🧪 This pipeline is for research purposes only.  Text-to-video ModelScope Text-to-Video Technical Report is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang. The abstract from the paper is: This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at https://modelscope.cn/models/damo/text-to-video-synthesis/summary. You can find additional information about Text-to-Video on the project page, original codebase, and try it out in a demo. Official checkpoints can be found at damo-vilab and cerspense.  Usage example  text-to-video-ms-1.7b Let’s start by generating a short video with the default length of 16 frames (2s at 8 fps):   Copied import torch