Foresight Boosts Text-to-Video Speed with Adaptive Layer Reuse (No Retraining)

Text-to-video generation has seen rapid progress thanks to large-scale diffusion models. However, maintaining generation quality while significantly reducing inference time remains a persistent challenge. The paper “Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation” introduces a clever solution: reuse internal model layers adaptively during inference—without retraining. This strategy allows for faster generation (1.63× improvement) while keeping the visual fidelity intact. In this post, we’ll break down the core idea behind Foresight, how it achieves speed without sacrificing quality, and why this could reshape efficient video generation pipelines in research and industry alike.

ethan.an

Oct 16, 2025

Foresight Boosts Text-to-Video Speed with Adaptive Layer Reuse (No Retraining)

Contents

Core idea Where it shines Caveats

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1 minute read

What it is: Foresight is an adaptive layer-reuse scheduler for Diffusion Transformer (DiT) video generators. It reuses block outputs across denoising steps, no fine-tuning or retraining required to cut redundant compute.
What it is: Foresight is an adaptive layer-reuse scheduler for Diffusion Transformer (DiT) video generators. It reuses block outputs across denoising steps, no fine-tuning or retraining required to cut redundant compute.
Why it matters: On OpenSora, Latte, and CogVideoX, it delivers up to 1.63× end-to-end speedup while preserving quality (single A100 GPU tests reported).
Paper(NeurIPS 2025): https://arxiv.org/abs/2506.00329
Code: https://github.com/STAR-Laboratory/foresight?utm_source=chatgpt.com

Core idea

Problem: Recomputing every layer at every denoising step multiplies cost (steps × layers).
Idea: Track each DiT block across steps and selectively reuse its output when it’s “safe,” otherwise recompute. The decision adapts to resolution and timestep schedule, so you avoid one-size-fits-all caching.
Performance: Up to 1.63× speedup end-to-end inference acceleration vs. static reuse baselines, with quality maintained.

Where it shines

Batch clip generation (marketing previews, A/B prompt tests) where cost per clip matters.
Any production path that already standardizes on DiT-style T2V.

Caveats

Gains are model/schedule-dependent; test across your resolutions/timesteps to find the sweet spot.
It’s an inference-time method, if you later change architectures/samplers, re-profile.