Instructions to use muchojarabe/muxoyara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use muchojarabe/muxoyara with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("muchojarabe/muxoyara", 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 muchojarabe/muxoyara: direct link, hf CLI and curl.
- Browser
- Download file 1.72 GB
-
https://huggingface.co/muchojarabe/muxoyara/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://muchojarabe/muxoyara/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/muchojarabe/muxoyara/resolve/main/unet/diffusion_pytorch_model.bin
1.72 GB
- Xet hash:
- 4861b62fdadbaa4e731232d4da51d0175ebe354eddc1c407f17029be1753f041
- Size of remote file:
- 1.72 GB
- SHA256:
- 7d8008f6907ed381bf86e2f48981282443f227009bcd1acbf04c2489e4becd9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.