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:
- 496ea615cf2adf50f350ff26e703eb5d77dac51ca30f6024166e171bfe78702e
- Size of remote file:
- 4.98 GB
- SHA256:
- 9a8099c14933e9238d5a40eb0f1dc84cb84482fef1cdb1c2161aae21be59e45a
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