BLIP3o-NEXT: Next Frontier of Native Image Generation
Authors: name: Jiuhai Chen, name: Le Xue, name: Zhiyang Xu, name: Xichen Pan, name: Shusheng Yang, name: Can Qin, name: An Yan, name: Honglu Zhou, name: Zeyuan Chen, name: Lifu Huang, name: Tianyi Zhou, name: Junnan Li, name: Silvio Savarese, name: Caiming Xiong, name: Ran Xu
Organization: Salesforce

We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3
series that advances the next frontier of native image generation. BLIP3o-NEXT
unifies text-to-image generation and image editing within a single
architecture, demonstrating strong image generation and image editing
capabilities. In developing the state-of-the-art native image generation model,
we identify four key insights: (1) Most architectural choices yield comparable
performance; an architecture can be deemed effective provided it scales
efficiently and supports fast inference; (2) The successful application of
reinforcement learning can further push the frontier of native image
generation; (3) Image editing still remains a challenging task, yet instruction
following and the consistency between generated and reference images can be
significantly enhanced through post-training and data engine; (4) Data quality
and scale continue to be decisive factors that determine the upper bound of
model performance. Building upon these insights, BLIP3o-NEXT leverages an
Autoregressive + Diffusion architecture in which an autoregressive model first
generates discrete image tokens conditioned on multimodal inputs, whose hidden
states are then used as conditioning signals for a diffusion model to generate
high-fidelity images. This architecture integrates the reasoning strength and
instruction following of autoregressive models with the fine-detail rendering
ability of diffusion models, achieving a new level of coherence and realism.
Extensive evaluations of various text-to-image and image-editing benchmarks
show that BLIP3o-NEXT achieves superior performance over existing models.