Foresight Diffusion: Improving Sampling Consistency in Predictive Diffusion Models
Yu Zhang, Xingzhuo Guo, Haoran Xu, Jialong Wu, Mingsheng Long
TL;DR
Foresight Diffusion introduces a novel framework that enhances sampling consistency in predictive diffusion models by decoupling condition understanding from denoising, leading to improved accuracy and reliability in complex forecasting tasks.
Contribution
The paper proposes Foresight Diffusion, a new approach that separates condition processing from denoising, addressing limitations in existing predictive diffusion models.
Findings
Improves sampling consistency in predictive diffusion models.
Enhances predictive accuracy in robot video prediction.
Outperforms strong baselines in scientific spatiotemporal forecasting.
Abstract
Diffusion and flow-based models have enabled significant progress in generation tasks across various modalities and have recently found applications in predictive learning. However, unlike typical generation tasks that encourage sample diversity, predictive learning entails different sources of stochasticity and requires sampling consistency aligned with the ground-truth trajectory, which is a limitation we empirically observe in diffusion models. We argue that a key bottleneck in learning sampling-consistent predictive diffusion models lies in suboptimal predictive ability, which we attribute to the entanglement of condition understanding and target denoising within shared architectures and co-training schemes. To address this, we propose Foresight Diffusion (ForeDiff), a framework for predictive diffusion models that improves sampling consistency by decoupling condition understanding…
Peer Reviews
Decision·ICLR 2026 Poster
This is a clear and well-motivated paper. It identifies an important issue with applying diffusion models to predictive tasks and proposes an effective solution. The experimental results on various tasks and multiple ablations studies provide evidence for the authors' claims. This work can potentially be applied to various fields such as forecasting, robotics, and scientific ML.
1. The proposed two-stage training scheme, while effective, introduces additional complexity to the training pipeline compared to a single-stage, end-to-end model. It requires a separate pretraining phase for the predictive branch, which may add to the overall engineering effort, training time, and need for hyperparameter tuning. A discussion of this trade-off (implementation complexity vs. consistency gain) would be beneficial. 2. Using a frozen, deterministic predictor could also be a potenti
- The paper presents a novel architectural approach by explicitly separating condition processing from denoising, which is a creative departure from standard conditional diffusion models. - The focus on sampling consistency as a distinct requirement for predictive tasks versus generative tasks is an important problem formulation. - Clear mathematical formulation and proof of the key lemma connecting diffusion and deterministic models - Comprehensive experimental evaluation across three diverse d
- As acknowledged by the authors, experiments are limited to moderate-scale settings (64×64 resolution, relatively small models). - Only DiT-based architectures are evaluated; generalization to U-Net or other diffusion backbones is assumed but not demonstrated - The connection between predictive ability and sampling consistency could be more rigorously established - The improvement margins, while consistent, are sometimes modest - The two-stage training could be seen as unfair comparison since
The method is simple and relatively easy to reproduce: the architectural split and the two-stage schedule are clearly described, and the paper includes ablations on the prediction head and the number of ViT blocks in the predictive stream. Some datasets show moderate improvements in PSNR/LPIPS and reduced reported variability, and removing the prediction head appears beneficial across tasks per the appendix tables.
(1) Limited novelty and weak theory. The main idea—strengthening a condition encoder via standalone pretraining and then freezing its features to condition the denoiser—tracks common practice in conditional diffusion and teacher-feature guidance. Theoretical support is thin: the central formal argument reduces the $t=1$ case to a deterministic model by zeroing the first-layer weights, which does not yield general consistency or error bounds for multi-step diffusion. (2) Consistency is proxied n
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Taxonomy
TopicsNeural Networks and Applications · Gaussian Processes and Bayesian Inference · Time Series Analysis and Forecasting
MethodsDiffusion