UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs
Yanwu Xu, Yang Zhao, Zhisheng Xiao, Tingbo Hou
Abstract
Text-to-image diffusion models have demonstrated re-markable capabilities in transforming text prompts into co-herent 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 de-signed 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, inte-grating 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 advance-ment in the landscape of efficient generative models.