Instructions to use HorizonRobotics/RoboTransfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HorizonRobotics/RoboTransfer 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("HorizonRobotics/RoboTransfer", 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:
- 8c5151a107767f71bcbfba4e589090c565e336c731f002892623f2fa07464aa7
- Size of remote file:
- 913 kB
- SHA256:
- 27bc4dee01766e74d7cf64e8c9db6ee897fe3ae9430327cfc7cffd2f9270d3b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.