Instructions to use stepfun-ai/stepvideo-ti2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stepfun-ai/stepvideo-ti2v 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("stepfun-ai/stepvideo-ti2v", 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
Commit History
-
Update README.md
Verified signature3d7471a
-
Update README.md
Verified signaturefb6c19e -
Update README.md
Verified signaturea2b7b03 -
Update README.md
Verified signatureeed18c7 -
Update README.md
Verified signatured599999
-
Update README.md
Verified signatureef5317a -
Update README.md
Verified signature5c481c2 -
Update README.md
Verified signaturebc9b6d4 -
Update README.md
Verified signature594ab4e -
Update README.md
Verified signaturee3ae15c -
Update README.md
Verified signature575cb78 -
Add files using upload-large-folder tool
Verified signature6db8f07
-
initial commit
Verified signature43ff58d