UFOGen: You Forward Once Large Scale Text-to-Image Generation via
  Diffusion GANs
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.
6556e52238754a6b38fd8e76
Yanwu Xu
6556e52238754a6b38fd8e77
Yang Zhao
claimed_verified
2023-11-17T16:28:23.468Z
631ba6aaf6bc4be4a653b2c7
/avatars/c026f47a042711127d3b8431f6217f55.svg
Yang Zhao
user
YangNaruto
6556e52238754a6b38fd8e78
Zhisheng Xiao
admin_assigned
2023-11-17T15:59:17.224Z
630846e82d33960e6b7a43ce
/avatars/058ad9a17d51ce9e1952d2677b0a47bc.svg
Xiao
user
Zhisheng
6556e52238754a6b38fd8e79
Tingbo Hou
claimed_verified
2023-11-18T12:06:04.782Z
655846d7ed8df83128f5826a
/avatars/d7ce174d7d1b8614d5f6f071225c0057.svg
Hou
user
Tingbo
UFOGen: You Forward Once Large Scale Text-to-Image Generation via
  Diffusion GANs
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.
UFOGen, a hybrid diffusion-GAN model, achieves efficient one-step text-to-image synthesis at high quality.
diffusion models GAN diffusion-GAN pre-trained diffusion models text-to-image synthesis efficient generative models