Instructions to use avonomis/9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use avonomis/9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("avonomis/9", 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:
- d2150643196b8951d081d89a368ec9e8e8aa2357e04a36a19b8d11ff8675415e
- Size of remote file:
- 222 MB
- SHA256:
- 559c0511fc8ae6b2e5287a23cb2a7605931b52e4e0d1259718e67a4ca8be88c5
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