Feature Extraction
Transformers
PyTorch
English
motion
vqvae
motion-tokenization
motion-generation
human-motion
vector-quantization
Instructions to use khania/motion-mgvqvae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khania/motion-mgvqvae with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="khania/motion-mgvqvae")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("khania/motion-mgvqvae", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9b8c116d8ea6ce468415318242625c10a1af13bc9d5091a0ce27b68ff46c713
- Size of remote file:
- 1.22 kB
- SHA256:
- 8f17f33d27b65ba4bef3f87889df0ab22bfaabc14a161cfcc9dc06ab6ba0819e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.