Instructions to use WALIDALI/viniciuslibya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WALIDALI/viniciuslibya with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WALIDALI/viniciuslibya", 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
- Xet hash:
- 52f1f01a87ca380d5cfb260d7e831891dea94b0922e650a0ca7eabba84f98f2e
- Size of remote file:
- 246 MB
- SHA256:
- 0a117a42b3a297cdf361d6b9edff906b418187fc1a57bd298e12f01ca2a7da0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.