  Although the capacity of deep generative models for image generation, such as
Diffusion Models (DMs) and Generative Adversarial Networks (GANs), has
dramatically improved in recent years, much of their success can be attributed
to computationally expensive architectures. This has limited their adoption and
use to research laboratories and companies with large resources, while
significantly raising the carbon footprint for training, fine-tuning, and
inference. In this work, we present LadaGAN, an efficient generative
adversarial network that is built upon a novel Transformer block named
Ladaformer. The main component of this block is a linear additive-attention
mechanism that computes a single attention vector per head instead of the
quadratic dot-product attention. We employ Ladaformer in both the generator and
discriminator, which reduces the computational complexity and overcomes the
training instabilities often associated with Transformer GANs. LadaGAN
consistently outperforms existing convolutional and Transformer GANs on
benchmark datasets at different resolutions while being significantly more
efficient. Moreover, LadaGAN shows competitive performance compared to
state-of-the-art multi-step generative models (e.g. DMs) using orders of
magnitude less computational resources.
