VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video
  Diffusion Models
Wenhao Wang Yi Yang
The arrival of Sora marks a new era for text-to-video diffusion models,
bringing significant advancements in video generation and potential
applications. However, Sora, as well as other text-to-video diffusion models,
highly relies on the prompts, and there is no publicly available dataset
featuring a study of text-to-video prompts. In this paper, we introduce
VidProM, the first large-scale dataset comprising 1.67 million unique
text-to-video prompts from real users. Additionally, the dataset includes 6.69
million videos generated by four state-of-the-art diffusion models and some
related data. We initially demonstrate the curation of this large-scale
dataset, which is a time-consuming and costly process. Subsequently, we show
how the proposed VidProM differs from DiffusionDB, a large-scale prompt-gallery
dataset for image generation. Based on the analysis of these prompts, we
identify the necessity for a new prompt dataset specifically designed for
text-to-video generation and gain insights into the preferences of real users
when creating videos. Our large-scale and diverse dataset also inspires many
exciting new research areas. For instance, to develop better, more efficient,
and safer text-to-video diffusion models, we suggest exploring text-to-video
prompt engineering, efficient video generation, and video copy detection for
diffusion models. We make the collected dataset VidProM publicly available at
GitHub and Hugging Face under the CC-BY- NC 4.0 License.