Instructions to use INCModel/Wan2.2-I2V-A14B-Diffusers-MXFP8-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use INCModel/Wan2.2-I2V-A14B-Diffusers-MXFP8-AutoRound with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("INCModel/Wan2.2-I2V-A14B-Diffusers-MXFP8-AutoRound", 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
Wan2.2-I2V-A14B-Diffusers-MXFP8-AutoRound / transformer /diffusion_pytorch_model-00003-of-00006.safetensors
- Xet hash:
- 88f9bf78c3f0253a7d1c34b488f73fab508743d2485aba1c8e0b86ae93d5b7f4
- Size of remote file:
- 5 GB
- SHA256:
- 249bb504d6e9ebaa182f0d73ad789b04fbdf2cfc5f77b3c1a71135d8e3713d85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.