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
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README.md
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1. Set the frame rate to match your input video. This allows audio to match with the output video.
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1. DWPose will download checkpoint itself on its first run.
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1. DWPose may get stuck in UI, but the pipeline is actually still running in the background. Check ComfyUI log and your output folder.
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1. This simple pipeline will scene change due to limited context window. More sophisticated pipeline, such as IP-Adapter for identity preservation, matting, or i2v recurring generation can be explored.
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1. Set the frame rate to match your input video. This allows audio to match with the output video.
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1. DWPose will download checkpoint itself on its first run.
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1. DWPose may get stuck in UI, but the pipeline is actually still running in the background. Check ComfyUI log and your output folder.
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