title: DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture
    Design in Text to Image Generation
summary: In this work, we empirically study Diffusion Transformers (DiTs) for
  text-to-image generation, focusing on architectural choices, text-conditioning
  strategies, and training protocols. We evaluate a range of DiT-based
  architectures--including PixArt-style and MMDiT variants--and compare them with
  a standard DiT variant which directly processes concatenated text and noise
  inputs. Surprisingly, our findings reveal that the performance of standard DiT
  is comparable with those specialized models, while demonstrating superior
  parameter-efficiency, especially when scaled up. Leveraging the layer-wise
  parameter sharing strategy, we achieve a further reduction of 66% in model size
  compared to an MMDiT architecture, with minimal performance impact. Building on
  an in-depth analysis of critical components such as text encoders and
  Variational Auto-Encoders (VAEs), we introduce DiT-Air and DiT-Air-Lite. With
  supervised and reward fine-tuning, DiT-Air achieves state-of-the-art
  performance on GenEval and T2I CompBench, while DiT-Air-Lite remains highly
  competitive, surpassing most existing models despite its compact size.
paper_authors._id: 67d3d2dec4a225b653154b3a
paper_authors.name: Chen Chen
paper_authors.status: claimed_verified
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paper_authors.fullname: Chen Chen
paper_authors.type: user
paper_authors.user: ultra7chen
paper_authors._id: 67d3d2dec4a225b653154b3b
paper_authors.name: Rui Qian
paper_authors._id: 67d3d2dec4a225b653154b3c
paper_authors.name: Wenze Hu
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paper_authors.fullname: wenze
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paper_authors.user: wenzehu
paper_authors._id: 67d3d2dec4a225b653154b3d
paper_authors.name: Tsu-Jui Fu
paper_authors.status: admin_assigned
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paper_authors.fullname: Tsu-Jui Fu
paper_authors.type: user
paper_authors.user: tsujuifu
paper_authors._id: 67d3d2dec4a225b653154b3e
paper_authors.name: Lezhi Li
paper_authors._id: 67d3d2dec4a225b653154b3f
paper_authors.name: Bowen Zhang
paper_authors._id: 67d3d2dec4a225b653154b40
paper_authors.name: Alex Schwing
paper_authors._id: 67d3d2dec4a225b653154b41
paper_authors.name: Wei Liu
paper_authors._id: 67d3d2dec4a225b653154b42
paper_authors.name: Yinfei Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:50:24.690Z
paper_authors._id: 64b762568c632fbca942a405
paper_authors.avatarUrl: /avatars/1eb737ec169967872f1ebf5ff29f1e6b.svg
paper_authors.fullname: Yinfei Yang
paper_authors.type: user
paper_authors.user: yinfeiy
paper_title: DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture
    Design in Text to Image Generation
paper_summary: In this work, we empirically study Diffusion Transformers (DiTs) for
  text-to-image generation, focusing on architectural choices, text-conditioning
  strategies, and training protocols. We evaluate a range of DiT-based
  architectures--including PixArt-style and MMDiT variants--and compare them with
  a standard DiT variant which directly processes concatenated text and noise
  inputs. Surprisingly, our findings reveal that the performance of standard DiT
  is comparable with those specialized models, while demonstrating superior
  parameter-efficiency, especially when scaled up. Leveraging the layer-wise
  parameter sharing strategy, we achieve a further reduction of 66% in model size
  compared to an MMDiT architecture, with minimal performance impact. Building on
  an in-depth analysis of critical components such as text encoders and
  Variational Auto-Encoders (VAEs), we introduce DiT-Air and DiT-Air-Lite. With
  supervised and reward fine-tuning, DiT-Air achieves state-of-the-art
  performance on GenEval and T2I CompBench, while DiT-Air-Lite remains highly
  competitive, surpassing most existing models despite its compact size.
paper_ai_keywords: Diffusion Transformers (DiTs) text-to-image generation architectural choices text-conditioning strategies training protocols PixArt-style MMDiT variants concatenated text and noise inputs parameter-efficiency layer-wise parameter sharing strategy Variational Auto-Encoders (VAEs) DiT-Air DiT-Air-Lite supervised and reward fine-tuning GenEval T2I CompBench state-of-the-art performance