Instructions to use EnD-Diffusers/art-of-wave with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EnD-Diffusers/art-of-wave with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("EnD-Diffusers/art-of-wave", dtype=torch.bfloat16, device_map="cuda") prompt = "wvert1" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 3b23bccc5611c309e1d5c190b0a64c53d5d08ce6cb6c2e3da0c7ad6223b26ac8
- Size of remote file:
- 2.13 GB
- SHA256:
- a55a4240585daa1b9bfc8ede264449ed591e494cc5558ea98c19a4d040361d88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.