Plug-and-Play Fidelity Optimization for Diffusion Transformer Acceleration via Cumulative Error Minimization
Authors: Tong Shao
Yusen Fu
Guoying Sun
Jingde Kong
Zhuotao Tian
Jingyong Su
Venue: ICLR 2026 Poster (2026)
Keywords: Training-free acceleration
Diffusion transformer
Error correction
Primary area: generative models
TL;DR: A novel plug-and-play acceleration for diffusion transformers, which significantly improves generation fidelity on top of existing acceleration methods.

Abstract: Although Diffusion Transformer (DiT) has emerged as a predominant architecture for image and video generation, its iterative denoising process results in slow inference, which hinders broader applicability and development. Caching-based methods achieve training-free acceleration, while suffering from considerable computational error. Existing methods typically incorporate error correction strategies such as pruning or prediction to mitigate it. However, their fixed caching strategy fails to adapt to the complex error variations during denoising, which limits the full potential of error correction. To tackle this challenge, we propose a novel fidelity-optimization plugin for existing error correction methods via cumulative error minimization, named CEM. CEM predefines the error to characterize the sensitivity of model to acceleration jointly influenced by timesteps and cache intervals. Guided by this prior, we formulate a dynamic programming algorithm with cumulative error approximation for strategy optimization, which achieves the caching error minimization, resulting in a substantial improvement in generation fidelity. CEM is model-agnostic and exhibits strong generalization, which is adaptable to arbitrary acceleration budgets. It can be seamlessly integrated into existing error correction frameworks and quantized models without introducing any additional computational overhead. Extensive experiments conducted on nine generation models and quantized methods across three tasks demonstrate that CEM significantly improves generation fidelity of existing acceleration models, and outperforms the original generation performance on FLUX.1-dev, PixArt-$\alpha$, StableDiffusion1.5 and Hunyuan. Our code is released publicly at https://github.com/leaves162/CEM.

Published as a conference paper at ICLR 2026
PLUG-AND-PLAY FIDELITY OPTIMIZATION FOR DIF- FUSION TRANSFORMER ACCELERATION VIA CUMU- LATIVE ERROR MINIMIZATION
Tong Shao, Yusen Fu, Guoying Sun, Jingde Kong, Zhuotao Tian", Jingyong Su' School of Computer Science and Technology
Harbin Institute of Technology. Shenzhen, China
(shaotong, 220110511 6, sunguoying ng, 2023113058) hit. edu. cn, (tianzhuotao, su wiiggongggetttedd..oo
ABSTRACT
Although Diffusion Transformer (DiT) has emerged as predominant architec- ture for image and video generation, its iterative denoising process results slow inference, which hinders broader applicability and development. Caching- based methods achieve training- acceleration, while suffering from consid- erable computational error. Existing methods typically incorporate tion strategies such as pruning or prediction to mitigate it. However, their fixed caching strategy fails to adapt to the complex error variations during denoising, which limits the full potential of error correction. To tackle this challenge, we propose novel addlity-optimiizaiin plugin for existing error correction meth- ods via cumulative eттоr minimization, named CEM. CEM predefines the еπrог to characterize the sensitivity of model acceleration jointly influenced by timesteps and cache intervals. Guided by this prior, we formulate dynamic programming algorithm with cumulative error approximation for strategy optimization, which achieves the caching error minimization, resulting in a substantial improvement in generation fidelity. CEM is model-agnostic and exhibits strong generaliza- tion, which is adaptable to arbitrary acceleration budgets. It can be seamlessly integrated into existing eтror correction frameworks and quantized models with- out introducing any additional computational overhead. Extensive experiments conducted on nine generation models and quantized methods across three tasks demonstrate that CEM significantly improves generation fidelity of existing accel- eration models, and outperforms the original generation performance on FLUX. dev, PixArt-a, StableDiffusionl. and Hunyuan. Our code is released publicly https://gitubb.com/eeess 162/CCM.
correc-
INTRODUCTION
Diffusion models Ho et al. 2020); Rombach et al. (2022) have significantly advanced visual gen- eration tasks, including image Chen extended to other tasks Nie et al. (205); Hu et al. (202);; Sun et al. (2025). Diffusion Transform- ers (DiT) Peebles & Xie (2023) have emerged as dominant architecture for diffusion models, replacing U-Net RRonnebreer et al. (2015) due to their inherent scalability and superior generative capabilitics. Despite the impressive performance of these powerful models, their slow inference speed remains critical barrier to widespread adoption. Currently, image generation typically quires tens of seconds, while video generation can take up to several minutes or even longer. This limitation primarily stems from the sequential nature of the reverse denoising process, which pre- cludes parallel decoding. Moreover, the expansion of parameters, coupled with computationally intensive operations like attention Vaswani al. (2017). further worsens efficiency.
(2023) and video Zheng al. (2024) generation, and
To accelerate the DiT-based diffusion generation, researchers adopt techniques such as distilla- tion Yin et al. 2024 Salimans & Ho (2022): Gu et al. (2025). learning Huang al. (024a);
"Corresponding authors.

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Published as a conference paper at ICLR 2026
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Figure 1: Our CEM significantly reduces caching error while maintaining acceleration, thereby improving the generation fidelity of existing acceleration methods. Comprehensive experiments demonstrate the effectiveness and generalization of CEM.
Ma et al. (2024a) and quantization Shang et al. 2023 He et al. 2023 et al. 2025 aiming to
reduce inference latency by decreasing the model size. However, they necessitate training phases, which entails substantial computational cost, and lack generalization across models. An alternative approach is the caching mechanism Liu et al. (2025b); Saghatchian et al. (2025); Qiu et al. (2025), which leverages the similarity Ma et al. (2024b) between adjacent timesteps layers to reuse previ- ous cached hidden states, achieving training- free acceleration. Naive caching Selvaraju et al. (2024); Ma et al. (2024b) inevitably accumulates noise during the denoising, with error growing exponen- tially as the cache interval increases, resulting in a substantial deterioration in generation fidelity.
Therefore, existing caching optimization methods combined with additional mechanisms per- form error correction, aiming to mitigate the cumulative caching error. For example, methods such as ToCa et al. (2024a), DuCa Zou al. (2024b), and FastCache Liu et al. (2025a) incorporate the pruning operation when reusing cache to retain the computation of a subset of important tokens, thereby reducing error. In addition, methods such as ICC Chen et (2025b and TaylorSeer Liu GGun
et al. (2025c): Guan et al. (2025) leverage historical trends to predict the output variations across dif- ferent timesteps and guide the update of cached representations, rather than directly reusing cache.
Although the idea of error correction partially reduces the cumulative error for balancing the gener- ation quality and acceleattio efficiency, its effectiveness limited by the error of relatively simple or fixed caching strategies. For example, ToCa et al. (2024a) and DuCa et al. (2024b) adopt the cache schedule that changes linearly with the timestep, while the cache interval of methods like ICCIChen et al. (2025b) and TaylorSeer Liu et al. (2025c); Guan al. (2025) is constant. They incapable of handling the complex dynamics of sensitivity to caching during denoising. This limita- tion results in insufficient characterization of the model's 's intrinsic sensitivity to caching. thereby preventing the miligation of error accumulation during acceleration. Consequently, the potential of error correction is limited, leading degradation in generation fidelity.
To address this issue, we propose a training-free fidelity optimization plugin via Cumulative Error Minimization, CEM, that seamlessly integrates into existing acceleration methods and quantized models. CEM customizes an error to model the intrinsic sensitivity of generation models to dif- ferent cache intervals during denoising, which then leveraged an offline prior. Guided by this prior, CEM employs dynamic programming to derive the optimal cache strategy under a given ac- celeration budget by minimizing the cumulative error. When integrated as a plugin into existing emor correction methods and quantized models, CEM effectively mitigates the cumulative errors, significantly improves generation fidelity, and maintains or enhances their acceleration efficiency.
Although several prior works Qiu et al. (2025) like TeaCache Liu et al. (2025b) have explored caching optimization, they rely on real-time error estimation, introducing additional computational overhead that undermines efficiency gains. Furthermore, they are tied to specific acceleration ratios or model architectures, limiting compatibility with error correction. In contrast, our CEM conducts offline emror modeling prior inference. enabling the of prior knowledge without incurring runtime cost. CEM is aoges--g,ostic,, supports arbitrary acceleration budgets, and integrates seam- lessly with both error correction methods and quantized models.
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Extensive experiments demonstrate that our CEM, when used plug-in, significantly improves the generation fidelity of existing acceleration models across eight generation models while preserving their acceleration efficiency. Specifically, CEM helps TaylorSeer, ToCa, DuCa and FasterSD achieve fidelity surpassing the original models of Hunyuan, FLUUX. Ieev, PixArt-a and StableDDiffusion ..,, respectively, without sacrificing their acceleration efficiency. In addition, CEM seamlessly integrates with quantized models, achieving further 2x speed-up on Q-DiT beyond its original acceleration, along with enhanced generation fidelity. In summary, the contributions of this paper are fourfold:
We propose a novel training-free and plug-in stratgy optimization method, CEM, that can be seamlessly integrated into existing eгror correction methods and quantized models, sub- stantially improving generation fidelity while maintaining acceleration efficiency.
We propose offline error modeling, which constructs the model's intrinsic sensitivity under differ- ent cache intervals through random sample generation. This offline prior subsequently guides the optimization of caching strategy, requiring extra online computational cost.
We introduce dynamic programming based on prior errors to derive the optimal caching strategy that minimizes cumulative error, thereby reducing the error of existing caching- based methods.
Extensive experiments conducted eight generation models and one quantized model demon- strate that CEM can be effectively employed as a plug-in to improve the generation fidelity of state-of-the thrr-rrtt--rttt-- acceleration methods and quantized models.
2 RELATED WORK
Diffusion Transformer.
Diffusion models Ho et al. (2020); : Rombach et al. (2022) have demon-
strated remarkable capabilities in image Chen et al. (2023) and video Zheng et al. 2024 Kong
jet al. (202) generation. Upon this, DiT Peebles & Xie (2023) replace the convolutional modules in U-Net Ronneberge et al. (2155) with attention Vaswani et al. (2017). which enhances scalability and leads to breakthrough improvements in generation quality. Despite these advancements, the iterative nature of the diffusion combined with the computational complexity of attention results in slow inference. As a result, acceleration techniques have emerged as a key focus of research.
DiT Acceleration. General acceleration techniques such distillation [Mng et al. 2023); Yao eet al. (2024) and quantization Li et al.] (2023b); [iu et al. (2025d) require retraining models, which incur substantial time costs and lacks generalization across models. Consequently, training- free methods have emerged as a promising direction. Caching-baas based methods Selvaraju et al. (2024); Zou et al. (2025): [Lv et al.] (2024) accelerate inference by reusing previous hidden states with high similarity, while the reuse of cache inevitably introduces error, which accumulate over time and result in a significant degradation of generation quality. To correct this error, two strategies have been proposed: (1) Token pruning, which retains the computation of a subset of tokens during cache reuse to reduce the error caused by fully relying on cache. For example, DuCa Zou et al. (004bb) applies pruning at specific timesteps to balance generation quality and acceleration efficiency. (2) Predictive reuse, which estimates future representations based on historical evolution of cache rather than directly reusing them. For instance, TaylorSeer Liu et al.) (2025c); GGuan al. (2025) performs a taylor expansion on cache and utilizes gradient dynamics to predict features for reuse.
These methods alleviate caching error some extent, they overlook the optimization of the caching strategy itself, corrections are applied on top of relatively high cumulative error, limiting their effec- tiveness. Although few studies have explored strategy optimization, they are difficult to integrate with error correction for quality enhancement. For instance, AdaCache Kahatapitiya et al. (2024)
relies on motion trajectories and is restricted to video; AdaptiveDiffusion Ye et al. (2024) introduces overhead from third-order calculations and is specific to the U-Net; TeaCache Liu et al. (2025b) performs real-time estimation and polynomial fitting, while the extra cost offsets the benefits of cache-based acceleration. To address these issues, we propose a plug-and-play acceleration frame- work that optimizes the caching strategy by offline prior, seamlessly integrates with error correction methods without computational overhead, substantially improving their generation fidelity.
In addition, optimizing the sampling strategy also leads to acceleration. For example, DDIM Song et al.) (2020) introduces deterministic sampling to reduce the number of denoising steps. DPM- Solver Lu et al. (2022) and +PMM-Soler+++ Lu et al. (2025) leverage adaptive high-order solvers to accelerate denoising. Rectified flow Liu et al. (2022); Liu 2022) improves the transport of the distribution in the ordinary differential equation. Our method is orthogonal to these approaches and can be integrated across sampling strategies, even supporting compatibility with quantized models.
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Prior Modeling
Dynamic Caching Strategy
Modular Deployment
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Figure 2: Overview of our CEM framework. It first performs Offline Error Modeling to char- acterize the model's intrinsic sensitivity to caching under different timesteps and cache intervals, forming an offline prior. Guided by this prior, it employs Dynamic Caching Strategy with dy- namic programming to determine the optimal caching strategy that minimizes cumulative error and enhances generation fidelity. Finally, CEM supports Plug-and- Deployment and can be seam- lessly integrated into existing error correction methods and quantized models.
METHODOLOGY
To mitigate the caching error and address the limitations of simplistic caching strategies in error correction methods, we propose training-free, plug-and-play acceleration approach, CEM, that significantly improves generation fidelity while preserving the acceleration efficiency. As shown in Fig. CEM first performs (1) Offline Error Modeling, where it models the joint influence of denoising timesteps and cache intervals on caching eтrоr distribution. Then, we introduce (2) Dynamic Caching Strategy, which selects a set of cache intervals that minimizes the cumulative error through dynamic programming. Finally, (3) Plug-and-Plaa Deployment integrates the derived caching strategy seamlessly into the error correction methods, thereby improving generation fidelity.
3.1 OFFLINE ERROR MODELING
To maintain acceleration efficiency without incurring the computation overhead of real-time caching strategies, we propose offline error modeling that models caching error by analyzing under differ- ent caching strategies randomly generated content before inference.
Error definition. We model the error distribution by considering the joint variation of denoising steps and cache intervals, rather than relying solely on temporal changes as in real-time methods. Specifically, given the cache interval n (perform caching every n timesteps) and the current timestep t, we denote the output of the diffusion model with input x at specific timestep t as D(z, t). Furthermore, we flatten D into D' and compute the Cosine loss between the ground-truth output of I and the cached output of previous timestep & via normalized inner product:
D'(x, ((,,,))D'xx,L (x,t+n) E(t, = N. Σ1- [|D'(x, [D' t n))l2
N.-1
(1)
where E' is our error, N₂ denotes the number of generation, see Appendix. B.1 for error visualization.
Offline modeling. The modeling captures complex intrinsic error variations and provides the basis for adaptive caching by sensitivity errors. However, if the modeling is performed online, it in- evitably needs additional computational overhead, hindering acceleration efficiency. Moreover, the sensitivity is
making repeated modeling during inference redundant and wasteful.
Therefore, CEM performs the modeling offline by generating multiple (N, times in Eq. D random contents and averaging them, storing it as intrinsic prior. It makes the modeling content-agnostic, so each generation model only needs to be modeled once for permanent use.
Error distribution consistency. A key implicit assumption of error modeling is: The statistics from randomly generated samples are representative of those encountered during actual inference. To validate analyze the variation of error distributions across different samples in Fig.
Fig. 3 a)) the error exhibits small variances across different cache intervals, showing that the variation of both content and cache interval exert only minor effects the error distribution.
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Error
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Figure 3: Error Analysis. (a). Mean-variance of offline error modeling under different cache intervals. The enror variance remains relatively small across various contents and cache intervals. (b). Consistency between offfine modeling and actual inference. The error points obtained during inference fall within the prior-modeled distribution, indicating strong consistency between prior modeling and real inference. (c). Offline cumulative error vs. online error. The cumulative error approximation accurately captures the trend of error variation during actual inference.
Fig. b)) the errors from actual inference lie within the range the error distribution from offline modeling, demonstrating that this error distribution remains highly consistent between prior modeling and actual inference scenarios.
These results both demonstrate the robustness of the error distribution generated content. Further- more, we conduct extended experiments in Appendix. Dlxxx.... BB22using different prompt sources for offline modeling, and observe consistent behavior during inference. This provides further evidence that the error distribution captures the model's 's intrinsic sensitivity to acceleration, which is content- content-agnostic. Refer Sec. 4.3 for more results on robustness, offline cost and the effect of random sample size.
Joint modeling analysis. We model the error as joint function of denoising timesteps and cache intervals. Previous methods Zou et al. (2024a b): Liu al. (2025e) only consider differences across timesteps and determine cache intervals heuristically, limiting control over the acceleration budget. In contrast, we explicitly model cache intervals together with timesteps to capture the error distribu- tion and analyze the sensitivity of different denoising stages. This formulation is simple yet accurate, offering flexible acceleration control, while its offline design introduces no additional computational overhead and provides the foundation for our algorithm of cumulative error minimization in Sec. 3.2
3.2 DYNAMIC CACHING STRATEGY
After introducing offline error modeling to quantify the error, we can further leverage these errors to evaluate entire caching strategies, i.e., the combinations of cache intervals throughout the denoising. It inherently possesses an optimal substructure: the minimum cumulative error at each caching operation builds upon the optimal result of the preceding operation. So CEM employs dynamic programming, as shown in Fig. 2 to optimize combination of cache intervals.
Problem setup. Given acceleration budget (Ne), let n denote the current cache interval, t + n and t represent the timesteps with full computation with same setting as Sec. 3.1
Cumulative error approximation. Naturally, the modeled error serves as the dynamic cost for dynamic programming. Given a fixed number of N,, we compute the optimal combination of cache intervals that minimizes the error. However, the error in cache acceleration a continuously aceu- mulating process, which is not considered in the offline error modeling. This is because direct mod- eling of cumulative error is exponentially inefficient: Simple error modeling completes all timesteps in one forward pass, while cumulative modeling needs I passes for T timesteps.
To incorporate cumulative error effects without sacrificing efficiency, we introduce cumulative error approximation. It estimates the cumulative error based on Ett, n), achieving a balance between error fidelity and modeling efficiency. Specifically, we observe that simple cumulative integral over the E(t, n) yields effective approximation. The cumulative error E" (f, n) an arbitrary timestep is:
(t n) CUMSUM (E(t, n),, dim 0).
where CUMSUM(-) denotes the cumulative integral in Numpy and we apply weighting factors to amplify the differences across cache intervals. As shown in Fig. 3 this simple approximation
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closely matches the actual emror. It can be attributed to the high structural similarity between the input and output of each module in DiT, which causes imput perturbations to propagate to the output and accumulate across timesteps. Refer to Appendix. C.2 for more analysis.
Optimal substructure. Given the specific constraint on acceleration efficiency (the number of caching Ne), we construct dynamic programming array dp[t][s] based on the (t, n) described above. The array ]p[t][s] represents the minimum total caching error when denoising from beginning up timestep & with caching operations. We formulate the optimal substructure based dp[4][]]:
dp[t][j + 1] =
min nEN,1>0
+ dppt + nlbl),j E [cl. [T, 1],
(3)
where A denotes the set of candidate cache intervals. Our final objective is solve for the minimum value of dp[]][[No]- Then, a backtracking procedure is employed to recover the positions of the selected timesteps and associated cache intervals. See Appendix C.1 for implementation details.
Analysis. Dynamic programming enables CEM to obtain caching strategy with optimal error. As shown in Fig. T it effectively reduces the sensitivity error and improves generation fidelity under the same acceleration efficiency. The algorithm operates on offline errors, so it can derive the op- timal cache-interval combination that can be shared across multiple generations without additional overhead given an acceleration budget, analysis can be found in Sec. 4.3 and Appendix.
3.3 PLUG-AND-PLLAY DEPLOYMENT
Our optimized caching strategy can replace the existing caching components in current acceleration methods, significantly improving generation fidelity while maintaining the acceleration efficiency. It can also be directly applied to generation or quantized models to achieve high-fidelin acceleration. As illustrated in Sec. CEM can be integrated with pruning-based methods such DuCa and ToCa, and can also enhance prediction-based approaches like TaylorSeer in error correction. Moreover, CEM can also be directly incorporated into the generation process of quantized models such as Q-DīT, effectively doubling its acceleration. In summary, our method is compatible with various generation models, acceleration techniques, and quantized models.
EXPERIMENT
4.1 EXPERIMENT SETTINGS
Models and baselines. We conduct extensive experiments across three tasks: text-to-image gen- eration (using StableDiffusion (SD1.5) Rombaac et al. (2022), PixArt-or Chen et al. (2023) and FLUX. 1 -dev Labs )2244), text-to-video generation (Hunyuan Kong et al. (2024), Wan2.111.311 Wan ct al. (025)) and OpenSora Zheng et al. (0044), and class-to-image generation (DiT-XL/2 with DDPM al. 2020 and DDIM Song al. 2020) samplers). Our method is integrated into five SOTA acceleration methods: FasterSD Li et al. (2023a), TeaCache Liu et al. (2025b), ToCa Zou al. (024a)), DuCa Zou et al. (2024b) and TaylorSeer Liu al. (2025c). In addition, ensure compatibility with quantized models by applying our approach to the Q Q-DiT et al. (2025a).
captions of MS-COC02017 Lin et al. (2014) to generate images and evaluate performance using FID Heusel et al. (2017). CLIP- Score (CLIP) Hessel al. (2021). ImageReward (IR) Xu al. (003). Peak -oonal-to-Noose Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Simi- larity (LPIPS). Note that variations in sample counts (10K 50K) and CLIP versions stem from differences in the respective acceleration baselines. For text-to-video generation, we report the av- erage performance across 16 tasks from the VBench Huang al. (2024b). For the class-to-imase generation, we randomly sample 50,000 class labels from ImageNet and compute FID, sFID, Incep- tionScore (IS) [Salimans al. 2016), Precision (P) and Recall (R). Finally, we evaluate generation speed on RT'X0090 or A800 using both FLOPss and latency. Refer to Appendix. D.I for more details.
Evaluation and metrics. For text-to-image generation,
4.2 MAIN RESULTS
Text-to-image generation. In the results shown in Tab. I our CEM is integrated into four ac- celeration methods across three different generation models. Our method improves FasterSI of

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Table 1: Quantitative comparison on text-to-image gen-
Table 2: Quantitative comparison on text-to-video generation. N (or
eration. 1/f denotes lower/higher values indicate superior performance. "_" denotes the absence of reference results.
D): baseline hyperparameter control-
"+Ours" indicates the baseline with our CEM. Bold font highlights our better results.
ling acceleration efficiency.
OpenSora, rflow 30 steps, 480P, frames
StableDiffusion.... 1.5. DDIM 50 steps, 512x812
FLOPs
Latency
VBench
FLOPs
Lasency
FID
CLIP
FSNR
SSIM
LPIPS
(T)
[s)
(%(
(T))
[s)
10K]
[L]
Origin
3283.22 20
102.29
79.13
Origin
37.05
1.44
21.75
30.92
INF
1.00
0.00
50% steps
1641.60
51.16
76.55
50% steps
18.53
0.23
25.21
12.18
20.19
0.62
0.26
A-DIT
3166.47
98.31
78.21
FasterSD
27.35
0.33
21.62
32.54
16.42
0.56
0.36
ToCa
1294,03
54.70
78.34
+Ours
27.35
0.33
19.99
32.85
15.77
0.60
0.35
DuCa(Na)
1315.62
50.64
78.39
PixArt-a, DPM-Solver 20 stops, 2566226
+Ours
1315.62
50.24
78.45
FLOPs
Latency
FI1
CLIP
PSNN
SSIM
LPIPS
Iunyuan, solver 50 steps, 480P, frames
(T))
50K]
(L)t
t
f
FL.PPs
1 Latency
VRench
Origin
11.18
0,86
28.06
16.29
INP
1.00
0.00
(T)
(s)
(S)
50% steps
5.59
0.43
37.41
15.82
18.67
0,70
0.20
Origin
29773.00
441.76
78.46
FORA
5.66
29.67
16.40
25% steps
7741.11
70.89
ToCa
4.26
0.44
29.73
16.45
FORA
5960.40
93.92
78.83
DuCa(Na)
4.79
0.40
41.56
16.46
14.96
0.46
0.42
ToCa
7006.20
109.89
78.86
+Ours
4.75
27.57
16.37
18.25
0.68
0.21
TeuCachetlo.a)
6550.06
108.54
77.56
FLUXX.1dde Rectified Flow 50 steps, 1024xc1024
+Ours
6550.06
105.94
78.15
FLOPs
Larency
IR
CLIP
FSNR
SSTM
LPIPS
(aylorScer(N))
5939. 10
95.
79.78
(T))
[s)]]
(G))
+Ours
5939.110
95.01
81.24
Origin
3719.50
35,63
0.9649
32.57
INF
1.00
0.00
25% steps
967.07
0.9310
32.72
14.71
0,58
0.46
Wan2.1-1.38
unipe stops, 480P, 65 frames
PORA
1320.07
14.66
0.9227
FLOPs
Latency
VBench
ToCa(Na)
1263.22
14,60
0.9822
32.36
18.27
0.67
0.30
Spe."
(s)
(%)
+Ours
1263.22
1.051
32.67
17.72
0.67
0.31
Origin
1.00x
187.46
78.97
Rea (r
1115.85
16.57
0.7228
30.66
17.41
0.70
0.35
25% steps
3.85x
48.97
61
+Ours
1115.85
16.05
0.7362
31.13
17.89
0.71
0.33
A-DIT
1.24x
165.89
75.97
Tay Ne)
744.81
10.09
0.9410
32.57
15.59
0.60
0.41
TaylorSeer(Na)
5,56K
39.55
75.31
+Ours
744.81
10.09
0
32.89
16.11
0.61
0.39
+Ours
5.56x
39.37
76.18
mil aol
Backet
lorker
+Ours FaxterSD Origin
DnCa Origin
+Ours TaylerSeur Origin
OOurs
(a) StableDif
(b) PixArt-a
(c) FLUX.1-dev
Figure 4: Qualitative visualization comparison text-to-image generation. We highlight the
areas with red dashed boxes to emphasize the comparison. Our CEM achieves higher generation fidelity under the same or higher acceleration efficiency compared with baselines. More experiment results and visualizations are provided in the Appendix. D.2
E. and
SDL.5 by 1.63 on FID and 0.31 on CLIP, surpassing the performance of the original model. More- additional computational cost, both FLOPSs and latency
over, these improvements come remain unchanged. On PixArt-o, our enhancement of the DuCa leads to a 13.99 improvement in FID. Furthermore, thanks to our offline cache strategy optimization, we further reduce latency while maintaining the same FLOPs, ensuring improved efficiency without additional computational cost. Similarly, we improve both ToCa and TaylorSeer on FLUX.1 our CEM with the online optimization technique TeaCache, achieving 0.0134 improvement in IR while eliminating its extra computation during inference, thus reducing inference latency.
addition, we integrate
These text-to-image models span diverse backbones, sampling strategies and resolutions, demon- strating the generalization of CEM, even beyond DIT' (SD1.5 is UNet). As shown in the qualitative results in Fig. 4 our CEM produces higher-fidelity images that closely resemble those of the original model, like the cat's face in (a), the umbrella in (b), and the texture of the tennis ball in (c).
generation. We validate the effectiveness of our plug-in on Hunyuan, Wan21 and OpenSora. Taking Hunyuan as an example, we improve TaylorSeer in Tab. 2 achieving VBench gains of 1.46 at 9=6 (Our method replaces the design of N, when combined with our CEM, the baseline no longer requires the N to be specified). These improvements significantly improve the generation fidelity of the acceleration models, even surpassing the original model performance
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Table 3: Quantitative comparison on class-to-image generation with DiT-XL/2 and quantized model. W/A denotes the quantization bit-width of weights and activations. More experiment results
and visualizations are provided the Appendix. D.2 and E.4
Bit-width
Latency
FID
PSNR
SSIM
LPIPS
(WZA)
(MB)
[9)
Origin
16/6
1349
0.62
17.61
245.85
0.81
0.08
1.00
Q.DiT
68
518
0.45
5.44
17.61
237.34
0.80
0.68
31.10
0.95
0.04
+Ours
6/8
518
0.22
5.51
17.49
240.36
0.80
0.68
31.06
0.93
0.05
Q-DiT
4/8
347
0.39
6.31
17.81
209.30
0.76
0.69
24.88
0.82
0.14
+Ours
4/8
347
0.20
6.20
17.62
213.50
0.76
0.69
24.99
0.82
0.14
Sampling
FLOPs
Latency
FID
sFID
1S
PSNR
SSIM
LPIPS
iSteps
(T)
(9)
Origin
DDPM/250
2.51
2.23
1.00
50% steps
DDPM/250
59,34
1.26
2.42
5.04
270.40
0.82
0.57
8.55
0.14
0.78
FORA
DDPM/250
39.95
1,01
2.80
6.21
0.80
0.59
ToCo(Ne)
DDPM/250
36.30
0.84
3.08
6.58
244..99
0.79
0.59
20.92
0.71
0.21
+Ours
DDPM/250
36.48
0.82
3.09
6.00
248.58
0.80
0.59
22.63
0.76
0.16
Origin
DDIM/50
2399.3
1.00
33% steps
DDIM/50
8.07
0.18
4.24
5.52
214.35
0.77
0.56
9.22
0.17
0.81
AdaCache
DDIM/50
0.46
4.64
LazyDiT
DDIM/50
11.93
0,28
2.70
4.47
237..3
0.80
0.59
ToCa(Ng)
DDIM/50
7.44
0.20
6.37
7.09
199.48
0.74
0.53
16.56
0.53
0.40
+Ours
DDIM/50
7.14
0.18
4.68
6.41
212.13
0.77
0.55
21.59
0.72
DuCa(Ng)
DDIM/50
6.32
0.17
6.07
6.64
199.64
0.74
0.52
16.63
0.53
0.39
DDIM/50
6.73
0.17
3.96
218.66
0.78
0.55
23.00
0.76
TaylorSeer(Ne)
DDIM/50
4.76
0.14
3.56
7.52
223.83
0.79
0.56
24.69
0.80
0.13
+Ours
DDIM/50
4.76
0.13
3.08
6.43
231.10
0.80
0.57
25.64
0.83
0.10
A bacbel glone
Aburatfal count beach qrita, vavii on sand by lioksa, is to URipa.
A barcllazingg.o mec.
Oodic Tolecter Diee berere
+Ours Tay Saylorsonn Origin
(8)
Figure 5: Qualitative visualization comparison on Hunyuan. Our CEM improves the TaylorSeer for better consistency with the original model. See Appendix. E. for more visualizations.
of 80.66. Our method significantly reduces the cumulative error in caching strategies through of- fline error modeling and dynamic programming, without introducing any additional computational overhead, which is supported by the latency and performance metrics shown in the Tab.
Furthermore, the visualization examples in Fig. 5(a) also demonstrate that CEM is capable of better preserving fine-grained generation details, such the baseball glove and the village by the beach. Detailed VBench results in Fig. 5 a) also show that our method significantly enhances video gener- ation quality on TaylorSeer, particularly in multi-objects, semantics and quality evaluations.
Class-to-image generation. In Tab. 3 we report the results of DiT-XL/2 under DDPM and DDIM sampling strategies. Under DDPM, our CEM improves ToCa by 0.58 on sFID and 1.99 IS, respectively, Under the more commonly used DDIM, we explore optimizations for ToCa, DuCa and TaylorSeer. Taking DuCa as an example, our CEM achieves more than 3x acceleration, reducing FID by 2. increasing IS by 19.02, and improving PSNR by 6.37. The performance gain mainly arises from the fact that the baseline method inevitably introduces more caching error as the acceleration efficiency increases, leading to sharp degradation in fidelity. In contrast, our CEM effectively improves generation fidelity by combining offline error modeling with dynamic programming to identify the caching strategy that minimizes cumulative error.
In addition, to demonstrate the effectiveness and generalization of our method, we further integrate it into the quantized model Q-DT As shown in Tab. under the manually specified 2x acceleration budget (N.=25 in total 50 steps), our CEM not only further speeds up inference on top of the quantized model, but also improves the generation fidelity. Taking IS as example, improve it by 3.02 and 4.20 on W6A8 and W4A8, respectively.
In summary, DiT-XL/2 without compromising their efficiency, but also supports direct integration with quantized models, achieving substantial reductions in both computational time and memory usage.
method not only improves the fidelity of existing acceleration models
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Table 4: Ablation results on three generation models. "Vanilla" is naive caching with fixed cache intervals. "DCS" is Dynamic Caching Strategy, and "CEA" Cumulative Error Approximation.
PixAn-a FIDI
Hunyuan
DiT-XL22
FID
IST
Vanilla 30.04
Vanilla
77.64
Vanilla
3.83
213.12
+DCS 28.69
+DCS
79.21
+DCS
2.73
234.78
+DCS
+DCS
+DCS
27.94
80.44
2.65
235.11
w/ W/CA
w/ CEA
w/ CRA
4.3
ABLATION STUDIES
Module effectiveness. To demonstrate the effectiveness of CEM, we conduct ablation studies on three generation tasks (one model per task). As the offline error modeling cannot be ablated in isolation, we perform ablation primarily on the Dynamic Caching Strategy (DCS) built upon it, with particular attention to the effects of the Cumulative Error Approximation (CEA).
As shown in Tab. the dynamic programming in DCS module significantly improves model performance. It improves the FID by 1.35 on PixArt-a, increases the VBench by 1.57 on Hun- yuan, and achieves improvements of 1.1 on FID and 21.66 on IS on DiT-XL/2. On the one hand, demonstrates that the dynamic programming algorithm effectively optimizes the caching error, thereby improving the generation fidelity of generation models while maintaining acceleration. On the other hand, these results provide indirect evidence that our offline error modeling can effectively learn intrinsic caching error distributions of generation models, and apply them actual inference acceleration without incurring any additional computational cost.
Furthermore, by incorporating the CEA in DCS module, we observe further performance improve- ments across all three models. The performance increases by 0.75, 1 1.23, 0.08 and 0.33 across the four metrics on the three models after leveraging cumulative integral to better fit the caching error. We believe this is mainly attributed to the approximate estimation of caching error, which also plays a smoothing role to some extent, reducing the impact of data variations on the error distribution.
Comparison of error definitions. In the offiine error modeling, CEM defines the feature difference between the current and previous timesteps as the caching error, based on which it builds the offline model- ing and subsequent dynamic programming process. For error formulation, we com- pare three commonly used distance mea- sures: L1, L2 and Cosine distance. As shown in Fig. a)), the Cosine distance adopted our method clearly outperforms the other two.
Table 5: Cost analysis of our OEM and DCS module.
See the Appendix. B.4 C.3
for the cost of all models.
Models
FLUX.I-dev
Hunyuan 4.72h
DIT-XL/2
Time only generation
1.92h
19.63m
Time of OEM
2.08h
5.21h
25.52m
Memory of only generation
43,42GB
57.36GB
4.09GB
Memory of OEM
53.06GB
72.62GB
4.65GB
Time of DCS
1.10ms
0.71ms
1.13ms
Memory of error
0.88KB
0.88KB
0.88KB
We attribute this advantage to the fact that Cosine distance ignores the scale variance of features during computation, making it more robust to the sparse feature representations in DiT.
Offinee modeling cost. Our offline error modeling is obtained through random content generation before the actual inference, and it is performed only once for each generation model for permanent use. We report the cost of the offline process in Tab. [S] As this modeling relies random content generation, which inherently requires time and memory resources, we first list the cost of generation only and then the cost of our offline error modeling (OEM). For the three major generation models reported in Tab. 55] the additional time overhead of OEM mainly comes from computing caching errors across different timesteps and cache intervals. The extra memory overhead primarily arises from storing the feature representations of previous timesteps for subsequent error computation.
should be noted that this cost pertains solely the offline modeling stage. During inference, CEM only needs to load precomputed eπror matrix (of size NxT, where N is the number of cache intervals and T the number of timesteps) for dynamic programming. As shown in the lower part of Tab. 5 CEM introduces negligible overhead in both time and memory. Moreover, the dynamic programming solution computed once for a given acceleration setting and can be reused across multiple generations, further reducing batch-generation cost.
Effeet of the number of random samples. Our offline error modeling is derived from the random generation process and, as shown in Fig. 3 the estimated prior error remains consistent with that observed during actual inference. We further analyze the influence of the number of offline samples on model performance. As illustrated in Fig. b) (more analysis in Appendix. B.3), the modeling shows minimal sensitivity to sample count, the fidelity on FLUX. 1-dev quickly converges once more than 10 samples. This indicates that the error reflects a model-intrinsic sensitivity to caching, rather

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Published as a conference paper at ICLR 2026
Table 6: Robustness of our CEM across different seeds, CFGs, resolutions, and frames. We conduct all tests of Ta on the FLUX.1 1-..l-dvv for consistency.
Seed
Model
I8
CTIP
(Git
CPG
Model
IK
CLIF
(G))
Size
Model
IR
CLI
(G))
Frames
Model
Visench
(%)
TaylorSeer
0,8760
32.17
TaylorSeer
0.8760
32.17
TaylorSeer
0.5756
30.97
TeaCache
77.88
0
3.5
256
33
+Ours
0.9205
32.66
+Ours
0.9205
32.66
+Ours
0.6792
31.40
+Ours
77.96
TaylorSeer
0.8625
32.38
TayhonSeer
0.6571
31.34
TaylorSeer
0.7495
31.80
TeaCache
77.81
42
5.5
512
+Ours
0,9405
32.86
+Ours
0.8867
32.76
+Ours
0.7700
32.31
+Ours
78.01
TaylorSeer
0.8169
32.50
TaylorSeer 0.6924
31.75
TaylorSeer
32.17
TeaCache
77.56
2025
7.5
1024
65
+Ours
0,8875
32.69
+Ours
0.7336
32.29
+Ours
0.9205
32.66
+Ours
78.15
TaylorSecr
U.8217
31.95
TaylorSeer
0,6794
31.20
TaylorSeer
0.1552
31.26
TeaCache
76.21
3407
2048
+Ours
0,9118
32.99
+Ours
0.7935
32.17
+Ours
0,2564
31.31
+Ours
77.31
3.00
TID.
236.0
TS1
0:9207
32.67
Error
2.95
235.5
0.920
32.66
2.0
235.0
0.22
234.5
0.9204
0.20
2.85
214,0-
32.65
4.1x
2.80
213.5
0.9203
1R11
32.62
0.18
5.0-
0.9202
CLING))
6.3x
2.75
L1
1.2
COS
233.0
COS
10
50
100
200
500
500
1000
32.61
0.16
0.70
0.75
0.80
0.85
0,90
0.95
Error type
Error type
Number of random samples
ImageRoward?
(a)
(b)
(c)
Figure 6: (a). Comparison of generation fidelity with different error construction methods
DLT-XL/2. The Cosine distance adopted in our CEM achieves the best generation fidelity. (b). Ef- fect of offline sample size on generation fidelity. The offline error modeling captures the intrinsic sensitivity of the model to acceleration, which is content-agnostic and insensitive to the number of offline samples. (c). Relationship between error and generation fidelity under different cache strategies. Our quantitative analysis reveals negative correlation between generation fidelity and caching error, with the curvature of this relationship differing under various acceleration efficiencies.
than content dependence. Additional experiments in the Appendix. B.2 drawn from different datasets but evaluated the same benchmark, further validate this observation.
where offline samples are
The robustness of our CEM. To further demonstrate the robustness of our CEM and the offline error modeling, we extend the precomputed errors to various generation settings, as illustrated in Tab. 6 including different seeds, CFG, resolutions and frames. It can be clearly observed that the modeled errors are insensitive to such variations in generation settings, consistently exhibiting strong robustness of our CEM across diverse configurations.
Relationship between error and generation fidelity. Finally, by constructing the errors of caching strategies, we can quantitatively and intuitively analyze the relationship between caching strategies and their generation fidelity under the same acceleration efficiency. As shown in Fig. (c). multiple candidate caching strategies exist under same acceleration efficiency. We plot their corresponding error and fidelity, and fit a relationship curve between them. We observe that as the error of caching strategy increases, the generation fidelity tends decrease, and the curvature of this relationship varies with acceleration efficiency. This observation aligns with the empirical understanding, while CEM provides quantitative perspective to validate and extend this intuition.
5 CONCLUSION
Summary. To optimize the caching strategy and fully leverage the potential of caching error cor- rection methods, we propose novel plug-in acceleration method, CEM. It leverages offline error modeling and a dynamic programming algorithm to derive the optimal caching strategy that min- imizes the caching error. By integrating the caching error correction method, we significantly improve generation fidelity while maintaining acceleration, without introducing any additional com- putation. Extensive experiments eight generation models and one quantized model across three tasks demonstrate the plug-in effectiveness of our method on existing accelerated models.
Limitations. We provide training- ffree plug-an and-play acceleration for DiT, with compatibility across generation models, acceleration methods, and quantized models. However, our performance still lags behind training- based methods and cannot be applied to one-step generation models.
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ACKNOWLEDGMENTS
This work was supported by National Natural Science Foundation of China (grant No. 623507 by Guangdong Basic and Applied Basic Research Foundation (grant No.
2023B1515120065, 2025A15150115400 and by the Shenzhen Science and Technology Program 1CC220040 00000000000000000000000000000000 0TT.D///000000000000200000011110 2DCY3025000113300000
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Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412. 2024.
Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion transformers with token-wise feature caching. arXiv preprint arXiv:2410.05317, 2024a.
Chang Zou, Evelyn Zhang, Runlin Guo, Haohang Xu, Conghui He, Xuming Hu, and Linfeng Zhang- Accelcrating diffusion transformers with dual feature caching. arXiv preprint arX1v:2412.18911, 2024b.
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APPENDIX
This is the appendix for our submission titled Stug-and-Pal Fidelity Optimization for Diffussion Transformer Acceleration via Cumulative Error Minimization. This appendix supplements the main paper with the following content:
Statements
A.
Ethics Statement
Reproducibility Statement
A.3
The Use of Large Language Models
More Details of Offline Error Modeling
Visualization for Error Distribution
Impact of the Source of Offline Samples
B.
Impact of the Number Offline Samples
4
Offline Modeling Cost
Robustness Modeled Error
B.6
Scalability of Error Modeling
More Details of Dynamic Caching Strategy
Dynamic Programming Pseudocode
Cumulative Error vs. Real Error
Online Cost
D
More Experiments
D.1
More Implementation Details
Results Under Different Acceleration Efficiencies
Higher Resolutions and Longer Frames
D.4
Comparison with Learningbaase Methods
D.5
Comparison with One-step Diffusion
E More
Visualization
E.
StableDiiffsion1.5
2
PixArt-a
Hunyuan
DiT-XL/2
Visualization with Complex Prompts
A STATEMENTS
A. ETHICS STATEMENT
We affirm that the research and writing of this paper strictly adhere to the ICLR Code of Ethics, and do not involve any issues related to human subjects, ethical concerns, or potential conflicts of interest.
A.2 REPRODUCIBILITY STATEMENT
We affirm that all research presented in this paper fully reproducible, including the error modeling analysis in Sec. 3.1 the core implementation described in Sec. 3.2 and Appendix ??, and all results and visualizations shown in figures and tables. The source code will be released publicly later.
A.3 THE USE OF LARGE LANGUAGE MODELS
We only used GPT4 for minor writing polishing and grammar correction during the writing process. We affirm that no large language model was involved in any other aspect this work, including idea, coding, experiments and main writing.
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OpenSora
Hunyuan
DIT-XL2-DDPP
DIT-NL-2-DDIM
tommlop
Clcte Insora
Eacher inserate
Tonesig
Texi-to-video generation
-lmmaeto generation
StableDiffusonl...
PixAn--
FLUX.I-4 dedev
Cook hermals
Tonorop
Codbe intervls
Temolop
Text-to-image generation
Figure 7: Offline error modeling, We model caching error based on the joint variation of cache intervals and denoising timesteps, covering seven generation models across three tasks. In this figure, the caching error is normalized.
Table 7: Results on DrawBench with Different Data Sources. We perform offline error modeling on the FLUX. using prompts from DrawBench, COCO2017 Captions and GPT-Random, and evaluate generation fidelity on the Draw Bench benchmark.
Datasct of OEM/Actual inference
Cosine distance of CLIP
IR
CLIP(G)1
Random from GPT Drawbench COCO2017 captions/ Drawbench Drawbench/ / Drawbench
0.841
0.9205
32.66
0.895
0.9205
32.66
0.000
0.9205
32.66
B MORE DETAILS OF OFFLINE ERROR MODELING
B. VISUALIZATION FOR ERROR DISTRIBUTION
As described in the main paper, we compute the differences between the hidden states stored from previous timesteps in the cache and those com- puted at the current timestep to model error distribution across various cache intervals and de- noising steps. We present the corresponding er- distribution trends for all generation models. Fig. 7 illustrates the complete error distributions of the models evaluated sults clearly show that the error distributions vary notably across models, highlighting that previous fixed or linearly varying caching strategies lack sufficient generalization capability.
three tasks. The re-
DrawBench
COCC2017
Beyond their variation across denoising timesteps, caching errors exhibit non-uniform scaling behaviors under different cache intervals. For instance, on OpenSora, the caching error at interval of 7 is unexpectedly lower than at smaller intervals. On Hunyuan and SD1.5, the eπror distributions become progressively more pronounced as the cache interval increases, shifting from approximately linear pattern
GPTRandom
Figure 8: T-SNE visualization of prompt fea- tures from different sources. The prompt fea- tures from DrawBench, COC02017 captions, and GPT-Random exhibit clear separability, in- dicating that their distinct data sources result in substantially different feature distributions.
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Table 8: Impact of OEM random sample size on generation fidelity (DiT-XL/2). We perform
offline error modeling on DiT-XL/2 using different numbers of samples and evaluate the generation fidelity after acceleration on the DuCa baseline.
Sample Num
10
50
100
200
300
500
1000
Time of OEM
2.67m
12.7Im
25.52m
51.04m
1.28h
2.13h
4.25h
FIDI IS1
2.84
2.83
2.80
2.80
2.80
2.80
2.80
235.44
235.42
235.20
235.20
235.20
235.20
235.20
Table 9: Impact of OEM random sample size on generation fidelity (FLUX.1-dev). We per- form offline emro modeling FLUX. 1-ll- using different numbers of samples and evaluate the generation fidelity after acceleration on the TaylorSeer baseline.
Sample Num
100
200
300
500
1000
Time OEM
31.71m
1.05h
2.08h
4.15h
6.27h
10.38h
20.77h
IRT
0.9202
0.9205
0.9205
0.9205
0.9205
0.9205
0.9205
CLIP(G)
32.62
32,66
32.66
32.66
32.66
32.66
32,66
to a U-shaped curve. On FLUX, an anomalously high caching error occurs during the middle denoising steps when the interval is set to 10. These variations cannot be captured by treating the cache error solely as a function denoising timesteps, underscoring the importance of incorporating the cache interval key variable in error modeling.
This joint modeling approach enables us to simultaneously capture the influences denoising stages and caching strategies the resulting cache error. It naturally aligns with the dynamic program- ming proposed later, allowing our CEM to account for variations across denoising stages and scale differences across cache intervals, thereby deriving optimal caching strategy that minimizes the overall caching error.
B.2 IMPACT OF THE SOURCE OF OFFLINE SAMPLES
The offline error modeling captures the model's intrinsic sensitivity to different acceleration oper- ations, revealed through limited number of sample generation experiments. This sensitivity is model-intrinsic and content-agnostic, indicating that the prompts used during modeling have negli- gible impact on the results.
To validate this, we construct modeling prompt sets from three distinct data sources (random prompts from GPT, captions of COCO2017, and prompts of DrawBench) and evaluate the accel- crated generation quality on the DrawBench. As shown in Tab. [7] we quantify the distributional differences among the three sources using Cosine distance with CLIP, and include a t-SNE visual- ization in Fig. 8 which clearly highlights distributional variation. Nevertheless, the offline errors derived from these three sources yield identical quality results on DrawBench, confirming that the етror modeling process is indeed content-agnostce
B.3 IMPACT OF THE NUMBER OF OFFLINE SAMPLES
Our previous experiments have demonstrated that offline eтror modeling (OEM) reflects intrinsic model property independent of sample content. To further examine the modeling difficulty, we evaluate the generation fidelity constructed from different offline sample sets DiT-XI/22 and 1-ev.
As shown in Tab. for DIT-XL//2, generation fidelity remains largely unaffected by the number of modeling samples when the sample size exceeds 50, indicating convergence of the modeled error and resulting in consistent cache-interval configurations in the derived strategy. A similar trend observed on FLUX.1-deww when more than 10 samples, the generation fidelity remains fully consistent regardless of the number of offline samples. Collectively, the findings from both models indicate that the caching error can be easily captured from a small set random samples, thereby reflecting the intrinsic sensitivity inherent to the model.
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Table 10: Offiine modeling cost of all models. This modeling built upon random content gener- ation, which introduces additional overhead beyond generation itself due to the need to store inter- mediate results and compute feature errors.
Tasks
Text2lmage
Text2Video
Class2lnage
Models
FLUX.IIdev
PixArt-o
SDI.5
Hunyuan
Wan21
OpenSora
DIT-XL/2
Sample Num
TEXI
T00
T00
T0
10
10
100
Time 2,08h((8.35%)
13.52m
38.85m
19.63m
Time wi. OEM
(4.7lm(+1.8%)
43.83m (..88m(+122) 12.7%)
5.21h(+10.48)
4.15 55.0%%
1.85((+2811))
25.52m(+30.00%
Memory OEM
22,31GB
3,6GB
57.36GB
40.83G
Memory wi OEM 53.0608(+222.25) 22, 65.8() 3,67G1((005%) 72.620B( 63. (6..6(444444)))) 42000000000000000
B.4 OFFLINE MODELING CoST
In Tab. of the main paper, we summarize the costs and analyses of the primary models across three tasks. Here, we provide the costs for all models.
We report the time and memory overhead of offline error modeling for each generation model dis- cussed in the paper, with the following clarifications:
Offline error modeling performed once per model, and the resulting error can be permanently reused with strong generalization across configurations.
Random content generation inherently incurs overhead (see "w/o. OEM" in Tab. 10 Since
modeling measures the sensitivity of these generations to acceleration, the additional cost beyond content generation represents the true overhead of the modeling process.
The offline modeling overhead does not reflect the inference cost. After modeling, invoking CEM and performing sache-strategy optimization incur only negligible additional overhead (see Tab, I2 DCS overhead).
The additional memory overhead primarily results from storing intermediate features during infer- ence to compute differences across cache intervals. On average (including Wan21 we added), offline error modeling introduces only 15.8% increase in memory usage and 16.8% increase in model- ing time relative random content generation, both well below 20%. Considering the performance gains of CEM and its zero inference- sirence-timeeeee overhead, this cost is entirely acceptable.
B.5 ROBUSTNESS OF MODELED ERROR
We further evaluate the robustness of modeled eгror under varying conditions, based on Tab. in See. For each generation model, the caching error is modeled once and reused across all experimental settings to assess the stability of the offline modeling.
Specifically, we evaluate CEM under variations in random seeds, CFG values, resolutions and frames. As shown in Tab. 6 on FLUX.I-des CEM consistently improves the generation fidelity of the baseline (TaylorSeer) at equal acceleration efficiency, regardless of changes in seed, CFG, or resolution. Similarly, Hunyuan, CEM also enhances generation fidelity across different frame numbers.
Overall, the modeled error demonstrates strong robustness, remaining effective without ee-ooeeling when configurations vary. This confirms the practicality and ease of deployment of CEM as a training-free solution for real-world generative applications.
B.6 SCALABILITY OF ERROR MODELING
The overhead of offline error modeling is influenced by the model scale. The time overhead mainly depends on the model's inherent inference speed, which varies with its stochastic generation pro- cess, while additional cost remains relatively small (averaging 16.8% from Tab. IO). Similarly, the memory overhead is dominated by the model parameters themselves, with our caching and com- putation contributing only about 15.8% (from Tab. 10 on average.
Overall, larger models generally incur higher absolute overhead. However, two points should be noted:
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Algorithm Dynamic Programming Strategy
Input: Total steps T, number of caching Ne, cache interval candidate set N, dynamic approximate costs 8" (I,
Output: Cache interval set C
1: Initialize [p] < 0o, d + 0. prev[t][s] + None, V E [T. Nc.
2: for 1 to No do
3: for = down to do
4:
if []] < then
5:
for all n E W do
6:
i 0 then
7:
if d + 1] dpl +
(t,,,) then dplt] j + 1] dpit+ (t,,t)
8:
9:
prev [ + + (t,n)
Backtracking: C 0.100,1111 N.
11: while j > 0 do
12: (Knext, 11) 1 [rev[t][]]
13: C+CU
14:
t mmexa J f
15:
return C
Most of the overhead originates from the model itself rather than from our modeling process. The relationship not strictly monotonic, for example, SD15 smaller than DiT-X1/2, yet its modeling time is longer.
C MORE DETAILS OF DYNAMIC CACHING STRATEGY
C.I DYNAMIC PROGRAMMING PSEUDOCODE
We provide the pseudo-code of the dynamic pro- gramming algorithm in the appendix to clarify its implementation details. Due to the approximate error introduced by cumulative integration, the cost in our dynamic programming formulation is non-static it varies across denoising timesteps according to the selected cache interval. The algorithm is built upon the optimal substructure property described in the main paper and ulti- mately identifies the minimal cumulative error for
C-4
2.5
C-5
C=6
2.0
Cumulative error
C=7
1.5
1.0
the entire caching strategy. The optimal cache interval at each stage is obtained via backtrack- ing from the computed total error. Specifically, as shown in the last five lines of Alg. [ we itera- tively trace back the timesteps associated with the previously minimized eиror and record their cor- responding cache intervals, thereby constructing the complete caching strategy.
0.5
Offinee
Online
0.0
10
20
40
Timestep
Figure 9: Cumulative error vs. real error. The cumulative error approximation captures the trend of real-error variation during denois- ing under different cache intervals Hunyuan.
C.2 CUMULATIVE ERROR VS. ACTUAL ERROR
The cumulative error approximation models the continuous accumulation of caching error during accelerated denoising. To demonstrate its simplicity and effectiveness, provide supporting evi- dence from multiple perspectives.
Additional analysis cumulative simulates the progressive buildup of caching errors during denoising, grounded in the offiiee error modeling. Based this foundation, design motivations for this module follows:
approximation. The cumulative error approximation
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Table 11: Cumulative Error vs. Real Error. Using a cache interval of an example, we report the error comparison on ILUUX.1-der and Hunyuan.
Timestep
5
30
40
45
49
GT emoor
0.000
0.004
0.279
0.434
0.603
0.682
0.714
0.840
0.893
1.025
1.233
FLUX.I-des
Cumulative error
0.000
0.001
0.289
0.432
0.560
0.667
0.745
0.823
0.902
1.023
1.221
Hunyuan
error
0.000
0.154
0.391
0.526
0.794
0.971
1.079
1.285
1.532
1.755
2.141
Cumulative error
0.000 0.082
0.363
0.552
0.748
0.934
1.101
1.272
1.490
1.747
2.182
The offline error modeling omits the influence of previously accumulated errors on the current timestep, as directly modeling cumulative errors would incur exponentially increasing computa- tional costs, rendering the process highly inefficient.
We aim to realize this approximation through a simple operation, avoiding complex computations that could undermine the intended acceleration efficiency.
We conduct quantitative experiments comparing the comulative-integratio eaching error with the actual error and find that it effectively meets our design objective.
Quantitative difference between cumulative error and actual error. Building on the above anal- ysis, we conduct a quantitative evaluation to measure the difference between the cumulative error and the actual error.
As shown in Fig. 3 of the main paper, the evolution of these differences on FLUX.I-dee under var- ious acceleration efficiencies has already been illustrated. Here, we further report the key-timestep error differences FLUX.I-dev and Hunyuan, along with additional line-chart comparisons for Hunyuan.
As presented in Tab. 11 the final difference between cumulative and ground-trute -ruut (GT) errors is only 0.80% on FLUX. 1edev and 2.89% on Humyuan, indicating that the cumulative error closely approx- imates the actual error. The Fig- c and Fig. 9 further show highly consistent trajectories between cumulative and actual errors across cache intervals, confirming the accuracy and effectiveness of our approximation.
Additional theoretical analysis. In addition to the experimental evaluation of the approximation effect, further provide theoretical analysis of how the cumulative error approximates the real error. Together, these theoretical and empirical results comprehensively validate the rationality and effectiveness of our cumulative error approximation module.
The bound between the cumulative error and the actual error.
We obtained content-agnostic prior errors from the offline modeling and derived the estimated cu- mulative eиror E" through integral accumulation. To better illustrate the difference between them, we define the actual eтror under the same operation during formal inference as E (Unlike the de- fined in Eq. 1 the symbol here is introduced merely to distinguish notation within the current proof), while the true propagated error of the cached latent during the denoising process is defined as E.
We assume the DiT Lipschitz continuous (common in Diffusson models for bounded error propa- gation), i.e., [z,
Dvy, I) s L||= y/| for some Lipschitz constant L > O.
First, we need to establish the approximate relationship between the offline cumulative error E and the cumulative error & that occurs during actual inference. The offline Ex is an empirical estimate of the error distribution using N₂ random samples, while E is computed for a specific inference sample.
Theorem 1. Under the assumption of unified error distribution, the difference (E* n) E(t,n)) is bounded by:
log(2/6)
g" - E(t,n)) -((tt)))((tn))
+ Eyrr:
(4)
2N
where 5 (0, 1) confidence parameter, and Gaar a small variance term (empirically small, as per Fig. 3(a))
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Proof. Since diffusion- liffusion-generated content follows the same underlying distribution, assume that the caching error in DiT also follows the unified distribution.
Let put, n) be the true population mean of the cosine enror all possible contents. Then, (t, n)
for offline is the empirical mean p(t, n) = N, Σ, Et,,, n), and for online it's single-sample esti- mate (or small batch).
By Hoeffding's inequality (since cosine errors are bounded in [0,1]), with probability at least 1 - 8:
log(2/6)
(a(e,n) - (((, S
(5)
2N,
The online E(t,n) deviates from u(t,n) ) by at most the empirical variance Evar (small, as per the analysis: low variance across contents and intervals).
For cumulation: Since CUMSUM is a linear operator, the bound propagates:
- - - Σ (T, E(T, St (v
1og(2/5) 2.V.
+ Cvar
(6)
(For total timesteps T, the worst-case bound is O(T/ N.), but empirically tighter due low 6var-) This holds because extended experiments (Appendix, B.2 show consistency across prompt sources, confirming the i.i.d. assumption. Thus, E* se & with high probability (Appendix, C.2 supports this).
Theorem 2. Assuming Dil' modules have high structural similarity (perturbations propagate near- linearly, as observed in Fig. Bc)),, the difference (8(1, n1) Ett, is bounded by:
Ett. E((, S L. E(T,,) + Eprop:
r=1
where L is the Lipschitz constant of DD, and Cprup is small propagation residual (empirically near- zero, as the approximation matches GT).
Proof. In DiT, each timestep's output is D(r,, t) = (+))))+1,+++++000 where f is the transformer layer, and is noise/caching perturbation. Caching introduces error E(t, n) at each step, which propagates future steps. The true propagated error Et,, n) satisfies recurrence:
Ê(t, n) E(t, g(((t + 1,n)),
where g models propagation (approximately linear due to DiT's residual connections and atten- tion linearity). The CUMSUM approximation assumes g Id (identity, i.e., direct summation), which holds because of "high structural similarity between input and output" (as noted in Liu et al. (025b)).
By the Lipschitz assumption, propagation error is bounded: (g(e) el S L e Eprop- where Epoop captures non-linear residuals (small in DiT). Unrolling the recurrence over steps yields the bound via triangle inequality. For the caching strategy (DP-selected intervals), the bound extends to the full sequence, as DP preserves substructure:
18(t, n)) Ett, m)) < L. Σ E(r, + Cprup
(9)
Thus, & F E, with the bound tightening for smaller single-step errors.
Finally, by chaining Theorems and 2 (triangle inequality):
(1, E(1, s (t, E(1,))) + (E(1,)) - E(1,n))
(10)
<1 (1
1og(2/8)
+ 6var
+ L. Σ 8(T, + Eprop
(11)
2N,
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Table 12: Online cost of the DCS module across all models. During actual inference, DCS primar- ily loads the prior error matrix into memory and solves dynamic programming problem to obtain the optimal caching strategy, which can be shared across batch generation for improved efficiency.
Tasks
Texr2Image
Texe2Video
Class21mage
Models
FLUX. I-dev
PixArt-o
SD1.5
Hunyuan
Wan21
OpenSora
DIT-DDPM DiT-DDIM
Time of DPS
1.10ms
0.12ms
0.80ms
0.7lms
0.85ms
0.27ms
6,96ms
1.13ms
Shape of
50*9
20
50"9
50*9
50*9
3049
250
50*9
Memory crror
0.88KB
0.35KB
0.88KB
0.88KB
0.88KB
0.53KB
4,39KB
0.88KB
This upper bound is OTT/ + L. Eooaa),, where Evtal is total single-step emror. Empirically, it's small, confirming the approximation.
Theoretical analysis of dynamic programming.
Time Complexity: The DP table has OTT . N.) entries (t E [1, j E N2). For each entry dp[t]li + 1] (for j 2 0), we evaluate the min over [JV] possible intervals n, each requiring ((1) time to compute 8x (4, n)) + dp[( + ni[i]] (assuming E4, nt) is precomputed offline in O (W1) time, as it's content-agnostic). Thus, filling the table takes O(T- NN -NV) time. Backtracking: O(T) time (traverse the path of Ne choices, each step O(1)).
Overall Time Complexity: O(T N: [W]). which is efficient since T is small (c.g., 50), N: < (ondget-oosttrained) and [V] is small (e.g., 10 practical intervals). The analysis notes additional overhead during inference, as DP runs offline once per model. The actual time consumption of the dynamic programming (DP) process can be found in Appendix. C.3 and Tab. 12
Space Complexity: DP table: O(T No)) space (store floats for errors). Precomputed E* (1, n): O(T IWI) space. Backtracking can the table itself (no extra space) or a separate predecessor array (O(T'- Ne)). This complexity is polynomial and scalable, enabling "optimal cache-interva combination that can be shared across multiple generations without additional overhead" (Sec. 3.2 analysis).
Proof of Optimality: Suppose there exists an optimal strategy S* for dp[t]li + 1] that chooses interval n, but the sub-strategy S' for dplt + ni[ is not optimal (i.e., there exists better sub- strategy S" with lower error for dplt + n n)]. Then, replacing S' with S" in S* would yield new strategy with total error (1, n) + error(S") < (1, + erron(S"), contradicting the optimality of S*. Thus, subproblems must be optimal.
This holds because: 1). The denoising process is sequential and acyclic (timesteps decrease from T to 1). 2). Errors are additive E Et(t, n) is independent of future choices, depending only on the current interval and offline modeling).
3). The budget N. is fixed, and choices do not overlap (each caching operation covers distinct timestep segments).
C.3 ONLINE COST
During inference, the computational overhead of CEM mainly stems from loading the pre-modeled егror distribution and solving the dynamic programming optimization.
The memory cost is negligible, as each model only stores an array of size NxT (where N is the number of cache intervals and T the number of timesteps). The time overhead similarly minimal, the dynamic programming process involves at most T iterations, resulting in computation times on the order of milliseconds. Moreover, the derived optimal caching strategy can be shared across multiple generations with the same acceleration efficiency, further amortizing this minor cost.
D MORE EXPERIMENTS
D.1 MORE IMPLEMENTATION DETAILS
We conduct comprehensive experiments across three major generative tasks covering representa- tive text-to-image, text-to-video, and class-to class-to-image diffusion models, as well as multiple SOTA acceleration techniques.
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Generation models. Text-to-Image Generation: We evaluate three diffusion- text-to-image
generation models: (1) Stable Diffusion v1.5 (SD1.5) Rombach et al. (2022), a latent diffusion model (LDM) trained LAION-SB, generating images at resolution of 512x (2) PixArt- ar Chen et al. (2023), a transformerrbased diffusion model that performs efficient pixel-space mod- eling at 256x 256 resolution.
(3) FLJUX.1-de Labs (2024), state-of-the-art diffusion transformer trained at x x 1024 resolution, capable of producing high-fidelity, photorealistic images.
Text-to-Video Generation: We include three representative video diffusion models: (1) Hunyuan- Video Kong et al. 2024), generating 65 frames at 480p resolution, emphasizing realistic motion and temporal coherence. (2) Wan2.1-1.33 Wan et (2025), a lightweight and efficient video diffusion transformer generating 65 frames at 480p. (3) OpenSora Zheng et al. (2024), an open research model producing 2-second clips at 480p, enabling temporally consistent text-conditioned generation.
Class-to-Image Generation: We adopt Peeeles & Xie 2023), a large diffusion transformer trained on ImageNet, evaluated with both DDPM Ho et al. (2020) and DDIM [Song et al. (2020) samplers at 256x resolution.
Acceleration Baselines. Our method (CEM) is integrated into six representative acceleration or effi ciency improvement approaches: FasterSD Li et al. (2023a) accelerates diffusion by reusing features extracted from shallow network layers to reduce redundant computation in deeper ones; ToCa ZZu et al. (022a)) combines caching and pruning mechanisms to jointly optimize computational reuse and step reduction; DuCa Zou et al. (2024b) integrates conservative and aggressive caching strate- gies to maintain balance between fidelity and acceleration; TaylorSeer Liu et al. (2025c) predicts reusable features from historical cache states through a Taylor-series expansion rather than directly reusing cached results; TeaCache Liu al. (2025b) adaptively determines the caching policy based on the relationship between input and output activations; and Q-DiT Chen et al. (2025a) is a training- free quantized Diffusion Transformer that serves as our platform validate the compatibility of CEM with quantized models.
Dhen
Evaluation and Metries. For text-to-image generation, all models produce images conditioned on captions from the MS-COCO 2017 dataset Lin et al. (2014). For text-to-video generation, evalua- tions are conducted on VBench Huang et al. (2024b). comprehensive benchmark of 16 sub-tasks assessing spatial fidelity, motion consistency, and text-video alignment. For class-to-image gener- ation, DiT-XL/2 is evaluated on ImageNet Deng et al. (2009) by generating images for its 1,000 labeled categories.
We employ standard metries to assess fidelity, perceptual quality, diversity, and semantic alignment. Fréchet Inception Distance (FID) Heusel et al. (2017) measures distributional similarity between generated and real images; lower values indicate better fidelity. CLIPScore (CLIP) Hessel et al. (2021) evaluates semantic alignment between text and images, and higher scores denote stronger consistency. ImwgeReward (IR) [Xu et al.] (2023) reflects human aesthetic preference; higher bet- ter. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) evaluate pixel-level and structural fidelity, both maximized for better reconstruction quality. Learned Percep- tual Image Patch Similarity (L.IIS) quantifies perceptual difference in deep feature space; lower indicates higher similarity.
For video generation, VBench [uang al. (2024b) jointly evaluates realism, temporal smoothness, and motion-text consistency, with higher scores representing better video quality. In class-to-image generation, we report Sliced FID (sFID) for image fidelity, Inception Score (IS) Salimans et al. (2016) for diversity, and Precision (P) and Recall (R) to measure the trade-off between fidelity and diversity.
Finally, generation efficiency is reported using both theoretical FL.OPs and empirical latency. Image models are tested on RTX 4090, while FLUXX. dev and all video models are evaluated on A800 GPUs due to higher computational demands.
D.2 RESULTS UNDER DIFFERENT ACCELERATION EFFICIENCIES
Text-to-image generation. Tab. 1 presents comprehensive quantitative comparison across di- verse text-to-image generation settings, demonstrating the effectiveness and generality our pro- posed CEM when integrated into various acceleration frameworks.
23

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Published as a conference paper at ICLR 2026
Table 13: Quantitative comparison on text-to-image generation under different acceleration
efficiencies. J/t denotes lower/higher values indicate superior performance. "_" denotes the absence of reference results. "+Ours" indicates the baseline with CEM. Bold font highlights our better results.
SDI.5,
MSCOC02017 10K,
DDIM 50 steps, 512x512, RTX4090
FLOPS(T)
Spet
Lat(s)
Spe
FID.
CLIP(L)(
PSNR
SSIMT
LPIPS
Origin 50% steps
37.05
1.00x
1.44
1.00x
21.75
30.92
1.00
18.53
2.00x
0.73
1.97x
25.21
32.15
20.19
0.62
0.26
DeepCache
0.63
2.27x
21.53
30.80
FasterSD
27.35
1.35x
0.33
4.35%
21.62
32.54
16.42
0.56
0.36
+Ours
27.35
1.35x
0.33
4.35x
19.99
32.85
15.77
0.60
0.35
PixArt-o; MSCOC02017
30K,
DPM-Solver
20 steps, 256x256,
RTX4090
FLOPS(T)
Spet
Lat(s)
Spe7
FID.
CLIP(L)(
PSNRT
SSIMT
LPIPS
Origin
11.18
1.00x
0.86
1.0xx
1.00
0.00
50% steps
5,59
2.00x
0.43
2.00x
37.41
15.82
18.67
0.70
0.20
FORA
5.66
1.98x
0.52
1.64x
29.67
16.40
DeepCache
0.62
1.39x
31.57
16.24
TuCa
4.26
2.62x
0.44
1.97x
29.73
16.45
DuCa(N=3)
6.19
1.81x
0.50
1.72x
28.39
16.44
17.79
0.63
0.24
+Ours
6.54
1.71x
0.53
1.62x
27.06
16.44
21.45
0.79
0.13
DuCa(N=4)
5.90
1,89x
0.49
1.76x
35.36
16.45
15.99
0.52
0.35
+Ours
5.94
1,88x
0.49
1.76x
27.20
16.42
20.94
0.78
0.14
DuCa(N=S)
4.79
2.33x
0.40
2.15x
16.46
0.46
0.42
+Ours
4.75
2.35x
0.39
2.20x
27.57
16.37
18.25
0.68
0.21
FLUX.1-60 dev. DrawBench, Rectified Flow 50 steps.
1024x
A800
FLOPS(T)
Spet
Lat(s)]
Spet
IRT
CLIP(G)(S0)
PSNRT
SSIM
LPIPS]
Origin
3719.50
1.00x
35.63
1.00x
0.9649
32.57
INF
1.00
0.00
50% steps
1859.75
2,00x
17.82
2.00x
0.9874
32.77
17.23
0.67
0.32
25% steps
967.07
3,85x
8.91
4.00x
0.9310
32.72
14.71
0.58
0.46
A-DIT
1686.76
2.21x
18.27
1.95x
0.8561
FORA
1320.07
2.82x
14.66
2.43x
0.9227
ToCa(N=4)
1263.22
2.94x
14.60
2.44x
0.9822
32.36
18.27
0.67
0.30
+Ours
1263.22
2.94x
14.13
2.52x
1.0151
32.67
17.72
0.67
0.31
TeaCache(l=0.4)
1413.41
2.63x
25.45
1.40x
0.7040
30.72
18.70
0.74
0.29
+Ours
1413.41
2.63x
23.60
1.51x
0.7545
31.34
17.24
0.69
0.34
TeaCacho(I=0.6)
1115,85
3.33x
16.57
2.15x
0.7228
30.66
17.41
0.70
0.35
+Ours
1115.85
3,33x
16.05
2.22x
0.7362
31.13
17.89
0.71
0.33
TeaCache(I=0.8)
892.68
4.17x
12.33
2.89x
0.7136
30.74
16.50
0.66
0.40
892.68
4.17x
12.08
2.95x
0.7139
30.77
16.96
0.67
0.39
TaylorScer(N601)
744.81
4.99x
10.09
3.53x
0.9410
32.57
15.59
0.60
0.41
+Ours
744.81
4.99x
10.09
3.53x
0.9811
32.89
16.11
0.61
0.39
(ay1o)Seer(N701)
668.99 97
5,56x
8.61
4.14%
0.9233
32.55
14.94
0.57
0.46
+Ours
668.97
5.56x
8.59
4.15%
0.9449
32,59
15.71
0.58
0.42
TaylorSeer(N801)
595.12
6,25x
7.41
4.81x
0.8760
32.17
14.24
0.54
0.49
+Ours
595.12
6.25x
7.41
4.81x
0.9205
32.66
15.41
0.56
0.46
On SD1.5, CEM consistently improves generation quality over all baselines. Under identical accel- eration configurations (e.g., same FLOPs and latency as FasterSD), it reduces FID) from 21.62 to 19.99 and increases perceptual scores such CLIP and SSIM, indicating enhanced fidelity without extra computational cost.
On PixArt-a, CEM achieves superior speed trade-offs. Across different step-reduction lev- els in DuCa (N=3-5), our method markedly lowers FID (e.g., from 41.56 to 27.57 at N=5) while maintaining similar acceleration ratios. Perceptual metrics (SSIM/LPIPS) also improve consistently, confirming CEM's robustness across architectures and noise schedules.
On FLUX.I-dew evaluated with DrawBench, CEM further enhances both image realism (IR) and perceptual alignment (CLIP(G)) across all error correction baselines (ToCa, TeaCache, and Tay- lorSeer). These gains come with no additional FLOPs or latency, showing that CEM mitigates quality loss even under aggressive step reduction caching.
Overall, across all diffusion backbones- from SD1.5 and PixArt-a to FLUX. CEM con- sistently boosts generation fidelity under equal faster inference conditions. These results demon-
24

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Published as a conference paper at ICLR 2026
Quantitative comparison on class-to-image generation with DiT-XL/2 and quan- tized model under different acceleration efficiencies. W/A denotes the quantization bit-width of weights and activations.
Table 14:
DFT-XL ImageNet 50K.
DDPM 250
steps, 256x256, <FID]
RTX4090
FL.PP(()).
Spet
[at(s).
Spet
FIDJ
1S1
Pt
RT
PSNR
SSIMT
LPIPS]
Origin
118.68
L00x
2.51
1.00x
2.23
4.57
275.64
0.83
0.58
INF
1.00
0.00
50% steps
59.34
2,00x
1.26
1.99x
2.42
5.04
270.40
0.82
0.57
8.55
0.14
0.78
FORA
39.95
2.97x
1.01
2.49x
2.80
6.21
0.80
ToCa(N==)
43,42
2.73x
1.02
2.46x
2.59
5.73
255.93
0.80
0.59
22.61
0.77
0.17
+Ours
43.58
2.72x
0.99
2.54x
2.66
5.33
257.85
0.81
0.59
23.78
0.80
ToCa(N=5)
39.25
3.02x
0.92
2.73x
2.82
6.03
251.84
0.80
0.59
21.81
0.74
0.19
+Ours
39.39
3.01x
0.89
2.82x
2.82
5.82
252.47
0.80
0.59
23.04
0.78
ToCa(N=6)
36.30
3.27x
0.84
2.99x
3.08
6.58
246.59
0.79
0.59
20.92
0.71
0.21
+Ours
36.48
3.25x
0.82
3.06x
3.09
6.00
248.58
0.80
0.59
22.63
0.76
0.16
DIT-XL/2, ImageNet S0K, DDIM 50
steps,
256x256,
RTX4090
FLOPS(T).
Spet
Lat(s))
Spet
FIDI
sFID.
1S1
Pt
PSNR
SSIM
LPIPS
Origin
23.74
1.00x
0.53
1.00x
2.25
4.33
239.93
0.80
0.59
INF
1.00
0.00
50% steps
11.87
2.00x
0.27
1.96x
2.87
4.58
231.05
0.79
0.58
9.05
0.16
0.80
33% steps
8.07
2.94x
0.18
2.94x
4.24
5.52
214.35
0.77
0.56
9.22
0.17
0.81
AdaCache
0.46
1.15x
4.64
TeaCache
0.32
1.66x
5.09
A-DIT
16.14
1.47x
0.21
2.52x
3.75
5.70
207.57
FORA
8.58
2.77x
0.24
2.21x
3.55
6.36
229.02
LazyDiT
11.93
1.99x
0.28
1.89x
2.70
4.47
237.03
0.80
0.59
ToCa(N=4)
8.73
2.72x
0.25
2.12x
3.64
5.14
228.44
0.79
0.55
22.20
0.75
0.18
+Ours
8.70
2.73x
0.24
2.21x
3.19
5.12
229.59
0.79
0.57
23.57
0.79
0.14
ToCa(N=5)
7.44
3.19x
0.20
2.65x
6.37
7.09
199,48
0.74
0.53
16.56
0.53
+Ours
7.14
3,32x
0.18
2.94x
4.68
6.41
212.13
0.77
0.55
21.59
0.72
ToCa(N=6)
7.02
3,38x
0.18
2.94x
6.79
7.41
187.32
0.72
0.55
17.62
0.56
0.36
+Ours
6.72
3.53x
0.17
3.12x
5.38
6.84
205.52
0.76
0.55
20.83
0.69
DuCa(N=3)
9.58
2.48x
0.25
2.12x
3.05
4.66
233.11
0.80
0.57
24.62
0.82
0.12
+Ours
9.49
2.50x
0.25
2.12x
2.80
4.64
235.20
0.80
0.58
25.47
0.84
DuCa(N=4)
7.66
3.10x
0,20
2.65x
3.39
4.91
226.33
0.79
0.56
22.40
0.75
0.18
+Ours
7.40
3.21x
0.19
2.79x
3.36
5.15
226.59
0.79
0.57
23.69
0.79
0.14
DuCa(N=5)
6.32
3.76x
0.17
3.12x
6.07
6.64
199.64
0.74
0.52
16.63
0.53
0.39
+Ours
6.73
3.53x
0.17
3.12x
3,96
5.87
218.66
0.78
0.55
23.00
0.76
0.16
DuCa(N=6)
5.86
4.05x
0.15
3.53x
6.38
6.65
189.97
0.73
0.54
17.47
0.54
0.37
+Oury
5.69
4.17x
0.14
3.79x
5.06
6.75
206.03
0.77
0.54
21.62
0.70
0.21
TaylorSeer(N303)
8.55
2.78x
0.31
1.71x
2.34
4.69
238.42
0.80
0.59
35.13
0.96
0.02
+Ours
8,55
2.78x
0.30
1.77x
2.31
4.55
242.08
0.81
0.59
36.16
0.97
0.01
Taylo1Sec(N444)
6.66
3.56x
0.27
1.96x
2.49
5.19
235.83
0.80
0.59
30.74
0.93
0.04
+Ours
6.66
3.56x
0.27
1.96x
2.46
4.80
238.28
0.80
0.59
31.76
0.94
0.03
(oy50)Sce(NN503)
5.34
4.45x
0.22
2.41x
2.65
5.36
23
0.80
0.59
28.48
0.90
0.07
+Ours
5.34
4.45%
0.22
2.41x
2.64
5.48
233.75
0.80
0.59
28.74
0.90
0.07
TaylorSeer(N601)
4.76
4.99x
0.14
3.79x
3.56
7.52
223.83
0.79
0.56
24.69
0.80
0.13
+Ours
4.76
4.99x
0.13
4.08x
3,08
6.43
231.10
0.80
0.57
25.64
0.83
0.10
DIT-X1
ImageNett 10K,
DDIM
steps,
256x256,,
quantized,
RTX4090
Size(MBy
Comf
Lai(s)
Spet
FID]
sFID
ISt
Pt
PSNR
SSIM1
LPIPS!
Origin
1349
1.00x
0.62
1.00x
5.31
17.61
245.85
0.81
0.68
INF
1.00
0.00
Q-DIT(WGAS)
518
2.60x
0.45
1.38x
5.44
17.61
237.34
0.80
0.68
31.10
0.95
0.04
+Ours
518
2.60x
0.22
2.82x
5.51
17.49
240.36
0.80
0.68
31.06
0.93
0.05
Q-DIT(W4A8)
347
3.89x
0.39
1.59x
6.31
17.81
209.30
0.76
0.69
24.88
0.82
0.14
+Ours
347
3.89x
0.20
3.10x
6.20
17.62
213.50
0.76
0.69
24.99
0.82
0.14
strate that our offline error modeling and dynamic programming framework offer a simple yet robust enhancement that generalizes effectively across diverse text-to-image diffusion models.
Class-to-image generation. Tab. [4 summarizes the quantitative results for class-to-image gener- ation using DiT-XL/2 under various acceleration and quantization settings. The results show that integrating proposed CEM consistently enhances both visual quality and efficiency across dif- ferent diffusion configurations, sampling schedules, and precision levels.
Under the DDPM sampler, when combined with step-reduction and caching methods such ToCa, CEM improves quality while maintaining comparable computational cost. For example, at ToCa (N=4), sFID decreases from 5.73 to 5.33 and PSNR increases from 22.61 to 23.78, with no notice-
25

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Published as a conference paper at ICLR 2026
Table 16: Comparison with learning- -base acceleration methods on DIT-XL/2.
Method
Train
FLOPS(T)
Spet
Lat(s)
Spet
FID1
sFID]
151
P1
R1
Origin
23.74
1.00x
0.53
1.00%
2.25
4.33
239.93
0.80
0.59
L2C Ma et al 2024a yes
1.25x
2.62
4.50
233.26
0.79
0.59
HarmoniC Huung et al. 2024a TaylorSer++Ours
yes
1.30x
2.36
4.24
238.74
0.81
0.60
no
11.87
2.00
0.37
1.43x
2.27
4.42
242.45
0.81
0.59
able increase in FLOPs or latency. The stable Precision/Recall values further indicate that CEM preserves generation diversity while improving fidelity.
Under the DDIM sampler, across multiple acceleration baselines (ToCa, DuCa, TaylorSeer), CEM delivers consistent performance gains. Even in high-speed scenarios (over 3x acceleration), it ef- fectively mitigates quality degradation, reducing FID from 3.64 3.19 (ToCa, N=4) and from 6.79 to 5.38 (ToCa, N=6). Perceptual quality is also enhanced, as evidenced by higher SSIM and lower LPIPS scores, confirming that CEM accurately compensates for offline-modeled errors under ag- gressive acceleration.
In low-bit quantization settings, CEM maintains comparable fidelity while further improving ef- ficiency. For Q-DiT (W6A8), latency is reduced by more than 2x with nearly unchanged FID (5.44+5.51), while for Q-DiT (W4A8), FID remains stable (6.31-6.22) as runtime improves from 1.59x to 3.10x. These results demonstrate that CEM and quantization compatible, method effectively leverages hardware-level compression without introducing additional degradation.
Overall, CEM provides lightweight and general enhancement for diffusion transformers, robustly stabilizing the generation process across different acceleration ratios, sampling schedules, and quan- tization precisions, thereby achieving balanced improvement in both fidelity and efficiency.
D.3 HIGHER RESOLUTIONS AND LONGER FRAMES
Table 15: Improvement of our CEM on Hunyuan under higher resolutions or longer frame settings.
Experiments at 480p are conducted to en- sure fair comparisons with the baselines. We have additionally included compara- tive experiments at 720p resolution and with longer frame sequences.
Resolution/Frames
Method
VBench (
TeaCache
77.56
480P-65f
+Ours
78.15
According the Tab. [5] our CEM en- hances the VBench of the TeaCache base- line across both 480p and 720p resolu- tions, well for longer 129 frames. These results further validate the effective- ness and robustness of our approach under more challenging generation settings.
TeaCache
76.21
480P-129f
+Ours
77.31
TeaCache
78.13
720P-65I'
+Ours
78.42
TeaCache
77.22
720P-129F
+Ours
78.43
D.4 COMPARISON WITH LEARNING- BASED METHODS
The two methods mentioned, L2C and HarmoniCa, are learning-based approaches with relatively low acceleration ratios (below 2x). A direct comparison with our CEM would therefore be somewhat inequitable, as our method is completely training-fres free. Nevertheless, to better illustrate the superior- ity of our CEM, we adjust the acceleration efficiency to align with that of L.2C and HarmoniCa and conduct a comparative evaluation terms of generation quality.
It can be observed that in Tab. ou CEM achieves higher acceleration efficiency and better fidelity even under a training-free setting.
D.5 COMPARISON WITH ONE-STEP DIFFUSION
Both few-step diffusion and caching acceleration aim to speed up existing DiT by reducing the number denoising iterations. However, their motivations and focuses are fundamentally different in Tab. 17
26

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Published as a conference paper at ICLR 2026
Table 17: One-step difflusio vs. our CEM. Since direct comparison across models is not feasible, we report the results in percentage form.
Model
Method
Speedt
Training Costs!.
Origin
100.0
1.00
ImageNet
ShortCu([CCL225 Frans et al.
2024
42.9
~100
TPUv3 (1-2 days)
Ours
99.3
3,56
Free
Origin
100.0
1.00
PixArt-a
SIM(NIPS24)) Luo et al. 2024
81.2
~30
A100s (2 days)
Ours
100.1
2.35
Free
Origin
100.0
1.00
SDI.5
EDM(CVPR24) Yin 2024
76.4
28.7
A100s (36 hours)
Ours
108.8
4.35
Free
Speed-quality trade-off. Few-step diffusion models achieve few-step generation through retraining. leading to substantial acceleration but often at the expense of visual quality. In contrast, our CEM provides training-free acceleration while preserving the original model's generation fidelity.
Cost of Acceleration. The computational costs of the two approaches differ significantly. Few-step diffusion entails substantial retraining overhead (in above table). contrast, we offer training-free acceleration. Our CEM is plug-and-ple and-alay achieving superior balance between acceleration and generation quality without incurring additional computational cost.
Generalization. Few-step diffusion relies on complex, experience-driver modules and costly train- ing, limiting its ability to generalize across model. In contrast, our CEM plug-and-play and can be directly applied to various visual generative DiT models, demonstrating strong generalization.
Hence, caching- caching-based acceleration and few-step diffusion have remained independent research di- rections. Although this is beyond the scope of our current work, propose a potential direction: our offline error modeling can also be applied to capture pruning-induced errors, enabling adaptive pruning for one-step diffusion to further improve efficiency.
E MORE VISUALIZATION
STABLEDIFFUSION
We provide additional visual results and analyses for the text-to-image task, focusing on SD1.5 (Fig [T)p and PixArt-a [T). In SD1.3, integrating CEM into FasterSD effectively mitigates common generation artifacts such as feature distortion (e.g., the bike in column 3, the bear's head in column 10, and the commuter train in column 11) and incomplete synthesis (e.g.. the bird's head in column 6). The optimized FasterSD also shows improved structural consistency and fidelity with the original model (c.g.. the cat's 's posture in column the trailer in column 4, and the police motorcycle in column 9). Notably, in some cases it even surpasses the original model (e.g., the restored teddy bear eyes in column 5 and the refined airplane column 8), confirming the effectiveness of our proposed method.
E.2 PIXART-a
In PixArt-ax, our method significantly enhances the visual fidelity of the DuCa model, particularly in recovering fine-grained details. With CEM applied, DuCa produces outputs more consistent with those of the original model-for example, the seagull in column the donuts in column 8, and the soda can in column 11. Furthermore, our method effectively reduces generation failures, including visual artifacts (e.g., the distorted handle in column 4 and the inaceurate mirror in column 9) and blurry regions (e.g., the tree in column 2).
These visualizations further support the quantitative findings in Tab. 1 providing clear evidence that our method, when used as a plug-in, enhances caching strategies and improves the fidelity of existing acceleration approaches.
27

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Published as a conference paper at ICLR 2026
boding
+Ours FasterSD Origin
Figure 10: More sample visualizations on the text-to-image task with StableDiftusion..5
Alade
Soors
schokgoand
bectall
Origin
DuCa
+Ours
Figure 11: More sample visualizations on the text-to-image task with PixArt-a.
bayyk piding lorgh Maay
Abvibeddag aed foos tips loves
TwelerSer Seer
Aerees backes of spti Ielivee
Acrest amtrrede
Acdiptan ande book.
dkcc en tee KE PeelweN
TaylerSeen Ovigin
Figure 12: More sample visualizations on the text-to-video task with Hunyuan.
E.3 HUNYUAN
For the text-to- eex-to--video task, we primarily present visual comparisons on the advanced Hunyuan model. Although the original TaylorSeer achieves up to 5x acceleration, the visualizations in Fig. 12 reveal noticeable quality degradation, including blurriness (e.g., the bird row I, column 4, and the apple in row 2, column 1), undesired viewpoint shifts (e.g., the person in row column 2), and temporal inconsistencies (e.g., the car in row 2, column 2).
After integrating method, these artifacts effectively alleviated. For instance, in 1, column 2, the viewpoint discrepancy is significantly reduced, yielding a result much closer to the original. In row 2, column 1, the apple appears markedly clearer, and in row column 3, method better preserves suitcase details, achieving higher consistency with the original output.
E.4 DIT-XL/2
Finally, we provide additional visualizations in Fig. 14 based both the DDPM and DDIM sam- pling strategies using the DiT-XL/2 model. is worth noting that we also conduct extensive experi- ments the quantized model Q-DIT. In this case, method directly apply the caching strategy
28

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Published as a conference paper at ICLR 2026
Origia
DII-DDPM
ToCa
+Ours
+Our TaylorSeur Origin
DII-DDIM
Figure 13: More sample visualizations on the class-to-ine to-image task with DiT-XL/2.
Origin
TaylorSeer
+Ours
Origin
TaylorSeer
+Ours
"Ows TaylorSeet Origin
PHINEE CUT Bu and HouR copprnal oi Saaieei
bade
and
Figure 14: More sample visualizations with complex prompts on FLUX.I-dev and Hunyuan.
quantized model, resulting in an additional 2x acceleration while maintaining comparable or even better generation quality. The primary contribution of our method on Q-DiT lies further improv- ing acceleration efficiency and demonstrating compatibility with quantization techniques. However, since the fidelity improvements are not particularly significant this setting, we do not include additional visualizations of Q-DiT here.
DIT-XL/2 is trained using the 1,000 class IDs from ImageNet, which provides more structured and less ambiguous prompts than those in text-to-image models. This simple prompt constraint reduces complexity of the generation task and enables a more direct and interpretable analysis of the role of self-attention during the synthesis process.
Under the DDPM sampling, the generation results of the ToCa model combined with our method be- come more aligned with those of the original model when combined with our method. For instance, the keyboard in column 1, the bird in column 3, and the dog column are all more faithfully reproduced. Additionally, undesired artifacts to some extent, for example, the watermark on the pants in column is noticeably reduced. Under the DDIM sampling strategy, method also significantly improves generation fi- delity. It demonstrates stronger capability in preserving fine-grained details, rather than suffering from distortions caused by the omission of timesteps tokens during acceleration. For example, the dog's 's face in column 1, the dog's mouth in column 4, and the polar bear in column 9 all illustrate the effectiveness of our method in restoring detailed features.
observe that method unexpectedly suppresses the generation of
In summary, the visual results across various tasks and models, together with the quantitative results presented in the main paper, collectively demonstrate the effectiveness of our method.
E.5 VISUALIZATION WITH EXTREMELY COMPLEX PROMPTS
We further evaluate the CEM using complex prompts to examine its performance in challenging scenarios. As illustrated, CEM enhances the overall visual style and color consistency after accel- eration (e.g., Example 1), producing results closer to the original video. It also better preserves fine details, such as the textures on the snow mountain (e.g., Example 2). These results demonstrate that CEM effectively improves the generation fidelity of accelerated models, even in complex scenes.
29


Reviews:
[{"review_id": "QDkKwZxdTn", "reviewer": "Reviewer_WEPH", "summary": "This paper proposes CEM (Cumulative Error Minimization), a plug-and-play, training-free acceleration strategy for Diffusion Transformers (DiTs). CEM models caching error as a joint function of denoising timestep and cache interval, and then uses a dynamic-programming procedure to choose a caching schedule that minimizes cumulative error under a given acceleration budget. The method is model-agnostic, designed to be compatible with existing cache-correction pipelines and quantized models, and claims no extra runtime overhead (beyond an offline estimation step). Experiments across seven generative models and three tasks suggest that CEM can improve fidelity over existing acceleration methods and, in some cases, match or slightly exceed the original unaccelerated models.", "questions": "Several improvements are small. Could this indicate that random-sample–based estimation lacks coverage (e.g., prompt types, scene complexity, motion patterns for video)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear, well-structured presentation: The problem setup, motivation, and algorithm are easy to follow. The paper is readable and self-contained.\n\n2. Interesting angle via offline error modeling: Estimating an error distribution over (timestep, cache interval) and optimizing a schedule with DP is a neat, generally applicable idea.\n\n3. Plug-and-play applicability: The method is model-agnostic and integrates with existing cache-correction approaches and quantized variants, which increases potential practical value.\n\n4. Budget-aware optimization: Framing the schedule search under explicit acceleration budgets is sensible and aligns with deployment needs.", "weaknesses": "1. Limited practical gains in several settings: In Table 1 and Table 3 (and a few other results), improvements over strong baselines appear marginal, making it difficult to judge the real-world significance of CEM. In places where the paper claims to “even outperform the original,” the margins seem small or inconsistent.\n\n2. Representativeness of the offline estimate is unclear: The fidelity of the learned error prior depends on the sample set used for estimation. If the sample pool (prompts, content types, seeds) is not representative, the optimized schedule may not generalize, which could explain the limited improvements in several cells.\n\n3. Overhead vs. ‘no extra computation’ claim: While CEM adds no inference overhead, the offline estimation step has real cost. The paper does not quantify this cost or show its amortization across models/datasets/budgets.\n\n4. Robustness and stability not fully characterized: The sensitivity to the number of estimation samples, dataset/domain shifts, prompt distributions, and random seeds is not sufficiently explored.", "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": "x3dHFSwILQ", "reviewer": "Reviewer_XEtZ", "summary": "The paper targets training-free acceleration for Diffusion Transformer (DiT) models that already use cache-based methods such as ToCa, DuCa or TaylorSeer. The authors observe that these methods correct cache error (by pruning or prediction) but leave the cache schedule itself fixed or very simple, so a large part of the overall quality drop actually comes from a suboptimal schedule. They therefore propose CEM (Cumulative Error Minimization): first build, offline, a table that estimates the cache error for every pair of denoising timestep and cache interval; then, given an acceleration budget, run a dynamic-programming procedure to pick the sequence of cache/recompute steps that minimizes the accumulated error; finally, at inference time, just swap the original schedule for the optimized one, with zero extra runtime cost. They show that this plug-and-play schedule can be inserted into four existing accelerators and even quantized DiT models, giving better FID/IR/VBench and sometimes even surpassing the original unaccelerated model.", "questions": "1.How robust is the offline error prior to changes in prompt distribution, CFG/guidance strength, image resolution, or video length – do we need to rebuild the error table whenever the deployment setting shifts?\n2.Can you quantify the gap between the cumulative-error approximation used in the DP and the true accumulated error on long sampling trajectories, especially for video models?\n3.How does your offline schedule compare to a lightweight online/content-aware schedule under the same acceleration budget – is there a regime where online is clearly better?\n4.What is the actual offline cost (time, number of sampled prompts/videos) to build one error table for a large DiT, and can a single table be shared across multiple accelerators that use different cache intervals?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The motivation is clear and well aligned with current DiT practice: cumulative error in cache reuse is real, and current methods optimize everything except the schedule. The method stays training-free and keeps inference overhead unchanged because all error modeling is done offline. The DP formulation over timesteps and number of cache uses is simple, reproducible, and can target arbitrary acceleration budgets. The approach is model-agnostic and demonstrated on seven generators across text-to-image, text-to-video, and class-conditional DiT, and it improves four different cache-based accelerators plus a quantized DiT, which supports the “plug-and-play” claim. The paper also shows nice cases where accelerated+ours slightly outperforms the original model, which is a strong empirical signal that the schedule itself was the bottleneck.", "weaknesses": "The central assumption that an error table built from a small offline set can be reused for arbitrary prompts, CFG scales, resolutions, and even different video lengths is only illustrated on a fixed setting and not validated across harder regimes, so it is unclear how often the error prior must be rebuilt in practice. The cumulative-error approximation used in the DP is quite rough (essentially a cumsum of per-step errors) and the paper does not quantify how far this is from the true accumulated error on long denoising chains, where mismatches would matter most. The comparison to online, content-aware cache optimizers (AdaCache, AdaptiveDiffusion, TeaCache) is brief; these methods pay some runtime but are data-dependent, while the proposed method is data-agnostic, so the paper should spell out when the offline schedule is preferable. Many gains in the tables are modest (often 0.3–1 point) and look like “a better schedule on top of the same accelerator” rather than a fundamentally new acceleration mechanism; the offline profiling cost per model/task is also not clearly reported.", "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": "LKYVMY5KGf", "reviewer": "Reviewer_Hw5z", "summary": "This paper proposes a training-free, plug-and-play acceleration framework for Diffusion Transformers called Cumulative Error Minimization (CEM). The method performs offline error modeling to estimate the joint distribution of denoising steps and cache intervals, and then applies dynamic programming optimization to minimize cumulative cache errors under a given acceleration budget. CEM can be seamlessly integrated into existing acceleration and quantization frameworks, improving fidelity across multiple generation tasks without additional inference cost. Overall, the paper presents an efficient, general, and theoretically grounded acceleration strategy for diffusion models.", "questions": "1. Have the authors evaluated CEM on non-visual diffusion tasks? Cross-modal results would help validate the claimed generality.\n\n2. Can the authors quantify the relationship between cumulative error and perceptual generation quality, and analyze the stability of this relationship across samplers, resolutions, and long-sequence scenarios? Does cumulative error accumulation cause performance degradation in long-horizon tasks, and how might this be mitigated?\n\n3. Please provide more details about the offline modeling cost and complexity—for example, sample size, runtime, and scalability to larger models.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Introduces **dynamic programming** into diffusion caching optimization, combined with offline error modeling, which provides a structured alternative to previous heuristic caching methods.\n\n2. The **plug-and-play** design requires no additional training and can be easily integrated into existing acceleration or quantization frameworks, demonstrating strong engineering practicality.\n\n3. Comprehensive experiments across multiple tasks and models show consistent fidelity improvement at fixed acceleration ratios.", "weaknesses": "1. **Limited theoretical analysis.** The cumulative error approximation is only empirically motivated, lacking a formal discussion of convergence, stability, or optimality guarantees. The complexity and optimality conditions of the DP procedure are also not analyzed.\n\n2. **Restricted applicability.** CEM appears to be tailored for iterative denoising structures and may not extend to one-step or non-iterative diffusion models. The paper could further discuss potential adaptations to these architectures.", "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": "Hg6LoE0CjM", "reviewer": "Reviewer_98fT", "summary": "This paper proposes CEM (Cumulative Error Minimization), a training-free, plug-and-play acceleration method designed to improve the generation fidelity of Diffusion Transformer (DiT) models under caching-based acceleration. CEM tackles this by formulating caching strategy optimization as a cumulative error minimization problem. It first performs offline error modeling to characterize the joint effect of denoising timesteps and cache intervals, building a reusable prior without retraining or online computation. Then, a dynamic programming algorithm derives the optimal cache schedule that minimizes the total accumulated error under arbitrary acceleration budgets. CEM is model-agnostic, incurs no runtime overhead, and can be directly integrated with existing acceleration or quantization frameworks. Extensive experiments across seven diffusion models and three tasks (text-to-image, text-to-video, and class-to-image generation) show that CEM consistently improves generation fidelity while maintaining or even improving inference speed.", "questions": "Please see the above weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper introduces a novel formulation of the caching optimization problem for Diffusion Transformers as a cumulative error minimization task. Unlike prior caching-based accelerators that rely on fixed intervals or heuristic scheduling, CEM models the joint variation of denoising timesteps and cache intervals and solves for an optimal caching plan through dynamic programming. This method combining offline error modeling with discrete optimization is original and conceptually elegant, extending beyond prior local correction approaches such as ToCa, DuCa, and TaylorSeer.\n\n2. The methodology is technically sound and well-supported by extensive experiments. The paper conducts thorough evaluations across seven generative models and three task categories (text-to-image, text-to-video, and class-to-image), demonstrating consistent fidelity gains under identical FLOPs or latency.", "weaknesses": "1. Limited theoretical justification for the cumulative error approximation.\n\nThe proposed cumulative error approximation (Eq. 2) is empirically validated but lacks a clear theoretical foundation. The assumption that a cumulative sum over per-step error distributions sufficiently approximates the true propagation of caching error is plausible yet heuristic. A deeper analysis — for example, quantifying the approximation gap between estimated and actual cumulative error or providing theoretical error bounds — would make the dynamic programming framework more convincing.\n\n2. Limited analysis of computational trade-offs and scalability.\n\nAlthough CEM claims to introduce no runtime overhead, the paper does not detail the computational cost of the offline modeling phase, especially for large-scale models (e.g., FLUX or Hunyuan). Clarifying the one-time cost and memory footprint of building the offline error prior would help readers assess practical feasibility in industrial settings.\n\n3. Missing comparison with learned caching optimization methods.\n\nAlthough CEM is positioned as training-free, the paper does not compare against recent learning-based caching optimization approaches such as HarmoniCa [1] or Learning-to-Cache [2], which explicitly learn adaptive caching schedules from data. Such baselines would better contextualize how much performance CEM gains or sacrifices relative to methods that perform end-to-end cache learning. Without these, the claimed superiority of CEM’s offline optimization remains partially unquantified.\n\n[1] HarmoniCa: HarmonizingTraining and Inference for Better Feature Caching in Diffusion Transformer Acceleration, ICML 2025.\n\n[2] Learning to-cache: Accelerating diffusion transformer via layer caching, NeurIPS 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": {}}, {"review_id": "rcbETfBnYy", "reviewer": "Reviewer_m1Xm", "summary": "Diffusion Transformers suffer slow inference due to iterative denoising; training‑free cache acceleration helps but introduces sizable errors, and fixed caching cannot adapt to error variation across timesteps. This paper proposes CEM, a plug‑in, model‑agnostic strategy that models error jointly over denoising timesteps and cache intervals and uses dynamic programming with a cumulative‑error approximation to optimize the caching schedule, integrating seamlessly with existing cache‑correction pipelines and quantized models with negligible overhead. Across seven models and three tasks, CEM consistently improves the fidelity of accelerated generators and can even surpass the original unaccelerated baselines.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method is easy to implement.\n2. It is a plug-and-play framework that enhances the performance of previous methods without additional overhead.\n3. A training-free approach without relying heavily on computational resources.", "weaknesses": "1. The performance on SOTA video generation methods, e.g., Hunyuan and Wan2.1 on high-resolution generation, e.g., 720p and beyond, is missing. The acceleration of more powerful video generation models towards higher resolution should be more challenging and practical.\n2. The author should include the experiments on few-step diffusion models.\n3. As the author mentioned in Line 102, some works employ error compensation approaches. Although they incur some overhead, I believe it is better to compare this work with them to further show the superiority.\n4. The motivation and method of this work are a little bit trivial.\n5. The prompts for the visualization in this paper are too simple and short. I hope the authors could include more visualization with complex prompts. For example, video prompts with more complex motion descriptions.", "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)