  Developing generative models for interleaved image-text data has both
research and practical value. It requires models to understand the interleaved
sequences and subsequently generate images and text. However, existing attempts
are limited by the issue that the fixed number of visual tokens cannot
efficiently capture image details, which is particularly problematic in the
multi-image scenarios. To address this, this paper presents MM-Interleaved, an
end-to-end generative model for interleaved image-text data. It introduces a
multi-scale and multi-image feature synchronizer module, allowing direct access
to fine-grained image features in the previous context during the generation
process. MM-Interleaved is end-to-end pre-trained on both paired and
interleaved image-text corpora. It is further enhanced through a supervised
fine-tuning phase, wherein the model improves its ability to follow complex
multi-modal instructions. Experiments demonstrate the versatility of
MM-Interleaved in recognizing visual details following multi-modal instructions
and generating consistent images following both textual and visual conditions.
Code and models are available at
\url{https://github.com/OpenGVLab/MM-Interleaved}.
