Instructions to use camenduru/PASD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use camenduru/PASD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("camenduru/PASD", 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:
- 54e1f1a00f5730cde628c8af6a1b3df881ee46d77b0cc2f8e3926e35e28df8a9
- Size of remote file:
- 5.45 GB
- SHA256:
- 48eb5f434791f3d0d7c1b36c0aaf1040d935c0a065cae96d7e35336af9df93b7
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