Lumina Image 2.0

🚀 Lumina-Image-2.0

Lumina-Image-2.0 is a 2 billion parameter flow-based diffusion transformer that can generate images from text descriptions.

🚀 Quick Start

Lumina-Image-2.0 is a 2 billion parameter flow-based diffusion transformer capable of generating images from text descriptions. For more information, visit our GitHub.

✨ Features

• Capable of generating high - quality images from text descriptions.
• Implemented with Hugging Face Diffusers.

📦 Installation

The installation is mainly about setting up the necessary Python environment and dependencies. You can install the required libraries as shown in the usage code example.

💻 Usage Examples

Basic Usage

import torch
from diffusers import Lumina2Pipeline

pipe = Lumina2Pipeline.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

prompt = "A serene photograph capturing the golden reflection of the sun on a vast expanse of water. The sun is positioned at the top center, casting a brilliant, shimmering trail of light across the rippling surface. The water is textured with gentle waves, creating a rhythmic pattern that leads the eye towards the horizon. The entire scene is bathed in warm, golden hues, enhancing the tranquil and meditative atmosphere. High contrast, natural lighting, golden hour, photorealistic, expansive composition, reflective surface, peaceful, visually harmonious."
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=4.0,
    num_inference_steps=50,
    cfg_trunc_ratio=0.25,
    cfg_normalization=True,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("lumina_demo.png")

📚 Documentation

We provide an official Gradio demo. You can use the link we provided to try it out.

This is a Hugging Face Diffusers implementation of the paper Lumina-Image 2.0: A Unified and Efficient Image Generative Framework.

📄 License

This project is licensed under the Apache 2.0 license.

📚 Citation

If you find the provided code or models useful for your research, consider citing them as:

@misc{lumina2,
    author={Qi Qin and Le Zhuo and Yi Xin and Ruoyi Du and Zhen Li and Bin Fu and Yiting Lu and Xinyue Li and Dongyang Liu and Xiangyang Zhu and Will Beddow and Erwann Millon and Victor Perez,Wenhai Wang and Yu Qiao and Bo Zhang and Xiaohong Liu and Hongsheng Li and Chang Xu and Peng Gao},
    title={Lumina-Image 2.0: A Unified and Efficient Image Generative Framework},
    year={2025},
    eprint={2503.21758},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/pdf/2503.21758}, 
}