing methods, rather than on kernel development.\n\nNote that as our method introduces a post-Softmax attention rescaling step, it is not compatible with existing kernel solutions like FlexAttention, which only provides flexibility before the Softmax operation. That said, we believe that Re-ttention can be adapted to incorporate kernel fusion with further non-trivial engineering efforts, which is an interesting direction for future work.\n\n>**Q1. The acceleration is compared against naive full attention implementation or fused attention in PyTorch 2.0 or higher?**\n\nThey are compared against the fused attention in PyTorch 2.0.\n\n[1] G. Xiao, Y. Tian, B. Chen, S. Han, and M. Lewis, “Efficient streaming language models with attention sinks,” arXiv preprint arXiv:2309.17453, 2023."}, {"review_id": "OPdzOJAwSh", "reviewer": "Reviewer_SARk", "summary": "This paper presents a method for sparse attention in image and video generation. It re-scales the attention matrix of sparse attention to be close to full attention. It also adds residual from a previous layer to the following attent results. The method has been applied to text-to-image and text-to-video generation models. The proposed method shows better evaluation metrics when compared with previous methods.", "questions": "-  Is the degradation of DiTFastAttn on PixArt-Sigma related to token compression? How does Re-ttention handle this change?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n- The method is training-free, and can be applied to any diffusion models with attentions.\n- The method shows better metrics in evaluations.\n\nWeakness:\n- The paper brings limited novelty. Residual caching was introduced by DiTFastAttn. Apart from that, the contribution of this paper is the attention scaling, which is minor. \n- Unclearness about experimental results. For T2V, the proposed method is slightly better than previous method DiTFastAttn. However for T2I (Figure 6), DiTFastAttn performs well on PixArt-alpha, but severely degrades on other methods. The paper didn’t analyze the degradation.\n- In ablation studies, the paper only studies hyperparameter lambda.  In addition to that, the paper should ablate the components of the proposed method: attention rescaling and residual caching. \n- Writing can be improved.", "was_revised": true, "final_justification": "The authors addressed my concerns on experimental results and the significance. Although the proposed attention scaling is a small change to previous work, it's effective to improve attention sparsity from 70% to 95%. Additional experiments show that 95% sparsity has a significant impact to computation speed.", "rebuttal": ">**W1. The paper brings limited novelty. Residual caching was introduced by DiTFastAttn. Apart from that, the contribution of this paper is the attention scaling, which is minor.**\n\nWe would like to invite the reviewer to re-evaluate the value and significance of Re-ttention through the aspects of 1) the limitation of prior sparse attention and caching method for DiT and why these schemes cannot uphold their statistical assumption; 2) how our revised statistical assumption fills the important gap in DiT attention approximation, and the mathematically grounded statistical reshaping method in Re-ttention to cope with attention distribution drift that supports our assumption; 3) the significant performance improvement over existing schemes on both T2I and T2V tasks to achieve much higher sparsity levels that the literature has never reported.\nWe respectfully disagree with this comment, which might be a misunderstanding or at least an over-simplified/downgraded view of our contribution. In the following, we explain the key differences in detail:\n\n1. DiTFastAttn’s failed caching assumption: Sparse attention with residual caching in DiTFastAttn caches the difference (subtraction) between full and sparse attention in one step and uses this difference in subsequent steps, based on the assumption that the “residual” changes minimally from step to step. However, this assumption is not true, since it naturally assumes the attention scores of tokens within the window remain unchanged across timesteps. For example, given a row of full attention scores: $\\text{softmax}(QK) = [0.1, 0.4, 0.4, 0.1]$, DiTFastAttn computes the sparse attention scores as $\\text{softmax}(QK) = [0, 0.5, 0.5, 0]$ and the residual as $R = \\text{full attention - sparse attention} =  [0.1, -0.1, -0.1, 0.1]$. If in the next step the true full attention scores have a different distribution inside the window (the 2 middle positions), e.g. $\\text{softmax}(QK) = [0.1, 0.3, 0.5, 0.1]$, the sparse attention output becomes $\\text{softmax}(QK) = [0, 0.375, 0.625, 0]$. The attention reconstructed with the cached residual $R = [0.1, -0.1, -0.1, 0.1]$ is $[0.1, 0.275, 0.525, 0.1]$, which is incorrect in the sparse window (the most important part for attention). In addition to the abuse of this assumption, the common limitation of existing sparse attention methods, including DiTFastAttn, is that they ignore the importance of the denominator in softmax operation.\n\n2. Re-ttention’s new assumption, statistical reshaping and caching schemes: Re-ttention fixes this issue by reconstructing softmax denominator by a cached $\\rho$ (as detailed in Sec. 4.2) together with our specific residual caching for attention only outside the window. In other words, our corrected mathematical assumption is that attention outside the window changes minimally across steps, whereas sparse attention within the window is critical and could change from step to step and thus demands a whole new reshaping technique to reconstruct the full attention. Specifically, given the same row of attention scores $\\text{softmax}(QK) = [0.1, 0.4, 0.4, 0.1]$, Re-ttention caches [0.1, 0, 0, 0.1] as the residual, i.e., only the attention of masked tokens outside the sparse window (see Appendix A.3). In the next timestep, if $\\text{softmax}(QK) = [0.1, 0.3, 0.5, 0.1]$, the sparse attention output is $\\text{softmax}(QK) = [0, 0.375, 0.625, 0]$. After applying our proposed statistical reshaping, it becomes $[0, 0.3, 0.5, 0]$ (Please refer to Sec. 4.2 for how to reshape the sparse attention). Adding the residual attention outside the window $[0.1, 0, 0, 0.1]$ makes it match the full attention. Thus, we provide a mathematical grounded solution: as long as the ratio $\\rho$ as defined by Eq (8) is estimated accurately and residual attention outside the window does not change much, there is a good chance of approximating the full attention, even if attention distribution changes within the window. However, with the prior caching method in DiTFastAttn, we can see that the reconstruction is almost always wrong if the detailed distribution of sparse attention within the sparse window alters from time step to step, which happens frequently if a high sparsity level is applied (leading to even smaller windows).\n\n3. Experimental evidence: We largely surpass all the baseline methods in both quantitative and qualitative performance evaluation on multiple benchmarks and tasks. In quantitative metrics, Re-ttention outperforms DiTFastAttn across all T2V and T2I tasks. Especially in the T2I tasks, Re-ttention with 96.9% sparsity outperforms DiTFastAttn with 93.8% sparsity in Table 2, across 5 different benchmarks (HPSv2, GenEval, LPIPS, IR, CLIP). For qualitative visual comparison, Re-ttention preserves the visual quality and maintains high consistency even under 96.9% sparsity, while DiTFastAttn suffers from visual artifacts, degraded textures, and even semantic corruption in Figures 1, 6, 9, 10, 11, 12, 13, 14, 15.  Such a significant gap across a wide range of benchmarks and metrics would not have been possible if Re-ttention is a minor increment of prior sparse attention work. \n \nIn summary, Re-ttention is a holistic mathematically grounded method to revisit how full attention can be recovered from sparse attention through caching in DiT, and requires two necessary parts to work together: statistical reshaping of attention within the sparse window via the estimated denominator, and residual compensation by caching masked tokens’ attention. \n\n\n>**W2. Unclearness about experimental results. For T2V, the proposed method is slightly better than previous method DiTFastAttn. However for T2I (Figure 6), DiTFastAttn performs well on PixArt-α, but severely degrades on other methods. The paper didn’t analyze the degradation.**\n\nWe would like to note that all the results are authentically reported by following the implementation details provided in the original papers or by executing their publicly available codes, and analyzing the incompetence of other baseline methods is out of the main focus of this paper. However, we provide some plausible explanations for these results regarding DiTFastAttn.\n1. The more severe degradation observed in DiTFastAttn is primarily due to different latent spaces. PixArt-α uses the LDM’s visual autoencoder (VAE), while PixArt-$\\Sigma$ and Hunyuan use SDXL’s VAE. SDXL’s VAE uses the same architecture as LDM’s, but has superior performance due to the improved training procedure. This makes it more expressive, but likely more sensitive to minor changes in latent distribution. Due to the distribution shift of the attention score, DiTFastAttn deviates from the full attention at ultra-sparse settings, which causes varying degrees of degradation—in Sigma and Hunyuan, the degradation is more severe as the latent space is more sensitive to the deviation.\n2. We use the visual result in Figure 6 to explain our claim above. In PixArt-α, the latent space is more robust, and distribution shifts can lead to minor changes, although still noticeable, in semantic attributes such as the breed of the cat and the style or quality of the background (although there are still some visible artifacts in certain areas). In the other two models, which use more sensitive latent spaces, the impact of distribution shifts due to sparse attention is more severe—resulting in prominent visual artifacts, degraded textures, and even semantic corruption such as distorted object shapes or inconsistent attributes. In contrast, Re-ttention does not suffer from the same degree degradation—it preserves the visual quality and maintains high consistency under ultra sparse settings. Additional visual comparison can be found in Figure 13, 14, 15.\n3. Literature shows that compressing PixArt-$\\Sigma$ and Hunyuan are more challenging problems than PixArt-α. ViDiT-Q [1] shows that naive quantization only slightly decreases PixArt-α’s performance, while severely decreasing PixArt-$\\Sigma$’s performance.\n\n>**W3. In ablation studies, the paper only studies hyperparameter lambda. In addition to that, the paper should ablate the components of the proposed method: attention rescaling and residual caching.**\n\n1. Regarding attention rescaling and residual caching, these are not independent modules but are jointly designed as the core of our method. The attention rescaling recovers the attention distribution within the sliding window, but deviates from the intended probability formulation (sum probability < 1). Hence, we propose to apply the cached residual from the masked tokens, thereby compensating for the missing probability. \n2. We ablate the components of Re-ttention, and evaluate them on CogVideoX T2V models. All sparsity is set to 96.9% to be consistent with the Table 1 result. Removing either residual caching or attention scaling degrades all the evaluation metrics severely, re-affirming that Re-ttention is a holistic new method based on different mathematical assumptions than DiTFastAttn. In Re-ttention, all the components are indispensable and need to work together to achieve good performance.\n\n| Attention     | PSNR ↑ | SSIM ↑ | LPIPS ↓ | ImageQual ↑ | SubConsist ↑ |\n|---|---|---|---|---|---|\n| Re-ttention   | 27.96  | 0.894  | 0.059    | 64.87%       | 94.80%         |\n| No Residual   | 14.51  | 0.563  | 0.602    | 37.89%       | 94.49%         |\n| No Scaling    | 2.64   | 0.443  | 0.606    | 39.50%       | 90.02%         |\n\n>**Q1. Is the degradation of DiTFastAttn on PixArt-Sigma related to token compression? How does Re-ttention handle this change?**\n\nWe use the pipeline in HuggingFace’s diffusers to run our experiments. And to our knowledge, this token compression technique has not been implemented.\n\n[1] T. Zhao, T. Fang, H. Huang, et al., “Vidit-Q: Efficient and accurate quantization of diffusion transformers for image and video generation,” arXiv preprint arXiv:2406.02540, 2024."}, {"review_id": "NY7Hgli9qp", "reviewer": "Reviewer_phiV", "summary": "This paper proposes Re-ttention, a training-free sparse attention mechanism for Diffusion Transformers that enables ultra-sparse inference (up to 96.9% sparsity) without quality degradation. It addresses the softmax distribution shift caused by sparsity by reusing attention score statistics across denoising steps via a cached denominator ratio and residual correction. The method is simple to implement, incurs negligible overhead, and achieves over 92% self-attention latency reduction. Extensive experiments on T2I and T2V tasks demonstrate that Re-ttention outperforms prior sparse attention baselines like DiTFastAttn, MInference, and SVG in both quality and efficiency.", "questions": "1. How sensitive is the method to the static attention mask design? Since Re-ttention relies on fixed sparse masks, how would it perform under dynamically computed masks (e.g., block sorting)? Could the authors clarify whether the denominator approximation remains valid when token positions change?\n\n2. Is the ρ-ratio globally or locally applied? In Section 4.2, the paper defines ρ as a per-token ratio, but in practice it is unclear whether ρ is shared across heads, tokens, or averaged. A clarification and analysis of how local vs. global scaling affects quality would be helpful.\n\n3. Error accumulation in long denoising schedules: Re-ttention reuses ρ across denoising steps, adding a ramp-up λ. Could this accumulate estimation errors over time? Have the authors tested performance for longer denoising schedules (e.g., 100+ steps) or varied sampling schedules?\n\n4. Is residual caching practical under memory constraints? While self-attention latency is reduced, the method introduces a memory-time tradeoff by caching full attention outputs periodically. Could the authors provide quantitative memory overhead analysis?\n\n5. Does Re-ttention generalize to conditional control tasks? For example, text-to-image with spatial constraints or layout conditions. Can the statistical correction mechanism interfere with structured control, or can it be combined with existing conditioning modules?", "limitations": "The paper briefly discusses limitations in future work (e.g., extending to LLMs and dynamic masks), but does not thoroughly analyze potential risks such as mismatch in attention semantics, degradation under noisy prompts, or applicability to constrained generation tasks.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n1. The paper proposes a simple yet effective method to mitigate softmax distribution shift in sparse attention via cached normalization and residual correction.\n\n2. Re-ttention achieves ultra-high sparsity (96.9%) with minimal quality loss and over 92% self-attention latency reduction across T2I and T2V tasks.\n\n3. The method is training-free, generalizable across multiple DiT models, and thoroughly validated with strong empirical results.\n\n**Weakness**\n\n1. The statistical correction introduced by the ρ-scaling and residual caching lacks a theoretical grounding—there is no guarantee that the modified attention remains consistent with probabilistic interpretations or converges to full attention. \n\n2. While the method is empirically validated across several models, the sparse mask remains static. This limits generalization to dynamic attention scenarios, and the paper does not examine how Re-ttention interacts with token-level adaptivity or downstream control tasks.\n\n3. The ablation is relatively narrow: only one hyperparameter (λ) is tested on a subset of benchmarks, without broader sensitivity analysis or exploration of failure modes.", "was_revised": true, "final_justification": "The authors’ detailed response has satisfactorily addressed most of my concerns, which encouraged me to raise my score.", "rebuttal": ">**W1: The statistical correction introduced by the ρ-scaling and residual caching lacks a theoretical grounding—there is no guarantee that the modified attention remains consistent with probabilistic interpretations or converges to full attention.**\n\nWe offer a better approximation to full attention under ultra-sparse settings than prior works under a new assumption, emphasizing the importance of denominator rescaling. The residual caching in existing sparse attention methods can not guarantee convergence to full attention and in fact is relying on some assumption that may not hold. For example, given a row of true full attention scores: $\\text{softmax}(QK) = [0.1, 0.4, 0.4, 0.1]$, DiTFastAttn computes the sparse attention scores as $\\text{softmax}(QK) = [0, 0.5, 0.5, 0]$ and the residual as $R = \\text{full attention - sparse attention} =  [0.1, -0.1, -0.1, 0.1]$ via subtraction. DiTFastAttn’s residual contaminates the attention scores if the attention distribution inside the window (the middle two points) changes, i.e., given full attention $\\text{softmax}(QK) = [0.1, 0.3, 0.5, 0.1]$, the sparse attention is $\\text{softmax}(QK) = [0, 0.375, 0.625, 0]$. Reconstructed with residual: [0.1, 0.275, 0.525, 0.1], which is inaccurate in the sparse window. Our method rescales the sparse attention scores as $\\text{softmax}(QK) = [0, 0.4, 0.4, 0]$ and computes the residual as $R’ = [0.1, 0, 0, 0.1]$. Even if the full attention changes to $[0.1, 0.3, 0.5, 0.1]$ and the sparse attention is $[0, 0.375, 0.625, 0]$, after applying our proposed statistical reshaping, it becomes $[0, 0.3, 0.5, 0]$. Adding the residual $R'$ makes it match the full attention. That is to say, as long as the $\\rho$ defined by Eq. 8 is estimated accurately and residual attention outside the window does not change much, we approximate the full attention correctly. \n\n>**W2: While the method's sparse mask remains static. This limits generalization to dynamic attention scenarios, and the paper does not examine how Re-ttention interacts with token-level adaptivity or downstream control tasks.**\n\nThe focus of this paper is on static sparse attention, as it has been shown that static masks are a solid choice for implementing sparse attention, especially in the context of T2I/T2V Diffusion Models, where the generation process involves spatially or temporally structured visual tokens [1]. Compared to the dynamic mask, the static mask is easier to implement and introduces no overhead during inference. However, the statistical reshape for the dynamic mask is worth studying in future work, which is beyond our focus in this paper. That being said, we perform some tentative exploration on adapting Re-ttention on the dynamic sparse mask, please refer to the result shown in response to Q1. As for downstream control tasks, please refer to Q5's response.\n\n>**W3: The ablation is relatively narrow.**\n\nWe conduct more ablation in terms of sampling schedule (see Q.3) and applying dynamic mask (see Q.1).\n\n>**Q1: How sensitive is the method to the static attention mask design? Since Re-ttention relies on fixed sparse masks, how would it perform under dynamically computed masks? Could the authors clarify whether the denominator approximation remains valid when token positions change?**\n\n1. Based on our observations through tentative exploration (which is beyond the focus of this work), the conclusion we make about the denominator approximation still holds for dynamically computed masks, as the ratio ρ exhibits a predictable trend even with dynamically calculated masks. However, because the masking pattern changes at each step, the cached residual can not correctly represent the attention output of masked tokens, thereby resulting in suboptimal performance.\n2. We provide experiment results on directly applying Re-ttention to PixArt-$\\Sigma$ with dynamically calculated masks. The results show that the performance drops after replacing the static mask with the dynamic mask. Note that dynamically calculated masks impose additional computation compared to static attention mask. That being said, exploration of dynamic masks is subject to future investigations, as this paper focuses on maintaining the quality of sparse window attention (with static masks) for DiT. \n\n| Attention              | HPSv2 ↑ | GenEval ↑ |\n|--|--|--|\n| Static mask attention  | 27.72   | 0.512      |\n| Dynamic mask attention | 25.35   | 0.473      |\n\n>**Q2: Is the ρ-ratio globally or locally applied? In Section 4.2, the paper defines ρ as a per-token ratio, but in practice it is unclear whether ρ is shared across heads, tokens, or averaged. A clarification and analysis of how local vs. global scaling affects quality would be helpful.**\n\nThe ρ-ratio is locally applied per attention head, as we described in Section 4.2. More specifically, in Fig. 5, we show the ρ-ratio for a single token at a given attention head. In our observations, the ρ-ratio for a specific token varies greatly across different attention heads, and inaccurately predicting the scaling ρ with an averaged value will be harmful to the performance. Thus, we use a local scaling mechanism.\n\n>**Q3: Error accumulation in long denoising schedules: Re-ttention reuses ρ across denoising steps, adding a ramp-up λ. Could this accumulate estimation errors over time? Have the authors tested performance for longer denoising schedules (e.g., 100+ steps) or varied sampling schedules?**\n\nYes, the estimation error can accumulate over time when reusing ρ across denoising steps. To address this, we periodically insert full attention steps, which effectively act as a reset mechanism to correct any accumulated estimation error in the residuals. In the following, we further add the result on PixArt-$\\Sigma$ with 96.9% sparsity after changing the sampling schedule from $T=5$ to 3 and 7. Smaller $T$ means more full attention inferences and more accurate cached residual, thereby resulting in better performance. However, our Re-ttention outperforms DiTFastAttn regardless of $T$. We believe that the result demonstrates the effectiveness of our method across different sampling schedules.\n\n| Attention     | T = 3 GenEval ↑ | T = 3 HPSv2 ↑ | T = 5 GenEval ↑ | T = 5 HPSv2 ↑ | T = 7 GenEval ↑ | T = 7 HPSv2 ↑ |\n|---|---|---|---|---|---|---|\n| DiTFastAttn   | 0.376            | 26.81          | 0.233            | 22.79          | 0.147            | 20.87          |\n| Re-ttention   | 0.544            | 29.26          | 0.512            | 27.72          | 0.479            | 26.16          |\n\n>**Q4: Is residual caching practical under memory constraints? While self-attention latency is reduced, the method introduces a memory-time tradeoff by caching full attention outputs periodically. Could the authors provide quantitative memory overhead analysis?**\n\n1. Yes, it is practical and under memory constraints. In most DiT architectures [2], since the cached residual has the same shape as the attention output, its memory overhead is typically considered negligible relative to the model weights. We use PixArt-$\\Sigma$ as an example to perform a quantitative analysis on the memory consumption of the cached residual. We use the HuggingFace implementation to measure the memory consumption. Loading PixArt-$\\Sigma$ model takes 13080MiB, while the cached residual (for one 1024x1024 image) is 36 MiB, which is ignorable. \n2. Furthermore, it involves only simple addition and subtraction operations, which are memory-efficient and impose minimal computational burden [3]. \n\n>**Q5: Does Re-ttention generalize to conditional control tasks? Can the statistical correction mechanism interfere with structured control, or can it be combined with existing conditioning modules?**\n\n1. Yes, Re-ttention is applicable to the conditional control tasks as well. This potential has already been shown in our T2V experiment, which uses CogVideoX model and is a scenario that needs to handle conditional control tokens (i.e., text tokens as control tokens). This is because CogVideoX model follows the MMDiT architecture [4]. This architecture omits explicit cross-attention and instead employs a unified self-attention mechanism over both visual tokens and conditional tokens (including text, image, layout tokens). In Table 1, we can see that Re-ttention can adapt to this setting and achieves outstanding performance, outperforming other baseline methods across 5 different metrics related to quality, similarity, and consistency.\n2. Another setting of conditional control tasks involves cross-attention between control tokens and generated visual tokens, which is beyond the scope of this work. The focus of this paper is on ultra-sparse self-attention, since sparse attention is usually applied to self-attention, i.e., the bulk of attention computation cost is for the self-attention mechanism rather than cross-attention. In fact, it is common practice in the literature [5] to apply full attention to cross-attention layers, as the computational cost of cross-attention is relatively low, making sparse attention unnecessary.\n\n[1] H. Xi, S. Yang, Y. Zhao, et al., “Sparse VideoGen: Accelerating video diffusion transformers with spatial-temporal sparsity,” arXiv preprint arXiv:2502.01776, 2025.\n\n[2] W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2023, pp. 4195–4205.\n\n[3] T. Dao, D. Fu, S. Ermon, et al., “FlashAttention: Fast and memory-efficient exact attention with IO-awareness,” Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 35, pp. 16344–16359, 2022.\n\n[4] P. Esser, S. Kulal, A. Blattmann, et al., “Scaling rectified flow transformers for high-resolution image synthesis,” in Proc. 41st Int. Conf. Mach. Learn. (ICML), 2024.\n\n[5] Z. Yuan, H. Zhang, L. Pu, X. Ning, et al., “DiTFastAttn: Attention compression for diffusion transformer models,” Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 37, pp. 1196–1219, 2024."}]

Author rebuttal:

Meta-review: This paper introduces Re-ttention, a sparse attention mechanism that achieves ultra-high sparsity (up to 96.9%) with minimal quality degradation in diffusion transformers. The method is well-motivated, proposing a statistically grounded correction of softmax denominator shift. The authors provide extensive experiments across T2I and T2V models and strong ablations, showcasing both effectiveness and generality. During the rebuttal, the authors addressed most of reviewers’ concerns, including theoretical intuitions, implementation details, comparisons, and memory/efficiency trade-offs. Reviewers converged on borderline-to-positive recommendations. With strong empirical results and its potential for broad applicability, I recommend acceptance.
Decision: Accept (poster)