APEX: Asynchronous Prefix Denoising for Extreme Compression

Fewer latent tokens promise cheaper diffusion. Yet aggressive spatial compression creates a reconstruction-generation dilemma: adding channels improves reconstruction but makes generation harder. Our goal is to make extreme spatial compression competitive by shifting this frontier. We introduce APEX, short for Asynchronous Prefix denoising for EXtreme compression, which pairs a hierarchical latent space with asynchronous denoising over channel groups. Our autoencoder orders channel prefixes from structure to detail, while one shared denoiser generates them at staggered local times without adding tokens. We show that naively combining a channel hierarchy with asynchronous denoising can make generation worse. APEX succeeds by matching what each level represents, when it denoises, and how training is allocated across the trajectory. On ImageNet-512, APEX outperforms existing methods at every evaluated channel count under 64x and 128x spatial compression, substantially improving this frontier. At matched training and guided sampling compute, it also outperforms a 256-token model at every evaluated budget, with larger gains at lower compute. APEX makes extreme spatial compression practical for compute-efficient latent diffusion.