Instructions to use declare-lab/mustango-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/mustango-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="declare-lab/mustango-pretrained")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("declare-lab/mustango-pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ldm/pytorch_model_ldm.bin from declare-lab/mustango-pretrained: direct link, hf CLI and curl.
- Browser
- Download file 7.05 GB
-
https://huggingface.co/declare-lab/mustango-pretrained/resolve/main/ldm/pytorch_model_ldm.bin
- Command line
-
hf download hf://declare-lab/mustango-pretrained/ldm/pytorch_model_ldm.bin
-
curl -L -o pytorch_model_ldm.bin https://huggingface.co/declare-lab/mustango-pretrained/resolve/main/ldm/pytorch_model_ldm.bin
7.05 GB
- Xet hash:
- 67176ed014747340a0cba9d6627aa2f45421105f77ef77e33e8fdec34e832c32
- Size of remote file:
- 7.05 GB
- SHA256:
- c7cad0ff3dd6b346898b12b2e3627e8262684646ff82f59d0a69891e42fcaa66
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