Title: Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Text-to-Video Generation

Diffusion Transformers

Efficient Serving
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:

(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.

(2). Foresight formulation is elegant and lightweight: The design of temporal offset attention and residual modulation is simple yet effective.

(3). Empirical performance is strong: The method shows consistent gains on multiple backbones and video generation benchmarks.

(4). Good ablation and visualization: Visualizations (Figure 5) help illustrate how FAL enhances coherence and temporal structure.


Weaknesses:

(1). While intuitively motivated, the idea of “future conditioning” is not justified theoretically—e.g., what guarantees stability or convergence with foresight patches?

(2). No formal analysis is presented on why foresight improves video generation in a diffusion setting, beyond empirical results.

QUESTIONS:
See weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nForesight 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nQuestions 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}