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
Update readme
Browse files
README.md
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@@ -50,9 +50,9 @@ device = "cuda"
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dtype = torch.float16
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step = 4 # Options: [1,2,4,8]
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repo = "AnimateDiff-Lightning"
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ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
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base = "
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adapter = MotionAdapter().to(device, dtype)
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adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device))
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dtype = torch.float16
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step = 4 # Options: [1,2,4,8]
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repo = "ByteDance/AnimateDiff-Lightning"
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ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
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base = "emilianJR/epiCRealism" # Choose to your favorite base model.
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adapter = MotionAdapter().to(device, dtype)
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adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device))
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