  We present VisionLLM v2, an end-to-end generalist multimodal large model
(MLLM) that unifies visual perception, understanding, and generation within a
single framework. Unlike traditional MLLMs limited to text output, VisionLLM v2
significantly broadens its application scope. It excels not only in
conventional visual question answering (VQA) but also in open-ended,
cross-domain vision tasks such as object localization, pose estimation, and
image generation and editing. To this end, we propose a new information
transmission mechanism termed "super link", as a medium to connect MLLM with
task-specific decoders. It not only allows flexible transmission of task
information and gradient feedback between the MLLM and multiple downstream
decoders but also effectively resolves training conflicts in multi-tasking
scenarios. In addition, to support the diverse range of tasks, we carefully
collected and combed training data from hundreds of public vision and
vision-language tasks. In this way, our model can be joint-trained end-to-end
on hundreds of vision language tasks and generalize to these tasks using a set
of shared parameters through different user prompts, achieving performance
comparable to task-specific models. We believe VisionLLM v2 will offer a new
perspective on the generalization of MLLMs.
