Instructions to use Androidonnxfork/icbinpfinal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Androidonnxfork/icbinpfinal with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Androidonnxfork/icbinpfinal", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 433e3208d8be9ec4ba2048628b889c67c63597f59d23adece1c1f3513aa70bc7
- Size of remote file:
- 492 MB
- SHA256:
- 1550641ed85e9e091152c407c7a2ad5a8fc8fd74cc02c02548d5b2ed634f0e1b
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