Instructions to use GSAI-ML/LLaDA-o with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GSAI-ML/LLaDA-o with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="GSAI-ML/LLaDA-o")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GSAI-ML/LLaDA-o", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GSAI-ML/LLaDA-o with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GSAI-ML/LLaDA-o"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-o",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/GSAI-ML/LLaDA-o

SGLang

How to use GSAI-ML/LLaDA-o with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GSAI-ML/LLaDA-o" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-o",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GSAI-ML/LLaDA-o" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-o",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use GSAI-ML/LLaDA-o with Docker Model Runner:
```
docker model run hf.co/GSAI-ML/LLaDA-o
```

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.

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:

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:

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:

@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.

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Paper for GSAI-ML/LLaDA-o

LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model

Paper • 2603.01068 • Published Mar 1 • 22