This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a
pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task …

In this paper, we introduce PixArt-Σ, a Diffusion Transformer model (DiT) capable of directly
generating images at 4K resolution. PixArt-Σ represents a significant advancement over its …

We present WALT, a diffusion transformer for photorealistic video generation from text
prompts. Our approach has two key design decisions. First, we use a causal encoder to …

Recent advances in text-to-image generation have made remarkable progress in
synthesizing realistic human photos conditioned on given text prompts. However existing …

While next-token prediction is considered a promising path towards artificial general
intelligence, it has struggled to excel in multimodal tasks, which are still dominated by …

We present a unified transformer, ie, Show-o, that unifies multimodal understanding and
generation. Unlike fully autoregressive models, Show-o unifies autoregressive and …

In this paper, we study the harmlessness alignment problem of multimodal large language
models (MLLMs). We conduct a systematic empirical analysis of the harmlessness …

The diffusion model has been proven a powerful generative model in recent years, yet it
remains a challenge in generating visual text. Although existing work has endeavored to …

Generative AI has made remarkable strides to revolutionize fields such as image and video
generation. These advancements are driven by innovative algorithms, architecture, and …

VILA-U is a Unified foundation model that integrates Video, Image, Language understanding
and generation. Traditional visual language models (VLMs) use separate modules for …