Image-to-Video
Diffusers
Safetensors
English
Chinese
vace
video generation
video-to-video editing
refernce-to-video
Instructions to use Wan-AI/Wan2.1-VACE-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wan-AI/Wan2.1-VACE-1.3B 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("Wan-AI/Wan2.1-VACE-1.3B", torch_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
Improve model card: Update pipeline tag, add library name, tags, and link NABLA paper
#5
by nielsr HF Staff - opened
This PR enhances the model card for Wan2.1 by:
- Updating the
pipeline_tagfromimage-to-videotoany-to-anyto accurately reflect the model's versatile capabilities (Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio generation). This will improve discoverability on the Hugging Face Hub. - Adding
library_name: diffusersto the metadata. This enables the "Use in Diffusers" button and provides a seamless user experience, aligning with the provided code examples. - Adding relevant tags (
diffusion,dit) to further describe the model's architecture. - Adding a prominent link to the paper $\nabla$NABLA: Neighborhood Adaptive Block-Level Attention, which details a key architectural innovation incorporated into this model.
- Updating the citation section to refer to the NABLA paper as the primary scientific reference for this model.
Please review and merge this pull request.