Instructions to use issai/Beynele with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use issai/Beynele with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("issai/Beynele", 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
Commit History
18 days ago
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Redesign model card: separate evaluation data from training metadata, add gallery, provenance and component licences
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>4913f59
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Update README.md
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Update Beynele model card license wording
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Package Beynele as a full Diffusers pipeline
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Initial private Beynele release
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Initial private Beynele release
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Initial private Beynele release
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Initial private Beynele release
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Initial private Beynele release
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Initial private Beynele release
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initial commit
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