  Text-to-video is a rapidly growing research area that aims to generate a
semantic, identical, and temporal coherence sequence of frames that accurately
align with the input text prompt. This study focuses on zero-shot text-to-video
generation considering the data- and cost-efficient. To generate a
semantic-coherent video, exhibiting a rich portrayal of temporal semantics such
as 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, while
pre-trained latent diffusion models (LDMs) as the animator to generate the high
fidelity frames. Furthermore, to ensure temporal and identical coherence while
maintaining semantic coherence, we propose a series of annotative modifications
to adapting LDMs in the reverse process, including joint noise sampling,
step-aware attention shift, and dual-path interpolation. Without any video data
and training requirements, Free-Bloom generates vivid and high-quality videos,
awe-inspiring in generating complex scenes with semantic meaningful frame
sequences. In addition, Free-Bloom is naturally compatible with LDMs-based
extensions.
