Instructions to use easylearning/RoboImagine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use easylearning/RoboImagine with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("easylearning/RoboImagine", 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
Add release placeholder and project link
Browse files
README.md
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---
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tags:
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- robotics
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- world-model
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- video-prediction
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---
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# RoboImagine
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**Learning Robotic World Models with Semantic and Visual Feedback**
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This repository is the release hub for RoboImagine model checkpoints and associated data resources.
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## Release status
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This is a placeholder repository. Model weights, data-resource links, and evaluation assets are being prepared and will be added after manuscript submission. No weights or datasets are available here yet.
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## Project and code
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[GitHub: Alexander-wu/RoboImagine](https://github.com/Alexander-wu/RoboImagine)
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