Instructions to use MochunniaN1/One-to-All-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MochunniaN1/One-to-All-14b 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("MochunniaN1/One-to-All-14b", 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:
- bf26177ed96b9661fbbcd9b44bedf6f035e472c591eea02bcbb1172ff6d8873a
- Size of remote file:
- 4.93 GB
- SHA256:
- 0172deb4fbbcc32284dd5cc6f159801680cd33000bf1513c9149bb3089481a77
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