We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training convergence. To ensure training data quality, we establish a comprehensive data curation pipeline and manually annotate and train an MLLM-based video quality model to filter for the highest-quality samples. Furthermore, we provide detailed training and inference recipes to facilitate the generation of high-quality videos. Building on these contributions, Waver excels at capturing complex motion, achieving superior motion amplitude and temporal consistency in video synthesis. Notably, it ranks among the Top 3 on both the T2V and I2V leaderboards at Artificial Analysis (data as of 2025-07-30 10:00 GMT+8), consistently outperforming existing open-source models and matching or surpassing state-of-the-art commercial solutions. We hope this technical report will help the community more efficiently train high-quality video generation models and accelerate progress in video generation technologies. Official page: https://github.com/FoundationVision/Waver.

翻译：本文提出Waver，一种用于统一图像与视频生成的高性能基础模型。Waver能够直接生成时长为5至10秒、原生分辨率为720p的视频，并随后将其超分辨率提升至1080p。该模型在单一集成框架内同时支持文本到视频（T2V）、图像到视频（I2V）及文本到图像（T2I）生成。我们引入混合流式DiT架构以增强模态对齐并加速训练收敛。为确保训练数据质量，我们建立了完整的数据处理流程，并手动标注并训练了基于MLLM的视频质量模型以筛选最高质量样本。此外，我们提供了详细的训练与推断方案以促进高质量视频生成。基于这些贡献，Waver在捕捉复杂运动方面表现卓越，在视频合成中实现了优异的运动幅度与时间一致性。值得注意的是，该模型在Artificial Analysis平台的T2V与I2V排行榜（数据截至2025年7月30日格林尼治标准时间+8上午10时）均位列前三，持续超越现有开源模型，并达到或超越了最先进的商业解决方案。我们希望本技术报告能帮助学界更高效地训练高质量视频生成模型，并加速视频生成技术的发展。官方页面：https://github.com/FoundationVision/Waver。