Instructions to use mitchtech/klingon-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mitchtech/klingon-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mitchtech/klingon-diffusion", 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:
- 469e5a604376841c93e19f2ce85a4fd60e34f2f2b72ad4171ce20fc6fd3d78ec
- Size of remote file:
- 492 MB
- SHA256:
- 07174d46fd1d1f8326da84bf7bb23e8d04b5784635c22b3a090d164d4b698240
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.