  For multimodal LLMs, the synergy of visual comprehension (textual output) and
generation (visual output) presents an ongoing challenge. This is due to a
conflicting objective: for comprehension, an MLLM needs to abstract the
visuals; for generation, it needs to preserve the visuals as much as possible.
Thus, the objective is a dilemma for visual-tokens. To resolve the conflict, we
propose encoding images into morph-tokens to serve a dual purpose: for
comprehension, they act as visual prompts instructing MLLM to generate texts;
for generation, they take on a different, non-conflicting role as complete
visual-tokens for image reconstruction, where the missing visual cues are
recovered by the MLLM. Extensive experiments show that morph-tokens can achieve
a new SOTA for multimodal comprehension and generation simultaneously. Our
project is available at https://github.com/DCDmllm/MorphTokens.
