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
Published: 2025-10-17 17:50:58

Abstract: 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.