Instructions to use michaelz/MakotoShinkai-Style-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use michaelz/MakotoShinkai-Style-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("michaelz/MakotoShinkai-Style-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 text_encoder/pytorch_model.bin from michaelz/MakotoShinkai-Style-Diffusion: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/michaelz/MakotoShinkai-Style-Diffusion/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://michaelz/MakotoShinkai-Style-Diffusion/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/michaelz/MakotoShinkai-Style-Diffusion/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 359876a11d8af360f62a925c781ee71256f6f5e80fdd09e3f93dafb5d785dabd
- Size of remote file:
- 492 MB
- SHA256:
- ba0325044b25389523617d926293440d9ea6b38a6f49f1cddd767e01cc63e693
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.