alphaXiv(@askalphaxiv):"Sphere Encoder 2" This paper fixes why one-step autoencoder generation looks blurry. It trains the latent rotation all the way from an encoded image to the 90° equator region where random generation actually starts. Then instead of forcing those highly ambiguous latents to reconstruct the exact source image pixel-by-pixel, it uses semantic, latent-consistency, and feature-space score-matching losses so the decoder can generate a sharp plausible image rather than average many possibilities into blur. On ImageNet, it reaches FDr6 4.92 with just 4 steps and ~23x less sampling compute than PixNerd, though its gFID is worse.

2026.10.05 09:48

"Sphere Encoder 2" This paper fixes why one-step autoencoder generation looks blurry. It trains the latent rotation all the way from an encoded image to the 90° equator region where random generation actually starts. Then instead of forcing those highly ambiguous latents to reconstruct the exact source image pixel-by-pixel, it uses semantic, latent-consistency, and feature-space score-matching losses so the decoder can generate a sharp plausible image rather than average many possibilities into blur. On ImageNet, it reaches FDr6 4.92 with just 4 steps and ~23x less sampling compute than PixNerd, though its gFID is worse.