Step-by-Step Guide to Using modelscope Text-to-Video Generation Model

by

Putting the Model to Work

So, how do you get this magic to happen? The model is accessible on ModelScope Studio and Hugging Face, with a DIY option available on the Colab page. If you’re looking for a quick start, the Aliyun Notebook Tutorial is your go-to guide.

Requirements

You’ll need about 16GB of CPU and GPU RAM. Remember, this model is GPU-only for inference.

Setting Up

Install the necessary Python packages:

pip install modelscope==1.4.2
pip install open_clip_torch
pip install pytorch-lightning

And then dive into the code

from huggingface_hub import snapshot_download
from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys
import pathlib

model_dir = pathlib.Path('weights')
snapshot_download('damo-vilab/modelscope-damo-text-to-video-synthesis', repo_type='model', local_dir=model_dir)

pipe = pipeline('text-to-video-synthesis', model_dir.as_posix())
test_text = {'text': 'A panda eating bamboo on a rock.'}
output_video_path = pipe(test_text)[OutputKeys.OUTPUT_VIDEO]
print('output_video_path:', output_video_path)

Run the code, and voilà! You’ll get the path to your generated video, which you can view using VLC player.

Keep in Mind

  • The model’s output is influenced by its training data (Webvid, etc.).
  • It’s not Hollywood-quality, struggles with clear text, and is English-only.
  • Avoid misuse, like generating demeaning or false content.

Training Data and Citation

It’s trained on datasets like LAION5B and ImageNet, with a focus on quality and uniqueness. For academic purposes, don’t forget to cite their paper!

There you have it – your gateway to AI-powered video generation. Experiment, explore, and most importantly, use it responsibly. Happy coding! 🚀

Leave a Comment