DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer
DuSPiT is a new pixel-space diffusion transformer that uses a dual-branch architecture—a compact base branch for global reasoning and a high-capacity pixel branch for local details—connected via cross-attention, achieving richer image details and better quality-efficiency trade-off than prior methods.
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[Submitted on 20 Jul 2026]
Title:DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer
View a PDF of the paper titled DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer, by Yunpeng Bai and 2 other authors
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Abstract:Diffusion Transformers achieve strong image generation performance, but most operate in compressed latent spaces. Pixel-space diffusion avoids this information loss, yet existing approaches map each raw image patch to a single token, forcing one representation to handle both global communication and fine-grained details. We address this issue by proposing a new architecture, \textbf{DuSPiT}, a \textbf{Du}al-branch \textbf{S}ub\textbf{P}atch \textbf{Pi}xel \textbf{T}ransformer. This model separates global structural reasoning from local appearance modeling. DuSPiT uses a compact base branch for efficient global reasoning and a parallel, high-capacity pixel branch, organized into subpatch groups, to preserve detailed appearance, with the two branches interacting through cross-attention. Our results show that DuSPiT generates images with richer details and stronger fine-grained structures, while also achieving a better quality--efficiency trade-off than prior pixel-space diffusion transformers.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.18510 [cs.CV]
(or arXiv:2607.18510v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.18510
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yunpeng Bai [view email] [v1] Mon, 20 Jul 2026 21:08:56 UTC (1,604 KB)
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