AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Authors: Wenhao Sun
Rong-Cheng Tu
Jingyi Liao
Zhao Jin
Dacheng Tao
Venue: ICML 2025 poster (2025)
Keywords: Video Generation
Diffusion Models
Video Transformers
Efficient Diffusion
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: A training-free token-reduction approach to accelerate state-of-the-art video DiTs without compromising generation quality.

Abstract: Diffusion Transformers (DiTs) have proven effective in generating high-quality videos but are hindered by high computational costs. Existing video diffusion sampling acceleration methods often rely on costly fine-tuning or exhibit limited generalization capabilities. We propose Asymmetric Reduction and Restoration (**AsymRnR**), **a training-free and model-agnostic method to accelerate video DiTs**. It builds on the observation that redundancies of feature tokens in DiTs vary significantly across different model blocks, denoising steps, and feature types. Our AsymRnR asymmetrically reduces redundant tokens in the attention operation, achieving acceleration with negligible degradation in output quality and, in some cases, even improving it. We also tailored a reduction schedule to distribute the reduction across components adaptively. To further accelerate this process, we introduce a matching cache for more efficient reduction. Backed by theoretical foundations and extensive experimental validation, AsymRnR integrates into state-of-the-art video DiTs and offers substantial speedup.

AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Wenhao Sun
Rong-Chere Tu
Jingyi Liao 12
Zhao Jin
Dacheng Tao
Hunyuan Video
Video
+AsymRnR
SostVide--Hunyaene
+AsymRnR
avg. latency 913s (original)
avg. lateney 703s
avg. lateney 159s (original)
avg. latency 128s (24% faster)
(30% faster)
prompt: A person is kaitiing....
promp: A toddy hear swimming in the occon
Figure 1. Quality and speed comparison between baseline models, Hunyuan Video (Team, 2024c) and FastVideo-Hunyane (Team, 2024a), with our AsymRnR. Our approach enables training-fre free, lossless acceleration for statc-of-the theth-rrt video diffusion transformers.
Abstract
attention operation, achieving acceleration with negligible degradation in output quality and, in some cases, even improving it. We also tailored a reduction schedule to distribute the reduction across components adaptively. To further acceler- ate this process, we introduce a matching cache for more efficient reduction. Backed by theoreti- cal foundations and extensive experimental vali- dation, AsymRnR integrates into state-of-the-art video DiTs and offers substantial speedup. The code is available at https: //g/tttthb//////////// com/ wenhac722 /ss//ssmmmnn
Diffusion Transformers (DiTs) have proven ef- fective in generating high-quality videos but are hindered by high computational costs. Exist- ing video DiT sampling acceleration methods of- ten rely on costly fine-tuning or exhibit limited generalization capabilities. We propose Asym- metric Reduction and Restoration (AsymRnR), a training-free and model-agnostic method ac- celerate video DiTs. It builds the observation that redundancies of feature tokens in DiT's vary significantly across different model blocks, de- noising steps, and feature types. Our AsymRnR asymmetrically reduces redundant tokens in the
1. Introduction
"College of Computing and Data Science, Nanyang Techno- logical University, Singapore, Singapore "Institute for Infocomm Research (I2R), A Singapore, Singapore. Correspondence to: Long-Cheng Tu <rongcheng tu@ ntt.cdu.usg.... Dacheng Tao <dacheng.taod @ ntu.edu.sg>.
Recent progress in video generation has been largely pro- pelled by innovations in diffusion models "Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al., 2020). Building on these developments, the Diffusion Transformers (DiTs) (Peebles Xie, 2023) have achieved state-of-thh-an results across a range of generative tasks (Zhang et al., 2023; Xing et al., 2025; Shuai et al., 2024; Sun et al., 2024; Tu et al.,
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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AsymRnR: Video Diffussin Transformers Acceleration with Asymmetric Reduction and Restoration
reverse
switch
attn w/ 70%
XT
diffusion
Q tokens
diffusion
original
attn w/ 70%
transformer
attn block
K& V tokens
baseline
POG
(a) effeet of perturbing different features: Q or K&V
(b) effect perturbing different blocks: shallow, medium, or deep
(c) effect of perturbing different
denoising steps: early late
Figure 2. Altering different components in video DìT's leads to varying degradation. Green blocks represent original attention blocks. Blue blocks represent attention blocks where 30% of the query tokens randomly discarded, allowing only the remaining 70% contribute to output. Red blocks represent same perturbation applied to key and value tokens. The comparison includes perturbing: (a) different features: Q or K&V; (b) different DIT blocks: shallow, medium, deep: (c) different timesteps: early or later.
2024). Despite the advancements in video DiTs, the latency remains a critical bottleneck, often taking minutes or even hours to process a few seconds of video (Lin et al., 2024; Yang et al., 2024; Team, 2024b;c).
30% tokens from different features, blocks, and denoising timesteps. The results in Figure 2 highlight three key in- sights: 1) Perturbations in the query (Q) of the early blocks significantly degrade the quality of the generation, whereas similar perturbations in the key (K) and value (V) have less significant impact; 2) The intensity of degradation varies between the perturbed blocks; 3) Perturbing the Q across all blocks but at specific denoising timesteps show that early-timestep perturbations primarily affect semantic accuracy (e.g. temporal motion and spatial layout), while later-time timestep perturbations degrade visual details. Previous token reduction methods (Bolya Hoffman, 2023; Li et al., 2024; Kahatapitiya al., 2024a) apply uniform reductions across all components without accounting for their varying sensitivities. This uniformity disproportionately impacts the most vulnerable components, where even small perturba- tions can significantly degrade the quality of the generation. This phenomenon mirrors Liebig's law of the minimum, where a system's 's capacity is constrained by its weakest element, akin barrel limited by its shortest stave.
Optimizing the efficiency of vision diffusion models has been long-standing research focus. Distillation methods (Salimans & Ho, 2022; Meng al., 2023; Sauer et al., 2024; Luo et al., 2023; Team, 2024c) are widely used to reduce sampling steps and network complexity. However, they require extensive training and impose high computa- tional costs. Feature caching techniques (Zhang et al., 2024; Zhao al., 2024; Kahatapitiya et al., 2024b) provide an alternative acceleration strategy by avoiding redundant com- putations in specific layers. While promising, these methods often tailored to specific network architectures (Rom- bach et al., 2022; Ma et al., 2024b; Zheng et al., 2024; Lin et al., 2024), limiting their generalizability to diverse scenar- ios and broader model families. Another viable strategy is reducing the attention sequence length to mitigate the com- putational overhead in resource- intenssvve attention layers. For example, Token Merging (ToMe) approaches (Bolya & Hoffmann, 2023; Li et al., 2024) merge highly similar (i.e. redundant) tokens to accelerate image and video generation in Stable Diffusson (SD) (Rombach et al., 2022).
Inspired by these observations, propose Asymmetric Re- duction and Restoration (AsymRnR) as plug-and-play ap- proach to accelerate video DiTs. The core idea is to reduce the attention sequence length asymmetrically before self- attention and restore it afterward for subsequent operations. We also propose a reduction scheduling that adaptively ad- justs the reduction rate account for nonuniform redun- dancy. Finally, we introduce the matching cache, which bypasses unnecessary matching computations to acceler- ate further. We conducted extensive experiments to evalu-
However, directly extending ToMe methods to video DiTs often results in distortions and pixelation (as shown in Fig- ure 6). We attribute this issue to the neglect of the vary- ing contributions of different components the final out- put, and to validate this hypothesis, we randomly discard
2

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AsymRnR: Video Diffusson Transformers Acceleration with Asymmetric Reduction and Restoration
ate its effectiveness and design choices using state-of-the- art video DiT's, including CogVideoX (Yang et al., 2024), Mochi-1 (Team, 2024b), HunyuanVideo (Team, 2024c), and FastVideo (Team, 2024a). With AsymRnR, these models demonstrate significant acceleration with negligible degra- dation in video quality and, in some cases, even improve performance as evaluated on VBench (Huang et al., 2024).
cache methods are tightly coupled with specific architec- tures and have not been effectively integrated into few-step sampling models. In contrast, our sequence-level accel- eration method is training- maining-frre and compatible with SOTA video DiT's Our method can also accelerate distilled models or pipelines with caching, achieving additional speedup.
2.3. Token Reduction
2. Related Work
Recent advances in processing long contexts using Trans- formers span areas such as NLP (Leviathan et al., 2024;
2.1. Video Diffusion Networks
Xiao al., 2024; Wang et al., 2020; Choromanski et al., 2021), computer vision (Koner al., 2024; Rao et al., 2021; Yin et al., 2022; Choudhury al., 2024), and multimodal tasks (Darcet et al., 2024; Li et al., 2024; Tu et al., 2023; Ma et al., 2024c; Ji et al., 2023). Many studies have focused on shortening the sequence, primarily targeting discrimina- tive and autoregressive generation tasks. These approaches selectively truncate outputs from preceding layers, with the reductions compounding throughout the process. However, they are unsuitable for diffusion denoising, where the entire sequence must remain restorable.
State-of-the-arr video diffusion methods (Team, 2024b;d; Yang et al., 2024; Team, 2024c) employ DiT backbones.
The core module, self-attention, is defined as follows:
AHln(H) = softmax (QK T)V
(L)
softmax (HHW)) (HWV).
where Wo. WK. Wv E Rdxd denote the projection matri- ces. H E Raxd represents the input sequence, n represents the sequence length, and d represents the hidden dimensions. Certain operations, such as scaling, positional embeddings (Su et al., 2024), and normalization (Ba al., 2016), are omitted here for brevity.
Token Merging (ToMe) (Bolya et al., 2023) achieves to- ken reduction by merging tokens based on intra-sequence similarity, which has been generalized to image generation with SD (Bolya & Hoffman, 2023). However, directly ex- tending ToMe video DiT's poses challenges, often leading to excessive pixelation and blurriness. Moreover, some de- signs are empirical and lack theoretical justification. We revamp their design choices from practical and theoretical
The self-attention operation has a Onn²) time complexity. For video sequences, n is typically very large. Our goal is to simplify its calculation to improve efficiency.
2.2. Efficient Diffusion Models
Step Distillation. Diffusion step distillation studies reduce sampling steps to as few as 4-8 (Salimans & Ho, 2022; Meng et al., 2023; Sauer et al., 2024). InstaFlow (Liu al., 2024) introduces the integration of Rectify Flow (Liu et al., 2023) into distillation pipelines, enabling extreme model compression without sacrificing much quality. Consistency Models (CMs) (Song et al., 2023) propose regularizing the ODE trajectories during distillation. LCM-LoRA (Luo et al., 2023) introduces an efficient low-rank adaptation (Hu et al., 2022), facilitating the conversion of SD 4-step models.
perspectives for greater acceleration and consistent quality.
3. Method
Consider self-attention layer that processes an input ma- trix H E Igndd where is the sequence length and d is the feature dimension. The standard scaled dot- dot-producc self-attention as shown in Equation (1) results in a (nn) complexity, which is costly for long sequences in video gen- eration. We accelerate self-attention in DiT' by reducing the number of tokens n involved in the computation.
Feature Cache Recognizing the small variation in high- level features across adjacent denoising steps, studies (Ma et al., 2024a; Wimbauer et al., 2024; Habibian et al., 2024; Chen et al., 2024) reuse these features while updating the low-level ones. T-GATE (Zhang et al., 2024) caches cross- attention features during the fidelityimprooing stage. PAB (Zhao et al., 2024) caches both self-attention and cross- attention features across different broadcast ranges. Ada- Cache (Kahatapitiya et al., 2024b) adaptively caches fea- tures based on the computational needs of varying contexts.
3.1. Matching-Basn Based Reduction
The hidden states of vision transformers often exhibit redun- dancy (Bolya et al., 2023; Darcet et al.. 2024). These obser- vations motivate reducing the token sequence (h, ) to compact subsequence {14)%= C {h+ prior to computa- tion, thereby reducing computational costs by shortening the sequences. A feasible reducing strategy involves computing the pairwise similarity [Si]ax = [similarity (h; hy) mx The token pairs with the highest similarity are iteratively matched and merged, retaining single representative token
Despite significant progress, distillation methods necessitate lne-tuning to integrate new sampling paradigms. Feature
3

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AsymRnR: Video Diffussio Transformers Acceleration with Asymmetric Reduction and Restoration
to !
to 2
reduce
reduce
attention
restore
attention
restore
K&V
reduce
input
reduced input
Input
(a) Symmetric Reduction and Restoration (SymRnR)
(b) Asymmetrie Reduction and Restoration (AsymRnR)
Figure 3. Overview of (a) symmetric and (b) asymmetric strategies. Both methods reduce the processing sequence length before self-attention to enhance efficiency and subsequently restore to original length for dense prediction. SymRaR performs reduction before mapping to Q. K, and V. whereas AsymRnR applies reduction afterward. This flexibility allows for the adaptive assignment of varying reduction rates to individual features. Moreover, AsymRnR supports operations on Q, K, and before reducing sequence, such as 3D rotary position embedding (Su et al., 2024), offering better compatibility. We use image patches for illustrative purposes.
per pair until the sequence is reduced m tokens.
reduction strategies but also suggests promising directions for enhancement, which explore in subsequent sections.
While these methods align with intuition and perform well in practice, they lack theoretical analysis to explain their effectiveness, limiting opportunities for further optimization. From a distributional perspective, we provide a theoretical explanation motivating additional improvement. To prevent the pretrained network from being affected by covariate shift, the distribution of the reduced sequence should closely resemble that of the original sequence. Specifically, the re- duction process should minimize the Kullback-Leiblee (KL) divergence Dx x (P' (P)) between the reduced sequence P and the original distribution P. Given that the analytical forms of these distributions are typically inaccessible, we employ numerical estimation techniques. Inspired by (Wang al., 2009), we present the following corollary for estimat- ing the KL divergence, with further details provided in the Appendix A.
3.2. Prior Reduction Methods for Diffusion Acceleration
Prior works (Bolya & Hoffman, 2023; Li et al., 2024; Ka- hatapitiya et al., 2024a) reduce the input sequence H (so symmetrically reduce Q. K, and V/,, enabling self-attention to process a shorter sequence, as depicted in Figure (a). To maintain compatibility with diffusion denoising, the reduced sequences are restored to their original length by replicat- ing each reduced token according to its most similar match among the unreduced tokens. Formally, the attention opera- tion with Symmetric Reduction and Restoration (SymRnR) applied can be formulated as follows:
SymRuR(H) (R-1 1 Attn o R)(H),
Corollary 3.1. Suppose (X:Y and (X covari- ance stationary sequences sampled from P and p', respec- tively. A Monte Carlo estimator is given by:
where R(-) and R-1(-) represent the reduction and restora- tion operations, respectively. The symbol denotes compo- sition operator, meaning the connected operators are applied sequentially from right to left. Notably, this process is lossy, and the resulting error is often substantial in video DiTs.
v(i) log p(i)
1
Dv,(P'I(P)
+ log
Bipartite Soft Matching (BSM). Computing the naive similarity matrix is computationally expensive and may negate acceleration. BSM is introduced to improve the matching efficiency (Bolya et al., 2023): The tokens {h }n} are first partitioned into a set of source tokens (h hs.,, } of size n. and a set destination tokens (ha,...., hdn..! } of size n22, where n = ni + 122. Each source token matched with its closest destination token, Then, the top m matched source tokens are reduced.
where p(i) is the nearest-neighhor (NN) Euclidean distance of X among (X))izi and v(i) is the NN Euclidean dis- tance of X; among {Xi)/=ı- The bias and variance of this estimator (P vanish as 1,1' + oc.
This theoretical framework illuminates the effectiveness of matching-based reduction methods through two key mech- anisms: 1) the climination of redundant tokens increases the dispersion of P', resulting in larger p(i) values; and 2) careful control of the reduction ratio prevents excessive sparsification, maintaining small v(i) values. This analysis not only provides theoretical validation for existing token
Partitioning. ToMe (Bolya & Hoffman, 2023) partitions the image tokens into chunks using 2D stride (e.g. 2 x 2) and randomly selects one token from each chunk to populate the set of destinations. We extend this approach to 3D stride (e.g. 2 x 2) to accommodate video data.
This generally holds for distancce, where S S co.

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AsymRnR: Video Diffussin Transformers Acceleration with Asymmetric Reduction and Restoration
Block index: 1-8
Block index: 9-16
Block index: 17-24
Block index: 25-30
1.0
Input
Query
Smilarity
Value
0.4
30
Timestep
Timestep
Timestep
Timestep
Figure 4. CogVideoX (Yang et al., 2024) attention feature similarity distribution. The shaded areas indicate the confidence interval. Blocks are divided into four groups, each exhibiting distinct trends, with variations observed across different feature types. These patterns remain consistent across generations with diverse contents.
3.3. Asymmetric Reduction and Restoration
grees of degradation. Similarly, perturbations at different denoising timesteps exhibit obvious variations. This raises a natural question: how does redundancy evolve across blocks and denoising timesteps?
Unlike previous works that focus reducing H, we reduce Q and K&V independently, as depicted in Figure 3 (b). In this way. the asymmetric treatment of Q and &&V enables each feature to minimize its covariate shift independently during the matching process, as discussed in Section 3.1. The attention operation with our Asymmetric Reduction and Restoration (AsymRnR) is formulated as:
We examined the similarity across blocks and timesteps in video DiTss. Figure 4 illustrates temporal trends in similarity across blocks: 1) During the initial timesteps when the input to the network closely resembles random noise, higher similarity is observed across different blocks; 2) As the generation progresses, the similarity generally decreases and stabilizes; 3) The temporal trends vary significantly between blocks. For instance, in the shallow blocks, the similarity of V increases steadily after the first ten steps. Whereas in other blocks, it remains relatively constant. 4) Such pattern is model-specific but context-agnostic and can be considered an intrinsic property of the models.
Q' = RQ(Q), K", V' Rxv (K, V). AsymRnR(H)- Ro¹ ((timaax((((K"")))))
(4)
is worth noticing that K and must share the same reduc- tion scheme due their one-to-one correspondence, while Q can be reduced independently of the other features. Fur- thermore, since Q acts the "questioner" and its sequence length must match the original sequence for subsequent layer processing, while the information from K and V has already been encoded into the output features by attention weights, it sufficient to restore the Q sequence.
To optimize computational budgets, we can reduce com- putations for high-similarity blocks and timesteps while maintaining low-similarity components. A key challenge that the similarity values for the entire process are unknown in advance, complicating the decision which components to reduce. Fortunately, we can leverage the property that similarity patterns are context-agnostic, enabling the similar- ity distribution to be estimated in advance through arbitrary diffusion sampling. Specifically, let S(A, t, b) represent the similarity for feature E {H, Q, K, V}. reverse diffu- sion timestep I, and block b during the advanced sampling process. Using this similarity measure, reductions can be applied selectively by thresholding S(A, t, b), enabling the derivation of a scheduled reduction strategy:
Symmetric reduction H may not necessarily achieve op- timal divergence the levels of Q, K, and V but seeks to balance them. In contrast, our decoupled design allows for an asymmetrical reduction strategy and varying reduction rates across features, resulting in improved divergence for each feature and greater flexibility. Another benefit of the decoupled design is improved compatibility. For instance, techniques such 3D rotary position embedding (ROPE) (Su al., 2024) on Q and K require specific sequence lengths. SymRnR, which performs reduction before map- ping to Q, K, and V. cannot support arbitrary reduction rates. In contrast, AsymRnR applies reduction after these operations, enabling arbitrary reduction rates and ensuring better compatibility.
RA if (A,,,))) id
R.
otherwise
where T 's are the thresholding hyperparameters, which can be adjusted to optimize the trade-off between computational efficiency and output quality. RA denotes the reduction operation defined in Equation (4), while id represents the identity operation (i.e., no reduction). The restoration op-
3.4. Reduction Scheduling
Besides the asymmetric redundancy mentioned in Sec- tion 3.3, Figure also reveals that perturbations in Q across the shallow, middle, and deep blocks lead to varying de-
5

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AsymRnR: Video Difffusion Transformers Acceleration with Asymmetric Reduction and Restoration
timestep Eo
tim stee
timestep
Table I. Quantitative comparison of CogVideoX-28 (Yang et al., 2024) with ToMe (Bolya et al., 2023) and AsymRnR. FLOPs represent the floating-poin poaning-point operations required per video. Generation specifications: resolution 180 720 and 49 frames.
METHOD
FLOPs (x:10))
LATENCY (SECOND)
SPEEDUP
VBENCH 1 LPIPS J
Figure 5. Heatmap of matching similarity at different denoising timesteps. The similarities across successive timesteps are nearly identical, but divergence increases with larger step gap.
COGVIDROX-22
12,000
137.30
0,8008
+ TOME
10.549
123.59
1.11X
0.7825
0.5562
TOME-FAST
10.120
118.28
1.16x
0.7732
0.5602
OURS
10.342
121.70
1.13%
0.7917
0.5811
+ OURS-FAST
9.976
117.22
1.17%
0.7849
0,5632
eration R is modified correspondingly. This reduction scheduling is also asymmetric for A E {H, K, V). al- lowing for precise coordination of computational resources.
ToMe (Bolya & Hoffman, 2023) as the baseline method. We adjust the reduction rate, align the latency, and evaluate their generation quality on Cog VidcoX-2B (Yang al., 2024). Notably, ToMe is incompatible with 3D ROPE and cannot be directly integrated into the other DiTs as detailed in Section 3.3. Our AsymRnR integrates seamlessly with these methods, and we report its results across all video DiTs for comprehensive evaluation. Additional implementation details are provided in the Appendix.
Details of the hyperparameter tuning for 7A are presented in Appendix B.
3.5. Matching Cache
One drawback of matching- reduction methods is the additional cost incurred by the matching process during each reduction. Despite using BSM (Bolya et al., 2023), the matching process still significantly negates the speedup.
Benchmarks and Evaluation Metrics. We follow previous work and perform sampling on over 900 text prompts from the standard VBench suite (Huang et al., 2024). It assesses the quality of generated videos across 16 dimensions. The aggregated VBench score is reported and all dimensional scores will be provided in Appendix E.2. LPIPS (Zhang et al., 2018) is used as reference metric in the comparison analysis for semantic alignment evaluation. Note that no unique ground-truth video exists for given text prompt; multiple generations can be equally satisfactory. Therefore, visual quality and textual alignment (measured by VBench score) are the primary performance metrics. And LPIPS is only included as reference metric.
We observed that the matching similarity at successive de- noising steps exhibits only minor differences, as illustrated in Figure 5. Similar patterns were also observed in the hid- den states produced by the denoising network layers (Balaji et al., 2022; Ma al., 2024a; Chen et al., 2024). This ob- servation motivates to cache the matching results over denoising steps, avoiding repeating calculations. Formally, matching similarity with caching is defined as:
BSM((A,t) t, b) b)
if = 0 (mod s) otherwise
S(A, s- Lt/s],
where S(A,t, b) denotes the matching similarity of the atten- tion feature type A E {H, Q, K, V} at the reverse diffusion timestep and block 6. BSM(-) represents the bipartite soft matching (Bolya al., 2023) metioned in Section 3.2 and denotes the caching steps. Consequently, the matching cost is proportionally reduced by 1/s.
For efficiency evaluation, we use FLOPs and running la- tency² metrics from both theoretical and practical per- spectives. The relative speedup, Alatency + 1, is also provided.
4.2. Experimental Results
Quantitative Comparison. Table provides qualitative comparisons between two configurations: a base version with perceptually near-lossless quality and a fast version that achieves higher speed at the cost of slight quality degra- dation. We set the matching cache step to partition stride to 2 x 2 for both ToMe and AsymRnR. Our higher VBench scores and lower LPIPS, achieved at comparable FLOPPs and latency, demonstrate superior video quality and semantic preservation.
4. Experiments
4.1. Experimental Settings
Models and Methods. We exhibit experiments on Text- to- vidio (T2V) task on state-of-the-art open-sourced video DiTs: Vog CogVideoX-SB (Yang al., 2024), Mochi-1 (Team, 2024b), and Hunyuan (Team, 2024c). We also evaluate integrating our AsymRnR approach with the step distillation approach by combining it with a 6-step distilled version of Viseo (Team, 2024a). We focus on training-free free token reduction- acceleration and use
5 and the
Latency is measured using an NVIDIA A100 for CogVideoX variants and NVIDIA H100 for the rest of models due to the availability of hardware at the time.
6

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AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
CogVideoX-28
+ToMe
+AsymRnR
CogVideoX-2BB
+ToMe
+symRnR
avg. Intency 137s (original)
aeg, Intency 118s
avg. latency 117
avg. Intency 137s (original)
avg. lateney 118s
avg. larency 1176
(17% faster)
(17% faster)
prompt
prompt golden shway
shrough sxn-dappled
Figure Qualitative comparison on Cog (Yang al., 2024). (Bolya Hoffmann, 2023) exhibits blurriness (left) pixelation (right), whereas our AsymRnR consistently performs well. The video examples are provided in the Supplementary Materials.
C9gVidenN-5W
Iatency 348x
(original)
Machi-1
latency 2004
(eriginal)
AAgmitk
arg. Iitency 109s
(13% faster)
+AsyMKoR
arg Intency 1788
(13% faster)
bensh cieule
Hude
avg. latency 913s
(orighnol)
FastVideo Hunyuan
ang, latency 15%
(original)
+AsymBoR
EVg. latency
(30% faster)
AsymMikk
ng knency faster)
pronge busse gallaping ararefidly sevenu fubl.
groupe A porda stnddy auffourd is decusoas sweust...
Figure 7. Qualitative results CogVideoX-SB (Yang al., 2024), Mochi-1 (Team, 2024b), Hunyuan Video (Team, 2024c), and RastVideo-Hunyunn (Team, 2024a). ToMe (Bolya & Hoffman, 2023) is incompatible with these models; we present videos generated by the baseline models and our proposed AsymRnR.
Generalization to SOTA Video DiT's Table presents additional results of applying AsymRnR to DiTs across various architectures, parameter sizes, and denoising sched- ulers. Applying AsymRnR to the Cog VideoXX51 demon- strates less quality degradation than the 2B variant. This phenomenon is more pronounced in larger models Mochi-1 and Hunyuan Video: AsymRnR even achieves superior re- sults over baseline models while reducing computational costs. In Video--ooHu,,,,,n a step-distilled variant of Hun- yuan Video, which is capable sampling in just denois- ing steps, AsymRnR achieves an over 24% speedup while
maintaining perceptual quality. Our consistently strong per- formance underscores effectiveness and generalizability.
Qualitative Results. Figure 6 presents a qualitative com- parison on CogVideoX-2B. ToMe generations exhibit no- ticeable blurriness and pixelation. Although generations with AsymRnR deviate from the baselines at the pixel level, they consistently preserve high quality and semantic coher- ence. Figure 7 also presents qualitative results on other video DiT's, which are incompatible with ToMe. Asym- RnR demonstrates accelcration without compromising vi- sual quality in various baseline models and contents.

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AsymRnR: Video Difffssin Transformers Acceleration with Asymmetric Reduction and Restoration
Table Quantitative evaluation of V (Yang et al., 2024), Mochi-1 (Team, 2024b), Hunyuan Video (Team, 2024c), and Fast Vaseooaa-uunn (Team, 2024a). The generation specifi- cations are detailed in the Appendix C.
Table 3. Integration with the caching- method PAB. AsymRnR and PAB (Zhao et al., 2024) on Hunyuan Video (Team. 2024c) further improve efficiency, achieving a total 1.71x speedup with negligible performance degradation.
FLOPS (x10")
LATENCY (SECOND)
FLOPS (x10"*)
LATENCY (SECOND)
METHOD
SPREDUP
VBENCH
METHOD
SPEEDUP
VBENCH
CUGVIDEOX SB
33.220
347.50
0.8074
HUNYUANVIDEO
128.599
912.89
0.8157
+ OURS
29.876
316.12
1.10X
0.8061
PAB
96.582
715.71
1.28x
0.8153
+ OURS-FAST
29.190
308.86
1.13x
0.8056
PAB OURS
73.224
534.93
1.718
0.8140
MOCII-1
35.877
201.85
0.7911
+ OURS
32.086
182.64
1.10%
0.7973
OURS-FAS
31.583
178.26
1.13x
0.7996
Table 4. Integration with the UNet-based video diffusion model
HUNYUANVIDEO
128.559
912.98
0.8157
AnimateDiff (Guo et al., 2024). AsymRnR achieves 1.2x speedup. Generation specifications: 16-frame videos at 512 x 512
+ OURS
104.795
738.18
1.24%
0.8240
+ OURS-FAST
100.110
703.29
1.30x
0.8237
using 50-step DDIM Euler solver (Song et al., 2021).
FASTVIDEO
21.720
158.66
0.8225
+ OURS
17.746
132.52
1.20x
0,8172
OURS-FAST
17.086
128.39
1.24x
0,8140
FLOPs (x10'
LATENCY (SECOND)
METHOD
SPEEDUP
VBENCH
Hunyaan Video
+PAB
+PAB & AsymRaR
ANIMATEDIFF
1.775
37.21
0.7748
TOME
1.636
31.14
1.19x
0.7693
OURS
1.635
31.09
1.20x
0.7738
AnimateDiff. The corresponding qualitative results are pro- vided in Table 4. This integration yields a 1.20x speedup without perceptible quality degradation.
4.3. Ablation Study
Quality- Trade-off for Individual Features. Fig- ure 9 illustrates the quality-latency curve for different fea- ture types: H, Q. K, and V. To isolate the influence of other factors, the scheduling discussed in Section 3.4 is dis- abled unless explicitly mentioned otherwise in this section. We observe that reducing individual features leads to hier- archy of V > H > K > Q. As K and V require identical reduction behavior, we use V's matching to decide K&V reduction throughout the paper.
avg. latency 913s (origimal)
avg. Jalency 716s (28% faster)
avg. latency 535s (71% faster)
pronpe: A fuff whice sboce...
Figure 8. Quantitative evaluation on Hunyuan AsymRnR is compatible with the feature caching method PAB (Zhao et al., 2024), and together they achieve 1.71 x overall accelcration.
Integration with Feature Caching. Our AsymRnR acceler- ates sampling by reducing the computation of the attention operator and is orthogonal to other acceleration approaches. Experiments on VastVideo-Hunyuan (Team, 2024a) demon- strate its compatibility with the step-distillation method (Wang et al., 2024). We further assess its compatibility with feature caching techniques by integrating AsymRnR with PAB (Zhao et al., 2024); the results are summarized in Table 1. When stacked on top of PAB, AsymRnR achieves a 1.71x overall speedup without compromising generation quality, The quantitative results are presented in Figure
Reduction Scheduling. Table 5 presents a comparison of AsymRnR with and without scheduling. Under capped latency budget, adaptively adjusting reducible blocks and timesteps based on the scheduling strategy significantly improves performance. Moreover, the improvement in Q surpasses V. suggesting that Q is more sensitive in low- redundancy blocks and timesteps but exhibits greater robust- ness to substantial reductions in high-redundancy regions. Specifically, reducing the high-similarity parts of Q by 80% causes no perceptible artifacts in human evaluations. In con- trast, reducing the low-similarit components by just 10% leads noticeable distortions. It underscores the necessity of implementing such reduction schedule.
Integration with UNet-based video diffusion models. AsymRnR is designed to operate on attention layers, which are prevalent in diffusion models, including bNed- (Ronneberger et al., 2015) architectures such as Animate- Diff (Guo et al., 2024). We apply AsymRuR to the spatial self-attention modules at the highest-resolutic stages of
Matching Cache. One major efficiency bottleneck match- ing. Our newly proposed matching cache reduces the match- ing cost by a factor of 1/s, but may intuitively result in
8

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AsymRnR: Video Difflssion Transformers Acceleration with Asymmetric Reduction and Restoration
0.80
Table 6. The effect of matching cache on V reduction. Increas- ing the caching steps does not significantly degrade performance but reduces latency when < 5. This evaluation focuses on feature V without scheduling to isolate the impact of caching steps.
10%
0.78
VBench score
F 20%
0.76
Baseline Input
30%
FLOPS (x10'5)
LATENCY (SECOND)
0.74
VBENCH t
Query
0.72
Key K
1
10.210
134.68
0.7859
40%
Value V
2
10.161
124.24
0.7822
0.70
3
9.968
120.77
0.7798
110
115
120
125
130
135
140
$ 4
9.943
118.61
0.7763
Latency (second)
8 5
9.917
118.16
0.7796
6
9.909
117.51
0.7747
Figure 9. Quality-latency trade-off for individual features. Uni- formly reducing V shows superior quality, whereas reducing Q in isolation leads to substantial quality decline.
Table 7. Comparison of similarity and reduction operations. The cosine similarity in ToMe (Bolya et al., 2023) is suboptimal compared Euclidean similarity for AsymRnR. The re- duction operation introduces extra processing time and degrades quality, Directly discarding yields the best results.
Table The effectiveness of the reduction schedule. With the reduction schedule. the reductions in Q and V demonstrate sig- nificant improvements without increasing CollbocCoolbboratvelly reducing Q and V further improves performance while maintain- ing the same latency. Our default configurations are highlighted
FLOPs (x10'*)
LATENCY (SECOND)
SIMILARITY
REDUCTION
VBENCH
FLOPs (x10'5)
LATENCY (SECOND)
FEATURE
SCHEDULE
VBENCH 1
RANDOM
DISCARD
9.843
113.79
0.7112
DOT
DISCARD
9.914
117.92
0.7395
10.204
123.42
0.7641
COSINE
DISCARD
9.914
118.08
0.7716
10.276
122.34
0.7765
EUCLIDEAN
DISCARD
9.917
118.16
0.7796
EUCLIDEAN
9.917
119.90
0.7630
10.620
123.59
0.7893
10.403
122.19
0.7787
Q ++V V
V
10.342
121.70
0.7917
reduction and the reduction rate, which require tuning for each baseline model. Furthermore, AsymRnR offers signif- icant advantages in processing longer sequences, whereas it provides minor acceleration for image-based DīT's due to their inherently shorter sequence lengths.
potential quality degradation. Table 6 presents the VBench score across various caching steps. Increasing the caching step significantly reduces FLOPs and latency, with only minor quality degradation. We adopt 5 as the default configuration, balancing latency and quality.
6. Conclusion
This paper presents AsymRnR, training-free sampling ac- celeration approach for video DiTs. AsymRnR decouples sequence length reduction between attention features and allows the reduction scheduling to adaptively distribute re- duced computations across blocks and denoising timesteps. To further enhance efficiency, we introduce a matching cache mechanism that minimizes matching overhead, en- suring that acceleration gains are fully realized. Applied to saate-of-the-ane art video DiT's in comprehensive experiments, our approach achieves significant speedups while maintain- ing high-quality generation. The successful integration with diverse models, including step-distilled models, highlights the generalizability of our approach. These results high- light the potential of AsymRnR to drive practical efficiency improvements in video DiTs generation.
Similarity Metric and Reduction Operation. Table 7 analyzes the impact of various design choices for matching and reduction. ToMe (Bolya et al., 2023) utilizes cosine similarity, which lacks the metric properties and cannot be interpreted via Corollary 3.1. Experimental results reveal that switching (negative) Euclidean distance improves performance, offering theoretical soundness and accounting for magnitude. Additionally, ToMe's default mean- based reduction operation increases latency and causes blurriness and distortions. In contrast, our approach directly discards redundant tokens, enhancing efficiency and output quality.
5. Limitation
One limitation of our approach is the presence of visual discrepancies in the generated outputs as shown in Figures 6 and 7, despite maintaining semantic consistency. Addition- ally, acceleration and generation quality depend on hyperpa- rameter configurations, such as the similarity threshold for

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AsymRnR: Video Difflusion Transformers Acceleration with Asymmetric Reduction and Restoration
Acknowledgements
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F, Podell, D., Dockhorn, T., English, Z., and Rombach, R. Scaling rectified flow transformers for high-resolution image synthesis. In ICMI. OpenReview.net, 2024.
This project is supported by the National Research Foun- dation, Singapore, under its NRF Professorship Award No. NRF-P2024-001.
Impact Statement
Guo, Y., Yang, C., Rao, A., Liang, Z., Wang, Y., Qiao, Y., Agrawala, M., Lin, D., and Dai, B. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In ICLR. OpenReview.net, 2024.
Generative methods carry the risk of producing biased, privacy-violating, or harmful content. Our method, designed to improve the generation efficiency of video generative models, may also inherit these potential negative impacts, Researchers, users, and service providers should take re- sponsibility for the generated content and strive ensure positive social impacts.
Habibian, A., Ghodrati, A., Fathima, N., Sautière, G., Gar- repalli, R., Porikli, F., and Petersen, J. Clockwork diffu- sion: Efficient generation with model-step distillation. In CVPR, pp. 2352-83612 IEEE, 2024.
Ho, J., Jain, A., and Abbeel, Denoising diffusion proba- bilistic models. In NeurIPS, 2020.
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AsymRnR: Video Difffusin Transformers Acceleration with Asymmetric Reduction and Restoration
A. Details of Corollary 3.1
B. Hyperparameter Tuning
The derivation of the original N-NN estimator is detailed in (Wang et al., 2009). For completeness, we present its core concept here. We refer the reader the cited work for comprehensive derivation and coverage analysis.
The hyperparameters are manually tuned through only a simple and efficient process. typically within 10 iterations, with each iteration requiring only inference. In practice:
We start the first iteration with a low similarity thresh- old of 0.5 and a low reduction rate of 0.3.
Suppose p and are the densities of two continuous distribu- tions on 1d, where p(x) = 0 almost everywhere ((x) = O.
The KL-divergence is defined as:
2. We run 1 inference with arbitrary text prompt. If the generation maintains good, we increase the reduction rate by 0.2 to encourage more aggressive reduction.
(xt(Fll9)= - Jpc plxx) log) log p(x) ((x)
dx.
(6)
3. When a poor generation occurs, we revert to the previ- ous reduction rate, lift the threshold by 0.1, and repeat step
Let (x))=1 and [Y:1/=1 be i.i.d. samples drawn from p and q, respectively, and p and ④ denote consistent estimators of p and q- By the law of large numbers, the following statistic provides consistent estimator for DKL. (pllg):
This simple heuristic guides the tuning process with minimal effort. The hyperparameter we used in the experiments will be provided in Appendix C later.
(Xx)) n ((x))
1
log
C. Implementation Details for Experiments
To define á in Equation (7), consider the closure of the d- dimensional Fuclidean ball B(X,, Pk (i)), centered at X; with radius (i()), where pu (i) is the Euclidean distance to the k-th nearest-neighbor of X, in {Xz]) Since p is continuous density, the ball B BX,;, Pk (i)) contains k samples from (Xj)it almost surely. The density estimate of at X; is
Video Specification. In terms of output specifications, Cog VideoXX2B and CogVideoX-555 generate 49 frames at a resolution of 480 x 720, following their default set- ting. Mochi-1 generates 85 frames with resolution of x80 Hunyuan produces 129 frames at x60. SesVideo-HHuyuuee outputs 65 frames at 720 x 1280 reso- lution. Memory-efficient attention (Rabe & Staats, 2021) is enabled by default in all experiments.
1
p(X.) = n- 1--1a (1))
(8)
where ei (d) 4//2 ((d22 + 1) is the volume of the unit ball. Similarly the k-NN density estimate of q at X, is
Diffusion Specification. The sampling of Cog CogVideoX-2B (Yang et al., 2024) utilizes the 50-step DDIM solver (Song et al., 2021), while Cog Vide employs the 50-step DPM solver (Lu et al., 2022). Mochi-1 (Team, 2024b), Hun- yuanVides (Team, 2024c), and SestVideo-Hunyune (Team, 2024a) use the flow-matching (Esser et al., 2024) Euler solver with 30, 30, and 6 sampling steps, respectively.
k ci (d))
((X,)
where vs (1) is the Euclidean distance to the k-th nearest- neighbor of X, in {y,))=-. Substituting Equations (8)
and (9) into Equation (7) yields the following estimator:
BSM Specification. To adapt ToMe to the video scenario, we employ a 3D partition with a x Cog VideoX-2B experiments. This setup ensures consis- tency with the configuration outlined in Section 3.2. In the experiments involving AsymRnR on Mochi-1, Hun- yuan Video, and FastVideo-Hunyuall the partition stride is expanded to 6 x 2 x 2 to accommodate the higher num- ber of generated frames in these models. As outlined in Section 4.2, we set the matching cache steps to s = 5 for Cog variants and for Mochi-1 I and Hunyuan- Video. For Fast the matching cache is disabled.
stride in the
Dnmm (pllg) = Σ log
m
+ log
(10)
Pa (7)
The original paper (Wang et al., 2009) demonstrated that, for any fixed k the bias and variance of the estimator diminish as the sample size n increases. Therefore, l is omitted in Corollary 3.1 for simplicity.
The i.i.d. assumption in the derivation process, together with the convergence analysis from Theorems 1 and 2 in (Wang et al., 2009), is used to apply the law of large numbers. This assumption can be relaxed to the weaker condition of covariance stationarity by applying Chebyshev's 's law of large numbers, which leads to the same conclusion and facilitates the derivation of Corollary 3.1.
Similarity Standardization. The negative Euclidean dis- tance used as the similarity metric spans the range [0, oo). To improve visualization and usability, as illustrated in Fig- ure 4, we apply standardization approach. Specifically,
13

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AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Table Reduction scheduling details for Section 4.
Table 9, Destination partition stride and ratio. Increasing the number of destination tokens excessively leads to higher latency, while reducing them too much compromises video quality.
METHOD
FEATURE
REDUCTION
COGVIDERS
+ TOME
{0.0:0.1}
STRIDE
DESTINATION Fa (%)
FLOPs (x10'))
LATENCY (SECOND)
+ TOME-FAST
(0.0:0.13)
(t.h, w)
VBENCH
Q
{0.6:0.4, 0.7:0.8}
+ OURS
[0.8:0.3]
(1,2,2)
24.44%
9.979
123.60
0.7624
(2,2,2)
11.28%
9.917
118.16
0.7796
(0.6:0.6, 0.7:0.8)
+ QURS-FAST
(3, 2,
7.52%
9.894
115.66
0.7417
V
(0.7:0.3, 0.9:0.4)
(4,2
5.64%
9.882
114.55
0.7356
MOCHI-1
(2,3,3)
5.13%
9.879
114.10
0.7372
Q
[0.5:0.7]
(2,4,
2.63%
9.862
112.58
0.7231
+ OURS
V
[0.7:0.3)
(0.45:0.6, 0.5:0.7)
+ FUSRS-FAT
[0.7:0.3]
Table 10. Comparison of additional reduction rates and fea-
HUNYUANVIDEO
tures. Simply scaling the reduction rate outperforms the inclusion of and SymRaR lower latency.
Q
[0.7:0.9]
+ OURS
V
(0.8:0.3)
(0.5:0.3, 0.7:0.9}
+ OURS-FAST
(0.8:0.3)
FEATURE
(x10'*)
FLOPS
LATENCY (SECOND)
VBENCH 1
FASTVIDEO
Q
[0.7:0.9]
12.000
137.30
0.8008
+ OURS
V
[0.8:0.3)
Q +
9.9755
117.29
0.7849
[0.7:0.9]
H + QVQV
10.0404
117.23
0.7766
+ OURS-FAST
V
(0.8:0.4)
the importance of our asymmetric design, which enables greater flexibility and optimizes acceleration potential.
during the registration of similarity distributions in Sec- tion 3.4, values outside the 5th-95th percentile range are
truncated, followed by min-max standardization. This en- sures that all similarity values are scaled the range [0, 1].
D. More Ablation Results
Destination Partition. The partitioning of source and destination tokens in BSM (as detailed Section 3.1) is critical in final quality. BSM exhibits a complexity of O(ra(1 ra)m?), which increases monotonically for < ra < 1/2, where ra denotes the fraction of destination tokens and is the total number of tokens.
Reduction Scheduling. The reduction schedule is defined by two hyperparameters: the similarity threshold for re- duction and the reduction rates. The similarity threshold is tuned individually for each DiT model to maintain the quality. Specifically, it is determined through visual inspec- tion of several cases, providing generally effective results, though not necessarily optimal for every sample. On the other hand, the reduction rates adjusted to achieve the desired accelcration (e.g., 1.30x speedup).
As shown in Table 9, smaller results in significant quality degradation due to less accurate matching, while a larger ra slightly reduces quality and increases latency due to weakened temporal regularization. adopt a stride of (2, 2) as the default setting to balance these trade-offs.
The reduction schedule is represented as JSON dictionary. For instance, in Cog V AsymRnR, the schedule specified as [ {'Q': {0. 06:0... 0. 0.7:0 'V' {o. 00:8:0. 3}). This indicates that the query sequence will be reduced by 40% when the estimated similarity S(Q, t, exceeds TQ1 0.6 timestep f and block b, as outlined in Section 3.4. The reduction rate increases to 80% S(Q, b,
Combining SymRnR and AsymRnR. A key question is whether increasing the reduction rate or further reducing additional features is more effective in squeezing the latency. Intuitively, SymRnR and AsymRnR can be combined for greater speedup. We explore the parallel integration of Sym- RnR and AsymRnR: when a block deemed redundant in Q V a given timestep, AsymRnR takes precedence for reduction. Then, the unreduced components can be further processed using SymRnR. The result of this integration is shown in the last row of Table 10. Incorporating additional reductions through SymRnR results lower quality at the same latency, whereas directly scaling the reduction rate of AsymRnR yields superior performance. We focus on
exceeds TQ2 = 0.8. Similarly, the value (and key) sequences reduced by 30% when the similarity exceeds Ty 0.8.
All schedule specifications are summarized in Table 8.
We observed that query sequences are less sensitive to high reduction rates in high-similarity blocks and timesteps, whereas key and value sequences benefit from more bal- anced reduction rate across components. This underscores
14

---

AsymRnR: Video Diffusson Transformers Acceleration with Asymmetric Reduction and Restoration
95th: 12.46
CogVideo
+AsymRnR
0.20
Density
0.15
Input
Value
0.10
0.05
0.00
25
75
Figure 10. The distribution of feature Euclidean norms. The dashed line indicates the 95th percentile. Compared to input H, the value V norm distribution exhibits longer tail, which can cause distortion when using cosine similarity for matching.
AsymRnR and leave their integration to future work.
Similarity Metric for Matching, The attention operation relies on the dot product metric (i.e. cosine similarity) for calculating the attention map (Kim et al., 2017; Dong et al., 2021). Therefore, it is regarded as the standard approach for token similarity in previous works (Bolya et al., 2023; Bolya & Hoffman, 2023; Li et al., 2024; Kahatapitiya al., 2024a). However, cosine similarity cannot be analyzed through Corollary 3.1 because it lacks metric properties. Empirically, this limitation results in dark spots and dim appearance in the generated videos, particularly when V is reduced, as quantitatively shown Table
avg. latency 348s (original)
avg. latency 309s (13% faster)
CogVideoX-5B
+AsymRnR
To investigate the root cause of this issue, visualized the distribution of feature norms in Figure 10. The norm distributions for V exhibit significantly long tails: small proportion of tokens show significantly larger norms. Using cosine similarity disregards the magnitude of these tokens, which can result in matching tokens with greatly different magnitudes, thereby causing instability. We use the (neg- ative) Euclidean distance, which effectively captures both directional and magnitude differences between paired vec- tors, and is supported by theoretical foundation.
E. More Results
E.1. More Qualitative Results
Figure presents additional comparisons of ToMe (Bolya Hoffman, 2023) on CogVideoX-2B (Yang et al., 2024). Since ToMe is incompatible with -S, Mochi- 1 (Team, 2024b), Hunyuan Video (Team, 2024c), and FastVideo-Hunyuan (Team, 2024a), as discussed in Sec- tion 3.3, Figures 11, 13 and 14 illustrate further comparisons of our AsymRnR against the baseline models. More video examples are provided the Supplementary Materials.
avg. latency 348s (original) Figure 11. Additional qualitative comparison on CogVideoX. 5B (Yang et al., 2024).
latency 309s (13% faster)
(Huang et al., 2024), serving as an extended reference Tables and 2.
E.2. More Quantitive Results
Table provides additional dimensional metrics of our methods compared to baseline models (Yang et al., 2024; Team, 2024b;c;a) and ToMe (Bolya et al., 2023) on VBench
15

---

AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
VogiioeX-22B
+ToMe
+AsymRnR
avg. latency 137s (original) CogVidcoX-2B
avg. latency 118s
avg. latency 117s
(17% faster)
+ToMe
+AsymRnR
avg. latency 137s
avg. latency 118s
avg. latency 117s
(original)
(17% faster)
Figure 12. Additional qualitative comparison on Cog VideoX-2B (Yang et al., 2024).
16

---

AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Hunyuan Video
+AsymRnR
BastVideo-Hunyaee
+AsymRnR
latency 913s (original) Mochi-1
avg. latency 703s (30% faster)
+AsymRnR
avg. latency 159s
latency 128s (24% faster)
(original)
Figure 14. Additional qualitative comparison on FastVideo- Hunyuan (Team, 2024a)
avg. latency 202s (original)
avg. latency 178s (13% faster)
Figure 13. Additional qualitative comparison Hunyuan- Video (Team, 2024c) and Mochi-1 (Team, 2024b).
17

---

AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Table 11. Quantitative results for VBench dimensions (Huang et al., 2024) comparing CogVideoX-2B,, CogVideoX-SB (Yang et al., 2024), Mochi-1 (Team, 2024b), Hunyuan Video (Team, 2024c), and FastVideo- HastVidee-Hunyuaa Hunyuan (Team, 2024a).
AESTHETIC QUALITY
APPEARANCE STYLE1
BACKGROUND CONSISTENCY f
DYNAMIC DEGREE
HUMAN ACTION
IMAGING QUALITY
MOTION SMOOTHNESS
METROD
COLOR
COGVIDEOX-22
0.6334
0.2514
0.9390
0.8776
0.6944
0.9400
0.6147
0.9721
+ TOME
0.6036
0.2505
0.9328
0.8315
0,5833
0.9600
0.5977
0.9750
+ TOME-FAST
0.5978
0.2500
0.9315
0.8530
0,5000
0.9700
0.5788
0.9759
+ OURS
0.6121
0.2504
0.9369
0.8252
0.6667
0.9600
0.6082
0.9699
OURS-FAST
0.6099
0.2500
0.9366
0.8762
0.5972
0.9600
0.5986
0.9694
METHOD
MUTLIPLE OBJECTS
ORJECTS CLASS
OVERALL CONSISTENCY f
SCENE 1
SPATIAL RELATIONSUIP
SUBJECT CONSISTENCY
TEMPORAL FLICKERING
TEMPORAL
STYLE"
COGVIDEOX-2
0.6502
0.8505
0.2697
0.5378
0.6587
0.9711
0.2569
TOME
0.5473
0.8323
0.2695
0.4586
0.7085
0.9025
0.9747
0.2473
TOME-FAST
0.5198
0.7880
0.2665
0.4804
0.7027
0.8949
0,9766
0.2453
0.5480
0.8402
0.2750
0.5029
0.7071
0.9725
0.2475
OURS-FAST
0.5686
0.8188
0.2693
0.5254
0.6957
0.9058
0.9729
0.2443
AESTHETIC QUALITY
APPEARANCE STYLE
BACKGROUND CONSISTENCY
DYNAMIC DEGREE
HUMAN ACTION
IMAGING QUALITY
METHOD
COLOR
SMOOTHNESS
COGVIDEOX-51 -B
0.6350
0.2516
0.9577
0.8002
0.6667
0.9600
0.6342
0.9741
OURS
0.6298
0.2540
0.9581
0.8300
0.6806
0.9700
0.6284
0.9699
+ OURS-FAST
0.6392
0.2509
0.9588
0.8633
0.6250
0.9700
0.6228
0.9741
MOCHI-1
0.5773
0.7989
0.9344
0.7658
0.4375
0.9800
0.4830
0.9537
+ OURS
0.5864
0.8008
0.9422
0.7789
0.4028
0,9800
0.4945
0.9712
+ OURS-FAST
0.5870
0.8051
0.9428
0.7505
0.4097
1,0000
0.4906
0,9721
HUNYUANVIDEO
0.6315
0.7210
0.9392
0,8409
0.4097
0,9700
0.5804
0.9717
OURS
0.6407
0.7216
0,9473
0,8251
0.4097
0,9700
OURS-FAST
0.6406
0.7216
0.9472
0.8239
0.4097
0,9700
0.6053
0.9716
FASTVIDEO
0.6318
0.7444
0.9616
0.8857
0.4167
0.9700
0.5783
0.9671
OURS
0.6270
0.7427
0.9647
0.8819
0.4028
0,9600
0.5662
0.9566
+ OURS-FAST
0.6248
0.7442
0.9617
0.8451
0.4097
0.9600
0.5567
0.9476
MUTLIPLE OBJECTS
OVERALL CONSISTENCY
SPATIAL RELATIONSHIP
TEMPORAL FLICKERING
TEMPORAL
METHOD
SCENE
CLASS
CONSISTENCY
STYLE
COGVIDEOX-SB
0.6555
0.8426
0.2694
0.5560
0.6782
0.9227
0.9752
0.2555
+ OURS
0.6707
0.9922
0.2717
0.5349
0.6633
0.8981
0.9770
0.2545
OURS-FAST
0.6601
0.8180
0.2764
0.4964
0.6448
0.9331
0.9775
0.2564
MOCHI-1
0,4794
0.8212
0.7357
0.6417
0.6740
0.8810
0.9683
0.7099
+ OURRS
0.5465
0.8505
0.7266
0.6408
0.6449
0.9077
0.9705
0.6942
0.7308
0.9704
HUNYCANVIDEO
0.5838
0.8560
0.7385
0.6019
0.7253
0.9020
0.9767
0.7013
OURS
0.5991
0.8655
0.7269
0.6285
0.7662
0.9129
0.9797
0.6977
OURS-FAST
0.5983
0.8584
0.7269
0.6196
0.7704
0.9129
0.9797
0.6976
FASTVIDEO
0.6151
0.8536
0.7308
0.6240
0.7266
0.9213
0.9688
0.7016
0.5877
0.8489
0.7378
0.6391
0.7253
0.9206
0.9692
0.7057
OURS-FAST
0.6425
0.8457
0.7351
0.6133
0.7208
0.9155
0.9673
0.7055
18


Reviews:
[{"review_id": "szot9ULV6f", "reviewer": "Reviewer_wNPy", "summary": "This paper proposes to asymmetrically reduce the sequence length of attention features to accelerate video DiTs. The proposed approach, called AsymRnR, leverages the observation that different components and stages exhibit varying levels of redundancy. The method introduces a reduction schedule to adaptively distribute reductions across components and a matching cache to enhance efficiency. The authors demonstrate the effectiveness on several video DiTs.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the Reviewer **wNPy** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:\n\n**Q1**: While maintaining semantic consistency and the experimental results show high VBench scores and low LPIPS values, some visual cases in **Fig 1, 6, 7** show visual discrepancies (eg, misaligned motions) to baseline models. I wonder how to align these results in practice.\n\n**A1**: Thank you for the comment. We agree that there are visual discrepancies; however, no unique ground-truth video exists for a given text prompt, multiple generations can be equally satisfactory. Therefore, visual quality and textual alignment (measured by VBench score) are the primary performance metrics.\n\nNote that some visual discrepancies occur in cases where AsymRnR produces better results than the baseline model, as illustrated in **supplementary Fig 1**, despite we were not intending to do that. A similar phenomenon is also reported in related works (eg, *Selective Attention Improves Transformer*).\n\nWe include baseline generations in the figures to demonstrate that AsymRnR achieves comparable visual quality and textual alignment in most cases, without implying that the outputs should be visually identical. We will include the text prompts alongside the figures in the revision to avoid potential misunderstanding.\n\n---\n\n**Q2**: The performance of AsymRnR depends on hyperparameter configurations, may require tuning for different models.\n\n**A2**: We acknowledge that the hyperparameters (HPs) searching is performed manually; however, it is very efficient—typically within 10 iterations, with each iteration requiring only 1 inference. Due to the shared concern and word limit, we kindly refer you to our response **A1** to Reviewer **LhNZ** for detailed process of HPs searching.\n\nMoreover, the HPs are transferable:\n\n1. across text prompts\n2. across models with \n    1. Different architecture: CogVideo-2B HPs are reused for the 5B variant.\n    2. Different ODE schedulers: FastVideo HPs are transferred to HunyuanVideo.\n3. Moreover, when integrating AsymRnR with cached methods (detailed in the **A3** below), we can also maintain the same HPs as in Sec 4 and Appendix B.\n\nNotably, other acceleration methods also involve tuning efforts: step distillation approaches require substantial training, and caching-based methods still necessitate model-specific HPs tuning. In comparison, AsymRnR’s HPs tuning is lightweight.\n\n---\n\n**Q3**: Can the caching method be combined with distillation methods? For example, can the caching approach be applied on top of a model that has already been accelerated through distillation to achieve further speedup?\n\n**A3**: Thank you for the question. \n\n- AsymRnR is compatible with step-distilled models such as FastVideo, as discussed in Sec 4. Notably, despite FastVideo being a distilled 6-step video generation model (5x speed up vs the 30-step HunyuanVideo), AsymRnR achieves a 1.24× further speedup, resulting in a total speedup of **6.18x** over the original HunyuanVideo.\n- Moreover, AsymRnR is also compatible with other caching methods. We compared PAB and PAB + AsymRnR on HunyuanVideo. PAB is configured using the official settings provided on the authors’ GitHub. AsymRnR reuses the HPs from Sec 4 and Appendix B without modification. **The supplementary Tab 1 and Fig 4** shows the compatibility of AsymRnR with the PAB cache method, achieving a total **1.71×** speedup without significant performance loss.\n- To the best of our knowledge, caching-based methods such as PAB are not compatible with step-distilled video diffusions. AsymRnR can be integrated with either caching methods or step-distilled models for acceleration, but their joint integration is beyond the scope of this work.\n\nIn summary, although AsymRnR alone does not provide huge acceleration, its compatibility and ease of integration allow it to work seamlessly with other acceleration methods, offering additional benefits.\n\n---\n\n**Q4**: What is the meaning of $d$ in Eq 2?\n\n**A4**: The $d$ denotes the dimensionality of the vector samples $X_i$, consistent with the definitions throughout the paper. We will explicitly clarify this notation in Collary 3.1 in the revision.\n\n---\n\n**Q5**: After calculating $S$ using the formula in Sec 3.5, how is $\\hat{S}$ in Eq 5 determined for $H, Q, K, V$? Is it simply the average value?\n\n**A5**: Thank you for the comment. The notation $S(A, t, b) = \\mathrm{BSM}(A, t, b)$ appears in Sec 3.5 (P6, left column, line 297), where $A \\in {H, Q, K, V}$ represents the feature type, and $t$ and $b$ denote the timestep and block. This indicates that $S$ (and $\\hat{S}$) is computed separately for each of $H$, $Q$, $K$, and $V$. No averaging or aggregation is applied for our method. We will include the definition of $S$ and the explanation above immediately after Eq 5 in the revision.", "claims_and_evidence": "Most claims made in the submission are generally well-supported. However, the paper claims that AsymRnR achieves \"negligible degradation in output quality\" in some cases and even improves it. While the experimental results show high VBench scores and low LPIPS values, some visual cases in Figure.1, 6, and 7 show misaligned motions with the original results. I wonder how to align these results in practice.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are appropriate for the problem of accelerating video diffusion transformers.", "theoretical_claims": "The paper includes theoretical analysis to motivate the reduction strategy, specifically through the estimation of KL divergence using Monte Carlo methods.", "experimental_designs_or_analyses": "Yes. The authors compare AsymRnR with existing token reduction methods and show performance improvements in terms of efficiency and quality. However, the speedups are very limted, at most 1.3x in Tab.", "relation_to_broader_scientific_literature": "Diffusion models, efficient neural network architectures, and video generation acceleration.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "## Strengths:\n* The proposed method of asymmetric reduction is reasonable. \n* The paper provides theoretical analysis to support the proposed reduction strategy, enhancing the credibility of the approach.\n* The paper is easy to follow.\n\n## Weaknesses:\n*  Despite maintaining semantic consistency, the generated videos may exhibit visual discrepancies compared to baseline models.\n* The performance of AsymRnR depends on hyperparameter configurations (e.g., similarity thresholds and reduction rates), which may require tuning for different models.\n* The acceleration effects shown in Tables 1 and 2 are very limited, **with a maximum speedup of only 1.3 times**. Can the caching method be combined with distillation methods? For example, **can the caching approach be applied on top of a model that has already been accelerated through distillation to achieve further speedup**?", "other_comments_or_suggestions": "Misalignment of the semantics, the limited speedups and the combination of distillation methods are important for my assesment. I suggest the authors to solve these issues during the rebuttal.", "questions_for_authors": "1. What is the meaning of (d) in Equation 2?\n\n2. Figures 1, 6, and 7 may alter the semantic content of the video.\n\n3. In Equation 5, (S) needs to be calculated using the formula in Section 3.5. After calculation, how is ($\\hat{S}$) determined for \\( H \\)/\\( Q \\)/\\( K \\)/\\( V \\)? Is it simply the average value?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GHFe4LVwg6", "reviewer": "Reviewer_LhNZ", "summary": "The paper presents AsymRnR, a method to accelerate video DiTs without requiring retraining. It exploits the variability in redundancy among different feature tokens across various model blocks and denoising steps. By asymmetrically reducing the computational load during the attention operations, AsymRnR achieves speedups with small loss in output quality. It integrates seamlessly with existing state-of-the-art DiT architectures, enhancing their efficiency across multiple benchmarks.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the Reviewer **LhNZ** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:\n\n---\n\n**Q1**: How were these hyperparameters (similarity thresholds and reduction rate) tuned? Are they manually tuned per model, or is there an automated selection process?\n\n**A1**: Thank you for the comment. The hyperparameters are manually tuned through only a simple and efficient process——typically within 10 iterations, with each iteration requiring only 1 inference. In practive:\n\n1. We start the first iteration with a low similarity threshold of 0.5 and a low reduction rate of 0.3.\n2. We run 1 inference with an arbitrary text prompt. If the generation maintains good, we increase the reduction rate by 0.2 to encourage more aggressive reduction.\n3. When a poor generation occurs, we revert to the previous reduction rate, lift the threshold by 0.1, and repeat the step 2.\n\nMoreover, hyperparameter re-tuning is not always necessary. In practice, we are able to reuse the same hyperparameters across different model architectures and even models employing different diffusion schedulers. Due to space limitations, we kindly refer Reviewer **LhNZ** to our **A3** response to Reviewer **wNPy** for further details.\n\nThis simple heuristic guides the tuning process with minimal effort. We will include the hyperparameter tuning process in the revision.\n\n---\n\n**Q2**: What is the computational overhead of the matching cache? Does it introduce significant latency in some cases?\n\n**A2**: The matching cache itself does not introduce additional computation. The complexity of a single matching step is analyzed in Appendix C and depends solely on the video size, which is typically fixed to the training resolution. It doesn’t depend on text prompt or reduction rate. With a matching cache step of $s$, the total matching cost can be further reduced by a factor of $1/s$. \n\nIn practice, the matching overhead takes approximately **7 seconds** for each generation in the HunyuanVideo experiments (see Sec 4 and Appendix B for more detailed configurations), which is negligible compared to the **over 200 seconds of total acceleration** achieved.\n\n---\n\n**Q3**: Does AsymRnR ever degrade performance compared to a baseline? If so, under what conditions?\n\n**A3**: Yes, AsymRnR may lead to visible quality degradation.\n\n1. Under aggressive reduction settings, such as extremely low similarity thresholds or high reduction rates, AsymRnR may introduce distortions, pixelation, or blurring.\n    1. The quantitative analysis for varying reduction rates is provided in Sec 4.3. \n    2. In addition, the **supplementary Fig 2** shows a qualitative study, where we vary the similarity threshold and reduction rate of HunyuanVideo. Under aggressive reduction settings (eg, similarity threshold 0.6 and reduction rate 0.7), noticeable distortion is observed.\n2. Additionally, when the baseline model already produces unsatisfactory outputs (eg, under extremely fast motion or characters, as shown in the **supplementary Fig 3**), AsymRnR may amplify these issues. However, in most cases where the baseline performs well, AsymRnR maintains stable performance without introducing topic-specific degradation.\n\nWe will include the bad case analysis in the revision.\n\n---\n\n**Q4**: Could the method be extended to latent-space DiTs (e.g., Video LDMs)?\n\n**A4**: The experiments in Sec 4 show our applications of AsymRnR on latent DiTs such as CogVideoX and HunyuanVideo. AsymRnR can also be extended to UNet-based video diffusion models, as these models include attention blocks where AsymRnR operates.\n\nAs Video LDM is not open-soruced, we use AnimateDiff to present our extensibility. We apply AsymRnR to the spatial self-attention modules in the highest-resolution stages of AnimateDiff. The corresponding quantitative and qualitative results are presented in the **supplementary Fig 5 and Tab 2.** We achieve 1.20x speedup, with invisable quality degradation. We will include the additional UNet-based experiments in the revision.\n\nNote that, UNet models adopt factorized spatiotemporal blocks have worse performance than the full 3D DiTs at the same parameter scale, as shown in the **supplementary Tab 3**. And it has seen less adopted since early 2024. Our work primarily focuses on SOTA video DiTs in the main manuscript. Nonetheless, AsymRnR remains compatible with the legacy UNet-based video diffusion models.", "claims_and_evidence": "The paper makes several key claims, which are well-supported by theoretical analysis and experimental results:\n\n1. Claim: AsymRnR provides significant speedup in video DiTs without retraining.  \nEvidence: Experiments on multiple SOTA models show 24–30% reduction in latency while maintaining high perceptual quality.\n\n2. Claim: The asymmetric reduction strategy improves efficiency while minimizing quality loss.  \nEvidence: Ablation studies demonstrate that reducing Q tokens too aggressively degrades quality, while reducing K&V is more forgiving. The asymmetric approach balances quality and efficiency better than prior uniform reduction methods (e.g., ToMe).\n\n3. Claim: Matching cache reduces computational overhead while maintaining accuracy.  \nEvidence: Ablations in Table 4 show that increasing cache steps significantly reduces latency (from 134s to 118s) with only minor quality degradation.", "methods_and_evaluation_criteria": "1. The benchmark datasets and evaluation metrics are appropriate for assessing video generation quality and efficiency.\n2. The method is tested on multiple state-of-the-art DiT architectures, and follows standard evaluation protocols for video generation.", "theoretical_claims": "1. The KL divergence-based analysis (Corollary 3.1) is mathematically sound and provides a formal justification for token reduction strategies.\n2. The derivation of the Monte Carlo estimator for KL divergence is based on prior work (Wang et al., 2009) and appears correct.\n3. The discussion on token similarity metrics (dot product vs. Euclidean distance) is insightful and supported by empirical findings.", "experimental_designs_or_analyses": "Potential concerns:\n1. The paper does not explicitly discuss the worst-case computational overhead introduced by matching cache and dynamic scheduling.\n2. The choice of similarity thresholds for reduction scheduling is not well-explained—how were these hyperparameters tuned?", "relation_to_broader_scientific_literature": "The paper builds on prior token reduction techniques (e.g., ToMe (Bolya & Hoffman, 2023)) but extends them to video DiTs with asymmetric scheduling and caching. Connections to diffusion model acceleration (e.g., distillation approaches like InstaFlow (Liu et al., 2024)) are discussed, highlighting how AsymRnR differs by being training-free. The work is related to efficient attention mechanisms (e.g., Linformer, Performer) but is more specialized for diffusion models.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Strengths:\n1. No fine-tuning required, making the method easily adaptable.\n2. The aymmetric design is reasonsable with theoretical analysis.\n3. Experiments conducted on various SOTA models have demonstrated strong performance. The ablation study is well-conducted.\n4. The overall writing is well and logical flow is clear.\n\nWeakness:\n1. Hyperparameter sensitivity: Similarity thresholds for reduction scheduling lack clear explanation.\n2. Computational overhead of matching cache should be discussed more explicitly.\n3. Limited discussion on worst-case performance—are there cases where AsymRnR degrades performance?", "other_comments_or_suggestions": "1. The paper could clarify the trade-offs between speedup and quality in more detail—e.g., when does AsymRnR start degrading output?\n2. A qualitative analysis of artifacts introduced by aggressive reduction would be useful.", "questions_for_authors": "1. How are the similarity thresholds for reduction scheduling chosen? Are they manually tuned per model, or is there an automated selection process?\n2. What is the computational overhead of the matching cache? Does it introduce significant latency in some cases?\n3. Does AsymRnR ever degrade performance compared to a baseline? If so, under what conditions?\n4. Could the method be extended to latent-space DiTs (e.g., Video LDMs)?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QhVQB0sy3Z", "reviewer": "Reviewer_Mipk", "summary": "This paper studies the importance of different components in video DiTs and proposes Asymmetric Reduction and Restoration (AsymRnR) as a plug-and-play approach to accelerate video DiTs based on previous findings. Experiments on multiple open-source video generation models demonstrate the effectiveness of the proposed method.\n\n## update after rebuttal\nThe additional experiments of u-net based structures and integration with existing speedup methods address most of my concerns. Therefore, the rating is updated.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the Reviewer **Mipk** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:\n\n**Q1**: The authors choosed different models of CogVideoX in Tab 1 and Tab 2. Is there a specific reason for this experimental setup?\n\n**A1**: Thank you for the comment. CogVideoX 2B and 5B are distinct models with different architectures. We include both to comprehensively evaluate the effectiveness of AsymRnR. \n\nAs clarified in **P6 (left, line 322-326)**, ToMe is only compatible with CogVideoX 2B and cannot be applied to other models, such as CogVideoX 5B and HunyuanVideo. In contrast, AsymRnR is compatible with all these models. Therefore, the comparison with ToMe is presented in **Tab 1**, while results on the other models are reported in **Tab 2**.\n\n---\n\n**Q2**: Shown in Tab 2, the proposed method could result in improvement in VBench scores at certain cases and it is suggested to provide some justification why this could happen. One possibility is that it is caused by the variance of VBench scores, and repeated evaluation may help to eliminate this effect.\n\n**A2**: We agree that variance exists in VBench results, as is common in generative benchmarks. However, the experiments on VBench has already includes over 950 text prompts, and for each prompt, 5 videos have already been generated to mitigate the impact of randomness.\n\nAnother possible explanation is the inherent redundancies in the overparameterized models, which may introduce minor negative effects. AsymRnR prunes these redundancies, potentially leading to slight improvements. This hypothesis is empirically supported by **Tab 1 and 2**, where AsymRnR exhibits minimal degradation in larger models with higher FLOPs—and in some cases, performance gains. **The supplementary Fig 1** also show cases where AsymRnR improves the baseline generation.\n\n---\n\n**Q3**: It is mentioned that 'Latency is measured using an NVIDIA A100 for CogVideoX variants and a H100 for the rest of models', it would be better to provide some justification on this setup.\n\n**A3**: Thank you for the question. Both the baseline models and AsymRnR impose no constraints on the underlying hardware. The experiments were conducted on different devices purely due to the availability of hardware at the time.\n\n---\n\nAdditionally, we would like to clarify a few points raised by Reviewer **Mipk**.\n\n---\n\n**Q4**: AsymRnR could only work on transformer structure. It has certain limitation in the generalization to UNet backboned video diffusion models.\n\n**A4**: AsymRnR is designed to operate on attention layers, which are commonly present in diffusion models—including UNet models such as Stable Diffusion.\n\nDue to space constraints, we refer the readers to our **A4** response to Reviewer **LhNZ**, which includes additional experiments on the UNet-based video diffusion, AnimateDiff.\n\n---\n\n**Q5**: The improvement in efficiency seems limited compared to other lines of methods, such as distillation methods or feature caching techniques, which could achieve beyond 10x acceleration.\n\n**A5**: To the best of our knowledge, open-sourced step-distilled video DiTs (eg, FastVideo) can achieve approximately 5× speedup but require substantial training resources. Feature caching methods generally yield around 1.3× acceleration in video DiTs, as shown in **A3** response to Reviewer **wNPy**—on par with AsymRnR.\n\nAdditionally, AsymRnR is complementary to these methods and can be integrated with them for additional acceleration.\n\n- The integration with the step-distilled FastVideo method is presented in Sec 4, achieving a total **6.18× speedup over HunyuanVideo**.\n- Integration with the caching-based method PAB results in a **1.71× speedup** without visible distortions.\n\nWe also refer Reviewer **Mipk** to response A3 to Reviewer **wNPy** for detailed results.\n\n---\n\n**Q6**: The proposed method seems to be an improved version of ToMe based on the empirical findings in video DiT models.\n\n**A6**: Both ToMe and AsymRnR accelerate through reducing the number of tokens. However, ToMe relies heavily on heuristic designs and lacks theoretical foundation (eg, the use of cosine similarity). In contrast, we provide a theoretical justification for the matching-based reduction methods in **Corollary 3.1**, which directly informs our design choices (eg, the matching metric in Tab 5).\n\nFurthermore, inspired by the QKV-specific behavior from our exploration, we propose several key components: asymmetric strategy (Sec 3.3), scheduling mechanism (Sec 3.4), and the matching cache (Sec 3.5). \n\nTogether, our theoretical insights, empirical analysis, and extensive experiments drive the design of AsymRnR and lay the groundwork for future research across a broader range of token reduction methods.", "claims_and_evidence": "Yes, the claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria make sense for the problem or application at hand.", "theoretical_claims": "No.", "experimental_designs_or_analyses": "Yes, I have checked the soundness of the experimental designs and analyses. Seems fine to me.", "relation_to_broader_scientific_literature": "This work shares similar motivation with ToMe, i.e., reducing the tokens to enable efficient inference, but exhibits some improvement on DiT-based video generation models.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths:\n1. The paper provides analysis on the importance of different components in video DiT and provides several key insights which may benefit latter works when designing the methods.\n2. The authors provide extensive analysis on different open-source video generation models and qualitative / quantitative evaluation validates the effectiveness of the proposed method.\n3. The paper is well-organized and the writing is clear.\n\nWeaknesses:\n1. While the proposed method has shown some improvement over the baseline method ToMe, ToMe is original proposed on U-Net based architectures and the comparisons are only conducted on DiT based models. It can be explained by that AsymRnR could only work on transformer structure which contains the designs of Q, K, V which has certain limitation in the generalization ability.\n2. Although the proposed method can boost the efficiency of current video generation models to some extend, the improvement in efficiency seems limited compared to other lines of methods, such as distillation methods or feature caching techniques, which could achieve beyond 10x acceleration.\n3. The proposed method seems to be an improved version of ToMe based on the empirical findings in video DiT models, the technical contribution needs further justification.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "1. It is noted that the authors choose different models of CogVideoX in Tab. 1 and Tab. 2, is there a specific reason for this experimental setup?\n2. Shown in Tab. 2, the proposed method could result in improvement in VBench scores at certain cases and it is suggested to provide some justification why this could happen. One possibility is that it is caused by the variance of VBench scores, and repeated evaluation may help to eliminate this effect.\n3. While the author mentioned that 'Latency is measured using an NVIDIA A100 for CogVideoX variants and an NVIDIA H100 for the rest of models', it would be better to provide some justification on this setup.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "v1w8JKvmrM", "reviewer": "Reviewer_fiPH", "summary": "The authors claim that existing methods for accelerating video DiT sampling often rely on expensive fine-tuning or exhibit limited generalization capabilities. To this end, the authors propose a training-free and model-agnostic method to accelerate video DiTs. Specifically, the authors decouples sequence length reduction between attention features and allows the reduction scheduling to adaptively distribute reduced computations across blocks and denoising timesteps. In addition, the authors introduce a matching cache mechanism to minimize matching overhead. The authors provide extensive experimental validation.\n\n## update after rebuttal\nThe authors have addressed the majority of my concerns. I therefore maintain my positive rating.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the Reviewer **fiPH** for the valuable questions and comments. For the concerns and questions, here are our responses, along with supplementary figures and tables available at https://anon0728.github.io/icml-230-supplementary:\n\n**Q1**: The authors mentioned a variety of Prior Reduction Methods (such as Bolya & Hoffman, 2023 [1]; Li et al., 2024 [6]; Kahatapitiya et al., 2024a [7]) in the paper, but only compared ToMe [1] in the experimental analysis. More experimental comparisons are needed to verify the effectiveness of the paper's methods.\n\n**A1**: We thanks for the comment. While various prior reduction methods have been proposed, they are not directly designed for text-to-video diffusion models. Some approaches [1, 2, 3] target discriminative tasks such as image classification, while others [4, 5] focus on autoregressive generation in NLP. These methods typically truncate sequence lengths, making them incompatible with diffusion denoising tasks, which require the input and output sequence lengths to remain identical, as discussed in Sec 2.3.\n\nLi et al. [6] and Kahatapitiya et al. [7], on the other hand, focus on adapting image diffusion models for video editing tasks. However, their task setups and model designs differ significantly from native video diffusion models, making direct comparison inappropriate.\n\nSequence length reduction for accelerating video diffusion remains relatively underexplored, despite its high potential as demonstrated in our work. Therefore, we primarily compare against ToMe, the only applicable baseline—even though it was originally proposed for image diffusion. Additionally, we evaluate and integrate our method with other acceleration techniques, such as the step-distilled FastVideo method (in Sec 4). We further demonstrate compatibility with caching-based methods through additional experiments in the **A3** of our response to Reviewer **wNPy**, which show that AsymRnR can provide further acceleration when combined with such techniques.\n\n**Reference**\n\n[1] Bolya, Daniel, and Judy Hoffman. \"Token merging for fast stable diffusion.\" CVPR, 2023.\n\n[2] Koner, Rajat, et al. \"Lookupvit: Compressing visual information to a limited number of tokens.\" ECCV, 2024.\n\n[3] Rao, Yongming, et al. \"Dynamicvit: Efficient vision transformers with dynamic token sparsification.\" NIPS, 2021.\n\n[4] Leviathan, Yaniv, Matan Kalman, and Yossi Matias. \"Selective attention improves transformer.\" preprint, 2024.\n\n[5] Xiao, Guangxuan, et al. \"Duoattention: Efficient long-context llm inference with retrieval and streaming heads.\" preprint, 2024.\n\n[6] Li, Xirui, et al. \"Vidtome: Video token merging for zero-shot video editing.\" CVPR. 2024.\n\n[7] Kahatapitiya, Kumara, et al. \"Object-centric diffusion for efficient video editing.\" ECCV, 2024.\n\n---\n\n**Q2**: In Fig. 2, the author conducted a comparative experiment on Q in shallow/medium/deep and early/late. I want to know how K and V perform in these positions.\n\n**A2**: Thank you for the comment. This observation also motivated our curiosity during the analysis, and we conducted a similar analysis on the $K$ and $V$. Since the analysis is based on random perturbation and $K$ and $V$ tokens are one-to-one corresponding, perturbing $K$ or $V$ yields equivalent effects. The feature-type sensitivity of matching-based (non-random) reduction is further analyzed in Sec 4.3.\n\nThe performance trends for $K$ and $V$ mirror those of $Q$ mentioned in Sec 1, but the degradation is less obvious:\n\n1. Perturbations in later blocks result in greater performance degradation.\n2. Perturbing early timesteps primarily affects semantic accuracy, while perturbing later timesteps degrades visual details.\n\nQualitative results are provided in the **supplementary Fig 6**. To improve readability and due to page constraints, we present only the main analysis results in the **main manuscript Fig 2**. We will include the full analysis in the revision.", "claims_and_evidence": "The author's claims are clear and provide sufficient theoretical support.", "methods_and_evaluation_criteria": "The proposed method and evaluation datasets used by the authors are reasonable.", "theoretical_claims": "I have carefully checked the correctness of the proofs for theoretical claims and found no relevant problems.", "experimental_designs_or_analyses": "I have carefully checked the soundness/validity of any experimental designs and analyses, and there are the following problems: \n\n(1) The authors mentioned a variety of Prior Reduction Methods in the paper (such as Bolya & Hoffman, 2023; Li et al., 2024; Kahatapitiya et al., 2024a), but only compared ToMe in the experimental analysis. More experimental comparisons are needed to verify the effectiveness of the paper's methods. \n\n(2) In Fig. 2, the author conducted a comparative experiment on Q in shallow/medium/deep and early/late. I want to know how K and V perform in these positions.", "relation_to_broader_scientific_literature": "The paper provides new improvement ideas for existing methods and provides new potential for improving the actual generation efficiency of video DiTs.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "None", "other_comments_or_suggestions": "None", "questions_for_authors": "(1) The authors mentioned a variety of Prior Reduction Methods in the paper (such as Bolya & Hoffman, 2023; Li et al., 2024; Kahatapitiya et al., 2024a), but only compared ToMe in the experimental analysis. More experimental comparisons are needed to verify the effectiveness of the paper's methods. \n\n(2) In Fig. 2, the author conducted a comparative experiment on Q in shallow/medium/deep and early/late. I want to know how K and V perform in these positions.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The final rating for this paper is 4 weak accept.

Before rebuttal, reviewers' main concerns are: 1) hyperparameter need to be changed for different methods; 2) acceleration effects are very limited, with a maximum speedup of only 1.3x; 3) limited technical contribution; 4) more comparison with other methods are needed.

After rebuttal, all reviewers confirmed they had read the authors' response and would update reviews if needed. Reviewers wNPy suggested their concerns had been mostly addressed and raise the score. Reviewer fiPH mentioned authors addressed all their concersn and maintained rating. Reviewer LhNZ and Mipk didn't give further comments and maintain their score.

Consider all these AC gave weak accept recommendation for this paper which aligned with all the reviewers' rating.
Decision: Accept (poster)