title: UFOGen: You Forward Once Large Scale Text-to-Image Generation via
    Diffusion GANs
summary: Text-to-image diffusion models have demonstrated remarkable capabilities in
  transforming textual prompts into coherent images, yet the computational cost
  of their inference remains a persistent challenge. To address this issue, we
  present UFOGen, a novel generative model designed for ultra-fast, one-step
  text-to-image synthesis. In contrast to conventional approaches that focus on
  improving samplers or employing distillation techniques for diffusion models,
  UFOGen adopts a hybrid methodology, integrating diffusion models with a GAN
  objective. Leveraging a newly introduced diffusion-GAN objective and
  initialization with pre-trained diffusion models, UFOGen excels in efficiently
  generating high-quality images conditioned on textual descriptions in a single
  step. Beyond traditional text-to-image generation, UFOGen showcases versatility
  in applications. Notably, UFOGen stands among the pioneering models enabling
  one-step text-to-image generation and diverse downstream tasks, presenting a
  significant advancement in the landscape of efficient generative models.
  \blfootnote{*Work done as a student researcher of Google, dagger indicates
  equal contribution.
paper_authors._id: 6556e52238754a6b38fd8e76
paper_authors.name: Yanwu Xu
paper_authors._id: 6556e52238754a6b38fd8e77
paper_authors.name: Yang Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-17T16:28:23.468Z
paper_authors._id: 631ba6aaf6bc4be4a653b2c7
paper_authors.avatarUrl: /avatars/c026f47a042711127d3b8431f6217f55.svg
paper_authors.fullname: Yang Zhao
paper_authors.type: user
paper_authors.user: YangNaruto
paper_authors._id: 6556e52238754a6b38fd8e78
paper_authors.name: Zhisheng Xiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-17T15:59:17.224Z
paper_authors._id: 630846e82d33960e6b7a43ce
paper_authors.avatarUrl: /avatars/058ad9a17d51ce9e1952d2677b0a47bc.svg
paper_authors.fullname: Xiao
paper_authors.type: user
paper_authors.user: Zhisheng
paper_authors._id: 6556e52238754a6b38fd8e79
paper_authors.name: Tingbo Hou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-18T12:06:04.782Z
paper_authors._id: 655846d7ed8df83128f5826a
paper_authors.avatarUrl: /avatars/d7ce174d7d1b8614d5f6f071225c0057.svg
paper_authors.fullname: Hou
paper_authors.type: user
paper_authors.user: Tingbo
paper_title: UFOGen: You Forward Once Large Scale Text-to-Image Generation via
    Diffusion GANs
paper_summary: Text-to-image diffusion models have demonstrated remarkable capabilities in
  transforming textual prompts into coherent images, yet the computational cost
  of their inference remains a persistent challenge. To address this issue, we
  present UFOGen, a novel generative model designed for ultra-fast, one-step
  text-to-image synthesis. In contrast to conventional approaches that focus on
  improving samplers or employing distillation techniques for diffusion models,
  UFOGen adopts a hybrid methodology, integrating diffusion models with a GAN
  objective. Leveraging a newly introduced diffusion-GAN objective and
  initialization with pre-trained diffusion models, UFOGen excels in efficiently
  generating high-quality images conditioned on textual descriptions in a single
  step. Beyond traditional text-to-image generation, UFOGen showcases versatility
  in applications. Notably, UFOGen stands among the pioneering models enabling
  one-step text-to-image generation and diverse downstream tasks, presenting a
  significant advancement in the landscape of efficient generative models.
  \blfootnote{*Work done as a student researcher of Google, dagger indicates
  equal contribution.
paper_ai_keywords: text-image diffusion models diffusion models textual prompts coherent images inference speculative inference UFOGen generative model text-to-image synthesis diffusion-GAN objective pre-trained diffusion models high-quality images textual descriptions one-step text-to-image generation downstream tasks efficient generative models