Instructions to use gzzyyxy/orv-gen-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gzzyyxy/orv-gen-model 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("gzzyyxy/orv-gen-model", 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
Add model card (#1)
Browse files- Add model card (a0c644b9ec957e0165f7a9eb5d9f837852964316)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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---
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pipeline_tag: image-to-video
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library_name: diffusers
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---
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This repository contains the model described in [ORV: 4D Occupancy-centric Robot Video Generation](https://huggingface.co/papers/2506.03079).
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- **Project page:** https://orangesodahub.github.io/ORV/
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- **Code:** https://github.com/OrangeSodahub/ORV
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The model is an image-to-video generation model for robot manipulation, based on CogVideoX and trained with action and occupancy-derived guidance.
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