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:
- 420a7646489310f7472ddcab2c9520245c97962948de7fc7184e41c3dfed271b
- Size of remote file:
- 221 MB
- SHA256:
- f9fbefc2b31c85d1dabe98e53d09ac88039af411162a7e641040a9c2b5f62364
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