Instructions to use codemichaeld/wan1.3b_cf_FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codemichaeld/wan1.3b_cf_FP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codemichaeld/wan1.3b_cf_FP8", 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:
- 9726b94dcac4ebdc0b108c5e98f548f8929730e56e8ddc732f99178070228564
- Size of remote file:
- 1.42 GB
- SHA256:
- 258f0e74bb14877095fa52b6510a42430aac14dd5e731eb22531a49e9298de20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.