ﬁne-tune the model to improve temporal consistency across
video frames.

While Large Language Models (LLMs) are commonly
used as foundational models across various modalities
including language [3, 8], code [37, 42], audio [50],
speech [1], and robotics [19, 84], the diffusion model re-
mains the predominant approach for video generation. Al-
though early research has demonstrated the effectiveness
of LLMs in text-to-image generation (e.g., DALL-E [47],
Parti [73] and [18]) and text-to-video (e.g., [35]), language
models have not reached a level of quality on par with video
diffusion models in tasks like text-to-video generation as
shown in previous studies [41, 64]. In contrast to training
exclusively for text-to-video tasks, the generative model of
LLMs in the language domain emphasizes a large pretrain-
ing stage to learn a foundation [6, 58] by examining pre-
VideoPoet