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.
