Astraea: A Token-wise Acceleration Framework for Video Diffusion Transformers
Authors: Haosong Liu
Yuge Cheng
Wenxuan Miao
Zihan Liu
Aiyue Chen
Jing Lin
Yiwu Yao
Chen Chen
Jingwen Leng
Minyi Guo
Yu Feng
Venue: ICLR 2026 Poster (2026)
Keywords: video diffusion acceleration
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a token-wise acceleration framework for video diffusion transformers and achieve the best image consistency (10dB higher) and the highest speedup (up to 13.2x) against the state-of-the-art methods.

Abstract: Video diffusion transformers (vDiTs) have made tremendous progress in text-to-video generation, but their high computational demands pose a major challenge for practical deployment. While existing studies propose acceleration methods to reduce workload at various granularities, they often rely on heuristics, limiting their applicability.
We introduce Astraea, a framework that searches for near-optimal configurations for vDiT-based video generation with a performance target. At its core, Astraea proposes a lightweight token selection mechanism and a memory-efficient, GPU-parallel sparse attention strategy, enabling linear reductions in execution time with minimal impact on generation quality. Meanwhile, to determine optimal token reduction for different timesteps, we further design a search framework that leverages a classic evolutionary algorithm to automatically determine the distribution of the token budget effectively. Together, Astraea achieves up to 2.4x inference speedup on a single GPU with great scalability (up to 13.2x speedup on 8 GPUs) while retaining better video quality compared to the state-of-the-art methods (<0.5% loss on the VBench score compared to the baseline vDiT models).

Published as a conference paper at ICLR 2026
ASTRAEA: A TOKEN-WISE ACCELERATION FRAME- WORK FOR VIDEO DIFFUSION TRANSFORMERS
Haosong Liu'
Yuge Cheng Wenxuan Miao'
Zihan Liu
Aiyue Chen Jing Lin
Yiwu Yao Chen Chen' Jingwen Leng!.2 Minyi Guo Yu Fengl.2 .2#
Shhnggaai Jiao Tong University Shhnngai Qizhi Institute
"Huawei Technologies Co.,Lid
"Equal Contribution # *Corresponding Author
(2436824987, chengyuge, miaowenxuan, altair. 1iu, chen-chenl@sjtu.edddcc cn (leng- jw, guo-my)&cs. stmmeycsssttta sjt....uuui edu.. cn {chenaiyue, linjing28, (oyyiiiwe@hueeeei com
Project site: cn
ABSTRACT
Video diffusion transformers (vDiTs) have made tremendous progress in text-to- video generation, but their high compute demands pose major challenge for prac- tical deployment. While studies propose acceleration methods to reduce workload at various granularities, they often rely on heuristies, limiting their applicability.
We introduce ASTRAEA, a framework that searches for near-optimal configura- tions for vDiT-based video generation under performance target. At its core, ASTRAEA proposes lightweight token selection mechanism and a memory- efficient, GPU-friendly sparse attention strategy, enabling linear savings on exe- cution time with minimal impact on generation quality. Meanwhile, to determine optimal token reduction for different timesteps, we further design a search frame- work that leverages classic evolutionary algorithm to automatically determine the distribution of the token budget effectively. Together, ASTRAEA achieves up to 2.4x inference speedup a single GPU with great scalability (up to 13.2 speedup on GPUs) while achieving up to over 10 dB video quality compared to the state-of-the the--rt methods (<0.5% loss on VBench compared to baselines).
INTRODUCTION
Visual imagination has always been at the core of humanity's 's nature for creativity. After the lease of Sora by OpenAl in early (OpenAl 2024), there are numerous video generative frameworks from text input, including Kuaishou's 's Kling (Kuaishou 2024p, Google's Veo (Google) 2024), and Tencent's 's Hunyuan Video (Tencent 2024). Despite the abundance of frameworks, video diffusion transformers (vDiTs) remain the core of these frameworks, widely regarded the most effective paradigm for high-fidelity video generation. However, its computational demand poses challenge for any industrial-level deployment. For instance, 4-second 720x 1280 video clip using Hunyuan- Video (Kong et al. 2024) takes over 0.5 hours on single Nvidia H100 GPU. This high computa- tional cost comes from the extensive denoising steps and the long token sequence. For example, a vDiT model often requires 50-100 denoising steps, with each step involving millions of tokens.
To address the inference inefficiency, various acceleration methods have been proposed reduce computations at different granularities, such as denoising step reduction (Li et al. 2023 Yin et al.)
al. 2024 Fang et al. 2023), ,, token selection (Zou et al. 2024b), etc. However, these methods often require it- cratively fine-tuning typerparameters, i.c., which steps or blocks to skip, with a human in the loop to achieve target performance in industrial-level deployments. Fundamentally, previous approaches propose various acceleration heuristics, without addressing a key question: given a performance target, which tokens are worth computing at each denoising step achieve optimal accuracy?
2024 Gu et al. (202)), block caching (Zhao et al. 2024 Chen al., 2024] Kahatapitiya
To answer this question, we propose ASTRAEA, a GPU-friendly acceleration framework that op- erates at the token level, the smallest primitive in vDiT models. Specifically, we propose sparse

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Published as a conference paper at ICLR 2026
Reversing Diffusion Process
Froode
TIXT.
lleraaive Dendieing
6xx'.
Decadi
Takens Timestap (T]
Tokans
Tokans@ Timestep (0)
Nolsing Video,
Timestep (T-1)
Denoised Video, X
Fig. 1: Diffusion models work by reversing diffusion process, where they iteratively predict and remove noise at each timestep to gradually reconstruct the original images or videos.
diffusion inference with lightweight token selection mechanism and GPU-efficient sparse attention to accelerate each denoising step by dynamically selecting important tokens. Meanwhile, to deter- mine optimal token budget allocation, search framework is proposed to determine the number of tokens that should be assigned in each denoising step to achieve the target performance.
Algorithm. One key downside of prior work (Zou al., 2024b Zhao al. 2024) is the extensive GPU memory usage. Unlike prior studies, which require storing the entire attention map, our mech- anism introduces negligible memory overhead by only caching previous tokens. Thus, the memory requirement of our selection metric scales linearly with the token length to avoid a memory explo- sion. In addition, we purposely design our sparse attention to be natively parallelizable; thus, it can be integrated with existing acceleration techniques, such FlashAttention (Dao) 2023). On existing GPUs, our sparse attention achieves linear speedup with the number of reduced input tokens.
Search Framework. Although our algorithm can achieve linear acceleration for each denoising timestep, it is still unknown how many tokens should be selected for individual timesteps. While numerous search techniques exist in the literature, such neural architecture search (White et al.
2023 Elsken et al. 2019) and network pruning (Hoefler et al. [022 Cheng et al.] 2024). none can be applied to vDiI's due to their substantial search times. Here, we propose search framework based on the classic evolutionary search algorithms (Whitley et al.) 1996| AAshookk] (006). Our approach
guarantees achieve the target performance while achieving the minimal accuracy loss.
ASTRAEA achieves up to 2.4x speedup with up to > 10 dB better quality in PSNR compared to the state-of-the-are algorithms, and achieves <0.5% loss VBench. ASTRAEA also shows great seal- ability and achieves 13.2x speedup across 8 GPUs. Our contributions are summarized as follows:
We propose a lightweight token selection mechanism to reduce the computation workload of cach denoising step with negligible latency and memory overhead.
We design a sparse attention computation method that achieves linear speedup on existing GPUs, as the number of selected tokens decreases.
We introduce search framework that meets the target performance while achieving the minimal accuracy loss against prior studies.
2 RELATED WORK
Diffusion models have been widely used in video generation. It learns to generate data from Gaus- sian noise through a reverse Markov process (Fig. DT. The input of this diffusion process is ran- domly generated Gaussian noise, z/y. The diffusion model recovers the original data by progres- sively predicting and removing noise, 2/, from x/ each timestep f. The hope is that, through this process, the prediction of the diffusion model, zn can be close to the original data, zo. Mathemati- cally, the denoising step can be expressed as,
ar ar +o z ((x(,t),
where & is the prediction function that predicts the noise z/.- Both and B, are onnh is the renoising term to add randomness to the denoising step.
Normally, diffusion models often require hundreds videos (Yang et al. 2022). The common practice to reduce the diffusion workload is to encode the initial noising inputs into the latent space, then decode the tokens the end (Fig. 1
thousands of steps to denoise images or
Prior methods to enhance the efficiency diffusion models fall into two categories: step reduction, which reduces the number of denoising steps, and block caching, which seeks minimize the computational demands within each denoising step. The following paragraphs provide an overview of these two categories of acceleration techniques separately.
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Step Reduction. Overall, step reduction methods can be classified into two types: requires retraining, and the other is training friening-free. The retraining methods (Yin et al. 2024, Salimans Hoj 2022 Gu et al. 2023] Habibian et al. 2024 Kim et al. 2024), such distillation, can reduce the number of denoising steps to as few as one. However, the major downside of these methods is that they require as much training time the original model training. For this reason, current distillation
methods are primarily applied to image generation rather than video generation (Salimans & Ho
2022
Gu al. 2023 Yin et al. 2024; Habibian al. 2024).
On the other hand, training-free methods do not require re-training and are generally less effective. The intuition of these training-free methods is to leverage the insignificance predicted noises be- tween steps, allowing diffusion models to skip these less critical steps. For instance, PAB (Zhao) et al. 2024) periodically skips two out of every three intermediate steps to reduce the overall com- putation. AutoDiffusion (Li et al. 2023) applies heuristic search to iteratively develop better step- sampling strategies. FasterCache (Lv et al., 2024) and Gradient- Peafeent-Ottmized Cache (Qiu et al.) 2025) both propose some caching mechanisms accelerate video diffusion. Fast Video Generation ZZang ctt al. 2025c) introduces a tile-based attention pattern that accelerates video diffusion without requir- ing model retraining. AdaDiff (Zhang et al. 2023) and similar work (Fang et al. 2023 dynamically select steps to skip during inference.
Block Caching. The execution time of diffusion transformers is dominated by either 2D or 3D attention (Lin et al., 22224] IZheng et al. 2024). To reduce the computation in self-attention, existing methods exploit the similarity of intermediate results across denoising steps and cache the interme- diate results to avoid extra computations (Zhao et al., 2024; Chen et al., 2024 Kahatapitiya et al.)
2024
Ma et al. 2024 Wimbauer et al. 2024 Liu
2024a
Primary caching methods operate at the block level. They reuse the intermediate result of a block from the previous denoising step and skip the corresponding block computation in the current de- noising step entirely (Zhao et al. [2024] Kahatapitiya et al. Liu et al. 20e4b/Chhen
Chen et al. 2024]
Liu et al., [2224a] Zhang al., [025b]] Ma et al. 2024] Wimbauer et al.j 2024; Zhang et al. 2025a).
The key difference among block- wise methods hes in the strategies they apply to reuse the interme-
diate results. For instance, PAB (Zhao al.] 2024) applies a fixed step-skipping scheme throughout the entire inference process, whereas A-DiT (Chen et al., (024) and AdaptiveCache (Kahatapitiya 2024) adopt an adaptive scheme to tailor the step reuse for each input. SmoothCache (Liu cet al.
ee al. 2024b) and BlockDance (Zhang et al. 2025b) exploit different features in DiTs to enable continuous reuse of transformer blocks. Nevertheless, the underlying concept remains unchanged.
Recently, a few studies (Zou et al. 2024b Bolya & Hoffman [023 Zou et al. 2025 have started to explore reuse at a finer granularity, the token level. For instance, ToCa (ou et al. 2024b) proposes a composed metric that identifies the unimportant tokens during the inference and uses the previously cached results for attention computation. However, its token selection process introduces non-trivial compute and memory overheads. Neither is affordable on modern GPUs. Thus, token-wise methods are still not fully exploited and require careful algorithmic design to enable practical usage.
3 METHODOLOGY
This section describes our framework, ASTRAEA. The goal of ASTRAEA is to determine which tokens worth computing throughout the diffusion process. Sec. 3 .2 first describes our selection method that determines which specific tokens should be computed at each timestep under a token budget. Sec. 3 3.3 then describes search algorithm that determines the token budget each timestep.
3 .1 PRELIMINARY
Self-Attention. We first introduce one of the key operations in vDiTs: self-attention. The input to the self-attention, Xin- sequence of tokens with a shape of (N, d). N the number of tokens and d is the token channel dimension. The computation of self-attention can be expressed as,
(Q,ention(,,AA V) = Softmax QKT
Va V
where query Q, key K, and value V are generated by performing three independent linear projec- tions on input tokens, Xm. These three values have the same dimensions as Xi.- The product QKT
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Reversed Diflusion Process
Legend
Input
Token Selection
Compulc Block (Bx)
compuiee block
Token Selection
Compute Block (Bo)
Taken
x
Timestap t-1
Computed
Timestop
Taken
Repeat for
Cached
x
Token
Not
Rysut
Select
62:1
Reuse
Op.
Cache
Update
Cache
Fig. 2: The general diffusion process with token caching. For each compute block, a selection mod- ule determines which tokens should be computed. Only the selected tokens perform computation. The unselected tokens skip the compute block and query the cache for their results.
commonly referred to as the attention map, A, with a shape of (N,N). The attention map is then divided by /dg where de the channel dimension of K. Finally, each row of the attention map, A,, is normalized by the softmax function, where
A
Softmax (A.j) ΣE eAiL"
(3)
before performing a dot product with V. i and j are the and column indices of the attention map, A. Note that, Ei=1 e often called the Log-Sum Sagg-Suu-xppp (LSE) score.
TOKEN SELECTION
Execution Flow. Fig. 2 illustrates how our token selection integrates into the general diffusion process. For instance, at timestep I, there are four input tokens, (fo..t T3,t). Before these tokens pass through compute block (typically stack of self-attention layer, cross-attention layer, and a multilayer perceptron in vDiTs), the token selection module first determines which tokens should be computed. This module computes the importance of each token and selects the top significant tokens, under a pre-defined computation budget, 0"
The selected tokens then processed through the compute block, and their outputs are used to update the cache. contrast, unselected tokens would directly query their outputs from the cache, which stores results from the same compute block earlier timesteps. The final output token quence a combination of both computed and cached tokens, e.g., (Wo.rs 31,t, U2,e', V3.z) )) in Fig. The same process is applied all compute blocks in the subsequent process.
Selection Metric. Given the execution flow above, we next describe our token selection mecha- nism. The goal of token selection is skip unimportant tokens while retaining the same generation quality. Despite studies (Zou et al. 2024b; Bolya et al., 2022, Bolya & Hoffman 2023) proposing various token selection metries, they either incur high computational and memory overhead (Zou
el al. 2024b)
specifically tailored to particular tasks (Bolya et al.) [022] Bolya & Hoffmmnn
2023). Thus, we propose a general token selection metric, Stokens that applies to vDiTs. Tbl. shows that Stoke achieves superior performance compared to prior metrics.
Mathematically, Stoken has two components,
Suken = W., Ssig + W
(4)
where Ssig stands for the significance of this token and Spenatty represents the penalty for repeatedly choosing the same token across multiple timesteps. Both wa and wy are the hyperparameters that are used to weigh the contributions of Ssip and Spenatty Ssig is expressed as,
Saig SLSE. 1-1 Auoken
(5)
where SL.F, 1-1 is the LSE score of each token computed in the softmax function in the previous timestep, 1. Here, we use the LSE score in the previous timestep because LSE scores do not vary across timesteps. To verify this point, calculate the cosine similarity of the LSE of the attention for each block in adjacent timesteps during the inference process of the Wan 2. (Wang et al. 2025). The average cosine similarity of adjacent L.E is 99.1 % with standard error of 0.00736%.
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Computed Tokens
Masked Takens
Cached
SoftMax
IV4.
Output Takens
SoftMax
/Va.
Output Tokens
Attantion Map
Attention Map
Naive Sparse Attention
Our Sparse Attention
Fig. 3: An illustration of our sparse attention computation with selected tokens. While naive sparse attention reduces overall computation quadratically by computing the selected tokens on Q, K, and V, it suffers from computational inaccuracies and requires excessive memory usage. Our sparse attention only computes the selected tokens on Q and keeps all tokens for and V. The pseudocode of our sparse attention is shown in Algo. in the appendix.
Also, SLSE, 1-1 is included in Ssig because its value is proportional to the attention score in self- attention, which reflects the token importance. Meanwhile, SLSE, t-1 the byproduct of softmax and incurs no additional computational overhead.
Aokkn, is the value difference of individual input tokens across two adjacent computed timesteps. Here, a timestep is considered computed if the token is evaluated at that timestep rather than reused from cache. Note that, we calculate Auken, the difference of input tokens, so that we can select the important tokens without performing the attention computation. Spenalty is expressed as,
Spenalty
(6)
where is the number of times the ith token has not been selected consecutively. This penalty term inspired by ToCa (Zou al. 2024b), and we claim no contribution.
Sparse Attention. Next, we describe how to perform vDîT inference with the selected tokens. There are three main computation operations in vDiT: self-atteniion, cross-attention, and multilayer perceptron (MLP) (Zheng et al., 2024 [in et al. 2024). Both cross-attention and MI. operate on individual tokens; thus, we can directly skip the unselected tokens reduce the computation.
However, naively performing sparse self-attention with selected tokens, e.g., in ToCa (Zou et al.) 2024b), would alter the semantics of self-attention, as shown Fig. 3 Only computing the un- masked positions in the attention map would lead to two issues: incorrect results and substantial memory overhead. This is because self-attention requires calculating the LSE score for each row in the attention map. Just computing the unmasked positions is semantically incorrect. Even reusing the results the same attention a previous denoising timestep would lead to accuracy loss. Mean- while, it requires caching the entire attention map, which introduces high memory overhead. Tbl. shows that our technique achieves smaller memory overhead (roughly 2x reduction) and much higher accuracy J0dB in PSNR) against the prior methods.
In contrast, we propose a seemingly "counterintuitive" sparse attention computation, as shown in Fig. 3 where we only selectively compute the queries Q while computing all tokens for the keys K and values V. Although this approach saves less computation than naive sparse attention, it offers the following advantages. First, by computing the entire row of elements in the attention map, we ensure the individual output token is computed correctly. Second, our sparse attention is natively GPU-parallelizable and can be integrated with existing attention acceleration techniques, such as FlashAttention (Dao et al.) 20022). Third, sparse attention does not require any additional GPU memory, except for the cached tokens, which are negligible compared to the all attention scores.
3 .3 TOKEN-WISE SEARCH FRAMEWORK
Problem Setup. Sec. 3 explains how to select tokens of a compute block under given token budget, 0*, The next question is what token budget should be allocated for each compute block. Ideally, to achieve the best performance, we should search for token budgets at the block level. However, searching the block level would make the search space intractable. Thus, we fix the token budget at the timestep level and formulate the problem as follows: Given a total number of selected tokens, how should the token budget be allocated for each timestep?
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Haratlvvly Generate P New Candidites
Marttaring K Candidates
New
0,000
0.3
10.7
0.3
Range
last ...
Su oct Top Qan dandi Out (C-P)) Prpuration
...]
Ph
(hip
OR
(8) phyn
Newe
Nawor
(1.0)
10.71
04 ..)
New
Uniform
Crossover
Mutetion
Reppiir
Fig. 4: An illustration of EA with its three key steps. Each new candidate would go through these three steps sequentially before being added to the existing population. For each candidate, we would randomly pick one out of two crossover methods. The detailed EA procedure is shown in Algo. 2
Search Space. In our framework, the search space is O which can expressed as,
θ { i E [1. T] and 0, E {0, 10%, 20%, .... 100%),
where 02 is the percentage of selected tokens at the denoising timestep i. T is the maximal timestep.
Algorithm. We now introduce our search algorithm, which adopts the classic stochastic search framework, evolutionary algorithm (EA) (Whitley et al., 1996] Ashlock, 2006), to search for the optimal token allocation across denoising steps. EA simulates the process of natural selection, which evolves population of candidates over generations using operations, e.g., selection, crossover, and mutation, to find a near-optimal solution. In EA, we start by spawning the initial generation of K candidates. We set K to be 50 in this paper. Each candidate has list of selected token percentages, {o & E [0, K). Each 0, stands for selecting O: percent of tokens at the ith timestep. All {0.)x values together need to fit the total token budget constraint, Og.
At each generation, the top-K number of parents with smaller MSE losses are selected from the previous generation. We then generate P' number of new candidates from these top-K parents (P= 30). For each new candidate, we randomly pick a parent pair from the top-K parents. The selected parent pair then goes through three key steps: crossover, mutation, and repair, to generate those new candidates. These newly generated candidates are added to the current population. These new candidates are then evaluated based on the MSE between the original video output and the output generated using the selected tokens. Finally, the lop-K candidates with the lowest MSEs are selected for the next generation. After G generations (G = 30), we will pick the best candidate as the solution for our token allocation. Next, we explain the three key steps in this process, as shown in Fig,
This step aims to generate a new candidate by combining two randomly picked candidates from the previous population. We propose two crossover strategies: uniform crossover and block crossover. Given two parent candidates, {0.) [0.}po and (6) hyt, uniform crossover randomly selects each 8, from either parent with equal probability to form new candidate. In contrast, block crossover first randomly creates a contiguous subset of timesteps within the range [0, T). The new candidate then inherits all 0, values within this subset from one parent, and the remaining values from the other parent. In the crossover step, we randomly pick between uniform crossover and block crossover.
Crossover:
Mutation, Once a new candidate {0, new is generated, the goal of mutation is introduce possibly better candidate by randomly mutating this candidate. We decide whether each timestep would be mutated based on the probability, Pmmtatinn = Po B_ Prayy (Po - Pmmaa), where Po and Pinall are the initial and the final probability of mutation, respectively. We find that gradually decreasing the mutation probability over generations leads to better convergence. 8 is the ith evolution generation and 3mmx is the maximal evolution generation. If timestep were mutated, our algorithm would randomly change its 0, from the valid value range, i.e., {0, 0.1,... 1.0].
Repair, A new candidate {0:)new' from the previous two steps might no longer satisfy the token budget constraint, Og. This repair step would ensure that the total token budget falls within the ac- ceptable range, [0.90,, 1. 1.10g]. If the total budget exceeds the upper bound, randomly decrease one or more 0.. Conversely, if the total budget is below the lower bound, we randomly increase one or more 8, values until the constraint is met, shown in Fig.
Generality. Standard EA typically requires generating ground truth videos for multiple sample prompts, which introduces additional computational overhead. In our implementation, we simply select prompts from different genres. While including more prompts could theoretically improve generalization, we observe that it leads to minimal improvement in search outcomes while sub-

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Published as a conference paper at ICLR 2026
20
10-2
Timestep
(a) OpenSora (4s).
(b) Wan (4s).
Fig. Different prompts show a similar trend when removing one specific timestep out the entire denoising process. The MSE is calculated against the original result without skipping timesteps.
stantially increasing the search cost. The reason is that different prompts often exhibit a similar robustness trend shown in Fig. 5 Specifically, for each prompt, we skip one timestep during the denoising process and calculate the MSE loss against the ground truth. By sweeping the timesteps, find that different prompts show a similar MSE trend both OpenSora and Wan (Fig. (5)
Why EA? There are two main reasons why we choose EA over other search methods. First, EA keeps the original model intact. Search methods, such NAS (Elsken et al.] 20T9) network pruning (Cheng et al. 2024). would modify the network architecture or model weights. In contrast, EA can well fit in the token selection problem without altering the original vDiT models. Second, both NAS and Network Pruning are several orders of magnitude more computationally intensive than ours. For instance, NASNet (Zoph et al. (018)) requires approximately 2,000 GPU hours to complete its architecture search, and Diff-Pruning (Fang et al., (023) demanded up to 20% of the original model training time. In contrast, our work only requires, on average, 82 GPU hours. So we choose lightweight EA instead.
4
EVALUATION
4.1 EXPERIMENTAL SETUP
Baselines. We evaluate three widely used text-to-video generation frameworks: Hunyuan Video-
T2V (Kong et al. 2024), Wan v2.1 1.3B (Wang al. 2025 and OpenSora v1.2 (Zheng al. 2024).
For Hunyuan VidunnVidee ViVeo-T2V, we generate 5-second videos with a resolution of 544 x 960. For Wan and OpenSora, test both short-sequence videos (2-second 480P) and long-sequence videos (4-second
480P). Our evaluation compares with five the state-of-the-art training-free methods: A-DIT (Chen 2024). PAB (Zhao et al. 2024), TOCA (Zou al. 2024b), SVG (Xi al. 2025 and SVG2 Yang et al. 2025).
our EA algorithm, we set the maximal generation to be 30, K and P are set to be 50 and 30 in each generation. To ensure diversity of candidates in the early stages and structural stability of good candidates in the later stages, we set Po and Pinal be 0.1 and 0.01, respectively. Across all four evaluated models, the results converge after searching 30 generations.
EA Configuration.
Metrics and Hardware. Following prior works (Zhao et al., 2024 Zou al.] 2024b Xi et al,, 2025 Chen et al.) 2024), we use the VBench score (Huang et al.) 2024) as the video quality met- ric. During the experiments, we generate videos for each of the 950 benchmark prompts using different random seeds. The generated videos are then evaluated across 16 aspects. We report the average value of the aspects. In addition, we compare the generated videos by different acceleration methods against the original video results frame-by-frane and I.PIS. For performance, we report end-to-end generation latency, GPU memory consumption, and computational complexity (FLOPs). Our evaluation uses two GPUs as hardware platforms: Nvidia A6000 with 48 GB of memory and Nvidia 100 with 80 GB of memory.
image quality, using PSNR, SSIM,
4 .2 PERFORMANCE AND ACCURACY
Video Quality. Tbl presents the overall comparison of generation quality with different methods. Across all vDiT models, ASTRAFA achieves the highest speedup (2.4x) while maintaining the best VBench score compared to other baselines. On the VBench metrie, almost all ASTRABA variants retain the accuracy loss within 0.5%. In contrast, the strongest baseline, ToCA2,554 achieves only 79.2%, which is 1.0% lower than the original model's ss score Wan (4s). Similarly, OpenSora,

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Table 1: Quantitative evaluation of our method against the state-of-the-arts (Chen et al. 2024
Zou et al. 2024b] Zhao et al. 2024, Xi et al. 2025 Yang et al. (025) on three vDii mod- els: Hunyuan Video-
T2V ViVeo-T2V (Kong et al. 2024). Wan v2.1 1.3B (Wang et al. 2025), and OpenSora
v1.2 Zheng et al., 2024).
and denote the best and second-best results among all methods,
respectively. PAB: The subscript numbers represent the reuse strides for spatial, temporal, and
cross-attention. TOCA: The subscript numbers denote the timestep reuse stride and MLP reuse
ratio. SVG/SVG2: The percentage indicates the skipped attention computation. ASTRAEA: The
percentage indicates the total token budget. Hunyuan Video cannot run Nvidia A6000.
Model
Method
Metric
(76)
Quality Metries
PSNR (JB)
SSIM LPPISS
(10%))
(scc:)]
Luuro
Performance Metries
Speedup (A100)
Laoo Speodup Mem. (A600)
[sce]]
(CB))
Otiggina
80.28
217.27
1226.99
1.00
45.81
A-DrT
Chen et ml 2024
79.45
26.00
0.862
0.111
145.05
996.44
1.23
46.55
PA
79
0.906
0.082
104
1.17
66.17
PAB e
78.90
0.778
0.084
145.55
1009.10
1.22
66.17
Hunyuan Video (56)
SVG
TOCA
Zou
Xi
21
ct al
20246
2025
79.97
25.78
0.843
0.153
8 121.51
726.03
1.69
50.87
0OM
SVG2
Tang
2025
80,80
21.52
0.747
0.259
121.51
804.23
1.53
58.61
ASTRAEA 101g
79.79
27.61
0.895
0.076
113.27
514.84
2:38
89.01
ASTRAUIA
80.20
28.71
0.913
0.058
130
@ 636.35
@ 1.93
69.01
881.55
1.39
69.01
Original
$1,46
7.29
68.48
1,00
108.30
1,00
7.8
A-DIT
Chen al.
2026
78.37
15.13
0.499
0.408
5.87
60.52
1.13
87.81
1.23
7,81
PAB2.
Zhan
2014
80.05
18.02
0.667
0.246
4.67
50,36
1.36
79.84
1.35
11.59
7866
0.638
0.250
1,70
11.59
Wan (2x)
2024h
81.06
18.01
0.611
0.254
4.14
44.43
1.54
75.02
1.44
17.66
TGCAg.00
20220
80.89
18.02
0.653
0.252
4.07
43.86
1.56
71,28
1.52
17.66
SVOnm
2025
77.51
18.87
0,695
0.237
4.11
56.58
1.21
100.08
1.08
21.99
SVGG2uu
Fang
2025
79.48
22.23
0.787
0.127
4.11
60.09
1.14
95.84
1.13
20.70
ASTRXEA mn
0.826
0.144
3.05
2:27
ASTRAEA
8 81.11
8 25.67
0.884
0.071
3.85
49 37.29
88 2.01
56.77
1.91
9.04
81.28
30,83
0.948
0.026
5.38
44.71
1.53
77.91
1.39
Original
80.28
19.87
155.01
1.00
253.62
8.97
A-DIT
Ches al
2024
76.81
16.14
0.6622
0.376
15.96
135.96
1.14
205.117 17
1.24
8.97
PAB2.c
ZJia 11
78.76
19.95
0.761
0.194
12.79
113.41
1.37
183.37
1.38
15.96
PAB:
77.21
19.44
0.729
0.234
8.99
90.72
1.71
148.58
1.71
15:96
ToC
al
b02
79.01
0.689
0.269
11.04
1.60
154.83
38.40
ToC
Zou al
201240
79.28
18.13
0.694
0.264
10.92
95.07
1.63
152.34
1.66
38.40
SVG
Xi ml.
2025
77.74
0.678
0.255
129.50
1.30
216.50
1.17
22.38
SVG2
Tang 0
2015
79.17
22,95
0.833
0.116
11.69
116.55
1.33
193.60
1.31
21.27
0,072
8.20
2.29
11.71
ASTRAEA mox
8 79.96
B 28.12
0.918
0.053
10.32
C 53.34
0 1.56
132.62
1.91
11.71
ASTRAEA
80.18
33.00
0.958
0.021
14.42
114,20
1.36
184.89
1.37
11.71
Orisinal
3.79
1.00
A-DIT
Chen
78.09
0.906
0.066
2.84
77.23
23.78
PAB
7hha 21 m-
77.50
26.78
0.884
0.089
2.1
44.09
1.23
59.87
1.31
27:20
FAB.a
2124
75,52
22.60
0,500
0.191
2.53
37.68
1.44
55.75
1.40
27:20
OpenSora (2s)
ToCAAs.u
77.13
20.28
0.766
0.209
1.80
32.04
41.27
ToCAsaa0
Zaxrel al
202446
76.89
20.02
0.760
0,216
1.84
31,74
1.70
$2.89
1.48
41.27
ASTRAEN 402
0.095
2:35
20.08
ÄSTRASA 9m
77.45
B 29.52
0.908
0.067
8 1.52
@ 28.36
@ 1.91
41.13
1.52
20.08
ASTRARI
788088
31,78
0.932
0.039
2.48
37.15
1.46
54.54
1.43
20.08
Original
109.15
173.07
A-DIT
Chun el al.
2026
78.46
28.15
0.856
0.084
5.68
108.93
1.00
171.84
1.01
25.83
PAB
Zhin e1 R1
78,40
0 28.65
0.896
0.088
5.82
76.48
1.43
139.52
1.24
41.55
PABga
7haa o1
76.63
23.36
0.804
0.192
5.10
70.71
1.54
129.52
1.34
41.55
OpenSorn
ToCAs
77.69
21.02
0.773
0.212
3.79
65.48
1.67
61.17
TaC
202201
77.68
20.72
0.767
0.219
8 3.56
64.46
1.69
COM
00M
61.17
a/k
0.145
2:31
27.95
ASTRABA
7k.07
28.51
0.891
0.086
3.65
48 5X.62
88 1.56
92.54
1.87
27.98
ASTRAEA
78.65
30.92
0.920
0.056
4.97
76.13
1.43
121.57
1:42
27.98
Original Model
CoCa
PAB
SVG
Ours(50%)
Ours(708)
mi
Synaes
Symana
HunyLanvidee
Wan (2x)
OpentSors (26)
OpentSonalag)
Fig. 6: VBench metrics, speedup, and memory consumption of ASTRAEA against other methods.
we can achieve the best accuracy while achieving the highest speedup. Although A-DIT achieves the best VBench score on OpenSora (2s), A-DIT achieves no speedup. In contrast, ASTRAEA 70% is almost the best on all quality metrics with much higher speedup. ASTRAFA 50% achieves the third
8

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Published as a conference paper at ICLR 2026
Table 2: The ablation study on Wan (4s).
Quality Metries
Performance Metrics
Method
VBench (%6)
PSNR
FL.PPs (10%))
L,0000 (sec.))
Speedup (A100)
L
Speedup (A6000)
Mem.
SSIM
LPIPS
(dB)t
(sec.)
(GB))
Original
80.28
16.54
155.01
253.62
8,97
SELECTO&K K
79.01
18,10
0.689
0.269
11.04
96.84
1.60
154.83
1.64
38.40
TIMSTEP-1 LEVEL
79.50
22.71
0.802
0.169
10.32
78.51
1.97
127.72
1.99
8.97
FIXED-TOKEE
77.92
19.75
0.753
0.214
10.32
83,20
1.86
132.19
1.91
11.71
ASTRAEA
79.96
28.12
0.918
0.053
10.32
83.34
1.86
132.62
1.91
11.71
taken select
self
cross
other
HunyuanVidee
Wan
OpenSora
Org.
Org.
Org:
Actraca
Actraca
Actraca
250
500
75g
1000
1250
50
100
150
100
(seconds)
(seconds)
(seconds)
Fig. 7: VBench metrics, speedup, and memory consumption of ASTRAEA against other methods.
best on quality metrics while achieving higher speedup with large margin. Sec. C C.6,shows detailed VBench scores on five vDiT models. Across VBench scores, our variants are closely matched with the original baselines. The qualitative comparison of ASTRABA against other methods is shown in the Appendix. Qualitatively, ASTRAEA achieves the best consistency against the original models.
Daxaline
Zapelie
Sanalis
1665
160
1200
400
(a) Hunyuan Vidco.
(b) Wan (2s).
(c) Wan (4s).
(d) OpenSora (2s). (c) OpenSora (4s).
Fig. 8: The speedup of ASTRAEA against the baseline models across various numbers GPUs.
Image consistency. addition to VBench scores, we also perform frame-to-frame comparisons against the outputs from the original models to assess image consistency. Our results show that As- TRAEA consistently preserves higher image consistency across all evaluated models. In particular, ASTRAEA outperforms all methods by significant margin Wan (4s) across all image quality metrics. On Wan, ASTRAEA 70% and ASTRAEA 50% outperform the strongest baseline by 10 dB.
Performance. Tbl. alsoo shows the performance comparison across various models. ASTRARA consistently outperforms all baselines in both inference speed and GPU memory. Across models, ASTRAEA 40% delivers the highest speedup 2.4x on both A100 and A6000 with the lowest memory usage. We configure all baselines to achieve their best quality; however, none of them can achieve over 2x speedup. In contrast, ASTRAFA can be easily configured to achieve any target speedup (see sensitivity study Sec. 4 .b with the minimal quality tradeoff.
Execution Breakdown. Fig. 7 shows the execution breakdown of three vDiT models: three vDiT models: Hunyuan Video-T21 (Kong et al., (2224), Wan v2.1 1.3B (Wang et al., 2025), and OpenSora v1.2 (Zheng et al. 2024). Here, Wan and OpenSora are all 4-second videos. From Fig. 7 the latencies of all original compute blocks are proportionally decreased via our token selection technique. In addition, our token selection only takes 2.3% of the total execution time. Results indicate that our selection mechanism has low overhead.
Scalability. ASTRAEA shows strong performance scalability across various vDiT models. As shown in Fig. g our method demonstrates sublinear speedups as the number of GPUs increases across four different models. Specifically, our ASTRAEA 50% can achieve 13.2x speedup on Open- Sora with 8 GPUs. Overall, ASTRAEA can achieve over 10x speedup with 8 GPUs across all models. This shows the high parallclizability of our sparse attention described in Sec. 3 .2
4 .3 ABLATION STUDY
In the ablation study, we compare three different variants: SELECTQ&K, TIMSTEP-LEVEL and FIXED-TOKEN. SELECTQ&K sparsely select both Q and K for every block (similar to naive sparse

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Published as a conference paper at ICLR 2026
100
100
100
b Volenc (S)
35
35
PSWR
V-
POWR
70
5
10 30 60 80
10 20 30 40 50 60
10 20 50 70
10 20 30 40 60 70 Computation Budgott
Computation Budeet
Computation Budgett9)
Compuration Budget
(a) VBench vs. latency on (b) PSNR on Wan (2s).
(c) VBench vs. latency on (d) PSNR on OpenSora (2s).
Wan (2s).
OpenSora (2s).
Fig. 9: Sensitivity of quality metrics and performance computational budget percentage.
attention). TIMSTEP-LEVEL only selects timesteps. Each timestep either computes all tokens or skips computation entirely. FIXED-TOKEN selects tokens at the granularity of timesteps instead of blocks. All selected tokens within timestep are computed, while unselected ones are skipped.
Our experiments show that all three variants achieve much lower VBench scores compared to our method. Specifically, FIXED-TOKEN drops the VBench score significantly (>2.0%). This shows that the important tokens vary across compute blocks. On the other hand, TIMSTEP-LEVEL drops the VBench scores modestly, while achieving slightly higher speedup compared to our method under the same token budget. This suggests that selecting at the timestep level may be a viable approach when trading off accuracy for higher performance. However, TIMESTEP-LEVEL suffers from noticeably lower image consistency compared to ASTRAEA.
.4 SENSITIVITY STUDY
Fig. shows the sensitivity of the computation budget (expressed percentage) both the overall VBench score and execution latency on Wan (2s) and OpenSora (2s). On both models, we observe that the VBench score degrades rapidly when the computation budget drops below around 30%. In contrast, execution latency increases linearly with the computation budget. These results suggest that selecting a computation budget in the range between 30% and 50% offers a favorable trade-off.
Fig. I0 shows the sensitivity of the hyperparameters in Equ. to PSNR on Wan (4s). Recall, Wa is the hyperparameter for the significance term, Ssig, and W/ is the hyperparameter for the penalty term, Spenatty for non-selected tokens. In this sensitivity study, we fix Wa
26.0
25.8
and only vary Wg. The overall results show that ASTRAEA achieves the best quality when Wa and Wy 1 and 0.5, respectively. However, the overall variation is extremely small (< 0.2 PSNR), indicating that our method robust these hyperparameters. Also, would like to emphasize that this penalty term is adapted directly from ToCA (Zou et al. 2024b), and our sensitivity trend matches the trend reported in ToCA (Zou et al. 2024b). We claim no contribution to this term
25.6
25.4
25.2
25.0
0.0
0.2
0.4
0.6
0.8
1.0
We
Fig. 10: The sensitivity of hyperpa- rameters in Eqn. on Wan (4s).
CONCLUSION
As vDiTs continue to drive breakthroughs in text-to-video generation, their deployment remains limited by computational demands. This work presents ASTRAEA, a framework that systemati- cally accelerates vDiT inference through fine-grained token-level selection. By combining our three optimizations in Sec. 3 ASTRAEA dynamically determines the optimal token selection at each de- noising step. We demonstrate that our method not only delivers almost linear speedup in inference latency under certain performance target but also preserves the highest generation quality.
ACKNOWLEDGMENT
This work was supported by the National Natural Science Foundation of China (NSFC) Grants (62532006 and 62402312), Shanghai Qi Zhi Institute Innovation Program (SQZ202316), and Fun- damental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry Education of China (YYB2025XDXM1
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Published as a conference paper at ICLR 2026
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Algorithm Efficient Attention via Temporal Token Selection.
Require: Xin, Xin. E RR
b Token features at timestep and Cached output tokens at timestep - >> The token budget for this attention block Projection matrices D Updated token features at t D Per-token squared difference
Require: 1 E RRN
Require: 0
Require: Wq, WK. Wv
Ensure: Xout, E IRN
1: Sug + Mean((Xm,,
i-), = 1) SLSE.11
2: Swken Wa Sale + W3 Spenalty
Calculate proposed selection metric Selected top-& percentile of token indices
3: Sel, Temain f- Topoo (Sioknn 0)
4: K, < Xin. WK. Vi Xin. Wv
D Project and V D Select important Query tokens and perform projection
5: Qsel. Xin. ((sol] We
6: XouL [[Tse] < Softmax QQeelt
K KVVVVV
Compute attention only for selected Q Unselected tokens reuse the previously cached results
7: Xout [rremain] t- Xout, 1-1 rremain
8: return Xout,
A CODE OF ETHICS
I acknowledge that all co-authors of this work have read and commit adhering the ICLR Code of Ethics.
B
LLM USAGE
We did use LLM throughout the entire submission.
C SUPPLEMENTARY
C.1 TOKEN SELECTION WITH SELF-ATTENTION
Algo. illustrates how our lightweight token selection mechanism is integrated with the self- attention computation. Overall, our algorithm dynamically selects tokens required for computation based on their significance and imposes a penalty for consecutive non-selected tokens.
Inputs. Our self-attention algorithm requires two kinds input tokens, X,-1 and X,, which are the input tokens of the given attention block at the timestep ( - and f, respectively. Meanwhile, our algorithm requires a token budget, which is between and 100%. stands for select & e-percent of top important tokens. Xout, 1-1 is the previously cached token results from the early timesteps.
Output. The output our attention is the output token sequence, Xour,
Procedure. We now describe the detailed process of how our token selection mechanism is inte- grated with the self-attention. The overall procedure consists of six steps.
The first step is to compute the significance, Saig. of each token. Sagg is the weighted absolute difference between the input tokens of timestep t- and The absolute difference is then weighted by SLSE.
Once Saig is computed, we then obtain our selection metric, Spoen- which is the weighted sum between Ssig and Spenalty- Here, Spenally is a penalty term that prevents one particular token from being selected repeatedly.
The third step is find the top-0 percentile of token indices. Here, I the indices of the selected tokens, and Tremain are the indices of the remaining unselected tokens.
The fourth step is to compute the key K, and value V, This process is similar to the conventional attention computation. However, computing query Q is different. Here, we only compute the queries for the selected tokens, as shown in Line 5. The fifth step is perform the attention computation for the selected tokens.
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Algorithm 2 Evolutionary Search for Token Scheduling.
Require: T
> Total timesteps (e.g., 100)
Require: Og
& Computation budget (e.g., total cost S 50) Parent candidates for evaluation
Require: P
Require:
> New population size
Require: G
> Number of generations
Ensure: Best schedule hest = 100, B1, 01.....,rr:)
1: Initialize population S = {A(0) ((K 0(6) (0000) with E 0
I Og
2: for to G do
3: for all (R) S
((k) MSE f Evaluate
4:
Compute fitness:
5: end for
6: Select top individuals as parents: P C 8
7: Initialize new population: Snew <
8: while |Snew < K + IP| do
9:
Sample parents (a), (b) E P
10:
Oehild + Crossover (66)
child Mutate chila)
12:
echald < RepairlfNeeded (eeeedddddeeiiss Qs)
if Ochald Snew then
14:
Add Ochia to Snew
end if end while
16:
17:
S - Spew
18:
end for
19:
return arg minges MSE(O)
The last step is reuse the cached token results from the previous timesteps for the unse- lected tokens.
C.2 TOKEN-WISE WISE SEARCH ALGORITHM.
Algo. Nuttines our token-wise search algorithm via an evolutionary algorithm. This algorithm is to determine the optimal token budget allocation across denoising timesteps, discussed in Sec.
The overall logic of this algorithm iteratively refines candidates through crossover, mutation, and repair operations to achieve target performance with minimal accuracy loss.
Inputs. Our token-wise search algorithm requires four input parameters. T is the total number of timesteps of the vDiT model. Oş is the computational budget that we can afford for a specific performance target. K is the population size of the potential candidates in the evolutionary algorithm (EA). G is the total number of generations in EA.
Output. The output of our algorithm is the best schedule of the token selection after G generations.
Procedure. The overall process of our evolutionary algorithm is described follows:
The first step is to initialize the first generation, S, shown in Line Each candidate (k)) represents token allocation schedule, which is constrained such that the total token budget across all timesteps, E g(*) does not exceed the overall token budget Og.
Then, we iterate through number of generations. For each generation, we compute the mean squared error (MSE) loss (k) between the output of the baseline model and each candidate schedule. Once obtain the MSE loss of each candidate, we select the top candidates among P and obtain the subset S.
Then, we create the next generation of population based on S. Here, we first initialize an empty set, Snew- Next, we spawn K number of new candidates based the procedure as we describe in Sec. .3 following Crossover, Mutate, and Repair steps.
Once we create the new set candidates Snew- we then start over the next generation utill iterate over G number of generations.
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After completing all generations, select the candidate with the lowest MSE loss the final output, denoted as beat-
C.3 EXPERIMENTAL. SETUP
We conduct both the performance and accuracy measurements two Hardware Platforms.
hardware platforms:
NVIDIA A6000 with 38.71 TFLOPS (FP32) and 48 GB memory;
NVIDIA A100 with 19.49 TFLOPS (FP32) and 80 GB memory.
Video Generation Frameworks. We measure the performance of various acceleration techniques on three widely-used video generation Trameworks:Hunyuaar Video- T2V (Kong et al.) 2024), Wan v2.1 1.3 B Wang et al. (2025) and OpenSora v1.2 (Zheng et al. (024). We test Hunyuan Video- T2V (Kong et al. 2024) on 5-second videos with 544x960 resolution and both Wan v2.1 1.3 B Wang
et al. (2025) and OpenSora v1.2 (Zheng et al. 2024) on 2-second videos and 4-second videos with
480p resolution.
Baselines. We compare against five different training-free acceleration techniques:
A-DIT (Chen al. 2024). The parameter b represents the timestep at which the reusing strategy of the block residuals switches. The parameter N represents the timestep intervals that skip full computation. In Opensora, The partial computation starts at timestep and ends at timestep 28. We also preserve the residuals of the first 10 blocks the last 10 blocks. In Wan model, we set b and N to be 25 and 3, respectively. The partial computation starts at timestep 5 and ends at timestep 45. We also preserve the residuals of the first 10 blocks the last 10 blocks.
set and N to be 15 and 3, respectively.
PAB (Zhao et al. 2024). The broadcast range n represents the timestep intervals that skip full computation. In the OpenSora model, the broadcast ranges of spatial attention, temporal attention, and cross- attention are set to be (2, 4, 6) or (5, 7, 9). Similarly, in the Wan model, the broadcast ranges of self-attention and cross-attention (5, 9). For both models, we keep the first 15% and last 15% of the timesteps untouched.
set to be (2, 6) or
ToCA (Zou et al. 2024b): This variant is similar to PAB. However, ToCA also performs a certain level of token-wiss wise skipping similar to our work. We faithfully reimplement their work according to their released code (Zou et al. 2024a), which only applies the token selection on cross-attention and MLP layers. In OpenSora, their broadcast ranges of spatial attention, temporal attention, cross-attention. and MLP are 3, 6, and 3, respectively. The reuse ratios MLP in their two variants are set to 80% and 85%, respectively. In Wan model, their broadcast ranges of spatial attention, temporal attention, cross-attention, and MI.P are 2, 2, 2, and 2, respectively. The reuse ratios of MI.P in their two variants are also set 80% and 85%, respectively.
SVG (Xi et al. 2025/SVG2 (aag et al. 2025): This variant leverages the sparsity in self-attention computation and proposes to skip some unimportant tokens during the self- attention.
Evaluation Metrics. Next, we describe how we obtain various performance and quality metrics.
Video Quality. We used the VBench score (Huang et al. 2024) as the primary video qual- ity metric. For each of the 946 benchmark prompts, 5 videos were generated using different random seeds. The generated videos are then evaluated across 16 aspects. We then report the average value of the aspects. The VBench score is obtained from the VBENCH bench- mark suite (Huang et al. 2024). Specific APIs within this suite are used to evaluate aspects like Motion Fidelity. Temporal Consistency, Aesthetic Quality, etc.
Image Consistency (PSNR, SSIM, LPIPS). To assess frame-to-frame im- age consistency, we compared generated videos by different acceleration meth- ods against the original video results on image quality, using PSNR, SSIM, and IPIPS. PSNR is calculated using standard image processing libraries,
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e.g.skimage eeeeeiietsisee ppw...iigml..nnns..raa..... Similarly. SSIM is cal- culated using skimage using the ipips library in Python.
htt.::s.ssrrutttrra.silllaaaaaa. LPIPS is measured
Performance. For performance, report end-to-end generation latency, GPU memory consumption, and computational complexity (FLOPs). For end-to-end latency, we mea- sure the total time elapsed during video generation. Here, we use Python's torch. cuda module. We capture the start and end timestamps using torch. cuda. event () and use the difference between these two the end-to-end latency, For GPU memory consumption, we use the built-in measurement to monitor the peak GPU memory usage during inference. For FLOPs, we design analytic model to calculate the floating-point operations. Please see Sec.
C.4 FLOPS CALCULATION FORMULATIONS
Input Representation. We first define the symbols we used in our FLOPs computation:
B: Batch size.
N: Number of tokens (sequence length).
H: Embedding dimension (hidden dimension).
Mhed: Number of attention heads.
d H/Nhead: Dimension per head.
Attention FLOPs Calculation. We abstract the computation of attention module into two parts: linear projections for Query (Q), Key (K), and Value (V), and the subsequent attention score computations.
Linear Projection. The imput X e BXNI is projected into Q, K, and V tensors, each of shape _ A single linear transformation of shape [H H] has a FLOPs count 2 H.H.. Therefore, the total FLOPs for Q, K, and V projections can be expressed as,
Syku
(2 x B Xx 6BNNH.
6
Attention Score Computation. This step includes calculating the attention scores, applying soft- max, computing the attention output, and output linear projection. We next describe these four parts.
1. Compute Attention Scores (QK?). Initially, the input shapes of and K are both B, Nnead, N₂ d]. Their product, QKT results in a tensor of shape [B, Nuead N, NJ. The FLOPs for computing QKT per head are 2 x N d N. If we sum across all heads and batches, the total FLOPs becomes: FLOPs = 2 x B x Nuead X N x x
2B 2BN
2. Softmax Operation. The softmax operation is applied to the QK" scores. Its computa- tional cost is relatively small compared matrix multiplications and can be approximated as: FLOP FLOPssottmas Sootumax D B x Nuead X N x N. For overall computational complexity, its contribution often considered negligible.
3. Attention Output (A- V). There are two inputs in this step: the attention map. A, which has a shape of [B, Nucud N, N]; and the value, V. with a shape of [B, Nuead N, d]. The attention output has a shape of [B, Nuand, N, d]. The FLOPS to compute A V is expressed as, FT.OPsAv = 2 x B x Nbest X N x d.
4. Output Linear Projection (Head Merging). Finally, the outputs from all heads are con- catenated and passed through a linear layer of shape [H x H] to produce the final attention output. FLOP FLOPspe Speuj 2 x B x N H².
Total Sell-Attention FLOPs. By substituting d = H/Nnend and combining the above calculations, the total FLOPs for self-attention layer simplify to,
FLOP P.OPPsatn.toom Sattn total
2 + 4BN² H.
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5.005
0.009
0.06
0.003
0.034
0.00
N00: Lox
NBE Low
NBE Low
0.013
0.039
0.000
0.00
0.010
0.022
0.001
30
30
0.001
10
0.01
15
0.003
25
(a) OpenSora (2s, 34 hrs). (b) OpenSora (4s, 85 hrs).
(c) Wan (2s, 69hrs).
(d) Wan (4s, 139hrs).
Fig. 11: The MSE loss trend in the EA search process. Here, we only show one case: the perfor- mance target is 50% of the token budget reduction. The trend is similar for other cases. The first number in parentheses is the video length, and the second number is the total GPU search hours.
Cross-Attention FLOPs Calculation. Cross-attention is similar to self-attention, but its Q and K often come from different input tokens. Here, we define N₂ be the number of query tokens and Ns: be the number of key/value tokens. The FLOPs calculation can expressed as, FLOP P Scross-attn 4BN, H + 4BNg.H2 + No.H.
MLP FLOPs Caleulation. The MLP in Transformer typically consists of two linear layers separated by an activation function (c.g.. GELU), Here, we ignore the computation of the activation function and only consider the FLOPs in two linear layers. The total FLOPs for an MLP block are,
FLOPsmmp = 8BNH2 + 8 = 16BNH²
C.5 DETAILED EXPERIMENT RESULTS
Video Quality. Tbl. [ presents the overall comparison of generation quality across different tech- niques. On Hunyuan, ASTRAEA achieves the highest speedup (roughly 2.4x) while maintaining a better VBench score compared other baselines. On VBench metric, all variants, ASTRAEA 40%+
ASTRAEA 50% and ASTRAEA 70% can retain the accuracy loss within 0.5%. On other metries, As- TRAEA also achieves the best quality. For instance, ASTRAEA 50% achieves 1.9x speedup and has better quality metrics compared all other methods. In contrast, the strongest baseline, SVG+0% achieves only 1.6x speedup and has much lower generative quality (e.g.. PSNR, SSIM) compared ASTRAEA 5U%- On Wan, both 2-second and 4-second video sequences, ASTRAEA 50% achieves the
better quality in terms of all quality metries compared to other baselines. Specifically, PSNR metric, ASTRAFA is almost 10 dB higher than other baselines. This shows that ASTR ARA OpenSora,
can preserve better quality. Similarly, achieving the highest speedup. Although A-DIT achieves the best VBench score on OpenSora (2s), A-DIT can only achieve 1.01 x speedup. In contrast, ASTRAEA 70% is almost the best on all quality metrics with much higher speedup. ASTRAEA 50% can achieve the second or third best on quality metrics while achieving higher speedup with a large margin. More qualitative comparisons of As- TRAEA against other methods are shown in Fig. 12 Fig. I3 and Fig. [4 Qualitatively, ASTRAEA achieves better consistency with the original models compared to other methods.
can achieve almost the best accuracy while
Image consistency. In addition to evaluating VBench scores, we also perform frame-to-frame comparisons against the outputs from the original models to assess image consistency. Our results show that ASTRAEA consistently preserves higher image consistency across all evaluated models. In partieular, ASTRABA outperforms all baseline methods by a significant margin on Wan (2s), Wan (4s), and OpenSora (2s) across all image quality metrics. For instance, on both Wan (2s) and Wan (4s), the ASTRAFA 70% outperforms the strongest baseline by 10 dB.
Performance. Tbl. I also shows the performance comparison across various models. ASTRAEA consistently outperforms all baselines in both inference speed and GPU memory. For Wan (2s), ASTRAEA 50% can deliver the highest speedup 1.9x on both A100 and A6000 with lowest memory usage. Meanwhile, it still delivers the second-best quality against the other methods. On other models, our method also achieves significantly higher speedup. Specifically, when we set the token budget to be 40%, can achieve 2.4x speedup while the model accuracy is still competitive.
EA Search Time. In addition, Fig. shows the EA search process of one case, 50% of the token budget reduction. Across all four evaluated models, the results converge after approximately 10 generations, indicating that a certain reduction of search time is possible. All EA searches are conducted on 8 1000 GPUs with average search time of 82 GPU hours.
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While EA might appear computationally expensive at first glance, emphasize that EA achieves near-optimal schedules without any human-in in-the-oo- tuning. In contrast, prior training-fres tech- niques, despite not requiring explicit training, rely extensive manual effort to investigate corre- lations or redundancies inside models. This implicit "human training" cost rarely accounted for, whereas EA automates this process and avoids suboptimal hand-crafted designs.
Moreover, EA can be accelerated through two complementary techniques. First, EA is inherently parallelizable: different individuals can be evaluated independently on different GPUs. Using GPUs yields a 7.6x speedup, enabling the entire EA procedure to finish within one day. Sec- ond, many EA candidates share identical prefix schedules (e.g.. the same decisions in the first 20 timesteps). We can evaluate those candidates together by saving the intermediate results to avoid redundant computations. Overall, we find that dynamic programming can further provide 1.5x speedup. Combining both techniques, the EA process can achieve up to 1 1..x overall acceleration.
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Prompt: a zebra running to join a herd of kind
Origin PSNR: Speedup: 1
Ours 70%
PSNR: 29.14
Speedup: 1.39
Ours 50%
PSNR: 26.07
Speedup: 1,93
PAB 26
PSNR: 23.18
Speedup: 1.17
SVG 70%
PSNR: 22.68
Speedup: 1.69
Prompt: The bund Shanghai, tilt down
Origin PSNR: Speedup: 1
Ours 70%
PSNR: 29.63
fuu وجاة ألاة
02 7M
Speedup: 1.39
Po-
Ours 50%
PSNR: 25.33
Speedup: 1.93
PAB 26
PSNR: 23.19
-
Speedup: 1.17
SVG 70%
PSNR: 19.33
ilee
A 09 :
Speedup: 1.69
Fig. 12: The qualitative comparison of ASTRAEA against other methods on Hunyuan
C66 FULL PERFORMANCE METRICS
In the remaining section, show the detailed experimental results.
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Prompt: raccoon dressed in suit playing the trumpet, stage background
Origin PSNR: Speedup: 1
Ours 70%
PSNR: 35.00
Speedup: 1.36
Ours 50%
PSNR: 25.76
Speedup: 1.86
PAB 26
PSNR: 18.28
Speedup: 1.37
ToCa 80%
PSNR: 24.08
Speedup: 1.60
Prompt: A robot DJ is playing the turnable, in heavy raining futuristic
tokyo rooftop cyberpunk night, sci-fi, fantasy
Origin PSNR: Speedup: 1
Ours 70%
PSNR: 27.58
Speedup: 1.36
Ours 50%
PSNR: 23.51
Speedup: 1.86
PAB 26
PSNR: 16.37
Speedup: 1.37
ToCa 80%
PSNR: 18.87
Speedup: 1.60
Fig. 13: The qualitative comparison of ASTRAEA against other methods on Wan (4s).
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Prompt: happy fuzzy panda playing guitar nearby campfire, snow mountain in the background
Origin PSNR: Speedup: 1
Ours 70%
PSNR: 22.14
Speedup: 1.46
Ours 50%
PSNR: 20.74
Speedup: 1.91
PAB 26
PSNR: 18.64
Speedup: 1.23
ToCa 80%
PSNR: 12.37
Speedup: 1.69
Prompt: A couple in formal evening wear going home get caught in a heavy downpour with umbrellas by Hokusai, in the style of Ukiyo
Origin PSNR: Speedup: 1
Ours 70%
PSNR: 22.89
Speedup: 1.46
Ours 50%
PSNR: 21.67
Speedup: 1.91
26
PSNR: 18.24
Speedup: 1.23
ToCa 80%
PSNR: 17.56
Speedup: 1.69
Fig. 14: The qualitative comparison of ASTRAEA against other methods on OpenSora (4s).
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Table 3: Individual VBench scores for Hunyuan Video model.
Metric
Original
ASTRARA 70%
ASTRARA 50%
ASTRARA 40%
SVG
SVG2
PABg:
PAB
A-DIT
Subject Consittnncy
0,9164
0.5236
0.2270
0.9216
0.91C0
0.9622
0.9155
0.9142
0.9015
Medion Smoothaess
0,9501
0.5000
0.55112
0.9000
119556
0,9919
0.55015
0.2915
0.9819
Dynamic Degree
0.8356
0.571
0.5671
0.8571
0.7778
0.2917
0.115
Acsthetic Quality
0.6234
0.126
0.5841
0.5765
0.5105
0.6206
Imaging Quality
0.6250
0.6110
Overall Consistency
0,2570
0.250
1127715
0.2063
0.000
0.2674
Background Consistency
0,9633
0.9088
0.9651
11.088)
0.3621
0.963
Object
0.6306
0.6172
0.6133
0.6367
0.7112
0.738-
0.6614
0.335
0,6915
Multiple Objects
0.5297
0.5480
0.586
0.5703
0.5168
0.7000
0.5030
03111
0.4277
Color
0.8750
0.7641
0 7357
0.7205
0.8 0.8000
0.5044
0.8917
0.836
0
Sputial Relationship
0.6383
0
0.7711
0.77105
11,6574
018088
0.63377
0.6470
066767
Scene
0.2586
0,2300
0.2831
0.2610
0.3000
0.5787
0.2536
0.1322
0.2929
Tempporal Style
0.2967
0,2550
0.2669
0.23557
11.2421
0.2371
0.2367
0.2335
0-2371
Tuman Action
0,6000
0,5500
0 0.000
0 0.5000
0.9220
0.8500
0.8400
0.5000
0.6000
Temporal I llickering
0,3861
0.5872
0.9874
0.98774
11,9522
0.3622
0.9869
0.5883
0.9761
Appearance Style
0,1908
0.1826
0.1841
0.1827
11.1942
0.1172
0.15011
0.1896
0.1898
Quality Store
0.8371
0U5455
0.5377
018548
1182254
0.82295
0.8316
0.5336
0.873
Semuntic Score
0.6857
0.6477
0.6504
0.6109
0.8812
0.7228
0.6357
0.6107
0.6727
Total Score
0.8322
0.5045
0.5020
0.7079
0.7067
0.9080
0.7564
0.7800
0.7945
Table 4: Individual VBench scores for Wan (4s) model.
Metric
Original
ASTRAFA 70%
ASTRAKA 50%
ASTRARA 40%
ToCa R0%
ToCa 85%
SVG 70%
SVG2
PAR.,
PARk.
A-DIT
Subject Conststency
0.00.7
0.1079
0.3555
0.1511
019778
0.913
0.5374
0.9012
0.953
0.9357
USSEU
Mution Smoothnees
0.08:20
0.1022
019K31
0.1031
0.916
0.3821
0.9700
0.976
09511
0.930
Dynamic Degres
0.618)
0.0250
0.6111
0.5831
0.7557
0.7500
0.5014
0.1199
0.5714
Acstbelic Quality
0.0102
0.6102
0.0004
0.3887
03517
0.5566
Imaging Quality
0.6331
0.6116
0.6285
0
0,214
Consialancy
0.2112
Backgronnd Cumisteacy
0.0858
0.0685
0.3301
Object Class
0.7650
0.755
0.7909
Mollipie Objectts
0.5013
0.5318
0.3500
0.5 5.50
0.550
0.5587
0.8219
0.3615
0. 0.1122
0.3001
0.3516
Caler
0-5751
0.875)
0.815
0.8845
0.8000
0.907U
0.9612
0.2541
0. 0.5000
0.756
036
Spotial Relationship
0,7255
0.7234
0.7001
0,0841
0.7717
0.7864
0.6489
0.7167
0 0.6888
0.117
0.7447
Scene
0,2504
0,2185
0.2655
0, 0.2233
0.2947
0.210
0.1544
0.1230
0.2122
0.2564
010000
Temspraal Style
0.2436
0,2411
1122244
0.2114
0.2451
0.2465
0.2276
01 0.222
0.225
0.3172
0.20:3
Human Action
0.7400
0.7300
0.5300
0,7000
0.7400
0.7600
0.730
0.7400
0.5000
0.6700
0.5000
Tompporal Flickering
0,25141
0,0028
0.5929
0,8524
0.0003
0
0.9888
0.2915
0.5841
0.9229
0.5858
Agueorance Style
1989
011857
0.1989
0.10015
0. 0919
0.223
0 0.907
0.107
0.186
0.9066
Quality Score
0.5370
0.5388
08855
0.83
0,8108
0.8227
0,8170
0
0
0.2012
0.0067
Sematlic Score
0.0050
0.0114
06567
0.6521
0.6710
0.6730
0.0189
04806
0:2300
0.060
0.6155
Soore
0,9018
0.71916
0.7978
0.71011
0,7038
0.7774
0.791T
0.757
0.777
0.7081
Table 5: Individual VBench scores for Wan (2s) model.
Metric
Original
ASTRABA 70%
ASTRARA 90%
ASTRARA 40%
TuCla 50%
ToCa 85%
SVG 70%
SVG2 79%
PABus
PAB:,
3.-6T
Subject Coasistency
B3719
0.9722
0.9715
0.9725
0.3075
0.300
0.9511
0.0513
0.97113
0.5884
0.2606
Madion Smoothness
89831
0.9822
019522
033866
0.311
0.3610
0.9721
113766
0.9815
0.9836
0.5979
Dysumic Degree
0.9972
0.3633
0.5831
0.7143
0.853
0.25))
0.6250
0.5417
Quality
0.501
0.6642
0.6717
3.0500
0.703
0.6630
0.6453
Overall Considency
B 03383
0/2178
01 13390
033300
0.3175
0.200M
012133
0.225
02280
0.3105
0.3335
Backgrrund Consiiency
0.884
0.0580
0.9587
0.9697
0.9636
0.9652
0.0321
0.0711
0.9885
0.9884
0.0750
Object Class
0.754
0. 1752
0.7627
0.8505
0.5022
0.7148
0.7453
0.738
0.6543
0.7191
Mahiipla Origects
00551
0.0700
0.111
080 15
0.4192
0.8971
0.330
0.1794
0.190
0.3577
0.17
Color
00088
0.857
0.714
015350
0.2075
0.5700
1.51.0
018622
0.661
0.8227
Spatial Redationship
8.293
0.7431
0.7575
0.5255
0.5000
0.6000
0.7968
Temporal Style
0.2505
0.2061
0.2000
Action
0.7500
0.
0.7500
0.7000
0.7100
0.5000
0.7200
Temparai Flickrring
04500)
0.960
0.9887
0.9633
0.9845
0.9852
0.90
0.9039
0.9907
0.991
0.0676
Appearunce Style
0.1594
0.1585
0. 11551
0.1586
0.1962
0.3130
0.2231
0.2590
[.136]
0.1566
0.314
Quality Sears
0.811
08816
0.111
0.8365
0.8585
0.5335
0.811
0.8397
08822
0.5300
0.53.6
Semantic Score
0.0594
0.0963
0.6206
0.0091
0.7100
06173
066338
0.3867
0.5071
Score
0.8100
08.25
08.11
0.8062
0.3106
0.7761
0.7455
0.8005
0.790
0.
23

---

Published as a conference paper at ICLR 2026
Table 6: Individual VBench scores for OpenSora (4s) model.
Metric
Original
ASTRARA 70%
ASTRARA 50%
ASTRARA 40%
ToCa 80%
ToCa 85%
PAB
PAB
A-DIT
Subject Consistency
0.9478
0.947)
0.9459
0.9380
0.9475
0.3462
0.5482
0.5860
0.5604
Motion Smoothmess
0.9551
0.5872
0.9665
0.975
0.9644
0.5642
0.5685
0.5889
0.5817
Dynamic Degree
0.5117
0.5000
0.4122
0.486]
0.5750
0.5730
0.4565
0.4025
0.822
Aesthetic Quality
0
0.5533
0.5483
0.5115
0.5681
0.5633
0.5300
0.5322
0.2611
Imaging Quality
0.5832
0.5851
0.5770
0.5496
0.5535
0.5537
0.5785
0.5509
0.3974
Overall Consistency
0,27422
0.2734
0.2118
0.3675
0.2710
0.27230
0.2774
0.2611
0.365
Background Consistency
0.9751
0.97.5
0.9718
0.9609
0.9855
0.9700
0.9715
0.5637
0.9767
Object Class
0,8022
0.8010
018501
0.992
Multiple Objects
0.4977
0.4703
0.4505
0.5213
0.5137
0.4764
0,799
Color
0.7922
0.7814
0.8400
0.179
Spatial Relationshp
0.1215
0.5574
Temuporal Style
0.2412
0.2090
0.2251
0.2451
0.2481
0.2019
0.2881
Human Action
0.8400
0U5800
0.8703
0,8600
0.9000
0.5500
0.5800
0.540
0,900
Tempprral Flickering
0.9932
0.5951
0.9846
0.9822
0.5946
0.39447
0.5851
Appearance Style
0.2530
0.2379
0.2374
0.2355
0.2381
0.2594
0.2377
0.2357
0323
Quality Score
0.8107
0.8064
0,8009
0.7855
0.7911
0.7509
0.8022
0.7873
0.7997
Semuntic Score
0.7068
0.7071
0.7003
0.08773
0.7200
0.7202
0.7111
0.6830
0.7239
Total Score
0.7809
0.7865
0.7807
0. 0.7802
0.7769
0.7708
0.7640
0.7083
0.7846
Table 7: Individual VBench scores for OpenSora (2s) model.
Metric
Original
ASTRABA 70%
ASTRAEA 50%
ASTRABA 40%
ToCu 89%
ToCa 85%
PAB:a
PA
A-DIT
Suhject Consistency
0.96544
0L9675
0.96N2
0.9615
B.9576
0.3571
0.3662
0.3591
0.5615
Medion Smoothness
0.9815
0.9864
0.9878
0.9825
0.9854
0.3858
0.5875
0.5885
018812
Dynamic Degree
0.3533
0.2917
0.3056
0.3611
0.5154
0.2917
0:2500
0.4286
0.4484
Aesthetic Quality
0.5681
0.5884
0.5675
0.5882
0.5502
0.5584
0.5685
0.5008
0.5465
Imaging Quality
0,5992
0.5030
0. 0.5627
0.5171
0.5545
0.5554
0.5798
0.5376
0.5550
Overall Consistency
0,2722
0.2721
0.2701
0.2638
0,2722
0.2722
0.2719
012624
0.2469
Background Consistency
0,9750
0.575)
0.0731
0.9713
0.9711
0.3601
0.9708
0.5650
0.9738
Object Class
0.8317
0U5-112
0.8412
0.8554
0.8473
0.5544
0.8576
0155351
0.5331
Multiple Objects
0.4177
0.4258
1040
0. 0.4070
0.4451
020200
0.3636
0.6602
Color
0.7947
0.8013
0.7761
11775711
0.7521
0.7288
0.7588
Spolial Relationship
0,5854
0.5779
0.5702
0.5673
1152225
0.5799
0.5221
0.4717
0.4256
0.4351
Temsporal
0.2470
02419
0.2662
0.3455
0.3455
0.2351
0.238
Action
0.8700
5600
0.8400
Temuporal llickering
0.9941
0.9147
0.9932
1..9942
0.2921
Appearance Style
0.2407
0.2397
0.2420
0.2423
0.2403
0.2066
Quality Score
0,8022
0,5012
0.7962
0.730S
0.7922
0.7583
0.7560
0.7780
0.7934
Semuntic Score
0.6982
0.600]
0.8878
0.6845
0.6878
0.8912
0.6912
0.6637
0.7308
Total Score
0.7514
0.7808
0.7745
0.7805
0.7713
0.7689
0.7750
0.7552
0.7800
Table 8: FLOPs Breakdown for Hunyuan Video model across different methods (in 15 FLOPs).
Method
Self
Cross
MLP
Original
168.34
45.93
Delta-DiT
107.73
37.36
PAB 26
131.31
45.09
PAB 59
101.01
44.55
SVG 70%
75.58
45.93
ASTRAEA 0.4
67.34
45.93
ASTRAEA 0.5
84.17
45.93
ASTRAEA 0.7
117.84
45.93
Table 9: FLOPs Breakdown for Wan (2s)
model across different methods (in 10 FLOPs).
Method
Self
Cross
MLP
Original
3.6830
0.1491
1.5900
Delta-DiT
2.9464
0.1192
1.2720
ToCa 0.8
1.9520
0.0790
0.9921
ToCa 0.85
1.9520
0.0790
0.9548
PAB 246
2.3940
0.0621
1.5900
PAB 579
1.6205
0.0563
1.5900
ASTRAEA 0.4
1.4732
0.0596
0.6360
ASTRAEA 0.5
1.8415
0.0745
0.7950
ASTRAFA 0.7
2.5781
0.1043
1.1130
24

---

Published as a conference paper at ICLR 2026
Table 10: FLOPs Breakdown for Wan (4s) model across different methods (in 10th FLOPs).
Method
Self
Cross
MLP
Original
13.0573
0.2816
3.0033
Delta-DiT
10.4458
0.2252
2.4026
ToCa 0.8
6.9204
0.1492
1.8741
ToCa 0.85
6.9204
0.1492
1.8035
PAB 246
8.4872
0.1173
3.0033
PAB 579
5.7452
0.1064
3.0033
ASTRAFA 0.4
5.2229
0.1126
1.2013
ASTRAEA 0.5
6.5286
0.1408
1.5016
ASTRAFA 0.7
9.1401
0.1971
2.1023
Table 11: FLOPs Breakdown for OpenSora (2s)
model across different methods (in 10 FLOPs).
Method
Spatial
Temporal
Cross
MLP
Original
0.7190
0.4282
0.4380
0.8508
Delta-DiT
0.5512
0.3283
0.3358
0.6523
ToCa 0.8
0.2876
0.1713
0.3504
0.4424
ToCa 0.85
0.2450
0.1287
0.1802
0.4169
PAB 246
0.4673
0.2034
0.1825
0.8508
PAB 579
0.3163
0.1713
0.1654
0.8508
ASTRAEA 0.4
0.2876
0.1713
0.1752
0.3403
ASTRAEA 0.5
0.3595
0.2141
0.2190
0.4254
ASTRAEA 0.7
0.5033
0.2997
0.3066
0.5956
Table 12: FLOPs Breakdown for OpenSora (4s) model across different methods (in 15 FLOPs).
Method
Spatial
Temporal
Cross
MLP
Original Delta-DiT
1.4379
0.8619
0.8759
1.7016
1.1024
0.6608
0.6715
1.3046
ToCa 0.8
0.5752
0.3447
0.7007
0.8848
ToCa 0.85
0.2450
0.1287
0.1802
0.8338
PAB 246
0.9347
0.4094
0.3650
1.7016
PAB 579
0.6327
0.3447
0.3309
1.7016
ASTRAFA 0.4
0.5752
0.3447
0.3504
0.6806
ASTRAEA 0.5
0.7190
0.4309
0.4380
0.8508
ASTRAEA 0.7
1.0065
0.6033
0.6131
1.1911
25


Reviews:
[{"review_id": "1hrQaWMgHT", "reviewer": "Reviewer_nMzk", "summary": "This paper proposes ASTRAEA, a token-wise acceleration framework for video diffusion transformers (vDiTs). Unlike prior acceleration methods that rely on step reduction or blocking caching, ASTRAEA introduces:\n\n1. A lightweight token selection mechanism that dynamically identifies important tokens at each denoising step with negligible overhead.\n2. A GPU-friendly sparse attention strategy that selectively computes queries while retaining full keys/values, ensuring correctness and parallelizability.\n3. An evolutionary search framework to automatically allocate token budgets across timesteps, avoiding manual hyperparameter tuning.", "questions": "1. Generalization: If the token budget allocation is searched on one dataset or prompt set, how well does it transfer to unseen prompts or domains?\n2. Integration with retraining methods: Could ASTRAEA be combined with distillation-based step reduction to achieve further acceleration?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Operating at the token level, it offers finer granularity than step- or block-level methods, addressing a previously underexplored dimension.\n2. Evolutionary algorithm effectively allocates token budgets across timesteps, reducing reliance on heuristics.\n3. Clear evidence of multi-GPU efficiency, which is crucial for industrial deployment.", "weaknesses": "1. Evolutionary search may still be computationally expensive, limiting practicality in large-scale or frequently updated deployments.\n2. Generality of prompts: Search is conducted on a small set of prompts; broader validation on diverse datasets would strengthen claims of generalization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "sWki9kMWgQ", "reviewer": "Reviewer_vjj7", "summary": "This paper presents  a framework for accelerating vDiTs via token selection. The method leverages the multi-timestep nature of diffusion models for token selection, resulting to a GPU-efficient sparse attention strategy, achieving linear inference speedup with minimal quality degradation.", "questions": "- Line 203, 204: `We use the LSE score in the previous timestep because LSE scores do not vary across timestep`. What is the basis for this claim?\n- Why does Table 1, lines 379–380 say `on two vDiT models` while in fact there are three models?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea is novel and conceptually makes sense. It cleverly leverages the multi-timestep nature of diffusion models for token selection.\n- The method is supported by solid experiments, and the resulting performance is highly impressive.", "weaknesses": "Since the paper claims to outperform native sparse attention in line 233 to line 241, would it be possible to include a performance comparison with state-of-the-art methods, such as SVG2 [1]?\n\n[1] Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "lnz7opakwW", "reviewer": "Reviewer_eRBx", "summary": "This paper presents **ASTRAEA**, a token-level acceleration framework designed to mitigate the high computational costs of video diffusion transformers (vDiTs). The method introduces a lightweight token selection mechanism, a GPU-friendly sparse attention strategy, and an evolutionary search framework to find optimal token budgets across denoising steps.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Excellent Scalability**: A key strength of this work is its demonstrated scalability. The framework achieves strong performance scaling across multiple GPUs, showing up to 13.2x speedup on 8 GPUs, which highlights its practical utility for large-scale inference.\n\n- **Good Performance Gains**: The proposed method delivers acceptable and noteworthy performance gains, achieving up to 2.4x inference speedup on a single GPU while maintaining high video quality (e.g., <0.5% VBench loss).\n\n- **Clear Presentation**: The paper is well-written and logically organized. The core concepts, including the token selection mechanism and the sparse attention strategy, are presented clearly, making the work easy to follow.", "weaknesses": "- **Evolutionary Algorithm (EA) Search Cost**: The EA search for finding the optimal token distribution is computationally expensive, with an average search time of 82 GPU hours and some models taking up to 139 hours. While the authors rightly point out this is an offline cost, this is a significant practical hurdle. Further work could be done to optimize this search or analyze potential inefficiencies in the EA process.\n- **Lack of Theoretical Justification for EA**: The paper's justification for using an EA is primarily empirical. While the experiments show great results, the paper would be stronger with a more detailed explanation of why EA is well-suited for this specific problem over other search methods. The current justification—that other methods like NAS have \"substantial search times\"—could be expanded upon with a brief analysis of the search space itself.\n- **Missed Structural Optimizations**: The token selection framework appears to treat all tokens uniformly. It does not seem to leverage the inherent structures of video models, such as spatial-temporal sparsity. Furthermore, it does not analyze or exploit the different roles of text tokens versus spatio-temporal video tokens, which represents a missed opportunity for a more granular and potentially more effective optimization.\n- **No Hyperparameter Sensitivity Analysis**: The core token selection metric in Equation 4 relies on two key hyperparameters, $w_{\\alpha}$ and $w_{\\beta}$, to weigh token significance and the non-selection penalty. The paper does not provide a sensitivity analysis for these hyperparameters. This makes it difficult to assess the robustness of the method and understand how critical fine-tuning these parameters is to achieving the reported results. The ablation study in Section 4.3 focuses on high-level design choices rather than these specific equation parameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "3o3RV5nHQJ", "reviewer": "Reviewer_hZdk", "summary": "This paper proposed ASTRAEA, a token-wise cache framework for efficient video generation. ASTRAEA proposes a lightweight token selection mechanism and a memory-efficient, GPU-friendly sparse attention strategy. ASTRAEA also proposed an evolutionary algorithm to automatically determine the distribution of the token budget across timesteps. Experiments show that ASTRAEA achieves better generation quality with lower latency under different video diffusion transformers.", "questions": "Please see above weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.The problem studied in the paper is quite valuable, and the acceleration of video DiT generation is crucial.\n\n2.ASTRAEA should be able to be migrated to various DiT architectures with good generalization.\n\n3.The method explanation is relatively clear and easy to understand.\n\n4.The performance and acceleration effect of ASTRAEA appear to be relatively good.", "weaknesses": "1.The acquisition of LSE Score should not be directly provided by existing attention methods such as FlashAttention. How is the efficient and memory friendly acquisition described in the paper achieved? Can specific resource consumption be reported?\n\n2.The sparse attention in Fig.3 appears to bring additional computational complexity, why does the paper claim less computational complexity compared to sparsification for QK? Sparse QK theoretically should be faster, and faster hardware implementation can be achieved using a kernel designed like in SVG. Can you provide a more detailed explanation.\n\n3.The evolutionary algorithm used in the paper appears to be very time-consuming, which puts it at a certain disadvantage compared to other training-free methods. Can you report the performance comparison without using the evolutionary algorithm?\n\n4.The paper lacks the performance analysis of individual components.\n\n5.It seems that the statement in line 463 is incorrect and contradictory to line 464.\n\n6.What are the hyperparameters set in Eq.4 and how should they be set? The paper lacks additional analysis on whether these hyperparameters are sensitive.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)