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:
- 036e4d857a7c5d9f17cf163353a727fa6621d0ebd41146a863818fb38b4bb8db
- Size of remote file:
- 9.19 MB
- SHA256:
- 624f6d1643cb9ad8c416728e73935bb4137043b7044c9ad88b6766a17abb16a5
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