OmniGen2: Exploration to Advanced Multimodal Generation
Authors: name: Chenyuan Wu, name: Pengfei Zheng, name: Ruiran Yan, name: Shitao Xiao, name: Xin Luo, name: Yueze Wang, name: Wanli Li, name: Xiyan Jiang, name: Yexin Liu, name: Junjie Zhou, name: Ze Liu, name: Ziyi Xia, name: Chaofan Li, name: Haoge Deng, name: Jiahao Wang, name: Kun Luo, name: Bo Zhang, name: Defu Lian, name: Xinlong Wang, name: Zhongyuan Wang, name: Tiejun Huang, name: Zheng Liu

In this work, we introduce OmniGen2, a versatile and open-source generative
model designed to provide a unified solution for diverse generation tasks,
including text-to-image, image editing, and in-context generation. Unlike
OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image
modalities, utilizing unshared parameters and a decoupled image tokenizer. This
design enables OmniGen2 to build upon existing multimodal understanding models
without the need to re-adapt VAE inputs, thereby preserving the original text
generation capabilities. To facilitate the training of OmniGen2, we developed
comprehensive data construction pipelines, encompassing image editing and
in-context generation data. Additionally, we introduce a reflection mechanism
tailored for image generation tasks and curate a dedicated reflection dataset
based on OmniGen2. Despite its relatively modest parameter size, OmniGen2
achieves competitive results on multiple task benchmarks, including
text-to-image and image editing. To further evaluate in-context generation,
also referred to as subject-driven tasks, we introduce a new benchmark named
OmniContext. OmniGen2 achieves state-of-the-art performance among open-source
models in terms of consistency. We will release our models, training code,
datasets, and data construction pipeline to support future research in this
field. Project Page: https://vectorspacelab.github.io/OmniGen2; GitHub Link:
https://github.com/VectorSpaceLab/OmniGen2