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
Download unet/diffusion_pytorch_model.bin from mitchtech/klingon-diffusion: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/mitchtech/klingon-diffusion/resolve/refs%2Fpr%2F2/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://mitchtech/klingon-diffusion@refs/pr/2/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/mitchtech/klingon-diffusion/resolve/refs%2Fpr%2F2/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- a9efa10af498907feb4983265a57856c9318ce7d18bea35b984d0265728205f2
- Size of remote file:
- 3.44 GB
- SHA256:
- 2e6cd0d0c65ba5eb1e75e6ba6630a28723d875e3c45eadc6d0e1e38ef925ea8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.