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:
- 77ac2391852662b439ac0e73c2231c622a7d6279752468c81750d6a136ea0717
- Size of remote file:
- 6.93 MB
- SHA256:
- 00c58c75ff700a19caa6d3b4b890bd49555167940c096639dff8cb78a2d495fc
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