Instructions to use jadechoghari/openmusic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jadechoghari/openmusic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jadechoghari/openmusic", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download offset_pretrained_checkpoints.json from jadechoghari/openmusic: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
-
https://huggingface.co/jadechoghari/openmusic/resolve/main/offset_pretrained_checkpoints.json
- Command line
-
hf download hf://jadechoghari/openmusic/offset_pretrained_checkpoints.json
-
curl -L -o offset_pretrained_checkpoints.json https://huggingface.co/jadechoghari/openmusic/resolve/main/offset_pretrained_checkpoints.json
267 Bytes
| { | |
| "clap_music" : "./qa_mdt/checkpoints/clap_music/music_speech_audioset_epoch_15_esc_89.98.pt", | |
| "flan_t5" : "./qa_mdt/checkpoints/flant5", | |
| "hifi-gan" : "./qa_mdt/checkpoints/hifi-gan/checkpoints", | |
| "roberta-base" : "./qa_mdt/checkpoints/robertabase" | |
| } | |