  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.
