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 vae/diffusion_pytorch_model.bin from mufasanft/Ghibli-Diffusion: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/mufasanft/Ghibli-Diffusion/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://mufasanft/Ghibli-Diffusion/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/mufasanft/Ghibli-Diffusion/resolve/main/vae/diffusion_pytorch_model.bin
134 Bytes
- Xet hash:
- 84d4931abd28c99ba0e556379b62b14ab792e0888df7c3608958c65fa88ad049
- Size of remote file:
- 134 Bytes
- SHA256:
- 49af6bece6c4f5b2c6f6d72461018b4477f8f423315bea70ac63c3d9a3a0dfd9
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