Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video generation and editing, while innovative, are often confined to extremely short videos (typically less than 24 frames) and are limited to a single text condition. These constraints significantly limit their applications given that real-world videos usually consist of multiple segments, each bearing different semantic information. To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency. We have implemented three mainstream text-driven video generation and editing methodologies and extended them to accommodate longer videos imbued with a variety of semantic segments with our proposed paradigm. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models, offering new possibilities for future research and applications. The code is available at https://github.com/G-U-N/Gen-L-Video.

翻译：利用大规模图像-文本数据集以及扩散模型的进展，文本驱动的生成模型在图像生成与编辑领域取得了显著突破。本研究探索将文本驱动能力扩展到多文本条件的长视频生成与编辑中的潜力。当前视频生成与编辑方法虽具创新性，但通常局限于极短视频（通常少于24帧），且仅能处理单一文本条件。这些限制大大降低了其应用价值，因为现实视频通常由多个片段组成，每个片段承载不同的语义信息。为解决这一挑战，我们提出一种称为Gen-L-Video的新范式，它能够扩展现有短视频扩散模型，在不引入额外训练的情况下生成和编辑包含数百帧、具有多样化语义片段的视频，同时保持内容一致性。我们实现了三种主流文本驱动视频生成与编辑方法，并通过所提出的范式将其扩展到能够容纳具有多种语义片段的更长视频。实验结果表明，我们的方法显著拓宽了视频扩散模型的生成与编辑能力，为未来研究与应用提供了新的可能性。代码已开源在https://github.com/G-U-N/Gen-L-Video。