Instructions to use ionet-official/bc8-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ionet-official/bc8-alpha with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ionet-official/bc8-alpha", 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
Upload pytorch_model.bin (#1)
Browse files- Upload pytorch_model.bin (1bd1f2d0fdde752402daae4715914b17da1d0cef)
text_encoder/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:1a544b59d46af5d2b99f013e8f79d6d0835f5b8d96a1a279f4be68d949738696
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size 492307041
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