Image-to-Video
Diffusers
Safetensors
StableVideoDiffusionPipeline
normal-estimation
video
diffusion
svd
Instructions to use AEmotionStudio/NormalCrafter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AEmotionStudio/NormalCrafter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AEmotionStudio/NormalCrafter", torch_dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52ca3f53d20a83ec27e5de7c0f71c54ae5c895348e3fee4903f2f7e7230e949c
- Size of remote file:
- 3.05 GB
- SHA256:
- 03095971efc7c439767c3a42d78ded3bc0acb3f51acbfc588c9de76c59bb27cb
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