Text-to-Image
Diffusers
TuneAVideoPipeline
stable-diffusion
stable-diffusion-diffusers
text-to-video
tune-a-video
Instructions to use Tune-A-Video-library/chicken-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Tune-A-Video-library/chicken-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tune-A-Video-library/chicken-1", 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
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- 4a7497940fa607f2ecdfbd8447050c85b535a78aa6fb27b669587e4620a928eb
- Size of remote file:
- 1.93 MB
- SHA256:
- 7445df1a36b586bf28a661300ab6d926d4e20637db5ffecf06b5051e4ebcea90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.