Causal Diffusion Transformers for Generative Modeling
Authors: name: Chaorui Deng, name: Deyao Zh, name: Kunchang Li, name: Shi Guan, name: Haoqi Fan

We introduce Causal Diffusion as the autoregressive (AR) counterpart of
Diffusion models. It is a next-token(s) forecasting framework that is friendly
to both discrete and continuous modalities and compatible with existing
next-token prediction models like LLaMA and GPT. While recent works attempt to
combine diffusion with AR models, we show that introducing sequential
factorization to a diffusion model can substantially improve its performance
and enables a smooth transition between AR and diffusion generation modes.
Hence, we propose CausalFusion - a decoder-only transformer that
dual-factorizes data across sequential tokens and diffusion noise levels,
leading to state-of-the-art results on the ImageNet generation benchmark while
also enjoying the AR advantage of generating an arbitrary number of tokens for
in-context reasoning. We further demonstrate CausalFusion's multimodal
capabilities through a joint image generation and captioning model, and
showcase CausalFusion's ability for zero-shot in-context image manipulations.
We hope that this work could provide the community with a fresh perspective on
training multimodal models over discrete and continuous data.