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", 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
Add README and MIT LICENSE
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README.md
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## License
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Apache-2.0 —
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## Links
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## License
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- **Model weights** (this repo): **Apache-2.0** — matching the upstream [Yanrui95/NormalCrafter](https://huggingface.co/Yanrui95/NormalCrafter) HuggingFace repo. See [LICENSE](LICENSE).
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- **Source code**: **MIT** — as published at [Binyr/NormalCrafter](https://github.com/Binyr/NormalCrafter) on GitHub.
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Both licenses are permissive and allow commercial use.
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## Links
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