---
pipeline_tag: image-text-to-text
library_name: transformers
license: apache-2.0
---

# LLaDA-o

We introduce **LLaDA-o**, an effective and length-adaptive omni diffusion model for unified multimodal understanding and generation.

LLaDA-o extends diffusion language modeling to a broader multimodal setting, supporting both visual understanding and visual generation within a single framework. The released codebase provides a practical inference pipeline for interleaved text-image processing and a notebook-based workflow for reproducible experiments.

It was presented in the paper [LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model](https://arxiv.org/abs/2603.01068).

Code: https://github.com/ML-GSAI/LLaDA-o

## Highlights

- Unified multimodal modeling for both understanding and generation
- Support for text-to-image generation
- Support for image understanding
- Support for instruction-based image editing
- Reproducible inference workflow through `multimodal_demo.ipynb`

## Supported Tasks

The current release is designed for the following multimodal inference settings:

- **Text-to-image**: generate images from natural language prompts
- **Image understanding**: produce textual responses conditioned on an input image
- **Image editing**: edit an image according to a textual instruction
- **Interleaved multimodal inference**: process text and image context within a shared diffusion-based framework

## Quick Start

Please first download the model checkpoint locally, then use the official repository for inference:

```bash
git clone https://github.com/ML-GSAI/LLaDA-o
cd LLaDA-o
bash init_env.sh 
```

The recommended inference entry point is:

- `multimodal_demo.ipynb`

In the notebook, set:

```python
MODEL_PATH = "/path/to/local/GSAI-ML-LLaDA-o"
```

and run the cells sequentially to perform text-to-image generation, image understanding, and image editing.

## Notes

- The current inference pipeline expects a local checkpoint path.
- The released demo is intended for GPU-based inference.
- For a complete inference workflow and implementation details, please refer to the official GitHub repository.

## Citation

If you find LLaDA-o useful in your research, please consider citing:

```bibtex
@article{you2026lladao,
  title={LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model},
  author={You, Zebin and Zhang, Xiaolu and Zhou, Jun and Li, Chongxuan and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2603.01068},
  year={2026}
}
```

## Contact

If you have any questions, please feel free to contact us at zebin@ruc.edu.cn.