In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

Humans can easily solve multimodal tasks in context with only a few demonstrations or
simple instructions which current multimodal systems largely struggle to imitate. In this work …

We introduce CogVLM, a powerful open-source visual language foundation model. Different
from the popular shallow alignment method which maps image features into the input space …

People are spending an enormous amount of time on digital devices through graphical user
interfaces (GUIs) eg computer or smartphone screens. Large language models (LLMs) such …

The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However the progress in vision and vision …

Multimodal large language models (MLLMs) building upon the foundation of powerful large
language models (LLMs) have recently demonstrated exceptional capabilities in generating …

Large multimodal models demonstrate remarkable generalist ability to perform diverse
multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute …

We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-
form text-image composition and comprehension. This model goes beyond conventional …

We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint
mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision …

Diffusion models have exhibit exceptional performance in text-to-image generation and
editing. However, existing methods often face challenges when handling complex text …