Instructions to use adamdad/videocrafterv2_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use adamdad/videocrafterv2_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("adamdad/videocrafterv2_diffusers", 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:
- 6938790ec150573bb29d8932c95cda220e49f416b5e898afc1e1493f145cd7d6
- Size of remote file:
- 5.65 GB
- SHA256:
- 3b27f434c9b21fd8c2dfc36e1975445b4686e8723ac0108a0a7ea6157ed06805
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.