Instructions to use engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-Diffusers 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("engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-Diffusers", 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:
- 168eb9d382320e4b39566f51af1512a5e7450f0aefd140679d4525a3b73f021c
- Size of remote file:
- 508 MB
- SHA256:
- d6e524b3fffede1787a74e81b30976dce5400c4439ba64222168e607ed19e793
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