Created
Apr 22, 2024 5:34 PM
Discipline
CNNsDiffusion ModelsMultimodal
Date
June 17, 2024
Journal
CVPR Workshop on Efficient Large Vision Models
Year
2024
Abstract
We present a one-shot text-to-image diffusion model that can generate high-resolution images from natural language descriptions. Our model employs a layered U-Net architecture that simultaneously synthesizes images at multiple resolution scales. We show that this method outperforms the baseline of synthesizing images only at the target resolution, while reducing the computational cost per step. We demonstrate that higher resolution synthesis can be achieved by layering convolutions at additional resolution scales, in contrast to other methods which require additional models for super-resolution synthesis.