ScalingCache: Extreme Acceleration of DiTs through Difference Scaling and Dynamic Interval Caching
Authors: Lihui Gu
Jingbin He
Lianghao Su
Kang He
Wenxiao Wang
Yuliang Liu
Venue: ICLR 2026 Poster (2026)
Keywords: ​​Diffusion Transformer
Image generation
Video generation
Model Acceleration
Feature Cache
Primary area: generative models
TL;DR: This paper introduces ​​a method that drastically speeds up Diffusion Transformer (DiT) inference by ​​reusing the feature cache​​ from previous denoising steps.

Abstract: Diffusion Transformers (DiTs) have emerged as powerful generative models, but their iterative denoising structure and deep transformer blocks incur substantial computational overhead, limiting the accessibility and practical deployment of high-quality video generation. To address this bottleneck, we propose ScalingCache, a training-free acceleration framework specifically designed for DiTs. ScalingCache exploits the inherent redundancy in model representations by performing lightweight offline analysis on a small number of samples and dynamically reusing previously computed activations during inference, thereby avoiding full computation at certain denoising steps. Experimental results demonstrate that ScalingCache achieves significant acceleration in both image and video generation tasks while maintaining near-lossless generation quality. On widely used video generation models including Wan2.1 and HunyuanVideo, it achieves approximately 2.5$\times$ acceleration with only 0.5$\%$ drop in VBench scores; on FLUX, it achieves 3.1$\times$ near-lossless acceleration, with human preference tests showing comparable quality to original outputs. Moreover, under similar acceleration ratios, ScalingCache outperforms prior state-of-the-art caching strategies, achieving a 45$\%$ reduction in LPIPS for text-to-image generation and 20$-$30$\%$ reduction for text-to-video generation, highlighting its superior fidelity preservation.

Published as a conference paper at ICLR 2026
SCALINGCACHE: EXTREME ACCELERATION OF DITs THROUGH DIFFERENCE SCALING AND DYNAMIC INTERVAL CACHING
Lihui Gu Jingbin He Lianghao Su', Kang Hc², Wenxiao Wang Yuliang Liu
'School of Software Technology. Zhejiang University AKlingAI Research
ABSTRACT
Diffusion Transformers (DiTs) have emerged as powerful generative models, but their iterative denoising structure and deep transformer blocks incur substantial computational overhead, limiting the accessibility and practical deployment of high- quality video generation. To address this bottleneck, we propose ScalingCache, training- ffree acceleration framework specifically designed for DiTs. Scaling- Cache exploits the inherent redundancy in model representations by performing lightweight offline analysis small number of samples and dynamically reusing previously computed activations during inference, thereby avoiding full computa- tion at certain denoising steps. Experimental results demonstrate that ScalingCache achieves significant acceleration in both image and video generation tasks while maintaining near-lossless generation quality. On widely used video generation models including Wan2.1 and Hunyuan Video, it achieves approximately 2.5x acceleration with only 0.5% drop VBench scores; on FLUX. achieves 3.1 x near-lossless acceleration, with human preference tests showing comparable quality original outputs. Moreover, under similar acceleration ratios, ScalingCache out- performs prior state-of-the-an caching strategies, achieving 45% reduction in LPIPS for text-to-image generation and 20-30% reduction for text-to-video gen- eration, highlighting its superior fidelity preservation. Our code is available at htc.perr/ggL..bb...oooo/LLgggeee
INTRODUCTION
Recent advances in visual generation have established Diffusion Transformers (DiTs) as the domi- nant paradigm(Peeble (Peebles & Xie, 2023), achieving state-of-the-art performance in modeling complex spatiotemporal patterns. However, their iterative denoising process incurs substantial computational cost, with generating even a few seconds of video often requiring several minutes(Sun al. 2024 Wan et al. 2025). This efficiency bottleneck motivates the development of effective lightweight acceleration strategies.
Acceleration methods for DiT' can be broadly categorized into training-bases and non-training-baser approaches. Training-basee methods, such as (Zhilaattion/hang al., 20256), require large-scale data and computation, whereas non-training-bai based
feature caching, sparsification(X Xi
cct al. 20255 Xia et al. 2025 Yang et al. 2025), and quantization(Shang et al., 2023 Li et al. 2025 Zhang et al. 2025a)
accelerate inference without additional training. Among these, feature caching leverages the temporal similarity between adjacent steps to improve efficiency. While all feature caching strategies are inherently lossy, the approximation error of existing methods remains too significant for professional-grade video generation, which demands near-lossless quality and high fidelity. This necessitates the development of more efficient and less destructive caching mechanisms.
The fundamental challenge of feature caching revolves around two core questions: how to use the cache and when to use it. For the former, naive approach directly reuse cached features, but this method faces a critical limitation: as temporal distance increases, feature similarity decays rapidly,
Work done during an internship KlingAl Research.
These authors contributed equally to this work.
ocCoreepponding authors. Email: wenxiaowang @ww.nadwww..uunnnn.m hhaaalang@kkuashhuu.ccom

---

Published as a conference paper at ICLR 2026
leading to significant divergence. For the latter, common strategy is recompute features at fixed intervals, yet this rigid approach lacks the flexibility needed to adapt to the model's 's dynamic behavior.
Building these two fundamental issues, prior studies have proposed various solutions. However, these approaches either lack flexibility et al. 2024 Liu et al. 2025b Zhao et al. 2023 or fail to
fully leverage the output features of each block to reduce prediction errors Zhou al. 2025 Liu et al.
2025a). To address these limitations, we propose two complementary strategies. First, we develop more efficient predictive paradigm that mitigates the limitations of relying solely on differential scaling, while avoiding the exponential growth of activation caches required by higher-order taylor expansions. Second, we design dynamic caching strategy that adaptively adjusts computational intervals during the denoising process.
Although Taylorseer(Liu al. 2025b) employs higher-order Taylor expansions for block-level feature prediction within each module, increasing the expansion order provides little improvement in final performance while significantly increasing the storage and read/write overhead of the caches. Moreover, relying solely on first-order differences is insufficient to capture dynamic feature variations. This limitation motivates us to explore more efficient and expressive prediction paradigms.
1.0
3,00
27
24
Tadassor, Si Naa2
2.75
Taylarmor, 3, Nin3 Caaks
2,50
block
18
15
1.1 relativee
speed
2.25
12
2.00
1.75
1,50
Owt
2.
6
10
14
18
22
26 step
30 34 38 42 46
step
Figure 1: Lower values of o (lly()) yll, (1) -yll)) indicate that the current feature is more similar to the zero-order feature.
Figure 2: ScalingCache consistently achieves a higher acceleration ratio and lower L1 relative error compared to Taylorseer when evaluating the hidden state of the last block at each step under different caching strategies.
We observed that for certain transformer blocks at specific time steps, directly reusing cached pre- timestep features y (0) yields smaller errors relative to full computation than applying first-order features (2)) as shown in Figure [ This finding suggests that combining the y and (1) is more effective than relying solely on either, motivating our introduction of differential scaling coefficients for each block are precomputed offline and applied during inference. During inference stage, we leverage precomputed differential scaling coefficient a for each time step and transformer block, enabling substantial improvements in generation quality.
The importance of different denoising steps varies significantly, making fixed cache intervals subopti- mal. For instance, the initial full-computation steps Sy are particularly critical: when Sy=2 and the full-computation interval ==2, the overall speedup is lower compared to the case where Sy=6 and N=3, yet the denoised output deviates more significantly from the original result as shown in Figure 2 Although several studies, including Teacache et al. 2025a) and Easycache(Zhou et al. 2025) have proposed dynamic caching strategies, these approaches typically base their policies only on the input to the first block and the output of the last block, without fully considering the dynamics of each intermediate block.
To this end, we propose ScalingCache, caching framework that operates directly hidden states of the transformer block to accelerate DiT inference. Our contributions are summarized as follows:
Cache prediction with differential scaling optimization. We computed differential scaling coefficients for each time step and transformer block offline using a small set of samples, enabling accurate cache-based prediction.
Runtime adaptive dynamic interval caching strategy. We propose an adaptive dynamic cache prediction approach that leverages both the outputs of each block and the variation of errors during computation, allowing the interval of full step computations be adjusted adaptively.
2

---

Published as a conference paper at ICLR 2026
Tnylorseer Baseline
Baseline
Ours Taylonser
Touns 10
Trans 23
Prate 20
Trenc
Trers 10
Fiatc
(a) Prompt: The camera orbits around a bustling city square at dusk. Taylorseer's 's scene generation is highly blurry, with significant discrepancy compared to the original video.
(b) Prompt: sleek motoreycle shreds through neon- lit alleyways, bullets sparking off dumpsters. Taylorseer's character is riding the motorcycle in reverse, and the motorcycle disappears the moment flies off.
The skvlne Ans Jeverror wask "JCLA 20z8 sertonn wkyferwanks
Téree dage and one caur sWiin grasi
Taylorseer Baxeline
under
Beseline
Iay 2016
Ours Thylorserr
1ay 2006
(c) Prompt: CG animation digital art, sleek and ad- vanced robot standing in the bustling center of Times Square. Taylorseer exhibited inconsistent lighting ef- fects the robot, with variations between the front and rear.
(d) In text-to-image generation task, Taylorseer( (1.9x) failed to generate the "ICLR 2026" text in the first im- age, produced only one ivory tusk in the second image, and omitted the cat in the third image. In contrast, method (2.2x) consistently ensured optimal results.
Figure 3: Video and image generation results on Wan2.1-14B and FLUX 1.dev. We highlight challenging scenarios where previous state-of-the-art methods (e.g.. Taylorseer) produce noticeable artifacts or inconsistencies, while our method achieves nearly identical visual fidelity to the original videos/images even under a high acceleration factor.
By synergistically combining the above modules for extreme acceleration, Scaling- Cache achieves significant speedup while maintaining near-lossless generation quality. Compared with prior state-of-the-art eaching strategies, ScalingCache also demonstrates superior fidelity, achieving a 45% reduction in LPIPS for image generation tasks and 20-30% reduction for video generation tasks.
RELATED WORK
Existing caching methods, including DeepCache (Ma et al. 2024b), Faster Diffusion (Li et al. 2023) and Mache-Me-if-You-CCe (Wimbauer al. 2024), are primarily designed for U-Net architectures. Their structural assumptions limit direct applicability to transformer- based DiTs, motivating dedicated caching mechanisms for this paradigm.
Predictive Hidden-State Caching in Diffusion Transformers. Caching approaches, such as AB Cache (Yu et al. 2025), PAB (Zhao et 2025), and TeaCache (Liu et al.) 2025a),, focus on directly reusing previously computed features without explicitly modeling their temporal evolution. While these methods reduce redundant computation, they often suffer from error accumulation and limited adaptability across different noise levels generation conditions. D-DIT (Chen et al.) 2024) first introduced delta-based caching by incrementally updating attention and MLP activations across timesteps. The ToCa series (Zou et al.) 2025| 2024) extend this idea by introducing dynamic feature correction, which alleviates iterative error accumulation during cache reuse. Building on this line, Taylorseer (Liu et al. 2025b) further enhances predictive caching by constructing cross-timeste noss-tinmsttep
3

---

Published as a conference paper at ICLR 2026
mappings that better preserve information flow in isotropic architectures. By leveraging the smooth continuity of hidden states across adjacent timesteps, these approaches achieve high efficiency without runtime scheduling, while offering stronger robustness than naive cache reuse.
Dynamic scheduling caching, Dynamic scheduling techniques adapt caching strategies at runtime by exploiting input characteristics timestep patterns. Rule-based methods such as PAB (Zhao et al. 2025) adopt frequenyy attention reuse, gaining efficiency but lacking adaptability. Data-driven approaches improve flexibility: TeaCache (Liu al. (022a) fits polynomial mappings of timestep embeddings, while AdaCache (Kahatapitiya et al. (0024) performs online similarity checks to reuse block outputs, though both incur profiling or computation overhead. FORA (Selvaraju al. 2024) reduces redundancy by selectively reusing spatio-temporal attention subsets. and later extensions such as I.2C (Ma et af., 2024a) and ABCache (Yu et al. 2025) enable learnable layer selection or multi-step reuse. While dynamically responsive to runtime states, these methods face common trade-off: heuristic rules risk quality degradation, whereas data-driven and adaptive schemes sacrifice efficiency due profiling or similarity computation costs.
METHOD
3.1 OVERVIEW
Diffusion Transformers (DiTs) follow hierarchical architecture, denoted as .M = B₁ o Bg ....0 B where each block B, consists of multiple distinct modules. For example, in Wan2.1 1 (Wan et al. 2025 each block comprises a cross-attention (CA) module conditioned on the time step and observations, self-attention (SA) module, and feed-forward network (FFN). This can be formally expressed as
B² B'= o FMLP, LE (1,2,...... L.),
where the superscript 1 denotes the block index. Each module incorporates residual connection, defined as y = a! + AdaLN o f(x)), where AdaLN denotes adaptive layer normalization, and [ax) represents the function implemented by one of the modules within the block, i.e., f E Fcs FMar). Given an input a/ at step t. the output of the (-th block is denoted as yf.
3.2 DIFFERENTIAL SCALING FOR PREDICTION
In DiTs, each denoising step requires full computation of intermediate features. Naive feature reuse often neglects the dynamic evolution of features over time, potentially leading to the accumulation of approximation errors. To address this, Taylorseer proposes a linear prediction-based caching strategy. Its core idea not only to cache feature values but also to record their temporal differences, enabling the prediction of features for future steps. Specifically, the first-order features at step can be predicted using the formula:
E T (8)
y1 3
11
vー-r),
where 7 = t k denote the most recent full full-computation step, and the second most recent full- computation step can be expressed as T and represents the caching interval. The term represeett the average rate of change in the feature between these two time steps. This first-order prediction strategy effectively captures the linear temporal trend of feature evolution, significantly improving prediction accuracy compared to directly reusing cached features.
Ay. (v'- / represents
We observe that, for different denoising steps and different blocks B/, the error first-order and zero-order features with respect to the full computation exhibits distinct regional patterns in Figure This indicates that, for certain blocks Bi, directly reusing features can outperform first-order linear prediction. Motivated by this observation, propose the following modified first-order linear prediction:
I
J v¹ + a.kDy'..
4

---

Published as a conference paper at ICLR 2026
where af denotes the first-order differential scaling coefficients. In order derive ai. conduct an offline estimation on collection of prompts. For each block Bi, we compute a by minimizing the discrepancy between the predicted and fully computed outputs via least-squares formulation:
min o
11 will min l/y v. t.kAAy-ll
(4)
of
During the practical phase of offline of estimation, use k=1, 7=1, the closed-form solution is given by:
a (9/11 vl. - A
(5)
where (.) denotes the inner product. To improve stability and generalization, update og using exponential moving average of f 3 ( + (1 - 3) at, where af is value obtained previously from set of prompts. To better illustrate this mechanism, practice, we precompute a for each block using approximately 50 prompts offline and 3=0.97. This offline computation introduces additional overhead during online inference.
provide a schematic overview in Figure 4 In
XB
X
obbain by eache
obtaiin by computation
Ay
Ay
dy
cache required for y!
The time step of the second-to-last computation
The time step of the computation
The current time step
Figure 4: The complete process of obtaining surrent-step features via differential scaling- saalngg-basd block-level prediction.
The complete computation process for predicting the features at the current time step using single- stage differential scaling is illustrated in Figure 4. Note that each full computation updates Ay, Since the scaling factors between two full computation steps is different, we use the following formula to obtain estimate of Ay":
Ayi J / (y: y. I 05
(6)
Our caching strategy requires storing two tensors per module: the cached feature vf- and the feature difference A In the Appendix G we analyze the additional memory and computational
overhead introduced by ScalingCache various mainstream generative models.
3.3 RUNTIME DYNAMIC INTERVAL CACHING
In conventional feature caching strategies, full computation and cache updates are typically performed fixed intervals based on predetermined thresholds. However, our observations reveal a U-shaped error pattern in cache predictions: when optimized using first-order differences, intermediate timesteps exhibit relatively low prediction errors, whereas the beginning and end the diffusion process show larger deviations. This indicates that static caching interval may ineur unnecessary computational overhead or lead to the accumulation of approximation errors.
To address this issue, propose runtime dynamic interval caching strategy, which adaptively adjusts caching intervals to improve computational efficiency while maintaining prediction accuracy. For each timestep I, the dynamic emor e, of step / defined as:
5

---

Published as a conference paper at ICLR 2026
Algorithm 1 ScalingCache inference strategy
Input: DiT model M, [= Paramter: Sy. initial warm-up steps
Output: ft =1.1... N,..,,N.. the output of the last block for each timestep
1: Initialize -,E=0
2: to do
3: Calculate 6, 1/| Eeeg
4:
i Sy 1] or > 0, then
5:
y M(xx)
6:
6, = Et
7:
E E Uee
8:
else
9:
for 1 to L do
10:
y = v + Af Ayi = d/
11:
end for
12:
= 6 + e, update cumulative error
end if
14:
end for
y y
& T.
y
where y denotes the output of the 1-th block at timestep t, and v-- corresponds to the last fully computed output. This metrie quantifies the relative deviation between the predicted and fully computed features, providing a principled criterion for cache updates.
We further define the cumulative error from the last full computation the current timestep as Er = D111 Jimr εt. The cache update rule then formulated as:
vl (f(x)) E Fos if > 6, or [0,S, - -1] ( + d'Aii
(8)
otherwise.
where Sy denotes the initial warm-up corm-up steps during which full computation is mandatory to capture the rapidly changing features of the early diffusion phase, and 6, is dynamic error threshold that bounds the deviation between predicted and fully computed features. Specifically, if the cumulative enror exceeds 8,, full computation is performed to prevent predictions from diverging significantly from the true features. Otherwise, the cache and predicted features are fused using an adaptive weight at to approximate the full computation output.
In practical scenarios, one key objective is to maximize the proportion of videos whose LPIPS falls below specified threshold or whose PSNR exceeds desired level, while maintaining the same acceleration ratio. We observe that as shown in Figure 5 for high-variation video generation tasks, a smaller threshold 6, is required to achieve desirable results, whereas for slow-variation scenarios, a larger 5, suffices and further yields higher speedup ratios. In practice, ô, can be estimated from the first Sy timesteps, enabling the model assign appropriate thresholds for different types of video generation tasks. Under a fixed S setting in ScalingCache, the computed 8, for samples intentionally lower than that for low-variation ones, thereby mitigating that high-variation samples require a lower ó, to achieve visual quality comparable to that of low-variation samples. To implement this adaptive caching mechanism, we design the inference procedure outlined in Algorithm Specifically, the algorithm maintains a cumulative error set & to estimate the dynamic threshold o, during inference. For each timestep. the model decides whether to perform full computa- tion reuse cached features by comparing the current error e against 8_. This enables the model to dynamically balance accuracy and efficiency, with aggressive reuse in stable regions and conservative updates in rapidly changing regions.

---

Published as a conference paper at ICLR 2026
light
10
0.16
0.75
0.12
0.50
30
0.08
10
0.45
0.04
0.30
0.80
0.00
0.15
0.1
0.2 0.3
0.1
0.2
0.3
0.4
0.1
0.2
0.3
0.4
10
14
16
18
6
51
Figure 5: Employing two distinct sets of prompts- one with descriptive cues such "still" (low variation) and another with "dramatically changing"(high) variation) -e evaluate the generation quality under various threshold configurations using the Wan2.1 1.3B model.
We acknowledge that the current strategy may not be applicable to all instances- for example, cases that begin statically but later transition into dynamic states represent a known limitation the present approach and warrant further investigation. Nevertheless, the method is effective in distinguishing the majority of samples and contributes to an overall improvement in performance.
4 EXPERIMENT
4.1 SETUPS
Models. We evaluate our proposed ScalingCache on text-to-video generation and extend the assess- ment its generalization capability text-to-image synthesis, with focus on inference efficiency as well as generation quality. The experiments are conducted on three state-of of-the-art visual generative models: the eet-to-image generation model FLUX,
2024), text-to-video generation
model including Wan2.1-1.31 1an2.1-14B (Wan al. 2025), and Hunyuan (Sun et al. 2024 to rigorously evaluate the acceleration and visual retention of ScalingCache.
Evaluation Metrics. For the primary task of text-to-video generation, we use default prompts in VBench (Huang et al., (2024) to assess visual retention. Specifically, we measure pixel-level fidelity, structural similarity, and perceptual consistency using PSNR. SSIM (Wang et al., 2004 and LPIPS (Zhang et al., 2018) against the original videos and images. We then systematically assess the generated results based 16 core evaluation dimensions defined by the VBench framework to provide a comprehensive evaluation of the model's 's performance. For the text-to-image generation task, perform inference on 200 DrawBench (Saharia et al.) prompts to generate images with resolution of 1360x 768. We then evaluate the generated samples using CL.IP Score (Hessel et al. 2021 as key metrics to assess image quality and text alignment.
To capture subtle quality differences, especially in high-quality generated images, automated eval- uation methods such CLIP-score may fully reflect these variations. To provide an objective assessment of the generated image quality, we employed a human preference-basee comparison evaluation method. Each evaluator, given a specific prompt, was asked to select the image they considered superior or to judge if both images were of equal quality.
Implementation Details. We determine the alpha values using text prompts and examine the convergence of alpha with respect to the number of prompts, as illustrated in Figure In the end, we use 20 prompts, and for each prompt, alpha is computed offline using five different random seeds. For the ablation studies video generation, only one video is generated per prompt. For the large-scale evaluation on VBench, we use an NVIDIA H800 GPU to generate five video samples with different random seeds for each prompt. resulting in total of 4,730 videos and 1,000 images generated on DrawBench. Our method requires no parameter tuning and only involves specifying the value of Sy.
2
MAIN RESULTS
Table reports the performance of ScalingCache compared to several representative acceleration strategies on three text-to-video generation models, evaluated in terms of inference efficiency, visual retention and human preference evaluation.
7

---

Published as a conference paper at ICLR 2026
66.7%
CA
55.2%
0 mecan
0.8
FFN
32
0.7
100
Sacleng
Taylorseer
ScalingCache
Teacache
Scaling
Original
prompts number
Figure 6: After performing 100 inference runs on Wan2.1 1.3B, the mean alpha values converge. For each number of inferences, we repeat the procedure five times and observe that the vari- ance across runs small.
Figure 7: For the human preference evaluation, we compared the FLUXI.des model's performance under a 3.1 acceler- ation factor, evaluating our method, ScalingCache(S)= against Taylorseer (Sy=4, T=4) and ScalingCache against Teacacheo.6(2.0x).
Table 1: Comparison of ScalingCache with other acceleration methods text-to-video tasks using the prompt-enhanced VBench dataset, reporting inference efficiency and visual quality metrics on representative models. Similar to Taylorseer, quantify the reduction in computational complexity (measured by the decrease FLOPs) to evaluate the theoretical speedup ratio.
Efficiency
Visual Retention
Methods
VBench (%)
Latency (8)
Speedup f
PSNR
SSIM
LPIPS 1
Wan2.1 1.3B Wan et al.
2025
(81 frames, 832x480)
Wan2.1 1.3B (T 50)
85.0
1x
83.31
40% steps
34.1
2.5x
14.50
0.523
0.437
80.30
Teacachea.os
42.6
2.0x
22.57
0.806
0.128
81.04
Taylorseer
1.9x
13.52
0.510
0.447
81.97
Easy Cache
34.2
2.5x
25.24
0.834
0.095
82.48
Scaling (cull
34.0
2,5x
26.61
0.890
0.071
82.92
Wan2.1 14B Wan et al.
2025)
(81 frames, 832x Ulyssesx2, RingAttention x
Wan2.1 14B (T' = 50)
137.8
1x
84.05
50% steps
68.9
2,0x
15.82
0.696
0.336
79.36
TeaCacheo.14
91.9
1.5x
18.60
0.688
0.244
83.95
MixCache
81.1
1.8x
23.45
0.814
0.124
83.97
ScalingCacherooourrs)
55.1
2.5x
25.63
0.861
0.083
83.87
Hunyuan Video (Sun al.
2024)
(129 frames, 960x544), Ulysses RingAttention x
Hunyuan
50)
199.8
1x
81.40
50% steps
100.1
2.0x
17.57
0.734
0.247
78.78
TeaCachou.1
133.7
1.5x
23.85
0.819
0.173
80.87
MixCache
110.5
1.8x
26.86
0.906
0.060
80.98
Taylorseer
72.2
2.8x
26.57
0.860
0.135
80.74
Cache
91.9
2.2x
29.20
0.904
0.063
80.69
(aalingCachez((ours))
88.4
2.3x
30.80
0.930
0.049
81.13
Inference efficiency. ScalingCache consistently achieves the highest speedup across all evaluated models while maintaining low latency. For instance, on the Wan2.1 model, ScalingCache(S)=11) attains 2.5x speedup. similar trend is observed Hunyuan Video, where CcalingCache(S5=111 achieves a 2.3x speedup, demonstrating its scalability across different model sizes and video lengths.
Visual retention. Despite aggressive acceleration, ScalingCache preserves superior visual qual- ity. Across widely used video generation models including Wan2.1 1 and Hunyuan Video, Scaling- Cache achieves approximately 2.3-2.5x acceleration with minimal impact VBench scores (0.3- 0.5% drop). On FLUX 1.dev, near-lossless 3.1 x acceleration is achieved, with all visual retention metrics significantly surpassing those of Taylorseer at 2.8x acceleration. Under comparable accelera-
8

---

Published as a conference paper at ICLR 2026
Table Comparison of ScalingCache with other acceleration strategies on the FLUX L.dev model.
Efficiency
Visual Retention
Methods
Clip Score (%)
Latency (8)
Speedup
PSNR
SSIM f LPIPS
FLUX 1.dev (T' = 50)
15.6
1x
80.17
50% steps
7.8
2.0x
29.36
0.683
0.318
78.88
TeaCacheo.d
7.8
2.0x
28.08
0.400
0.690
81.79
Taylorseer;
5.7
2.8x
30.76
0.780
0.230
80.17
ScalingCCccheooouurs)
5.1
3.1x
32.28
0.819
0.131
80.25
tion ratios, ScalingCache consistently outperforms prior state-of-the-ard caching methods, achieving a 45% reduction LPIPS for image tasks and 20-30% reduction for video tasks, demonstrating its superior fidelity preservation.
Human preference evaluation. The accelerated images produced by ScalingCache were selected at roughly equal rate the original images, demonstrating that the accelerated generation preserves visual quality to a level comparable with the originals as shown in Figure
The results demonstrate that ScalingCache effectively accelerates video and image generation with minimal quality loss. Its dynamic caching mechanism and predictive feature updates enable a superior trade-off between speed and fidelity, outperforming existing acceleration strategies across multiple models and video resolutions.
4.3 ABLATION STUDIES
Our ablation study on Fluxl deev and Wan2.1 1.3B demonstrates that both differential scaling coefficient(a) and dynamic caching intervals are critical for efficiency and generation quality. the ablation setting without a, we fix a=1 and strictly set the static caching interval to the maximum value smaller than the dynamic caching interval divided by the acceleration factor. Under these conditions, visual fidelity drops significantly at higher acceleration factors. Introducing mitigates feature prediction errors, improving PSNR, SSIM, and LPIPS with minimal overhead. Dynamic eaching alone boosts acceleration while maintaining quality, and combining both strategies yields the best overall performance--ubbsaniiillll -ubstantially faster inference with negligible loss in visual quality. These results highlight that ScalingCache effectively allocates computational resources and preserves high-quality generation under high-accele acceleccelrrtoonn settings as shown in Table S
ScalingCache only requires adjusting a single parameter, Sy. which can be tuned according the desired acceleration. As shown in the Figure 8 for S₁ $14, all evaluated models achieve over 2.0x end-to-end inference speedup. In Figure D we explore the impact of higher acceleration ratios on the performance of Vbench by analyzing the changes in its individual sub-metrics.
Table Ablation study on text-to-image and text-to-video task. We analyze the effect of and dynamic caching on efficiency and visual retention.
4.5
Wan2.1 1.3B
Wan2.1 14B
HunyuanVideo
Flux 1.dev
Settings dyn.
Visual Retention
Spocdun
3.5
Model
Speedup t
PSNR
SSIM
LPIPS $
3.0
2.9x
29.15
0.652
0.324
2,9x
29,83
0.701
0.259
Flux 1.dev
2.6x
31.04
0.772
0.192
2.0
3,0x
32.28
0.819
0.131
8
12 14
2.4x
24.53
0.857
0.092
Wan2.1 1 1.3B
/
2,4x
25.95
0.876
0.079
Figure 8: The speedup achieved across different models using
2.5x
22.50
0.809
0.129
2.5x
26.61
0.890
0.071
various Sy-

---

Published as a conference paper at ICLR 2026
Table 4: We analyze the stability of the a both within individual sub-tasks and across different sub-tasks for 1-.2.1-1.3B. Each sub-task contains 10 prompts, and each prompt is generated 5 times using 5 different random seeds.
Sub-task motion composition human material mechanics dynamic static random
0.008
0.007
0.009
0.022
0.015
0.011 0.017 0.006
4.4
ROBUSTNESS ANALYSIS
Based the VBench2 et al. 2025 dataset, selects 5 representative sub-tasks the evalu- ation foundation. To enhance the comprehensiveness of the assessment, we additionally construct two types custom-designed prompt sets dynamic prompts and static mrompts-resullee of 8 distinct evaluation sub-tasks including a "random" sub-task. Each sub-task includes 10 carefully crafted text prompts to ensure diversity and representativeness across tasks. For each prompt, the alpha value is computed using five different random seeds. We further evaluate both the standard deviation of alpha values within each sub-task and the global standard deviation across all sub-tasks, so as to systematically analyze the stability of alpha computation.
in total
As illustrated in the Table 4 we observe that the majority of subtasks yield alpha values within deviation of 2.5% from the global mean, indicating that a demonstrates good cross-task stability. Since the a calculated from randomly sampled subsets show smaller deviations from the global a, we therefore recommend using diverse set of prompts for a calculation in practical applications.
Original
2354
Table 5: Evaluation of the Wan2. 1.3B model yielded two key find- ings: Firstly, using custom al- pha calculated per sample yields videos with the highest visual fi- delity, Secondly, even in subtasks where the custom alpha deviates significantly from the mean alpha, using the mean alpha, while subop- timal, still produces results substan- tially superior to those generated without any alpha.
subject consiaenes
boiagorant conntstney
evenll consurmacy
xuporul Oikkerig
impoorllty
malkn stanthece
appkaranc abole
drnaii degree
speiia relesientipp
seitboic quilky
imaging grality
action
ORjAcT CIRN
eettiii onjects
Visual Retention
Figure 9: We vary Sy evaluate VBench and Visual Retention under higher acceleration. Re- ducing Sy from 6 noticeably degrades gener- ation quality. Although the overall VBench score drops by only 2%, higher acceleration significantly harms dynamic degree and human action perfor- mance.
Wan2.1 1.3B
PSNR
SSIM
LPIPS
custom
27.76
0.939
0.034
mean a
26.92
0.935
0.037
w/o
26.37
0.933
0.044
CONCLUSION
In this study, we propose ScalingCache, an efficient inference acceleration framework for Diffusion Transformers. By introducing first-order differential scaling coefficients, the method significantly reduces computational overhead while maintaining negligible loss in generation quality. This optimization leverages temporal feature evolution trends through linear prediction, combined with a runtime dynamic caching mechanism that adaptively updates cached features based on eumulative error. Specifically, the differential scaling formulation enables lightweight yet accurate estimation of intermediate features, substantially decreasing the need for full network evaluations. Experiments show that it achieves 2.3-3.1 1 x acceleration across text-to-vides and text-to-image generation, with minimal impact on visual fidelity and strong performance in human preference evaluations. Compared caching methods. ScalingCache consistently delivers superior visual
with prior state-of-the-ee retention, highlighting its effectiveness and scalability for high-quality, resource-efficieee generative inference.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENTS
This work was supported in part by The National Nature Science Foundation of China (Grant NO.: 62303406), in part by Ningbo Key R&D Program (NO.: 20252055), part by Yongjiang Talent Introduction Programme (Grant NO.: 2023A-194-G).
REFERENCES
Pengtao Chen, Mingzhu Shen, Peng Ye, Jianjian Cao, Chongjun Tu, Maristos-Savaar Bouganis, Yiren Zhao, and Tao Chen. 8-dit: A training-free acceleration method tailored for diffusion transformers, 2024.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference- free evaluation metric for image captioning. 2021.
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. pp. 21807-21818, 2024.
Kumara Kahatapitiya, Haozhe Liu, Sen He, Ding Liu, Menglin Jia, Chenyang Zhang, Michael Ryoo, and Tian Xie. Adaptive caching for faster video generation with diffusion transformers, 2024.
Black Forest Labs. Flux.
forest-labs/El http///hhh....ooma/oooooorroearr 2024.
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun-Yan Zhu, and Song Han. Svdquant: Absorbing outliers by low-rank component for 4-bit diffusion models. In International Conference on Learning Representations (ICLR), 2025.
Senmao Li, Taihang Hu, Fahad Shahbaz Khan. Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming Cheng, and Jian Yang. Faster diffusion: Rethinking the role unet encoder in diffusion models. CoRR, 2023.
Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It's time to cache for video diffusion model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 7353-7363, 2025a.
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Junjie Chen, and Linfeng Zhang. From reusing to forecasting: Accelerating diffusion models with taylorseers. arXiv preprint arXiv:2503.06923, 2025b.
Xinyin Ma, Gongfan Fang, Michael Bi Mi, and Xinchao Wang. Learning-to-cachhn Accelerating diffusion transformer via layer caching. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, and Cheng Zhang (eds.). Advances in Neural Information Processing Systems 38: Annual Conference Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 15, 2024, 2024a.
Xinyin Ma. Gongfan Fang, and Xinchao Wang. Deepcache: Accelerating diffusion models for free. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15772, June 2024b.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4195-4205, 2023.
Chitwan Saharia, William Chan. Saurabh Saxena, Lala Li, Jay Whang. Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. Photorealistic Imaae Diffusion Models with Deep Language Understanding.
Pratheba Selvaraju, Tianyu Ding. Tianyi Chen, Ilya Zharkov, and Luming Liang. Fora: Fast-forward caching in diffusion transformer acceleration, 2024.

---

Published as a conference paper at ICLR 2026
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. Post-training quantization on diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1972-1981, 2023.
Xingwu Sun, Yanfeng Chen, Yiqing Huang, Ruobing Xie, Jiaqi Zhu, Kai Zhang, Shuaipeng Li, Zhen Yang, Jonny Han, Xiaobo Shu, et al. Hunyuan-large: An open-source moe model with 52 billion activated parameters by tencent. arXiv preprint arXiv:2411.0222 2024.
Team Wan, Ang Wang, Baole Ai. Bin Wen, Chaojie Mao. Chen-War Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng. Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang. Mengyang Feng. Ningyi Zhang. Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng. Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang. Tianyi Gui, Tingyu Weng. Tong Shen, Wei Lin, Wei Wang. Wei Wang. Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang. Yingya Zhang, Yitong Huang. Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang. Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models. arXiv preprint 2503.
2025.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. 13(4):600-6) (144),000612, 2004.
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, et al. Cache me if you can: Accelerating diffusion models through block caching. In Proceedings of the IEEE/CVÉ Conference on Computer Vision and 2024.
Pattern Recognition, pp.
Haocheng Xi, Shuo Yang. Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, Jianfei Chen, Ion Stoica, Kurt Keutzer, and Song Han. Sparse video- gen: Accelerating video diffusion transformers with spatial-temporal sparsity. In Forly-second International Conference on Machine Learning, 2025.
Yifei Xia, Suhan Ling, Fangcheng Fu, Yujie Wang, Huixia Li, Xuefeng Xiao, and Bin Cui. Training- free and adaptive sparse attention for efficient long video generation, 2025.
Shuo Yang, Haocheng Xi, Yilong Zhao, Muyang Li, Jintao Zhang, Han Cai, Yujun Lin, Xiuyu Li, Chenfeng Xu, Kelly Peng, Jianfei Chen, Song Han, Kurt Keutzer, and Ion Stoica. Sparse videogen2: Accelerate video generation with sparse attention via semantic-aware permutation, 2025.
Zichao Yu, Zhen Zou, Guojiang Shao, Chengwei Zhang, Shengze Xu, Jie Huang, Feng Zhao, Xiaodong Cun, and Wenyi Zhang. Ab-cache: Training-free acceleration of diffusion models via adams-bashforth cached feature reuse. CoRR, abs/2504. 10540,, 2025. doi: 10.48550/ARXIV.2500. 10540.
Jintao Zhang, Jia Wei, Pengle Zhang, Jun Zhu, and Jianfei Chen. Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration. In International Conference Learning Representations (ICLR), 2025a.
Peiyuan Zhang, Haofeng Huang, Yonggi Chen, Will Lin, Zhengzhong Liu, lon Stoica, Eric Xing, and Hao Zhang. Vsa: Faster video diffusion with trainable sparse attention. arXiv preprint 1:Xi::2505513880 2025b.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. pp. 586-595, 2018
Xuanlei Zhao, Xiaolong Jin, Kai Wang, and Yang You. Real-time video generation with pyramid attention broadcast. In International Conference on Learning Representations (ICLR). 2025.
Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Lulu Gu, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng. Yu Qiao, and Ziwei Liu. Vbench- Advancing video generation benchmark suite for intrinsic faithfulness, 2025.
12

---

Published as a conference paper at ICLR 2026
Xin Zhou, Dingkang Liang, Kaijin Chen, Tianrui Feng. Xiwu Chen, Hongkai Lin, Yikang Ding, Feiyang Tan, Hengshuang Zhao, and Xiang Bai. Less enough: Training-free video diffusion acceleration via vuntime-adaptive caching. arXiv preprint arXhv:200700286, 2025.
Chang Zou, Evelyn Zhang, Runlin Guo, Haohang Xu, Conghui He, Xuming Hu, and Linfeng Zhang. Accelerating diffusion transformers with dual feature caching. arXiv preprint arXiv:2412.18811 2024.
Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion transformers with tosen-wise feature caching. In The Thirteenth International Conference Learning Reprexentations. 2025.
13

---

Published as a conference paper at ICLR 2026
A DECLARATION ON THE USE OF LLMS
In compliance with the conference policy on the use of Large Language Models (LLMs), declare that LLMs were employed solely as auxiliary tool for language polishing and for generating Python scripts dedicated to statistical analysis and visualization. The LLM contributed neither to research idcation, reasoning, has been included to describe LLM usage. The authors assume full responsibility for the entire manuscript, including all Al-assisted portions.
any substantive aspect of the content. Therefore, separate section
B DIFFERENTIAL SCALING COEFFICIENTS
The differential scaling coefficients remain consistent between the uncond stream and the cond stream, with most values falling within the range of 0.8 1.0, exceeding 1.2 are rarely observed.
Solfortention Module
Cross-attetiion Modiée
Modile
16
16
block
-1
-12
-11
466
-66
klop
stp
Figure 10: Wan2. 1.3B conditional stream
18
18
16
14
Nock
-12
110
Figure 11: Wan2.1 1.3B unconditional stream
C IMAGE-TO-VIDEO RESULT
As shown in Table 6 proposed method is evaluated the Image-to- image-to-Video (i2v) generation task. The experimental results demonstrate that the Scalingcache technique is equally effective when applied to this task.
14

---

Published as a conference paper at ICLR 2026
Table 6: Performance on the i2v task of Vbench2 using the ulyssess parallel configuration. Taylorseer uses an interval of 2 and enforces full computation for the first 4 steps and the last steps.
Efficiency
Visual Retention
Methods
Latency (s)
Speedup f PSNR
SSIM f LPIPS 1
Wan2.2 SB (T' 50)
234.0
1x
+ Taylorseer
137.6
1.7x
29.51
0.920
0.047
+ ScalingCachezo
111.4
2,1x
33.29
0.959
0.021
D TEXI-TO-IMAGE CASE STUDY
comparison between ScalingCache and Taylorseer text-to-image generation tasks. The results demonstrate a clear advantage of ScalingCache, although some failure cases remain (e.g.. the third row) in Figure 12 In most scenarios, however, the output is visually indistinguishable from the original, achieving near-lossless generation performance.
E WEIGHTED DIFFERENCE ACCUMULATION - DETAILED DERIVATION
We aim to estimate the hidden feature time step in layer / based on the previously cached feature at an earlier step T' (T' < T). The forward dynamics between these steps can be expressed as cumulative sum of residual updates:
y = y +
Σ Ayki.
k=r'+1
where y denotes the hidden feature at step k and Ayk = Ua 1h- represents the residual change.
E.1 EXPONENTIAL RESIDUAL DECAY ASSUMPTION
In practice, the magnitude of residuals tends to decrease as the sequence progresses, because earlier steps capture more global information and later steps mainly refine details. We model this behavior by assuming the residual norms decay approximately following multiplicative factor of E (0, 2):
| ss a Ayyll
(10)
This implies that the residuals (Avl) +2 form geometric sequence scaled by of.
E.2 RELATION TO THE OBSERVED DIFFERENCE
The difference between the two hidden states is equal to the sum of their intermediate residuals:
リ Vz
Ayk.
(11)
k=r
we denote Ayi as the most recent residual and back-propagatt its magnitude along the sequence using the decay factors, each earlier residual can be written as:
Il of IT of Ay,
Ayk.
(12)
15

---

Published as a conference paper at ICLR 2026
Siae 2
Tayle1 9X
Deep Learning
Deep Learning
Deep Learning
Deep Learning
Image
Tet Image
Text Image
Tex image.
Diffuision
Diffuston
Diffuston
Diffu Ston
Figure 12: A comparison between Scaling Cache and Taylorseer on the Flux 1.dev model for the text-to-image task and by adjusting the scaling factor Sy to 10 and 14, obtain different speedups of x3.0 and 12.1, respectively.
16

---

Published as a conference paper at ICLR 2026
E.3 SOLVING FOR THE LAST- STEP RESIDUAL
Plugging this relation into the telescoping sum constraint gives:
Il of k=T+-1 T of
y yー
Ay'.
(13)
Rearranging yields closed-form estimate of the most recent residual:
Avy (0' vtt, II Ii of
(14)
Ex-r44 11r a²
E.4 USAGE IN DYNAMIC CACUE UPDATES
This formulation provides a stable way to approximate the last-step residual from two known hidden states (y'., v²)) and set of estimated decay factors {aş}. It can be used to refine cached hidden states during dynamic cache updates without recomputing all intermediate steps, thus reducing computational overhead.
F DISTRIBUTED AND PARALLEL COMPUTING INTEGRATION
To enable efficient computation under sequence sequence-parallel DiT models (c.g.. Ulysses Ring Attention), we compute the dynamic error é, at each timestep in a distributed manner across all participating devices. Concretely, for each device d ..... D and each transformer layer E 1,..., L within stream, we first compute the **local relative change** between consecutive timesteps:
.
(d.))
(d,1)
yt
(15)
yi d.1)
We then aggregate the maximal local across all modules the same device for each layer, and average over layers to obtain the device-level local error:
(d...)
max Farn))
(16)
Finally, perform all-reduce operation over all participating devices to obtain the global dynamic error Ex:
1 D
4
6,
(17)
where D denotes the world size. This distributed reduction step ensures that consistently refleets the average prediction dynamics over all devices the parallel group, enabling our method to adapt caching intervals coherently in requence-parallee setting.
G OVERHEAD OF SCALINGCACHE ANALYSIS
We provide detailed analysis of the additional memory requirements and associated overheads introduced by the feature caching mechanism.
Memory Overhead. The memory footprint for caching intermediate features is substantial. Using the Wan2.1 14B model as a primary example, the model contains 40 layers (I), and computational streams (S=2). For each layer, features of dimension (B, L,, D)are stored. Since the algorithm
17

---

Published as a conference paper at ICLR 2026
Table 7: comparative analysis of generation quality and inference speed under different Sy for Hunyuan Video.
Visual Retention
Sf
Speedupt
Vbench %
PSNR
SSIM
LPIPS}.
4
5,8
18.59
0.684
0.341
75.18
6
4.5x
23.62
0.813
0.172
79.67
12
2.3x
30.80
0.930
0.049
81.13
requires caching both the previous feature and the delta (N=2), the total caching memory can be calculated as:
B BxXxxxxXXXX* 2Byles
using the following parameters:
B=1, 1.32760, D=5120
Data type: BF16 (2 bytes per element)
Applying the same method to other models yields the following additional memory requirements:
Wan2.1 1.3B: ~1.7GGB
Wan2.1 14B: ~50GB
Hunyuan Video: ~62.2GB
The substantial memory overhead can be effectively addressed with sequence parallelism methods like Ring Attention or Ulysses. In these approaches, each attention head processes only local sequence segment, meaning only the corresponding feature segments must be cached per device. This distributes the caching load evenly across the GPU cluster. For example, using 8-way sequence parallelism with the Wan2.1 14B model reduces the additional memory requirement to under 9 GB per GPU.
Computational Overhead. The core operations during the cache step are element-wise. making them memory- bound. Execution time therefore be estimated based the GPU's memory bandwidth. For an NVIDIA H800 with a memory bandwidth of approximately 3.3 TB/s, the estimated time per cache step for the Wan2.1 14B model is around 0.05 seconds.
Communication Overhead. The communication overhead is negligible. At the end of each cache step, synchronization is only required for a small scalar statistic computed per device. With Pdevices, the aggregated data size for communication is merely about 2 x P bytes per step, resulting in minimal communication cost.
18


Reviews:
[{"review_id": "BdUTY5ppDn", "reviewer": "Reviewer_mevD", "summary": "This paper presents ScalingCache, a training-free method to accelerate Diffusion Transformers (DiTs). It improves upon standard feature caching by introducing a pre-computed scaling factor (alpha) for more accurate feature prediction and a dynamic caching strategy to adaptively skip computation steps. The method achieves significant speedups (2.5-3.1x) with minimal quality loss on major text-to-video and text-to-image models.", "questions": "Please see the weakness.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Effective: The differential scaling with alpha is a lightweight approach to improve feature prediction accuracy.\n2. Strong Results: The method delivers impressive speedups while preserving high visual fidelity, outperforming prior methods in key metrics.\n3. Practical: As a training-free solution, it is easy to apply to existing models without expensive retraining.", "weaknesses": "1. Robustness of Alpha: Is calculating the alpha coefficient from only 50 prompts sufficient for generalization across diverse inputs? The paper should discuss the method's robustness and show potential failure cases.\n2. Analysis of Dynamic Caching: The ablation study confirms the dynamic caching strategy is useful, but lacks a deeper analysis. How does it adaptively change intervals for different content (e.g., static vs. dynamic scenes)?\n3. VBench Score Breakdown: The analysis of VBench scores is too general. A breakdown by dimension (e.g., image quality, temporal consistency) is needed to clarify where the method truly excels. An explanation for why it doesn't achieve top scores on all models would also be helpful.", "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": "z1nedzdQ0S", "reviewer": "Reviewer_PyFd", "summary": "The paper presents ScalingCache, a training-free inference acceleration framework specifically designed for Diffusion Transformers (DiTs), targeting image and video generation tasks. The core idea leverages the temporal redundancy in the hidden states during the denoising process in DiT. It conducts lightweight offline analysis on a small number of samples to precompute differential scaling factors and dynamically reuses previously computed activations during inference to bypass certain computation steps. This method achieves significant speedup while maintaining near-lossless generation quality, outperforming existing caching strategies. It particularly demonstrates better robustness in complex video generation scenarios.", "questions": "1. In Table 1, why does MixCache achieve the highest score on the 14B WAN2.1 model? Could the authors explain this anomaly?\n\n2. Concerns remain regarding the generalization of α. How does α perform in out-of-distribution prompts? If the selected prompts are not diverse enough, could this lead to suboptimal results?\n\n3. Regarding the human evaluation experiments, were the participants professionals or anonymous general users? Could this introduce bias?\n\n4. In the real-world deployment of 14B models, how much does the cache increase memory consumption?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. Important and Practical Problem: The high computational cost of DiT significantly limits its deployment in real-world scenarios like video generation. The proposed training-free acceleration approach holds clear practical value.\n\n2. Sophisticated Technical Design:\nThe introduction of the differential scaling factor α effectively combines zero-order and first-order predictions, addressing the issue of large prediction errors in certain layers seen with methods like Taylorseer.\n\n3. Comprehensive Experiments with Outstanding Results:\nCovers multiple SOTA models (Wan2.1-1.3B/14B, HunyuanVideo, FLUX);\nEvaluates both image and video tasks with metrics including PSNR/SSIM/LPIPS/VBench/CLIP Score and human preference.\n\n4. Engineering-Friendly: Only requires tuning one hyperparameter (Sf, i.e., number of initial full computation steps), without the need for training, fine-tuning, or complex scheduling logic, making it easy to integrate.", "weaknesses": "1. Generalization of α: The α coefficients need to be estimated offline using a small number of prompts (~50 prompts). While the paper claims convergence and low variance (Figure 6), it doesn’t verify its generalization to out-of-distribution prompts (e.g., extreme styles or rare objects). If α is sensitive to prompts, frequent re-estimation may be necessary for deployment.\n\n2. (Section 3.3) The authors acknowledge that their strategy may fail for \"static-to-dynamic\" videos (e.g., a scene suddenly transitioning from stillness to high-speed movement). Such scenarios are not uncommon in real-world videos. Furthermore, as the denoising process can theoretically access tokens from all frames at every step, this raises the question of why such scenarios would significantly impact this strategy and whether a reasonable threshold can still be estimated.\n\n3. While the authors claim \"no additional inference overhead,\" the calculation of dynamic errors (Equation 7) and all-reduce operations (Appendix F) under sequence parallelism still involve communication and computational overhead.\nThe authors fail to report the storage cost of caching (storing y and Δy per module) and do not discuss the potential impact on devices with limited memory.\n\n4. Lack of Comparisons: Why didn’t the authors compare their method with other acceleration approaches, such as Sparse VideoGen?", "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": "75N1jpTQqs", "reviewer": "Reviewer_6bkW", "summary": "The paper introduces ScalingCache, a training-free acceleration framework tailored for DiTs. By synergizing differential-scaling-based prediction with runtime-adaptive caching intervals, ScalingCache delivers significant speed-ups on both image and video generation while retaining near-lossless quality. Extensive experiments on Wan2.1, HunyuanVideo and FLUX show 2.3–3.1× acceleration with only 0.3–0.5 % VBench drop, and outperform prior state-of-the-art caching methods in LPIPS and other fidelity metrics, demonstrating superior fidelity-efficiency trade-offs.", "questions": "The authors are encouraged to further explore the applicability of the proposed approach to few-step distilled models.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe proposed algorithm is clearly described, with a well-defined formulation and solid explanation.\n2.\tThe manuscript is clearly structured and well-articulated, making it easy for readers to follow.", "weaknesses": "1.\tThe related work section overlooks the discussion of cache acceleration methods for UNet-based models, even though the cache acceleration technique for DiT-based models is an extension of and inspired by the earlier approaches developed for UNet-based models.\n2.\tIt would be great if the proposed method could further improve the sampling speed of the distilled models.\n3.\tIt's better to provide a user study to verify, through human evaluation, whether the generative performance of the method is close to the baseline.\n4.\tIs using 50 prompts sufficient to determine the appropriate scale?", "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": "7MCLxMzTCK", "reviewer": "Reviewer_BDzQ", "summary": "This paper presents ScalingCache, a training-free acceleration framework for Diffusion Transformers (DiTs), focused on visual generative models for both image and video generation. The approach introduces an adaptive dynamic caching mechanism, leveraging block-wise differential scaling coefficients (precomputed offline) and runtime error-adaptive cache interval selection to reduce redundant computation during denoising inference. Experimental results on multiple state-of-the-art image and video DiTs show substantial speedup (up to 3.1×) with minimal loss in visual quality, outperforming previously established caching baselines in both fidelity and efficiency on several standard benchmarks.", "questions": "1. Can the authors provide a more systematic analysis of scaling/failure scenarios? For instance, under what prompt or video conditions does the method’s error substantially increase, and what diagnostic measures could be advised in practice?\n2. How sensitive is the method to the quality and diversity of prompts used for offline estimation of $\\alpha$ coefficients? Figure 6 shows convergence, but quantitative analysis across tasks would clarify real-world robustness.\n3. Given the omission of several related works (see above), how does ScalingCache’s performance and computational overhead compare to FastCache, TokenCache, and GOC, both qualitatively and in speedup/fidelity metrics?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper addresses a pressing bottleneck in generative modeling—accelerating the slow inference of DiTs—through an approach requiring no retraining.\n2. The method section provides a systematic derivation of the prediction formula (Eq. for $\\hat{\\boldsymbol{y}}{t}^{l}$, Page 4), the estimation of scaling coefficients (Eq. for $\\alpha{t}^{l}$), and the adaptive error-based update rule. Full derivations and algorithmic steps are offered in the main text and appendices.\n3. Results across major models (Wan2.1, HunyuanVideo, FLUX) and diverse settings show consistently strong speedups and lower LPIPS/SSIM drops versus all baselines.", "weaknesses": "1. Several directly related and competitive caching acceleration methods—TokenCache (Lou et al., 2024) [1], Gradient-Optimized Cache (Qiu et al., 2025) [2], FastCache (Liu et al., 2025) [3], SpeCa (Liu et al., 2024) [4], DiTFastAttn (Kim et al., 2024) [5], and Dynamic Diffusion Transformer (Wang et al., 2024) [6]—are not cited, discussed, or included as baselines. While the paper does compare to prominent recent caching strategies, this omission leaves a gap in situating ScalingCache's novelty and advancement versus the current best practices.\n2. The derivation of the dynamic error threshold $\\delta_s$ in Algorithm 1 (Page 6) could benefit from further theoretical and empirical justification—currently, it is set based on an empirical mean of prior errors, and may be sensitive to outliers or sample diversity. The implications for worst-case quality loss (e.g., video flicker) as a function of $\\delta_s$ are left unexplored.\n3. While near-lossless acceleration is highlighted, the methodology is not extensively challenged at higher speedup factors, nor is there a rigorous exploration of when the trade-off between speed and quality breaks down (e.g., in especially long sequences, rare prompts, or highly dynamic scenes).\n4. In some equations, notation such as $\\Delta \\boldsymbol{y}_{\\tau}^{l}$ is used before being defined, and formatting is at times inconsistent (e.g., parameter lists in Algorithm 1). In Section 3.2 (Page 4), certain variables and indices are introduced abruptly, which may cause confusion for readers less familiar with blockwise DiT architectures.\n\n[1] Lou J, Luo W, Liu Y, et al. Token caching for diffusion transformer acceleration[J]. arXiv preprint arXiv:2409.18523, 2024.\n\n[2] Qiu J, Liu L, Wang S, et al. Accelerating diffusion transformer via gradient-optimized cache[J]. arXiv preprint arXiv:2503.05156, 2025.\n\n[3] Liu D, Yu Y, Zhang J, et al. Fastcache: Fast caching for diffusion transformer through learnable linear approximation[J]. arXiv preprint arXiv:2505.20353, 2025.\n\n[4] Liu J, Zou C, Lyu Y, et al. Speca: Accelerating diffusion transformers with speculative feature caching[J]. arXiv preprint arXiv:2509.11628, 2025.\n\n[5] Yuan Z, Zhang H, Pu L, et al. Ditfastattn: Attention compression for diffusion transformer models[J]. Advances in Neural Information Processing Systems, 2024, 37: 1196-1219.\n\n[6] Zhao W, Han Y, Tang J, et al. Dynamic diffusion transformer[J]. arXiv preprint arXiv:2410.03456, 2024.", "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)