Instructions to use peter168/output_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peter168/output_dir with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("peter168/output_dir", 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:
- f83768fbb18665128b5abaf9ecc88a9a34aa2752e9dfc5dbc399cf8e2bf902b8
- Size of remote file:
- 455 MB
- SHA256:
- 285318e8115ac81b974c2af4e9c194d83c8c14b8175d59b518ce0694252f9b09
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