Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos. However, they can only generate short video clips, normally around 10 seconds or 240 frames, due to computation limitations during training. In this work, we show that existing models can be naturally extended to autoregressive video diffusion models without changing the architectures. Our key idea is to assign the latent frames with progressively increasing noise levels rather than a single noise level, which allows for fine-grained condition among the latents and large overlaps between the attention windows. Such progressive video denoising allows our models to autoregressively generate video frames without quality degradation or abrupt scene changes. We present state-of-the-art results on long video generation at 1 minute (1440 frames at 24 FPS). Videos from this paper are available at https://desaixie.github.io/pa-vdm/.

本研究解决了现有视频扩散模型只能生成短视频片段的局限性，通过引入逐渐增加的噪声水平，有效地扩展了模型的能力。此创新方法实现了无质量衰退的自回归视频帧生成，取得了在长达1分钟（1440帧，24 FPS）视频生成上的最佳效果。

渐进式自回归视频扩散模型