Instructions to use Niggendar/RandomNYK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Niggendar/RandomNYK with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Niggendar/RandomNYK", 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:
- fa857ef1e9ec6fa324574243c084afc87c404b7fd2d81e0c78b46c646e7c9ec1
- Size of remote file:
- 246 MB
- SHA256:
- 2c33478aa02e54e3ffe16c7c828a3ffccfaf3937cd15aa58dcb93f327b7df0f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.