Instructions to use stepfun-ai/stepvideo-t2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stepfun-ai/stepvideo-t2v with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stepfun-ai/stepvideo-t2v", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
π© Report
The Code doesn't works: from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("stepfun-ai/stepvideo-t2v")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]
It gives error: ValueError: scheduler/stepvideo.py as defined in model_index.json does not exist in stepfun-ai/stepvideo-t2v and is not a module in 'diffusers/pipelines'.
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To download the model checkpoint, execute the following command in your terminal:
huggingface-cli download stepfun-ai/stepvideo-t2v --local-dir [YOUR_DIRECTORY]
For multi-GPU inference configurations, please follow the instructions detailed in our model card.
We additionally provide a quantized model variant that enables single-GPU deployment with reduced VRAM requirements. Implementation specifics for this optimized version can be found in our GitHub repository documentation.π