Instructions to use wangfuyun/AnimateLCM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wangfuyun/AnimateLCM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wangfuyun/AnimateLCM", 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
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# AnimateLCM for Fast Video Generation in 4 steps.
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[AnimateLCM:
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## We also support fast image-to-video generation, please see [AnimateLCM-SVD-xt](https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt) and [AnimateLCM-I2V](https://huggingface.co/wangfuyun/AnimateLCM-I2V).
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# AnimateLCM for Fast Video Generation in 4 steps.
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[AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data](https://arxiv.org/abs/2402.00769) by Fu-Yun Wang et al.
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## We also support fast image-to-video generation, please see [AnimateLCM-SVD-xt](https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt) and [AnimateLCM-I2V](https://huggingface.co/wangfuyun/AnimateLCM-I2V).
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