BLIP3o has achieved state-of-the-art performance across a wide range of image understanding and generation benchmarks. The model is trained on a large dataset of 20 million images with detailed captions and 4 million images with short captions. The dataset is compressed into tar archives, making it easy to download and use. The model is also supported by a demo that allows users to try out the model in their browser.


BLIP3o supports a variety of tasks, including text-to-text, image-to-text, text-to-image, image-to-image, and multitask training. The model also supports different image generation methods, such as CLIP + MSE, CLIP + Flow Matching, VAE + Flow Matching, Transfusion, and LMFusion. Additionally, the model supports different autoregressive backbones, including Qwen-2.5-VL and LLaMA 3. The model is designed to be flexible and adaptable to different use cases and applications.

Key Features

Unified multimodal model
Diffuses semantically rich CLIP image features
Fully open-source
State-of-the-art performance
Supports multiple tasks
Supports different image generation methods
Supports different autoregressive backbones
Flexible and adaptable

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