BLIP3o-NEXT: Next Frontier of Native Image Generation
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.
68f592b68589920bf4d320ad
Jiuhai Chen
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Le Xue
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Zhiyang Xu
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Xichen Pan
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Shusheng Yang
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Can Qin
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An Yan
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Honglu Zhou
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Zeyuan Chen
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Lifu Huang
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Tianyi Zhou
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Junnan Li
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Silvio Savarese
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Caiming Xiong
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Ran Xu
BLIP3o-NEXT: Next Frontier of Native Image Generation
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.
BLIP3o-NEXT, a unified text-to-image generation and image editing model, uses an Autoregressive + Diffusion architecture to achieve superior performance and realism.
Autoregressive Diffusion text-to-image generation image editing discrete image tokens multimodal inputs hidden states high-fidelity images coherence realism