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Developing generative models for interleaved image-text data has bothresearch and practical value. It requires models to understand the interleavedsequences and subsequently generate images and text. However, existing attemptsare limited by the issue that the fixed number of visual tokens cannotefficiently capture image details, which is particularly problematic in themulti-image scenarios. To address this, this paper presents MM-Interleaved, anend-to-end generative model for interleaved image-text data. It introduces amulti-scale and multi-image feature synchronizer module, allowing direct accessto fine-grained image features in the previous context during the generationprocess. MM-Interleaved is end-to-end pre-trained on both paired andinterleaved image-text corpora. It is further enhanced through a supervisedfine-tuning phase, wherein the model improves its ability to follow complexmulti-modal instructions. Experiments demonstrate the versatility ofMM-Interleaved in recognizing visual details following multi-modal instructionsand generating consistent images following both textual and visual conditions.Code and models are available at\url{https://github.com/OpenGVLab/MM-Interleaved}.

MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer