Instructions to use thuml/rt1-frame-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use thuml/rt1-frame-tokenizer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("thuml/rt1-frame-tokenizer", 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
File size: 424 Bytes
6ca7e0f 95fba0e a41f1ad 6ca7e0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ---
license: mit
tags:
- robotic-manipulation
- rt1
- tokenizer
- visual-tokenizer
---
See https://github.com/thuml/RLVR-World for examples for using this model.
## Citation
```
@article{wu2025rlvr,
title={RLVR-World: Training World Models with Reinforcement Learning},
author={Jialong Wu and Shaofeng Yin and Ningya Feng and Mingsheng Long},
journal={arXiv preprint arXiv:2505.13934},
year={2025},
}
``` |