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
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README.md
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The following table shows the requirements for running Step-Video-
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| GPU | height/width/frame | Peak GPU Memory | 50 steps |
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The following table shows the requirements for running Step-Video-TI2V model (batch size = 1, w/o cfg distillation) to generate videos:
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| GPU | height/width/frame | Peak GPU Memory | 50 steps |
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