Instructions to use WaveCut/LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static", 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:
- bd056980d92b35a094dc3565cebbc27b3f79e8caef3e5663bb6b229a8c95d4fb
- Size of remote file:
- 360 kB
- SHA256:
- e91a616e88278e062635d12bd204679caf879dafc107cb066cf4155c3efe2486
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.