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-lightningAnd 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! 🚀
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