The emergence of video diffusion models represents a significant advancement in the field of text-to-video generation. These models efficiently generate videos by leveraging recent improvements in diffusion-based techniques, showcasing the potential of LLMs in producing more coherent and high-quality visual content.
In our experiments, we compared our method against several state-of-the-art approaches, demonstrating how our innovative training strategy enhances video quality and expands the range of generated content. The pretraining task design specifically enables the model to refine its capabilities in generating complex video sequences.
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