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UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs

Yanwu Xu · Yang Zhao · Zhisheng Xiao · Tingbo Hou

Arch 4A-E Poster #330
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


Text-to-image diffusion models have demonstrated remarkable capabilities in transforming text prompts into coherent images, yet the computational cost of the multi-step 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 generation. 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.

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