Image-to-Video
Diffusers
world-model
robotics
teleoperation
video-generation
egocentric
diffusion
causal
streaming
real-time
Instructions to use Alibaba-DAMO-Academy/RynnWorld-Teleop-Causal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Alibaba-DAMO-Academy/RynnWorld-Teleop-Causal 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("Alibaba-DAMO-Academy/RynnWorld-Teleop-Causal", 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:
- 1a9a5201c1be80cc5e5e044078bfedcd504a15beba459e9722d7ccdfd3cd492c
- Size of remote file:
- 1.19 MB
- SHA256:
- 4254b817930fdbd10ae9b2bd7a76320e55e8b494b29eb55862f432380d23fd35
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