Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

In this work, we discuss building performant Multimodal Large Language Models (MLLMs).
In particular, we study the importance of various architecture components and data choices …

Image editing aims to edit the given synthetic or real image to meet the specific requirements
from users. It is widely studied in recent years as a promising and challenging field of …

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 …

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

Current instruction-based image editing methods such as InstructPix2Pix often fail to
produce satisfactory results in complex scenarios due to their dependence on the simple …

Denoising diffusion models have emerged as a powerful tool for various image generation
and editing tasks, facilitating the synthesis of visual content in an unconditional or input …

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in
processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization …

Despite the success achieved by existing image generation and editing methods, current
models still struggle with complex problems including intricate text prompts, and the …