Instructions to use ByteDance/BindWeave with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/BindWeave 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("ByteDance/BindWeave", 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:
- 4752ddda5c4f382827f904ad1a2e4bc2527919b1775a3b12cb8ddb1bb140b5b1
- Size of remote file:
- 4.85 GB
- SHA256:
- c6b50fa299fdef39024cf0070e86a8c3addf10f0eae64766166cf54a75aa14b7
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