Instructions to use ByteDance/AnimateDiff-Lightning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/AnimateDiff-Lightning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/AnimateDiff-Lightning", 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:
- e7b08fca497fd3892507addbc9e9f187df7681b5a9d10a971ea9f9197ae83aa5
- Size of remote file:
- 908 MB
- SHA256:
- ca4ea6e68f4634d72c222de2b0187c3a31f00278a55868420b4e66b987bb7ccb
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