title: Exploring the Deep Fusion of Large Language Models and Diffusion
    Transformers for Text-to-Image Synthesis
summary: This paper does not describe a new method; instead, it provides a thorough
  exploration of an important yet understudied design space related to recent
  advances in text-to-image synthesis -- specifically, the deep fusion of large
  language models (LLMs) and diffusion transformers (DiTs) for multi-modal
  generation. Previous studies mainly focused on overall system performance
  rather than detailed comparisons with alternative methods, and key design
  details and training recipes were often left undisclosed. These gaps create
  uncertainty about the real potential of this approach. To fill these gaps, we
  conduct an empirical study on text-to-image generation, performing controlled
  comparisons with established baselines, analyzing important design choices, and
  providing a clear, reproducible recipe for training at scale. We hope this work
  offers meaningful data points and practical guidelines for future research in
  multi-modal generation.
paper_authors._id: 6826b00ed4c8b864e5ed1c0f
paper_authors.name: Bingda Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:21:21.171Z
paper_authors._id: 666de415ea11ab8f28560962
paper_authors.avatarUrl: /avatars/c910b35e3128cf17ba1e45afff551820.svg
paper_authors.fullname: Bingda Tang
paper_authors.type: user
paper_authors.user: ooutlierr
paper_authors._id: 6826b00ed4c8b864e5ed1c10
paper_authors.name: Boyang Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:21:27.353Z
paper_authors._id: 6434226da4c9c55871a78052
paper_authors.avatarUrl: /avatars/3309832b3115bc6ad08ae1d10f43118b.svg
paper_authors.fullname: BoYang Zheng
paper_authors.type: user
paper_authors.user: bytetriper
paper_authors._id: 6826b00ed4c8b864e5ed1c11
paper_authors.name: Xichen Pan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:21:34.655Z
paper_authors._id: 63172831c92fd6fee3181f50
paper_authors.avatarUrl: /avatars/0f57068a138cb181e9451bfc1ed3d1c0.svg
paper_authors.fullname: Xichen Pan
paper_authors.type: user
paper_authors.user: xcpan
paper_authors._id: 6826b00ed4c8b864e5ed1c12
paper_authors.name: Sayak Paul
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T13:23:23.708Z
paper_authors._id: 5f7fbd813e94f16a85448745
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1649681653581-5f7fbd813e94f16a85448745.jpeg
paper_authors.fullname: Sayak Paul
paper_authors.type: user
paper_authors.user: sayakpaul
paper_authors._id: 6826b00ed4c8b864e5ed1c13
paper_authors.name: Saining Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:21:41.504Z
paper_authors._id: 6596422646624a86ff3b3bda
paper_authors.avatarUrl: /avatars/216e12b77e45ac5f1fa20932f5745411.svg
paper_authors.fullname: Saining Xie
paper_authors.type: user
paper_authors.user: sainx
paper_title: Exploring the Deep Fusion of Large Language Models and Diffusion
    Transformers for Text-to-Image Synthesis
paper_summary: This paper does not describe a new method; instead, it provides a thorough
  exploration of an important yet understudied design space related to recent
  advances in text-to-image synthesis -- specifically, the deep fusion of large
  language models (LLMs) and diffusion transformers (DiTs) for multi-modal
  generation. Previous studies mainly focused on overall system performance
  rather than detailed comparisons with alternative methods, and key design
  details and training recipes were often left undisclosed. These gaps create
  uncertainty about the real potential of this approach. To fill these gaps, we
  conduct an empirical study on text-to-image generation, performing controlled
  comparisons with established baselines, analyzing important design choices, and
  providing a clear, reproducible recipe for training at scale. We hope this work
  offers meaningful data points and practical guidelines for future research in
  multi-modal generation.
paper_ai_summary: Empirical exploration of text-to-image synthesis focuses on the deep fusion of large language models and diffusion transformers, providing reproducible guidelines and insights.
paper_ai_keywords: large language models diffusion transformers multi-modal generation empirical study controlled comparisons baseline design choices reproducible recipe training at scale