Image-to-Video
Diffusers
MLX
i2v
character-animation
video-generation
cross-identity-replacement
pose-driven
diffusion
apple-silicon
Instructions to use SceneWorks/scail2-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SceneWorks/scail2-mlx 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("SceneWorks/scail2-mlx", 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") - MLX
How to use SceneWorks/scail2-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir scail2-mlx SceneWorks/scail2-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 046d1cf39400ffcd7d3a8390760323951a1ecaa087a80be25e3a3717a38fb37e
- Size of remote file:
- 16.8 MB
- SHA256:
- 6e197b4d3dbd71da14b4eb255f4fa91c9c1f2068b20a2de2472967ca3d22602b
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