Instructions to use JCTN/AnimateDiff-Lightning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JCTN/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("JCTN/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:
- 843beae1fcc20e2cf5edb94e64976ee1ce2d11b8f70d091557afdf5ef7030362
- Size of remote file:
- 1.4 MB
- SHA256:
- f19d994a8a74def47039b65961e1f1b7e66b5fec0ac30716ceb04ab27245bc90
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