We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content. Extending video generation methods to maintain scenario diversity over long durations presents significant challenges. To address this, we propose a Segmented Cross-Attention (SCA) strategy, which splits hidden states into segments along the temporal dimension, allowing each segment to cross-attend to a corresponding sub-caption. SCA requires no additional parameters, enabling seamless incorporation into current DiT-based architectures. To facilitate high-quality long video generation, we build the LongTake-HD dataset, consisting of 261k content-rich videos with scenario coherence, annotated with an overall video caption and five progressive sub-captions. Experiments show that our Presto achieves 78.5% on the VBench Semantic Score and 100% on the Dynamic Degree, outperforming existing state-of-the-art video generation methods. This demonstrates that our proposed Presto significantly enhances content richness, maintains long-range coherence, and captures intricate textual details. More details are displayed on our project page: https://presto-video.github.io/.

翻译：本文介绍Presto，一种新颖的视频扩散模型，旨在生成具有长程连贯性和丰富内容的15秒视频。将视频生成方法扩展到长时间保持场景多样性面临重大挑战。为此，我们提出一种分段交叉注意力策略，该策略将隐藏状态沿时间维度分割为多个片段，使每个片段能够交叉关注对应的子描述。SCA无需额外参数，可无缝集成到当前基于DiT的架构中。为促进高质量长视频生成，我们构建了LongTake-HD数据集，包含26.1万个内容丰富且具有场景连贯性的视频，每个视频均标注有整体视频描述和五个递进子描述。实验表明，我们的Presto在VBench语义得分上达到78.5%，动态程度得分达到100%，优于现有最先进的视频生成方法。这证明我们提出的Presto显著增强了内容丰富性，保持了长程连贯性，并捕捉了复杂的文本细节。更多细节展示于我们的项目页面：https://presto-video.github.io/。