  Data processing plays an significant role in current multimodal model
training. In this paper. we provide an comprehensive review of common data
processing techniques used in modern multimodal model training with a focus on
diffusion models and multimodal large language models (MLLMs). We summarized
all techniques into four categories: data quality, data quantity, data
distribution and data safety. We further present our findings in the choice of
data process methods in different type of models. This study aims to provide
guidance to multimodal models developers with effective data processing
techniques.
