Q&C: When Quantization Meets Cache in Efficient Generation
Authors: Xin Ding
Xin Li
Haotong Qin
Zhibo Chen
Venue: ICLR 2026 Poster (2026)
Keywords: diffusion model
visual  generation models
cache
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: Quantization and cache mechanisms are typically applied individually in efficient generation tasks, each showing notable potential for acceleration. However, their joint effect on efficiency remains under-explored. Through both empirical investigation and theoretical analysis, we find that that combining quantization with caching is non-trivial, as it introduces two major challenges that severely degrade performance:
(i) the sample efficacy of calibration datasets in post-training quantization (PTQ) is significantly eliminated by cache operation; (ii) the joint use of the two mechanisms exacerbates exposure bias in the sampling distribution, leading to amplified error accumulation during generation. In this work, we take advantage of these two acceleration mechanisms and propose a hybrid acceleration method by tackling the above challenges, aiming to further improve the efficiency of tasks while maintaining excellent generation capability.
Concretely, a temporal-aware parallel clustering (TAP) is designed to dynamically improve the sample selection efficacy for the calibration within PTQ for different diffusion steps. A variance compensation (VC) strategy is derived to correct the sampling distribution. It mitigates exposure bias through an adaptive correction factor generation. Extensive experiments demonstrate that our method is broadly applicable to diverse generation tasks, achieving up to 12.7x acceleration while preserving competitive generation quality.

Published as a conference paper at ICLR 2026
Q&C: WHEN QUANTIZATION MEETS CACHE IN EFFI- CIENT GENERATION
Xin Ding'
Xin Li
Haotong Qin Zhibo Chen 13+
'University of Science and Technology of China
'ETH Zürich
Zhongguancun Academy
ABSTRACT
Quantization and cache mechanisms are typically applied individually in efficient generation tasks, each showing notable potential for acceleration. However, their joint effect on efficiency remains under-explored. Through both empirical inves- tigation and theoretical analysis, we find that that combining quantization with caching is non-trivial, as introduces two major challenges that severely degrade performance: (i) the sample efficacy of calibration datasets in post-training quanti- zation (PTQ) significantly eliminated by cache operation; (ii) the joint use of the two mechanisms exacerbates exposure bias in the sampling distribution, leading to amplified error accumulation during generation. In this work, we take advantage of these two acceleration mechanisms and propose hybrid acceleration method by tackling the above challenges, aiming to further improve the efficiency of tasks while maintaining excellent generation capability. Concretely, temporal-aware parallel clustering (TAP) is designed to dynamically improve the sample selection efficacy for the calibration within PTQ for different diffusion steps. A variance compensation (VC) strategy is derived to correct the sampling distribution. It mitigates exposure bias through an adaptive correction factor generation. Exten- sive experiments demonstrate that our method is broadly applicable to diverse generation tasks, achieving up 12.7 acceleration while preserving competitive generation quality.
INTRODUCTION
The rapid progress generative models has driven significant breakthroughs across a wide range of tasks, such image generation (Croitoru et al. (023) Yang et al. 2023 Peebles & Xie 2023), video generation (Liu et al. [2024b] Wang et al.] a (025). However, their widespread adoption is hindered by the immense computational complexity and large parameter counts, For instance, generating 512x512 resolution image using DiTs can take more than 20 seconds and 105 Gflops on an NVIDIA RTX A6000 GPU (Wu al., 2024). This substantial requirement makes them unacceptable or impractical for real-time applications, especially as model sizes and resolutions continue to increase
LLiu & Zhang
2024 Zhao al. 2024a).
Quantization Nagel et al. 2021 2020 Liu al. 2023 and cache (Ma et al. 2024h Wimbauer et al.
2024 So et al. 2023) have been independently explored as two effective acceleration mechanisms to alleviate the computational burden of generative models (Wu et al. 2024, Ma et al.) 2024a Seel
varaju et al. 2024: Chen et al., 2024b). Quantization accelerates inference by converting weights and activations into lower-bit representations, thereby reducing both runtime and memory usage. Among different paradigms, post-training quantization (PTQ) (He et al., (023) has received par- ticular attention, as it eliminates quantization errors using only small calibration dataset, making more resource-efficient compared with quantization-aware training (QAT) (Lu et al., 2024). In contrast, the cache mechanism accelerates generative models by exploiting the reusability of his- torical features in the diffusion process, thereby avoiding redundant computations during inference. Common strategies leverage the repetitive nature denoising steps, storing and reusing intermediate representations--uin such as those from attention and MLP layers-across across different timesteps.
Despite the effectiveness of both quantization and cache mechanisms, it remains under-explored "whether integrating these two mechanisms can further boost the efficiency of generative models?"
"Corresponding author; xin.li wel.liuttteeeiite
wwtrrhioo@ust.edduu

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Published as a conference paper at ICLR 2026
However, when quantization meets cache, notable decline in generation quality is often observed, even though impressive acceleration gains can be achieved. To investigate this phenomenon, we conduct an in-depth analysis of the sampling process and identify two crucial factors contributing to the performance degradation.
Firstly, shown in Figure[] we find that cache operation dramatically increases sample similarity in the calibration dataset used for PTQ, leading to a marked reduction in sample efficacy. This effect becomes increasingly severe with the number of generative steps, compromising the effectiveness of PTQ due to insufficient coverage of the overall distribution. Secondly, the synergy of quantization and cache amplifies exposure bias (See Appendix [.I] for a detailed definition) within the sampling distribution, problem that is less pronounced when exploring either quantization or cache individu- ally, which can be observed in Figure 2 Furthermore, this exposure bias induces an accumulated shift in the variance of denoised outputs as the sampling iterations increase.
To restore the generation capability of models while preserving the acceleration benefits from combining quantization and and cache mechanisms, we tackle the above challenges by developing two essential techniques, constituting our hybrid acceleration mechanism: (i) temporal-aware parallel clustering (TAP) and (ii) distribution variance compensation (VC).
In particular, our TAP aims to restore the reduced sample efficacy in calibration datasets caused by cache operation, thereby enabling more accurate identification and correction of quantization errors. Notably, a naïve method to overcome the reduction of sample efficacy is to increase the dataset size. However, it will introduce excessive redundant data and unnecessary computational costs (see Table [12 for supporting experiments). In contrast, TAP dynamically constructs calibration datasets by efficiently selecting the most informative and distinguishable samples from large-scale datasets through parallel clustering scheme. Unlike traditional spectral clustering, which suffers from prohibitive computational complexity of (n²)) (Yan et al. 2009: Li al. 2011: Chen & Cai 2011) or On²²) with optimized algorithms (Halko et al. 2011; Feng et al. 2018 Martin et al. 2018). TAP integrates temporal sequences with data distribution enable paraleel subsampling of size т. thereby reducing the complexity to (rn)), where < n. This design leverages the time-sensitive nature of calibration datasets in generative models, as highlighted in recent studies (Li et al.) 2023b) 2024b), allowing TAP to achieve effective clustering and sampling that faithfully represent the overall distribution while avoiding excessive redundancy and computational costs.
Our in-depth analysis of the image generation process reveals strong link between image variance and exposure bias, as shown in S.c To address this, we propose the VC, a tailored approach that adaptively mitigates exposure bias through variance correction. Unlike methods that introduce an additional neural network to predict errors in corrupted estimations (Wimbauer et al., 2024), our approach requires additional training. Instead, it utilizes small batch of intermediate samples to compute a reconstruction factor, which adaptively corrects feature variance at each timestep. This method effectively reduces exposure bias, resulting in notable improvements in overall model performance.
The contributions of this paper can be summarized as follows:
We are the first to systematically investigate the combined use of quantization and caching techniques from both theoretical and empirical perspectives, demonstrating the substantial potential of this approach to alleviate computational burdens.
We identify two critical challenges when integrating quantization and cache: (1) the gen- eration of highly redundant samples in calibration datasets; and (2) the emergence of exposure bias caused by distributional variance shifts in the model's output, which becomes exacerbated over iterations.
We propose two novel methods: (1) TAP dynamically selects informative and distinct samples from large-scale datasets to optimize calibration dataset efficacy. (2) VC, an adaptive approach that mitigates exposure bias by correcting feature variance at each timestep requiring no additional training.
Extensive empirical results demonstrate that our approach exhibits strong generalization, being broadly applicable across diverse model architectures, generation tasks, and various combinations of muantization-cache methods and settings, while simultaneously accelerating diffusion-based image generation by up to 12.7x without compromising generative quality.
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Published as a conference paper at ICLR 2026
10
10
20
57
30 20
33
37
20
20 30 43
250 Stp
200- 100-150-50pp
150-100
100-52.5xp
50.0
Figure 1: Cosine similarity analysis across time steps in DiT for calibration data. This visualization is based on 250-step DDIM sampling process. Calibration data were collected both without (up) and with (bottom) cache; samples positioned further the right represent data closer the final step To. The heatmap reveals high similarity in calibration datasets when quantization meets cache, particularly in later diffusion stages. This observation motivates calibration strategy, highlighting a clear requirement to reduce redundancy and improve efficacy. A more detailed theoretical and experimental analysis is provided Appendix D.I
2
BACKGROUND AND MOTIVATION
2.1
QUANTIZATION AND CACHE
Quantization, a pivotal stage model deployment, has often been scrutinized for its ability to reduce memory footprints and inference latencies. Typically, its quantizer Q(X]b)
(b) is defined follows:
X
Q(Xbb) clip(
2, -
(1)
Where (scale) and (zero-point) are quantization parameters determined by the lower bound and the upper bound u of X,which are usually defined as follow:
min (X), max(X)
- 2 clip
+ 2, 0,
8
Using a calibration dataset and Equation and 3 we can derive the statistical information for s and
Previous research (Williams & Aletras 2024 Lee et al., 2023 Wu et al., [003] Jaiswal et al. 2023) has examined the performance of downstream tasks across a variety of models, compression methods, and calibration data sources. Their findings indicate that the choice of calibration data can significantly impact the performance of compressed models.
Cache, technique that leverages the repetitive nature of denoising steps in diffusion models, significantly reduces computational costs while maintaining the quality of generated samples. cache mechanisms operate by storing and reusing intermediate outputs during the sampling process, avoiding the need for redundant calculations at each step. The key parameter in this approach is the cache interval N, which dictates how often features are recomputed and cached. Initially, features for all layers are cached, and at each time step f, if modN 0, the model recomputes and updates the cache. For the following N steps, the model reuses these cached features, bypassing the need for repeated full forward passes. This process efficiently reduces computational overhead, particularly in diffusion models, without sacrificing generative quality.
2.2 CHALLENGES THE SYNERGY OF QUANTIZATION AND CACHE EFFICIENT IMAGE GENERATION
The remarkable performance of quantization and cache naturally leads us to consider the significant potential of their combination for enhancing the efficiency of DiTs. To this end, leverage standard quantization (Nagel et al.) 202T) and caching mechanism (Ma et al. (024b) to conduct an in-depth analysis, through which identify critical issues.

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Challenge 1: Amplification of Exposure Bias Past research has consistently shown that expo- sure bias, resulting from the training- inference discrepancy, has a profound impact on text and image generation models (Ranzato al. 2015 Schmidt 2019 Rennie et al. 2017]] Ning et al.
training-inference
Original
Quantized model
Cache modd
Quant-Cache model
2023). Due the presence of exposure bias, gradually intensifies with the increase in the number of inference sampling steps, becoming major cause of error accumulation (Li et al.)
MSE 2.0
2023a Li & van der Schaar 2023) (See Ap- pendix G. for more detailed definition). To explore this further, we compared the changes in exposure bias under different acceleration methods and were surprised to find that when quantization meets cache, exposure bias sig- nificantly worsens, whereas it does not occur when either quantization isolation, as shown in Figure 2
Timestop
Figure 2: Analysis of exposure bias DiT mod- els. The mean squared errors between predicted samples and ground truth samples are computed at each time step. While the exposure bias remains relatively stable in both the cached and quantized models compared to the 50-timestep DiT, notice- able increase in exposure bias is observed when quantization meets cache, leading to accumulation during the generation process.
cache is used in
To analyze the underlying causes, we examined the distributional changes over the generation process using 5,000 images. We observed that this Amplification is due to a change in variance. Specifically, as shown in Figure 33 at the begin- ning of the denoising process, the span of variance is narrow, and the changes in variance remain stable, fluctuating around 1. As the noise gradually removed from the white noise, the variance distribution of the ground truth samples spans approximately (0, 0.6), reflecting the diversity of the sample distributions. However, when considering the synergy of quantization and cache, the distribution shifts the range of (0.1, 0.7), which aligns closely with the shift trend of exposure bias in Figure 2 We conducted the same experiment for the mean, but no similar phenomenon was observed, detailed analysis can be found in the Appendix [ This highlights the need to correct variance during the later stages of generation to mitigate its negative impact on exposure bias.
1.0
1.0
0.8
Var(x))
0.6
30.6
Avar
0.4
0.4
0.2
Avar
30
30 Timesteps
0.0
50
20
10
Timesteps
Figure 3: Comparison of the density distribution of the variance of 5000 ImageNet samples across different timesteps. The plots illustrate how the sample distribution variance evolves over time, shown without (left) and with (right) quant-cache. Detailed numerical results are reported in Table [4] in Appendix With the incorporation of quant-cache, as denoising progresses, the variance error converges to nearly 100%, and the sample distribution variance gradually deviates toward Gaussian white noise.
Challenge 2: Degradation Calibration Dataset Effectiveness diffusion quantization, previ- works (Liu et al. 2024a Zhao et al. 2024b Li al. 2023b) often randomly sample intermediate inputs uniformly across all time steps to generate a small calibration set. This strategy leverages the smooth transition between consecutive time steps, ensuring that limited calibration can still repre- sent the overall distribution effectively (Li et al.) 2024b). However, when quantization meets cache, this balance is disrupted, significantly reducing the effectiveness of the calibration dataset. In
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Appendix D.I we provide more comprehensive and detailed theoretical and experimental analysis
of this phenomenon.
To visualize this issue, followed the setup in (Wu et al. 2024) and constructed multiple calibration datasets, each consisting of 250-step samples. We then computed the cosine similarity between these samples and observed substantial rise in similarity compared to non-cached scenarios (see Figure D Furthermore, as the diffusion process approaches the final step To, sample similarity increases dramatically, with some exceeding 60%. Paradoxically, these later-stage samples are more reliable and valuable for accurate calibration. This indicates that a large portion of calibration samples, despite their computational cost, do not contribute additional useful information for quantization, significantly reducing the overall effectiveness of the calibration dataset.
METHOD
3.1 TEMPORAL-AWARE PARALLEL CLUSTERING FOR CALIBRATION
In this section, we present Cemporal-Aware Parallel Clustering (TAP), a novel method that integrates both spatial data distribution and temporal dynamics to address clustering challenges in datasets with complex feature interactions and inherent temporal patterns. TAP leverages parallel subsampling to efficiently combine spatial and temporal similarities, providing a robust approach for generating calibrated datasets.
Algorithm Overview Given a dataset with
N samples, TAP reduces computational com- plexity through subsampling, followed by par- allel processing across multiple subsampled sets, Each subsample is generated via random sam- pling, where the probability of selecting sample is n per subsample. By repeating this process, we ob- tain m subsampled sets S2..,,,.., Sm). The parallel subsampling approach offers two key ad- vantages: (1) it mitigates potential random noise and distributional biases within the dataset, and (2) it significantly improves computational effi- ciency.
Algorithm 1: Temporal-Aware Parallel Clus- tering (TAP)
Input: Dataset T with N samples, samples per subsample n
Output: Cluster assignments for dataset T
N. with n being the number of samples
for i = 1 to m in parallel do
Generate subsample S; from T with
|S,] = n,;
Compute A
for each (IT, vn.) E S; do
(1) Spatial,AA [ [x
Compute Aeemporal
A for each (l&, (L,,4n) E St do temporal, + cxp (- 1t4 tna)):
For each subsampled set, a similarity matrix A constructed. Next, spectral clustering applied to each weighted similarity matrix Ajnal to detect communities. First, we compute the normalized Laplacian matrix for each parallel subsampling set,as follow:
L
(6)
Combine to A Compute D. D
L )12
A innll )1/2,
[ = (D!O) A (D! E NR Xn
(4)
Extract the top & eigenvectors of ( and perform k-means clustering on the eigenvector matrix rows;
Given the subsampled similarity matrix A (5)
"imal
where D is diagonal matrix with the i-th di- agonal element being En Ass, for 1 < k < N, the degree matrices for the subsampled node set S; are defined as:
Aggregate cluster assignments from all subsamples to produce final results;
D = diag { (D
D diag (D
(5)
The top l eigenvectors of L are then extracted, and k-means clustering is performed the rows of the resulting eigenvector matrix to produce the final clustering results.
As the entire dataset is divided into à categories, we uniformly sample from these categories to construct the final calibration dataset, ensuring that its data distribution perfectly covers the overall distribution of the original dataset. The detailed algorithm flow is shown in Algorithm 1
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Definition of Similarity Matrices A
final Drawing from the prior work (Li al. 2023b He et al. 2024), datasets 7 exhibit complex feature distributions and inherent temporal patterns. To account for these aspects, we construct a comprchensive similarity measure by combining spatial and temporal similarities. Specifically, for each subset S,, we compute the data similarity matrix A data. based on the
feature space, and the temporal similarity matrix A which captures temporal correlations. We then construct weighted similarity matrix for each subsample, which combines Temporll similarities:
A + (1 a) 4
final
(6)
where a represents adjustable weights that balance the influence of data spatial and temporal proper- ties.
Spatial Similarity Matrix features. For each pair of samples Ia and 2₁ from the subsampled set S; the element A spatial, kh
spatial captures the similarity between samples in terms of their data
represents how similar these two samples are based on their feature vectors, which could be defined as:
Ik 2h
A-h
| |
Temporal Similarity Matrix A Aemponl captures the similarity between samples based on their temporal relationships. For each pair of samples with timestamps & and &n, the element A (i) defined as:
kemporal,kh could be
time ,oA = exp (-14+ - 1nl)
3.2 VARIANCE COMPENSATION (VC) FOR EXPOSURE BIAS
Assume that a random variable ff follows a normal distribution, denoted as f ~ . (N,, where represents the mean and 6² denotes the variance. To alter the variance of f. we can apply scaling transformation. If the objective is to modify the variance to a new value oeew the transformation can be defined as follows:
O (f
Y
In this formulation, will conform to a new normal distribution given by Y ~ N(u. dew),, where Pack serves as the scaling factor for the variance adjustment. However, in practice, directly determining dime may not be feasible. Consequently, we introduce simestep-dependee a
reconstruction scaling factor K SSC within the Intermediate samples á. where St indicates the number of denoising steps and C' signifies the number of channels corresponding to the estimated noise. The reconstructed Intermediate samples H at timestep t can thus be represented follows:
z; Me + K, (R. - -pe)
(10)
where (.) represents the channel-wiss multiplication. Next, need to select suitable optimization objective L to efficiently reconstruct ă,-
Inspired by existing (Finkelinll al. 2019 Nagel et al. 2019 Yao al. 2024). we adopt the
following objective to more comprehensively measure the discrepancy between the reconstructed feature and the target feature. In this formulation, the inverse root quantization-to-noiss ratio (rQNSR)
(Finkelstein et al. 22019: Yao et al. 2024) is integrated to enhance the MSE criterion's 's sensitivity to -hanncl-specific noise effects.
K, argmin(rQNSS((2..2)) K,
MSE(E,
(11)
Equation transforms the optimization problem into minimizing a function with respect to K, By taking the derivative of the function with respect to K, and setting the derivative zero, can obtain the analytical solution for K, In Appendix D.2 and we provide the theoretical foundation and detailed derivation of VC, from which the final formula is obtained follow:
Σn (ct.n - M.)(Een - 14) + EN 2
K, (n - 22 + ΣN
(12)
where N denotes the number of samples across the optimization, and the reference x' is obtained during calibration in the quantization process.

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4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Our experimental setup closely follows the original configuration used in the Diffusion Transformers
(DiTs) study (Peebles & Xie 2023). We evaluate the performance of our method on the ImageNet
dataset (Deng et al. 2009). using pre-trained, classcconditional DiT-XL/2 models (Peebles & Xie
2023) image resolutions of both 256 256 and 512 512. The DDPM solver (Ho al. (020) 2020)) with 250 sampling steps is employed for the primary generation process, while additional evaluations with reduced sampling steps of 100 and 50 are conducted to further test the robustness of our approach.
For fair benchmarking, all methods utilize uniform quantizers for all activations and weights, with channel-wise quantization for weights and tensor-wise for activations. To create calibration dataset, we generate large-scale samples across the ImageNet classes during the diffusion process, forming dataset Dj. We utilize the TAP algorithm to select a final set for quantization calibration. Specifically, three parallel sampling processes are performed, with each sampling selecting only 1/20 of the samples. This allows us to split Dj into 100 categories, from which we randomly choose 3-10 samples per category, ultimately forming set of 800 calibration samples-folloviin previous works (Wu et al. 2024). All experiments are conducted on NVIDIA RTX A100 GPUs, and our code is based on PyTorch (Paszke et al. 2019).
the implementation of
To comprehensively assess the quality of generated images, we employ four evaluation metrics: Fréchet Inception Distance (FID) (Heusel al. 2017), , spatial FID (sFID) (Salimans et al. 2016
Nash et al. 202T). Inception Score (IS) (Salimans et al.) 2016; Barratt & Sharma 2018), and Precision. All metrics are computed using the ADM toolkit (Dhariwal & Nichol (022)) l). For fair comparison across all methods, including the original models, we sample 10,000 images for ImageNet 256x256
and 5,000 images for ImageNet 512x512, consistent with the standards used in prior studies (Nichol
& Dhariwal 2021: Shang et al. 2023
4.2 COMPARISON ON PERFORMANCE
We conduct comprehensive evaluation of our method against prevalent baselines, being the first
to explore the combined effects of quantization and cache. Our benchmarking includes PTQ4DM
(Shang et al. 2023). Q-Diffusion (Li et al. 2023b), PTQD (He et al. 2024). PTQ4DM (Shang et al. 2023) sear-too-achh (Ma et 2024a). RepQ (Li et al.) (023c). and Fora (Selvaraju et al.
2024). All quantization methods use uniform quantizers, applying channel-win quantization to
weights and tensor-wise quantization to activations, while cache methods store and reuse outputs from self-attention and MLP layers.
Tables Ta and lb summarize results for large-scale, class-conditional image generation ImageNet at resolutions of 256x256 and 512x512 using DiT, demonstrating our method's ' effectiveness across different datasets, and timestep settings. Notably, under 8-bit quantization, our method closely matches the generative quality of original models while offering substantial computational efficiency.
4.3 GENERALITY OF THE METHOD
Generality Across Diverse Tasks We further extend our comparison to other unconditional gener- ation, video generation, and text-conditional generation tasks. Specifically, we conduct experiments with L.SUN-Bedroon and LSUN-Churee datasets Yuu al. (2015) on LDM (Rombach et al. 2022). Sora (Liu et al. 2024b) on VBench (Huang et al., 2024 FLUX.1 (Black-Forest-Labs] 2024) and PixArt-E (Chen al. 2024a) on MJHQ-30K (Li al. (024a), as well as PartiPrompt (Yu al.
2022) and MS-COCC Lin et al. 2014) on Stable Diffusion. The corresponding results are reported
in Tables Ic and 9
in Appendix and Appendix
Generality Across Methods and Settings We construct diverse baselines by randomly combining existing quantization and cache methods to examine the applicability of Q&C under different method compositions. The detailed procedures and results provided in Table In addition, investigate the generalization Q&C under bit and 6-bit settings, with the results summarized in Table [ Appendix C
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Table 1: Performance comparison across multiple datasets, where Q&C* denotes results using
PTQ4DiT (u et al., 2024) with Deepcache (Ma et al. 2024b), Q&Ci denotes results using
PTQ4DiT Wu et al. (024) with caum-to-cache (Ma et al. 2024a), Q&C denotes APQ-DM (Wang
al. 2023 with Deepeache (Ma al. 2024b).
(a) ImageNet 256 x 256 with W8A8
(b) ImageNet 512 512 with W4A8
Steps
Method
Speed
FID
sFID 1 IS
Precision f
Steps
Method
Speed
F[D
sFID
IS
Precision f
DDPM
1x
4.53
17.93
278.50
0.8231
DDPM
1x
9.06
37.58
239.03
0.8300
PTQ4DM
2x
21.65
99.98
134.22
0.6342
PTQ4DM
2.5x
70.63
57.73
33.82
0,4574
O-Diffusion
2x
5.57
18.22
227.50
0.7612
Q-Diff
2.5x
62.05
57.02
29.52
0.4786
PTQD
2x
5.69
18.4
224.26
0.7594
PTOD
2.5
81.17
66.58
35.67
0,5166
250
100
RepQ
2x
4.51
18.01
264.68
0.8076
RepQ
2.5x
62.70
73.29
31.44
0.3606
PTOADIT
2x
4.63
17.72
274.86
0.8299
PTQADiT
2.5x
19.00
50.71
121.35
0.7514
FORA
2.06x
5.06
18.12
263.84
0.8152
Q&C
3.75x
18.76
50.14
120.31
0.74.95
Q&C
3.02x
4.75
18.13
265.5
0,8210
Q&CT
19.05
50.71
121.11
0.7533
Q&CT
3.12x
4.68
17.84
268.65
0.8195
DDPM
2X
11.28
41.70
213.86
0.8100
DDPM
2,5x
5.00
17.65
274.78
0.8068
PTQ4DM
5x
71.69
59.10
33.77
0,4604
PTQ4DM
5x
15.36
79.31
172.37
0.6926
Q-Diff
5x
53.49
50,27
38.99
0.5430
Q-Diffusion
5x
7.93
19.46
202.84
0,7299
PTQD
5X
73.45
59.14
39.63
0,5508
100
PTQD
5x
8.12
19.64
199.00
0.7295
50
RepQ
5X
65.92
74.19
30.92
0.3542
RepQ
5x
5.20
19.87
254.70
0.7929
PTONDIT
5X
19.71
52.27
118.32
0.7336
PTQ4DIT
4.73
17.83
277.27
0.8270
Q&C
6.0%
20.18
52.45
117.34
0.7285
Q&C
6.95x
4.83
18.24
265.52
0.8056
Q&CI
6.5x
19.71 52.26 118.45
0.7342
Q&Cl
7.23x
4.75
18.02
267.96
0.8065
DDPM
3x
5.22
17.63
237.8
0.8056
(c) The Generality with more datasets (W8A8)
Q- g-Diffusion
PTQ4DM
10x
10x
14.61
17.52
84.28
27.57
154.08
153.01
0.6601
0.6574
Step Method
Speed
LSUN-Bedroom
LSUN-Church
50
PTQ4DiT
PTQD
RepQ
10x
10x
10x
15.21
7.17
5.45
23.67
27.52
19.50
224.83
250.68
151.60
0.7496
0,7882
0.6578
PTO4DM
DDIM
2x
1x
FID
6.39
7.48
[sFID
12.42
9.45
I LsFID
2.23
2.45 10.98
10.98
17.28
16.16 2.76
2.76
Learn-to-Cacee
Q&C"
12.05x
6.3x
5.21
5.56
17.60
19.73
245.45
248.96
0.8122
0.7812
100 Q-Diff
APQ-DM
2x
2x
7.04
6.46
11.82
12.24
2.55
2.27
12.72
9.04
16.96
16.74
2.84
2.72
Q&C
3
6.52
1183
2.55
16.73 2.83
Q&CT
12.7x
5,43
19.52
250.68
0.7895
The experimental findings demonstrate that Q&C exhibits strong generalization, being broadly applicable across diverse model architectures, generation tasks, and various combinations of quanti- methods and settings.
4.4 ABLATION STUDY
Individual Contributions of TAP and VC To assess the effectiveness of TAP and VC, conducted an ablation study using the W8A8 quantization setup on the ImageNet dataset at resolution of 256 256, employing 50 sampling timesteps. We evaluated three method variants: (i) Baseline, which leverages the latest quantization and cache techniques, specifically PTQ4DiT (Wul jt al. 2024) combined with Learn-to-Cache (Ma et al. (2224) on DiTs; (ii) Baseline TAP, which selects optimized calibration dataset via TAP; and (ii) Baseline TAP + VC, incorporating both components. The results, presented in Table demonstrate performance improvements with each added component, underscoring their effectiveness.
Notably, the results reveal that TAP and VC contribute significantly to the quality of generated outputs, indicating that our experiments in See 2.2 accurately identified key challenges in the combined use of quantization and cache, and that our methods effectively address these issues. Specifically, the simple stacking of state-of-the-an quantization and cache methods in the baseline led to sharp drop in generative quality, whereas adding TAP and VC resulted in substantial improvements, reducing FID by 8.24 and sFID by 6.34, significantly outperforming the baseline.
Table Ablation study on ImageNet 256 x 256 for 50 timesteps
Method
FID
sFID 4
Precision
Speed
5.22
17.63
237.8
0.8056
5x
PTQ4DIT
5,45
19.50
250.68
0.7882
10x
Baseline
13.67
25.86
189.65
0.7124
11.5x
VC
9.65
22.34
210.35
0.7445
12.1x
+ TAP
8.34
21.65
220.67
0.7566
12.3x
+TAP +VC
5.43
19.52
250.68
0.7895
12.7x
To demonstrate the superiority of the TAP method, we compare it with several common clustering methods ImageNet 256 x 256, covering representative algorithms
Effectiveness of TAP
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from vartition-basede density- based, and hierarchical clustering approaches. Specifically, select K-Means (Likas et al., 22003), DBSCAN (Deng 2020), and Agglomerative (Murtagh Legendre
2014
Clustering for comparison. The results are Tab 4
Table 3: Ablation for similarity Matrices a
Table 4: TAP with Different Clustering Methods
FID
L sFID
IS
Precision
Method
FID
sFID
IS
Precision
0.3
5.57
19.58
248.63
0.7823
KMeans
10.31
23.65
195.43
0.7332
5.46
19.55
250.62
0.7863
DBSCAN
10.12
23.21
201.35
0.7365
5.43
19.52
250.68
0.7895
Agglomerative
9.56
22.13
202.12
0.7345
5.36
19.56
249.53
0.7875
TAP (ours)
8.34
21.65
220.67
0.7566
0.7
5.45
19.46
248.96
0.7725
TAP leverages spatial data distribution and temporal dynamics to construct similarity matrices. To assess the impact the parameter a in Equation 6 we conducted ablation experiments on ImageNet 256 256, with the results shown in Table
Hyperparameters in TAP
Visualization of VC To examine whether the VC effectively mitigates exposure bias, we provide comprehensive visualizations in the Appendix G.2
5 DISCUSSION: WHY QAT Is NOT USED AND WHETHER IT SOLVES THE QUANTIZATION-
INTERACTION
Quantization-awae aware training (QAT) known to provide higher robustness than post-training quanti- zation (PTQ). However, QAT is not practical option for diffusion transformer (DiT) models in our target setting, and it does not resolve the core interaction between quantization and cache reuse. We summarize the key reasons below.
(1) QAT is prohibitively expensive for diffusion models. Performing QAT requires end-to-end retraining with simulated quantization throughout the forward pass. For large DiT architectures (e.g.. Open-Sora 11B), single epoch of QAT cost hundreds of GPU hours. Our goal, in contrast, is to enable a tightweight and scalable quantization pipeline that completes within minutes and applies to any pre- pre-trained model.
(2) QAT violates the plug-and-play requirement. The proposed method is designed support drop-in deployment, requiring: (i) no access to original training data, (ii) no fine-tuning or retraining pipeline, and (iii) only a small calibration set.
QAT, however, demands access to the full training dataset well as the full training infrastructure, making it impractical in realistic model deployment scenarios.
(3) QAT does not address the quantization-cache consistency issue. Even with QAT, the numerical stability of cached intermediate features across diffusion steps remains unresolved. QAT optimizes robustness against quantization noise but does not enforce the step-to-step cache consistency required by cache reuse mechanisms. Therefore, QAT is insufficient for stabilizing cached values under diffusion dynamics.
Experimental Comparison. Following prior work Nagel et al. 12021) we conduct QAT on DiT/XL-2 for ImageNet x 256 with 50 diffusion steps. QAT uses the full ImageNet training set and is trained on 8 A100 GPUs. In contrast, Q&C pipeline requires only one A100 and an 800-sample calibration set. Results are summarized in T
Table 5: QAT vs. Q&C on DIT/XL-2 for ImageNet 256x226 (50 steps).
Method
FID
sFIDJ
1St
Precision?
DDPM (FP)
5.22
17.63
237.8
0.8056
QAT
5.67
18.42
225.58
0.7565
QAT DeepCache
8.95
23.23
200.15
0.7351

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The results show that QAT alone does not recover full full-precision performance, and combining QAT with caching leads to further degradation due to unresolved cache- quantization inconsistency. These findings indicate that PTQ-aware cache mechanism remains necessary even in the presence of QAT.
6 RELATED WORK
Enhancing the efficiency of diffusion models has become increasingly necessary due to the high computational cost associated with larger models Gao et al. (2025); Ding et al. (2025); Lu et al. (2025); Xie et al. 2025 Li al. (2025). Quantization and cache mechanisms offer promising approaches to improve the computational efficiency of diffusion models.
Quantization methods such Post- Jost-Training Quantization (PTQ) Ding et al. (2023): Wu al.
(2023) have gained attention for their ability to reduce model size and inference time without requiring retraining, making them computationally efficient. Unlike Quantization- -wwai Training
(QAT), PTQ only requires minimal calibration and can be implemented in data-free manner by generating calibration datasets using the full-precision model. Techniques like Q-Diffusion (Li et (0233b apply PTQ methods proposed by BRECQ (Li et al. 2021) to optimize performance across various datasets, while PTQD (He et al.) 2024) mitigates quantization errors by integrating them with diffusion noise. More recent work, such EfficientDM (He et al., 2023). fine-tunes quantized diffusion models using QALoRA (Xu et al. 2023] Han et al. 2024). while HQ-DiT (Liu
& Zhang, 2024) adopts low-precision floating-poiis formats, utilizing data distribution analysis and
random Hadamard transforms to reduce outliers and enhance quantization performance with minimal computational cost.
Cache aims to mitigate the computational redundancy in diffusion model inference by leveraging the repetitive nature sequential diffusion steps. cache in diffusion models leverages the minimal change in high-level features across consecutive steps, enabling reuse of these features while updating
only the low-level details. For instance, studies (Ma et al.) 2024b) Wimbauer al. 2024) reuse feature maps from specific components within U-Net architectures, while (Hunter et al. 2023)
focuses on reusing attention maps. Further refinements by (Wimbauer et al., 2024 So et al., 2023
Ma et al. 2024b) involve adaptive lifetimes for cached features and adjusting scaling maximize reuse efficiency. Additionally, (Zhang et al., 2024) identifies redundancy in cross-atte coos-atttntion during fidelity improvement steps, which can be cached to reduce computation.
Previous research has accumulated extensive work in both quantization and caching. However, there has been little exploration into how these two acceleration mechanisms be combined effectively and the challenges that arise from their integration. This work aims to identify these challenges and address them systematically.
CONCLUSION
In this paper, we systematically investigated the impact of integrating quantization techniques with caching mechanisms in efficient generative models, from both theoretical and empirical perspectives. Our study identified key challenges arising when quantization applied in conjunction with cache, particularly redundancy in calibration datasets and the exacerbation of exposure bias. To address these issues, we proposed two complementary techniques: Temporal-Aware Parallel Clustering (TAP) to enhance calibration efficacy, and Variance Compensation (VC) strategy to mitigate exposure bias. Experimental results demonstrate that the combination of TAP and VC substantially improves generation quality while preserving computational efficiency. Furthermore, our findings show that the proposed Q&C framework exhibits strong generalization, being broadly applicable across diverse model architectures, generation tasks, and various quantization-cache configurations. We believe that this work paves the way for more efficient and effective generative pipelines.
ACKNOWLEDGEMENT
This work was supported in part by NSFC under Grant 62371434 and U25B2010, the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20252293, the China Postdoctoral Science Foundation-Anin Anhui Joint Support Program under Grant Number 2024T017AH, China Postdoctoral Science Foundation under Grant Number 2025M783529, Anhui Postdoctoral Scientifie Research
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Program Foundation (No.2025A1015), the Fundamental Research Funds for the Central Universities (No. WK2100250064), ZGCA
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A LARGE LANGUAGE MODELS (LLMS) IN PAPER WRITING
In this work, Large Language Models (LLMs) were only employed for proofreading, spelling, and formatting checks. LLMs did not contribute to any aspect of research idea generation, experimental design, or coding.
B THE COMPARISON WITH CONCURRENT WORK
We provide systematic comparison between our Q&C method and two representative contem- porancous works works-QuantCaee QuantCache (Wu et al.) 2025) and CacheQuant (Liu et al. 2025) from three perspectives: problem formulation, technical mechanisms, and empirical performance.
Moreover, the contemporaneous works did not effectively analyze the fundamental issues arising from the combination of quantization and caching. Instead, they adjusted the system dynamically to achieve better integration. In contrast, Q&C not only proposes innovative methods to improve the synergy between quantization and caching but also conducts extensive experiments to analyze the intrinsic phenomena of their interaction.
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Table 6: Differences in Problem Formulation Compared with Concurrent Works
Comparison
QuantCache
CacheQuant
Q&C
Focus the collaborative conflict problem of Q&C itself, and the first mechanism
Accelerate DITs
Accelerate diffusion models in image generation tasks
objectives
in Video Generation Tasks
to identify and deal with its linkage error
Broadly applicable to any task built upon diffusion- based architectures.
Application scope
Video-level inference
UNet and DiT
Table 7: Differences in Technical Mechanisms Compared with Concurrent Works
Comparison
QuantCache
CacheQuant
Q&C
Exposure Bias
Andirrettly mitigated
Explicitly modeled, with VC correcting distribution shift
Not uddressed
Modeling
via correction
Quantzation-Cuchee Synergy
Focused only on redundancy
Modeled via
Optimizes calibration quality via TAP, lightweight and direct
dynamic programming
Training / Scheduling
Requires retraining pruning stats
Training- -fee but needs path scheduling
@ Fully training-free frree,
Dependency
scheduling model changes
Core Advantage
Reduces cache redundancy only
Complex scheduling,
VC: & first training-free free drift correction:
limited ficxibility
TAP: boosts robustness & generality
C
THE ANALYSIS OF GENERALIZZAION
The Generalization on Different Quantization-Cache Combinations To further demonstrate the generality and robustness of our method across different quantization strategies and cache designs, include six additional baselines and conduct comparisons on both the ImageNet-256 and LSUN-Bedroom datasets:
Basel: Standard Quantization
NNaeel et al. 2021 DeepCache (Ma et al.
2024b
Base2: PTQ4DiT (Wu et al.
2024
Leam-tt- (Ma et al.)
2024a
Base3: APQ-DM Wang et al.
2023)
+ DeepCache (Ma al. 2024b
Base4: PTQ4DiT Wu et al.
2024)
+ DeepCache (Ma et al. 2024b)
Base5: PTQ4DiT (Wu et al.
2024
+ FORA (Selvarauu et al.
2024
Baseó: Q-Diff (Chen et al.
2025
+ DeepCache (Ma et al.
2024b)
The Generalization on Different Quantization Setting generality of our method, we validate Q&C using Q-DiT (Chen al. 2025) combined with Learn-to Cache (Ma et al. 2024a) under 4-bit and 6-bit settings. The experimental results are presented in Table
To further demonstrate the robustness and
All experiments were conducted on single NVIDIA A100 GPU. Note that for the W6A8 setting, due the lack of dedicated hardware acceleration operators, we only provide a theoretical acceleration ratio.
D THEORETICAL FOUNDATIONS OF Q&C
D.1 A MORE DETAILED THEORETICAL ANALYSIS OF THE CALIBRATION DATASET DEGRADATION
1. Loss of Diversity Due to Caching In traditional PTQ setups, intermediate features are sampled uniformly from the full diffusion process:
Dealib = {ax = ea (2o. t) |t ~ ((1TT)},
(13)
which leverages the smooth transition across steps to ensure coverage of diverse feature distributions.
However, when caching is used, the calibration set is reduced to features from few fixed timesteps:
Denche {xre 1 Teachey Teachel
T).
(14)
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Table 8: Performance Comparison with CacheQuant on Text-Conditional Generation Tasks
Datasets
Image256
MS-COCO
PartiPrompts FIDI
Metric
FIDJ
IS7
Speed
IS f
Speed
Speed
DiT/LDM
6.02
246.24
1x
41.02
lx
27.23
1x
CacheQuant
12.42
173.17
3.08x
39.41
5.18x
27.15
5.2x
Q&C
6.23
244.68
3.22x
40.95
5.53x
26.35
5.3x
Table Performance Comparison with Quanteache on Video-generation task
Motion
Subject
Aesthetic
Imaging
Dynamic Scene
Open-Sora
98.42
96.44
95.20
60.07
59.66
33.33
41.72
Q-DiT
95.72
95.01
91.68
58.68
56.54
38.88
34.06
PTQ4DiT
98.02
96.33
96.23
58.40
53.29
37.50
36.36
QuantCache
98.52
96.12
94.62
58.57
55.94
31.94
36,92
Q&C
98.62
96.34
95.10
59.15
57.33
32.34
38.52
Effectively, the calibration distribution becomes concentrated:
Peuche (It)
8(xx).
(15)
TE
We compute cosine similarity between features:
(It xz))
Sim(z;,
(16)
| ||æ,||
Caching increases [[]im]. indicating high redundancy and reduced diversity.
2. Redundancy in Late-Stage Features As / - (closer to mp), sample variance decreases:
lim Var(x,) +0
Sim(z,, 1.
(17)
We observe that as the diffusion proceeds towards denoised outputs (closer to æo), the sample similarity increases dramatically. In cached settings, late-stage representations are more reliable calibration dataset. However, despite their higher reliability, the redundancy among these samples grows, with similarity exceeding 60% in some cases. This saturation effect means that many cached samples are nearly identical, and thus fail to provide new calibration signal.
Inefficient Use of Calibration Budget A fixed calibration budget (c.g., 250 samples) is inefficient when many samples are near-duplicates. The quantization objective:
min Ex-Dam [[[((z) x||²] Q
(18)
fails to capture error in underrepresented regions, especially early/mid diffusion steps.
Summary Caching introduces redundancy and distributional bias in the calibration dataset:
Diversity is reduced by over-sampling fixed steps
Late-stage features dominate but offer limited marginal gain
Calibration becomes less representative, hurting quantization accuracy
This raises the following question: Can increasing calibration samples alone resolve the issue? To evaluate this, we followed the same setup as PTQ4DiT: 25 timesteps x32 samples for 256 x 256 resolution (total 800 samples).
We conducted experiments using DiT-XL/2 (50 steps) on ImageNet with PTO4DiT+Lean-tto (the current of-t--ofthhearn quantization and caching method), increasing the sample count 1600, 3200, and 6400.
Even with 6400 samples-requiring over 2.5 days of computation single A100 the performance still lagged behind our proposed Q&C method. This highlights that increasing sample size alone is insufficient, and our method achieves better results with far less cost, due its efficient calibration.
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Table 10: Performance under Different Quantization-Catt Combinations, Here, 5x denotes 5x speedup, i.e., using only 50 steps compared to the standard 250-step setting.
ImageNet 256
LSUN-Bed
Model
FID.
sFID4
1St
Pret
Speedt
Model
FID
sFID] 1St
Speed
DiT
5.22
17.63
237.8
0.8056
5x
LDM
6.39
9.45
2.45
1x
Basel
28.34
102.25
113.34 0.6023
12.1x
Base
15.56
23.32
1.85
3x
&&
14.35
26.55
190.89
0.7082
12.1x
BascI+Q&C
10.21
15.32
2.15
3x
Base2
13.67
25.86
189.65
0.7124
12.5x
Base3
11.38
16.55
1.96
3x
Base2+Q&C
5,43
19.52
250.68
0.7895
12.7x
Base3+Q&C
6.52
11.83
2.55
3x
Base4
14.56
27.71
188.63
0.7066
12.1x
Base6
12.87
17.94
1.95
3x
Q&
5.56
19.73
248.96
0.7812
12.05
Base6 Q&C
7.01
12.25
2.31
3x
Base5
15.97
27.62
175.35
0.7054
12.2x
Base5+Q&C
5.12
18.33
265.55 0.8149
12.3x
Table 11: Performance under Different Quantization Cache Combinations
W/A
Method
FIDI
sFID
IS 1
Precision Speed
16/16
FP
12.40
19.11
116.68
0.6605
1 X
6/8
Q-DIT
12.21
18.48
117.75
0.6631
2.6
6/8
15.56
21.65
100.56
0.6113
3.58 x
6/8
Q&C
12.32
18.56
116.56
0.6559
3.77
4/8
Q-DIT
15.76
19.84
98.78
0.6395
4
4/8
Q D-I--lear--o--aaee cache
18.95
22.34
85,92
0.5685
5.52 x
4/8
Q&C
16.12
20.05
99.52
0.6385
5.79 x
D.2 THEORETICAL OF VC FOR MITIGATING EXPOSURE BIAS
Let the intermediate feature representation at diffusion step be a random variable X,, whose distribution during calibration is
N
(19)
where μ4 is the mean and of is the variance.
During actual sampling, due to quantization error and caching mechanisms, the feature distribution shifts to
X, (i,, of)
(20)
The distributional drift that causes exposure bias can be written as:
Au
Pe:
A 0 -
(21)
where Au, and Aot respectively capture the mean shift and variance shift at timestep L. Extensive experiments in Appendix [nd Section 2.2 show that the mean drift generally negligible:
Apdl s 0,
(22)
while the variance drift is significant:
Ao >
(23)
The diffusion process heavily relies on the variance of features to regulate uncertainty and noise scale. Variance drift causes inaccurate noise estimation, which accumulates errors step-by-step. leading to exposure bias. Specifically:
1.if variance shrinks, the model becomes overconfident and reduces sample diversity;
2. variance expands, excessive noise destabilizes generation.
To address this, we introduce Variance Correction (VC) by defining correction factor:
K Tt 02
(24)
and rescale the sampled feature as
X K, (X. (a)) pt-
(25)
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Table 12: Impact of Increasing Calibration Samples
Model
Num of Sample
FID.
sFID 4
IS
Precision
Time(h) (single A100)
5.22
17.63
237.8
0,8056
800
13.67
25.86
189.65
0.7124
7.97
1600
13.52
25.67
191.34
0.7152
16.85
DiT-XI/2-256
3200
11.26
24.42
200.25
0.7226
32.62
6400
8.52
21.25
209.62
0.7345
64.34
Q&C(800)
5.43
19.52
250.68
0.7895
8.18
This operation restores the expected variance of the feature distribution, mitigating the adverse effects of variance drift. As result, it significantly reduces error accumulation, enhances sampling stability, and improves overall generation quality.
In summary, variance correction offers lightweight, training-free, and effective mechanism to compensate for the dominant form distribution shift-variance drift-thereby mitigating exposure bias without introducing additional model complexity.
D.3 THEORETICAL OF SYNERGY BETWEEN TAP AND VC
Let X, denote the feature distribution at timestep t and let Z be the set of calibration samples.
VC estimates correction term
A
Onfferecce 0Z-
The effectiveness of VC depends on the accuracy of in approximating Oinference
TAP enhances this approximation by minimizing the distributional shift
B [az,] Oinference l|2
across all relevant timesteps T.
Thus, TAP indirectly reduces the correction magnitude needed by VC and stabilizes the estimation of variance across channels.
D.4 THE DETAILED DERIVATION OF THE TRANSFORMATION PARAMETER K,
We define the optimal transformation parameter K, by minimizing a combined objective of the relative quadratic normalized squared residual (OONSR)(Finellnn and mean squared error (MSE) between the transformed prediction 2, and the reference z/:
al.) 2019 Yao et al.) 2024)
K, argmin (rQNSR(Z,, MSE(E,, xf))
(26)
K,
where N denotes the number of samples used during optimization, 5, is defined as the affine- transformed prediction:
Fin = Ki pt) +
Substituting equation 26 into the expanded objective, we obtain the loss function:
N
L(Ki In
KK (K, Me) + Me x'(,,))
N
Ki - 11) + thr I.e.
(27)
N
Expanding the terms gives:
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((K,) =
1
N
(Ei, - m1) K? +
1 N
Σ
itn - 141
K?
N
N
(rk
==1
n=1
2
(ft, (2)( Mc) Kk,
N
2
(āt,n M2)( Me)) K₁
N
I/200
+
N
(1 - -1)
1
Σ
N
(4 Ms) (T2
(28)
N
N
==
m=1
Since this is convex quadratic function with respect to K,, the optimal solution can be obtained by taking the derivative and setting it zero:
aC(K,)
(29)
oK,
Solving equation 29 leads to the following closed-form solution:
K, En 1 - (e)(E 14) + En (d U4)
(30)
En - M1) + Σ
(3r (4)
(
E RUNTIME OVERHEAD OF TAP
We conducted experiments single A100 GPU using PTQ4DiT example, with the DiT-XL/2- 256 model under sampling steps 50, and 100. We report the runtime breakdown to show the actual cost of each component. The comparison reveals that TAP brings substantial improvements while having minimal impact on the overall runtime- it accounts for only a small fraction (just 2%-3%) of the total quantization time.
Table 13: Runtime overhead of TAP
Method
Calibration weight gunnt éxtt Act ivatv i Longund Reconguant
Overheadrat
PTO4DiT 50
14.75
16.57
13.67
433.23
0
Q&C
27.6
16.57
13.67
433.23
2.6%
PTQ4DiT_ION Q&C
25.3
16.57
13.67
433.23
0
40.4
16.57
13.67
433.23
2.9%
F EVOLUTION OF SAMPLE VARIANCE WITH QUANT-CACHE
We provide table of numerical changes to more clearly illustrate this effect. As denoising progresses, we observe that the variance error approaches nearly 100% % highlighting the significant impact of the quantization-cache interaction.
Table 14: Runtime overhead of TAP
Variance
50
20
DiT
0.98
0.21 0.98 0.96
0.95
0.84
0.58
0.38
Quantization
Nagel et al.
2021
+ cache (M et al
2024b
0.92
0.71
0.52
0.38
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G ANALYSIS OF EXPOSURE BIAS IN DIT MODELS WITH OUR METHODS.
G.1 BACKGROUND OF EXPOSURE BIAS
Exposure bias is well-known issue observed in generative models, such as diffusion models and autoregressive text generation models. This bias stems from discrepancies between training and inference phases, which can lead to significant performance degradation during sampling.
When comparing the training and inference processes in diffusion models, such in line 4 of Algorithm 2 and line 8 of Algorithm 3 fundamental difference emerges. During training, diffusion models use Ey (f+:f).whee) where I₁ ground truth sample, to predict the noise. However, during inference co (0 t) is deployed, where d; is derived from the model's output at the prior step & + 1. this lead to discrepancy known as raingin-inference mismatch, where training relies on ground- truth sequences, while inference conditions previously generated outputs (Ranzato et al. 2015 Schmidt 2019 Rennie et al. 2017 Ning et al. 2023).
Algorithm 2: DDPM Training
Algorithm 3: DDPM Sampling
repeat
Sample दτ ~ (0, for T to do
20 ((x0)
to Uniform( TT)
if t > 1 then
c IO Compute Iz
Sample (O,
Take a gradient descent step on Volle c0(x,, 1)l2
else
Set
until
f1-1
converged
(1 1-a: co( + 0z2
vT-a.
return
do
G.2 VISUALIZATION OF METHOD EFFECTIVE
Visualization of Exposure Bias in DiT Models Using VC We conducted experiments on exposure bias using our methods and found that our approach significantly mitigates the exacerbation of exposure bias caused by the combined use of quantization and cache mechanisms.
Original model
Quant Cathe model
2.2
Ours
2.0
MSE
1.8
1.6
1.4
40
30
Timestop
Figure 4: Analysis of exposure bias in DIT models with our methods.
Visualization of Sample Efficacy in the Calibration Dataset for DiT Models Using TAP To demonstrate the improvement of the quantization calibration dataset after applying TAP, we conducted the following visualization experiment on DiT: First, we followed the current SOTA PTQ4DiT calibration dataset generation method compute the cosine similarity matrices under both cached
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and non-cached settings. Then, applied TAP and computed the improved cosine similarity matrix. can be observed that the effectiveness of the calibration dataset is improved after applying TAP.
H EXPERIMENTAL EVALUATION OF THE EFFECTIVENESS OF SIMILARITY MATRICES PINAL
TAP leverages spatial data distribution and temporal dynamics to construct similarity matrices. To assess the impact of the Spatial Similarity Matrix A (9) spatial and Temporal Similarity Matrix A we
conducted ablation experiments, with the results presented Table. [5 This fully demonstrates the superiority of our similarity matrices design.
Table 15: Ablation study on ImageNet 256 x 256 for similarity Matrices
method
FID 4
sFID
IS
Precision 1
Baseline
13.67
25.86
189.65
0.7124
w/ (8)
spoiia w/o A (1)
temporal
5.89
20.67
245.63
0.7713
w/ spocial w/ A Agmmpnal
5.43
19.52
250.68
0.7895
I SUPPLEMENTARY EXPERIMENT FOR CHALLENGE 2: ANALYSIS OF MEAN DISTRIBUTION ACROSS THE GENERATION PROCESS.
To comprehensively analyze the distributional changes over the generation process, we also examined the change in the mean of the sample distribution. As shown in Figure regardless of the method used, at the beginning of the denoising process, the span variance narrow, and the changes in mean remain stable, fluctuating around O. As the noise is gradually removed from the white noise, the variance distribution the ground truth samples spans approximately (0, 0.6). This indicates that the mean does not exhibit significant shifts during the process and highlights the need correct variance during the later stages generation to mitigate its negative impact on exposure bias.
1.0
0.3
Mean(x:)
Mean(x) (x.)
0.6
0.4
0.4
50
40
10
0.0
30
20
10
Timesteps
Timesteps
Figure 5: Comparision of the density distribution of the mean of 5000 samples from Imagenet across difference time steps. They illustrate the change in sample distribution mean at various time steps, shown for case without (left) and with (right) quant-cache. As the diffusion progresses, the variance of sample distribution starts to deviate towards Gassian white noise.
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12
12
Spead (1))
Spood (1X)
DDLM
DDEM
Q-Dxffusson
RepQ
RepQ
PIQADIT
PIQADIT
leant eache
Tecre to eache
trade-off across different settings. Bubble size represents the ratio of relative speed-up to generative quality compared to the DDPM baseline at 250 timesteps. We compare various methods in terms of FID (left) and sFID (right) performance across 50, 100, and 250 timesteps. Our method consistently appears the upper-left region across all settings, achieving maximum acceleration while preserving generative quality.
Figure 6:
Tigure@Jin the appendix further illustrates the afficiency-efficcn trade-off across different configu- rations, showing that our approach achieves performance comparable to original models (c.g.. 250 timesteps, DDPM) but with markedly reduced computational costs (up to 12.7x x improvement), providing a practical solution for high-quality image generation. Across all tested settings, our method consistently occupies the upper-left region of the performance-effictee mainstream alternatives and reinforcing its effectiveness and adaptability.
space, surpassing
J SPEED-UP COMPUTATION AND NON-MULTIPLICATIVE EFFECTS.
The overall acceleration reported in paper is measured end-to-end relative to the FP16 250-step diffusion baseline without cache reuse. Accordingly, the final acceleration integrates the contributions from (i) diffusion step reduction, (ii) PTQ4DiT quantization, and (iii) cache reuse:
Total Speed-up (Step Reduction Speed-up) (Quantization Speed-up) (Cache Speed-up).
Thus, the reported 12.7x speed-up already reflects the combined improvements of all three compo- nents.
Measurement Setup. For clarity and reproducibility, all latency measurements follow the configu- ration below:
Hardware: NVIDIA A100-SXM-80GB
Batch size:
Resolution: 256
Latency metric: end-to-end
Framework: PyTorch 2.3 with CUDA Graphs
The measured latencies are summarized in Table 16
Model
FP16
W8A8
W4A8
Learn-to-Cache
Latency (%)
100
51
39
78
Table 16: Measured latency ratios under the standard evaluation setup.
Correspondingly, the quantization-only speed-ups are:
W8A8: 1.96x,
W4A8: 2.56x,
Learntto-Cache: 1.28
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K
GENERALIZATION TO FLUX AND PIXART-E.
To further demonstrate the strong generalization ability of Q&C, we additionally evaluate the method on two recent high-quality diffusion models: FLUX.1 (Black-Forest-Labs 2024) and PixArt-
(Chen et al. 2024a). As shown in Table [7 Q&C consistently restores or improves performance across all metrics, even when the underlying INT4 quantizer and DeepCache introduce severe degradation.
MJHQ Metrics
Method
FID (L)
IR (1)
LPIPS (J)
PSNR (1)
SVDQuant (FLUX.I) (INT W4A4)
19.9
0.935
0.223
21.0
SVDQuant + DeepCache (FLUX.1)
23.85
0.890
0.277
18.9
SVDQuant + DeepCache + Q&C (FLUX.1)
20.5
0.93
0.23
20.6
SVDQuant (PixArt->i) (ITT W4A4)
19.2
0.878
0.323
17.6
SVDQuant DeepCache (PixArt-E)
25.6
0.762
0.585
14.3
SVDQuant + DeepCache + Q&C (PixArt-E)
20.1
0.87
0.323
17.9
Table 17: Evaluation on FLUX,1 and Pix
These results confirm that both the observed inconsistency problem and our proposed Q&C solution generalize reliably across architectures, quantizers, and model families.
L IMAGE GENERATIONS WITH OUR METHOD ON DIT.
Figure 7 Supplementary visualization results of our method, accelerated by 12.7 x compared to the 250-step DiT model, on ImageNet 256x256. Our method generates outputs that closely resemble those of the 250-step model, maintaining high fidelity to the original results.
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OBc
Figure 7: Image generations with our method on DiT. The image sizes are 256 256. The control group consists of images generated using DDPM with 250 steps.
25


Reviews:
[{"review_id": "YfjPw4EkNi", "reviewer": "Reviewer_yPDm", "summary": "The paper find two key challenges that degrade performance when combining quantization and cache. To address these issues, the authors propose a temporal-aware parallel clustering and a variance compensation strategy. Higher speedup is achieved with image quality slightly hurted.", "questions": "Please see the weaknesses above, and:\n\n1. Can the authors explicitly clarify the difference between their approach in Section 3.2 and the one presented in [1]?\n\n2. Can the authors provide results for the \"quantize-first, then-cache\" pipeline? (Perhaps also showing its performance when combined with Variance Compensation).", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. First paper I'm aware of to combine quantization and cache for image generation.\n2. The proposed Temporal-Aware Parallel Clustering is interesting. \n3. The ablation study is detailed and validate the effectiveness of proposed approaches.", "weaknesses": "1. Section 3.2 (Variance Compensation) lacks novelty. The approach appears almost identical to that in [1] (which the authors cite), making it difficult to count as a separate contribution.\n\n2. Why not perform quantization calibration before applying the cache? This way, the cache could better utilize the output of the quantized layers to decide what to store, and it would also avoid the calibration data issues mentioned.\n\n3. The chosen quantization method is relatively old. Combining the proposed cache mechanism with newer DiT quantization methods, such as SVDQuant[2] or ViDiT-Q[3], would strengthen the paper's impact.\n\n4. Experiments were only conducted on DiT-XL/2, showing a lack of generalizability. Experiments on more recent models like FLUX or PixArt would further enhance the paper's persuasiveness.\n\n5. The reported speedup ratios lack empirical validation. The authors didn't describe how the speedup is measured. Previous quantization works like Q-diffusion and PTQD did not provide latency results. Clearly, the actual performance often falls short of theoretical claims—for example, MixDQ[4]'s tests show that W4A8 reduces VRAM usage by 3x but only achieves a 1.45x speedup. Yet, without any explanation, the authors claim that W8A8 can deliver a 2x speedup and W4A8 a 2.5x speedup. Therefore, I have reasonable doubts about the reported speedup of the proposed approach.\n\n[1] Timestep-Aware Correction for Quantized Diffusion Models\n\n[2] Svdquant: Absorbing outliers by low-rank components for 4-bit diffusion models \n\n[3] ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation \n\n[4] MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization", "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": "owJVubafed", "reviewer": "Reviewer_gdd2", "summary": "This paper addresses the challenge of enabling both caching and quantization in DiT models.  The authors first identify two key obstacles: exposure bias amplification and reduced effectiveness of calibration datasets.  They then address these issues by proposing Temporal-Aware Parallel Clustering (TAP) and a variance compensation technique. Extensive experiments and ablation studies demonstrate that the proposed approach achieves notable speed improvements with only minimal degradation in downstream task performance.", "questions": "•\tHow is the speed-up calculated?\n\n•\tWhy is the combined approach not achieving greater speed-up compared to individual techniques? For example, PTQ4DiT reports a 10x speed-up and Learn-to-Cache achieves 6.3x. Why doesn’t the combined method exceed 12.7x?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "•\tThe paper tackles a well-motivated problem, clearly isolating the issues and addressing them with thoughtful solutions.\n\n•\tComprehensive experimental evaluation across multiple tasks, including detailed ablation studies, supports the claims of improved efficiency.", "weaknesses": "•\tThe results lack statistical rigor. Without confidence intervals or similar measures, it is difficult to assess the significance of the reported differences.\n\n•\tCompared to techniques that employ only caching or quantization, the observed speed-up is relatively modest and, in some cases, comes at the cost of performance degradation.", "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": "McvVcRLp9L", "reviewer": "Reviewer_2MVF", "summary": "This paper tackles a very practical problem in accelerating generative models, specifically diffusion transformers (like DiT). The goal is to combine two standard speedup techniques: quantization (using fewer bits for weights and activations) and caching (like the K-V cache in transformers, which saves past computations).\nThe authors' key finding is that just \"turning both on\" doesn't work—in fact, it severely degrades performance. The paper then diagnoses why this happens (they identify two major challenges) and proposes two novel methods TAP and VC, that allow these two techniques to work together effectively. The end result is a massive speedup (up to 12.7x) on ImageNet generation while maintaining the quality of the original, slow model.", "questions": "1.\tWhy the quantized model in Figure 2 does not show too much higher accumulated error than the original one? It’s inconsistent to the results in [1].\n\nReference:\n\n[1] Yanjing Li, Sheng Xu, Xianbin Cao, Xiao Sun, and Baochang Zhang. Q-dm: An efficient low-bit quantized diffusion model. Advances in Neural Information Processing Systems, 36, 2024.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tThis is a strong, high-quality paper. The originality doesn't come from inventing a brand-new algorithm, but from investigating a subtle, negative interaction between two known techniques that everyone assumed would just work together. Identifying, analyzing, and solving this kind of interaction problem is a very valuable contribution.\n\n2.\tThe significance is crystal clear. Diffusion models are notoriously slow. A 12.7x speedup on a state-of-the-art DiT model running on ImageNet is a massive practical win. It's the kind of work that could be immediately adopted by anyone trying to deploy these models in the real world.", "weaknesses": "1.\tA potential weakness is the focus on Post-Training Quantization (PTQ). PTQ is fast, but it's often outperformed by Quantization-Aware Training (QAT). My question is: Why not just use QAT? It's possible QAT would automatically learn to be robust to the cache interactions, making this complex PTQ-specific solution unnecessary.\n\n2.\tCould the authors include speed metric In Table 2 as well for a clear comparison?\n\nMinors:\n1.\tLine 411 ‘The results, presented in Table 6’ should be Table 2.", "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": "omZiaN5bL5", "reviewer": "Reviewer_itt9", "summary": "This paper proposes to jointly use quantization and cache mechanisms in image generation process. The authors identify two main challenges when integrating quantization and cache and propose the TAP and VC methods to address this. Experimental results show that the proposed method accelerates the image generation process by up to 12.7× without compromising quality.", "questions": "Please refer to weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper is well-motivated. This paper aims to combine quantization and cache techniques  simultaneously to further accelerate the image generation process. In this process, the authors identified two key challenges: (1) Amplification of Exposure Bias (2) Degradation in Calibration Dataset Effectiveness. To this end, the authors specifically propose TAP and VC as solutions. The authors also provide comprehensive comparative experiments and ablation study results to demonstrate the effectiveness of the proposed method.", "weaknesses": "Add a new column in Table 2 to include latency data for each configuration. I would like to see the impact of TAP and VC on latency.", "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)