MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Authors: Huanlin Gao
Ping Chen
Fuyuan Shi
Ruijia Wu
Li YanTao
Qiang Hui
Youyuren
Ting Lu
Chao Tan
Shaoan Zhao
Zhaoxiang Liu
Fang Zhao
Kai Wang
Shiguo Lian
Venue: ICLR 2026 Poster (2026)
Keywords: Flow Matching
Model Acceleration
Caching Mechanism
Training-Free
Primary area: generative models

Abstract: We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantaneous velocity information (e.g., feature caching), which often leads to severe trajectory deviations and error accumulation under high acceleration ratios. MeanCache introduces an average-velocity perspective: by leveraging cached Jacobian--vector products (JVP) to construct interval average velocities from instantaneous velocities, it effectively mitigates local error accumulation. To further improve cache timing and JVP reuse stability, we develop a trajectory-stability scheduling strategy as a practical tool, employing a Peak-Suppressed Shortest Path under budget constraints to determine the schedule. Experiments on FLUX.1, Qwen-Image, and HunyuanVideo demonstrate that MeanCache achieves $4.12\times$, $4.56\times$, and $3.59\times$ acceleration, respectively, while consistently outperforming state-of-the-art caching baselines in generation quality. We believe this simple yet effective approach provides a new perspective for Flow Matching inference and will inspire further exploration of stability-driven acceleration in commercial-scale generative models.

Published as a conference paper at ICLR 2026
MEANCACHE: FROM INSTANTANEOUS TO AVERAGE VELOCITY FOR ACCELERATING FLOW MATCHING IN- FERENCE
Huanlin Gaot.2, Ping Chen Fuyuan Shi..2, Ruijia Wu', Yantao Li Qiang Hui
Yuren You?, Ting Lu 1.2 Chao Tan Shaoan Zhao 1.2 Zhaoxiang Liu!.2
Fang Zhao!.2 1.2- Kai Wang Shiguo Lian 1.2+
Data Science & Artificial Intelligence Research Institute, China Unicom Unicom Data Intelligence, China Unicom @National Key Laboratory for Novel Software Technology, Nanjing University
Code: UnicomAI/MeanCachn
ABSTRACT
We present MeanCache, training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantaneous velocity information (e.g.. feature caching), which often leads to severe trajectory deviations and error accumulation under high acceleration ratios. MeanCache introduces an average-velocity perspective: by leveraging cached Jacobian-vector products (JVP) construct interval aver- age velocities from instantancous velocities, it effectively mitigates local error accumulation. To further improve cache timing and JVP reuse stability, we de- velop a enjjectoy--stabilig scheduling strategy as a practical tool, employing a Seak-Suppesssed Shortest Path under budget constraints determine the sched- ule. Experiments FLUX.1, Qwen-Image, and VuunyanVVideo demonstrate that MeanCache achieves 121,, 4.56x, and 3.59x acceleration, respectively, while consistently outperforming state-of-the-rs caching baselines in generation qual- ity. believe this simple yet effective approach provides a new perspective for Flow Matching inference and will inspire further exploration of stability-driven acceleration commercial-seal generative models.
Original 1.51
2.02
2.43
x 3.00
3.46
x 4.12
Mean Cache
TaylorSeer
TeaCache
Figure 1: Visualization of images generated by different methods on FLUX.1[dev] under varying acceleration ratios.
"Corresponding author
(gaoh151, zhaof50. liansg) @chinaunicoo.cn

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Published as a conference paper at ICLR 2026
INTRODUCTION
Flow Matching (Lipman et al., 2023; Albergo & Vanden-Eijnden, 2022) has recently demonstrated remarkable progress across image (Wu al., 2025), video (Zheng et al., 2024b; Kong et al., 2024), and multi-modal generation tasks (Hung et al., 2024). By modeling instantaneous velocity fields to learn continuous transport paths, offers concise and effective paradigm for generative model- ing. However, in commercial-scale models such as FLUX.1 (Labs, 2024), Qwen-Image (Wu et al., 2025), and Hunyuan Video (Kong et al., 2024), the large memory footprint, heavy per-step com- putational cost, and long inference latency significantly hinder its applicability in interactive or resource-constrained scenarios.
Traditional acceleration methods, such as distillation (Salimans & Ho, 2022; Kim al., 2023; Sauer et al., 2024b), pruning (Han et al., 2015), and quantization (Li al., 2023b), usually rely on architec- ture modification and large-scale retraining. In contrast, caching-based methods (Ma al., 2023a) offer a lightweight, training-free alternative. By reusing intermediate representations from selected timesteps, they reduce redundant computation and accelerate sampling. However, at high accelera- tion ratios, these methods often suffer from severe error accumulation: interval states reconstructed solely from instantaneous velocity or feature information amplify local deviations, causing the tra- jectory to drift away from the true path. As shown in Fig. 2 (left), instantaneous velocities fluctuate sharply along the denoising trajectory, making them unstable for reuse, whereas interval average velocities are much smoother and thus more stable for reconstruction.
losta ttaneuu Veldity Average Valcity
0.00
Instaanta VeQUE Velecity Eachiin
044
JV7 Cuchiing (5-2)
Hodece
[VP Carhling (K-
0.-
Sharp Flachation
0.12
Noden: by 67%
0.6
0.1
605
Timastop
Timastop
0.00
DiPusion Frecess PM
Cecha Intorve
Coche Interval
Figure 2: Instantaneous vs. Average Velocity and JVP Caching. (Left) Along the original tra- jectory, instantaneous velocity shows sharp fluctuations, while average velocity is much smoother. (Middle) At timestep 927, JVP Caching reduces error accumulation, though its effectiveness de- pends the cache interval and hyperparameter K. (Right) At timestep 551, it achieves stronger error mitigation, showing that effectiveness varies across timesteps. Both middle and right figures under the single-cache setting on the original trajectory.
This observation is consistent with the objective of Flow Matching, which encourages trajectories to satisfy linear characteristics. Under fixed input conditions, an ideal trajectory approximates linear interpolation between sample and noise; the more linear the trajectory, the more stable and higher- quality the generation results. Recent work such as McanFlow (Geng et al., 2025), further demon- strates that modeling and leveraging average velocity can significantly improve trajectory stability, underscoring the potential of the average-velocity domain for more robust generation.
Motivated by this insight, propose MeanCache, training-free caching paradigm that operates in the average-velocity domain rather than relying solely on instantaneous velocities. The key idea construct interval average velocities from instantaneous ones under limited budget, ensuring trajectory stability. MeanCache has two components. First, interval average velocities are approx- imated using cached Jacobian products (JVP), yielding smoother and more stable guidance signals that help mitigate local error accumulation. As shown in Fig. 2 (middle, right), JVP caching reduces errors at timesteps 927 and 551; however, its benefit varies with the timestep, cache inter- val, and hyperparameters, indicating that fixed caching rules are insufficient. Second, we develop a trajectory-stabile
scheduling strategy as a practical tool. Inspired by the graph-based modeling idea in ShortDF (Chen et al., 2025) and by trajectory drift mitigation (Lin et al., 2019) in sequence processing (Wang et al., 2019), timesteps are represented as nodes, deviations of average velocity under JVP caching define edge weights, and mudget-constraiie shortest- -attest-pathhhae search determines cache placement. This scheduling tool systematically improves cache timing and JVP reuse stability without retraining.
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The main contributions of this work summarized as follows:
Average- Velocity Perspective on Caching. We introduce MeanCache, which redefines the caching problem from an instantancous velocity view to the average-velocity domain, offering simpler and more stable perspective for high-acceleration generative modeling.
TrajectorySttaiiliy Scheduling Strategy. We develop a scheduling tool that scores timesteps by JVP-based stability deviation and uses a budget-constrained shortest-path search for cache placement, improving timing and reuse stability without retraining.
Outstanding Performance. MeanCache maintains generation quality under high accel- eration while significantly reducing inference cost. Compared to state-of-the-ar caching baselines, experiments on FLUX.I, Qwen-Image, and Hunyuan Video show speedups of 4.12x, 4.56 x, and 3.59x, respectively. Moreover, MeanCache consistently delivers higher generation quality across different acceleration ratios (Fig. 1), highlighting its acceleration potential on commercial-scale generative models.
METHODOLOGY
2.1 PRELIMINARIES
Flow Matching and McanFlow. Flow Matching (Lipman al., 2023; Albergo & Vanden- Eijnden, 2022) constructs continuous transport paths between a noise distribution πΙ and a data distribution To via velocity fields, typically defined by linear interpolation F (1 t) + tæ I E [0, 1]. This leads to the ODE dr. (mr æ1 )dl. Since on is unknown during generation, a neural network (0(2, 1) is trained predict the instantaneous velocity, yielding the dynamics
dā, = c0 t) dt.
(1)
Where & denotes the trajectory point predicted by the neural ODE. Building on this formulation, MeanFlow (Geng et al., 2025) offers a new perspective by modeling the average velocity over the interval [s, 1), defined as
u(z,, = -/11/ J." o(2+,T) dr;
Furthermore, the MeanFlow Identity provides thcoretical bridge between instantaneous and aver- age velocity:
0(2,; (, + (s d t,
(3)
In this identity, the derivative term 1 u can be expressed as Jacobian Product (JVP), where the Jacobian of u with respect to (2, s) is contracted with the tangent vector [o(2,, s), 1]. Observing this formulation, JVP can be regarded as a computational bridge that directly connects instantaneous velocity and average velocity.
Feature Caching in Diffusion Models. Cache, as a training-free acceleration method, speeds up the denoising process by storing intermediate fea- tures and reusing them across adjacent timesteps. In particular, the reuse strategy directly substitutes cached features from a previous step:
D(z,
Ern Nostmaat
fa tis)
M(H
((z,t v(a.t) (s DDVPe
F(z(-)) := ==x(( Vk [1 - 1],
(4)
Q
where F denotes the feature extraction function, and is the layer index. This approach avoids re- dundant computations and yields up to (N 1)x theoretical speedup. Nevertheless, two major lim- itations remain:
Goche and Reuse
Figure 3: From Instantaneous to Average Ve- locity. Directly caching the instantancous locity v(z. 1) over [t,8] easily leads to trajec- tory drift and accumulation, whereas the average velocity u(z,, t, s) accurately reaches the target McanCache introduces a prior timestep r and reuses JVP,- to estimate the average velocity â(2,, thereby correcting the trajectory and effectively mitigating error accumulation.
(i) Error Accumulation: Ignoring the temporal dynamics of features leads to exponential error ac- cumulation increases. Although cache-then- forecast methods have been proposed recently (Liu al., 2025), the extrapolated features still exhibit
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significant deviations from the true trajectories, limiting acceleration in generative tasks.
(ii) When to Cache: Existing methods for deciding when cache rely on fixed intervals (Ma al., 2023a) or manually tuned presshld-baseee strategies (Liu et al., 2024; Bu et al., 2025), but these approaches cause significant degradation in generative quality under high acceleration ratios.
2.2 INSTANTANEOUS TO AVERAGE VELOCITY TRANSFORMATION
Traditional feature caching methods operate in the instantancous-velocity domain, where velocity varies continuously along the trajectory and inevitably accumulates errors (Fig. 2). Inspired by the MeanFlow Identity, we instead reformulate eaching in the average-velocity domain. As shown in Fig. 3, transforming the instantaneous velocity v(2,, t) into the average velocity u(2,, t, s) over the interval [s. 4] can, in principle, correct the trajectory accurately and eliminate accumulated errors. MeanCache builds on this perspective by formally deriving and practically approximating u(=,, I, 8).
The MeanFlow Identity in Eq. 3 characterizes the instantaneous velocity only at the endpoint 8, leaving the starting point t unspecified. To close this gap. we derive an analogous relation at & (see A.I for details):
v(att) () ulzz, (s -)) di ())
(5)
Here, the derivative term $ (( (, can be expressed as a Jacobian-Vectov Product (JVP). Since the exact JVP is unavailable during inference, approximate it using cached values from carlier steps. This yields the following estimate for the average velocity:
()) == v( (s +s JVP,
(6)
where JVP denotes an approximation to the total derivative d u(2 t, s), and Ü(ze, t, represents the estimated average velocity.
2.3 JVP-BASED CACHE CONSTRUCTION
To construct practical cache estimator, we extend the start-point identity by introducing a reference point r preceding f, with r f > s. Intuitively, r serves as an earlier cached state that helps approximate the JVP between É and s, illustrated in Fig. 3. Applying the start-anchored identity on the interval [t, r] and rearranging gives:
JVP = dr u(2,,r,t)= = u(zr, r, u(z..))
d
t ビ-
Using the displacement form of the average velocity on (].
u(zr, r,) = 21 Zr
(8)
1 T
obtain the fully cacheable estimator:
(t r) v(2r, (t rt- r)
JVP
(9)
Plugging this into the start-point identity yields the predicted average velocity:
t, t) + (s t) 2, (t r) v(2r,r)
(10)
r)²
We denote by K the number of discrete timesteps between and t in the original trajectory. The final estimator is:
(2(2,)) (s- ((sst).ul JVPK K> 1, t,), v((,,)), t),
(11)
==
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where larger K corresponds to reusing cached information longer interval, while K = reduces the average velocity to the instantaneous one. In the original denoising trajectory (e.g., 50 steps), 2y, 24, and 2, are available, caching, however, JVPt->> is unavailable and must be approximated using JVPr-t- Thus, the choice of K is critical: it specifies the span of the preceding segment (r used to approxi- mate JVP: balancing approximation error and stability. This trade-off motivates the need for principled scheduling strategy.
exact average velocities and JVP can be computed. Under
2.4 TRAJECTORY-STARILITT SCHEDULING
Although JVP-based corrections mitigate local eπror accumulation, two key challenges remain: de- termining when to cache and how select the cache span K. Empirically, while latent values vary across samples (e.g., different prompts and seeds), their relative changes at fixed timesteps are highly consistent. This stability, also observed in adaptive schemes such as TeaCache (Liu et al., 2024), indicates that caching decisions can be guided by precomputed stability map rather than fixed heuristics.
Stability Map via Graph Representation. Specifically, we define the error from to as the deviation between the true average velocity and its cached approximation:
(t, t, ((2,, t, s)ll-
(12)
Expanding the cached estimator Ü( s) with JVP correction gives:
1 (t, s) = N |u( t) v(2,,)) t) - (s- JVPK
(13)
To support trajeitory-stability scheduling, we use a graph representation as a practical tool to orga- nize stability costs and possible transitions. Specifically, for convenience, this can be represented as a graph G = EV,E), where nodes V correspond to timesteps in the denoising process and edges & directed connections (t s) with & s, each representing potential caching transition. Each edge is assigned a weight:
(t 8) = (t,
t, E
(14)
where CK (t, s) is the error between predicted and true average velocities under cache span K. Since multiple cache spans may connect the same node pair, G is naturally modeled multigraph.
Peak-Suppressed Shortest Path. Given a Multigraph with error-weighted edges, the scheduling problem can be conveniently solved via a constrained shortest-path search. A challenge under small budgets that the solution may concentrate error into a few edges, leading to large error spikes. To address this, we adopt a peak-suppressee objective that penalizes high-error edges via a power- weighted path cost. The optimization problem is:
arg argmin TEP(T,0)
Ec(e)
B ST
(15)
where P(T, 0) the set of feasible multi-edge paths from the start node T the end node 0, C(e) is the error cost of edge e, 7 2 1 is the peak-suppression parameter (7 1 recovers the standard shortest path), and [x] the path length. This peak-suppressed shortest-path problem can be solved efficiently via dynamic programming. The budget B acts as a constraint on the original path cost and directly controls the acceleration ratio.
EXPERIMENTS
3.1 EXPERIMENTAL SETUP
Baselines and Compared Methods. We evaluate our method on representative diffusion-based generative models: FLUX.1 [dev] (Labs, 2024), Qwen-Image (Wu et al., 2025), and Hunyuan- Video (Kong et al., 2024). Baselines include TeaCache (Liu et al., 2024), DBCache (vipshop.com, 2025), DiCache (Bu al., 2025), ToCa (Zou et al., 2024a), DuCa (Zou al., 2024b), and Tay- lorSeer (Liu et al., 2025). Among them, TeaCache (Liu et al., 2024) and TaylorSeer (Liu et al., 2025) are two of the most representative mainstream approaches, spanning both text-to-image and text-to-video generation tasks.
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Table 1: Quantitative comparison text-to-image generation on FLUX.1 [dev] and Qwen-Image.
Method
Acceleration
Visual Quality
FLOPPTT) 1 Latencyis)
Speed
Image Reward
CLIP Score
LPIPS
SSIM
PSNR
Origimal: steps
steps 50% steps
2346.87
7.01
1,63x
31,242
0.217
1131,10
3.60
3.21
0.880
30,832
0.399
0.682
15.798
TewCache (1 0.25)
1949.7 73
4.62
2.50x
0.960
31.145
0.338
0.721
17:286
DSCache (5 0.5)
1032.51
4.32
2.68%
0.675
30,814
0.416
0.717
21
TaylorSeer (V 6
760.08
4.34
2.74x
0.971
31,310
0.415
0,633
16.278
TrylorSer (V 6,0
760,08
4.06
2.85
0.961
31.191
0.419
0.640
15831
MeanCache
TeaCache
DiCache
TaylorSoer
3.73
MeanCache (8 100)
759.18
2,81
4.12%
0.993
31.325
0.272
0.761
19.425
Image 1064x092
steps
steps
9.36
3.31x
5481.27
1,76x
DRCache (r 0.6)
2.74x
1.016
MasnCache (8 15)
3291.75
11.45
2.85%
1.159
33.636
0.075
0.938
27,663
DBCache (r 1.5%
2070.26
9.57
3.41
-2.089
15.499
0.880
0.120
5,559
DBCCashe + Taylorseer (r- 1.5.0 4y
2070.26
9.70
3.37%
-0.227
29.755
0.625
0.646
16.574
MeanCache (8 139)
2855 235
9.09
3.00%
1,147
33.799
0.113
0.907
24,802
MeunCache (8 10)
2200.77
7,36
4.56
1,142
33.621
0.236
0.815
18.983
Methods signtfiantt degradation Image Reward, leading to deterionation intage quality.
Metrics. For a fair comparison, we evaluate both efficiency and quality. Efficiency is measured by FLOPs and latency, while quality is assessed with task-specific and reconstruction metrics. For text-to-image generation, we follow the standard DrawBench (Saharia et al., 2022) protocol and report ImageReward (Xu et al., 2023) and CLIP Score (Hessel et al., 2021) to evaluate perceptual quality and text-image alignment. For text-to-video generation, we adopt VBench (Huang et al., 2024) to capture human preference on generated videos. In addition, for both tasks, report LPIPS (Zhang et al., 2018) (perceptual similarity), SSIM (Wang & Bovik, 2002) (structural con- sistency), and PSNR (pixel-level accuracy) to quantify potential degradation in content and fidelity introduced by acceleration.
Implementation details. Experiments are conducted on NVIDIA H100 GPUs using PyTorch. To construct the multigraph, we sample 50 prompts (10 per attribute) from T2V-CompBench (Sun al., 2024), following standard practice (Sun et al., 2024; Liu et al., 2024). This procedure is applied consistently across both text-to-image and text-to-video experiments, even though the dataset was originally not designed for text-to-image generation. Sampling is repeated 5 times with different seeds, and results averaged reduce bias. For all experiments, FlashAttention (Dao al., 2022) is enabled by default to accelerate attention computation. Notably, since TaylorSeer encounters out-of of-mymory (OOM) issues under Humyuan Video, uniformly adopt the cpu-offload setting to ensure fair comparison.
3.2 TEXT-TO-IMAGE GENERATION.
As shown in Table 1, MeanCache achieves clear quantitative gains on two advanced text-to-image models, FLUX and Qwen-Image. We Im- ageReward (Xu et al., 2023) and CLIP Score (Hes- sel et al., 2021) as perceptual metries, and re- construction metrics to measure content and de- tail preservation. On FLUX, 2.91 x accelera- tion, MeanCache surpasses TaylorSeer and Tea- Cache in both image quality and detail preserva- tion, and remains robust at higher ratios. Even at 4.12x, where competitors collapse in quality, method still attains an IewgeReward Scoret of 0.993 and an LPIPS of 0.272. On Owen-Image (1664x928 resolution), MeanCache likewise im-
Original
MeanCache x4.12
TaylorSeer
TeaCache
DiCache
Diffuiooe
Duffuioo
Diffcon
Diffusie
Figure 4: Comparison of different methods at high acceleration ratios on FLUX.1[[dvv..

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Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison in text-to-video generation on Hunyuan Video ?.
Method
Acceleration
Visual Quality
HunyuanVideo
Latency(s) + Speed
VBench
LPIPS &
SSIM 1
PSNR f
Original: 50 steps
105.92
x
80.39%
30% steps
39.53
2.68 x
79.84%
0.381
0.659
17.335
ToCa (N
36.17
2.93 x
79.51%
0.454
0.590
15.765
Duca (N
34.32
3.09
79.54%
0.454
0.595
15,807
DiCache (ó 0.8)
33.76
3.11x
74.09%
0.382
0.701
22.053
TaylorSeer (N = 5, 0 1)
34.95
3.03
79.95%
0,428
0.603
16.026
Teacache (1 0.33)
34.06
3.11
80.02%
0.363
0,651
17.957
MeanCache (B 12)
33.05
3.21
80.01 %
0.176
0.809
24.002
DiCache (6 3.0)
31.81
3
70.86%
0.583
0.490
19.098
Teacache (I 0.39)
31,86
3.32
79.75%
0.396
0.631
17.382
TaylorSeer (N = 7 = 1)
31,50
3.36
79.76%
0,480
0.595
15.444
MeanCache (B 10)
29.48
3.59 x
80.08 %
0.269
0.732
20.464
TaylurSeer may encoumeer OOM: for faimess, methods ure run with CPU-oflood enabled.
proves both quality and speed, reaching an LPIPS 0.075 at 2.85x acceleration, indicating near- lossless sampling. As shown in Fig. 4, on FLUXX11 [dev], when the acceleration exceeds 3.5x, base- line methods suffer from severe blurring, detail loss, and structural distortions, whereas MeanCache consistently preserves perceptual quality and fidelity close the original outputs. On Qwen-Image, MeanCache also demonstrates strong robustness under high acceleration ratios, outperforming other baselines as illustrated Fig.
3.3 TEXT-TO-VIDES GENERATION.
On the Hunyuan Table 2 demonstrates the acceleration performance of MeanCache across VBench and three reconstruction metrics. With a speedup of 3,42x, , our method significantly out- performs the main competitors, achieving 0.809 in SSIM1 and 24.002 in PSNR1. Performance continues to improve with a further 3.97 speedup, while maintaining VBench score of 80.08%. In terms content preservation, our method effectively preserves both the content and intricate de- tails of the original video, surpassing all baseline methods in this regard. As shown in Fig. 5, when the acceleration exceeds 3.0x, baseline methods suffer from visual degradation or blurring, whereas MeanCache maintains superior video quality and fidelity to the original videos.
bascholl glove and skutchrar.
hird and
couple evening... Gogh
Hunyuan 50 step
3.59
Visual distortion, ghosting
Visual distortion
Missing Van Gugh style, distortion
TeaCache 3.32
TaylorSeer 3.36
DiCache
3.30
3.09
Figure 5: Comparison of different methods at high acceleration ratios on Hunyuan Video.

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Renorskip)
Pull
Cached Ivrk-1
Carbed avreedd
Cachal avppkcl
Cacked aveykest
40
1.16x
37
Budget B (NFE)
34
28
25
19
16-
10
1000
825
259
Timestep (From 1000 NFE
Figure 6: Shortest paths on a multigraph under different budgets FLUX. (UU.I.IIee)
[dev]
3.4 ABLATION STUDY
Shortest Path Analysis. Trajectory-Stability Scheduling is realized by constructing a multigraph and solving for its shortest paths. Given this representation, the shortest path under any step budget B can be obtained via edge-weighted optimization. The budget B (equivalent to the Number of Function Evaluations, NFE) directly controls the acceleration ratio: smaller values correspond to greater speedups. Figure 6 illustrates the shortest-path patterns FLJUX as B decreases from 40 to 10. The horizontal axis corresponds to the 50-step denoising trajectory, while the vertical axis indicates different budget levels. Darker cells represent larger cached JVP spans K, and gray cells indicate reuse (skips). This analysis reveals that early timesteps are crucial for denoising quality, whereas later timesteps, particularly in the latter half, contribute less and more suitable for skipping. Moreover, the optimal JVP span is not fixed but depends jointly on the budget and timestep, underscoring the necessity of multigraph-based modeling for analyzing acceleration from the average-velocity perspective.
Effect of Peak-Suppression Parameter y. The peak-suppression parameter, controls the de- gree of peak suppression in the shortest path, effectively mitigating the concentration of error into small number of edges. We selected a moderate budget size B 15 and varied 7 within the range [1. The results, shown in Table 3, indicate that when y the image quality metrics fail reach optimal performance, suggesting the presence of eπror spikes within the shortest path. In contrast, when y all evaluation metrics achieve their best performance, highlighting the effectiveness of peak suppression.
Original
N 1.51
282
x11
1.00
3346
x
Table 3: Impact of peak-suppression pa- rameter 7 on quality metrics.
Tocade ToploeSee Mon
Rewandf
CLIP1
LPIPS
SSIM
PSNR1
1.0136
31.201
0.192
0.826
22.376
1.0072
31.195
0.148
0.860
24.147
3
1.0066
31.208
0.145
0.862
24.183
1.0199
31.291
24.569
Figure 7: Content consistency under rare-word prompts "Matutinal" across acceleration ratios.
5
1.0177
31.271
0.140
0.871
24.568
Content Consistency Content consistency before and after acceleration is key criterion for eval- uating acceleration methods. Rare words, due to their ambiguous semantics and infrequent usage, pose stringent challenge for text-to-image generation. To assess consistency under acceleration, we compare MeanCache with two baselines, TaylorSeer (Liu et al., 2025) and TeaCache (Liu et al., 2024), using prompts containing words. As shown in Figure 7, all three methods maintain good consistency at low acceleration ratios (< 2.43x). However, as the ratio increases, TaylorSeer and TeaCache exhibit severe content drift and quality degradation, whereas MeanCache preserves most of the original content and details even at a 4.12x speedup.
4 RELATED WORK
Diffusion Model Acceleration/Flow Models. Diffusion models have achieved remarkable suc- cess across modalities, yet their iterative denoising procedure incurs high inference latency, making
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acceleration central challenge. A large body of work has therefore focused on reducing sampling steps. For example, DDIM (Song et al., 2020a) extends the original DDPM (Ho et al., 2020) to non- Markovian dynamics for faster sampling, while EDM (Karras et al., 2022) introduces principled design choices to improve efficiency. In parallel, advanced numerical solvers for SDEs/ODEs (Song al., 2021; Lu al., 2022; Chen al., 2025) significantly im-
al., 2020b; prove the trade-off between accuracy and speed. Another line of work leverages knowledge distil- lation (Hinton al., 2015), compressing multi-step trajectories into compact few-step models (Luo et al., 2023). Representative approaches include Progressive Distillation (Salimans & Ho, 2022), Consistency Distillation (Song et al., 2023; Kim et al., 2023; Geng al., 2024; Wang et al., 2025; Zheng et al., 2024a), Adversarial Diffusion Distillation (Sauer et al., 2024b;a), and Score Distilla- tion Sampling (Yin et al., 2024b;a). Orthogonal strategies such as quantization (Li al., 2023b; So et al., 2023; Shang et al., 2023), pruning (Han et al., 2015; Ma et al., 2023b), system-level optimiza- tion (Liu ct al., 2023), and parallelization frameworks (Zhao et al., 2024a; Li et al., 2024; Fang ct al., 2024; Chen ct al., 2024b) have also been explored to enhance efficiency.
Beyond these efforts, Flow Matching (Lipman et al., 2023; Albergo & Vanden- Mijede, 2022) has emerged as promising alternative. Unlike diffusion models (Song et al., 2020a;b) that rely on noise injection and SDE solvers, it learns velocity fields for distributional transformations and can be viewed continuous-time normalizing flow (Rezende & Mohamed, 2015). Extensions include Flow Map (Boffi et al., 2024) for integral displacements, Shortcut Models (Frans et al., 2025) for interval cofffs-onistenyy...o and Inductive Moment Matching (Zhou et al., 2025) for stochastic consis- tency. MeanFlow (Geng et al., 2025) further shifts the focus from instantaneous to average velocity, offering a new perspective on efficient generative modeling. Nevertheless, most methods still require heavy computation, large data, or complex engineering, limiting practical adoption.
Cache in Diffusion Models. Recently, caching strategies (Smith, 1982) have emerged as a promis- ing retraining-fif free approach for accelerating diffusion inference (Wimbauer et al., 2023; Ma et al., 2024). The core idea is to reuse intermediate results from selected timesteps during sampling to re- duce redundant computation (Selvaraju et al., 2024). Early attempts such as DeepCache (Ma al., 2023a) accelerated the UNet backbone with handcrafted rules. Later, T-GATE (Zhang et al., 2024) and A-DIT (Chen et al., 2024a) extended this idea to DiT architectures (Peebles & Xie, 2023), achieving significant speed-ups in image synthesis (Li et al., 2023a). With the breakthrough of Sora (OpenAI. 2024) in video generation, these techniques have also been extended temporal domains. For instance, PAB (Zhao et al., 2024b) identified a U-shaped trajectory of attention differ- ande--nd--brraccss strategy. More recently, TaylorSeer (Liu et al., 2025) combined multi-step cached features in a Vaylor-expansion-lik like manner to enhance fea- ture reuse; TeaCache (Liu et al., 2024) exploits the correlation between timestep embeddings and model outputs, employing thresholding and polynomial fitting to guide its caching strategy. Di- Cache (Bu et al., 2025) enhances this idea with online shallow-layer probes, while DBCache (vip- shop.com, 2025) extends TeaCache's thresholding scheme to additional boundary blocks. LeM- iCa (Gao et al., 2025) proposes a global caching mechanism based on a DAG structure to accelerate video synthesis. While effective, these methods mainly adopt an instantaneous- velocity perspective, performing feature caching at the step-wise level. This view is inherently unstable (Fig. 2), often leading to trajectory drift and error accumulation under high accelcration ratios.
ences across timesteps and proposed
DISCUSSION AND CONCLUSION
In this work, we presented MeanCache, a lightweight and training-freee caching framework for Flow Matching. By shifting the perspective from instantaneous to average velocities and combining JVP-based estimation with trajectory-stability scheduling, MeanCache effectively mitigates error accumulation and improves cache placement. Experiments on commercial-scake models demon- strate that it achieves significant acceleration while preserving high-fidelity generation. Beyond its empirical performance, MeanCache contributes a new perspective to caching methods: rather than reusing instantaneous quantities, it leverages vellgi-veloccty formulations as more stable founda- tion. This not only enriches the design space of caching strategies, but also extends the applicability of average- based velocities ideas, such as those in MeanFlow, to practical large-scale generative models. We hope that MeanCache provides fresh insights for accelerating commercial-scal gener- ative models.

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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation China Enterprise Innovation and Development Joint Fund Project U24B20181
ETHICS STATEMENT
This work does not introduce any new datasets or sensitive content, and all experiments are con- ducted on publicly available models. Furthermore, this study does not involve human subjects, ani- mal experiments, personally identifiable information. All datasets used are publicly available and strictly follow their usage licenses. We adhere to data usage guidelines throughout the experiments ensure ethical or privacy risks arise.
REPRODUCIBILITY STATEMENT
We have made every effort ensure the reproducibility of our results. The experimental setup, including baseline parameter settings, model configurations, and hardware details, is described in detail in both the main paper and the supplementary material. addition, we provide compre- hensive ablation studies and analyses to further support reproducibility. To better illustrate the ef- fectiveness of our approach, we also include high-resolution videos in the supplementary material. Furthermore, plan to make key resources related to MeanCache, including core code (within permissible scope), available to the community. This is intended to encourage further exploration of MeanCache in compliance with relevant guidelines.
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MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Appendix
We organize appendix follows:
Proofs and Experimental Settings:
Section A.I:
MeanFlow Identity at the Start Point
Section A.2: Model Configuration
Section A.3:
Baselines generation settings
Section A.4: Metrics
Additional Experimental Results and Analysis:
Section B.1: MeanCache vs. Distillation
Section B.2:
Trade-off
Section B.3: Additional quantitative results
LLM Statement:
Section C: Use of Large Language Models
A PROOFS AND EXPERIMENTAL SETTINGS
A.1 MEANFLOW IDENTITY AT THE START POINT
Start-point identity from the integral definition. average velocity over the interval [s, is defined
Let < s. In the MeanFlow formulation, the
5- -. v(2r, () dr.
u( 1,
(16)
Since the average velocity is uniquely determined by the interval [s, 4). it can be equivalently indexed by the state at the start point 2,. For notational consistency with the original McanFlow identity, we write
u(z,, t) s)
u(=s,t, t)
(17)
Equivalently, the definition can be expressed as
1) u(2,),,)) [ T((2,,T)dt dr.
(18)
Differentiate both sides with respect to I.
While the original MeanFlow identity is obtained by differentiating with respect the end variable here we instead differentiate the definition equation 18 with respect to the start variable t (holding fixed).
On the left-hand side, by the product rule, a.[(s t) u(z,, t, s)] u(ze,t, 8) + (s - 1) d,u(z,, t, On the right-hand side, by the Leibniz rule for lower limit depending on t,
(19)
ie (, ((((er)))) ((
v(2,,))
(20)
Equating equation and equation 20 and rearranging yields the MeanFlow Identity at the start point:
u(2,,)) t) = u(2,,t, s) - (s-t) u(22 t).
(21)
dt
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A.2 MODEL CONFIGURATION
For MeanCache, the primary hyperparameter is the peak-suppression parameter 7, Based on ab- lation studies, different 7 values are adopted across the three models to achieve better results. For the step size parameter K of JVP caching, values from 2 to 5 are considered. Trajectory-Stabiin Scheduling is further applied combination with multigraph construction and the reak-Suppressed Shortest Path to determine the optimal acceleration path. Under different constraints B, where B denotes the number of full computation steps, MeanCache flexibly balances runtime cost and accel- eration ratio. The detailed parameter settings are summarized in Table 4.
Table 4: MeanCache configuration across different models.
Model
Peak-suppression 7 Budget B
FLUX. [dee]
[10, 15]
Qwen-Image
[10, 13, 15]
Hunyuan Video
3
[10, 12]
A.3 BASELINES GENERATION SETTINGS
Experiments are conducted on three representative models from different tasks: FLUX.1- [dev] (Labs, 2024) and Qwen-Image (Wu et al., 2025) for text-to-image generation, and Hunyuan- Video (Kong et al., 2024) for text-to-video generation. The detailed configuration for each model is summarized below.
FLUX.I[dev] (Labs, 2024): TeaCache (Liu et al., 2024) is evaluated with accumulation ror thresholds | 0.25 and 1.5, corresponding to different acceleration ratios. DiCache (Bu al., 2025) replaces the threshold discriminator of the first block in TeaCache with probe- based perspective, where 5 serves as the control factor. In the experiments, ò is set to 0.8 and 2.0. TaylorSeer (Liu et al., 2025) reformulates cache reuse as cache prediction, where N' denotes the caching interval and O the order of derivatives. For moderate acceleration and N 6,
(2.50-2.91 x), the settings N 6, O
are adopted, while higher acceleration setting of N 20, O = 1 is applied to align with MeanCache.
Qwen-Image (Wu et al., 2025): DBCache (vipshop.com, 2025) adjusts acceleration via r, with default hyperparameters F, = and B, 4. The compositional setting DB- Mache+TaylorSee is further considered, configured with 1.5 and O 4. Community implementations of TeaCache are also included, although significant quality degradation is observed when thresholds are large.
Hunyuan Video (Kong al., 2024): In addition to TeaCache, DiCache, and TaylorSeer, comparisons are conducted with ToCa (Zou et al., 2024a) and DuCa (Zou et al., 2024b), both configured with N 5, consistent with the TaylorSeer setting. Since TaylorSeer frequently encounters out-of-memory (OOM) under CannyuanVided all methods are eval- uated with CPU-offload enabled ensure faimesss.
A.4 METRICS
For fair and consistent evaluation, we consider both efficiency and generation quality. Efficiency is quantified using FLOPs and runtime latency. Quality is evaluated with the following five metrics:
ImageReward. ImageReward (Xu et al., 2023) is a learned metric designed to align with human preferences in text-to-image generation. It uses a reward model trained on human-annotated com- parisons to provide scores that capture both fidelity and semantic alignment with the input text. Higher scores indicate better alignment with human judgment.
CLIP Score. CLIP Score (Hessel al., 2021) leverages pretrained CLIP embeddings to evaluate text-image alignment. computes the cosine similarity between image and text embeddings, with higher values indicating stronger semantic alignment. This metric complements ImageReward by
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providing embedding- based, zero-shot evaluation of alignment quality. For more precise assess- ment, we adopt ViT-G as the visual encoder in this paper.
VBench, VBench (Huang et al., 2024) evaluates video generation quality along 16 dimensions, in- cluding Subject Consistency, Background Consistency, Temporal Flickering, Motion Smoothness, Dynamic Degree, Aesthetic Quality, Imaging Quality, Object Class, Multiple Objects, Human Ac- tion, Color, Spatial Relationship. Scene, Appearance Style, Temporal Style. and Overall Consis- tency. We adopt the official implementation and weighted scoring to comprehensively assess video quality. practice, we randomly select 350 generated video samples, which are evenly distributed across the 16 evaluation dimensions, and evaluate them using the official VBench protocol to ensure fairness and consistency.
PSNR. Peak Signal-to-Noise Ratio (PSNR) is widely used to measure pixel-level fidelity:
R MSE
PSNR 10 logio
(22)
where is the maximum possible pixel value and MSE is the mean squared error between reference and generated images. Higher PSNR indicates better reconstruction fidelity, For videos, we compute PSNR per frame and report the average.
LPIPS. Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al., 2018) measures per- ceptual similarity using deep features:
LPIPS = Σ Dist(F(())
(23)
where F; denotes feature maps from pretrained network, Dist is typically the L2 distance, and o; are layer-specific weights. Lower LPIPS values indicate higher perceptual similarity.
SSIM. The Structural Similarity Index Measure (SSIM) (Wang & Bovik, 2002) evaluates lumi- nance, contrast, and structural consistency:
(2urlly + (20 + C2) SSIM u) (n + Ci)( Ci) + o C2)
(24)
where Pzz By are mean values, 22 of are variances, Ozy is covariance, and C1, C2 are constants for stability. SSIM ranges from -1 to 1, with larger values indicating stronger structural similarity.
B ADDITIONAL EXPERIMENTAL RESULTS AND ANALYSIS
B. MEANCACHE vs. DISTILLATION
To better understand the difference between training-free free caching and retraining- distillation, compare them the commercial-scale text-to-image model Qwen-Image. Caching requires no extra training, whereas distillation depends on costly large-scale retraining. As Qwen-Image newly released, distilled variants are limited; therefore include the 15-step distilled model from DiffSynth-Studio as a representative baseline. Here, NFE (Number of Function Evaluations) denotes the number of sampling steps during inference. Table 5 shows that the distilled model achieves the highest ImageReward score, but MeanCache remains competitive, reaching 1.142 with only 10 steps. On CLIP Score, a measure of text-image alignment, MeanCache performs favorably. Distilled models also tend to drift from the original outputs, while caching better preserves fidelity to the backbone. This is reflected reconstruction metrics, where MeanCache achieves lower LPIPS and higher SSIM and PSNR. Overall, distillation can improve some aspects through additional train- ing, but our results suggest that caching- baching-based approaches such as MeanCache provide practical alternative. They maintain semantic consistency, deliver good perceptual quality, and achieve strong reconstruction accuracy, all without retraining. Caching thus appears to be a scalable acceleration strategy for commercial-grade generative models.
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Published as a conference paper at ICLR 2026
Table 5: Quantitative comparison between distillation and MeanCache on Qwen-Image.
Method
Visual Quality
NFE:
Training Free
Qwen-Image
ImageReward
CLIP Score
LPIPS 4
SSIM
PSNR 1
Original (50 steps)
50
1,180
33.626
Qwen Image Distill Full
15
x
1.162
33,062
0.594
0.505
11.019
Dwen--nage-Distille Distill-Full
10
1.118
32.802
0.583
0.524
11.483
MeanCache
15
1.159
33.636
0.075
0.938
27.663
MeunCache
10
1.142
33.621
0.236
0.815
18.983
B.2
TRADE-OFF
Our analysis in Fig. compares MeanCache, TaylorSeer, TeaCache, and DiCache on FLUX.1 across three reconstruction metrics (LPIPS, SSIM, and PSNR). Across wide range of latency configurations, MeanCache consistently delivers higher reconstruction fidelity while requiring less inference time. This advantage is particularly clear in the low-latency regime: even when the total runtime falls below seconds, MeanCache maintains stable performance across metrics, whereas baseline methods exhibit severe degradation, including loss of structural consistency and perceptual quality. These results highlight that MeanCache not only achieves a favorable quality-efficieng balance, but also extends the usable acceleration range far beyond prior caching strategies, enabling practical deployment in interactive or resource-constrained scenarios.
NeunCache
0.5
DICache
0.4
orleee
TeCCahe
ThaCache
10
Cache
Dicache
Latency
Latency
Figure 8: Efficiency-Peeformamr Trade-off on (dev].
B.3 ADDITIONAL QUALITATIVE RESULTS
We provide further qualitative results to complement the main paper. These experiments cover high- acceleration scenarios Qwen- Qwen-Image, different acceleration ratios on FLUX.I rare-word prompts for testing content consistency, and additional video examples on Hunyuan Video.
Qwen-Image under High Acceleration. Figure 9 shows results Qwen-Image at high acceler- ation ratios. Compared with baselines, McanCache achieves more faithful preservation of structural details and visual quality, even when runtime is significantly reduced. Competing methods display blurring or noticeable artifacts, while MeanCache remains robust.
FLUX.1 across Acceleration Ratios. Figures 10 and 11 illustrate results FLUX. across multiple acceleration ratios. MeanCache consistently outperforms TaylorSeer and TeaCache in pre- serving fidelity and perceptual quality. Notably, even at 4.12x speedup, where baselines collapse in quality, MeanCache maintains coherent textures and stable global structure.
Content Consistency under Rare-Word Prompts. In Figure 12, compare MeanCache with TaylorSeer and TeaCache using the rare-word prompt "Peristeronic". While all methods retain reasonable content under mild acceleration (< 2.5x), TaylorSeer and TeaCache quickly degrade as speedup increases, showing severe semantic drift and detail loss. In contrast, MeanCache preserves both the intended concept and fine-graine details even 4.03x acceleration.
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Published as a conference paper at ICLR 2026
Original 50 step
MeanCache x 4.56
TBCache+Taylorseee x 3.37
DBCache 3.41
Figure 9: Qualitative comparison of different methods high acceleration ratios Qwen-Image.
Original
1.51
2.02
2.43
3.00
x 3.46
x 4.12
Mean Cache
TeaCache TaylorSeer
Figure 10: Qualitative comparison on FLUX.1[dev] under different acceleration ratios (Supplemen-
tary 1).
Hunyuan Video under High Acceleration. Figure 13 presents supplementary video results on VidneaaVVieo. When the acceleration exceeds 3x, baseline methods suffer from heavy motion artifacts, visual degradation, and temporal inconsistency. MeanCache, however, continues to deliver stable frame quality and temporal coherence, closely matching the reference trajectory and confirm- ing its robustness in video generation tasks.
C USE OF LARGE LANGUAGE MODELS
We used large language models (LLMs) solely for grammar checking and minor language polishing. No part the method design, experimental setup, analysis, results was generated by LLMs. All technical contributions and empirical findings in this paper are entirely by the authors.
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Published as a conference paper at ICLR 2026
Original
1.51
x 2.02
x x.44
x 3.00
3.46
x 4.12
McanCache
TaylorSeer
TeaCache
Figure 11: Qualitative comparison on FLUX.1[[ev] under different acceleration ratios (Supplemen-
tary 2).
Original
2.02
2.43
3.00
x 3.46
4.12
Mean Cache
TaylorSeer
TeaCache
Figure 12: Content consistency under rare-word prompts "Peristeronic" across acceleration ratios.
person movoryeling."
person tying
"A person""
HunnyannVidew 50 step
Meant 'cche 3.59
Motorbike distortion
Hand deformation
Gender changes, Marring
TeuCache 3.32
TaylorSeer
3.36
DxCache
3.30
Duch
Figure 13: Comparison of different methods at high acceleration ratios on Hunyuan Video. Video.
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Reviews:
[{"review_id": "wuaGCIDYr3", "reviewer": "Reviewer_Egs2", "summary": "This paper proposes MeanCache, a training-free caching framework for accelerating Flow Matching diffusion models. Unlike prior methods that cache instantaneous model outputs (velocities) at fixed intervals, MeanCache operates in the average-velocity domain. Specifically, it uses cached Jacobian–vector products (JVPs) from earlier timesteps to construct interval-average velocities over longer spans, which are significantly smoother than instantaneous velocities. By reusing these interval-average velocities, MeanCache mitigates the local error accumulation that plagues existing caching schemes. To decide when and how far to cache, the paper introduces a trajectory-stability scheduling strategy: denoising steps are nodes in a graph, edges represent caching intervals weighted by their induced velocity-error, and a peak-suppressed shortest-path search (a budget-constrained graph optimization) selects the caching schedule that avoids large error spikes. In experiments on large-scale models (FLUX.1 image model, Qwen-Image, and HunyuanVideo), MeanCache achieves substantial speedups (e.g. 4.12×, 4.56×, and 3.59×) while maintaining or even improving generation quality compared to state-of-the-art caching baselines. The authors demonstrate that MeanCache preserves both perceptual quality and content consistency under high acceleration. e.g. rare-word prompts remain consistent under 4× speedup for MeanCache, whereas prior methods exhibit severe drift. The key contributions are a new average-velocity perspective for caching and a principled stability-driven scheduling algorithm, together yielding a simple yet effective acceleration scheme.", "questions": "* How sensitive is the chosen cache schedule to the specific prompts or seeds used to build the stability map? In practice, do you compute a single schedule offline per model, or would it need adjustment at runtime for different inputs?\n\n* If the diffusion model is updated (e.g. fine-tuned or improved), must the whole schedule and hyperparameters (like K spans) be recomputed? Is there a way to adapt the existing cache without re-running the full graph optimization?\n\n* How much overhead is incurred when solving the shortest-path?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "* MeanCache presents a conceptually simple shift (using average vs instantaneous velocity) yet this new perspective is powerful. The paper explains this insight clearly.\n\n* It achieves large acceleration on realistic large models while retaining high fidelity, outperforming prior caching schemes across image/video tasks.\n\n* The use of the MeanFlow identity and JVP to bridge instantaneous and average velocity is well-founded.\n\n* The trajectory-stability scheduling (peak-suppressed shortest path) is a novel, effective tool for deciding cache placement under budget.\n\n* The method is training-free and was tested on diverse tasks (text-to-image, text-to-video), suggesting broad applicability.", "weaknesses": "* The method depends on approximating a future JVP from past states. As the authors note, the choice of interval K is “critical” and this is a trade-off between error and stability. If the model’s dynamics are highly non-linear, the approximation might degrade. The paper addresses this with scheduling, but it remains an approximation-dependent approach.\n\n* There are several hyperparameters (cache span $K$, budget $\\mathcal{B}$, peak-penalty $\\gamma$). While the paper provides ablation studies, choosing these may be non-trivial in new settings.\n\n* The scheduling step involves building a multigraph of possible cache edges and solving a shortest-path; the paper does not discuss this overhead.", "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": "YXmHOs72wr", "reviewer": "Reviewer_4J2V", "summary": "This paper presents MeanCache, a training-free framework to accelerate Flow Matching inference. It addresses error accumulation in prior caching methods by shifting from an instantaneous to an average velocity perspective. The core idea is to approximate the interval average velocity using a cached Jacobian-Vector Product for better stability. This is combined with a principled scheduling strategy, formulated as a shortest-path problem. Experiments on large-scale models (FLUX, Qwen-Image, HunyuanVideo) demonstrate significant speedups while maintaining or improving generation quality over state-of-the-art caching baselines.", "questions": "1. How does the preprocessing time scale with budget $B$?\n2. Is the stability map computed on 50 prompts transferable to the entire prompt distribution?\n3. What is the memory footprint of storing cached JVPs and the multigraph?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. MeanCache consistently outperforms state-of-the-art baselines, especially at high acceleration ratios where competing methods collapse.\n2. The inclusion of both perceptual metrics and reconstruction metrics provides thorough quality assessment.\n3. The paper is very well-written, clearly motivating the problem and lucidly explaining the proposed methodology.", "weaknesses": "1. The paper lacks theoretical analysis (e.g., error bounds) explaining why JVP-based average velocity outperforms TaylorSeer's Taylor expansion, leaving the source of empirical gains unclear.\n2. Constructing the multigraph and computing shortest paths incurs preprocessing cost. Table 1-2 report only inference latency, not total time including preprocessing.\n3. TaylorSeer encounters OOM on HunyuanVideo, forcing CPU-offload for all methods. This may artificially inflate latency measurements and doesn't reflect realistic deployment scenarios.", "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": "NfY6obJNdy", "reviewer": "Reviewer_5MTc", "summary": "This paper introduces MeanCache, a training free caching framework for Flow Matching (FM) inference. The method reframes caching from the instantaneous velocity domain to the average velocity domain, using a JVP based estimator to approximate interval average velocities and mitigate local error accumulation. A trajectory stability scheduling strategy casts timestep selection as a peak suppressed shortest path problem on a multigraph; dynamic programming yields the cache placement under a step budget. Experiments on FLUX.1, Qwen Image, and HunyuanVideo report ∼3–4.5× speedups with competitive perceptual quality vs. strong caching baselines.", "questions": "- Schedule generalization: If the stability graph is built on one prompt pool/domain, how well does it transfer to another (e.g., different prompt sets or resolutions)? Any results on cross domain reuse?\n- Offline cost: Please report the wall clock overhead (and FLOPs) to build the multigraph and the proportion relative to the downstream inference savings.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "+ Clear, practical idea: Average velocity caching with a JVP estimator is simple yet impactful; it addresses the well known error accumulation issue at high acceleration ratios.\n+ Scheduling formalization: Casting cache placement as a constrained shortest path problem is principled and provides a tunable trade off via $\\gamma$ and budget $B$.\n+ Strong empirical results: Consistent improvements over strong baselines across image and video; qualitative figures align with quantitative gains.", "weaknesses": "- Heuristic JVP reuse: Approximating $JVP_{t\\to s}$ by $JVP_{r\\to t}$ is empirically motivated. The paper does not provide theoretical guarantees or error bounds beyond the MeanFlow identities. Performance depends on the span $K$, which is tuned and schedule dependent.\n- Stability assumption: The scheduling assumes that “relative changes at fixed timesteps are highly consistent across samples.” This is plausible but not statistically substantiated. Some quantification (e.g., cross prompt correlation of stability costs) would strengthen the claim.\n- Cost of graph construction: The paper states the graph is built offline with 50 prompts $\\times$ 5 seeds, but the actual overhead (FLOPs/GPU hours) and sensitivity to the prompt set are not reported. It remains unclear how often this needs to be rebuilt across domains or resolutions.", "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": "BXVI6BxzSV", "reviewer": "Reviewer_pV1g", "summary": "The paper introduces MeanCache, a training-free caching framework designed to accelerate flow model inference in large-scale generative models. Traditional caching methods reuse intermediate representations to save computation but rely on instantaneous velocity fields, which lead to error accumulation. MeanCache addresses this by reframing caching in the average-velocity domain, inspired by the MeanFlow formulation.", "questions": "Why caching in MeanFlow would be better than caching in flow models? JVP term is approximated in MeanFlow too. This part is most confusing and concerning to me.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "Overall, the paper’s motivation and structure are sound. I was able to follow the authors’ intuitions and main ideas, though the mathematical formulations lack sufficient explanation and rigor (see weaknesses). Additionally, I appreciate the comprehensive empirical evaluation on large-scale generative models.", "weaknesses": "The biggest concern I have is the claim that MeanFlow-based caching is more robust than caching in Flow Matching. Although the authors provide an explanatory figure (Fig. 3), this is not convincing, since error accumulation can also be observed in MeanFlow. Can the authors provide high-level intuition and mathematical proofs to justify this claim? Additionally, MeanFlow is an existing idea. The main contributions of the paper come from implementing feature caching within the MeanFlow model. To assess the core contribution, I would like to see how vanilla MeanFlow (without caching) performs. This would help isolate and evaluate the effects of the proposed method more clearly. \n\n\nI find some mathematical formulations and claims inaccurate and presented without sufficient explanation.\nFirst, the JVP approximation error can be large when $t - r \\gg \\delta$. Can the authors show whether this error is negligible (or quantify it)?\nSecond, the metric used in Eq. (13) depends on timestep discretization, which can vary across applications and the number of sampling steps. This is also connected to my first point, since $u(z_s, t, s)$ is already an approximation of the true vector in Eq. (2). However, I do not find discussions or mathematical proofs that show the approximation of the error.\nLastly, I found some minor details regarding the formulations: \n\n\n- L.106 lower case paragraph not in consistency\n- Eq. (1) $\\hat{x} $ is introduced without explanation\n- Eq. (2) is $s > t$? This indicates forward velocity towards prior? Might be better to represent in reverse time consistently in line with L.122 $x_1 - x_0$ sounds more natural.\n- Eq. (4) $\\mathcal{F}$ and l are not defined elsewhere \n- Eq. (5) JVP is a function of $z, t, s$. Might be better to express explicitly instead of $\\hat{JVP}$\n- Eq. (7) $\\hat{JVP} \\approx \\frac{d}{dr}$. Not equality by definition. \n- Eq. (15) no explanation of $\\mathcal{B}$", "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)