CVPR 2024 · highlight
UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs
Abstract
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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