视频生成提速1.5倍：多数像素根本不用精修
现在的AI视频生成，每一帧的每个像素都要过一遍「去噪」工序，哪怕它早就已经清晰了。研究者发现，很多像素在第一步就已经接近最终效果，只有少数需要一路精修到最后。他们据此做了个「投机加速」:让模型先快速出个草稿，再判断哪些像素可以提前定稿、直接冻结，不再参与后续计算，同时保留它的缓存供其他像素参考。在长视频和世界生成任务上，质量不变，速度提升1.5倍以上。这不是你明天能下载的插件，但它指向一个趋势：AI生成正在从「无脑全量计算」转向「按需计算」，就像人画画不会把每个角落都涂十遍。
📄 原文摘要(英文)
Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much refinement it needs. We find this uniform computation is often unnecessary: many tokens already closely match their eventual full-step outputs in the initial steps, while others require refinement until the end. We introduce Speculative Forcing, a framework that treats intermediate clean estimates as drafts and predicts when individual tokens can be safely accepted early. Once accepted, a token is frozen as its final output and undergoes no further denoising, while its cached keys and values remain available as context for the remaining tokens. We explore two alternative verifier designs: a training-free one based on similarity to clean content and stability across steps, and a learned one that reads the frozen generator's intermediate features to estimate each token's remaining gap to its full-step output. Across short-video, long-video, and world generation, Speculative Forcing preserves the quality of full-step sampling while substantially reducing computation. On LongLive, it matches the four-step generator with ~1.7 denoising steps per token, running over 1.5x faster at 41.7 and 37.8 fps. On Lingbot-World 2.0, where camera motion continually changes the visible content, it maintains quality with ~2.5 denoising steps per token and runs 1.35x faster at 24.5 fps.