Instructions to use Zenni069/ALTc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Zenni069/ALTc with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RunDiffusion/Juggernaut-Z-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Zenni069/ALTc") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 28440333e6005abca93cf6b8fb5aaeefcae0a34a9e74cd5861713f0390cf8f53
- Size of remote file:
- 74.8 MB
- SHA256:
- b0290570e6f9034482febf996195f5edbe7d72587a4efce6b8f33f2a0b49325e
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