ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion
  Transformer
The recent surge of interest in comprehensive multimodal models has
necessitated the unification of diverse modalities. However, the unification
suffers from disparate methodologies. Continuous visual generation necessitates
the full-sequence diffusion-based approach, despite its divergence from the
autoregressive modeling in the text domain. We posit that autoregressive
modeling, i.e., predicting the future based on past deterministic experience,
remains crucial in developing both a visual generation model and a potential
unified multimodal model. In this paper, we explore an interpolation between
the autoregressive modeling and full-parameters diffusion to model visual
information. At its core, we present ACDiT, an Autoregressive blockwise
Conditional Diffusion Transformer, where the block size of diffusion, i.e., the
size of autoregressive units, can be flexibly adjusted to interpolate between
token-wise autoregression and full-sequence diffusion. ACDiT is easy to
implement, as simple as creating a Skip-Causal Attention Mask (SCAM) during
training. During inference, the process iterates between diffusion denoising
and autoregressive decoding that can make full use of KV-Cache. We verify the
effectiveness of ACDiT on image and video generation tasks. We also demonstrate
that benefitted from autoregressive modeling, ACDiT can be seamlessly used in
visual understanding tasks despite being trained on the diffusion objective.
The analysis of the trade-off between autoregressive modeling and diffusion
demonstrates the potential of ACDiT to be used in long-horizon visual
generation tasks. These strengths make it promising as the backbone of future
unified models.
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Jinyi Hu
claimed_verified
2024-12-12T08:21:37.595Z
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Jinyi Hu
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JamesHujy
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Shengding Hu
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Yuxuan Song
claimed_verified
2025-08-06T19:43:59.203Z
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Yuxuan Song
user
yxsong
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Yufei Huang
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Mingxuan Wang
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Hao Zhou
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Zhiyuan Liu
claimed_verified
2025-06-02T07:54:58.874Z
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/avatars/79dca137583875191ea883b193b630b7.svg
Zhiyuan Liu
user
zibuyu9
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Wei-Ying Ma
6759b7dbd98f3b3f28b5cadc
Maosong Sun
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion
  Transformer
The recent surge of interest in comprehensive multimodal models has
necessitated the unification of diverse modalities. However, the unification
suffers from disparate methodologies. Continuous visual generation necessitates
the full-sequence diffusion-based approach, despite its divergence from the
autoregressive modeling in the text domain. We posit that autoregressive
modeling, i.e., predicting the future based on past deterministic experience,
remains crucial in developing both a visual generation model and a potential
unified multimodal model. In this paper, we explore an interpolation between
the autoregressive modeling and full-parameters diffusion to model visual
information. At its core, we present ACDiT, an Autoregressive blockwise
Conditional Diffusion Transformer, where the block size of diffusion, i.e., the
size of autoregressive units, can be flexibly adjusted to interpolate between
token-wise autoregression and full-sequence diffusion. ACDiT is easy to
implement, as simple as creating a Skip-Causal Attention Mask (SCAM) during
training. During inference, the process iterates between diffusion denoising
and autoregressive decoding that can make full use of KV-Cache. We verify the
effectiveness of ACDiT on image and video generation tasks. We also demonstrate
that benefitted from autoregressive modeling, ACDiT can be seamlessly used in
visual understanding tasks despite being trained on the diffusion objective.
The analysis of the trade-off between autoregressive modeling and diffusion
demonstrates the potential of ACDiT to be used in long-horizon visual
generation tasks. These strengths make it promising as the backbone of future
unified models.
ACDiT, an Autoregressive blockwise Conditional Diffusion Transformer, bridges visual generation and autoregressive modeling, demonstrating effectiveness in image and video generation and potential for visual understanding tasks.
autoregressive modeling diffusion-based approach full-sequence diffusion visual generation model block size of diffusion autoregressive units token-wise autoregression Skip-Causal Attention Mask diffusion denoising KV-Cache visual understanding tasks long-horizon visual generation unified models