vidfetch 0.0.1a1

Latte, a novel latent diffusion transformer for video generation, utilizes spatio-temporal tokens extracted from input videos and employs a series of Transformer blocks to model the distribution of videos in the latent space. Latte achieves state-of-the-art performance on four standard video generation datasets FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. paper, code, pretrained

However, Latte still falls short in terms of video generation length and quality compared to Sora. To achieve training and generation effects close to Sora, the Latte model requires more high-quality text-video paired datasets. Therefore, we have created VidFetch, an open-source dataset download tool to obtain copyright-free videos from various free video websites.

Free Video Support

How to use

Install related dependency packages

pip install -r doc/requirements.txt

You only need three lines of code to start downloading the video

from vidfetch.website import MixkitVideoDataset

mixkit = MixkitVideoDataset(root_dir="mixkit")
mixkit.download(platform="windows")

Click to view examples we have implemented

• Download videos from Mixkit

• When you interrupt the download, the monitor will record the video information you downloaded successfully last time and continue downloading based on this information

VidFetch's design philosophy