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Causal Diffusion Transformers for Generative Modeling

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arxiv 2412.12095 v2 pith:L4UQ4RCN submitted 2024-12-16 cs.CV

Chaorui Deng , Deyao Zhu , Kunchang Li , Shi Guang , Haoqi Fan This is my paper
classification cs.CV
keywords diffusionmodelscausalfusiongenerationcausalcontinuousdatadiscrete
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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