DC-SAE splits image tokenization into semantic and pixel branches, hitting 32x compression with 3.37 gFID and 29.79 PSNR on ImageNet and a 4.41x speedup over DC-AE
Peking University and Singapore University of Technology and Design propose DC-SAE, a decoupled compact semantic autoencoder that pairs a frozen semantic encoder for a generation-friendly compact latent with a trainable pixel encoder for low-level detail, achieving 29.79 PSNR and 3.37 gFID on ImageNet at 32x spatial compression, improving over DC-AE by 13.5% in PSNR and 54.9% in gFID with a 4.41x speedup, while a B-parameter DiT reaches 0.84 on GenEval and 86.007 on DPG-Bench.