Instructions to use vidfom/wan-t2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/wan-t2v with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vidfom/wan-t2v", 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:
- 87fb6b524a54bd3560524f12f891aa72281ca6a0f00a0f6c9460e10b0377a194
- Size of remote file:
- 116 kB
- SHA256:
- 552cb5a1fbf3f4cc4724d91c6a54f15f3f0ed2738006a0c405e4f017430ad849
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.