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", torch_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:
- 6e5da63566df814d52e1d5a143ef492578ae45c1feb12b2744bbb8511237ed29
- Size of remote file:
- 909 MB
- SHA256:
- 5173ea8209053dd5de9b973baefac1801fbe7a433d07fa0122664121751885cf
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