Instructions to use fangqi/WMPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fangqi/WMPO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fangqi/WMPO", 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
- Xet hash:
- b14b0f34589dbefef81a8d3418faeea27a35164623c4e5aa3acb80f5a6730395
- Size of remote file:
- 335 MB
- SHA256:
- 1857495e1d4e28140013764ffd620f6aa1fb0311dd43d7cf083f72704c69e3ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.