LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation
Authors: Huanlin Gao
Ping Chen
Fuyuan Shi
Chao Tan
Zhaoxiang Liu
Fang Zhao
Kai Wang
Shiguo Lian
Venue: NeurIPS 2025 spotlight ()
Keywords: Video Generation
Model Acceleration
Caching Mechanism
Training-Free
Primary area: deep_learning
TL;DR: We propose LeMiCa, a training-free acceleration method that improves content consistency in diffusion-based video generation by minimizing worst-case global errors through lexicographic minimax path optimization

Abstract: We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local heuristic errors, they often overlook the accumulation of global errors, leading to noticeable content degradation between accelerated and original videos. To address this issue, we formulate cache scheduling as a directed graph with error-weighted edges and introduce a Lexicographic Minimax Path Optimization strategy that explicitly bounds the worst-case path error. This approach substantially improves the consistency of global content and style across generated frames. Extensive experiments on multiple text-to-video benchmarks demonstrate that LeMiCa delivers dual improvements in both inference speed and generation quality. Notably, our method achieves a 2.9× speedup on the Latte model and reaches an LPIPS score of 0.05 on Open-Sora, outperforming prior caching techniques. Importantly, these gains come with minimal perceptual quality degradation, making LeMiCa a robust and generalizable paradigm for accelerating diffusion-based video generation. We believe this approach can serve as a strong foundation for future research on efficient and reliable video synthesis.

LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation
Huanlin Gao 12 Ping Chen 1.2+ Fuyuan Shi 22 Chao Tan 1:2 Zhaoxiang Liu' 1.2
Fang Zhao Kai Wang Shiguo Lian
Data Science & Artificial Intelligence Research Institute, China Unicom' Unicom Data Intelligence, China Unicom
{gaoh151, chenp181, shify15, tanc10, liuzx178, zhaof50, wangk115, Ltanss))chinaunicom https://unicomai github. github.io/LeMice.. iooLLMiCCa
Abstract
We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local heuristic errors, they often overlook the accumulation global errors, leading noticeable content degradation between accelerated and original videos. To address this issue, we formulate cache scheduling as a directed graph with error-weighted edges and introduce Lexicographic Minimax Path Optimization strategy that explicitly bounds the worst-case path error. This approach substantially improves the consistency of global content and style across generated frames. Extensive experiments on multiple text-to-video benchmarks demonstrate that LeMiCa delivers dual improvements in both inference speed and generation quality. Notably, our method achieves a 2.9x speedup on the Latte model and reaches an LPIPS score of 0.05 on Open-Sora, outperforming prior caching techniques. Importantly, these gains come with minimal perceptual quality degradation, making LeMiCa a robust and generalizable paradigm for accelerating iiltusiion-baeed video generation. We believe this approach can serve strong foundation for future research on efficient and reliable video synthesis. Our code is available https: thub. com/ cwm/unmiiiiicccccceccccccooi /un//nniiiiii. ceiiccmII/LMMC..
1 Introduction
Diffusion models [10, 38] have made significant advancements in video generation [24, 53, 45], particularly with DiT-based architectures [29], which greatly enhance visual quality. However, these methods are often hindered by high memory usage, substantial computational costs, and long inference latencies, limiting their in interactive applications. This has led to increased interest in more efficient and cost-effective generation strategies.
Existing approaches such model distillation [39, 30, 42], pruning [7, 27]. and quantization [34, 37, 8, 18] have been widely adopted accelerate inference. While effective, these methods require careful architectural design and retraining on large datasets, incurring high costs. Caching mechanisms [35, 26]. in contrast, offer a retraining-fri free alternative for accelerating diffusion model inference. The core idea is to reuse model outputs from specific timesteps during sampling reduce redundant computations and speed up the process [20, 44]. Selecting optimal cache timesteps, while balancing video quality and inference speed, remains open problem in video generation.
Ideally, a lossless video acceleration method should meet two essential criteria: High visual quality and (ii) Consistency between accelerated and original videos. However, existing cache-
"quual contribution
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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based methods [20, 51] maintain a certain level of visual quality, but they often introduce content deviations and loss of high-frequency details, increasing the risk of uncontrolled degradation.
Timesteps
No Cache
Lecnl-Greody Cache
Outcome-Aware Cache
Originul
B =00
Traditional
B 12
LeMiCa
F4-1
XT-2
X
B 10
Directed Acyelic Graph (DAG)
Speed up (w/o)
Original
Speed up 2
Traditional
(looaasd))
Speed up 2.64x
LeMiCa
Figure I: Comparison between our globally controlled cache mechanism (LeMiCa) and traditional local greedy cache methods. Top: The second row shows the traditional Local-Greedy approach, which uses local eгror estimation and fixed thresholds for caching decisions. It assumes uniform denoising contributions across time steps and ignores temporal heterogeneity and error propagation. Our method (third row) introduces a Global Outcome-Aware Cache, evaluating cache segment impacts through multiple prompts along fixed sampling path, creating a static directed acyclic graph (DAG). We then use Lexicographic MiniMax Path Optimization (LeMiCa) to find the optimal cache path under fixed inference budget (B, model forward steps). Bottom: LeMiCa outperforms traditional methods (e.g., TeaCache) in maintaining structural consistency with faster inference and better control over cache errors and distortions.
Upon further analysis, we identify two key limitations. First, representative methods [44, 20] typically compute local errors between adjacent timesteps and apply fixed thresholds to decide whether to cache. However, the diffusion denoising process exhibits significant temporal heterogeneity, with varying noise levels and semantic richness across timesteps. Applying a uniform threshold throughout the process may disrupt semantic alignment and introduce inconsistencies in to inaccurate caching behavior. Second, these methods mainly focus minimizing local differences between consecutive ateps-what we refer to as Local-Greeda error. While this may reduce short- term discrepancies, it overlooks how small errors accumulate over time, potentially resulting in dual loss in both video quality and content consistency. These issues are evident in TeaCache (a state-of-fe of-the-art Local-Greede method), shown in Figure 1, particularly with the three frames in the top and second rows, where caching introduces noticeable content deviations and visual quality degradation.
leading
To address these limitations, we propose Lexicographic Minimax Caching (LeMiCa), static caching framework that is model-agnostic and cechittctuurr-nddeeeeeee Instead of using local greedy

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strategies, LeMiCa treats cache scheduling as a global path planning problem. This is based on the observation that well-trained' diffusion models remain stable along fixed sampling path.
LeMiCa takes global view of error by introducing the Global Outcome-Aware error, which quantifies the impact of each cache segment on the final output, effectively eliminating temporal heterogeneity and mitigating error propagation. Based on this metric, LeMiCa constructs a Directed Acyclic Graph (DAG), where each edge represents a possible cache segment and is weighted by its global impact on output quality. This graph is generated offline using multiple prompts and full sampling trajectories.
We then apply lexicographic minimax optimization to identify the path that minimizes worst-case degradation. Among all feasible paths under a fixed budget, the one with the smallest maximum error selected. If multiple paths have the same maximum errrr, the next largest error is compared, and so on. This strategy explicitly constrains the worst-case error, effectively preventing global degradation caused by locally unstable cache decisions, and significantly improving content consistency and video quality in accelerated generation.
In summary, the contributions of this paper are:
We propose LeMiCa, novel, training- free cache scheduling framework that formulates the generation process as a globally optimized DAG traversal task, offering a principled alternative heuristic and locally greedy approaches.
We conduct in-depth analysis of the cache optimization problem and appropriately introduce the Lexicographic Minimax Path Optimization strategy to solve the graph under a fixed cache budget, effectively suppressing error peaks and enhancing global consistency.
Experiments show that, compared to existing cache techniques, ours achieves dual improve- ments in inference speed and generation quality across various base models, such as a 2.9X speedup on Latte and an LPIPS of 0.05 on Open-Sora.
2 Related Work
Diffusion Model Acceleration. Diffusion models exhibit strong versatility across domains, but their iterative nature incurs high computational costs, positioning inference acceleration central research challenge. Current efforts to accelerate diffusion model sampling focus primarily on reduc- ing sampling steps via schedulers. Denoising Diffusion Implicit Models (DDIM) [38] represents one of the earliest attempts to accelerate sampling by extending the original Denoising Diffusion Proba- bilistic Model (DDPM) [10] non-Markovian settings. The Efficient Denoising Model (EDM) [13] introduces a design framework that optimizes specific aspects of the diffusion process. Concurrently, there is growing attention to more efficient and accurate methods for solving stochastic differential equations (SDEs) and ordinary differential equations (ODEs) [40, 12, 21, 3]. Other approaches introduce knowledge distillation [9], training a student model to condense the multi-step outputs of the original diffusion model into fewer steps [22], including Progressive Distillation [30], Consistency Distillation [39. 14, 42, 52]. Adversarial Diffusion Distillation [32, 31], and Score Distillation
Sampling [47, 46]. Additionally, methods such as quantization [17, 36. 34J. pruning [7, 27]. optimiza-
tion [19], and parallelism [50, 15, 5, 4] have been proposed and applied to various diffusion-baser generative tasks. However, these methods often require large amounts of computational resources and
data for training or intricate engineering designs, which increases the complexity of their application.
Cache in Diffusion Models. Caching mechanisms [35] have recently attracted attention retraining-free alternative for accelerating diffusion model inference [44, 25]. The core idea is to
reuse model outputs from certain timesteps during sampling reduce redundant computations [33].
DeepCache [26] accelerates the Unet structure using manually set rules. T-GATE [49] and A-DiT [2] apply this idea DiT-based networks [29]. achieving advanced image generation acceleration [54, 16].
With the breakthrough of Sora [28] in video generation, researchers have extended this acceleration concept from image generation to video generation. In this context, PAB [51] observed U-shaped pattern in attention differences across timesteps in the diffusion process, and based on this, proposed a strategy to cache and broadcast intermediate features at various timestep intervals. FasterCache [23] realized the significant redundancy in conditional generation (CFG) and further enhanced inference speed by utilizing a dynamic feature- based caching mechanism. TeaCache [20] leverages the correlation between timestep embeddings and model outputs, incorporating threshold-bart based indicators
3

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and polynomial fitting to guide caching. Although these methods have improved the efficiency of diffusion-based generation, the core challenge remains how to accelerate inference while maintaining content consistency and preserving details.
3 Method
3.1 Background: Denoising Diffussio Models
Denoising Diffusion Models achieve generative modeling by simulating the gradual noising and denoising process of data. The core of these models consists of two key stages: diffusion and denoising. During the forward diffusion process, the model starts from real sample zo ~ g(z) and gradually adds Gaussian noise over I' timesteps. The noised sample Iy at timestep t given by:
va Va I + v1 Or Rt, 2 ~00, 1 1=
(I)
where a controls the noise strength at each step. As t increases, the samples converge to a standard normal distribution (O0, f). In the reverse denoising process, the model reconstructs the original data distribution by iteratively denoising through neural network. The conditional probability for each step is modeled as:
Pa - x4) = 1 t), Eg(2s, t)).
(2)
where Hi and Eg are learned mean and covariance functions. Due to the multi-step nature of denoising, diffusion models typically incur significant computational overhead during generation.
3.2
Rethinking Cache in Diffusion Sampling
L1,é
0.8
0.5
len = 1
len
len 2
0.4
len = 3
len
Error
0.3
Error
len
0.4
len 6
len
0.2
len 8
0.2
0.1
0.0
0.0
20
40
60
80
100
25
50
75
100
Denoising Process (96)
Denoising Process (96)
(a) Local vs. Global Cache Error Control
(b) Segment-wise Error Trends
Figure Rethinking cache reuse in denoising diffusion via error estimation. (a) The traditional Local- Greedy (Llyet) strategy uses fixed thresholds on local output differences between adjacent timesteps decide when to cache. This assumes uniform temporal sensitivity, which can be misleading- instance, caching at f2 yields lower final eтror than f1, despite t seeming smoother locally. This highlights the role of temporal heterogeneity. (b) Our Global Outcome-Awe (segment-wize error) strategy estimates final output етror when caching outputs over segments of length len, starting from timestep The plot shows that early caches cause greater error, supporting an outcome-sensitive, trajectory-awanl strategy over fixed local heuristics.
Traditional cache reuse in diffusion sampling typically adopts a Local-Greedy strategy (Figure 2a), where caching is based on local differences between adjacent model outputs, often measured by the relative L. distance [20]:
(0,et(O,t) 10, O
(3)
||0

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where O₂ is the output at timestep t. High local differences prompt full inference; low differences lead to cache reuse. This step-wise strategy assumes uniform importance across timesteps.
However, diffusion processes are inherently lemporally heterogeneous- structure, while later steps refine details. Thus, as illustrated in Figure 2a, seemingly minor change at an early step (c.g., f1) can have a larger impact on the final output than a larger change at a later step (e.g., t2). Local metries fail account for this asymmetric error propagation, motivating rethinking of cache strategies.
steps shape global
To address this, propose Global Outcome-Aware view that considers the long- impact of cache reuse over time. Specifically, we define a cache segment (i.j) means full inference is performed at timesteps and j. while all intermediate steps t (i,j) reuse cached outputs:
Alglob(( (i +] N
cache(i-+1) original
original is the output with no caching, and #g Here, zu As shown in Figure 2b, the global error depends not just segment length but also its temporal position-early caches induce amplified downstream errors, while later caches are less disruptive.
cache(i-+)) is the output with segment-level cache.
These findings reveal two key insights: (1) Global error propagation is non- non-uniform and time- dependent, invalidating shed-threshold heuristies; (2) The position of the cache segment matters more than its length. Building these insights, we formulate cache planning as a graph-based constrained path optimization problem over the sampling trajectory.
3.3 Lexicographic Minimax Path Caching
Based on the rethinking of cache in Sec 3.2, we propose LeMiCa, a method that integrates sparse directed graph construction with optimal graph search under peak error control.
Graph Construction. We construct a directed acyelic graph, as shown in Figure 1, where each edge represents candidate cache segment along the original sampling trajectory. To reduce complexity, we impose a maximum skip length based on the prior that long-range reuse typically leads to large errors, thus avoiding full graph construction. Edge weights using intermediate states from a full denoising pass. To ensure generality, we build a static graph by averaging edge errors across diverse prompts and noise seeds.
evaluated by replaying cached segments
Graph Optimization. Given a directed acyclic graph G with globally aror-weighted edges, we frame the caching problem selecting path from source s to target that includes exactly B full computation steps and an arbitrary number of cached segments. This budget-constrained formulation allows flexible reuse while bounding computational cost.
As shown in Figure 2b, carly-stage cache errors amplify exponentially during denoising, while late-stage eтгоrs remain more localized. This asymmetric eлror propagation renders traditional shortest-path heuristics- which minimize only additive cust-suboptimal, as they fail control the dominant sources of degradation.
To better address this imbalance, adopt lexicographic minimax criterion that explicitly min- imizes the highest cache error along the path, followed by the second highest, and so on. Unlike training-based approaches such ShortDF [3], which directly seek the shortest error path, our for- used in control systems for robust worst-case optimization improved stability in error-sensitive settings. Formally, the optimization problem is defined as:
min LexMax (isort_descc ([w(e) e E P Poache PB)
(5)
Here, P!B)) denotes the set of paths from 8 to / with exactly B full steps, and Paache C P are the cached segments within given path P. The operator LexMax lexicographically minimizes the sorted error vector, ensuring worst-case robustness. The detailed algorithm pseudocode is provided in the Appendix (Section A).
5

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Table Comparison inference efficiency and visual quality across different models and accelera- tion strategies on a single GPU.
Efficiency
Visual Quality
Method
FLOPS (P)
Speedupt
Latency (s)
VBench
LPIPS|
SSIM
PSNR
Open-Sora 1.2 (51 frames, 480P)
Original
3.15
1x
26.54
79.22%
A-DiT
3.09
1.03x
25.87
78.21%
0.569
0.481
11.91
T-GATE
1.19x
22.22
77.61%
0.350
0.676
15.50
PAB
2.50
1.43x
18.52
76.95%
0.174
0.822
23.58
TeaCache-slow
2.40
1,50x
17.58
79.20%
0.134
0.837
23,50
TeaCache-fast
1.64
2.10x
12.63
78.24%
0.252
0.743
19.03
LeMiCa-slow
2.30
1.52x
17.43
79.26%
0,050
0.923
31.32
LeMiCa-fast
1.45
2,44x
10.86
78.34%
0.187
0.798
21.76
Latte (16 frames, 512x512)
Original
3.36
1x
11.18
77.40%
A-DiT
3.36
1.02x
10.85
52.00%
0,851
0.108
8.65
T-GATE
2.99
1.13x
9.88
75.42%
0.261
0.693
19.55
PAB
2.52
1.36x
73.13%
0.390
0.642
17.16
TeaCache-slow
1.94
1.65x
6.76
77.40%
0.195
0.775
21.52
TeaCache-fast
1.15
2.60x
4.30
76.09%
0,318
0,674
18.04
LcMiCa-slow
1.88
1.69
6.60
77.45%
0.091
0.865
27.65
LeMiCa-fast
1.00
2.93x
3.81
76.75%
0.273
0.70
19.43
Cog VideoX (49 frames, 480P)
Original
12.45
1x
43.08
77.13%
PAB
9.26
1,43x
32.07
75.95%
0.064
0.916
29.85
TeaCache-slow
6.93
1.70x
25.34
76.79%
0,053
0.928
31.07
TeaCache-fast
4.53
2.45x
17.58
76.06%
0.176
0.804
22.95
LeMiCa-slow
6.91
1.72x
25.02
76.89 % 76.20%
0,023
0.958
35.93
LeMiCa-fast
4.26
2.61
16.48
0.132
0.846
25.59
4 Experiments
4.1 Experimental Setup
For fair comparison, we follow prior works and report both efficiency and visual quality metrics. Efficiency is measured by FLOPs and latency. Visual quality is evaluated using VBench [11] (human preference), LPIPS [48] (perceptual similarity), SSIM [43] (structural consistency), and PSNR (pixel-level accuracy).
Metrics
Baselines and Compared Methods We evaluate our method on representative diffusion- -ased video models: Open-Sora [53], Latte [24], and Cog VidexX [45]. Baselines include DiT [2],
T-GATE [49], PAB [51], J. and TeaCache [20]. Among them, T-GATE and A-DiT are designed for images, while PAB and TeaCache target video. Accordingly, we compare against PAB and TeaCache
on Cog VideoX, and against all four baselines on Open-Sora and Latte.
Implementation Details Experiments conducted NVIDIA H100 GPUs using PyTorch. To construct the DAG for Global Muttome-AAare error modeling, we sample 70 prompts (10 per attribute) from T2V-CompBench [41], following standard practice [41, 20]. The DAG construction and forward inference use distinct datasets to ensure fair and robust evaluation. Sampling is repeated 10 times with different seeds, and results averaged to reduce bias.
4.2 Comparison with State-of-the-Art Methods
Quantitative Comparison Table compares LeMiCa with baselines across four metrics: VBench, LPIPS, SSIM, and PSNR. LcMiCa includes two variants: LcMiCa-slow fidelity-focusede and

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LeMiCa-fast (speed-focused). It consistently outperforms training-fiee free acceleration baselines across models, schedulers, resolutions, and video lengths. LeMiCa-slow achieves the best reconstruction quality, reducing LPIPS from 0.134 to 0.05 on Open-Sora and from 0.195 to 0.091 on Latte-over 2x improvement vs. TeaCache-slow. LeMiCa-fast improves inference speed from 2.60x to 2.93x on Latte compared to TeaCache-fast, while preserving visual quality. Unlike prior methods relying on online greedy strategies, LeMiCa precomputes its caching policy, eliminating runtime overhead. Overall, LeMiCa provides efficient video generation with minimal perceptual quality degradation.
Original (w/o Speed up)
TeaCache
LeMiCa (ours)
Open-sora
Latte
CogVideoX
Figure 3: Visual comparison under the fidelity- focused setting (LeMiCa-slow vs. TeaCache-slow) across different models. Differences are highlighted in red boxes.
Visualization We compare video acceleration methods from both quality and speed perspectives. As shown in Fig. 3, under the fidelity-focused setting, LeMiCa excels in preserving content consistency and fine details, as highlighted red boxes. This demonstrates its ability to maintain high-quality visuals even when prioritizing fidelity. In contrast, Fig. 4 illustrates that under the speed-focused se-
Original (w/o Speed up)
TeaCache
LeMiCa (ours)
Lafenicy; 26,54g
Latency: 12.63s (2.10x)
Latency: 10.66s (2:44x)
Open-sora
Latency: 11.18s
Latency: 4,30s (2.60x)
Latency: 3.81s (2.93x)
Latte
Latency:
Latency: 17.58s (2.45x)
Latency:16 6.. (2.61x)
CogVideoX
Figure Visual comparison under speed-focused setting (LeMiCa-fast vs. fasts, LeMiCa- fast better preserves content consistency and video quality under a high speedup (>2x).
7

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tting, LeMiCa-fast significantly outperforms TeaCache-fase achieving superior acceleration rates while still maintaining competitive performance. These results highlight LeMiCa's ability balance quality and speed across different configurations. Additional qualitative examples can be found in the Appendix (Section E).
4.3 Ablation Studies
Acceleration vs. Performance trade-off Figure presents the quality- trade-off between proposed LeMiCa and TeaCache. To ensure comparable computational budgets, LeMiCa is configured with 19, 12, 9, and inference steps (i.e., inference budget B), corresponding to TeaCache thresholds of 0.1, 0.2, 0.3, and 0.5, respectively. Across all latency regimes, LeMiCa consistently achieves superior quality-efficiency balance, outperforming TeaCache on all reference-basee metrics. Importantly, under extreme acceleration (latencies below seconds), LeMiCa maintains robust and high-quality performance.
79.5
0.96
LeMiCa
LeMiCa
0.90
LeMiCa
78.0
0.88
28-
LeMiCa
LPIPS
SSIM
PSNR
vBench
76.5
24
TeaCache
0.78
0.72
TeaCache
0.72
TeaCache
20
0.64
TeaCache
0.66
73.5
12
16
20
Latency (s)
Latency (s)
Latency (9)
Latency (s)
Figure 5: Quality-latency trade-off comparison between LeMiCa and TeaCache,
Sample Requirements for Graph Construction To investigate how many samples LcMiCa requires to offline construct the DAG, randomly select {1,5, 10, 20} from the original 350 samples (70 prompts x 5 seeds), and compute the optimal caching path under the lexicographic minimax criterion. Each setting repeated 20 times to reduce randomness. Importantly. distinct datasets are used for DAG construction and forward inference to guarantee fairness and robustness evaluation. Video quality then evaluated on selected VBench prompts, with average results reported. Table shows that LeMiCa achieves strong performance with single sample (e.g.. PSNR 24.51), rapidly approaching the upper bound with samples and essentially saturating 20 samples across all metrics. This demonstrates LeMiCa's 's ability construct high-quality cache paths with minimal samples and underscores the robustness of its static caching strategy across varying prompts and seeds.
Table 2: Impact of sample size cache path graph quality.
Table 3: Impact of different path strategies video reconstruction quality.
Number Samples
VBunch?
LPIPS
SSIM
PSNR
Path Strategy
VBencht
LPIPS}
SSIM
PSNR
78.58
0.164
24.51
0.161
24.57
Original
79.24
78.95
0.158
0.844
24.56
Shortest Path
76.04
0.203
0.809
22.90
20
79.16
0.152
0.843
24.00
350
70.27
0.143
0.851
24
MiniMax Path
79.27
0.143
0.851
24.67
Trajectory Robustness Since the cache mechanism is inherently tied the original denoising trajectory, it is essential to assess whether a training-free free cache method remains effective when the trajectory changes. To this end, we vary the trajectory scale parameter the sampling schedule from its default value of 1.0 to several alternative values (0.5, 0.75, 1.25, 1.5). introducing different diffusion paths during inference. As shown Figure 6, the left panel illustrates the effect of trajectory scaling the denoising paths, while the right panel demonstrates that LeMiCa consistently outperforms the current state-of-the-ar method, TeaCache, across trajectories in terms of LPIIS. These results confirm that our method remains effective even under varying denoising paths.

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1000
Denoising Trajectory
0.30
Teacache
LeMiCa
800
0.25
Timestep
600
LPIPS
0.20
scale 0.5
scale 0.75
0.15
scale 1.0
scale 1.25
0.10
scale 1.5
0.05
0
20
40
60
80
100
0.5
0.75
1.0
1.25
1.5
Denoising Process (%)
Scale
Figure Performance comparison between LeMiCa and TeaCache across different denoising trajectories. Left: Denoising step trajectories under different scale settings. Right: LPIPS performance across various denoising trajectories.
Shortest Path vs. Lexicographic MiniMax Path We compare the performance of the Shortest Path strategy and the Lexicographic MiniMax Path strategy video reconstruction tasks. As shown in Table 3, the MiniMax Path strategy consistently outperforms the baseline Shortest Path strategy both VBench scores and reconstruction metries. This observation is consistent with our analysis: the edge eπгоrs cached during the sampling process are not independent and thus cannot be simply accumulated linearly.
Performance at different resolutions and lengths Our method incorporates Dynamic Sequence Parallelism (DSP) 1i]to support nigh-resolution long-video generation across multiple GPUs. To assess its sampling acceleration performance across varying video sizes, we conducted tests on videos with different lengths and resolutions. As shown in Figure 7, our method maintains stable acceleration even video resolution and frame count increase, highlighting its potential for handling longer and nigheroresolution videos.
4,53X
Latexcyts
22.
Originll
4.GUU GPU
Originsl
GRU 1GPU GPU
Ongiral [.PP) 4GGII 3.PUU
Onginal
(b)
(c) 720P,
(d)
Figure 7: LeMiCa inference efficiency under various video durations and resolutions.
Conclusion
We propose LeMiCa, general and efficient caching framework for accelerating Liffusion-based video generation. Unlike locally greedy strategies, LeMiCa formulates cache scheduling as a global path optimization problem using lexicographic minimax worst-case degradation. With the introduction of the Global Outcome-Aware error, our method captures the long-term impact of caching decisions. mitigating temporal heterogeneity and error accumulation. Extensive experiments demonstrate that LeMiCa consistently improves both efficiency and visual quality across diverse diffusion models. More broadly, LeMiCa offers a new perspective on structured caching in generative modeling, which may inspire future research in other domains such 3D, multi view, or multi-modas generation where controllable acceleration remains an open challenge.
static DAG, effectively constraining

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LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation
Appendix
A
Pseudocode: Lexicographic Minimax Path Selection
Below we present the full pseudocode implementation of the lexicographic minimax path selection algorithm, introduced in Section 3.3. The algorithm leverages dynamic programming efficiently compute an optimal caching path from source s to target under step budget constraint B. Unlike standard shortest-paid -ath methods. our approach handles the non-additive and non-Markovian nature of сπтог accumulation by minimizing edge weights in a lexicographically ordered manner.
Algorithm Lexicographic Minimax Path Selection
Input: Directed acyelic graph (V, E). start node s, node 4, step limit
2: Output: Lexicographic Minimax Path p
3: Initialization:
4: dp[v][K]: maximum edge weight any k-step path to
5: pothsswv[[k], edges [A]]:: corresponding node and edge sequences
6: dp[+][0] t 0, paths[s] s| + [[+]]. edges[>[[)) <- [[]
7: Main Loop:
for
do
9: for each node with dp(u][A] [k] < do
10:
for each neighbor of do
11:
weight of edge (e, u)
12:
< max(dd[pv]]]),.. w)
13:
if m < [k] + then
14:
dp[u] [ + +
Update paths(u][ 1]. edges(u][k from v
dp [k] + 1] then
17:
Append paths and edges from paths/u][[: 1], edges[u] [k
18: end if
19: end for
20: end for
21: end for
22: Final Selection:
23: P (ann://ppootassttBB)eeees////// x),,e) reveee(e,
B
Experiment Settings
B.1
Models
In this paper, introduce LeMiCa, novel caching technique designed to accelerate and enhance a range state-of-the-art video synthesis models, including Open-Sora 1.2 [53], Latte [24], and Cog VideoX [45]. Open-Sora 1.2 integrates 2D/3D VAEs and ST-DIT blocks for efficient video compression and generation. Latte leverages spatio-temporal tokenization and Transformer layers to model video distributions in the latent space. CogVideox employs a 3D VAE and expert Transformers with adaptive LayerNorm for modality fusion and high-fidelity generation. In our experiments, we adopt the CogVié VideoX-2B variant.
B.2 Details of the Compared Methods
PAB introduces a pyramid-style broadcasting mechanism to reduce redundant attention computations in diffusion models. By observing a U-shaped pattern in attention differences across steps. PAB
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applies adaptive broadcast strategies based on the variance of different attention types (e.g., spatial, temporal, cross-modal). Stable attention outputs are efficiently reused later steps, reducing computation. All experiments use PAB's ss default parameter settings.
TeaCache is training-free, architecture-agnosii caching method that exploits the correlation between timestep embedding changes and model output differences across adjacent steps. By introducing a unified threshold-based strategy, TeaCache decides when activate caching through an accumulated error-based discriminator. Since this method operates solely along the temporal dimension without modifying specific model components, offers strong generalization and broad applicability.
B.3
Model Forward Steps
Model Forward Steps. this work, we control the acceleration efficiency of LeMiCa via the Model Forward Steps B. Smaller values of B reduce the denoising time, leading higher speed-up ratios. We consider two variants: LeMiCa-slow, which emphasizes visual fidelity, and LeMiCa-fast, which prioritizes inference efficiency. The corresponding values for each variant across different models are listed in Table 4.
Table 4: Model forward steps B under different configurations.
Model
Configuration
Model Forward Steps B
Original
Open-Sora 1.2
LeMiCa-slow
19
LeMiCa-fast
11
Original
50
Latte
LeMiCa-slow
27
LeMiCa-fast
14
Original
50
Cog VidexX
LeMiCa-slow
27
LeMiCa-fast
16
C OOD Generalization Analysis
We analyze the out-of of-distribuutin (OOD) generalization ability of LeMiCa through two evaluation setups: VBench and IP- VBenc....
OOD Evaluation on VBench. LeMiCa evaluated on Vßench [11], which follows a distribution distinct from 12V-CompBench [41] used for DAG construction. We quantify the distributional shift by computing prompt-level distances using text embeddings and PCA (see Table 5).
Table 5: Distributional distance analysis between VBench and T2V- Bey-Comp
Attribute
Description
VBench
Distance
VBench T2V-CompBench
0.61
Radius
Std. Deviation of T2V- Comp
0.39
Distance Radius
Ratio of distance to radius
1.58
OOD Status
Is it OOD?
Despite this evident OOD setting, LeMiCa consistently maintains strong acceleration and visual quality (Table 1), demonstrating robustness unseen prompt distributions.
OOD Evaluation on IP-VBench. We further test LeMiCa on IP-VBench [1], which contains intentionally unrealistic and semantically diverse prompts across four domains: Physical, Biological, Social, and Geographical. These prompts differ significantly from training data, with Distance/Radius nearly doubling compared T2V-CompBench (see Table 6).
Across all domains, LeMiCa substantially outperforms TeaCache in LPIPS, SSIM, and PSNR, underscoring its strong generalization and robustness under severe OOD conditions.
21

---

Table 6: Quantitative OOD performance on IP-V -P-hhh across four semantic domains.
Method
Domain
LPIPS ())
SSIM (1)
PSNR (T)
Distance/Radius
OOD Status"
TeaCache
Physical
0.093
0,911
26.7
2.09
Biological
0.839
24.0
Social
0,144
0.842
24,9
1.93
Geographical
0.072
0.914
Overall
0.120
0.877
26.4
2.01
LcMiCa
Physical
0.039
0.954
34.6
2.09
Biological
0.054
0.905
31.4
1.90
Social
0.040
0.946
33.1
1.93
Gengraphical
0.038
0.945
2.13
Overall
0.042
0.938
33.5
2.01
D
Offline Cost
The graph construction in LeMiCa is entirely offline, three-stage process. First, Edge Weight Estimation estimates reconstruction errors by running full-generation passes on approximately 20 sampled prompts; this task is fully parallelizable (leveraging 8 GPUs in experiments) and only needs to be run once per model configuration. The subsequent stages, Graph Construction (fusing jump edges into sparse DAG) and Path Optimization (employing lexicographic minimax search to find acceleration paths), are both highly efficient, each completing under second. As detailed in Table 7, these offline procedures incur negligible overhead, yet this low-cost offline computation yields up to 2.44x acceleration during inference generation.
Table 7: Offline cost analysis of LeMiCa on OpenSora.
Stage
Description
Time Cost (Ref)
Affects Inference
Edge Weight Estimation
Full- tull-generation error estimation
~3.18 min prompt
No
Graph Construction
Build sparse DAG
<1 sec
No
Path Optimization
Minimax search for jump paths
<1 sec
No
Inference Acceleration
Execute jump paths with caching
Up 2.44x faster
Yes
More Visual Results
We present additional visual comparisons across three foundational models: Open-Sora [53], Latte [24], and VogVid [45]. Results are grouped into two settings: fidelity-focusee and speed- focused.
E.1 Fidelity-Focused
We perform frame-by-framd comparisons to assess fine-grained differences in quality (LeMiCa-slow vs. TeaCache-slow). Since this setting uses relatively low acceleration ratios, artifacts are less obvious real-time playback. To address this, we extract representative frames that highlight detail preservation, object integrity, and temporal consistency. As shown in Figures 10, 11, and 12, method consistently produces more coherent results across all baselines.
E.2 Speed-Focuse
To evaluate robustness under aggressive acceleration, compare videos generated with higher speed-up ratios (Lesi vs. TeaCache-fastl This setting is designed to prioritize generation speed without significantly compromising visual quality. Under such conditions, baseline methods are more prone to issues such as flickering, object drift, and reduced temporal consistency. In contrast, method maintains strong temporal and semantic coherence,
high generation speeds.
As part of the supplementary material, we include the following video files: Speed-Focused Open- Sora.mp4, Speed-Focusee Latte.mp4, and Speed-Foouseed Cog X.de
22

---

banana and a keyboard
Original
TeaCache
LeMiCa
bottle
Original
TeaCache
LeMiCa
confused panda calculus class
Original
S.TT
2.12
TeaCache
35
3.55
33
355
LeMiCa
LA7
38
LA7
28
LA7
16
3.0
38
Figure 8: More visual results Open-Sora (Part I).
23

---

a teddy bear on the right of potted plant, front view
Original
TeaCache
LeMiCa
A tranquil tableau of a picturesque barn
Original
TeaCache
LeMiCa
tranquil tableau of apple
Original
TeaCache
LeMiCa
Figure 9: More visual results Open-Sora (Part II).
24

---

astronaut flying in space, zoom
Original
TeaCache
LeMiCa
Vampire makeup face beautiful girl, contact lenses
Original
TeaCache
LeMiCa
Gwen Stacy reading book,
featuring steady and smooth perspective
Original
TeaCache
LeMiCa
Figure 10: More visual results on Cog VideoX.
25

---

a white clock
12
12
Original
2
10
2
2
9
9
9
8
4
8
4
8
4
7
6
5
7
6
5
11
III
TeaCache
2
1
9
4
9
3
9
4
5
6
5
7
6
5
12
12
12
LeMiCa
10
2
10
2
10
2
3
9
3
9
3
8
8
4
8
4
7
7
an apple
Original
TeaCache
LeMiCa
Figure 11: More visual results on Latte (Part I).
26

---

An astronaut is riding horse in the space inaphotorealistic style
Original
TeaCache
LeMiCa
boat sailing leisurely along the Seine River with the Eiffel Tower background, surrealism style
Original
TeaCache
LeMiCa
Figure 12: More visual results on Latte (Part II).
27

---

Limitation
Although our method achieves strong performance in both acceleration and video fidelity, it still has certain limitations. First, when the original video quality is low, particularly in scenarios involving complex motion dynamics, struggles to consistently generate satisfactory results. This reflects dependency on the representational capacity of the underlying diffusion model. Second, under high acceleration ratios, some degree of quality degradation remains inevitable due to the significantly reduced number of model forward steps. We believe that continued progress in foundational video generation models will help alleviate these issues. Moreover, since our approach focuses solely on temporal step scheduling and is agnostic to model architecture, it can be quickly adapted to future, more powerful diffusion models.
G
Social Impact
Diffusion-baser video generation models often limited by high inference time and computational cost. Our method alleviates this by significantly improving efficiency without requiring additional training. This enables broader access to high-quality video synthesis, particularly in resource- constrained settings. By reducing computation during the inference process, our approach also lowers energy use and carbon emissions, contributing to more sustainable AI development. Furthermore, will release our code to support future research.
28


Reviews:
[{"review_id": "xTh9OIoSZs", "reviewer": "Reviewer_KfV5", "summary": "LeMiCa (Lexicographic Minimax Path Caching) is a new training-free acceleration method for text-to-video diffusion models. The core idea is to reuse intermediate model outputs (caching) while carefully controlling errors on a global scale. Unlike prior “local” caching heuristics that decide frame by frame, LeMiCa treats the whole generation process as a path through a directed acyclic graph (DAG) of diffusion timesteps. Each possible skip (cache segment) is an edge weighted by its impact on final output quality. The authors introduce a lexicographic minimax optimization to choose a caching path that minimizes the worst-case generation error under a fixed computation budget. This strategy explicitly bounds the largest error between the accelerated video and a full-quality video, preventing cumulative drift. In experiments on multiple video generation benchmarks, LeMiCa achieves significant speed-ups (e.g. ~2.9× faster on the Latte model) while maintaining high fidelity (e.g. LPIPS ≈0.05 on Open-Sora), outperforming previous caching techniques in both efficiency and visual consistency. The method requires no model retraining or architectural changes, highlighting its practical utility for faster diffusion-based video synthesis.", "questions": "- **Generality to New Content:** The approach builds a static error-weighted DAG using multiple prompts to estimate segment errors. How sensitive is this graph to the choice of calibration prompts or data domain? In other words, if one were to apply LeMiCa to very different videos or prompts not seen during graph construction, would the caching decisions remain effective? It would be helpful to discuss the method’s robustness to domain shifts or how one might update the cache strategy for new types of content.\n- **Combination with Other Accelerations:** Since LeMiCa is *training-free* and model-agnostic, have the authors considered combining it with other inference acceleration techniques (e.g., model distillation or quantization)? For instance, could one first apply LeMiCa to reduce steps and also use a distilled model for larger jumps, or would that risk compounding errors? A brief discussion on how this caching strategy might complement (or differ from) training-based accelerations could clarify its place in the broader landscape of efficient diffusion methods.", "limitations": "No. The submission does not explicitly include a dedicated *limitations* section or discuss potential **societal impacts** of the work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n- **Practical Impact:** LeMiCa offers a *model-agnostic, training-free* acceleration framework that can be applied to existing diffusion video generators without retraining. This makes it highly practical for real-world use, enabling much faster inference (2–3× speed-ups in tested cases) while introducing only minimal perceptual degradation. Such immediate efficiency gains will be valuable for deploying diffusion models in interactive or production settings.\n- **Robust Generation Quality:** The proposed lexicographic minimax strategy effectively controls error accumulation, leading to improved global consistency across frames. The results show that accelerated videos remain very close to originals in structure and detail (e.g., extremely low LPIPS error of 0.05), addressing a key weakness of prior caching methods that often caused content drift or loss of fine details. Notably, the paper demonstrates better structural alignment and style consistency than the state-of-the-art TeaCache baseline, indicating strong preservation of video quality even at high acceleration.\n- **Thorough Evaluation:** The submission includes extensive experiments on multiple text-to-video benchmarks and base models (such as Latte and Open-Sora). It consistently shows **dual improvements** – faster inference and higher generation fidelity – across the board. Ablation studies and analyses of error metrics are provided, giving credibility to the approach’s advantages. The empirical results are convincing and reinforce the significance of the proposed method.\n\n**Weaknesses:**\n\n- **Complexity and Generalization:** LeMiCa’s global DAG construction involves evaluating error impacts with multiple prompts and selecting an optimal path. This added complexity means implementing the method requires extra analysis offline. The authors should clarify how general the precomputed graph is, for instance, does it reliably cover arbitrary prompts and scenes, or could certain out-of-distribution videos see different error dynamics? This is a minor issue, but discussing the method’s generalization beyond the tested data would strengthen the work.", "was_revised": true, "final_justification": "I appreciate the authors' response and will maintain my rating as borderline accept.", "rebuttal": "We extend our gratitude for your insightful feedback and suggestions.\n\n***\n\n>**W1&Q1:DAG construction sensitivity and OOD generalization**\n\nThank you for the insightful and closely related questions. We provide a unified response structured around three dimensions:\n\n(1) **Unified DAG construction:** \nFor each model, LeMiCa constructs a single global DAG using just 4~70 prompts sampled from T2V-CompBench[1] (Line 174). This graph remains fixed across all evaluations, and performance saturates with these prompts.\n\n(2) **OOD Evaluation (VBench):** \nIn our primary evaluation (Table 1), we assess LeMiCa’s effectiveness on VBench[2], which is actually a completely different distribution than T2V-CompBench. We quantified the distributional shift by computing prompt-level distances using text embeddings and PCA. \n\n| **Attribute**     | **Description**                       | **VBench** |\n| ----------------- | ------------------------------------- | ---------- |\n| Distance          | VBench vs. T2V-CompBench              | 0.61       |\n| Radius            | 1 Standard Deviation of T2V-CompBench | 0.39       |\n| Distance / Radius | Ratio of distance to radius           | **1.58**   |\n| OOD Status\\*      | Is it OOD?                            | **✓**      |\n\nDespite the clear OOD status, LeMiCa consistently shows strong acceleration and quality maintenance on VBench.（Table 1, line161）\n\n(3) **Further OOD Evaluation (IP-VBench):** \nTo test LeMiCa under more extreme OOD conditions, we evaluate on IPVBench[3], which contains intentionally unrealistic and unnatural prompts across four domains: Physical, Biological, Social, and Geographical. These prompts differ not only statistically but also conceptually from training data（Distance/Radiu nearly 2×）\n\n| **Method** | **IP-Vbench** | **LPIPS (↓)** | **SSIM (↑)** | **PSNR (↑)** | **Distance/Radius** | **OOD Status*** |\n| --------- | ------------- | ------------- | ------------ | ------------ | ------------------- | --------------- |\n| TeaCache  | Physical      | 0.093         | 0.911        | 26.7         | 2.09                | ✓               |\n|           | Biological    | 0.171         | 0.839        | 24.0         | 1.90                | ✓               |\n|           | Social        | 0.144         | 0.842        | 24.9         | 1.93                | ✓               |\n|           | Geographical  | 0.072         | 0.914        | 29.8         | 2.13                | ✓               |\n|           | **Overall**   | **0.12**      | **0.877**    | **26.4**     | **2.01**            | ✓               |\n| LeMiCa    | Physical      | 0.039         | 0.954        | 34.6         | 2.09                | ✓               |\n|           | Biological    | 0.054         | 0.905        | 31.4         | 1.90                | ✓               |\n|           | Social        | 0.040         | 0.946        | 33.1         | 1.93                | ✓               |\n|           | Geographical  | 0.038         | 0.945        | 34.9         | 2.13                | ✓               |\n|           | **Overall**   | **0.042**     | **0.938**    | **33.5**     | **2.01**            | ✓               |\n\nDespite the severity, LeMiCa consistently and significantly outperforms the Teacache baseline on all metrics, showing strong robustness in abstract and diverse scenarios.\n\n**References:**\n\n[1] T2v-compbench: A comprehensive benchmark for compositional text-to-video generation CVPR 2025\n\n[2] Vbench: Comprehensive benchmark suite for video generative models. CVPR 2024\n\n[3] Impossible Videos. ICML 2025\n***\n\n> **Q2: Combined with distillation or quantization methods?**\n\nThank you for the insightful suggestion. LeMiCa is fully compatible with other acceleration techniques such as distillation and quantization.\n\n(1) **Inference with Distilled Models:** We applied LeMiCa to a distilled 8-step model (WAN2.1-T2V within the DCM[1] framework) and further reduced it to 6 steps. Despite the model already being heavily compressed via distillation, LeMiCa brought additional acceleration with minimal quality degradation:\n\n| **Method**           | **NFE** | **LPIPS↓** | **SSIM↑** | **PSNR↑** |\n|---------------------|--------:|-----------|-----------|-----------|\n| DCM                 | 8       | —         | —         | —         |\n| DCM + TeaCache      | 6       | 0.29      | 0.63      | 14.07     |\n| **DCM + LeMiCa**    | 6       | **0.08**  | **0.87**  | **22.04** |\n\nThis suggests LeMiCa can provide further gains even on top of state-of-the-art distillation methods, highlighting its strong complementarity in the inference phase.\n\n(2) **Other Possibilities (Distillation Training, Quantization):** LeMiCa’s training-free nature also makes it a potential companion to the distillation process itself.  Although we have not empirically evaluated this combination yet, their compatibility is clear in theory and suggests potential for additive speedups. Use LeMiCa to accelerate teacher model inference during training (e.g., for generating supervision signals) is an interesting direction that could reduce training costs. We consider this a promising avenue for future exploration.\n\nAdditionally, For quantization model, LeMiCa are orthogonal to them in principle: the former reduces the number of steps, while the latter reduces the cost per step. Though we have not yet tested this combination, they are theoretically compatible and could offer additive speedups.\n\n**References:**\n\n[1] DCM: Dual-Expert Consistency Model for Efficient and High-Quality Video Generation 2025\n\n***\n\nPlease let us know if additional clarification would be helpful. We truly appreciate your constructive feedback.\n\n— Authors"}, {"review_id": "P2vgmEb2l6", "reviewer": "Reviewer_J7ti", "summary": "The paper introduces a training‑free framework for accelerating diffusion‑based video generators. Instead of re‑using UNet/DiT activations with simple “local‐greedy” rules (e.g., TeaCache, PAB), LeMiCa does the following: 1. Defines a “global outcome‑aware” error: how much a whole segment of cached steps changes the final decoded video, averaged over several prompts/seeds; 2. Builds an offline Directed Acyclic Graph (DAG) whose edges are candidate cache segments weighted by that global error; 3. Searches under a fixed inference‑budget B the path that lexicographically minimizes the worst‑case edge weight (the minimax path). At run‑time only those B timesteps are fully computed; all others reuse cached features, incurring virtually no online overhead.", "questions": "N/A", "limitations": "N/A", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n-- The paper articulates a genuine failure mode visible in prior work, providing a clear diagnosis of “local‑greedy” caching weaknesses (temporal heterogeneity & error accumulation).\n\n-- The formulation is novel: casting cache selection as a global path search with a lexicographic minimax objective.\n\n-- The paper provides thorough ablations: effect of sample count for graph construction, minimax vs shortest path, trajectory scale, resolution & length scaling.\n\n\nWeakness:\n\n-- The biggest problem of this work is the lack of convincing qualitative results. Firstly, I can only play the open-sora video in the supplementary material, not the other two; secondly, only three examples are shown, where none of them has large, rigid motions, but rather static scene with small motions; thirdly, for the three simple examples, I still observe significant artifacts: several parts are blurred out, and there is significant motion artifacts, e.g. where a snowflake blends with the background. These examples are not convincing for me that the approach does not suffer from much quality degradation, especially when comparing with TeaCache.", "was_revised": true, "final_justification": "The authors have addressed my concerns by providing a complete rebuttal. I would be happy to raise my score.", "rebuttal": "We sincerely apologize for the inconvenience caused by the failure to play the supplementary videos. We fully understand that this may have affected your experience evaluating our work, and we greatly appreciate you bringing this issue to our attention.\n\n---\n\n> **W1: Only the open-sora video was playable; the other two failed to play.**\n\nUpon receiving this feedback, we immediately investigated the issue and found that it was likely due to **video encoding incompatibility**. While the videos played correctly on some systems, certain decoders may not support the original encoding.\n\nTo address this, we have tested and confirmed two reliable solutions across **macOS, Linux, and Windows** platforms:\n\n1. **Re-encoding using the H.264 codec:**\n   ```bash\n   ffmpeg -i input.mp4 -vcodec h264 output.mp4\n2. Using **VLC media player**, a free, open-source, cross-platform player that supports a wide range of formats.\n\nOnce again, we sincerely apologize for the inconvenience and thank you for your understanding and patience.\n\n---\n\n> **W2: Only three examples are shown, with no large, rigid motions—just static scenes with small movements.**\n\nWe understand the concern regarding the lack of large, rigid motion in the current supplementary videos. We have supplemented our evaluation with additional motion-related metrics from VBench as well as a new dataset containing extensive rigid motions.\n\n(1) **VBench Motion Subsets:** LeMiCa outperforms TeaCache in three motion-related sub-tests:\n\n| **Method**   | **Human Action (↑)** | **Motion Smoothness (↑)** | **Dynamic Degree (↑)** |\n|----------|---------------|--------------------|----------------|\n| TeaCache | 0.83          | 0.981              | 0.49           |\n| LeMiCa   | 0.87          | 0.986              | 0.51           |\n\n(2) **IP-VBench Evaluation:** We further include evaluations on IP-VBench [1], which contains strong, diverse motion scenes—again confirming LeMiCa\\'s robustness.\n\n| **Method** | **LPIPS (↓)** | **SSIM (↑)** | **PSNR (↑)** |\n| --------- | ------------- | ------------ | ------------ |\n| TeaCache  | 0.12          | 0.87         | 26.4         |\n| LeMiCa    | 0.04          | 0.93         | 33.5         |\n\nAdditionally, we agree that visual examples of such scenes would better highlight our method’s capabilities. We will include more examples with large, rigid motions on the project website upon release.\n\n**References:**\n\n[1] Vbench: Comprehensive benchmark suite for video generative models. CVPR 2024\n\n[2] Impossible Videos. ICML 2025\n\n---\n> **W3: The examples shown still have significant artifacts.**\n\nThank you for the careful observation. We\\'d like to clarify an important distinction in our method design:\n\n**Motion Blur (artifacts) ≠ Algorithmic Failure**\n\nA key design principle of LeMiCa is to **preserve consistency** between the accelerated and original videos (Line 31). Many of the “artifacts” observed, such as snowflake blending or motion blur, are inherent to the original video and result from camera motion or depth-of-field effects. LeMiCa intentionally preserves these natural dynamics, rather than suppressing them. In contrast, in two other videos where no noticeable artifacts were present in the original, LeMiCa also shows no artifacts, while retaining more fine-grained details and scene integrity than TeaCache.\n\n**Better Preservation of Spatial Relationships and Semantic Content:**\n\nLeMiCa maintains spatial consistency more effectively than TeaCache. For example, snowflakes consistently appear in front of the house, preserving depth cues. In contrast, TeaCache often misplaces or prematurely removes snowflakes, disrupting the scene’s spatial coherence.\n\nWe also conducted a detailed comparison of key semantic elements between the original video, TeaCache, and LeMiCa outputs. As shown in the table below, LeMiCa consistently retains more important visual content, even under challenging and dynamic scenarios:\n\n| **Semantic Element**                  | **Original** | **TeaCache** | **LeMiCa** |\n|--------------------------------------|--------------|--------------|------------|\n| The Broken Bridge in the Lake        | ✓            | x            | ✓          |\n| The House in the Bottom-Left Corner  | ✓            | x            | ✓          |\n| Christmas Tree                       | ✓            | x            | ✓          |\n| Snowflake Density                    | ✓            | x            | ✓          |\n\n\n---\n> **W4: These examples are not convincing for me that the approach does not suffer from much quality degradation, especially when comparing with TeaCache:**\n\nwe respectfully disagree with the view that LeMiCa degrades more than TeaCache, and we support our claim with:\n\n**Quantitative Evidence:** In VBench, LeMiCa-fast consistently outperforms TeaCache-fast in LPIPS, SSIM, and PSNR (see Table 1, Line 162).\n\n**Qualitative Evidence:** LeMiCa-fast better preserves semantic details such as the shark, laptop, and background objects. (Figure 4 and the supplementary videos)\n\n**Robustness Evidence:** In our ablation study (Line 196), even under 3.54× acceleration, LeMiCa remains more stable than TeaCache.\n\n\n***\n\nPlease let us know if additional clarification would be helpful. We truly appreciate your constructive feedback.\n\n— Authors"}, {"review_id": "YRA8ONxhJu", "reviewer": "Reviewer_iNvD", "summary": "The paper proposes LeMiCa, a training-free acceleration framework for diffusion-based video generation that improves upon existing caching methods. The authors identify that \"local-greedy\" caching strategies, which make decisions based on adjacent timesteps, fail to control the global accumulation of errors. To solve this, LeMiCa formulates the cache scheduling process as a path optimization problem on a Directed Acyclic Graph (DAG). Each edge in the DAG represents a potential cache segment, weighted by its global impact on the final generated video output quality. The framework then uses a Lexicographic Minimax optimization strategy to find the optimal path that minimizes the worst-case error under a fixed inference budget. Experiments on state-of-the-art models like Open-Sora and Latte show that LeMiCa achieves significant speedups of up to 2.9x while better preserving video quality better compared to prior methods.", "questions": "My questions are listed in the weaknesses section.", "limitations": "yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n-\tThe paper proposes a novel approach to the caching problem. Shifting from a local decision process to a global path optimization problem is a logical and effective idea. The formulation of cache scheduling as a lexicographic minimax problem on a DAG is also novel/interesting and addresses the issue of compounding errors.\n\n-\tThe experimental results are thorough and convincing. LeMiCa consistently outperforms SOTA caching methods like TeaCache across multiple models (Open-Sora, Latte, CogVideoX) and metrics. Table 1 shows LeMiCa achieving a better trade-off between speed and quality, and the qualitative results in Figures 3 and 4 visually confirm its superior editing quality as do the examples in the supplementary materials.\n\n-\tThe ablation studies are thorough. \n\nWeaknesses:\n\n-\tDoes the computational complexity of graph construction lead to any slowdown? Can a performance analysis over the cost of the graph construction also be done similar to the ablations provided on performance at different resolutions and lengths.\n\n-\tThe number of video examples provided in the supplementary are very limited (only 3), which leads to the possibility of the framework not being effective across a broad set of videos and only effective at maintaining quality on a small subset or cherry-picked examples.", "was_revised": true, "final_justification": "I maintain my borderline accept rating. I still believe the videos provided for evaluation are too limited and this concern was not addressed, but my concerns on the graph construction are addressed in the rebuttal. The overall work is impactful so I maintain my score.", "rebuttal": "We sincerely thank **Reviewer iNvD** for the thoughtful questions and constructive feedback. Below are our detailed responses.\n\n---\n\n> **W1: Does the computational complexity of graph construction lead to any slowdown?**\n\nThe graph construction in LeMiCa does **not introduce any slowdown** during model inference. It is performed entirely offline as a one-time preprocessing step. Once constructed, the Directed Acyclic Graph (DAG) is used to compute a static acceleration path, which guides the model’s forward passes without adding any runtime overhead. \n\n---\n\n> **W2: Performance analysis over the cost of the graph construction.**\n\nIn LeMiCa, the graph construction is performed entirely offline and consists of the following three stages:\n\n(1) **Edge Weight Estimation:** We estimate reconstruction errors of candidate jump edges by running full-generation passes on ~20 sampled prompts. This step is relatively time-consuming but fully parallelizable and only needs to be run once per model configuration.\n\n(2) **Graph Construction:** Valid jump edges are fused into a sparse DAG. Thanks to explicit constraints on skip length, the resulting structure is simple and this step completes in under 1 second.\n\n(3) **Path Optimization:** A lexicographic minimax search is performed over the sparse graph to identify acceleration paths that minimize the worst-case reconstruction error. This process is also highly efficient, completing in under 1 sec.\n\nThe table below summarizes the offline cost measured on OpenSora:\n\n| **Stage**               | **Description**                              | **Time Cost (Ref)** | **Affects Inference** |\n|-------------------------|-----------------------------------------------|----------------------|------------------------|\n| Edge Weight Estimation  | Full-gen error estimation (~20 prompts)       | ~3.18 min / prompt   | No                     |\n| Graph Construction      | Build sparse DAG                              | < 1 sec              | No                     |\n| Path Optimization       | Minimax search for jump paths                 | < 1 sec              | No                     |\n| Inference Acceleration  | Execute jump paths with caching               | Up to 2.44× faster   | Yes                    |\n\n\n\n> **W3: Does LeMiCa generalize well to a broader range of videos beyond those shown in the supplementary?**\n\nAddressing LeMiCa’s generalization capability, we highlight the following:\n\n(1) **OOD Evaluation (VBench):** For DAG construction, we used **T2V-CompBench**[1] (Line 174), while evaluations were done on **VBench**[2] (Line 165). These two datasets originate from entirely different sources and distributions. LeMiCa’s strong performance on VBench actually has already confirms its robustness to unseen prompts and scenes.（See the table below for OOD analysis.）\n\n(2) **Further OOD Evaluation (IP-VBench):** We further evaluate LeMiCa on **IP-VBench**[3] with the same constructed DAG path , a dataset featuring intentionally non-realistic, ceative video prompts. Its distributional distance from T2V-CompBench is nearly **2 standard deviations**, representing a truly diverse OOD space.\n  \n* **OOD Analysis:** \nWe computed OOD status by extracting text embeddings, applying PCA for dimensionality reduction, and measuring prompt-level distances. Specifically, we compared T2V-CompBench against VBench and IP-Vbench. The results are as follows:\n\n| **Attribute**     | **Description**                       | **VBench** |**IP-Vbench** |\n| ----------------- | ------------------------------------- | ---------- |--------------|\n| Distance          | T2V-CompBench  vs. ~              | 0.61       |0.85         |\n| Radius            | 1 Standard Deviation of T2V-CompBench | 0.39       |0.42         |\n| Distance / Radius | Ratio of distance to radius           | 1.58   | 2.01         |\n| OOD Status\\*      | Is it OOD?                            | ✓      | ✓            |\n\n\n**All datasets > 1.5× radius, confirming OOD status.*\n\n* **IP-VBench Results:**\n\n| **Model**   | **LPIPS (↓)** | **SSIM (↑)** | **PSNR (↑)** |\n|-------------|---------------|--------------|--------------|\n| TeaCache    | 0.12          | 0.87         | 26.4         |\n| LeMiCa      | 0.04          | 0.93         | 33.5         |\n\nThe table demonstrates that LeMiCa maintains strong performance on this challenging and diverse dataset.\n\n**References:**\n\n[1] T2v-compbench: A comprehensive benchmark for compositional text-to-video generation CVPR 2025\n\n[2] Vbench: Comprehensive benchmark suite for video generative models. CVPR 2024\n\n[3] Impossible Videos. ICML 2025\n\n\n***\n\nWe are grateful again for your insightful comments and believe the above clarifications address your key concerns. Please let us know if any further elaboration is needed.\n\n— Authors"}, {"review_id": "YywZK4L6tw", "reviewer": "Reviewer_Jva8", "summary": "This paper introduces LeMiCa, a novel, training-free, and model-agnostic framework for accelerating diffusion-based video generation. The key idea is to improve caching decisions by framing them as a global path planning problem using a Lexicographic Minimax Path Optimization approach. Rather than relying on local heuristics for cache selection, LeMiCa constructs a directed acyclic graph (DAG) where each edge represents a potential cache segment, weighted by its global error impact. This enables selection of caching paths that bound the worst-case degradation.", "questions": "1. How sensitive is LeMiCa to the choice of prompts used for DAG construction?\nAlthough ablations are provided (e.g., 1–350 samples), can performance degrade under domain shifts, such as out-of-distribution prompts or styles?\n\n2. Why choose lexicographic minimax over simpler bounded-error strategies?\nCould a hybrid strategy (e.g., combining minimax with average-case bounding) perform better in certain quality-speed regimes?\n\n3. What is the overhead of the offline DAG construction in practice?\nPlease provide empirical numbers (e.g., time, memory) for building the cache DAG on large models like CogVideoX.\n\n4. Could LeMiCa generalize to adaptive or online caching?\nThe current setup is entirely static. In latency-constrained deployments (e.g., real-time rendering), could partial DAG updates or dynamic adjustments help?\n\n5. Missing error bars/statistical significance.\nGiven the strong claims of performance gains, please provide error bars or confidence intervals over multiple seeds or trials. This would improve confidence in reported improvements and potentially increase the final score.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Clear conceptual novelty: The formulation of caching as a global lexicographic minimax optimization problem is a well-justified and innovative leap from local greedy heuristics.\n2. Strong empirical results: Extensive experiments across three T2V models consistently show LeMiCa's advantages in LPIPS, SSIM, PSNR, and VBench metrics.\n3. Training-free and model-agnostic: The framework does not require architectural changes or fine-tuning, broadening its applicability.\n4. Scalability and robustness: Effective across varying resolutions, frame counts, and denoising trajectories.\n5. Well-organized experimental section, including ablations and robustness checks.\n\nWeaknesses:\n1. Error estimation remains heuristic: While global in scope, the edge weights used in the DAG are derived from averaging over prompts and may miss task-specific subtleties.\n2. No runtime overhead claim may underplay preprocessing: The offline DAG construction phase is significant, especially for real-time applications.\n3. Limited theoretical insight into optimization guarantees: The use of lexicographic minimax is intuitive, but theoretical convergence or optimality bounds are not discussed.\n4. No uncertainty quantification: Error bars/statistical significance are missing from results, which limits rigor.", "was_revised": true, "final_justification": "The authors have addressed my concerns by providing a complete rebuttal. I would be happy to raise my score.", "rebuttal": "We sincerely thank the reviewer for the insightful comment.\n\n> **W1: The globally constructed DAG may overlook task-specific nuances due to prompt-averaged edge weights.**\n\nWe average errors across prompts when computing DAG edge weights for two key reasons:\n\n**Stability**: Prompt-specific errors can vary due to stochasticity, especially in early diffusion steps. Averaging reduces variance and yields more stable estimates.\n\n**Simplicity and generality**: Mean aggregation is model-agnostic, easy to compute, and aligns well with our lexicographic minimax path objective.\n\nWhile LeMiCa may not always yield the best path for every individual prompt, it generalizes well. As shown in our ablation (Line 203), it builds strong schedules with just 20 samples and performs effectively on unseen prompts and seeds.\n\n---\n\n> **Q1: How sensitive is LeMiCa to the choice of prompts used for DAG construction? Can its performance degrade under domain shifts?**\n\nWe appreciate the reviewer for raising a concern similar to those mentioned by the other two reviewers.\n\nLeMiCa is **not sensitive** to the specific prompts used for DAG construction.  Despite clear **out of distribution (OOD)** conditions, LeMiCa consistently delivers strong acceleration and maintains high generation quality.\n\nOur experiments support this conclusion across multiple dimensions:\n- **Cross-dataset generalization**: DAGs built on T2V-CompBench perform well on both VBench and IP-VBench, despite substantial distribution gaps. \n- **Sample Requirements**: LeMiCa constructs effective cache paths using as few as 20 samples.\n- **OOD Evaluation**: IP-VBench exhibits over a 2× standard deviation shift from T2V-CompBench, yet LeMiCa still significantly outperforms TeaCache, achieving an LPIPS of only 0.04.\n\nFor detailed results and OOD analysis, please refer to our response to **Reviewer KfV5’s W1 & Q1** and **Reviewer iNvD’s W3**. \n\n---\n> **Q2-1: Why choose lexicographic minimax over simpler bounded-error strategies?**\n \nWe choose lexicographic minimax because it enables global optimization and directly controls the worst-case error, which is crucial for stable and high-quality video generation.\n\nIn contrast, bounded-error strategies suffer from two key limitations:\n\n* **Temporal heterogeneity**: The error characteristics across different time steps in the denoising process vary significantly, making a single threshold inadequate for all situations.（Line 36）\n* **Local greediness**: Even with dynamic thresholds (e.g., TeaCache), bounded-error strategies still fall into local greedy errors. (Line 47)\n\n---\n> **Q2-2: Could a hybrid strategy (e.g., combining minimax with average-case bounding) perform better in certain quality-speed regimes?**\n\nThe idea of combining minimax with average-case bounding for path search in a DAG is intriguing, but we believe it may not yield better results in certain quality-speed regimes, for the following reasons:\n\n- **Offline Nature of Graph Construction and Path Search**  \n  Both graph construction and path search are performed offline. As a result, any computational overhead introduced by a hybrid strategy would not affect the model\\'s real-time inference speed, nor improve inference efficiency.\n\n- **Path Selection Comparison and Experiments**  \n  We compared various path selection methods, including Shortest Path and MiniMax Path (Line 220). We also tested using statistical bounds, such as average error and variance, for path selection. However, our results consistently show that MiniMax Path is most effective at controlling worst-case errors.\n\n---\n\n> **W2&Q3: Performance analysis over the cost of the graph construction.**\n\nIn LeMiCa, the graph construction is performed entirely offline and consists of the following three stages:\n\n(1) **Edge Weight Estimation:** We estimate reconstruction errors of candidate jump edges by running full-generation passes on ~20 sampled prompts. This step is relatively time-consuming but fully parallelizable and only needs to be run once per model configuration.\n\n(2) **Graph Construction:** Valid jump edges are fused into a sparse DAG. Thanks to explicit constraints on skip length, the resulting structure is simple and this step completes in under 1 second.\n\n(3) **Path Optimization:** A lexicographic minimax search is performed over the sparse graph to identify acceleration paths that minimize the worst-case reconstruction error. This process is also highly efficient, completing in under 1 sec.\n\nThe table below summarizes the offline cost measured on CogVideoX:\n\n| Stage                  | Time Cost (Ref)     | VRAM       | \n| ---------------------- | ------------------- | ---------- | \n| Edge Weight Estimation  | 9.74 min / prompt  | 18718 MiB  |\n| Graph Construction      | < 1 sec             |  /         |\n| Path Optimization       | < 1 sec             |   /        | \n\nOpenSora:\n\n| Stage                  | Time Cost (Ref)     | VRAM       |\n| ---------------------- | ------------------- | ---------- |\n| Edge Weight Estimation  | 3.18 min / prompt  | 21718 MiB  |\n| Graph Construction      | < 1 sec             |  /         |\n| Path Optimization       | < 1 sec             |   /        |\n\nLatte:\n\n| Stage                  | Time Cost (Ref)     | VRAM       |\n| ---------------------- | ------------------- | ---------- |\n| Edge Weight Estimation  | 2.57 min / prompt  | 15664 MiB  |\n| Graph Construction      | < 1 sec             |  /         |\n| Path Optimization       | < 1 sec             |   /        |\n\n---\n\n> **W3: Theoretical convergence or optimality bounds are not discussed.**\n\nHere are the proof of theoretical convergence optimality bounds. The full details of the procedure can be found in the supplementary material as **Algorithm 1**  (Lexicographic Minimax Path Selection).\n\n**Theorem 1: Convergence Guarantee**\n\n**Statement**: Given a directed acyclic graph $G=(V,E)$, source $s$, target $t$, and budget $B$, Algorithm 1 terminates in finite steps.\n\n**Proof**:\n\n(1) **Finite State Space:** The DP table has $|V| × B$ entries, each representing a finite optimization subproblem.\n\n(2) **Monotonic Updates:** For any state $(v,k)$, the value $dp[v][k]$ can only improve \n(decrease lexicographically) during iterations.\n\n(3) **Bounded Iterations:** The algorithm runs exactly $B$ outer iterations, processing each edge at most once per iteration.\n\n---\n**Optimality Bound Interpretation：**\n\nThe core advantage of lexicographic minimax optimization lies in its explicit control of worst-case local errors, a principle rooted in robust control theory [1]. Unlike additive objectives, it prevents large errors from dominating global behavior.\n\n**Classical Shortest Path is Suboptimal in Our Context**\n\nShortest-path algorithms (e.g., Dijkstra [2]) minimize total cost, but they fail here due to:\n\n- **Exponential Error Amplification**: As shown in Figure 2b, early-stage cache errors get amplified nonlinearly. A single high-error edge early in the trajectory can lead to total collapse, even if total additive error is low.\n- **Over-Aggressive Skipping**: Shortest-path often chooses long skips (fewer nodes) to reduce total cost. However, long-range cache reuse often incurs very high local error due to non-local denoising mismatches.\n\n**References:**\n\n[1] Robust and optimal control. 1996\n\n[2] A note on two problems in connexion with graphs. 2022\n\n---\n\n> **Q4: Could LeMiCa generalize to adaptive or online caching?**\n\nThank you for the thoughtful suggestions.\n\n(1) Adaptive or Online Caching:\nLeMiCa currently uses a static offline DAG for acceleration. Extending it to adaptive or online caching is non-trivial, especially under latency constraints where real-time updates are required. We agree this is an important direction and plan to investigate efficient dynamic scheduling strategies in future work.\n\n(2) Partial DAG Updates:\nWe see promise in partial DAG updates as a practical compromise. By locally adjusting edge weights or dependencies—akin to low-rank adaptations like LoRA—we can reduce recomputation while preserving graph structure.\n\nFuture Outlook:\nIn real-time tasks (e.g., interactive rendering), where prompts or scenes evolve smoothly, temporal coherence can be exploited to incrementally update the DAG. We believe this opens a promising path for extending LeMiCa to dynamic, low-latency settings.\n\n\n> **W4&Q5: Missing error bars/statistical significance.**\n\nThank you for your feedback. We have now included them, along with statistical significance indicators, in the main experimental table. These updates will be reflected in the revised version of the paper.\n\n\n\n| Dataset           | Method     | LPIPS ↓            | SSIM ↑            | PSNR ↑          |\n| ----------------- | ---------- | ------------------ | ----------------- | --------------- |\n| **Open-Sora 1.2** | Original   |  —                  | —                 | —               |\n|                   | TeaCache   |  0.134 ± 0.012      | 0.837 ± 0.024     | 23.50 ± 1.3     |\n|                   | **LeMiCa** |  **0.050 ± 0.010**  | **0.923 ± 0.022** | **31.32 ± 1.1** |\n| **Latte**         | Original   |  —                  | —                 | —               |\n|                   | TeaCache   |  0.195 ± 0.019      | 0.775 ± 0.014     | 21.52 ± 1.2     |\n|                   | **LeMiCa** |  **0.091 ± 0.012**  | **0.865 ± 0.024** | **27.65 ± 1.2** |\n| **CogVideoX**     | Original   |  —                  | —                 | —               |\n|                   | TeaCache   |  0.053 ± 0.0095     | 0.928 ± 0.014     | 31.07 ± 1.5     |\n|                   | **LeMiCa** |  **0.023 ± 0.0039** | **0.958 ± 0.011** | **35.93 ± 1.3** |\n\n**Paired *t*-tests**  confirm that the improvements in LPIPS, SSIM, and PSNR are statistically significant at the ***p* < 0.01** level.\n\n\n***\n\nWe are grateful again for your insightful comments and believe the above clarifications address your key concerns. Please let us know if any further elaboration is needed.\n\n— Authors"}]

Author rebuttal:

Meta-review: AC finds the paper interesting where a DAG is constructed offline with some prompts which is then used to guide the caching for speedup. What immediately comes to mind is whether this offline method, constructed with a few prompts, is generalizable and whether the prompts could cause a big difference. Reviewers pointed out the same concerns. There were also concerns with the quality of the videos in the supp.

After rebuttal, we see that the authors provided extra experiments on OOD which convinced the reviewers. The AC felt that more scientific insight can be provided why the method would generalize and why only a few prompts are needed, in addition to the new experimental results, which will make the paper stronger. Authors also clarified that the video quality is bottlenecked by the original model's performance, which is acceptable.

Overall, AC thinks the biggest strength of the paper is its novelty in using a DAG to conduct caching that results in a more global effect, which outweighs the concerns, and decides to accept the paper.
Decision: Accept (spotlight)