Instructions to use raw-vitor/danny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use raw-vitor/danny with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("raw-vitor/danny", 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
- Xet hash:
- ceda47dfee6c7bdf4588eded267a51409a01cfc884a44922c33f9e5e5d24a9b8
- Size of remote file:
- 246 MB
- SHA256:
- 3479700976e3594bdbaeb9e6cda73d05a97db1b833936dea709f9f33ccb5ee9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.