Generative Latent Zoning Networks

Recent advances in AI have been driven largely by generative models such as diffusion models (DMs), autoregressive (AR) models, and generative adversarial networks (GANs). However, each paradigm has inherent limitations in generation efficiency, training stability, latent-space flexibility, or other desirable properties. We propose a new class of generative models called Generative Latent Zoning Networks (G-LZNs). G-LZNs map each sample to a latent zone with learnable boundaries and decode latents within the zone back to the sample. By design, G-LZNs simultaneously provide six desirable properties: (1) One-step generation, unlike DMs and AR; (2) Stable training with only a reconstruction loss, avoiding competing objectives such as those in GANs; (3) Flexible latent dimensionality and latent-to-data mapping, unlike AR, which lacks an explicit latent space, and DMs, whose latent space is constrained by their formulation; (4) Native inversion from samples to latent space, unlike GANs and AR; (5) Likelihood estimation, unlike GANs; and (6) Flexible architecture, unlike normalizing flows, which require invertible architectures. Their compact latent space and native inversion further enable image editing without task-specific training, including class changes, class and latent interpolation, fine-grained attribute editing, super-resolution, and inpainting. Crucially, G-LZNs achieve these properties without sacrificing generation quality. Among one-step generative models trained from scratch, G-LZNs set new state-of-the-art image generation results, achieving FID 1.88 on CIFAR-10 and FDr6 2.49 on ImageNet 256×256. Code and models will be released upon acceptance.