Image-to-Video
Diffusers
Safetensors
world-model
video-generation
text-to-video
action-conditioned
causal
Instructions to use seedleap/zing-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use seedleap/zing-0.5 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("seedleap/zing-0.5", 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:
- 829080e8a6d9dbf0f706af8b19dabe4f3e0e74f4f000144ab918b043a891def1
- Size of remote file:
- 20 GB
- SHA256:
- e14ca90b64b3cec82be863b0459be2cb24558f9dc615a8a29ad6f51a8f3e85e4
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