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:
- 534b0104347f9284d0d7b9375321b6e33b27ac09eb5ff0878cb8da35012bdbaa
- Size of remote file:
- 909 MB
- SHA256:
- 29b4879520056657b897f48821760285b3eef371576e80c86c2ef1fdf95f6c1d
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