Title: Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape
Venue: NeurIPS 2025
Accepted: yes
Keywords: Sparse Attention

Diffusion Transformer

Image and Video Generation
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

Abstract

Diffusion Transformers (DiT) have become the de-facto model for generating highquality 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.

1 Introduction

Diffusion Transformers (DiT) [33, 3, 2, 23, 10] combine the attention [38] mechanism with the iterative denoising of Diffusion Models [35] to generate high-quality visual content such as videos [47, 54] and images [20, 44, 41, 31]. 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.e., resolution and video length.

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], as well as XAttention [46] achieve ∼ 50% sparsity (i.e., reducing only 50% of the attention computations) by relying on downsampling the attention map or 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 as the progressively denoising structure and the spatial/temporal locality of attention.

Full-Attention

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(87.5%)

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(96.9%)

Ours:

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While these methods can reduce more than half of the attention computation, their effectiveness remains 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. However, 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, it is simple to implement and incurs negligible overhead compared to standard sparse attention at the same sparsity level. Figure 1 provides sample content from our technique compared to other sparse attention methods. Our detailed contributions are as follows:

We relate the failure to achieve degradation-free >95% sparsity without training from scratch to the distributional shift in attention scores caused by the reduced softmax denominator term, i.e., the row-wise sum of the exponentials of involved elements. We design an experiment to illustrate the importance of preserving this term and the impact on visual generation.

We discover the softmax distribution redundancy among neighboring denoising steps. Although the actual value of denominator changes unpredictably, the ratio between the sparse and full denominator is relatively stable.

We propose that the attention scores shifted by sparse attention are viable to be recovered by approximating the real softmax denominator.

The recovered attention scores deviate from a valid probability distribution, as their sum is less than one, violating the normalization property of softmax. We leverage the redundancy among neighboring denoising steps to compensate the missing probability with residual.

We apply Re-ttention to T2V models such as CogVideoX [47] 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 [3, 2] and others [23] to maximize performance on Human Preference Score v2 (HPSv2) [41] and GenEval [12] while achieving a high sparsity of 96.9%.

2 Related Work

Diffusion Models (DM) [14] dominate visual generation tasks. Early DMs [35, 34] use convolutional U-Net structures [36] as their backbones. Later, Diffusion Transformers (DiT) [33, 5] adopt the attention-based [38] of Vision Transformers (ViT) [9] to increase scalability and visual generation quality. In addition to being the favored backbone structure for text-to-image (T2I) DMs [3, 2, 23, 10, 20, 44], the DiT structure is extensible to video generation [40, 19] as well. Specifically, Latte [30] proposes a 2D+1D attention block for video generation, which performs spatial and temporal attention separately. Subsequent works like CogVideoX [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 [1] 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 a factor or 8 × 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 T ) into blocks then dynamically select the topk 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 StreamingLLM [43], DiTFastAttn [48] and Sparse VideoGen [42] identify the special attention patterns in LLM and DiT and propose efficient attention masking based on those patterns. However, the sparsity achievable by these methods is limited to < 70% , which is much higher compared to 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 is a 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 it well-suited for the application of caching techniques. Recent methods [4, 27, 53] 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 to improve the visual quality.

3 Background: The Attention Mechanism and Sparsity

The attention mechanism [38] is the foundation of transformer architectures like DiTs. Let X ∈ R T × d be an input token/patch sequence, where T is the sequence length, dependent on the input size (e.g., image resolution or video length) and d is the embedding dimension, a hyperparameter of the transformer model. The attention mechanism contains h heads such that d h = d h ; d h ∈ Z + . We first map X into three representations, Query ( Q ), Key ( K ) and Value ( V ) of identical size R h × T × d h , then compute the attention as follows,

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Re-ttention for Ultra-Sparse Visual Content Generation

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We can decompose this mechanism into several intermittent matrices, specifically the product of Q and K before ( A pre ) and after ( A ) the softmax operation:

The computation of these matrices is very expensive [11]. To make matter worse, their size scales quadratically with T , which depends on the image/video resolution and video length. However, the softmax operation is computed row-wise and produces a probability distribution ∑ T j =1 A : , : ,j = 1 , which empirically produces a 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 to omit less relevant values of A pre , 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 } h × T × T where 1 means an index of A pre will be included in the softmax, while the rest are excluded. The indexes of the included values in A pre form a set S = ⋃ h k =1 ⋃ T i =1 S k,i , where S k,i = { ( k, i, j ) | M k,i,j = 1 , 0 ≤ j ≤ T } .

Given an arbitrary element of the pre-softmax matrix A pre k,i,j , the normal and sparse softmax computation are given by

respectively. Ultimately, the mask matrix M determines the potential amount of computational savings. M can be computed statically [7] prior to inference or 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 a percentage, e.g., 10%, 50%, 90%, etc., simply by computing the ratio of values in M that are 0 as follows:

where a higher value for sparsity corresponds to a 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 to detrimental performance. As we 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 a hypothesis regarding how distributional shift in softmax statistics prevents current training-free sparse attention methods from satisfactorily operating at high sparsity, e.g.,

> 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 2 provides a high-level overview of our proposed technique in comparison to full and sparse attention.

4.1 Importance of the Softmax Denominator

As a preliminary investigation, we gauge the performance of several existing sparse attention techniques [18, 48, 42]. We consider the GenEval [12] benchmark and evaluate performance across a spectrum of sparsity values, i.e., starting at the highest sparsity these techniques consider in their original manuscript and then further increasing the sparsity.

Figure 3 illustrates our findings. We observe that existing approaches suffer a monotonic performance drop when the sparsity is further increased beyond their proposed value (denoted with ⋆ ). Per Eq. 5, a higher value of sparsity corresponds the inclusion of fewer tokens in the softmax denominator as S k,i shrinks. Thus, the further removal of tokens, i.e, increasing sparsity closer to 100%, has a larger impact on the overall denominator value [43]. This phenomenon, introduces a detrimental distribution shift in the overall attention scores.

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We design a toy experiment to test this hypothesis and showcase the significance of the softmax denominator term. Specifically, we define a post-softmax masking operation as

where A is the output of the original full softmax attention via Eq. 4, ◦ denotes element-wise multiplication and A ′ is masked attention. We emphasize that Equation 7 is not a proper sparse attention calculation and does not entail speedup. However , it mimics the output of sparse attention as M still zeroes out the same indices of A , yet preserves the denominator of the full softmax.

Pre-Softmax

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 for comparison. Figure 4 provides a comparison, though we provide additional examples and prompts in the supplementary due to space constraints. We observe how the post-softmax attention preserves the guitar-playing panda, chair and background while the pre-softmax attention creates a 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 to preserve this information in an efficient sparse attention setup.

Post-Softmax

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 of the DM denoising process and taking inspiration from DiT caching [4, 27, 53] and redundancy [37] methods.

Denominator Approximation. Figure 5 tracks the magnitude of the softmax denominator for a single token in the 9th head of the 12th DiT block in PixArtα Specifically, we calculate the

denominator using both the full and sparse attention (with 87.5% sparsity) as well as the ratio ρ between these statistics,

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This yields an insightful observation: While the actual value of the denominators may change unpredictably and non-monotonically over the denoising process, given a statically computed mask M , the ratio ρ follows a predictable trend. Therefore, we propose a simple method to recover the attention distribution by modifying the output of the sparse softmax operation Eq. 5 as follows:

where we cache 0 < ρ ≤ 1 from a previous step. Multiplying the Softmax output of sparse attention by ρ approximates the full attention value, mitigating the distribution shift. In practice, since ρ is not a constant per Fig. 5, we empirically find that slightly increasing the cached ρ after each denoising step achieves better performance. Thus, we parameterize ρ t = ρ t -1 + λ after each denoising step, where λ is a ramp-up hyperparameter.

Residual Caching. Although we can modify the softmax to make the sparse attention more closely match that of full attention, Equation 8 does not yield proper probability distributions as ∑ T j =1 A : , : .j < 1 . Practically, this reduces the magnitude of overall attention outputs per Equation 1, 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 ρ -reshaped attention via Equations 5 and 9:

We can later add R to 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 statistics from the full attention in some steps, which is common in DiT sparse attention methods [48, 50].

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 CogVideoX (2B) [47], PixArtα / Σ (0.6B) [3, 2] and Hunyuan-DiT (1.6B) [23]. We generate 720 × 480 resolution, 6 second videos at 8 fps and 1024 × 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-free guidance (CFG) scale and number of denoising steps - 50 for CogVideoX/Hunyuan and 20 for the 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 PixArt DiTs, Hunyuan and CogVideoX models, respectively, and then apply the sparse attention mechanism for the remainder of the denoising process. Further, we set a caching period of 5 steps for DiTFastAttn and Re-ttenion, where we

perform full attention to 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 a video containing a single frame. We provide further baseline experimental details in the supplementary.

The rest of this section is organized as follows: We enumerate our T2V and T2I 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-Video Evaluation

Weperform quantitative T2V evaluation using the Animal and Architecture categories of VBench [16], 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, we compute the Peak Signal-to-Noise Ratio (PSNR) [15], 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 an apples-to-apples performance investigation. However, some methods exhibit substantial degradation at this level and produce very noisy/black frames, so we additionally report results at a less aggressive setting of 87.5% sparsity.

Table 1 presents our findings. Results demonstrate that Re-ttention consistently outperforms all 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 on all other metrics at this sparsity level. Furthermore, 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 in T2V applications.

We provide some sample frames from videos generated by Re-ttention, baseline sparse attention methods and full attention. Specifically, recall Figure 1 in the introduction. The video generated by Re-ttention shows the best clarity and temporal consistency across frames for the main subject, and it 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 DiTFastAttn has noisy texture artifacts both in the background and the subject. For SVG and MInference, the subject is inconsistent and deformed despite using a 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 [41], 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, we generate 10k

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Prompt: 'A cat on a city street with people.'

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.

Table 2 lists our results on the T2I task. Re-ttention outperforms all other sparse attention methods across models and metrics, showing consistently better performance. Additionally, Re-ttention achieves this while operating under an extremely high sparsity of 96.9%, which reduces the token/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, a feat that no other sparse attention method attains. Moreover, while DiTFastAttn attains similar T2V performance to 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 of our technique.

Next, we present the visual (qualitative) comparisons on PixArtα [3], PixArtΣ [2] and Hunyuan [23] T2I models in Figures 6. More visual comparisons can be found in the Appendix. Overall, Rettention 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 PixArtα [3] and PixArtΣ [2], Re-ttention generates clean, high-quality images that are well aligned to the prompts. Whereas the other methods often generate colored noise artifacts, distorted subjects, and lower quality images. For Hunyuan [23], 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.

5.3 Ablation Studies

Finally, we ablate the effect of the ramp-up hyperparameter λ on PixArtΣ [2] on overall performance. Specifically, we evaluate on HPSv2 overall as well as the 'animation' and 'conceptart' categories. Table 3 reports our findings. These findings demonstrate the robustness of Re-ttention as it is possible to forgo the λ parameter, yet it is better to select a moderate value.

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-ttention and address limitations should aim to repurpose our contributions in the application domain of LLMs 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 a static mask, though further investigation is merited to validate it in the context of dynamically-generated 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 a wider range of sequence modeling and generative tasks.

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A Appendix

We provide additional information about our work. Sections A.1 and A.2 provide statements about the broader impacts of our work and limitations, respectively. Further, we provide additional details on our methodology in Sec. A.3 and baselines in Sec. A.4. Section A.5 provides elaborates on our pre vs. post-softmax example from Figure 4. 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 attention. 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 on 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-masked attention of LLMs which may more often feature different attention patterns such as columns [7, 43]. Additionally, Re-ttention exploits the sequential nature of DMs and is 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 a 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 of 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 A in Eq. 3 and the set S that contains the included values (by sparse attention) in A . Hence, the A can be decomposed into two parts:

where 1 ∈ S is the indicator matrix that is 1 where ( k, i, j ) ∈ S , and 0 elsewhere. Conversely, 1 / ∈ S is 1 where ( k, i, j ) / ∈ S and 0 elsewhere.

At the caching timestep, we have the ratio ρ between the denominator of full and sparse attention according to Eq. 8. Because we compute full attention in the caching step, the ratio ρ is not an approximation but an accurate value. Hence, we have:

Therefore, the residual R in Eq. 10 is:

which is mathematically equivalent to the attention output of the masked tokens at the caching timestep.

pre-softmax post-softmax

pre-softmax

post-softmax

A.4 Details of Baseline Implementation

We compare Re-ttention to three different baseline methods: Sparse VideoGen (SVG) [42], MInference [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 DiTFastAttn, we use sliding window attention, which restricts each token's attention to a local neighborhood and will repeat the same mask at each frame of the video.

Text-to-Image Since T2I generation lacks a temporal dimension, we 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 7 expands on Fig. 4 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

large panda to vanish. This example further validates our assumption regarding the importance of maintaining the softmax denominator.

Further, Figure 8 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 probabilities-specifically, 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 on video generation using the CogVideoX2B [47] model in Figures 9, 10, 11 and 12. For example, in Figure 10, Re-ttention has the best looking otter as well as the food with the most similar shape as 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 to 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 on image generation using the PixArtα [3], PixArtΣ [2], and Hunyuan [23] models in Figures 13, 14 and 15, 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 are 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.

Full-Attention

SVG

(87.5%)

MInference

(87.5%)

DiTFastAttn

(96.9%)

Ours:

Re-ttention

(96.9%)

Full-Attention

SVG

(87.5%)

MInference

(87.5%)

DiTFastAttn

(96.9%)

Ours:

Re-ttention

(96.9%)

Full-Attention

SVG

(87.5%)

MInference

(87.5%)

DiTFastAttn

(96.9%)

Ours:

Re-ttention

(96.9%)

Full-Attention

SVG

(87.5%)

MInference

(87.5%)

DiTFastAttn

(96.9%)

Ours:

Re-ttention

(96.9%)

Full-

Attention

SVG

(75%)

DiTFastAttn

(93.8%)

Full-

Attention

SVG

(75%)

DiTFastAttn

(93.8%)

L

Full-

Attention

SVG

(75%)

MInference

(75%)

DiTFastAttn

(93.8%)

DiTFastAttn

(96.9%)

Re-ttention

(93.8%)

Re-ttention

(96.9%)

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strength:
- The method is training-free, and can be applied to any diffusion models with attentions.
- The method shows better metrics in evaluations.

Weakness:
- 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. 
- 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.
- 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. 
- Writing can be improved.

QUESTIONS:
-  Is the degradation of DiTFastAttn on PixArt-Sigma related to token compression? How does Re-ttention handle this change?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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?\n\nQUESTIONS:\n* 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.\n\nLIMITATIONS:\nThe authors have discussed the limitations in the supplement.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: 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.

STRENGTHS AND WEAKNESSES:
**Strengths**  
1. The paper is well-motivated, with abundant analyses and examples that clearly demonstrate the reasoning behind its technical designs.  
2. It provides extensive experimental results across various models, effectively showcasing its robustness and effectiveness.  
3. The figures are highly informative and significantly aid in understanding the core concepts of the paper.  

**Weaknesses**  
1. 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.  
2. 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.

QUESTIONS:
see above

LIMITATIONS:
see above
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03