Instructions to use chenguolin/sv3d-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenguolin/sv3d-diffusers 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("chenguolin/sv3d-diffusers", 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

- Xet hash:
- 7afa515b860961b6ea1c584e2c7f9473d023e2f63c343536b31f0d8f4f755fe3
- Size of remote file:
- 1.88 MB
- SHA256:
- 9744275a633043e1cd50bd6f1e855229f3ac145f77f4e28d63ada0be79dea865
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