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Text-to-video is a rapidly growing research area that aims to generate asemantic, identical, and temporal coherence sequence of frames that accuratelyalign with the input text prompt. This study focuses on zero-shot text-to-videogeneration considering the data- and cost-efficient. To generate asemantic-coherent video, exhibiting a rich portrayal of temporal semantics suchas the whole process of flower blooming rather than a set of "moving images",we propose a novel Free-Bloom pipeline that harnesses large language models(LLMs) as the director to generate a semantic-coherence prompt sequence, whilepre-trained latent diffusion models (LDMs) as the animator to generate the highfidelity frames. Furthermore, to ensure temporal and identical coherence whilemaintaining semantic coherence, we propose a series of annotative modificationsto adapting LDMs in the reverse process, including joint noise sampling,step-aware attention shift, and dual-path interpolation. Without any video dataand training requirements, Free-Bloom generates vivid and high-quality videos,awe-inspiring in generating complex scenes with semantic meaningful framesequences. In addition, Free-Bloom is naturally compatible with LDMs-basedextensions.

Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator