Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation

We thank the reviewer for their thoughtful feedback. Below, we address each point.

• Q1 - Convergence with Foresight

Foresight 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.

• Q2 - Formal Analysis

Foresight 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.

We will add this analysis using the denoising diffusion probabilistic model (DDPM) sampling phase [1] in the camera-ready version.

[1] https://hal.science/hal-04642649/file/Diffusion_tutorial.pdf

Please let us know if further clarification is needed. We again thank the reviewer for their valuable feedback.

Thank you for sharing the remaining concerns.

1. Hyperparameters Sensitivity

We use an MSE-based threshold for adaptive reuse, computed during the warmup phase & customized per model & video configuration. This ensures a quantitative reuse criterion, avoiding unstable, heuristic thresholds. Foresight requires no tuning & uses a fixed configuration across all experiments. We present sensitivity analysis in Figure 6 and Tables 2, 3, with detailed hyperparameter breakdowns below.

Warmup (W)

Reuse (N/R)

Threshold ($\gamma$)

2. Formal Analysis: Convergence

Let

• $\mathbf{x}_0$ be the generated (denoised) video,
• $\mathbf{x}_t$ be the intermediate latent at denoising step $t$ ($T \leq t \leq 1$).

For layer $l$ of the spatio-temporal diffusion transformer, denote its output by $f_{\theta}^{l}(\mathbf{x_t})$. During timesteps $T_{\text{reuse}} \subseteq \{1, \dots, T\}$, adaptive reuse approximates this output using the cached value from the previous step:

$$\tilde{f}\_{\theta}^{l}(\mathbf{x}\_t) = f\_{\theta}^{l}(\mathbf{x}\_{t-1}) \quad \text{if } \text{MSE}(\mathbf{x}\_t, \mathbf{x}\_{t-1}) < \lambda\_l$$ $$\tilde{f}\_{\theta}^{l}(\mathbf{x}\_t) = f\_{\theta}^{l}(\mathbf{x}\_t) \quad \text{otherwise}$$

where $\lambda_l$ is a layer-specific threshold learned during warm-up. Since reuse preserves weights $\theta$ & transition kernels $p_{\theta}(\mathbf{x_t} | \mathbf{x_{t-1}})$, the original diffusion process guarantees hold.

Even when recomputed (worst case), the baseline forward pass is recovered. For any reused layer $l$ at step $t$, the error is bounded:

$$\| f\_{\theta}^{l}(\mathbf{x}\_t) - \tilde{f}\_{\theta}^{l}(\mathbf{x}\_t) \|\_2 \leq \epsilon\_l, \quad \epsilon\_l \leq \sqrt{\gamma \lambda\_l}.$$

Activations vary slowly in space/time when reused, making perturbations negligible.

The reuse error at layer $l$ and step $t$ is:

$$\varepsilon\_t^l = \| f\_{\theta}^{l}(\mathbf{x}\_t) - \tilde{f}\_{\theta}^{l}(\mathbf{x}\_t) \|\_2 \leq \underbrace{\sqrt{\gamma \lambda\_x^l}}\_{\varepsilon\_{\text{max}}^l}.$$

Thus, $\varepsilon_t^l$ is uniformly bounded by a constant controllable via $\gamma$.

Let $\mathbf{x}_t^*$ be the baseline latent and $\hat{\mathbf{x}}_t$ the latent under adaptive reuse. After one reverse step:

$$\| \hat{\mathbf{x}}\_{t-1} - \mathbf{x}\_{t-1}^* \| \leq \sqrt{1 - \beta\_t} \| \hat{\mathbf{x}}\_t - \mathbf{x}\_t^* \| + \sum\_{l=1}^L L\_l \varepsilon\_t^l.$$

• Term 1 contracts by $\sqrt{1 - \beta_t} < 1$,
• Term 2 is bounded by $\varepsilon_{\text{tot}} = \sum_l L_l \varepsilon_{\text{max}}^l$.

Assume:

1. Each block is $L_l$-Lipschitz with $L_l < 1$,
2. $\varepsilon_t^l \leq \varepsilon_{\text{max}}^l$ for all $(t, l)$,

Unroll the recursion for $k$ steps:

$| \hat{\mathbf{x}}\_{t-k} - \mathbf{x}\_{t-k}^* | \leq \underbrace{\left( \prod\_{s=t-k+1}^t \sqrt{1 - \beta\_s} \right) | \hat{\mathbf{x}}\_t - \mathbf{x}\_t^* |}\_{\text{(I)}} + \underbrace{\varepsilon\_{\text{tot}} \sum\_{j=0}^{k-1} \prod\_{s=t-j+1}^t \sqrt{1 - \beta\_s}}\_{\text{(II)}}.$

• (I): As $k \to t$, $\prod_s \sqrt{1 - \beta_s} \to 0$ since $\sum_s \beta_s \to \infty$ implies $\prod_s (1 - \beta_s) \to 0$.
• (II): Geometric series bounded by $\varepsilon_{\text{tot}} / (1 - \rho)$ where $\rho = \max_s \sqrt{1 - \beta_s} < 1$.

As $t \to T$ and $T \to \infty$, (I) vanishes and (II) remains finite. Tightening $\gamma$ reduces $\varepsilon_{\text{tot}}$, yielding arbitrary closeness to the baseline.

Adaptive reuse introduces bounded, vanishing perturbations into a contractive reverse Markov chain. Thus, Foresight maintains theoretical fidelity to the original diffusion model.

We thank the reviewer for their thoughtful feedback. Below, we address each point.

• Q1 & W2 - Acceleration for Short Denoising Schedules

Most 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.

Nevertheless, Foresight is orthogonal to distillation and it can also be applied on top of distilled models for up to 1.1x further speedup.

• W1 & W5 - Hyperparameters and Modest Gains over PAB

Foresight 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.

Regarding 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.

To ensure a fair comparison, the table below reports speedups for both methods using a Foresight configuration that matches PAB’s output quality across models.

PAB versus Foresight Performance Improvement (higher is better) Measured at Equal Video Quality

• Q2 - Learned Thresholds

Foresight 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.

• Q3 - GPU Memory Footprint

Memory usage varies by model. For instance, HunyuanVideo requires 60GB of GPU memory (FP16) to generate a 5-second 720p video.

• Q4 & W3 - Applicability to Other Diffusion Tasks

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:

FLUX - Text-to-Image Generation

Foresight reduces Wall-Clock time (in seconds) by nearly 2x while maintaining the same image quality as baseline.

• W3 - Robustness Across Settings

To 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).

[1] https://arxiv.org/pdf/2412.03603

[2] https://arxiv.org/pdf/2410.13720

We 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.

We thank the reviewer for their thoughtful feedback. Below, we address each point.

• W1 - Visual Evidence for Adaptivity

We 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.

• Q1 & W3 - Application to MLPs

Yes, 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.

• W2 & Q2 - Hyperparameter Design

The 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.

• Q3 - Low-Step Regimes and Fast Samplers

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.

• W4 -Hyperparameter Complexity

Foresight 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.

In 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.

• Q4 & W5 - Robustness and Latency Variance

The 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.

The table below shows the VBench wall clock time for 500 prompts for OpenSora model.

OpenSora Cumulative Wall Clock Time

• Q5 - Resolution Sensitivity

Foresight’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.

• W6 - Modest Gains with High Variance

Regarding 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.

To ensure a fair comparison, the table below reports speedups for both methods using a Foresight configuration that matches PAB’s output quality across models.

PAB versus Foresight: Performance Improvement (higher is better)

[1] https://arxiv.org/pdf/2412.03603

[2] https://arxiv.org/pdf/2410.13720

[3] https://arxiv.org/pdf/2412.15689

[4] https://arxiv.org/pdf/2506.03123

We 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.

We thank the reviewer for their thoughtful feedback and constructive suggestions. Below, we respond to the major concerns.

• W1 & W3 – Comparison with Related Work and Evaluation on SOTA Models

As 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.

HunyuanVideo Results

Wan-2.1 Results

• W2 - Heuristic Design and Hyperparameters

Foresight 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.

Even 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.

• Q1 - Comparison to Step Distillation

We 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.

Nevertheless, 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.

[1] https://arxiv.org/pdf/2412.03603

[2] https://arxiv.org/pdf/2410.13720

[3] https://arxiv.org/pdf/2412.15689

[4] https://arxiv.org/pdf/2506.03123

We 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.