Instructions to use jdp8/audioldm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdp8/audioldm2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jdp8/audioldm2", 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
- Xet hash:
- 825ceb0b369c254979764094466de8b0ffa0cc497f356f0465ceeebdd0635d50
- Size of remote file:
- 776 MB
- SHA256:
- a4a47b4a637dd58e9edb7b64a06acf37328b7cc3eafb0b8a85df895cc9e45d09
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