Instructions to use mufasanft/Ghibli-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mufasanft/Ghibli-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("mufasanft/Ghibli-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 mufasanft/Ghibli-Diffusion: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/mufasanft/Ghibli-Diffusion/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://mufasanft/Ghibli-Diffusion/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/mufasanft/Ghibli-Diffusion/resolve/main/unet/diffusion_pytorch_model.bin
135 Bytes
- Xet hash:
- fb452e5d2cee6918413e615c9c6318f34f67d6e0c6bcb680fdadb402c2b94b15
- Size of remote file:
- 135 Bytes
- SHA256:
- 28b8d48b658971b9621bcab2eea7220054255968f7e63804e4a0d9eadd1a26a9
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