Test-Time Iterative Error Correction for Efficient Diffusion Models
Authors: Yunshan Zhong
Weiqi Yan
Yuxin Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Test-time; diffusion;
Primary area: generative models

Abstract: With the growing demand for high-quality image generation on resource-constrained devices, efficient diffusion models have received increasing attention. However, such models suffer from approximation errors introduced by efficiency techniques, which significantly degrade generation quality. Once deployed, these errors are difficult to correct, as modifying the model is typically infeasible in deployment environments. Through an analysis of error propagation across diffusion timesteps, we reveal that these approximation errors can accumulate exponentially, severely impairing output quality. Motivated by this insight, we propose Iterative Error Correction (IEC), a novel test-time method that mitigates inference-time errors by iteratively refining the model’s output. IEC is theoretically proven to reduce error propagation from exponential to linear growth, without requiring any retraining or architectural changes. IEC can seamlessly integrate into the inference process of existing diffusion models, enabling a flexible trade-off between performance and efficiency. Extensive experiments show that IEC consistently improves generation quality across various datasets, efficiency techniques, and model architectures, establishing it as a practical and generalizable solution for test-time enhancement of efficient diffusion models. The code is available at
https://github.com/zysxmu/IEC.

Published as a conference paper at ICLR 2026
TEST-TIME ITERATIVE ERROR CORRECTION FOR EFFI- CIENT DIFFUSION MODELS
Yunshan Zhong', Weiqi Yan?, Yuxin Zhang
"choool of Computer Science and Technology. Hainan University
2 MAC Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University ys pszhong01@gmas com, weigl_yanBoutlook com, yuxinzhangestu xmu, edu.
ABSTRACT
With the growing demand for high-quality image generation resource- constrained devices, efficient diffusion models have received increasing attention. However, such models suffer from approximation errors introduced by efficiency techniques, which significantly degrade generation quality. Once deployed, these errors are difficult correct, as modifying the model typically infeasible in de- ployment environments. Through an analysis of error propagation across diffusion timesteps, we reveal that these approximation errors can accumulate exponentially, severely impairing output quality. Motivated by this insight, we propose Iterative Error Correction (IEC), a novel test-time method that mitigates inference-time errors by iteratively refining the model's output. IEC is theoretically proven to reduce error propagation from exponential linear growth, without requiring any retraining architectural changes. IEC can seamlessly integrate into the infer- ence process of existing diffusion models, enabling a flexible trade-off between performance and efficiency. Extensive experiments show that IEC consistently improves generation quality across various datasets, efficiency techniques, and model architectures, establishing it practical and generalizable solution for test-time enhancement of efficient diffusion models. The code is available at https:: /ttpp:://gitann com/ z sxmu/ IEC
INTRODUCTION
Diffusion models (Ho et al. 2020; Rombach et al.
2022 Nichol & Dhariwal 2021) have achieved
state-of-the-ard generative performance across a wide range tasks, including image synthesis (Song
et al. 20206 Choi et al., 2021 1 Saharia et al. 2022a
Tumanyan et al. 2023 Li et al.
2022 Saharia
al. 2022c Gao et al. 2023 Avrahami et al. 2022
Kawar et al. [023 Meng et al., 2021) text-to-
image generation (Nichol et al.
|021] Ramesh et al.
2022
Saharia et al. 2022b; Zhang et al. 2023b),
text-to-3D generation (Lin al. [023] Luo & Hu] 2021
Poole et al. 2022), video generation (Mei
& Patel 2023), and audio generation (Huang et al.
2022 Zhang et al.] 2023a). However, diffusion
models typically require hundreds of iterative denoising steps and contain billions of parameters, resulting in high computational costs that hinder deployment in
environments. To address this limitation, extensive efficiency techniques have been devoted to developing efficient
model techniques for diffusion models (Song et al. 2020a Zhang et al.) 2022
Zheng al. 2023
Liu et al. 2025b
Shang et al. 2023 Li et al. 2023
2024c).
Among these techniques, network quantization (Shang et al.
2023
Li al. 2023 2024c Zheng et al.
Ma et al. (024b) have emerged as two promising approaches. The former reduces data precision low-bit representations simultaneously shrink model size and accelerate inference, while the latter caches intermediate features to eliminate redundant computations across diffusion timesteps. Although these methods effectively reduce overhead, they inevitably suffer from approximation errors between the efficient model and the original counterpart, which degrade the generation quality of the model. Prior studies have attempted to mitigate such errors through specialized mechanisms. For example, timestep-wise quantization parameters (Wang et al. 2023 Liu et al. 2024b) have been introduced to capture the dynamics of
2024) and feature caching (Chen et al., 20244) Zou et al. 2024
"Corresponding Author; yuxinzhang www.nngwtttttuuutttcmmmmm .dw.cdu.cn

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Published as a conference paper at ICLR 2026
vare-ig activations, while non-uniform caching strategies (Ma et al. 2024b) exploit similarity patterns between adjacent timesteps to improve performance. Despite their effectiveness, these methods remain pre-deployment solutions that require the ability to re-execute the model-efficiency pipeline and the original model.
In practice, such requirements often do not hold once model has been deployed in edge production environments. On the one hand, reapplying the model-efficiency pipeline and redeploying the model incurs significant engineering overhead, making it impractical in many cases. Also, deployed models are typically immutable due to storage limitations, deployment policies, or system design constraints. On the other hand, after being deployed, the original model is often irretrievable, making re-executing the model-efficiency pipeline unfeasible. For instance, a quantized model may no longer retain its original high-precision weights, making re-quantization infeasible. Inspired by recent advances in test-time scaling (Snell et al. 2024, Lightman et al. 2023; Muennighoff et al. [025 Ma et al. 2025 Singhal et al., 2025 Prabhudesai et al. al.]]2022)) |020]2222200] Xiao & Snoek (202)), where model behavior is adjusted
inference time without retraining, ask: Is it possible improve the performance of already
deployed diffusion model without repeating the model-efficiency pipeline?
To answer this question, we begin by analyzing how errors propagate through diffusion timesteps and reveal that they accumulate exponentially, which significantly degrades the final generation quality, Motivated by this finding, we propose Iterative Error Correction (IEC), novel test-time method that mitigates these errors by iteratively refining the model's output. Theoretically, we show that IEC reduces accumulation from exponential to linear growth. IEC is plug-and-play method that operates entirely at test-time. It requires no re-running the model-efficieng: pipeline, no fine-tuning weights, and no changes in model architecture, making compatible with deployed diffusion models. While IEC introduces additional computational overhead, it is highly flexible and can be selectively applied to a subset of all diffusion timesteps. Applying IEC to more timesteps yields greater quality improvements, while using fewer timesteps reduces computational overhead. Skipping IEC entirely preserves the model's original performance. This flexibility provides users with fine-grained control over the trade-off between efficiency and generation quality, depending on resource constraints and application needs. By enhancing the performance of efficient diffusion models in deployment, IEC preserves the compatibility and reusability of these models, making it a practical and generalizable solution for real-world applications.
RELATED WORK
2.1 DIFFUSION MODEL
Diffusion models (Ho et al. 2020 Rombach et al. 2022 Karras et al. 2022 2024 Peebles & Xie
2023) consist of forward process and reverse process. In the forward process, given input data distribution 2o q(x), diffusion models add series of Gaussian noise to 2o to resulting in sequence of noisy samples z,, 0 S T:
T
((11T(Z0) II ((
(I)
(x N(z Va B.I).
where or 1 Pr. 9e is I-based parameters. the reverse process, given randomly sampled Gaussian noise 2T (0, diffusion models progressively generate images by:
(It 12x.) = N ((
For DDIM (Song et al. 2020a), the 3e 0 and p is defined by:
Øt-1 va-1 Tt a.co t)
+ /1 (6-160(
(3)
2.2 EFFICIENT DIFFUSION
Significant efforts have been devoted to developing efficient diffusion models, which can be broadly categorized into two types: temporal efficiency and structural efficiency (Liu et al. 2025b). Temporal
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61
1=02
A-
05
1
B
A
17
14
11
20
80
100
80
100
Timestep
Timestep
(a) ||A Jal-
(b) |V with varying
VG(=)]l under varying A. Reported values are averaged over 100 sample generations using a DDIM pretrained on CIFAR-I0.
Figure I: Empirical results of (a) ||A, + B,Jell: :.lel::00 (b)
efficiency methods focus on reducing the number of sampling timesteps. For instance, methods such DDIM (Song al. 2020a) and GDDIM (Zhang et al. (2022) modify the denoising equations to enable fewer sampling steps, while others (Zheng et al. [Sih et al. 2023) accelerate inference by
performing multiple denoising timesteps in parallel. DistriFusion (Li et al. 2024a) further improves
efficiency by dividing imput patches across multiple GPUs. Additionally, fast solvers for diffusion (Liu
al. 2022 Dockhorn et al. 2022 Yin al., 2024b a Lu al. 2022a have been proposed to reduce the computational overhead associated with sampling. Structural efficiency methods, on the other hand, aim to reduce model complexity and complementary temporal efficiency techniques, making them increasingly popular. For example, network quantization has emerged as key approach
for efficient diffusion model (Li al. 2024c Zheng al. [2024] Yang al. [024 Deng et al.) [0255]
Zhao et al. 2024: Huang et al. [024aa] Chen et al. 2024a Dong & Zhang 2025). auantization-awan
training (QAT)Li et al. [024c] Zheng et al. 2024) restores model performance at low bit-widths but
incurs significant training overhead. In contrast, post-training quantization (PTQ)S et al. 2023
ei al. 2023 Wu et al. 2024a) requires minimal computational resources and small calibration datasets. making more suitable for scenarios with limited resources. PTQ4DM (Shang al. 2023
and Q-Diffusion (Li et al.) (223)) introduced time-aware calibration to realize quantized diftusion models. Subsequent works have refined these methods by addressing quantization error, feature re-balancing, calibration data collection, model reconstruction, mixed-precision strategies, and so
on (He al., [2024] [Huang al. 2024b Wang al., 2024a b. Feng al. 2025 Liu et al. 2024c
Sun et [ Wu et al. 2024b).. Moreover, techniques such as LoRA based optimization HHu 202 have been applied further enhance quantized diffusion models eLi al. 2024b
al.
He
et al.
2023). In addition to quantization, feature caching has also gained attention (Tang et al. 2024
Chen et al. 20244b Zou et al. 2024, Ma al.) 2024b) Wimbauer al.) 2024 Liu el. 2025a 2024a
Ma et al., 2024a). These methods aim to reduce inference time by reusing pre-computed features
across diffusion steps. Recent studies, such as CacheQuant (Liu et al. 2025b), combine caching with
quantization to achieve even greater efficiency. Other structural efficiency techniques include token
pruning (Fang et al., 2024) and sparsity (Fan et al. 2025). In this work, we focus on mitigating the errors introduced by structural efficiency methods.
3 METHOD
3.1 ANALYSIS OF ERROR ACCUMULATION IN EFFCIENT DIFFUSION MODELS
In this subsection, we present a theoretical analysis of error propagation and accumulation across diffusion timesteps, using the deterministic DDIM sampling procedure example.
Preliminaries.
We consider the deterministic DDIM sampling process defined as:
/1
( + v1 (r-166(
Pt-1 vat-1
(4)
Vaa
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For brevity of notation, we define the following coefficients:
A, val-1
B₁ = v 04-1
var-1
04
(5)
Val
V0t
thereby simplifying the rule to:
Tt-1 A,a Brea( t).
(6)
Modeling Error. In efficient diffusion model methods, such as quantized or cached models, two primary types of errors are introduced at each timestep. First, due error propagation from the previous timestep, there exists discrepancy &, between the perturbed input ž, and the ideal input Tt, defined as 7 2t + 6t. Second, perturbations from network quantization or feature caching introduce an eгror eg in the model's prediction. Specifically, the perturbed prediction én (z,, t) differs from the ideal case co (xt, i) is defined as (0 1) = <o 1) + cg.
Incorporating these errors, the perturbed update rule timestep becomes:
7t- = A, + (a,
Ar(x; + 6z) + BBE + 5,,
2 Ar(z; + 6.) + B, (Ea( 1) Jzōz)
A,( + 6.) B, (en 1) + E J(o,)
where the approximation uses the first-order Taylor expansion, and Jj (2,(2.)) Jacobian of the model output with respect to its input.
represents the
On
lama
Error Propagation. By subtracting the ideal update in Eq. from the perturbed update in Eq. derive the recursive relation for error propagation:
8 1 = žt-1 24-1
~ A,,: + B.(e) + 1(0.)
(A, + B,J+)o, BiG
(
Recursively expanding Eq. from timestep T to 0, and assuming the initial error 87 = 0 (since dr is drawn from ideal Gaussian prior), the accumulated error at t = 0 is given by:
T
II (A, + B3J,)) (Baca). /=++1
60- =
Eq. reveals that the final error is weighted sum of per-timestep prediction errors, where each contribution is scaled by the product of the matrices (A₃ + BBJ,) from timestep + to T.
Analysis of Error Growth. To understand whether the propagated error amplifies or decays over time, we analyze the spectral norm ||A, + B,J, which quantifies the maximum amplification of the linear transformation each timestep. Specifically, ||A, + B.Jell error component is amplified, and the error can grow exponentially if such amplification persists, leading to instability. In contrast, if A| + B.J.ll < for all i, the propagated error decays over time, indicating a relatively stable and robust sampling process. Empirical observations, shown in Fig. Ia] demonstrate that [|A, + Je |I consistently exceeds across timesteps, suggesting that the errors introduced by efficient diffusion methods tend to accumulate exponentially, posing challenge in stability.
the corresponding
As shown in Eq. 9 the key reducing error lies in breaking the exponential growth trend during the accumulation process. In the following subsection, we propose a novel and effective method that transforms the error growth from exponential to linear.
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3.2 ITERATIVE ERROR CORRECTION
To mitigate error accumulation, we introduce Iterative Error Correction (IEC), a theoretically mo- tivated, plug-and-ple method designed reduce error accumulation from exponential linear growth at test-time. The core idea of IEC is to introduce correction steps within diffusion timesteps. Specifically, timestep 1, initial estimate ( is computed using the standard DDIM update defined in Eq. =(_1 Aræ; + Bre t). We then iteratively refine this estimate by repeatedly applying the following update rule:
(R) (Acit + Bica(s -x(-1) 1-1 1-1
(10)
== 0,,,2,.....
where À is a tunable hyperparameter. The iteration proceeds until the difference || falls below predefined threshold the maximum number of iterations reached, yielding the final estimate 17-1-
(k+1) [
Convergence Analysis. We provide mathematical justification for the convergence of IEC using fixed fixed-point theory. Eq. 10 can be reformulated
(k+1) (1 ft-1
{)a (R)
(K) > (Arse + (reo(((11:))
(11)
This allows to define the mapping (iz) as:
G(x) A)x + (Ai,, + B,co(x,
(12)
The iterative procedure in Eq. 10 is thus equivalent to applying fixed-point iteration to solve:
x
(13)
According to Banach's fixed-poin theorem (Banach) 1922), the iterative procedure converges to unique fixed point wi-1 if the mapping G(xr) a contraction mapping, i.e., there exists Lipschitz constant O < T, < 1 such that:
[[G(x) G x L| yll. Vz,
(14)
To estimate the Lipschitz constant L, we compute the Jacobian of G(z):
VG(x) = (1 - A) + +B,J,,
(15)
where Je is the Jacobian matrix. Then, the Lipschitz constant L is given by:
L = VG(x)] = [[ - -A]I + AB,Jell-
(16)
To guarantee convergence, necessary to satisfy 0 L 1. Since B. < 0, an appropriately
chosen positive À reduces the Lipschitz constant L via the term AB,J,, ensuring L << 1. Empirically, as shown Fig. Tbb we observe that setting within the range [0.1, 0.7] consistently ensures that |[G(()] across all timesteps. In this paper, set À to 0.5 practical choice based these observations. Moreover, since G(x) is continuously differentiable, Eq. [4 holds by the Mean Value Inequality for vector-valued functions (Munkres) 2018), confirming that G is contraction mapping and IEC can converge to the fixed-point solution.
Error Accumulation in IEC. The proposed IEC effectively suppresses error accumulation. To demonstrate this, we define the error iteration &: 1 of IEC as:
8 8
#11 It-1 G( (k) - Pt-1
(k+1)
G( + ((1)) G(I(11) + G( I1-1
(17)
Cirst term
second torm
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where 6/ =t-1 - is the accumulated error iteration k. The first term of Eq. 17 can be
(K)
approximated by G( + ) G(xx-1) VG 8/
By considering the noisy input and noisy model prediction, the mapping ((x) is defined as:
G(x) (1 )) A(A,;; + (x,
(18)
where ü, x4 de and (o( c + represent the noisy input and noisy model prediction, respectively. Consequently, the second term in Eq. 17 can be approximated by:
G( 21-1
(1 -1- 11-A)==-1-- + MAr(ar + 6,) (c(e +e)) - It-1
= X(re + Beea( 11) + A( MAad. + Bieq)
( e0(0r, ()) + A(A,o; + BeeE),
where the last equality uses the relationship zz-1 A + Bica( t). Taking the norm of 6, and applying the triangle inequality, we obtain:
| || < -
(E ca( t)) ||A,ee + Bieall)
$ 1110/=111 C
where
L
(2:-)\1)) is the Lipschitz constant, and
(() - ca( t)
+ ||Aro + B,eell) is bounded constant independent of ((k)
Recursively applying Eq.
19 yields:
1- 1*
Deepcodhe-Nell
Deepaxche-Nant--te WSA5+1IC
1 s 11 11
(19)
-
As k + 00, L* + 0, and the error converges to:
15.
C 1-L'
(20)
This result demonstrates that IEC effectively suppresses error accumulation by ensuring that the propagated error each timestep is bounded by T-I. Crucially, since IEC eliminates dependency on errors from previous timesteps, the total accumulated error after timesteps grows only linearly:0 Σ 57, where each 87. is independently bounded. Thus, IEC can prevent exponential eтrоr amplification and achieve linear eтrоr propagation in theory. In practice, we set the maximum iteration K to and the threshold T to le-5, As shown in Fig. 2 IEC effectively reduces errors across timesteps, demonstrating its effectiveness in real case.
0
200
40
60
80
100
Timestep
Figure 2: Error comparison across
timesteps.
4 EXPERIMENT
4.1
EXPERIMENT SETTINGS
Models, Baselines, Datasets, Metrics, and Implementation Details. All experiments are con- ducted using Py Torch on an NVIDIA 3090 GPU. To evaluate the effectiveness of IEC, we apply it to various diffusion models, including DDPM, LDM, and Stable Diffusion (Song et al. 2020a Rombach et al. 2 as well as efficiency techniques such as timestep-wist network quantization [Liu et al. 2024b). Deepeache (Ma et al., 2024b), and CacheQuant (Liu et al., 2025b), a hybrid technique com- bining quantization and feature caching. For model quantization, we adopt channel-wis. quantization for weights and layer-wise quantization for activations, considering both W4A8 and W8A8 cases. For W4A8, local reconstruction is performed to enhance performance. For feature caching, following
We adopt the Comp Vis codebase and the official v1.4 checkpoint.

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Published as a conference paper at ICLR 2026
4.52
0.56
9.5
9.5
3.89
3.66
8.5-
3.8
2.7
7.00
86
Al Sigs
21/
s//
41/10
#1/21
Seg
Deedom
A0
41/a
s1/10
x1/21
All AMstppdk-S
Stspekiiityy
Applied timesteps
Applied timesteps
(a) Ablation study of applying IEC on W8A8 (b) Ablation study of applying IEC on Deep- quantization.
cache (N 10).
Figure 3: Ablation study of applying IEC on quantization and Deepcache. The baseline does not use IEC, while "All Steps" applies IEC to all timesteps. The "+AA" indicates that IEC is applied both the first and last A timesteps.
CacheQuant (Ma et al. 2024b), the cached blocks are set to last 3, 1, and blocks for DDIM, LDM, and Stable Diffusion, respectively. We evaluate IEC on several widely used datasets, including
CIFAR-10 (Krizhevsky et al. 2009). LSUN-Churchs (Yu et al. (2015), L-SUN-Bedrooms (Yu et al.
2015). ImageNet (Deng et al. 2009). and MS-COCO (Lin et al. 2014). For Stable Diffusion, we
generate 5,000 images using MS MO-C captions as prompts, following the protocols in (Ma et al.
2024b; Chen et al.) 2024b) Wu et al. 2024a). For all other datasets, 50,000 images are generated to
evaluate the quality of image synthesis. The evaluation metries include FID (Heusel et al., 2017 7). Inception Score (IS) (Salimans et al. 2016), and CLIP Score (evaluated on ViT-g/14) (Hessel et al. methods, IEC is applied at every timestep. For DeepCache and CacheQuant, IEC is applied only to the non-cached timesteps, except for MS-COCO, where IEC is only applied at the first timestep. Note that IEC is applied solely to the efficient diffusion models and does not interfere with the baseline model efficiency pipelines.
2021). For guantization-baat
Table 1: Results of combining IEC with timestep-wisg wise quantization (Liu et al. 2024b) on CIFAR-10, ASUN-Churceh and LSUN-Bedroood Bedrooms datasets.
CIFAR-10 x 32
LSUN-Churehs 256 256
LSUN-Bedrooms 256
256
(T=100)
(T=100)
(T=100)
W/A
FID
W/A
FID L
W/A
FID
DDIM
4.19
LDM-8
3.99
LDM-4
3.37
W8A8
4.32
W8A8
3.57
W8A8
8.97
+IRC
3.76
+IEC
3.29
+IEC
7.78
W4A8
6.82
W4A8
6.27
+IEC
5.96
+IEC
6.10
4.2 ABLATION STUDY
Fig. illustrates the impact of applying IEC to different subsets of timesteps. Specifically, we examine the effects of applying IEC only to the first and last timesteps, as these timesteps exhibit the largest values of ||A, + B,J, in Fig. Ia As shown in Fig. 3al for the W8A8 case, applying IEC to all timesteps achieves the best FID of 3.76. Notably, the performance gains remain significant even when IEC is applied to only a small number of timesteps. For instance, applying IEC to the first and last 1/10 ("=1//10)) or 1/20 1"20/) of timesteps yields FID improvements of 0.44 and 0.35, respectively. These results suggest that partial application of IEC still provides meaningful benefits. similar trend is observed in results on feature caching in Fig. 3b Applying IEC to all timesteps improves the FID by 1.97. while employing it to the first and last I/10 or 1/20 of timesteps leads to gains of 0.16 and 0.19, respectively. These findings demonstrate that IEC effective and flexible, as it can be selectively applied to subset of timesteps to balance performance improvements with
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Table 2: Results of combining IEC with Deepeache (Ma and
2024b) on CIFAR-IO, hhhurhss
datasets.
CIFAR 32 x 32
LSUN-Churchs 256 x 256
LSUN-Bedrooms 256 x 256
(T=100)
(T=100)
(T=100)
Method
FID
Method
FID L
Method
FID L
DDIM
4.19
LDM-8
3.99
LDM-4
3.37
Deepeache-N=3
4.70
Deepeache-N=3
5.10
Deepcache-N-2
11.21
+IEC
3.96
+IEC
4.73
+IEC
7.99
Deepcache-N-5
5.73
Deepeache-N-5 +IEC
6.74
Neepcache-N=3 11.86
+IEC
4.83
6.00
+IEC
8.16
Despcache-N=10
9.74
Deepeache-Nas
14.81
Deepcache-N=5
14.28
+IEC
7.77
+IEC
13.17
+IEC
9.20
Deepcache-N=15 +IFC
17.21
Deepeache-Ner 15
25.27
Deepcache-N=10 +IRC
26.09
14.58
+IEC
22.42
16.91
computational cos
Moreover, increasing the number of correction iterations (e.g.. == or 3) leads to marginal improvements, indicating that IEC already effective even with single correction step.
Table Results of combining IEC with CacheQuant (Liu et al. 2025b) (Denoted as CacheQ) on CIFAR-10, LSUN-Churchs, and L.UUN-Bddroos datasets.
CIFAR-10
LSUN-Churchs
W/A
32 x 32 (T=100)
256 x 256 (T=100)
256 x 256 (T=100)
Method
FID
Method
FID
Method
FID
DDIM
4.19
LDM-8
3.99
LDM-4
3.37
CacheQ-N=3 +IBC
CacheQ-N=3
3.66
CacheQ-N=2
8.85
3.93
+IBC
3.39
+IEC
7.51
CacheQ-N=5
5.28
CacheQ-N=5 +IBC
3.71
CacheQ-N=3 +IEC
8/8
+1BC
5.09
3.24
7.33
CacheQ-N=10
8.19
CacheQ-N=10 +IBC
5.54
CacheQ-N=5 +IEC
10.29
+IBC
6.47
4.25
7.67
CacheQ-N=15
13.42
CacheQ-N=11 +IBC
9.47
CacheQ-N=10 +IEC
17.53
+IBC
10.77
6.90
11.07
CacheQ-N=3
7.27
CacheQ-N=3 +IBC
7.08
+IBC
6.42
6.85
CacheQ-N=5
8.15
CacheQ-N=5 +IBC
7.24
4/8
+1BC
6.90
6.79
CacheQ-N=10
11.36
CacheQ-N=10 +IBC
10.75
+IBC
10.69
9.69
CacheQ-N=15 +IBC
16.76
CacheQ-N=15 +IBC
13.28
15.50
11.50
4.3
MAIN RESULTS
This subsection includes the quantitative results, while the visualizations are included in the appendix.
Evaluation on IEC on Network Quantization. Tab. Ppresents the quantization performance. For LSUN-Bedrooms, we report only W8A8 results, W4A8 quantization leads to model collapse.
IIn Sec. 4.4 we provide discussion about the time overhead of IEC.
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Across all datasets, applying IEC consistently improves performance. For example, in the W4A8 case, IEC reduces the FID from 6.82 to 5.96 on CIFAR-I0, and from 6.27 to 6.10 on ChNhhhhrhhs
Evaluation IEC on Feature Caching. The performance of IEC on DeepCache (Ma et al., 2024b) demonstrating consistent improvements. For example, on CIFAR-I0. IEC reduces FID from 4.70 to 3.96 when N 3. Similar trends are observed for larger N. with FID reduced from 17.21 to 14.58 when N = 13. On
shown Tab. 2
IEC reduces FID from 25.27 to 22.42 at N = 15, from 26.09 16.91 at N 10.
and L.SUN-Bedrrom
Combined With Ceantizatton-aachiig The results of IEC on CacheQuant (Liu et al. 2025b), which integrates quantization and feature caching, shown in Tab. 3 and Tab. On CIFAR- IEC achieves consistent improvements. For instance, under W8A8 with N = FID is reduced from 4.61 to 3.93. With N 15. FID improves from 13.42 to 10.77. Similar improvements are observed under W4A8. For example, at N 10, FID improves from 11.36 to 10.69. On LSUN-Churchs, for W8A8 and N = 3, IEC reduces FID from 3.66 3.39. At N = 15, FID drops from 9.47 to 6.90. Under W4A8, IEC improves FID by 0.23, 0.45, 1.06, and 1.78 for N 3, 5, 10, and 15, respectively, On -SUN-Bedrooms, IEC also provides consistent gains. For example, under W8A8 with FID improves from 8.85 to 7.51. As shown in Tab. 4 on ImageNet, IEC enhances performance across all baselines. For instance, under W8A8 with N = 10, FID improves from 4.68 to 4.15 and IS from 184.38 to 196.20. At N 20, FID decreases from 7.21 1 to 6.53, and IS increases from 160.68 to 169.71. Under W4A8, IEC also improves performance. For example, at = 10, FID improves from 6.90 6.50 and IS from 158.27 to 161.86, demonstrating IEC's robustness in low-precision scenarios. On MS-COCO, IEC also brings improvements. For example, under W8A8 with N = 10, FID improves from 23.65 23.36, IS from 36.71 to 37.02, and CLIP Score from 26.41 to 26.45. At 5 FID improves from 23.74 to 22.83, IS from 39.81 81 to 40.91, and CLIP Score from 26.87 to 26.94.
In summarry across all datasets and efficiency techniques, the integration of IEC consistently enhances performance, demonstrating its effectiveness.
Table 4: Results of combining IEC with CacheQuant (Liu et al. 2025b (Denoted as CacheQ) on ImageNet and MS-COCO datasets.
ImageNet 256 x 256 (T=250)
W/A
Method
FID
IS f
MS-COCO 256 x 256 (T=50)
LDM-4
204.56
CLIP Score t
W/A
Method
FID 4
IS f
CacheQ-N=10
4.68
184.38
+IEC
4.15
196.20
PLMS
22.41
41.02
26.89
8/8
CacheQ-N=15
5.51
174.81
CacheQ-N=10
23.65
36.71
26.41
+IEC
5.18
182.30
+IBC
23.36
37.02
26.45
8/8
CacheQ-N=20
7.21
160.68
CacheQ-N=5
23.74
39,81
26.87
+IEC
6.53
169.71
+IBC
22.83
40.91
26.94
CacheQ-N=10
6.90
158.27
CacheQ-N=10
26.63
34.57
26.23
+IEC
161.86
+IEC
24.82
36.18
26.25
4/8
4/8
CacheQ-N=155
9.40
139.64 64
CacheQ-N=5
23.85
39.53
26.78
+IEC
8.66
144.41
+IBC
23.80
40.27
26.80
CacheQ-N=20
12.65
124.13
+IEC
11.10
130.01
4.4 OVERHEAD DISCUSSION
Table presents the overhead and FID when combining our IEC with timestep-wise quantization (Luu et al. 2024b) and Deepcache (Ma et al. 2024b) on the CIFAR-10 dataset. Here, we measure the time overhead by comparing with the baseline without IEC. For W8A8 quantization, IEC achieves substantial improvements in FID with minimal overhead. For instance. selectively applying IEC to 1/10 or 11/20 the steps achieves improved FID of 3,88 and 3.97 with only 20% and 10% overhead, respectively. In the case of DeepCache, the IEC is only applied to the non-cached timesteps,

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Published as a conference paper at ICLR 2026
thereby its overhead is minimal. For example, applying IEC to all timesteps yields FID of 7.77 with only 14% overhead, while applying IEC to 1/10 or =1/0 reduces the FID to 9.58 and 9.55, respectively, with overheads as low as 2.8% and 1.4%. These results demonstrate that IEC provides flexible trade-off between efficiency and generation quality, making it easy to use and suitable for real-world applications.
Table 5: Overhead of combining IEC with timestep-wise quantization (Liu al. 2024b) and Deepeache (N=10) (Ma et al. 2024b) on CIFAR-10. "Naive + T indicates increasing "I iteration count.
CIFAR-10 32 32 (T=100)
CIFAR-10 32 x 32 (T=100)
W/A
FID J
Time Overhead
Method
FID J
Time Overhead
DDIM
4.19
DDIM
4.19
W8A8
4.32
0%
Deepeache-Nes
9.74
0%
Naive + 100
3.99
100%
Naive + 14
8,58
14%
All Step (K=1)
3.76
100%
All Step (K=1)
7.77
14%
1/4
3.85
50%
+:/
7.92
7%
1/8
3.86
25%
+ 1/8
9.31
4.2%
+/10
3.88
20%
+ 1/10
9.58
2.8%
1/20
3.97
10%
- 1/20
9.55
1.4%
4.5 COMPARISON WITH NAIVELY ADDING ITERATION
In this subsection, compare IEC with two alternative approaches: naively increasing the number of iterations in üimetep-wise quantization and DeepCache. Specifically, simply adding 100% more iterations to the timestep-wit wise quantization method yields FID score of 3.99, which is worse than our IEC with 100% overhead (3.76 FID) and even inferior to IEC with only 10% overhead (3.97 FID), shown in Tab. [ Similarly, increasing the iteration count by 14% for Deepeach-N=10 leads an FID of 8.58, which is also worse than the 7.77 FID achieved by our IEC. These results demonstrate that merely increasing the number of iterations does not lead satisfactory performance and justify the effectiveness of IEC.
5 DISCUSSION
The analysis in 3.1 suggests that more robust models can be achieved by modifying the scheduling schemes of A, and Bi, or by explicitly fine-tuning the model to control the norm of the Jacobian. Additionally, as discussed in Sec. 4.2 identifying small subset of critical timesteps for applying the proposed IEC significantly reduce inference overhead while still improving generation quality. Finally, this paper presents a conceptual and experimental validation of the test-time method for diffusion, while further exploring other diffusion models, samplers, and efficiency techniques remains an interesting topic. Although these directions are promising, we leave their exploration to future work due current resource limitations.
CONCLUSION
In this paper, we address the challenge of improving the performance of efficient diffusion models at test-time. We begin by analyzing the error accumulation such models and show that these errors can grow exponentially during the denoising process. To mitigate this, we introduce Iterative Error Correction (IEC), a simple yet effective method that iteratively refines the model's output to suppress error propagation. Our theoretical analysis demonstrates that IEC converges to fixed-point solution, reducing the rate of error growth from exponential to linear. As plug-and-play model-agnostic method, IEC offers flexible trade-off between performance and efficiency, allowing users to adapt to various practical scenarios. We validate IEC through extensive experiments, showing consistent performance gains across various datasets, efficiency techniques, and diffusion model architectures.
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A APPENDIX
A.1 ALGORITHM PROCESS
Alg. presents the overall process of the proposed IEC.
Algorithm Iterative Error Correction (IEC)
Input: 24, timestep t, hyperparameter X, threshold т, max iterations K
2: Output: Final corrected estimate r;-1
3: Initialize x²-1 using Eq.
6
4: while k < K do
5: Obtain -i-1 (k++1)
using Eq. 10
6: if [|>1)=-1]
x 7-11
(4)
then
7: break
8: end if
9: k ++1
10:
end while
11: Return: 27-1 21-1
(k+1)
A.2 EXPLANATION OF THE MISS OF W4A8 RESULTS ON LSUN- L.SUN-BEDROCE BEDROOMS
Applying W4A8 quantization on the LSUN-Bedrooms dataset results in severe performance degrada- tion. For instance, with CacheQ (N=10) under W4A8 settings, the baseline FID on LSUN-Bedroome degrades to 156.3. Due this extreme baseline failure, we omit these specific configurations from the main comparative results (Tab. D and Tab. However, this severe degradation provides valu- able testbed for evaluating recovery capabilities our IEC under extreme conditions. Notably, even with such damaged baseline, applying IEC drastically improves the FID from 156.3 to 52.6, demonstrating its capacity to recover signal from heavily quantized models.
A.3 DISCUSSION OF IEC'S BOUNDARIES
IEC designed correct the denoising trajectory deviations resulting from the in efficiency techniques. However, in cases total mode collapse or an under-trained model, where the model's prediction becomes entirely random, since IEC cannot hallucinate correct features that the model has totally lost.
A.4 APPLYING IEC ACROSS VARYING SETTINGS
We further explore the IEC's effectiveness across varying DDIM sampling steps and hyperparameters in efficiency methods in Fig. It can be seen that for any given setting, IEC consistently presents better performance, demonstrating its effectiveness.
A.5
VISUALIZATION
In this section, we provide visualization comparisons between the proposed IEC and the corresponding baseline. Fig and Fig. show the visualization results of Stable Diffusion on the COCO dataset. Fig. 7 Fig. and Fig. show the visualization results of LDM-8 on the LSUN-Church datasets. Fig. 10 and Fig. show the visualization results of LDM-4 the
datasets.
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Published as a conference paper at ICLR 2026
W-4AR
25
25-
W4AS-BP IBC
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WEAS
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FID
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Figure 4: (a) Performance comparison of quantization (W4A8/W8A8) with and without IEC the CIFAR-10 dataset across varying sampling steps. (b) Performance comparison of DeepCache (N {3, 5, 10}}) combined with IEC across varying sampling steps.
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fat orange cat sits touch with
There is bunch of flowers
A cat is sitting and watch- ing computer screen.
Two black bears park during the daytime.
Booeitee
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W-SA--Caa WWs.B-Caelli (Ours)
Figure 5: Qualitative comparison Stable Diffusion the COCO dataset: Baseline vs. QucheQuant
(W8A8, N=5) with and without IEC.
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Published as a conference paper at ICLR 2026
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there black bird sitting
dog that standing the beat.
giraffe eating tree.
bear peeking beyond some shrubbery bank onto the water.
Buselite
Watt- Cww.-oCoh-------
WEAS DieAe- 10FFEE (Ourr)
Figure Qualitative comparison of Stable Diffusion on the COCO dataset: Baseline vs. CacheQuant (W4A8, N=10) with and without IEC.
WSA
shott
WRASTIB (Ourr)
Figure 7: Qualitative comparison of L.M-88 the LSUN-Churcht dataset: W8A8 WSA8+IEC.
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Published as a conference paper at ICLR 2026
Deppocte N-10
Dieppaale 10 IEC (Ours)
Figure 8: Qualitative comparison of LDM-8 on the LSUN-Churchs dataset: DeepCache (N=10) vs. DeepCache+1EC (N=10).
WWA--- CAs--CadoQ---11
WWAN Dwel---eeli22e 11- (Ours)
Figure 9: Qualitative comparison of LDM-8 on the L.SUNCcurrch dataset: CacheQuant (W8A8,
N=10) vs. CacheQuant+IEC (W8A8, N=10).
WBAS
SSSAFTEE (Oum)
Figure 10:
Qualitative comparison of LDM-4 the LSUN-Bedrooms dataset: W8A8 us.
W8A
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Published as a conference paper at ICLR 2026
Depravbee N-10
Lhupese And N0 10 (Oura)
Figure 11: Qualitative comparison of LDM-4 on the LSUN-Bedrooms dataset: DeepCache (N=10) vs. DeepCache+IEC (N=10).
WSiLS Chrbse Q-100
WISSS CAe.e IIFICC (Oure)
Figure 12: Qualitative comparison of LDM-4 on the dataset: CacheQuant (W8A8,
N=10) vs. CacheQuant+IEC (W8A8, N=10).
21


Reviews:
[{"review_id": "rbvJWeh5wu", "reviewer": "Reviewer_gGVw", "summary": "This paper addresses the degradation in generation quality that with quantization or feature caching. The authors first analyze how approximation errors propagate across diffusion timesteps. Motivated by this, they propose Iterative Error Correction (IEC), a plug-and-play, test-time refinement that injects a fixed-point iteration at selected timesteps. They provide a convergence argument via Banach's fixed-point theorem and show that IEC reduces error growth from exponential to linear in theory. Empirically, IEC consistently improves metrics across multiple datasets and efficiency techniques, with a tunable compute–quality trade-off.", "questions": "Please check Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- IEC is model-agnostic, requires no retraining, and can be dropped into existing inference pipelines. This plug-and-play nature makes it highly practical for real deployments.\n- The method is evaluated across several models and multiple datasets. Improvements are consistent, with especially notable gains for aggressive efficiency settings.\n- The paper includes ablations over λ, iteration count K, and which timesteps are refined, and a brief comparison to naïvely adding iterations, helping understand the method better.", "weaknesses": "- For Stable Diffusion, IEC is applied only at the first timestep, yielding modest improvements. This raises concerns about the practical benefits at scale if the method cannot be applied more broadly due to compute.\n- W4A8 on LSUN-Bedrooms “collapses” without detail. A short analysis of when IEC helps vs. cannot rescue severe degradation would be valuable, potentially guiding users to regimes where IEC is most beneficial.\n- Most experiments use T=100 (or 250). Since many efficient systems run at T≤20–50, it would be useful to report results in that low-step regime to demonstrate relevance to contemporary fast samplers.\n- Showing ||At + BtJt|| > 1 indicates potential amplification but does not alone establish exponential growth of the end-to-end error. More direct measurement across models/datasets would strengthen the claim.", "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": "TyNDCcHxig", "reviewer": "Reviewer_NVsH", "summary": "This paper addresses the performance degradation problem in efficient diffusion models, which arises from approximation errors introduced by techniques like quantization and feature caching. It shows that these errors can accumulate exponentially throughout the sampling.\n\nThe core contribution is a test-time-scaling-like method called Iterative Error Correction (IEC). It requires no retraining or architectural changes, and operates by introducing an inner-loop fixed-point iteration at each sampling timestep to refine the model's output, which reduces the error accumulation from exponential to linear.\n\nThe paper provides both theoretical and empirical analysis showing that IEC can reduce the error accumulation. Experiments on various models (DDPM, LDM, SD), datasets (CIFAR, LSUN, ImageNet), and efficiency techniques (quant, cache, quant+cache) demonstrate that IEC consistently improves generation quality.", "questions": "Please refer to the weaknesses about generalization and compute-performance trade-off.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "-- Problem: It targets the practical, post-deployment scenario for efficient models, which is somewhat under-explored.\n\n-- Idea: It is related to test-time scaling, and potentially provides another new dimension in which we can scale (the inner-loop iteration at each timestep).\n\n-- Analysis: The theoretical analysis is of good quality. It is also appreciated that the authors try to validate their method on multiple models and settings.", "weaknesses": "-- The theoretical framework seems general to not be limited to errors from efficiency techniques.\nIt is unclear how the definition of $\\tilde x_t = x_t + \\delta_t$ and $\\tilde\\epsilon_\\theta = \\epsilon_\\theta + \\epsilon_\\theta^\\delta$ are necessarily linked to quantization or caching.\nIt seems potentially applicable to any diffusion model, where $x_t$ and $\\epsilon_\\theta$ correspond to an ideal denoiser (please refer to Figure 1 in EDM).\nIt would strengthen the paper if the authors could show the method can also improve regular diffusion models (e.g., trained with fewer iterations and thus have imperfections).\n\n--The compute-performance trade-off is not fully explored.\nAccording to my understanding, the total inference compute is determined by at least three factors: (1) DDIM sampling steps (2) hyper-parameters in efficiency methods (3) hyper-parameters in IEC. Each of these can be adjusted to trade more computation for better performance.\nWhile IEC shows improvements when (1) and (2) are fixed, it is unclear whether it provides a better Pareto frontier in the broader context. For example, a real, comprehensive compute-performance plot (please refer to Figure 9 in EDM2 and Figure 10 in DiT) with respect to all these factors would be helpful. The current discussion in A.1 and A.2 is insightful but seems too simple.\n\n--Theoretical and empirical analysis is based only on DDPM/DDIM variants, without showing applicability to modern flow-based models.\n\n--Presentation\nTables: Table 1 and Table 3 take too much space, and their subtables could be merged like Table 2. Additionally, it is unclear why results for certain settings are omitted (e.g., W4A8 in Table 1 for LSUN-Bedrooms, and W8A8 in Table 3). I assume W8A8 and W4A8 are both meaningful in both tables.\nSection A.2: some cited data (e.g., 3.99, 8.58) do not appear in Table 5. The overhead and trade-off (claimed in Abstract) analysis is important and should be moved to the main text.\nTypo: \"ImageNet 256x25\" in Table 4.\nCitation: most should be in parenthesis using \\citep{}.\n\nEDM: Elucidating the Design Space of Diffusion-Based Generative Models, NeurIPS 2022\n\nEDM2: Analyzing and Improving the Training Dynamics of Diffusion Models, CVPR 2024\n\nDiT: Scalable Diffusion Models with Transformers, ICCV 2023", "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": "u6dy1C4gGO", "reviewer": "Reviewer_cbcn", "summary": "This paper proposes Iterative Error Correction (IEC), a test-time method to improve the quality of efficient diffusion models suffering from approximation errors (e.g., from quantization or feature caching). The authors analyze error propagation in DDIM sampling and claim errors accumulate exponentially. IEC iteratively refines outputs at each timestep using a fixed-point iteration scheme, theoretically proven to reduce error growth from exponential to linear. Experiments on CIFAR-10, LSUN, ImageNet, and MS-COCO demonstrate FID improvements when combining IEC with quantization and caching methods.", "questions": "1. What is the FID when using T=200 sampling steps without IEC, matching the computational budget of T=100 with IEC (K=1)?\n2. How does IEC compare to other methods that increase sampling quality with more compute (e.g., DPM-Solver++ with more steps)?\n3. Can you provide rigorous proof that errors grow exponentially without IEC? The current analysis only shows they propagate through products of matrices.\n4. How does IEC compare to consistency models or other iterative refinement approaches?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Clear problem motivation (deployed models can't be easily modified) thus a training free method is needed.\n2. The ability to apply IEC to selected timesteps (±1/10, ±1/20) provides efficiency options, though this flexibility is expected from any refinement method\n3. IEC shows gains across all tested scenarios, demonstrating broad applicability\n4. The paper is generally easy to follow with good visual aids", "weaknesses": "Fundamentally Unfair Comparison:\n1. Baseline uses 100 forward passes, while IEC with K=1 applied to all T=100 steps uses 200 forward pass.\n2. Missing crucial baseline: what is the FID with T=200 steps without IEC?\n3. This is like claiming \"Method A is better than Method B\" when Method A uses 2× computation\n\nIEC is Multi-Step Refinement essentially\n1. Eq. 10 is mathematically equivalent to: repeatedly calling the denoising function with corrected inputs\n2. This is essentially running multiple sampling steps per timestep\n3. Authors admit in A.2 that \"naively adding 100% iterations\" achieves FID 3.99, close to IEC's 3.76", "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": "F7kJqhjbMx", "reviewer": "Reviewer_roVY", "summary": "This paper proposes a novel inference-time method called Iterative Error Correction (IEC) to enhance the performance of efficient diffusion models that often degrade in quality due to approximations introduced by quantization, feature caching, or other efficiency techniques. The authors introduce IEC, a test-time, plug-and-play refinement procedure that corrects inference-time errors without modifying model parameters. IEC is evaluated on multiple diffusion models (DDPM, LDM, and Stable Diffusion) and efficiency schemes (quantization, feature caching, and hybrid quantization-caching), across datasets such as CIFAR-10, LSUN-Church, LSUN-Bedroom, ImageNet, and MS-COCO.", "questions": "1. Would this strategy also applicable in other acceleration methods such as adaptive computation[1]? Could you provide some empirical or theoretical analysis for this?\n\n[1] Tang S, Wang Y, Ding C, et al. Adadiff: Accelerating diffusion models through step-wise adaptive computation[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 73-90.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces Iterative Error Correction (IEC), a new test-time method for improving diffusion model performance without retraining or architecture changes. This is highly practical because deployed diffusion models—especially quantized or cached versions—are often immutable, and IEC offers a plug-and-play solution that directly operates at inference time.\n\n2. The authors provide a clear mathematical analysis of error accumulation in efficient diffusion models and show that approximation errors grow exponentially across timesteps. They then rigorously prove that IEC reduces this exponential growth to linear error propagation through a fixed-point convergence analysis. This theoretical grounding strengthens the credibility and generality of the proposed method.\n\n3. Since IEC operates purely at test time, it does not require access to the original full-precision model or re-application of the efficiency pipeline. This feature is particularly valuable for real-world scenarios where deployed models are frozen or stored under strict hardware and policy constraints.", "weaknesses": "1. The experiments are limited in CNN-based methods. The effectiveness of the proposed methods are not validated in Transformer-based methods such as DiT and Flux.\n\n2. The theoretical and experimental analysis builds on top of DDIM sampler. I wonder if this error reduction strategy is compatible with other samplers such as DPM-Solver?\n\n3. Some critical implementation details are missing such as the exact version of SD.", "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)