Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation
Authors: Muhammad Adnan
Nithesh Kurella
Akhil Arunkumar
Prashant J. Nair
Venue: NeurIPS 2025 poster ()
Keywords: Text-to-Video Generation
Diffusion Transformers
Efficient Serving
Primary area: infrastructure
TL;DR: We propose an adaptive layer reuse technique that dynamically reuse intermediate feature across adjacent denoising steps to enable efficient inference of text-to-video generation models

Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art results in text-to-image, text-to-video generation, and editing. However, their large model size and the quadratic cost of spatial-temporal attention over multiple denoising steps make video generation computationally expensive. Static caching mitigates this by reusing features across fixed steps but fails to adapt to generation dynamics, leading to suboptimal trade-offs between speed and quality.

We propose Foresight, an adaptive layer-reuse technique that reduces computational redundancy across denoising steps while preserving baseline performance. Foresight dynamically identifies and reuses DiT block outputs for all layers across steps, adapting to generation parameters such as resolution and denoising schedules to optimize efficiency. Applied to OpenSora, Latte, and CogVideoX, Foresight achieves up to 1.63x end-to-end speedup, end-to-end speedup, while maintaining video quality.

Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation
Muhammad Adnan"
Nithesh Kurella d-Matrix ttheshhedmmatri....... ai
The University of British Columbia adnanθece ubc. ca
Akhil Arunkumar
Prashant J. Nair
d-Matrix mrrunkuuma@d-mmariie
The University of British Columbia prashantnair@ece.cm www.uucc..oo
Abstract
Diffusion Transformers (DiTs) achieve state-of-the-an art results text-to-image, text-to-video generation, and editing. However, their large model size and the quadratic cost of spatial-temporal attention over multiple denoising steps make video generation computationally expensive. Static caching mitigates this by reusing features across fixed steps but fails to adapt to generation dynamics, leading suboptimal trade-off' between speed and quality.
We propose Foresight, an adaptive layer-reuse technique that reduces computational redundancy across denoising steps while preserving baseline performance. Fore- sight dynamically identifies and reuses DiT block outputs for all layers across steps, adapting to generation parameters such resolution and denoising schedules to op- timize efficiency. Applied to OpenSora, Latte, and Cog VideoX, Foresight achieves up to 1.63 x end-to-end speedup, while maintaining video quality. The source code Foresight available at https://github..com wttp:ghhuu..ooSSAAA... -Ar.T-AAAaaattorffrrreiieep
Introduction
Diffusion Transformers (DiTs) [Bao et al., 2023, Peebles and Xie, 2023] are the of-te-of-the-rr architecture for text-to-image [Chen et al., 2024a] and text-to-video [Brooks et al., 2024, Genmo, 2024, Ma al., 2024, Liu al., 2024c] generation. By leveraging the scaling properties of self- attention (Vaswani et al., 2017], DiTs enable high-fidelity video synthesis. However, this comes at high computational cost. Self-attention has quadratic complexity ( in the token length which increases with both spatial resolution (more patches per frame) and temporal extent (more frames). Combined with the tens of denoising steps in typical diffusion pipelines, this leads to prohibitive inference latency for high-resolution or long-duration videos.
Inference latency diffusion models is further increased by classifier-free guidance (CFG) [Ho and Salimans, 2022] and full- full-precision weights. Techniques like pruning [Ma et al., 2023b]. quantiza- tion [Lin al., 2024b], distillation [Hsich al., 2023], and architectural optimizations [Li al.. 2023b, Zhao et al., 2024c,a, Fang et al., 2023, Bolya and Hoffman, 2023] can reduce model size and compute cost. However, they often require retraining or hardware-speciff tuning, making them impractical for deploying large, pretrained DiT's across diverse hardware environments.
Feature caching offers a complementary strategy for accelerating diffusion by reusing intermediate activations across adjacent denoising steps. Early methods for U-Net models cache skip-connection
A large part of this work was done while the author was interning with d-Matrix.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Baseline
Static
PAB
Foresight
Open Groape
27.5
CogVideoX
6P48
25.0
Latte
CogVideox
Quality (PSNR
22.5
Toneug
Bisolis
@Saltic
FAR
17.5
15.0
12.5
20
40
60
80
100
120
Joggudrrannd scarap the brancher, Novens and bokes socks The pork is Lumrii mrnas lo wcy detames alww
Latency (sec)
(a) Qualitative
(b) Quantitative
We compare the effectiveness of Adaptive Layer Reuse (Foresight) with prior state-of- the-art static techniques. The red circle indicates the frame variations in prior techniques. Videos are generated using OpenSora Zheng et al. [2024] at 720p, Latte Ma et al. [2024] at 512x512, and Cog VideoX Yang et al. [2024] at 480x720, all with a fixed length of 2 seconds. We report average inference time single A100 GPU using Open-Sone prompts.
Figure I:
outputs between steps and t-1 [Ma et al., 2023a, Wimbauer et al., 2024], while recent work extends this idea DiTs [Chen al., 2024b, Liu et al., 2024b, Selvaraju al., 2024, Yuan al., 2024, Zou et al., 2024, Kahatapitiya et al., 2024, Liu et al., 2024a]. However, these approaches apply static, uniform reuse policy across all layers and steps, which yields limited speedup and often degrades video quality and temporal coherence. As shown in Figure 1, static methods [Chen et al., 2024b, Liu al., 2024b, Zhao al., 2024b] introduce visible artifacts (highlighted in red), driven by latency- first design that ignores layer- and step-specific reuse dynamics.
To better balance inference speed and generation quality, analyze reuse patterns in DiT blocks and find that static reuse fails to preserve critical updates. Our analysis reveals two key findings: (1) the potential for reuse varies significantly across both layers and denoising steps, and (2) even minor changes in generation conditions, such as prompt content, resolution, or step count, can dramatically alter activation behavior. Figure 2(Left) quantifies the layer-wise Mean Squared Error (MSE) of spatial DiT features between successive timesteps in OpenSora, revealing pronounced layer-wise heterogeneity. Figure 2(Middle, Right) further highlights respeution-dependent reuse variation within the same layer. Motivated by these observations, we propose Foresight, a training-free per-layer adaptive reuse scheduler. Foresight dynamically decides, at runtime, whether to recompute or reuse each block's 's activation, thereby providing accelerated inference while maintaining video quality.
Our comprehensive experiments NVIDIA A100 GPUs for text-to-video generation benchmarks demonstrate the effectiveness of Foresight compared to static reuse baselines on OpenSora, Latte, and CogVideox Foresight achieves up 1.63 end-to-end inference speedup, with additional training, while preserving video quality.
2 Related Work
2.1 Diffusion Transformers
Diffusion Models (DMs) [Ho et al., 2020. Chen al., 2024a] generate images and videos by iteratively denoising random noise across multiple timesteps. Early models used convolutional U-Net backbones (Sohl-Dickstein et al., 2015, Rombach al., 2022], achieving strong results but with limited scalability. Diffusion Transformers (DiTs) [Bao et al., 2023, Peebles and Xie, 2023] replace convolutions with self-attention, offering better scalability and generation quality. DiTs now set state-of-the-and art benchmarks in text-to-image [Liu et al., 2024c] and eoxt-to-vide generation [Tim Brooks and Ramesh.J. Notably, Sora [Brooks et al., 2024] demonstrated high-quality video synthesis, motivating open-source projects like OpenSora [Zheng et al., 2024], Latte [Ma et al., 2024], Cog VideoX (Yang et al., 2024], and Mochi [Genmo, 2024). However, iterative denoising and attention introduce high inference latency, limiting practical deployment.

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101
60
144p
60
Prompt
Prompt
240p
720p
Prompt 2
Prompt
20
10a
Layers
15140
360p
Prompt 3
MSF
MSE
M
40
10-1
10'2
20
20
10a*
0
0
10
20
10
20
30
10 20
30
Denoising Steps
Denoising Steps
Denoising Steps
Figure Mean squared error (MSE) between consecutive Spatial DiT block outputs in Open- Sora [Zheng et al., 2024] during the generation of 2-second, 480p video. Lower MSE values indicate greater similarity and thus higher reuse potential. (I.eft) Heatmap across layers and denois- ing steps reveals distinct, layer-specific reuse patterns. (Middle) Variation of MSE for Layer 28 across different resolutions. (Right) Variation of MSE for Layer 28 across different text prompts. Note that Temporal DiT blocks demonstrate similar reuse trends as Spatial DiT blocks.
2.2 Accelerating Diffusion Transformers
Efforts to accelerate diffuuson models fall into two main categories: (1) reducing denoising steps and (2) speeding up the denoising network. The first reduces inference latency by shortening the sampling schedule. Deterministic methods like DDIM [Song et al., 2020] preserve quality with fewer steps, while higher-order solvers such DPM-Solvers [Lu et al., 2022a.b] improve efficiency through advanced numerical integration. Rectified Flow [Liu et al., 2022] enhances ODE-based sampling for faster inference. Step distillation [Meng al., 2023, Salimans and Ho, 2022] compresses multi-step models into fewer-step variants. Consistency Models [Song et al., 2023] enable single-step generation by mapping noise directly to data while maintaining consistency across timesteps.
The second direction accelerates the denoising network itself using techniques such as quantization [Li et al., 2023a, Zhao al., 2024a, Shang al., 2023], token reduction [Bolya and Hoffman, 2023, Zhang et al., 2025], knowledge distillation [Li et al., 2023b], and pruning [Wang et al., 2023]. More recently, training-free caching methods have gained attention for reusing intermediate activations across timesteps. Methods like DeepCache [Ma et al., 2023a], BlockCache [Wimbauer et al., 2024], and Faster Diffusion [Li al., 2024] achieve speedups via feature reuse but designed for U-Net architectures, limiting their applicability to transformer-based models.
To overcome the limitations of U-Net, recent work adapts caching techniques to Diffusion Transform- ers (DiTs). Methods like A-DiT [Chen et al., 2024b]. T-GATE [Liu et al., 2024b], and FORA [Sel- varaju et al., 2024] apply caching to image synthesis. For video. PAB [Zhao et al., 2024b] hierar- chically broadcasts attention, DiTFastAttn [Yuan et al., 2024] reduces redundancy across condition and timestep dimensions, and ToCA [Zou et al., 2025] selectively caches tokens. TeaCache [Liu et al., 2024a] adds timestep embedding awareness to caching. Other methods [Kahatapitiya et al., 2024, Xia et al., 2025, Lv et al., 2024, Anagnostidis et al., 2025] explore different reuse granularities. However, they all apply static reuse uniformly across layers and steps. Notably, these techniques prioritize speedup often at the cost of degraded output quality.
3 Foresight: From Static to Adaptive Layer Reuse
We begin by outlining background on diffusion models and static caching for inference acceleration. We then analyze the limitations of static reuse in Diffusion Transformers (DiTs), motivating Foresight, a training-free free framework for adaptive layer reuse.
3.1 Preliminary
Diffusion models generate images and videos through two complementary processes. A forward process that gradually introduces noise into clean data, and a reverse process that iteratively denoises to reconstruct data conditioned on input prompts. Starting from an initial clean sample xo, the forward process adds Gaussian noise over I' timesteps using variance schedule {8.]T=
3

---

x= = V1 - Be x vBr z1:
X,
21 (),
1=
As 1- the distribution of x, approaches (0, I). The reverse denoising process seeks to recover the original sample Xp by iteratively estimating:
Po Po x 1 xx) = N(x no( 1), Eo(x,, t))
where je and Eg are the mean and variance predicted by a denoising neural network (p) that is parameterized by 0.
State-of-the-an text-to-video models adopt Spatial-Temporal DiT (ST-DiT) layers that alternate spatial and temporal transformer blocks:
spTssaa = [fSA, fCA, fMLP), DiT = (fTA, SCA. SMLP]
where SSA and JAA apply spatial and temporal self-attention, and fCa performs cross-attention with text tokens. At timestep I, the input is token sequence obtained by flattening all H W latent patches in each of the frames:
X2 1 } }
3.2 Static Layer Reuse in Diffusion Models
Recent methods accelerate DiTs via static caching [Zhao al., 2024b, Chen al., 2024b, Liu et al., 2024b,a, Zou et al., 2024]. For a model with L layers and T denoising steps, let x, denote the output of layer at step f, comprising spatial and temporal components { (xppaial (t), x! (emporaa These activations are stored cache
(xx) <
{x (xapatia (O), x(emporai())
1= L.
All layers computed and cached chosen step /1. For the next N timesteps, the model reuses those activations:
x =x,,),,
1= 1= 1= t
1 + N
44)
This reuse significantly reduces computational costs by skipping redundant calculations across spatial and temporal DiT blocks for N consecutive steps. Once the reuse window expires (at timestep + N + 1), all layers are recomputed, and the cache is refreshed with the latest outputs +(x))+ enabling reuse in subsequent steps.
After step ( N, every layer is recomputed, the cache refreshed, and the eycle can repeat. Caching strategies can be applied at different granularities: fine-grained reuse (e.g.. individual attention or MLP blocks) coarse-grained reuse (entire DiT blocks). Prior methods, such as [Zhao al., 2024b), emphasize fine-grained reuse. In contrast, our approach investigates coarse coarse-grained reuse at the DiT block level, motivated by the observation that adjacent-step features exhibit significant similarity, as illustrated in Figure 2.
3.3 Limitations of Static Reuse
Reuse suitability text-to-video diffusion varies along three key axes: prompt, layer, and config- uration dynamics. Prompts differ in visual complexity. For instance, some prompts induce static scenes while others cause rapid changes. This leads significant variation in feature similarity across timesteps (Figure 3a). Static reuse applies uniform caching and cannot adapt to such prompt-specific behavior. Layer-wiss wise sensitivity analysis shows that reusing late layers degrades quality most, as these layers exhibit greater feature variation (Figure 3b, Figure 2 (left)), yet static methods fail to account for this variation. Moreover, video configuration parameters such as resolution, length, and denoising schedule can drastically alter reuse patterns, even under the same prompt (Figure 2 (middle); Appendix C). These findings underscore the limitations static reuse under dynamic generation conditions.

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Ztuet buit ce rocty nutrmpping slong ohe Amai Cavel
Hhrsugt the Thdye auturtt.
Average MSE
1260
1.0
0.710
1240
0.717
1220
20.25
0212
1200
1180
20.20
0.715
0.210
Denoising Steps
Denoising Steps
(a) Prompt Dynamies
(b) Layer Dynamics
Figure 3: (a) Prompt-dependent dynamics of intermediate spatial features (Spatial DiT block outputs) across layers. Prompts featuring rapid scene changes exhibit sharper variations between consecutive steps, indicating higher sensitivity and less reuse potential. The red circles indicate these variations. (b) Sensitivity analysis across DiT layers grouped into early, middle, and late stages. Applying static reuse (N = 1) within each group shows that later-stage layers disproportionately contribute to quality degradation under static caching. All experiments use the OpenSora model for 2-second, 720p videos with prompts selected from the OpenSora prompt set.
Spatial
Temporal
Spatial Block
Temporal
MLP
Reuse
Attention
Attention
Attention
Black
Static (Coarse Grained)
Static (Fine Grained PAB)
Adaptive (Foresight)
SSY YENT
M
SOT
50N
Layers
000
SDIT
SEAT
Denoising Steps
Figure 4: Overview of Foresight as compared to Static Reuse Techniques. Both Coarse-Grained
and Fine- Grainee [Zhao al., 2024b] techniques have preset reuse strategy and do not adapt reuse dynamically with layers and denoising steps.
3.4 Foresight: Adaptive Layer Reuse Framework
To address the limitations of static reuse, we propose Foresight, an adaptive layer reuse framework
that balances inference speed and generation quality. Figure 4 shows the overall flow, contrasting Foresight with coarse-grained static caching and fine-grained methods like PAB [Zhao al., 2024b].
Unlike static approaches, Foresight makes dynamic reuse decisions through a two-phase process: warmup phase and a reuse phase, driven by layer-specific reuse thresholds (A) and reuse metrics (6).
Warmup Phase: In contrast to prior caching methods, Foresight does not initialize the cache (C) immediately after the first step. Instead, all spatial, temporal, cross-attention, and MLP blocks are computed for the first W denoising steps, allowing intermediate features to stabilize. At timestep t= W, the cache is initialized with the latest outputs from each layer as described by Eq. 3.
Foresight also establishes reuse threshold (A) for each layer based on the mean squared er- ror (MSE) of intermediate fea- tures between consecutive steps during the warmup phase. Let X, denote features at step Given stabilized features by t W, we compute thresholds as weighted sum of MSEs from the final three warmup steps, scaled geometrically to reduce bias:
Threshgid(AA
Prompt 1
0.3
Spatial
Prompt
Temporal DIT
0.4
0.2
0.2
0.1
0.0
0.0
15 Layers(L)
Layers(L)
Figure (Left) Spatial block thresholds for different prompts using OpenSora (240p, 2s). (Right) Spatial and temporal block thresholds for the same prompt using OpenSora (720p. 2s).
5

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Σ (x(()) - xt(t -tt-1))) 1))² x (Xspatial: Xtemporal); ) == l,...,
X
t=W-> 10W-r
(5)
Here, P H x W for spatial blocks and F for temporal blocks. Figure shows the adaptive thresholds, showing variations for different prompts and resolutions. It shows how the reuse threshold varies if the resolution changes from 240p to 720p.
Reuse Phase:
The reuse phase employs the initialized cache (C) and thresholds (A), introducing dynamic reuse metric (d) for each layer and block to guide reuse decisions. This phase alternates between reuse and recomputation steps. Specifically, reuse occurs for N steps, after which all layers are recomputed for every R step. The reuse metric is updated based on MSE between the current and cached features each recomputation step, as defined in Eq 6. This update enables Foresight to adapt and reuse dynamically based on feature changes.
P E (x(()) (t 1))², xe (Xspatial, Xtemporat)
(1)
I....,
(6)
After updating the reuse metric, the cache is refreshed with the latest intermediate features defined in Eq. 3. At the subsequent timestep (L + 1), reuse decisions are made by comparing the reuse metric (6) against the threshold (A):
C(x+),
if (t)
x/+1
? (0,2), 1= I....,L
Compute,
otherwise
We introduce scaling factor (7) to control the reuse threshold, enabling adjustable trade-offs between speed and quality: higher y emphasizes quality (less reuse), while lower y favors speed (more reuse). Algorithm describes the end-to-end flow of the Foresight framework.
Algorithm Foresight
1: Inputs: Video tokens xfew Steps T, Warmup steps W, Reuse steps N, Compute interval Parameters: Reuse threshold X, scaling factor
Initialize cache C
Warnup
4: for t to W do
5: (1) + (xmm =I= I....,
6: XL + MSE(x (+), (t-1))
7: end for
8: C² x.W., & X
------ Rews Pluse
for W to T do
10:
ift mod R then
11:
(t) + Compute(x"),
vI I.....L
12:
MSF( (t), CX) C² (t)
14:
else
15:
for = to T. do
16:
if < >Xx then
17:
x' (t) + C*
18:
else
19:
x' (t) +- Compute(x))
20:
& MSF (1). Ctl
21:
C < (t)
22:
end
end for
end
25:
end for

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Warmup Phase
Reuse Phase
DiT Reuse
DiTrwrpard Reuse
Computation
11
24
10
1 16
11
20
26
T=1
1=10
T=15
T=20
T-25
T=30
Figure 6: Example of Foresight's ' adaptive layer reuse on the OpenSora model (240p. 4s) with a warmup phase of W = 15%, reuse window of N 1, computation of R = 2 and y 0.5. The evolution of the video frame is shown at the bottom. The prompt is "A playful black Labrador, adorned in vibrant pumpkin-themed Halloween costume, surrounded by fallen leaves."
Intuition with an Example: Figure shows how Foresight works using OpenSora using an example prompt (as showcased in the caption). Checkmarks (v/) indicate computation, arrows (-)) represent reuse. During the warmup phase (W 15%), all blocks are computed, initializing the cache and thresholds (A) scaled by 2 results alternating reuse and recomputation steps. Notably, later layers (e.g., 24-28) are recomputed more frequently, demonstrating Foresight's adaptive capability to maintain quality. The video frame evolution is shown at the bottom of Figure 6.
0.5. In the reuse phase, reuse window of N 1 and
recomputation interval
3.5 Foresight: Convergence Analysis
We provide a formal analysis showing that adaptive reuse, the key mechanism of Foresight, does not affect model convergence. Let X be the generated (denoised) video, be the intermediate latent at demoising step (T < < 1). For layer I the spatio-temporal diffusion transformer, denote its output by (x,)). During timesteps Trese {1....,T) T)), adaptive reuse approximates this output using the cached value from the previous step as per Eq 8.
(x) (xi)
if MSE(X,, x.-1) N t 1 1= 1- otherwise
(xx))
(8)
Since reuse preserves weights 0 and transition kernels pa (X;-1 xt). the original diffusion process is guaranteed hold. Even when recomputed (in the worst case), the baseline forward pass is recovered. For any reused layer I at denoising step t, the error is bounded as shown in Eq 9.
(x.)) (x))ll S E,, as 7
Activations vary slowly in space/time when reused, making perturbations negligible. The reuse error at layer and step is shown in Eq 10.
E = (x,)) - fx(x)))12 & VAAL
(10)

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Thus, of is uniformly bounded by a constant controllable via Let x be the baseline latent and X the latent under adaptive reuse. After one reverse step term contracts by vI 3 1 and term bounded by Euo E1 L in Eq 11.
7 Σ
xi-1ll S v Bel1x; xil +
Laet
(11)
1=1
Assuming each block T--Lipschitz with L < 1 and ot S e for all (1, unrolling the recursion for k steps is shown in Eq 12.
1
[ - xi-Al1 -
II
V1- Be
||:: - -xi Eoot Σ
II
V1 - B.
(12)
k+1
j=0
(I)
(2)
1. As k f, VT. - 0 since E, Ag - oo implies II, - (z) O.
2. Geometric series bounded by Sux/(1 where p = max, v1 Bg <
As - T and T co, (1) vanishes and (2) remains finite. Tightening reduces Est, yielding arbitrary closeness to the baseline. Adaptive reuse introduces bounded, vanishing perturbations into contractive reverse Markov chain. Thus, Foresight maintains theoretical fidelity to the original diffusion model.
Experiments
4.1
Experimental Setup
We evaluated Foresight on three popular text-to-video models: Open-Sora-v1., 1..2 [Zheng al., 2024], Latte- 1.0 [Ma et al., 2024], and CogVideoX-2b [Yang et al., 2024], using NVIDIA A100 80GB GPUs with FlashAttention [Dao et al., 2022] enabled. Open-Sora used rflow [Liu et al., 2022] sampling with 30 denoising steps and CFG scale of 7.5. Latte and Cog VideoX used DDIM [Song et al., 2020] with 50 steps, and CFG scales of 7.5 and 6.0, respectively.
Models:
Evaluation Datasets and Metries: For comprehensive evaluation, assess Foresight the VBench benchmark suite [Huang et al., 2024), using VBench accuracy, PSNR, SSIM [Wang and Bovik, 2002], LPIPS [Zhang al., 2018], and FVD [Unterthiner et al., 2019] to quantify video quality. We report results on the UCF-101 [Soomro et al., 2012] and EvalCrafter [Liu et al., 2024d] prompt sets evaluate performance dynamic scenes. Following EvalCrafter, also include CLIPSIM, CLIP-Temp, and DOVER's VQA score [Wu et al., 2023] for fine-grained quality assessment.
Baselines:
For comprehensive comparison, evaluate Foresight against four training-free caching methods for accelerating diffusion models: Static, A-DiT, T-GATE, and PAB. Implemen- tation details for each baseline appear in Appendix D.2.
4.2 Results
Quality Comparison: Table compares the video quality of Foresight with static reuse methods. We generate 550 videos from the VBench Huang et al. [2024] prompt set, covering 550 prompts across 11 dimensions (50 per dimension). Video quality is evaluated using VBench accuracy (%) and standard metrics: PSNR. SSIM, LPIPS, and FVD, all relative to the baseline. We report two Foresight configurations, varying reuse steps from = 1 = 2 and computation intervals from R = 2 to R 3, with fixed scaling factor 0.5) balancing speed and quality. Additional results are provided in Appendix E.
Qualitative results further highlight that Foresight surpasses static reuse methods by adaptively select- ing which layers to reuse at each denoising step. This strategy preserves critical intermediate features, maintaining video quality and achieving up to 1.63x speedup. Foresight consistently outperforms

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Model
Method
PSNR
SSIM f
LPIPS &
FVD J
Latency(s)
Speedup 1
Boseline
75.63
12.65 (# 0.06)
Statie
73.10
20.25
0.71
0.21
1257.50
9.27 (# 0.03)
1.3xx
A-DFT
74.20
22.45
1009.65
0.03)
Open-Sora
T-GATE
73.80
21.78
0.71
0.20
890.45
11.1 (# 0.02)
1.14x
PAB
75.32
25.67
0.85
0.10
541.53
10.01 (+ 0.03)
1.26x
Foresight (g)
75.90
29.67
0.90
0.05
306.66
0.92)
1.28x
Foresight, (N2
75.62
27.49
0.87
0.07
457.69
8,80 (+ 1.12)
1.44x
Boseline
73.02
33.53 (2: 0.09)
Statie
3937.29
25,90 0.09)
A-DIT
65.14
14.85
0.51
0.48
275164
32.84 (# 0.02)
1,0XX
Latte
T-GATE
62.48
12.56
0.48
0.56
1894.45
30.54 (1 0.05)
1.0xx
PAB
72.64
21.22
0.75
0.23
882.96
25,93 (2 0.02)
1.29X
Foresight (2)
73.08
26,02
0.86
0,12
443.05
28.40 (# 1.36)
L.8X
Foresight (Ng)
73.05
23.12
0.82
0.18
683.18 18
25.54 (+ 1.56)
1,31x
Baseline
77.78
30.23 (2 0.03)
Statie
76.65
14,05
0.63
0.42
1811,77
22.44 0.01)
1.35x
A-DIT
54.16
11.94
0.49
0.65
3085.41
29.12 0.01)
1.03x
CogVideoX
T-GATE
12.56
2845.64
27.64 0.03)
PAB
77.89
29.04
0.91
0.07
340.24
22.05 (# 0.01)
1.37x
Foresight: (N1 Rt))
77.94
34.75
0.95
0.03
130.65
20.67 (+ 1.10)
1.66x
Foresight (g)
77.84
28.45
0.87
0.12
18.53
Table I: Qualitative comparison of Foresight and static reuse methods on the VBench prompt set, which spans 11 dimensions with 50 prompts each. Videos generated 240p with OpenSora, 512x512 with Latte, and 480x720 with Videe,, all with a fixed duration of 2 seconds. Metrics including PSNR, SSIM, LPIPS, and FVD are reported relative to the baseline.
all baselines across varying noise schedulers, denoising steps, and architectures, demonstrating robustness and generalizability.
Memory Overhead:
Open-Sora
CogVideoX
While caching techniques require no training, they in- troduce memory overhead from storing intermediate features. This overhead grows with model depth, resolution, and video length. Prior work [Zhao al., 2024b] employs fine-grained caching across spatial, temporal, cross- attention, and MLP blocks in all layers, resulting in cache size of 6LHWF, where L is the number of layers, F the number of frames, and each frame has spatial size x W. In contrast, Foresight adopts a coarser strategy, caching only full DiT blocks (two per layer), reducing the cost to = 2LHWF. Thus, Foresight provides 3x higher memory reduction.
Bäseline Lateney
BSseelie Latency
Latencyls)
-0
PSNR
35
-35
10
20
10%
209
Wwrmup(W)
Warmup(W)
Figure 7: Varying warmup steps (W) with fixed reuse settings: N R and scaling factor y 0.5 in Foresight.
4.3 Ablation Studies
We conduct ablation studies to analyze Foresight's behavior by varying its key hyperparameters and examining the trade-offs between speed and video quality.
Varying Warmup Phase (W): To study the impact of the warmup phase, vary its length from to 40%, keeping all other Foresightsettings fixed. As shown in Figure 7, increasing the warmup percentage reduces the number of denoising steps available for reuse. This leads to higher video quality but limits performance gains, as reuse is applied to fewer steps.
Varying Reuse Settings (N, R): Reuse settings determine the maximum reuse across adjacent timesteps. We conducted ablation study to analyze their impact by varying the reuse steps and the compute interval. Table summarizes the inference latency and video quality trade-off Foresight relative to PAB vary the reuse window (N) and compute interval (R). Larger N increases reuse

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Table Effect of reuse settings on Open-Sora
Table 3: Effect of scaling factor on Open-Sora
(240p, 2s, T--00, 1515% 7=0.5), Com- pared to PAB (Latency = 19.88s, PSNR- 28.12).
(N=1, R-2, 240p, 2s, T-60, 1-15%)
Compared to PAB (Latency = 19.88s,
PSNR 28.12).
Settings
Latency
PSNR f
7
Latency 4
PSNR
N=1, R=2
18.70 (-1.17)
32.38 (+4.26)
0.25
20.50 (40.62)
38.09 (49.97)
N-2, R=3
16.37 (-3.50)
30.42 (42.30)
0.5
18.70 (-1.17)
32.38 (44.26)
N=3, R=4
14.79 (-5.08)
29.03 (+0.9))
1.0
17.03 (-2.84)
30.43 (+2.31)
N=4, R-5
13.49 (-6.38)
27.91 (-0.20)
2.0
16.02 (-3.85) 29.51 (+1.39)
opportunities but lowers the frequency of cache and reuse metric updates, eventually harming fidelity. In practice, we observe that for the range of N=1 to N=3 delivers the best balance, after which the video quality drops compared to the PAB baseline, mainly due to not updating the reuse metric. For N=3, Foresight yields a 1.35x speed-up while surpassing PAB in video quality.
Varying Scaling Factor (7): The scaling factor 7 governs the speed-quality balance by adjusting how strictly reuse is applied. The reuse threshold is dynamic and set at the end of the warmup phase. The scaling factor be applied uniformly across all layers or adjusted per layer account for varying sensitivity. Table shows that smaller values increase latency but enhance fidelity. With y=0.25, Foresight reaches PSNR of 39, which is 9.97 points above PAB, at the cost of just 0.62 s latency increase. This reflects slight reduction in the reuse profile.
5 Limitations and Discussion
Foresight operates at runtime without altering model architecture application. Its plug-and- play design enables efficient deployment of text-to-video models, reducing computational costs and expanding access to generative Al for creative and educational purposes. However, broader accessibility may also increase the risk of misuse, such as creating deepfakes for misinformation or fraud, While Foresight does not inherently heighten these risks, its widespread use underscores the need for safeguards and ethical guidelines with controlled use the model and usage guidelines. Foresight accelerates inference by reusing intermediate activations, its speed and video quality envelope are governed by the reuse window N and warm-up length W. Essentially, a larger N or smaller W could yield greater speed-up at the risk of quality loss. Furthermore, Foresight can be expanded. Our experiments adopt coarse, block-level caching, yet the framework can operate at finer granularity.
Conclusions
We introduced Foresight, training-free framework that accelerates diffusion transformers for text- to-video generation by making layer-wise reuse decisions at run time. Rather than relying on static caching, Foresight dynamically tracks mrean-squaredeevee (MSE) feature change, maintains per- layer reuse thresholds, and selectively decides whether to reuse or recompute each block. Evaluated on Open-Sora-vll .2, Latte-1.0, and Cog VideoX-2b, Foresight achieves up 1.63 speed-up in inference latency while consistently improving PSNR, SSIM, LPIPS, and FVD compared to state-of- the-art baselines. Its coarse, block-level caching strategy also reduces memory usage by 3x, without requiring retraining or architectural modifications.
Acknowledgments
We thank the anonymous reviewers for their valuable feedback and Haoran Qiu for his insightful comments. Muhammad Adnan's Ph.D. is supported by the Intel TSA and Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2019-05559] Grants.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claim is that static reuse methods are limited, and adaptive reuse is needed to handle the dynamic nature reuse, as shown both qualitatively and quantitatively.
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The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
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is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
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Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: Section discuss the limitations of Foresight.
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The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
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Justification:
Section 3.5 provides the assumptions along with theoratical convergence
analysis.
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The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
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Appendix
A Background
The success of Sora Brooks al. [2024] has shown the strong potential of diffusion transformers for text-to-video generation, inspiring a wave of open-source models such as Open-Sora [Zheng et al., 2024], Open-Sora-Plan [Lin et al., 2024a], Latte [Ma al., 2024], Cog VideoX [Yang al., 2024], Vchitect [Fan et al., 2025], and Mochi [Genmo, 2024].
Figure 8 shows high-level overview of DiT-based text-to-video models. These models take input Gaussian initialiii latent video frames, timesteps for scheduling, and a text prompt. They Spatial-Tempone DiT (ST-DiT) blocks: Spatial-DiT captures intra-frame spatial structure, while Temporal-De captures frter-frmm temporal dynamics. Cross-attention injects text conditioning into each block, and feedforward networks (FFNs) apply learned nonlinearities. Timestep embeddings guide denoising across both spatial and temporal layers.
B Workload Characterization
To analyze inference bottlenecks in text-to-video generation, profile the Open-Sora [Zheng et al., 2024] model across various resolutions and timeframes, using fixed 30-step RFlow scheduler using single NVIDIA A100 (80GB) GPU with flash attention [Dao et al., 2022].
Figure shows the end-to-end latency and its breakdown by operator type. As resolution increases from 480p to 720p, latency rises 2.5x. driven largely by the quadratic complexity of attention operations. Attention modules (spatial, temporal, and cross-attention) account for 50% of inference time, FFNs for 15%, and non-linear layers- LayerNomm,, scaling, and residuals- for 35%. The sizable cost of non-linear operations highlights the need to target them in optimization efforts.
To identify system bottlenecks during inference, for Spatial and Temporal Attention blocks in the Open-Sora [Zheng et al., 2024] model using a single NVIDIA A100 (80GB) GPU with batch size (Figure 10). For Spatial Attention, we varied resolution from 144p to 1080p with fixed timeframe of seconds. For Temporal Attention, we varied timeframes from 2 to 16 seconds at fixed resolution of 720p.
measured compute and memory throughput
In Spatial Attention, increasing resolution increases the number of spatial tokens, leading to higher compute demands due to the quadratic complexity of attention. This keeps the block compute-bound In contrast, Temporal Attention sees a smaller increase in sequence length with longer timeframes, while the batch equal to the number of token patches in trame-remains large. As result, flash attention becomes suboptimal and performance becomes memory-bonnd. Overall, ST-DiT layers are primarily compute-bound, suggesting that reusing redundant computations can improve inference efficiency.
Noise
Video Embedding Time Embedding Text Embedding
Spatial DiT
Temporal DiT
Final MLP
Timesteps
Prompt
Spatial
Cross Attention
Temporal
Cross
FFN
FFN
Attention
Attention
Attention
Figure 8: Overview of diffusion transformer (DIT) based text-to-video generation models. It com- prises of mainly spatial and temporal diffusion transformer blocks with spatial and temporal attention. Cross attention takes into acccount prompt conditioning for generated video.
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Spatial Attention
FFN
Residual Attention
Temporal Attention
Modulate Attention Modulate FFN
Residual FFN
Cross Attention
Inference Time (sec)
Inference Time (%)
100%
80%
100
60%
50
40%
20%
144p
240p 360p 480p 720p Resolution
144p 240p 360p 480p 720p
Resolution
Figure 9: Left: End-to-end inference time across resolutions. Right: Inference time breakdown by operator. Results based on Open-Sora [Zheng et al., 2024] using single NVIDIA A100 (80GB) GPU with batch size 1.
Compute Throughput
Memory Throughput
Spatial Attention
Temporal Attention
60%
Percentage
Percentage
40%
40%
0%
0%
144p
360p
720p
4s
16s
Resolution
Timeframe
Figure 10: Compute vs. memory throughput for Spatial and Temporal Attention blocks in Open- Sora [Zheng al., 2024], measured on a single NVIDIA A100 (80GB) GPU with batch size
Layers (480p. 25)
Prompts (480p, 2s)
Seeds (480p, 2s)
40
Layer 1
Layer 18
40
Prompt 1
Prompt 4
Seed 1024
Layer 22
Promet
Seed 2048
Layer 10
Layer 25
Prompt
MSE
Layer 15
Layer 28
20
20
30
30
20
30
Resolutions (2s)
Timeframes (480p)
Denoising Steps (480p, 2s)
144p
480p
seconds seconds
40
Steps 30
240p
720p
60
360p
Steps
MSE
40
20-
20
20
0.
0
20
30
10
20
30
50
100
Denoising Steps
Denoising Steps
Denoising Steps
Figure 11: Quantitave analysis of Spatial DiT output using mean squared difference across different layer, prompts, seeds, video resolutions, video timeframes and denoising steps for OpenSora Zheng al. (2024) model Layer 28 if layer not specified. The prompt is " " narrow, cobblestone alleyway, buthed in the soft glow of vintage street lamps, stretches between tall, weathered brick buildings adorned with ivy. The scene begins with a gentle drizzle, creating reflective sheen on the cobble- stones. As the camera pans, black cat with piercing green eyes darts across the path, adding touch of mystery. The alley is lined with quaint, shuttered windows and wooden doors, some slightly ajar. hinting at hidden stories within. A soft breeze rustles the leaves of potted plants and hanging flower baskets, while distant, muffled sounds of city life create a serene yet vibrant atnosphere."
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C Feature Variations and Reuse Metric
To analyze feature variation across video configurations, we vary one parameter time while keeping others fixed, and measure changes in intermediate features at specific layers. Figure 11 shows the effect of varying prompts, noise seeds, resolution, timeframes, and denoising steps. The results indicate that intermediate features are sensitive to these configurations. Therefore, adaptive reuse must account for such variations to minimize quality loss in video generation.
C.1 Reuse Metric
To analyze the dynamic behavior of reuse and intermediate feature variation, we measure how features evolve across layers and timesteps using cosine similarity.
C.1.1 Across Condition
Figure 12 illustrates the evolution of spatial features during conditional generation, across layers and denoising steps.
Spatial Comelation Conditional Generatior
1.00
0.04
0.58
0:57
0.56
0V153501 AAA.NA 103A3610090000
Figure 12: Cosine similarity of Spatial-DiT features across conditioning steps in the OpenSora model.
C.1.2
Across Layers
Figure 13 shows the cosine similarity of spatial-DiT features across layers and denoising steps for the OpenSora model.
C.1.3 Across Denoising Steps
Figure 14 shows the cosine similarity of Spatial-DiT features across denoising steps for different layers the OpenSora model. Later layers exhibit greater feature variation than early and middle layers.
D Evaluation
D.1 Evaluation Metrics
We evaluate video generation quality using established metrics that assess both perceptual quality and similarity to baseline outputs. Specifically, we use VBench [Huang et al.. 2024]. a comprehensive benchmark capturing multiple perceptual dimensions, and complement with similarity metrics comparing generated videos to baseline outputs without reuse.
VBench [Huang et al., 2024] is video generation benchmark that evaluates model quality across 16 well-defined dimensions. It uses 11 prompt categories, each designed to probe specific aspects of these dimensions, with weighted scores assigned each. For evaluation, we select the first 50 prompts from each category, totaling 550 prompts.
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Spatial DIT Correcction (Timestap-S)
Spatia DIT Corre ation (Timestgp-1))
0.4
0.
D2
00
0.0
(a) T 5
5
(b) 10
Spatia DIT Correlation (Timetep=11)
Spatia DTT Come ation, [Tmestep=20)
0.
00
04
04
02
02
0.
0.0
6y115581 01200
(c) 15
(d) 20
Conrlation
1.0
3.0
08
06
0.6
0.6
04
04
03
(c) 25
(f) T 30
Figure 13: Cosine similarity of Spatial-DiT features across layers at different denoising steps in the OpenSora model.
PSNR. measures pixel-level mean squared error between a baseline video (without reuse) and reused-version. Higher PSNR indicates lower error and better quality.
SSTM [Wagg and Bovik, 2002] measures image similarity by comparing struetural information, luminance, and contrast between two images.
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Spatial DIT Corrlationn (Layer=4)
Spatial DIT Corclaatin (Layer-9)
10
0.4
0.4
D2
00
0.0
(a) Layer 4
(b) Layer 9
Spatial DNT Correlation (Layer=14)
Spatial DNT Comrelation ation (Layer
0.
0.
04
044
02
022
0.0
0.0
Tieetops
Timestpss
(c) Layer 14
(d) Layer 19
Correlation
Spatial Correaaiio
1.0
3.0
08
06
0.6
0.6
04
04
03
02
(c) Layer 21
(f) Layer 27
Figure 14: Cosine similarity of Spatial-DiT features across denoising steps for different layers the OpenSora model.
I.PIPS [Zhang al., 2018] measures perceptual similarity between images using deep neural network features, capturing differences more effectively than pixel-based metrics. It computes the distance between features extracted from pre-trained models trained on large-scale datasets.
FVD [Unterthiner et al., 2019] based on the Fréchet Inception Distance (FID) images, extends the concept to videos. It measures the distance between real and generated video distributions in the feature space of pretrained network. By capturing both spatial quality and temporal consistency, FVD reflects how closely generated vidcos match real ones in appearance and motion.
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We compute all the above similarity metrics per frame and report the average across all frames as the final video score.
D.2 Baselines Generation Settings
We evaluate four static cache reuse methods- Static,, A-DiT[Chen et al., 2024b], T-GATE(Liu al., 2024b], and PAB[Zhao et al., 2024b]
configurations detailed below.
Static method caches coarse-grained features using reuse window of size N and updates the cache at fixed compute interval R, as shown Table 4.
Table 4: Static baseline Settings
Denoising Steps (7)
Reuse Window (N)
Compute
Static
Interval (R)
Open-Sora
30
Latte
50
2
Cog VideoX
50
2
A-DiT caches feature map deviations instead of full feature maps. It applies to back blocks during early outline generation and front blocks during the detail refinement stage. Cache reuse is controlled by a gating hyperparameter b, which defines the boundary between front and back blocks, and cache interval k shown in Table 5.
Table 5: A-DiT baseline Settings
Denoising Steps (T)
Cache Interval (k)
Gate Step (6)
Block
A-DIT
Range
Open-Sora
30
25
[0. 5]
Latte
48
[0, 2]
Cog VideoX
50
2
48
[0, 2]
T-GATE divides the diffusion process into two phases: semantics planning and fidelity improve- ment, with the transition defined by gate step m. During the cemantics-planning phase, cross-attention (CA) remains active, while self-attention (SA) is computed and reused every k steps after initial warm-up. After step m. CA is replaced by cached features, while SA continues as shown Table
Table 6: T-GATE baseline Settings
Denoising Steps (T)
Cache Interval (k)
Gate
T-GATE
Step (b)
Open-Sora
30
12
Latte
50
20
Cog VidcoX
50
20
PAB employs Pyramid Attention Broadcast, where the broadcast range forms hierarchy: cross- attention (y) at the base, temporal attention (8) in the middle, and spatial attention (a) at the top. Reuse occurs during designated broadcast timesteps. Each DiT block's MLP follows a separate reuse schedule, as detailed in Table 7, based on empirical evaluation.
Table 7: PAB baseline Settings
Denoising Steps (T)
Broadcast Range and Timesteps
MI Broadeast
Spatial (a)
Temporal (3) Cross (t)
Timestops
Reuse
Blocks
Timestops
Open-Sora
1930-450]
3,4
67611
Latte
50
2
4
[800- 100)
[0, 2,
[720, 640, 560, 480, 400]]
CogVideox
[850,
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E Extended Results
E.1 Adaptive behavior for different prompts
To quantify the adaptive behavior of Foresight, we plot absolute latency for all methods across prompts from the Open-Sora set. As shown Figure 15, static reuse methods exhibit consistent latency due to fixed reuse schedules. In contrast, Foresight balances speed and quality based on scene complexity, enabling dynamic reuse for improved video quality and inference speedup. Table 15 shows the VBench wall clock time for 500 prompts for OpenSora model for more realistic comparison.
Baseline
PAB
Static
Foresight
Cumulative Wall Clock Time in seconds (lower better)
Method
Latency
Baseline
7092.42
PAB
5509.06
1.00
Foresight (N; R2)
5455.77
90
Foresight (N₂
4842.95
OpenSoraPrompts
Latency variation across prompts from the Open-Sora set. Baseline and Static methods yield constant inference latency due to fixed reuse schedules. In contrast, Foresight adapts reuse based on prompt complexity, improving video quality with dynamic latency. Prompts are sorted by latency (ascending) using the Open-Sora model at 720p for 2-second generations. Right: Open-Sora cumulative wall clock time in seconds for VBench prompt set 500 prompts.
Figure 15: Left:
E.2 Additional results
To assess the efficacy of Foresight, we evaluate it on state-of-the-an models Hunyuan Video [Kong al., 2024] and 1 [Wan et al., 2025] using the Penguin BideoBenrrc and compare it with TeaCache [Liu et al., 2024a]. As shown in Table 8, Foresight consistently outperforms prior work in quality while delivering competitive speedups. Unlike static methods, Foresight adapts to balance speed and quality. For fairness, we also report performance for the Foresight configuration that matches the quality of prior work.
Model
Method Baseline
PSNR
SSIM f
LPIPS 4
FVD 4
Latency
Speedup 1
415.94 (# 1.04)
(c.1)
37.31
0.96
0.02
170.16
258.08 (1: 3.28)
1.61 x
Hunyuan Video
Foresight (N, R2)
41.79
0.97
47.45
256.75 (+ 6.54)
1.62x
Foresight (N2Rs)
37.25
0.02
172.85
231.08 (# 7.42)
1.80x
Baseline
74.87 (# 0.45)
TeaCache
21.49
0.77
0.14
1222.56
38,9 (1 0.25)
1,90x
Wan-2.1
Foresight (N₂ R2))
25.12
0.86
0.08
543.40
50.93 (+ 1.14)
1.47x
Foresight (N₂Rs)
21.64
0.77
0.15
1234.48
33.57 (= 1.32)
2.23x
Table 8: Qualitative comparison of Foresight with recent related work and state-of-the-an models on the Penguin BineoBen Results are shown for 10 randomly sampled prompts: 720p videos of 2 seconds with Hunyuan Video, and 480p videos of 5 seconds with 1an-2.1.
E.3 Performance comparison with same generation quality
Foresight uses fixed hyperparameters across all experiments and adapts consistently across models, prompts, and configurations. These parameters provide practical way to balance quality and performance without additional tuning. By contrast, prior work [Zhao et al., 2024b, Liu et al., 2024a]
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requires model--peeinfe adjustments: PAB fixes MLP reuse empirically, while TeaCache thresholds vary by model. For fair comparison, Table reports speedups for both methods using a Foresight configuration that matches PAB's output quality across models.
Table 9; Performance improvement measured matched video quality
Model
PSNR
PAB Speedup
Foresight Speedup
OpenSora
25.67
1.26x
1.68x
Latte
21.22
x.29
1.58x
Videox
29.04
1.37x
1.95
E.4 Additional results across other benchmarks and prompts set
To complement Section 4.2, which reports results using VBench [Huang et al., 2024] prompts and standard similarity metrics, we further evaluate Foresight on two additional prompt sets: UCF- 101 [Soomro et al., 2012] and EvalCrafter [Liu et al., 2024d]. We include CLIPSIM and CLIP--Tem to assess tixeo alignment and temporal consistency, along with DOVER's 's [Wu al., 2023] VQA metrics for both aesthetic and technical quality, shown in Table 10.
E.5 Extension to other diffusion tasks
To evaluate the efficacy of Foresight across other diffusion tasks, we apply Foresight to the FLUX [Labs et al., 2025] text-to-image generation model, which does not include a temporal dimension. In this setting, Foresight reuses spatial and cross-attention layers and MLPs.
Model
Method
CLIP
CLIP
VQA Austhetic
VQA Technical
VQA Overall
Latency(s)
Speedup
SIM
Temp
UCF-101
[Soomro
2012]
(101 Prompts)
Baseline
20.51
99.64
14.56
25.02
19.75
12.71 (+ 0.04)
Open-Sora
PAB
20.47
99.76
6.57
15.66
10.32
10.05 (1: 0.06)
1.26x
Foresight (N,Rg) Rg)
20.73
99.88
15.17
26.68
21.11
9.72 (+: 0.84)
1.30x
Foresight (N2
20.70
99.86
14.01
26.
20.32
7.32 (+ 0.80)
1.73x
Baseline
20.09
99.39
20.71
12.93
14.10
32.48 (# 0.02)
PAB
20.09
99.39
20.71
12.69
13.79
25.11 (1 0.01)
1.29x
Latte
Foresight (N)
20.12
99.14
22.72
21.04
20.34
21.48 0.05)
x.51
Foresight (NaRg)
20.09
99.16
21.95
19.14
18.92
0.22)
2.08x
Baseline
20.22
99.43
40,25
42.34
41.19
29.35 (+ 0.02)
PAB
20.20
99.43
39.44
41.86
40.52
22.65 0.05)
x,29
CogVideoX
Foresight (a)
20.21
99.42
40.64
40.65
40.17
19.57 0.96)
x.49x
Foresight (N)
20.17
99.44
41.46
43.34
42.15
17.00 (# 1.18)
1.72x
EvalCrafter [L.iu et al., 2024d]
(150 prompts)
Baseline
20.07
99.55
17.48
23.49
0.09)
PAB
19.91
99.76
9.16
20.82
14.15
10.07 0.03)
1,24x
Open-Sora
Foresight (N, R2)
20.05
99.54
16.85
29.56
23.10
9.36 (+ 0.77)
1.34x
Foresight (N2Rg)
20.05
99.50
15.67
29.21
22.24
7.06 (#: 0.93)
1.78x
Baseline
20.69
99.50
$3.05
45.59
48.37
32.48 0.02)
PAB
19.98
99.65
53.35
35.39
40.84
25.11 (1: 0.01)
1,29x
Latte
Foresight (NyRg)
20.60
99.51
$5.85
45.80
49.27
28.37 (t: 1.14)
1.14x
Foresight (Nalg)
20.54
99.50
$4.00
44.03
47.29
25.59 (L 1.36)
1.26x
Baseline
20.66
99.51
$4.72
$7.67
56.53
30.43 (+ 0.12)
PAB
20.66
99.51
$2.49
56.10
54.85
22.95 (# 0.07)
1.32x
VidexX
Foresight (N, Rg)
20.66
99.55
53.34
56.73
55.64
25.80
0.84)
1.17x
Foresight (NaRts
20.64
99.55
52.54
56.34
54.98
24.56
1.08)
1.23x
Table 10: Qualitative comparison of Foresight and PAB on UCF [Soomro et al., 2012] and Eval- Crafter [Liu et al., 2024d] prompts set. Videos are generated 240p with OpenSora, 512x512 with Latte, and 480x720 with Cog Vigeo,, all with a fixed duration of 2 seconds. Metrics including EvalCrafter's Liu al. [2024d] CL.P score and DOVER's [Wu et al., 2023] VQA in Aesthetics, Technical and Overall score.
29

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Table 11 reports the performance, showing that Foresight reduces wall-clock time by approximately 2x while maintaining comparable image quality to the baseline.
Table 11: Open-Sora cumulative wall clock time seconds
Method
Latency (seconds)
Bascline
14.02
Foresight (N1
7.1
E.6 Qualitative Comparison
Figures 16 and 17 present a qualitative comparison of videos generated by Foresight and the baseline across multiple frames. Foresight maintains consistent video quality while achieving notable performance improvements.
30

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Frame 1
Frame 25
Frame50
Baseline
Foresight
Prompt: A cargt vingging Ineef tropical Moui.
Baseline
Foresigh
Prompt: A drone comera circles around a beowijul historic church built on rocky outcropping along the Amolfi Coust, the view showauses crashing against
pathways and patios, waves overlooks the horizon of the coastol waters and billy londscapes of the Amolji Coost itoly, several distant people are seen wolking and magicai romantic fooling
magnificent architectural details
below
enjoying vistos patios of dramatic scene, the view is stunning coptured with heautiful photography.
glow the ofternace
Baseline
Foresight
Aroipt:Aerial view of Santorini during the blue hour, showcosing the stanning architecture white Cycledic buildings with blue domes. The coldera views are breothtoking, and the lighting creotes beoutiful, serene utmosphere.
Foresight Baseline
Prompt: Drone view of waves crashing against the nugged clifs aiong Big Sur's garay point beach. The crashing blue waters create white- golden light of setting sun illuminates rocky shore.
tipped waves, green shrubbery covers the clif's edge. The steep droe from the rood down to the beach a dromatic feat, with the clff's edges jutting out the Coast Nightwy..
lighthouse sits distance,
over sea.
coptures row beauty of coost
rugged
Figure 16: Qualitative Comparison for Open-Sora [Zheng et al., 2024] with 720p, 2sec video generation across multiple frames.
31

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Frame 1
Frame 8
Frame16
Baseline
Foresight
Prompt: A beautiful mosque stands toil the center.
Baseline
Foresight
Prompt: flowing gently rustic
Baseline
Foresight
Prompt: Clear take reflecting surrounding mountoios.
Baseline
Foresight
Prompt: Sond dunes mesmerizing under the
Figure 17: Qualitative Comparison for Cog VideoX (Yang al., 2024) with 720p, 2sec video generation across multiple frames.
32


Reviews:
[{"review_id": "2VR044TwPI", "reviewer": "Reviewer_GnU7", "summary": "This paper introduces Foresight, an adaptive layer-reuse technique designed to accelerate text-to-video generation using Diffusion Transformers (DiTs) while preserving output quality. Unlike static caching methods, which apply uniform reuse policies across layers and timesteps and often degrade quality, Foresight dynamically decides whether to recompute or reuse each DiT block's output at runtime. It achieves this by analyzing layer-wise feature changes (via Mean Squared Error) during a warmup phase, setting adaptive reuse thresholds per layer, and selectively reusing activations based on real-time similarity metrics. Evaluated on OpenSora, Latte, and CogVideoX models, Foresight achieves up to 1.63× end-to-end speedup, maintains or improves video quality metrics (PSNR, SSIM, FVD), and reduces memory overhead by 3× compared to prior methods, all without requiring model retraining.", "questions": "- The speedup ratio is not significant enough; step distillation methods for diffusion models (e.g., Consistency Distillation (CD)) can achieve much more notable acceleration. Step distillation is the mainstream approach for practical deployment of diffusion models. If Foresight is to be applied in real-world scenarios, how much improvement does it bring when combined with CD?", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n+ The writing of this paper is clear and easy to understand, providing a thorough analysis of the denoising process in diffusion models and offering motivation behind the proposed method. \n+ The proposed approach is straightforward to implement.\n\nWeaknesses\n- There is no comparison with related works such as TeaCache [1] and TaylorSeer [2] in terms of performance.\n- The technical contribution of the method is somewhat limited—it is relatively heuristic and relies on three hyperparameters.\n- The baseline model chosen is not the current state-of-the-art; it is recommended to test open-source models like Wan-2.1 or HunyuanVideo.\n\n[1] https://arxiv.org/abs/2411.19108\n\n[2] https://arxiv.org/abs/2503.06923", "was_revised": true, "final_justification": "After reading the authors' rebuttal, I choose to maintain my original score, considering the limitations of the work in terms of methodological novelty and significance of the results.", "rebuttal": "We thank the reviewer for their thoughtful feedback and constructive suggestions. Below, we respond to the major concerns.\n\n- **W1 & W3 – Comparison with Related Work and Evaluation on SOTA Models**\n\nAs requested, we now compare **Foresight** against **TeaCache** on open-source SOTA models, **HunyuanVideo** and **Wan-2.1**, using prompts from the Penguin Video Benchmark. The results below show that Foresight **consistently outperforms TeaCache in quality** while providing competitive performance as compared to baseline implementation.\n\n***HunyuanVideo Results***\n| | | | | | |\n|:-|:-:|:-:|:-:|:-:|:-:|\n|**Method**|**PSNR**|**SSIM**|**LPIPS**|**FVD**|**Speedup**|\n|$\\mathrm{TeaCache (0.1)}$|37.31|0.96|0.027|170.16|1.6$\\times$|\n|$\\mathrm{Foresight}~(N_1R_2)$|**41.79**|**0.97**|**0.016**|**47.45**|**1.62$\\times$**|\n\n***Wan-2.1 Results***\n| | | | | | |\n|:-|:-:|:-:|:-:|:-:|:-:|\n|**Method**|**PSNR**|**SSIM**|**LPIPS**|**FVD**|**Speedup**|\n|$\\mathrm{TeaCache (0.1)}$|21.49|0.774|0.147|1222.56|**1.9$\\times$**|\n|$\\mathrm{Foresight}~(N_1R_2)$|**25.12**|**0.863**|**0.081**|**543.4**|1.47$\\times$|\n\n\n- **W2 - Heuristic Design and Hyperparameters**\n\nForesight uses the well-established heuristic that early timesteps drive major generation changes, as adopted in HunyuanVideo [1] and MovieGen [2]. Static reuse methods cannot adapt to runtime variability across models and prompts. Foresight captures early dynamics via a brief warmup phase (see Fig. 2/3), then adapts reuse in real time.\n\nEven though Foresight uses four hyperparameters, it maintains a fixed configuration across all evaluations and does not need tuning. Prior work (e.g., PAB and TeaCache) requires model-specific tuning. For instance, PAB empirically fixes MLP reuse, and TeaCache thresholds vary across model versions.\n\n\n- **Q1 - Comparison to Step Distillation**\n\nWe agree that **step distillation** offers larger speedups. However, it **requires extensive compute of thousands of GPU-hours per model variant** and distillation per configuration [3,4]. **Foresight is an inference-time solution by design** and does not require any of the overheads of step distillation. It is lightweight, model-agnostic, and deployable without any retraining. Its strength lies in improving runtime efficiency at inference, offering a practical alternative to distillation-based approaches.\n\nNevertheless, the insights used by Foresight can be applied on the top of step distillation as well. Given consistency distillation reduces the number of denoising steps to less than 10 (usually 4-8), applying Foresight on top of CD would provide an additional speedup of upto 1.1x.\n\n[1] https://arxiv.org/pdf/2412.03603\n\n[2] https://arxiv.org/pdf/2410.13720\n\n[3] https://arxiv.org/pdf/2412.15689\n\n[4] https://arxiv.org/pdf/2506.03123\n\nWe will add these results and explanations into the final paper. Please let us know if further clarification is needed. We again thank the reviewer for their valuable feedback."}, {"review_id": "GOwl4ViOwg", "reviewer": "Reviewer_ocPP", "summary": "This paper proposes Foresight, a training-free, adaptive caching strategy for accelerating text-to-video diffusion models. Unlike prior methods that statically reuse intermediate features across fixed denoising steps or layers, Foresight adaptively determines whether to reuse or recompute each DiT block's output based on per-layer mean squared error (MSE) thresholds. The method consists of a warmup phase to initialize reuse thresholds, and a reuse phase that dynamically evaluates reuse eligibility via MSE. Foresight is evaluated across multiple video generation backbones (OpenSora, Latte, CogVideoX), achieving up to 1.63× speedup with minimal quality degradation, outperforming static caching methods like PAB and T-GATE.", "questions": "1. Application Beyond Attention Blocks: Does Foresight apply to MLPs in DiT blocks or only attention layers? If not, why exclude them?\n\n2. Hyperparameter Design: Why was the reuse threshold weighted as 10^(W−t)? What is the intuition behind this formulation, and how does it generalize across models or resolutions?\n\n3. Low-Step Regimes or Fast Samplers: How does Foresight perform in low-step (e.g., 4–8 steps) settings or under solvers like Consistency Models or DPM-Solver? Can it retain benefit with limited warmup?\n\n4. Robustness Across Prompts and Scenes: Given the latency variance, could you report the cumulative time for a full benchmark (e.g., 500 videos) and identify cases where Foresight regresses? This would give a more honest view of worst-case behavior.\n\n5. Sensitivity to Resolution: Can the thresholds or reuse policies trained on 240p generalize to 720p or 1080p videos, or do you need to recompute them for each setting?", "limitations": "Yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n1. Training-Free and Plug-and-Play: Foresight can be applied to pretrained models without retraining or architecture modification.\n\n2. Dynamic Reuse Strategy: The use of per-layer MSE thresholds allows Foresight to adaptively select which layers and steps to reuse, improving the speed-quality trade-off.\n\n3. Generalization Across Models: Evaluated on OpenSora, Latte, and CogVideoX with various prompt conditions and resolutions.\n\n4. Comprehensive Ablations: Extensive studies on warmup steps, reuse intervals, and threshold scaling are provided.\n\n## Weaknesses\n1. Unconvincing Visual Evidence for Adaptivity: The claim that “even minor changes in generation conditions… can dramatically alter activation behavior” (Figure 2) is not strongly supported. The middle and right plots show very similar trends, and Figure 3 lacks sharp contrasts to justify the adaptive strategy's necessity.\n\n2. Unclear Hyperparameter Justification: The reuse threshold formula involving 10^(W-t) lacks theoretical backing. The rationale behind choosing the parameter P (e.g., number of tokens for spatial/temporal blocks) is not explained, especially when applying it to models like CogVideoX with full attention in both dimensions.\n\n3. Limited Discussion of MLP Reuse: It is unclear whether the method applies to MLP blocks or is restricted to attention modules. Given that MLPs are part of DiT blocks, their reuse policy should be clarified.\n\n4. Complex Hyperparameter Space: The method introduces many knobs (W, N, R, γ), and their selection seems ad hoc. This complexity makes the method less elegant and potentially harder to adapt across resolutions or prompt domains.\n\n5. Latency Variance and Evaluation Fairness: Table 1 shows a large variance in latency, suggesting instability or outlier prompts where performance degrades. Reporting mean latency for 500 videos may be misleading—total wall-clock time for entire benchmark runs (e.g., VBench) would provide a more realistic comparison.\n\n5. Modest Performance Gains with High Variance: While the average speedup is competitive, some configurations (e.g., OpenSora N1R2 vs. PAB) show marginal gains (1.28× vs. 1.26×). The improvement in quality (e.g., PSNR) often comes with large standard deviations, indicating inconsistent results.", "was_revised": true, "final_justification": "After reading the authors' rebuttal and other reviews, I choose to maintain my recommendation.", "rebuttal": "We thank the reviewer for their thoughtful feedback. Below, we address each point.\n\n- **W1 - Visual Evidence for Adaptivity**\n\nWe agree that the diffusion process follows a general trend. However, we want to clarify that the magnitude of change varies by configuration. For instance, as shown in **Figure 2 (middle)**, increasing resolution from **144p to 720p** reduces MSE between the first two timesteps from **60 to 15**. **Figure 3 (middle)** highlights spikes tied to prompt changes (y-axis is linear; log-scale shows activation dynamics). **Figure 11 (appendix)** further shows activation patterns across changes in resolution, seeds, layers, prompts, and denoising steps.\n\n- **Q1 & W3 - Application to MLPs**\n\nYes, Foresight applies reuse to MLPs, as detailed in **Equation 6** and **Figure 4**, on top of attention modules in both spatial and temporal DiT blocks. We will clarify this further in the camera-ready version.\n\n\n- **W2 & Q2 - Hyperparameter Design**\n\nThe reuse threshold is weighted as $10^{W-t}$ during the last three warmup steps, reflecting the well-established heuristic that early timesteps drive most changes [1,2]. This scaling reduces bias toward volatile early steps. Parameter $𝑃$ is not a hyperparameter, it denotes averaging across spatial or temporal tokens in the respective DiT blocks.\n\n- **Q3 - Low-Step Regimes and Fast Samplers**\n\n**Consistency models** reduce denoising steps (e.g., 4–8), but **require expensive distillation over thousands of GPU-hours per configuration** [3,4]. **Foresight is an inference-time approach by design**. Foresight, by contrast, is lightweight, model-agnostic, and retraining-free. While step distillation offers higher speedups, Foresight is orthogonal to these and can still deliver up to 1.1x gain when applied on top of such models.\n\n- **W4 -Hyperparameter Complexity**\n\nForesight uses four hyperparameters but keeps them fixed across all evaluations. It consistently adapts across models, prompts, and configurations. These parameters allow users to balance performance and quality. Foresight maintains a fixed configuration across all evaluations and does not need tuning. \n\nIn contrast, prior work (e.g., PAB, TeaCache) requires model-specific tuning. For instance, PAB fixes MLP reuse empirically and TeaCache thresholds vary across model versions.\n\n- **Q4 & W5 - Robustness and Latency Variance**\n\nThe latency variance in Table 1 highlights Foresight’s adaptivity. Static reuse methods degrade in quality under complex prompts, while Foresight balances speed and quality based on scene complexity. **Figure 15 (appendix)** shows per-prompt latency across 50 prompts from OpenSora. Even in the worst case, Foresight offers 1.1x speedup over baseline without quality loss.\n\nThe table below shows the **VBench wall clock time for 500 prompts for OpenSora model**.\n\n***OpenSora Cumulative Wall Clock Time***\n| | |\n|:-|:-:|\n|**Method**|**Cumulative Wall Clock Time in Seconds (lower is better)**|\n|$\\mathrm{Baseline}$|7092.42|\n|$\\mathrm{PAB}$|5509.06|\n|$\\mathrm{Foresight}~(N_1R_2)$|5455.77|\n|$\\mathrm{Foresight}~(N_2R_3)$|**4842.95**|\n\n- **Q5 - Resolution Sensitivity**\n\nForesight’s thresholds and reuse policies are *determined dynamically at inference time*. They are *not* trained or tuned for any specific model or configuration, making Foresight plug-and-play across diverse setups.\n\n- **W6 - Modest Gains with High Variance**\n\nRegarding performance gains, **Foresight is intentionally designed to balance speed and quality**. We argue that directly comparing Foresight's speedup to PAB without accounting for PAB’s quality degradation paints an incomplete and misleading picture.\n\nTo ensure a fair comparison, the table below reports **speedups for both methods** using a Foresight **configuration that matches PAB’s output quality** across models.\n\n***PAB versus Foresight: Performance Improvement (higher is better)***\n| | | | |\n|:-|:-:|:-:|:-:|\n|**Model**|**PSNR**|**PAB** |**Foresight**|\n|OpenSora|25.67|1.26$\\times$|**1.68$\\times$**|\n|Latte|21.22|1.29$\\times$|**1.58$\\times$**|\n|CogVideoX|29.04|1.37$\\times$|**1.95$\\times$** |\n\n[1] https://arxiv.org/pdf/2412.03603\n\n[2] https://arxiv.org/pdf/2410.13720\n\n[3] https://arxiv.org/pdf/2412.15689\n\n[4] https://arxiv.org/pdf/2506.03123\n\nWe will add all these results and explanations into the final paper. Please let us know if further clarification is needed. We again thank the reviewer for their valuable feedback."}, {"review_id": "fvLMsFgMjQ", "reviewer": "Reviewer_hiCs", "summary": "Foresight is an adaptive layer reuse framework for accelerating text-to-video diffusion transformer inference. Unlike static caching methods that reuse all layers uniformly across timesteps, the authors propose computation per-layer, per-prompt reuse thresholds based on feature MSE during a warmup phase and then dynamically decides whether to reuse or recompute each layer. The method is training-free and shows up to 1.63× speedup while maintaining video quality.", "questions": "Questions for the Authors\n\n- How does Foresight’s warmup phase impact overall latency in shorter or real-time video generation?\n\n- Have authors tested or considered learned reuse threshold schedules to improve robustness?\n\n- What is the absolute GPU memory footprint at typical resolutions (512×512, 720p)?\n\n- Can you show results on other diffusion tasks to support the claimed generality?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- The problem is highly relevant as inference latency is a bottleneck in video generation tasks using DiT\n\n- Novel adaptive reuse mechanism with MSE-based thresholds is proposed to address prompt and layer variability.\n\n- Fully training-free design is appealing \n\n- Experiments are thorough on standard video generation benchmarks with multiple quality metrics.\n\nWeakness\n\n- While adaptive reuse is an interesting idea, the improvement over PAB in speedup (1.26× to ~1.28x/1.44×) is relatively modest. The key claim of \"up to 1.63×\" is only for one model. In many cases, the practical benefit seems marginal.\n\n- The method needs a warmup phase (15% of steps computed fully). This can reduce the effective acceleration for shorter videos or small denoising schedules. The impact of this overhead on real-time or low-latency settings is underexplored.\n\n- The paper claims Foresight is broadly applicable, but only tests text-to-video. There is no demonstration on image-to-video, or other diffusion tasks—even though those are prominent applications.\n\n- The MSE-based reuse threshold is entirely heuristic, computed on-the-fly without any learned component. This can lead to suboptimal reuse schedules in unseen conditions. It’s not clear how robust the method is to highly dynamic prompts, long videos (>10s), or changing denoising schedules.\n\n- Foresight introduces per-layer adaptive scheduling logic, multiple hyperparameters (warmup length, reuse steps, compute interval, γ), and runtime MSE calculations. It’s not clear if the benefit justifies this added engineering and inference complexity, particularly given the modest speedups over strong baselines like PAB.", "was_revised": true, "final_justification": "Authors have addressed my concerns satisfactorily and clarified my doubts.", "rebuttal": "We thank the reviewer for their thoughtful feedback. Below, we address each point.\n\n- **Q1 & W2 - Acceleration for Short Denoising Schedules**\n\nMost open-sourced video generation models use 30–50 denoising steps to produce high-quality outputs. This is the range we evaluate. **Consistency distillation can reduce this to 4–8 steps, but it demands costly retraining—thousands of GPU-hours per configuration** [1,2]. In contrast, **Foresight is an inference-time approach by design**.  Foresight is lightweight, model-agnostic, and training-free. \n\nNevertheless, Foresight is orthogonal to distillation and it can also be applied on top of distilled models for up to 1.1x further speedup.\n\n- **W1 & W5 - Hyperparameters and Modest Gains over PAB**\n\nForesight uses four fixed hyperparameters across all experiments and adapts well across models, prompts, and configurations. These parameters let users trade off quality and performance. Foresight maintains a fixed configuration across all evaluations and does not need tuning. In contrast, Prior work (e.g., PAB, TeaCache) requires model-specific tuning. For instance, PAB fixes MLP reuse empirically, and TeaCache thresholds vary by model.\n\nRegarding performance gains, **Foresight is intentionally designed to balance speed and quality**. We argue that directly comparing Foresight's speedup to PAB without accounting for PAB’s quality degradation paints an incomplete and misleading picture.\n\nTo ensure a fair comparison, the table below reports speedups for both methods using a Foresight configuration that matches PAB’s output quality across models.\n\n***PAB versus Foresight Performance Improvement (higher is better) Measured at Equal Video Quality***\n| | | | |\n|:-|:-:|:-:|:-:|\n|**Model**|**PSNR**|**$\\mathrm{PAB}$** Speedup|**$\\mathrm{Foresight}$** Speedup|\n|OpenSora|25.67|1.26$\\times$|**1.68$\\times$**|\n|Latte|21.22|1.29$\\times$|**1.58$\\times$**|\n|CogVideoX|29.04|1.37$\\times$|**1.95$\\times$** |\n\n- **Q2 - Learned Thresholds**\n\nForesight is designed to work at inference time without retraining. Learning reuse thresholds would introduce high computational cost and limit generality across architectures. Since models like OpenSora, Latte, and CogVideoX differ significantly, learned thresholds would reduce portability. Supporting diverse generation settings would also be harder.\n\n- **Q3 - GPU Memory Footprint**\n\nMemory usage varies by model. For instance, HunyuanVideo requires 60GB of GPU memory (FP16) to generate a 5-second 720p video.\n\n- **Q4 & W3 - Applicability to Other Diffusion Tasks**\n\n**We applied Foresight to the FLUX text-to-image model, which lacks a temporal dimension**. Foresight reuses spatial and cross attention and MLP layers. It yields the following latency gains:\n\n***FLUX - Text-to-Image Generation***\n| | |\n|:-|:-:|\n|**Method**|**Latency (sec)**|\n|$\\mathrm{Baseline}$|14.02|\n|$\\mathrm{Foresight}$|**7.1**|\n\nForesight **reduces Wall-Clock time (in seconds) by nearly 2x** while maintaining the same image quality as baseline.\n\n- **W3 - Robustness Across Settings**\n\nTo test robustness, we evaluated Foresight on diverse prompt sets (UCF-101, EvalCrafter), varying denoising steps, and longer generations (up to 16 seconds). Results are reported in **Table 8 (Appendix)**.\n\n[1] https://arxiv.org/pdf/2412.03603\n\n[2] https://arxiv.org/pdf/2410.13720\n\nWe will add all these results and explanations into the final paper. Please let us know if further clarification is needed. We again thank the reviewer for their valuable feedback."}, {"review_id": "9zdFofGB8K", "reviewer": "Reviewer_nygB", "summary": "This paper introduces Foresight Adaptive Layer (FAL), a plug-and-play module designed to improve temporal modeling in Diffusion Transformers for text-to-video (T2V) generation. Motivated by the tendency of current DiT models to process video frames independently (spatial-first), FAL injects a temporal adaptation mechanism that looks into future timesteps.", "questions": "See weaknesses.", "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:\n\n(1). Well-motivated problem: The lack of effective temporal modeling in existing diffusion transformers is an open issue. Addressing this gap is timely and valuable.\n\n(2). Foresight formulation is elegant and lightweight: The design of temporal offset attention and residual modulation is simple yet effective.\n\n(3). Empirical performance is strong: The method shows consistent gains on multiple backbones and video generation benchmarks.\n\n(4). Good ablation and visualization: Visualizations (Figure 5) help illustrate how FAL enhances coherence and temporal structure.\n\n\nWeaknesses:\n\n(1). While intuitively motivated, the idea of “future conditioning” is not justified theoretically—e.g., what guarantees stability or convergence with foresight patches?\n\n(2). No formal analysis is presented on why foresight improves video generation in a diffusion setting, beyond empirical results.", "was_revised": true, "final_justification": "Authors have addressed my concerns and clarified my doubts. I will raise my score to 5.", "rebuttal": "We thank the reviewer for their thoughtful feedback. Below, we address each point.\n\n- **Q1 - Convergence with Foresight**\n\nForesight adaptively reuses activations. When a denoising step involves significant activation changes, it recomputes the result rather than reusing the last denoising step data. In the worst case, no reuse occurs, and Foresight defaults to baseline execution. Since it does not alter model structure or scheduling, its convergence remains identical to the baseline, with performance gains only when safe reuse is possible.\n\n- **Q2 - Formal Analysis**\n\nForesight preserves the original model and denoising schedule. It leverages the observation that some layers exhibit minimal change across steps in spatial and temporal dimensions. Its contribution lies in identifying these stable layers and selectively skipping their recomputation. This yields speedup without compromising video quality. \n\nWe will add this analysis using the denoising diffusion probabilistic model (DDPM) sampling phase [1] in the camera-ready version.\n\n[1] https://hal.science/hal-04642649/file/Diffusion_tutorial.pdf\n\nPlease let us know if further clarification is needed. We again thank the reviewer for their valuable feedback."}]

Author rebuttal:

Meta-review: This paper introduces Foresight, an adaptive, training-free mechanism for accelerating text-to-video diffusion transformer models by selectively reusing layer outputs based on MSE-calibrated similarity thresholds during inference. The reviewers agree on the (1) relevance of the problem of diffusion model latency, (2) plug-and-play applicability without retraining, (3) adaptive reuse strategy guided by dynamic similarity, and (4) solid empirical evaluation across several backbones with detailed ablations. However, they note (1) modest speedup gains relative to strong baselines like PAB, (2) reliance on heuristic thresholds and multiple hyperparameters that raise concerns about robustness and generalization, (3) lack of clarity around effectiveness in low-latency or distillation-accelerated regimes, and (4) added complexity that may outweigh the practical benefit in real-time settings. The authors provided thorough responses, including new experiments on SOTA models, fair comparisons with TeaCache and PAB at matched quality, results on non-video diffusion tasks, hyperparameter sensitivity analysis, and a formal convergence argument. These efforts convinced two reviewers to raise or affirm strong scores, while the others maintained borderline positions due to remaining skepticism around practical impact and methodological elegance. The AC leans to accept this submission.
Decision: Accept (poster)