Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis
This addresses the need for efficient and high-quality text-to-image synthesis for applications in creative and media industries, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of generating high-resolution images from text descriptions in a single step, achieving improved performance over baseline methods while reducing computational cost per step.
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.