Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented
performance in response generation, especially with visual inputs, enabling more creative …

This survey reviews text-to-image diffusion models in the context that diffusion models have
emerged to be popular for a wide range of generative tasks. As a self-contained work, this …

This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions
relevant to a set of multi-modal data in one model. Our key insight is–learning diffusion …

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

Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is
increasingly used in representation learning. However, optimizing the codevectors in …

This work studies discrete diffusion probabilistic models with applications to natural
language generation. We derive an alternative yet equivalent formulation of the sampling …

Non-autoregressive (NAR) text generation has attracted much attention in the field of natural
language processing, which greatly reduces the inference latency but has to sacrifice the …

Diffusion models have gained prominence in generating high-quality sequences of text.
Nevertheless, current approaches predominantly represent discrete text within a continuous …

Recent advances in generative models like Stable Diffusion enable the generation of highly
photo-realistic images. Our objective in this paper is to probe the diffusion network to …

Text-conditional diffusion models are able to generate high-fidelity images with diverse
contents. However, linguistic representations frequently exhibit ambiguous descriptions of …