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:
- 068151a71f4683b3f90874aefbc6454e29028cd20ed2fce1a6f3126dae0d9e15
- Size of remote file:
- 11.8 MB
- SHA256:
- 9876209aeec6c68d8df1c337cce8d1f0fd51ec867bb0f65eadc3c453dd590aaf
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