PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generation
Authors: Jiangshan Wang
Kang Zhao
Jiayi Guo
Jiayu Wang
Hang Guo
Chenyang Zhu
Xiu Li
Xiangyu Yue
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Model
Primary area: generative models

Abstract: High computational costs and slow inference hinder the practical application of video generation models. While prior works accelerate the generation process through feature caching, they often suffer from notable quality degradation. In this work, we reveal that this issue arises from their inability to distinguish truly redundant features, which leads to the unintended skipping of computations on important features. To address this, we propose \textbf{PreciseCache}, a plug-and-play framework that precisely detects and skips truly redundant computations, thereby accelerating inference without sacrificing quality. Specifically, PreciseCache contains two components: LFCache for step-wise caching and BlockCache for block-wise caching. For LFCache, we compute the Low-Frequency Difference (LFD) between the prediction features of the current step and those from the previous cached step. Empirically, we observe that LFD serves as an effective measure of step-wise redundancy, accurately detecting highly redundant steps whose computation can be skipped through reusing cached features. To further accelerate generation within each non-skipped step, we propose BlockCache, which precisely detects and skips redundant computations at the block level within the network. Extensive experiments on various backbones demonstrate the effectiveness of our PreciseCache, which achieves an average of $2.6\times$ speedup without noticeable quality loss. Source code will be released.

Published as a conference paper at ICLR 2026
PRECISECACHE: PRECISE FEATURE CACHING FOR EFFICIENT AND HIGH-FIDELITY VIDEO GENERATION
Jiangshan Wang!.2. 12.3
Kang Zhao?
Jiayi Guo
Jiayu Wang
Hang Guo
Chenyang Zhu
Xiu Li TTiinguaa University
Xiangyu Yuelt
MMLab, CUHK
Tongyi Lab, Alibaba
Wan2. 1-14B (1x)
+Ours (-2.6X speedup)
+TeaCache (-1.9x speedup)
Figure I:
Qualitative Results of PreciseCache on Wan2.1-14B (Wang et al. 2025). Compared with previous methods, our PreciseCache achieves higher acceleration (about 2.6x speedup) of the base model without sacrificing the quality of generated videos.
ABSTRACT
High computational costs and slow inference hinder the practical application of video generation models. While prior works accelerate the generation process through feature caching, they often suffer from notable quality degradation. In this work, we reveal that this issue arises from their inability to distinguish truly redundant features, which leads to the unintended skipping of computations on important features. To address this, we propose PreciseCache, a plug-and-play framework that precisely detects and skips truly redundant computations, thereby accelerating inference without sacrificing quality. Specifically, PreciseCache con- tains two components: LFCache for step-wise caching and BlockCache for block- wise caching. For LFCache, we compute the Low-Frequency Difference (LFD) between the prediction features of the current step and those from the previous effective measure
cached step. Empirically, observe that LFD serves of step-wise redundancy, accurately detecting highly redundant steps whose com- putation can be skipped through reusing cached features. To further accelerate generation within each non-skipped step, we propose BlockCache, which pre- cisely detects and skips redundant computations at the block level within the work. Extensive experiments on various backbones demonstrate the effectiveness of our PreciseCache, such achieving average of 2.6 speedup Wan2. 14B without noticeable quality loss.
INTRODUCTION
Video generation models (Zheng et al., 2024 Yang et al., 2024 Kong et al., 2024 Wang et al.,
2025) have demonstrated impressive capabilities in producing high-fidelity and temporally coherent videos. However, they always suffer from extremely slow inference speed, posing significant chal- lenge their application. Although some works attempt alleviate the problem through distillation
Corresponding authors.

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Published as a conference paper at ICLR 2026
(Geng et al. intensive. To address this, feature caching (Zhao et al. 2024b Liu et al. 2025b Lv et al. (024) has emerged as popular approach to accelerate the process of video generation, which skips the network inference in several denoising steps by reusing the cached features from previous steps. However, these works usually adopt uniform caching scheme (i.e., performing a full inference every n steps, caching the features, and reusing them until the next full inference), which over- looks the varying importance of different timesteps in determining the output quality, resulting in insufficient speedup or noticeable quality degradation. As result, some recent works (Liu et al., |025a| Kahatapitiya et al. 2024 Chu et al. (2025) propose adaptive caching mechanisms that design metrics to adaptively decide whether to perform the full model inference or reuse cached features at each denoising timestep. However, these methods require complicated additional fitting or extensive hyperparameter tuning, and their cache decision criteria remain suboptimal, leading to unsatisfying generated results. Consequently, designing adaptive run-time caching mechanisms that maximizes acceleration while preserving video quality remains challenging.
2025 Song et al. 2023), , they always need additional training, which is computationally
In this work, we propose PreciseCache, an adaptive video generation acceleration frame- work that precisely identifies redundant fea- tures and skips their computation through feature caching, thereby enabling maximal speedup without compromising video quality. To this end, at each denoising step, we ana- lyze the influence of reusing cached features on the final generation quality. The results (Fig- ure 3R) indicate that as the denoising process progresses from high to low noise stages, the in- fluence of reusing cached features gradually di- minishes (Figure 2). This is consistent with the intuition that the diffusion process models low- frequency structural information high-noise steps while refining the generated content with high-frequency details at low-noise steps (Wan) 2025). The structural information is crucial for video generation, while high-frequed frequencyyncy details are usually perceptually insignificant, where the computation can be skipped to achieve ac- celeration. Consequently, we propose Low- Frequency Difference (LFD),, which measures the difference between the low-frequeet steps. Experiments in Figure 4 illustrate that LFD effectively estimates the redundancy of each denoising step (as illustrated in Figure 33) and therefore can be leveraged to indicate caching.
Denoising Process
Noise Space
Data Space
Latent
Model Prediction
Cached Prodiction
The Illustration of the Denoising Process for Video Generation. At high-noise timesteps, the prediction of the model varies sig- nificantly. Reusing the cached features in this stage (the red line) can significantly affect both the content and the quality of generated videos com- pared to the videos generated without caching (the orange line). In contrast, the feature caching dur- ing low-noise timesteps only introduces negligible impacts (the green line).
Figure 2:
components of the model's outputs at adjacent denoising
Based on the above analysis, we propose LFCache for step-wise caching. Specifically, at each denoising step, aim to leverage the LFD between the network prediction at the current step and that at the previous cached step as the criterion to indicate caching. However, directly calculating LFD cannot achieve acceleration because it requires caleulating the current-step predictions through the full model inference. To address this challenge, we observe that the L.DD exhibits low sensitivity to the resolution of the input latent (Figure 6) suggesting that lightweight downsampled latent is sufficient for its estimation. Consequently, at each denoising step in our LFCache framework, the noisy latent is firstly downsampled and fed into the model for quick "trial" inference, obtaining an estimated prediction. The LFD is calculated between this prediction and the cached prediction, which is then used to indicate caching. Due to the reduced latent size, the additional overhead for the inference of the downsampled latent is negligible compared to the overall generation time.
The LFCache identifies and eliminates the redundancy at the timestep level, focusing on the final output of the entire network at each denoising step. Beyond this, we introduce BlockCache, which delves into the network and further accelerates the generation process by performing the block- wise caching inside each non-skipped timestep identified by LFCache. Specifically, we assess the redundancy of each transformer block by measuring the difference between its input and output features. Our analysis (Figure 7 reveals that only subset of transformer blocks make substantial
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Published as a conference paper at ICLR 2026
modifications to the imput feature (which refer to as pivotal blocks), while others have minimal impact (which we refer to as non-pivotal blocks). BlockCache caches and reuses the outputs of these non-pivotal blocks to reduce redundant computation.
We evaluate our PreciseCache on various state-of-thh-an video diffusion models, including Open- sora (Zheng et al., 2024), Hunyuan Video (Kong et al.] 2024), Cog VideoX (Yang al. (0024), and 1 (Wang et al. 2025). Experimental results demonstrate that our approach can achieve an average of 2.6x speedup while preserving video generation quality, outperforming a wide range of previous caching-basee acceleration methods.
RELATED WORK
2.1 VIDEO DIFFUSION MODEL
Diffusion models (Ho et al. 2020: Rombach et al. 2022 Peebles & Xie (023) have become the leading paradigm for high-quality generative modeling in recent years. Within the video genera- tion domain, diffusion-based approaches have attracted increasing attention, driven by the rising demand for temporally coherent and high-resolution dynamic content (Blattmann et al.) 2023b a, 2023: Wang et al. 2024b| Recent advances have consequently seen shift from conventional U-Net architectures (Ronneberger et al. 2013) towards more scalable Diffusion Trans- formers (DiTs) (Peebles & Xiel 2023), which offer enhanced capacity to model intricate temporal dynamics across frames. State-of-the DiT-based video diffusion models such as Sora (Brooks) al. 2024 Zheng al. 2024), CogvideoX (Yang al., 2024), Hunyuan Video (Kong al. (0224). and Wan2.1 (Wang et al.
Hong et al.
2025) have demonstrated impressive performance in synthesizing coherent and high-fidelity videos. Despite these advancements, the inherently iterative denoising process in diffusion models introduces considerable inference latency, which remains critical challenge for real-time or large-scale deployment.
2.2 DIFFUSION MODEL INFERENCE- A ACCELERATION
Distillation pruning common methods for model acceleration Gou al. 2021 Cheng et al.
Liu (202)) Liu et al.] 20:8); Wang et al. (2024d); Fang et al.] which is also widely applied in diffusion
models (Sng et al. 2023 Meng et al. 2023 Sauer et al. 2024 Wang et al. 2026). However, they usually require large-scale training, which is time-consuming and resource-intensive. As an
alternative, training-fir free inference acceleration methods (Song et al. 2021 Karras al.) 2022 Lu iet al., 22022; Bolya & Hoffman 2023; Wang et al.) 2024a] Zou et al. 2025 Zhang et al. 2025b a
Xi et al. [025] Ye et al.) 2024) have gained considerable attention for speeding up diffusion model inference without costly retraining. Among these methods, Feature caching is one of the most popular methods for training- free video generation acceleration, which leverages redundancy across
iterative denoising steps. Early static caching methods (Selvaraju et al. 2024 Chen et al.) 2024 Zhao et al.] 2024b) rely on fixed schemes, but lack flexibility to adapt to varying process dynamics.
To overcome this, adaptive caching approaches (Wimbauer et al. 2024 Liu et al. 2025a Chu al.,
2025 Kahatapitiya et al.] 20244] Zhou et al.) [2025] Guo et al. 2025 2026) propose to adaptively decide when to apply the caching and reusing mechanism during the denoising process. However,
these methods usually suffer from notable quality degradation or extensive hyperparameter tuning.
METHOD
In this section, we introduce the PreciseCache method in detail. First, we analyze the influence of reusing the cached feature at each timestep on the final generated result, proposing the Low- Frequency Difference (LFD) metric to precisely estimate this influence at each timestep during the video generation process. Then, we introduce LFCache for timestep-level caching. At each denoising step, our LFCache framework first feeds downsampled latent into the model, obtaining an estimated output at this step. LFD calculated between this output and the cached output, which is used to determine whether to apply caching. Finally, we further propose BlockCache, which performs the caching and reusing mechanism at the block level within the non-skipped timesteps. The overall algorithm of our PreciseCache is shown in Figure 5 and Algorithm
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M00
Zu
Tunnsoog
2,
Zaa
Teessigg
2,
Thersuy
4.
(x) Caching Fr
(b). Caching P
(c). Caching P!r
Figure 3: The Impact of Reusing the Cached Model Prediction at Each Timestep. Considering clean latent Zo. we respectively reuse the model output at timestep f for each i E {49, 48, ... OO, and perform the subsequent denoising steps generate the final video. We then compare each resulting video with the baseline (i.e., generated without caching and reusing) to evaluate the impact of reusing cached predictions. Different colors indicate different prompts.
50-step denoising process from the Gaussian Noise Z30
MIE
MEEE
7o
250
Tiwesiig
A50
Theestop
2
(4).
[F; Finll
(b).
||
Pi ll.
(c)
||at
I.
Figure 4: The Difference between Model Predictions at Adjacent Timesteps. Although the dif- ference is relatively large in the low-noise stage, it primarily arises from high-frequency components, which have limited influence on the perceptual quality of the generated results. Different colors in- dicate different prompts.
3.1 PRELIMINARIES
Rectified Flow (Liu et 2022) models a linear path between the data distribution mo and Gaussian noise via an ODE: d2: v Zi, l)dl, E 10, where is parameterized by neural network Eg. Given samples X, ~ To. X, ~ T1 the forward trajectory defined by X, yielding the differential form dX, (X1 dt)dt. The training objective minimizes the regression loss between the ground truth velocity and the network prediction:
min /' E [|X
Xx) co( dt.
At inference, Gaussian noise N NO0 I) is iteratively updated using the ODE Solver (Lu al.
Wang et al. 2024c) represented by the Fuler Method: Z,-1 Z, (4:-1 t4) cgl Zz, t.)- Compared to DDPM (Ho et al. 2020). RF achieves high-quality generation with significantly fewer
2022
steps due to its linear sampling path. This efficiency makes it well-suited for T2V generation tasks
(Zheng al. 2024 Wang et al. 2025 Yang al. 2024 Kong al. 2024).
Feature Caching, DiT-based video generation remains computationally intensive due to the com- plexity of modeling spatiotemporal dependencies and the need for iterative denoising over numerous steps. To address this, feature caching is a widely adopted technique to accelerate video generation, where most works focus on step-wiss caching. Considering the noisy latent Z, at the ith denois- ing step, full inference of network co is performed, i.e., F, co Zz. tz), and the output F; is cached. the subsequent n timesteps {f;-1, 44-2. ... f- instead of performing a full inference as F. E Zi- 4i-k) where k (1,... n.., the cached F; is reused for updating the noisy latent, i.e., F, = F. Although this vanilla feature caching mechanism achieves significant ac- celeration, the interval n is fixed. On the other hand, different denoising steps have varying degrees of influence on the final output. Accurately identifying the redundant features within the generation process to achieve adaptive caching remains challenging problem.
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Z
Zm
Fei
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FE.
[1 la
cts
Z
1st Block
Di,
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top 5%
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Mth Block
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Fhi-t
(a). Overall Process
(b). Pipeline of PrechseCache
The Non-cached Step i3
Following Step kt-t
Video Generation
Example of Inference with BlockCache
Figure 5: Pipeline of PreciiseCche.
3.2 LOW-FREQUENCY DIFFERENCE
Adaptive caching requires the mechanism to dynamically decide whether to perform a full network inference at each denoising step i, (i.e., F; co (2,(4)), or to reuse the cached predictions of the model from the previous step. Intuitively, this depends on its impact on the final generated videos: reusing the previous cached prediction at t; significantly influences the content and quality of the generated video, a full inference of the network needs to be conducted; otherwise, the computation of this step can be skipped through reusing the cached prediction.
Based on this intuition, we begin by analyzing the impact of reusing the cached feature each step on the final generated video. Considering the video generation process consisting of N steps, we respectively skip the computation each step & (where i (N 1, N 2, , 0)) by reusing the model's prediction from the previous step 1,+1. and then generate the final videos. We measure the Mean Squared Error (MSE) between videos generated with caching and the ground truth, which are generated without caching. Generally, our results (Figure 3h) indicate that reusing the cached feature early high-noise steps significantly affects the generated results, whereas later low-noise steps, its impact is negligible.
The above analysis precisely quantifies the influence of applying caching at each single denoising step. Intuitively, if this influence is estimated immediately at &; during the denoising process, it can then be leveraged decide whether the computation at f, can skipped through feature caching. However, is non-trivial task because the influence of each step in B cannot be obtained be- fore the corresponding video is generated. As an alternative, most prior works like (Liu et al. (2025a) directly leverage the difference between the current network prediction F; and the cached prediction as the metric to indicate caching (Figure 4a), which does not align with the above observation and would lead to sub-optimal caching strategies. In this work, propose to further decompose the model prediction F, into www-frquency and nigh-frequency components (F... and F..F. through the Fast Fourier Transform (FFT), and investigate their separate effects during denoising, i.e.,
FLF = JFFT((E( (Z,, (t))to))) F.FF" FFT (Eo Z,, ti)ggh
(2)
We observe that caching F.F. predominantly affects the generated results (Figure 3p), whereas FHF has negligible influence (Figure 3b). Based on this insight, we further calculate their differ- ence between adjacent denoising steps, I.e.,
A.F |F. - F4211, AFF |FFF - FAF 2 2. iE {N - -,... .0
(3)
We find that the Low-Frequecd Fww-FFeuuency Difference (LFD) A.F closely aligns with the observation in Figure p.. This implies that at high-noise timesteps, the network generates critical structural and content information for the video, while at low-n timesteps, it primarily produces high- geghfrrquency details that perceptually insignificant and thus can be safely cached to accelerate generation.
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3.3 LFCACHE
Downsampled
Directly applying Low-Frequency Difference to indicate caching cannot accelerate the video generation process because obtaining L. re- quires performing full forward pass the timestep i, to calculate Fz. To address this, we propose LFCache framework, where down- sampled latent is first fed into the model for "trail" each denoising step. Specifically, given the latent Z E R XTTHHxW> xxxxxxxXC at the timestep ti (where T, H. W and C represent the temporal, height, width, and channel of the latent). We first downsample the latent on its temporal, height and width dimensions, i.e.,
Full-resolutien Latent
MSE
Z
Timestep
Zu
Figure 6: The Relationship between Latent Resolution and LFD. We observe that down- sampling has little effect on the computation of LFD. The experiment is conducted 1an2.1-
Z: = Downsample ZZ:),
(4)
where Z, ( denotes the downsample factor at the temporal dimen- sion and denotes the downsample factor at the spatial dimension. Then, feed the downsam- pled latent Z; into the network Eo obtain an estimated output F,, i.e. F. (2( Due to the reduced size of the downsampled latent, this process is highly efficient, taking negligi- ble computational overhead within the overall video generation process. Similarly, we downsam- ple the cached prediction Fi+1, obtaining F.41 and calculate the low-frequency difference, i.e., A.F F
14B (Wang et al. 2025
The analysis Figure indicates that the A.F is highly consistent with ALF, which can be used as an effective caching indicator during the process of generation.
FLF ++1
Following prior works (Liu al. 2025a), at each denoising step, as the final indicator to indicate caching. Specifically, after doing full inference and obtaining the output of the model Fa at timestep fa. we accumulate the low-frequency difference at subsequent timesteps, i.e., Σ._ A.P.P Ei A.F greater than a pre-defines threshold 4, the com- putation of this timestep cannot be skipped and F3 should be calculated through the inference of the network. Otherwise, we reuse the Fa to update the latent at ts. This procedure is shown in Algorithm
the accumulated differences
0.25
3.4 BLOCKCACHE
LFCache effectively identifies which timesteps in the denoising process can be directly skipped by reusing the cached output of the entire DiT at
MSE
model (i.e., Eq). On the other hand, those non-skipped timesteps, redundancy still exists within the computations of individual transformer blocks. To address this and achieve further acceleration, we propose Clock,ache, which eliminates the redundancy at the block level in those non-skipped steps identified by LFCache. Specifically, considering the non- skipped timestep t4, E [N. - .......... .N kn) identified in section 3.3 the inference of the DiT model with M transformer blocks (i.e.,
Block
39
Figure 7:
The Importance of Each Trans- former Block within the Video Diffusion Trans- former. Different colors represent different de- noising steps. The experiment is conducted on Wan2.1 141
FE. Eo (Z (k:)) be decomposed
(Wang et al. 2025).
(5) where j E (1...., M) and Bi indicates the jth block in Eg. We analyze the redundancy of each block B3 by calculating the difference between its input and output. The results in Figure fillusttate that only a subset of blocks (which we refer to as pivotal blocks) make notable modifications of the input, while remaining blocks have minimal impact (which refer non-pivotal blocks).
F ZE.
F = B (tk))
Ft, =. F.M.

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Published as a conference paper at ICLR 2026
Algorithm Video Generation with PreciseCache.
1: Initialize Eg, Zn (O.
2: Initialize <-
Accumulated error Always do the full inference at the first timestep i.v
3: Fy & Eo( L,,,NN
4: FLF - Downsamplo(FFTT (o)) UpdateLatent {ZN, FN)
5: ZN-1
6: for = N N, 2,...,I
7: Z, < Downsample(2 Zi)
8:
F, + co ZZ, (4)
Obtain the network output of downsampled input // Calculate the low-frequency component
9:
FLF FFT(F) (F)) low
FLF- FFT (PT((Dwnsampl) Fi+1))
Calculate the low-frequency component at 4++1
11:
AF - F.PP- - FFFF
// Calculate the Low- Now-Frequency Difference (LFD)
E+ E' ÂLF
// Update the accumulate error
13:
i < then
F. + F,+1
// Directly reuse the cached output
15:
else
F; + (Z(
Inference with BlockCache // Reset error
17:
E+0
18:
end if
19:
7,-1 UpdateLatent(Zy
// Update latent
20:
end for
21:
Output: Zo
Based on this observation, our BlockCache aims to eliminate the redundant computation of non- pivotal blocks. Specifically, considering the non-skipped step (kp full inference of the network conducted, and the difference D/, between the input and output of each block is cached, i.e., D = F - - Then, we select the blocks with top e% largest difference, which are identified the pivotal blocks: In. = { | is in the top c% of all values}. Other blocks non- pivotal blocks, which are highly redundant and thus can be skipped. In the following L non-skipped denoising steps la, (1 {1,.... L)), we use the cached difference D to estimate the output of non-pivotal blocks. The inference procedure with BlockCache can be represented as
1. F +
j E
F
ZR.
FV P
FE.
FN R.-I"
(6)
j I
With the skipping of the non non-pivotal blocks, BlockCache minimizes redundancy during generation without compromising the quality of the results. For implementation, our BlockCache is easier to in- tegrate into diverse model architectures for acceleration and only requires minimal hyper- parameter tuning compared to previous block-level caching methods such as (Kahatapitiya et al. 2024).
4 EXPERIMENTS
4.1 SETUP
Baselines. To validate the efficacy of PreciseCache, we implement our method on various state-of-
the-art base models for video generation, including Open-Sora 1.2 (Zheng et al.) 2024). Hunyuan- Video (Kong al. 2024), Cog VideoX (Yang al., 2024), and Wan2.1 (Wang et al. 2025). We compare methods with previous SOTA cached-based acceleration methods for video generation models, including PAB (Zhao et al., 2024b)) TeaCache (Liu et al.) 2025a)), and FasterCache (Lv et al. 2024). For these methods, utilize their official implementations available GitHub. For base models not directly supported by their official code, we implement the method ourselves.
Evaluation Metrics and Datasets. We evaluate inference efficiency and generated video quality of PreciseCache. To measure the inference efficiency, we report Multiply-Acoumule Operations (MACs) and inference latency. For assessing visual quality, we generate videos using the prompt

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Published as conference paper at ICLR 2026
from VBench (Huang et al. 2024) and evaluate performance using VBench's comprehensive met- rics. We also report some widely adopted perceptual and fidelity metrics, including LPIPS, PSNR, and SSIM, which measure the similarity between videos generated with cache-based acceleration methods and those directly generated by base models without caching.
Implementation Details. Determining an appropriate threshold for LFCache is non-trivial task, as the optimal value tends to vary across different base models and prompts. To address this chal- lenge, we convert determining specific threshold value into determining relative factor a. Specif- ically, in our implementation, caching is disabled for the first 5 timesteps during which we record the maximum low-frequency difference observed, i.e., A LF maz" We then set the threshold as A LF maz x a. This strategy substantially reduces the difficulty of manually tuning the threshold parameter. For LFCache, we provide two basic configurations, i.e., PreciseCache-DD Base and PreciseCache- where a is set to 0.5 and 0.7 for all the models. Based on PreciseCache-Turee faster configuration, Le., PreciseCache-Flan rate set to 40% and L set to 3. The downsample rate LFCache is set to [2. 4, 4] in the tem- poral, height, and width dimensions, respectively. To separate frequency components using FFT, we define a low-frequency region as a centered circular mask with radius equal to of the min- imum spatial dimension, i.e., radius = min H. W). All experiments are executed on NVIDIA A800 80GB GPUs utilizing Py Torch, with FlashAttention (Dao al. 2022) enabled by default to optimize computational efficiency.
we further provide where the BlockCache is enabled with the cache
Table 1: Quantitative Comparison of efficiency and visual quality 4 A800 GPUs.
Efficiency Speedup
Visual Quality LPIPS
Method
MAC' (P)
Latency (8) | VBench
SSIM
PSNR
Open-Sora 1.2 (4S0P, 192 Trames)
Open-Sora (72 30)
6.30
47.23
78.79%
PAB
5,33
1.26x
38.40
78.15%
0.1041
0.8821
26.43
TeaCache
3,29
1,95x
24.73
78.23%
0.0974
0.8897
26.84
FasterCache
4.13
1.67x
29.15
78.46%
0.0835
0,8932
27.03
Ours-base
3.73
1.72x
27.95
78.71 %
0.0617
0.9081
28.78
Ours-turho
3.10
2.07x
23.27
78.49 %
0.0786
0.8971
27.11
Ours-flash
2,45
2.60x
18.38
78.19%
0.0979
0.8903
26.78
Hunyuan Video
(480P, 63 frames)
Hunyuan Video (7 50)
14.92
1x
73.64
80.66%
PAB
10.73
L35x
54.54
79.37%
0.1143
0,8732
27.01
TeaCache
8.93
1.64x
44,90
80.51
0.091
0.8952
28.15
FastcrCachc
10.29
1.43x
51.50
80.59%
0.0893
0.9017
28.
Ours-base
9.15
L61x
45.74
80.65 5
0.0654
0.9102
29.15
Ours-turbo
7.49
1.95x
37.76
80.49 %
0.0884
0.9043
29.06
Ours-flash
6.04
2.44x
30.18
80.02 %
0.0902
0.8977
28.64
Cog Videox (480P, Trames)
Cog VideoX 50)
6.03
21.13
18%
PAB
4.45
1.3x
16.01
79.76%
0.0860
0.8978
28.04
TeaCache
3,33
1.79x
11.80
79.79%
0.0802
0.9013
28.76
FasterCache
3.71
1.60x
13.21
79.83%
0.0766
0.9066
28.93
Ours-base
3,59
L655x
12.81
80.14%
0.0619
0.9110
29.23
Ours-turhe
2.96
2.02x
10.46
79.91 %
0.0742
0.9021
28.97
Ours-flash
2.31
2.58x
79.80 %
0.0849
0.9001
28.79
Wan2.1-14B
(720P, 81 frames)
Wan2. 1-1BB (7' 50)
329,2
1x
907.3
83.62%
PAB
233.5
138x
657.5
82.91% 56
0.1853
0.8607
26. 18
TeaCache
166.3
1.9xx
467.7
83.24%
0.1012
0.8719
27.22
FasterCache
183.9
1.73x
524.5
83.47%
0.0741
0.9078
28.45
burs-base
2044.5
1,59
570.6
83.56
0.0451
0.9189
29.12
Ours-turbo
151.0
2.15x
422.1
83.52 %
0.0633
0.9127
28.98
Ours-flash
122.4
2.63x
344.9
83.43 %
0.0812
0.9035
28.76
4.2 MAIN RESULTS
Quantitative Evaluation. Table 0 reports a detailed quantitative assessment comparing our ap- proach with state of-i---f-th-arrr acceleration methods: PAB (Zhao et al. 2024b), TeaCache (Liu al.) 2025a), and FasterCache (Lv et al., 2024), focusing on both computational efficiency and visual fidelity. Our PreciseCache consistently illustrates notable speedup while strictly maintaining the vi-
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duuck a pond model ship floats nearby
knits and plays yur
Furry dog fetching rubber ball
Wan2.1-14B
FasterCache
TeaCache
HunyuanVideo
Ours
PasterCache
TeaCache
Figure 8: Qualitative Comparison. Zoom in for the best views.
sual quality of the base model, demonstrating robustness across diverse base architectures, sampling strategies, video resolutions, and durations.
Qualitative Comparison. Figure illustrates qualitative results comparing videos generated using PreciseCache-flan flash and several baseline methods. Visual comparisons demonstrate that method achieves significant acceleration without altering the generated video content or compromising qual- ity. In contrast, existing baselines often produce different content and suboptimal quality videos Additional qualitative examples are provided in Figure 9 for further reference.
4.3 ABLATION STUDIES
To comprehensively evaluate the effectiveness of PreciseCache, we conduct ablation studies to in- vestigate the performance under different number GPU, the downsampling size in LFCache, and the feature reusing strategy. Without loss of generality, experiments conducted Wan2.1-144 (Wang et al., 2025) and Hunyuan Video (Kong et al.) (024)..
Table 2: Latency on Different Number of GPUs with
Table 3: Influence of Downsample Size.
DSP (Zhao al. 2024a). Without loss of generality, we use Wan2.1 1- 14B (Wang et al. 2025) and Hunyuan- Video (Kong et al. 2024) as the base models and gen- erate the 1080P videos, reporting the latency (s) under different numbers A800 GPUs.
Without loss of generality, experiments are conducted 2025) with 4 A800 GPUs.
Wan2. 1-14B (Wang al.
Factor (T H x W)
Latency L
VBench +
LPIPS
Baseline
(Ix)
83.62%
1x2x2
918 (0.98x)
83.57%
0.0797
#GPU
Vidy
PrechseCuche
Wan-2.1
PPrecisCachhe
1x4x4
$25 (1.73x)
83.49%
0.0801
(1x)
470 (2.08xc
3326 (1x)
1330 (2.50x)
(1.73x)
275 (3.57x)
1732 (1.92x)
753 (4.41x)
1x8x8
401 (2.26x)
88.18%
0.1946
(2.98x)
161
(3.67x)
(8.00x)
2 2x4x4
416 (2.18x)
83.52%
0.0793
(5.662)
88 (11.166)
(7.25x)
239 (14.52x)
4x4x4
403 (2.25x)
83.02%
0,1875
Performances on Different Number of GPUs. Following previous works (L et al.
2024). we
adopt the Dynamic Sequence Parallelism (DSP) facilitate multi-GPU inference. Table ilustrates

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Published as a conference paper at ICLR 2026
the inference latency of PreciseCache-turbe under different numbers of A800 GPUs, where methods consistently achieves significantly lower inference latency than base models. Notably, PrecisCCache-tubbee can achieve even further acceleration ratio on Wan2.1-144 (Wang et al. 2025) under fewer number of GPU, e.g., it can achieve about 2.5x acceleration using GPU. These results highlight the effectiveness of our PreciseCache in various number of GPUs.
Size of Downsampling. As illustrated in section 3.3 downsampled latent is fed into the model to obtain the estimated output each denoising step. We conduct experiments illustrate the impact of downsampling size (Table 3 Experiments show that a small downsampling ratio results in a large latent size, which significantly increases the inference time. Conversely, over-downsampling can yield predictions that fail to adequately estimate the output at the current timestep. leading to suboptimal caching strategies and degraded video generation quality. Empirically, we find that sampling rate of 2 x 4 x 4 along the temporal (7") and spatial (H, W) dimensions can achieve a satisfying trade-off between acceleration and generation quality.
Feature Reusing Strategy. For the LFCache, we directly store the model's final prediction F; (i.e.. the results after classifier-free guidance) at each non- skipped timestep and reuse this cached prediction in the subsequent skipped steps. On the other hand, we notice that some prior works adopt different fea- ture reusing strategies, such as caching the residual (Liu et al., 2025a) (i.e., R; = F; Z,) at the non- skipped steps fg. At the skipped steps, the prediction is estimated according to this cached residual and the input noisy latent. Some works such as TaylorSeer (Liu et al., 2025b) also design more sophisticated reuse strategies with better performance. We con- ducted experiments to compare these strategies and found that their performances are comparable (Table under our PreciseCache. As a result, we adopt the vanilla approach for simplicity. This observation further implies that designing methods to identify where and when to cache could be more important than exploring how to cache for training-freee video generation acceleration.
Table 4: Feature Reusing Strategy for Step-wise Caching. Without loss of general- ity, conduct experiments Wan2.1-14BB (Wang et al. (025), generating videos with TOSOP resolution.
Strategy
VBench
LPIPS 1
Reuse prediction (F)
83.52%
0.0793
Reuse residuals (ft)
83.50%
0.0791
TaylorSeer
83.54%
0.0801
CONCLUSION
In this work, propose PreciseCache, effective training-free method for accelerating the video generation process, containing LFCache for step-wise caching and BlockCache for block-wise caching. First, we introduce the low-frequency difference, which can precisely reflect the redun- dancy of each denoising step. Then, we propose LFCache which indicates step-wise caching through the low-frequency difference between the downsampled output the current step and that of the cached step. Furthermore, we propose the BlockCache to reduce the redundancy at the non-skipped timesteps by caching the blocks which has minimal impact on the input feature. Extensive experi- ments illustrate the effectiveness of our method with different base models under various numbers of GPUs, highlighting its potential for real-world applications.
ACKNOWLEDGMENTS
This work is partially supported by the National Natural Science Foundation of China (No. 62306261), HK RGC-Early Career Scheme (No. 24211525). ITSP Platform Project (No. ITS/600/24FP)and the SHIAE Grant (No. 81 8110074). This study was supported in part by the Cen- tre for Perceptual and Interactive Intelligence, a CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government. This work is also partially supported by Hong Kong RGC Strategic Topics Grant (No. STG1/E-403/24-N) and SEEKK-CHHSSZ)-OOON
Joint Collaboration Fund (No. YSP26- 4760949). This work is partially supported by Alibaba Research Intern Program.
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A APPENDIX
A.
MORE QUALITATIVE RESULTS
We provide qualitative results of our PreciseCache in Figure illustrating the effectiveness of our method.
A.2 LIMITATIONS AND FUTURE WORKS
Although PreciseCache can achieve significant acceleration of video generation without training, its BlockCache component requires caching the features of each transformer block, which leads to increased GPU memory usage. Consequently, running PreciseCache-flan with BlockCache on Wan2.1114B to generate 1080P videos cannot be completed on a single 80G A800 GPU. This issue can be addressed through multi-GPU inference. We notice that the increase in GPU memory usage is common problem of cache-based acceleration methods for the need store features, which remains to be explored by future work. The application of PreciseCache on more powerful CFG methods (Karras et al.] 20244] Chen al.] 2025b) other diffusion-based scenarios such as control- lable generation (Cao et al. 2025 Lin et al. 2025b) Chen et al.) 2025a Lin et al. (2025a), unified
2025b a). detecting Chen al. (2023) and segmentation (Fang et al. 2024) are also promising directions for future works.
models (Xie
2024 Xiao
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Baseline (1.0x)
+PreciseCache (2.6x speedup)
Figure 9: More Qualitative Results of PreciseCache on Wan2.1-14B, Zoom in for the best views.
15


Reviews:
[{"review_id": "7BEB2Sa40l", "reviewer": "Reviewer_KbQo", "summary": "The paper presents PreciseCache, a unified inference-acceleration framework for Diffusion Transformers (DiTs) that leverages the Low-Frequency Difference (LFD) between consecutive steps to identify redundant computations.\nTwo complementary caching schemes are proposed:\n - 1. LFCache, which adaptively skips diffusion steps based on LFD-estimated redundancy;\t\n - 2. BlockCache, which reuses spatial feature blocks within non-skipped steps to further reduce computation.\n\nExperiments on multiple DiT-based video diffusion models show up to 2.6 × speed-up with minimal quality degradation, suggesting that LFD is an effective redundancy proxy.\n\nOverall, the paper tackles a practical and timely question—how to accelerate DiT inference without retraining or architectural modification—and offers an empirically validated, conceptually simple solution.", "questions": "1. How stable are the gains under different LFD thresholds $\\delta$ or block sampling ratios", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "PreciseCache demonstrates outstanding performance in **redundancy detection precision** and **cross-model adaptability**, particularly excelling in video generation—a highly complex task—where it effectively balances the core trade-off between *acceleration ratio* and *quality preservation*.\n\n1. **Frequency-Domain-Based Precise Redundancy Detection, Overcoming Blind Caching**  \n   Existing caching methods (e.g., TeaCache, FasterCache) often rely on *uniform time intervals* or *global feature differences* to detect redundancy, overlooking the fact that *different frequency components have varying impacts on video quality*:\n   - **Key insight via frequency decomposition:** By applying FFT to decompose predicted features into low-frequency and high-frequency components, experiments show that low-frequency components determine structural and content consistency, while high-frequency components mainly affect minor details. For instance, reusing high-frequency features results in negligible MSE error, but reusing low-frequency ones causes severe degradation.  \n   - **Low-Frequency Difference (LFD)** serves as a quantitative measure of temporal redundancy, and its variation trend aligns closely with caching impact on final quality — large LFD in early noisy stages implies high caching risk, while small LFD in late denoising stages indicates safety.  \n   This design fundamentally addresses the traditional issue of *misidentifying critical timesteps*, explaining the near-lossless quality at high acceleration.\n\n2. **Two-Level Caching: Step-Level + Block-Level Synergy for Maximum Acceleration**  \n   PreciseCache achieves *compound acceleration* by combining **LFCache** (temporal redundancy filtering) and **BlockCache**:  \n   - **LFCache (Step-Level):** To avoid full inference for LFD computation, a *downsampled latent probing* strategy is proposed—performing temporal-spatial downsampling (e.g., T/2×H/4×W/4) on current-step latents and running lightweight inference to obtain low-frequency components. This introduces negligible overhead. Experiments show >95% consistency between downsampled and full-resolution LFD, ensuring accurate caching decisions.  \n   - **BlockCache (Module-Level):** Within *non-redundant timesteps* selected by LFCache, PreciseCache further analyzes Transformer module importance—computing input-output differences to identify *pivotal blocks* (top c%) and *non-pivotal blocks* (low-difference). Outputs of redundant modules are approximated using cached input–output deltas, skipping redundant computation (Fig. 6).  \n   This *“step filtering + module compression”* design boosts acceleration from 1.9× (TeaCache) to 2.6× (PreciseCache-Flash) with negligible quality loss.\n\n3. **Adaptable to Mainstream Video Generation Models and Multi-GPU Scaling**  \n   PreciseCache maintains consistent performance across architectures, resolutions, and GPU configurations, demonstrating strong engineering generality:  \n   - **Cross-model adaptability:** Tested on Open-Sora 1.2, HunyuanVideo, CogVideoX, and Wan2.1-14B, achieving an average 2.6× acceleration with only 0.1–0.6% VBench score drops.  \n   - **Multi-GPU scalability:** With Dynamic Sequence Parallelism (DSP), acceleration scales almost linearly.  \n   - **Resolution robustness:** At 1080P, a single GPU achieves 2.5× speed-up, effectively mitigating *latency explosion* in high-resolution video generation.", "weaknesses": "1. **BlockCache Increases Memory Overhead, Limiting Single-GPU Deployment**  \n   BlockCache caches *input–output deltas* for each Transformer block, significantly increasing GPU memory usage—especially in large-scale or high-resolution setups:  \n   - Appendix A.2 states that for Wan2.1-14B (1080P), PreciseCache-Flash cannot run on a single 80 GB A800 GPU and requires multi-GPU execution.  \n   - The paper reports latency and MACs but omits explicit memory growth metrics (e.g., +20% or +50% vs baseline), leaving uncertainty for edge devices with limited VRAM.  \n   While this issue affects most module-level caching methods, the paper proposes no mitigation (e.g., delta compression or dynamic cache eviction), restricting its single-GPU applicability.\n\n2. **Untapped Acceleration Potential in High-Noise Phases**  \n   LFCache disables caching in early high-noise steps (≈ first 10–15 iterations) due to large LFD, yielding no acceleration during this phase.  \n   - However, module-wise redundancy may still exist even in high-noise phases. Yet BlockCache is only applied after LFCache filtering, missing these opportunities.  \n\n3. **Insufficient Analysis of Parameter Sensitivity in Frequency Decomposition and Downsampling**  \n   The paper defines a low-frequency radius of $1/5 × \\min(H,W)$ and a downsampling ratio of (2×4×4), but does not analyze their sensitivity:  \n   - **Low-frequency radius:** Would LFD accuracy degrade if set to 1/10 or 1/3? A too-small radius may miss important low-frequency content, while too-large introduces high-frequency noise, reducing discriminability.  \n   - **Downsampling ratio:** Table 3 tests 1×2×2 to 4×4×4 only, omitting extreme cases (e.g., 8×8×8) and ignoring interaction with model scale or resolution (e.g., smaller models may tolerate higher ratios).  \n   Lack of such analysis forces users to fine-tune these parameters per model, raising deployment costs.\n\n4. **Limited Theoretical Explanation for the Link Between LFD and Video Quality**  \n   While experiments empirically demonstrate the correlation between LFD and quality loss, the paper lacks theoretical grounding:  \n   - No mathematical derivation relating LFD magnitude to video quality degradation, thus the optimal α threshold is empirically chosen (0.5/0.7).  \n   - No analysis on how video type (e.g., dynamic action vs static landscape) affects LFD thresholds—dynamic videos likely require stricter criteria (smaller α), but this remains untested.  \n   Consequently, the method’s adaptability across diverse video generation scenarios is uncertain.", "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": "maXR41hXgp", "reviewer": "Reviewer_CSSt", "summary": "This paper achieves adaptive skipping—dynamically determining whether to adopt a full computation strategy or a caching strategy at each step—with minimal computational overhead. This is accomplished by first correlating output differences with low-frequency differences and then integrating these correlations with lightweight subsampling. At a finer granularity, for steps where the full computation strategy is employed, a BlockCache method is proposed. This method evaluates block importance based on block differences: it performs full computation for critical blocks while accelerating the processing of non-critical blocks by caching their differences.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "This paper proposes a novel novel adaptive-adaptive skipping strategy, which achieves adaptive skipping with minimal computation by correlating output differences with low-frequency differences and further associating them with lightweight subsampling.", "weaknesses": "1. The LFD method proposed in this paper essentially implements adaptive skipping. How does this method perform compared to AdaptiveDiffusion(Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy)?\n\n2. The BlockCache method achieves acceleration by caching block differences. How does this method perform in comparison with ∆-DiT(∆-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers)?\n\n3. Can the method in this paper be presented more clearly in the form of a network structure?\n\n4. Is this method equally effective in image generation?", "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": "hgNKVtUqqz", "reviewer": "Reviewer_GzPo", "summary": "PreciseCache is a training-free, plug-and-play framework that accelerates video diffusion models by precisely detecting and skipping truly redundant computations through Low-Frequency Difference (LFD)-based step-wise caching (LFCache) and block-wise caching (BlockCache), achieving up to 2.6× speed-up without noticeable quality loss.", "questions": "See Weakness.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The step-wise LFCache determines when to skip entire denoising steps, while the block-wise BlockCache further skips redundant network blocks within the key steps, forming a hierarchical strategy that compresses redundant computation in a simple yet effective manner.\n\n+ By performing a spatiotemporal downsampled “trial run” on the latent to estimate the LFD, the method greatly reduces the decision overhead compared to a full forward pass, which is an idea both interesting and effective.\n\n+ The experiments cover diverse backbones, resolutions, denoising steps, and multi-GPU settings, reporting metrics such as MACs, latency, VBench, and similarity scores (LPIPS, SSIM, PSNR) relative to non-caching baselines. Comparisons with PAB, TeaCache, and FasterCache demonstrate that the proposed method achieves higher acceleration while maintaining high fidelity, with LPIPS remaining below 0.1.\n\n+ The paper is clearly written and easy to follow.", "weaknesses": "- The paper involves several hyperparameter settings, such as the low-frequency cutoff radius, the cache window size L, and the key-block selection ratio Top-c% in BlockCache. However, the current version lacks experiments or analyses demonstrating the robustness of these choices. It would strengthen the work to include additional studies or sensitivity analyses that show how variations in these hyperparameters affect performance and stability.\n\n- The method suffers from excessive memory consumption; however, as noted by the authors in the limitation section, this is a common issue among cache-based approaches and can be mitigated through sequence parallelism.", "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": "K58jhiecJR", "reviewer": "Reviewer_j8AN", "summary": "This paper introduces **PreciseCache**, a training-free acceleration framework for video diffusion models that reuses redundant computations both across denoising steps (LFCache) and within transformer blocks (BlockCache). The method identifies truly redundant features based on low-frequency difference (LFD) analysis, achieving up to 2.6× speedup without noticeable quality degradation. The paper evaluates performance across several open-source video generation backbones.", "questions": "**Evaluation on compact video generation models:**\n\n Please evaluate PreciseCache on a smaller, efficient video generation model (e.g., with reduced transformer depth or parameter count) to demonstrate whether the proposed caching mechanism provides benefits beyond over-parameterized models. Please **specify the exact training datasets** and report **metric scores per model** (LPIPS, SSIM, PSNR, VBench) and training time for at least one video geneartion model.  I am willing to increase score if the authors can help reduce some concerns about the video generation models.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper identifies redundancy in transformer-based video generation models, distinguishing between *pivotal* and *non-pivotal* blocks, which aligns with observations of over-parameterization in large diffusion models. Training-free and plug-and-play: The proposed method requires no fine-tuning or retraining, making it applicable to existing models with minimal integration cost.", "weaknesses": "Limited generalization evidence. The proposed method relies on the redundancy of existing open-weight video generation models. Its effectiveness may diminish when applied to smaller or more efficient architectures with less redundancy. The paper does not test PreciseCache on small-scale, parameter-efficient video generation models, leaving uncertainty about its general applicability.", "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)