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:
- 83414860639d25b5cb897ed5c505868575750ec7e4e673d21669b5eacef8eb3d
- Size of remote file:
- 498 MB
- SHA256:
- 80896089a320949684e5150079143ee3061df687124216292da482e3b79ddc64
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