GenFirst: generation-first training makes end-to-end latent generative models stable
ByteDance Seed
GenFirst replaces the standard two-stage VAE-then-generator recipe with a schedule where the generative objective shapes the latent space first under weak reconstruction pressure, with reconstruction ramped up afterwards — avoiding latent collapse in fully end-to-end training. It reaches gFID 0.97 on ImageNet-256 with a SiT prior and a GenEval score of 0.90 for text-to-image, and extends to unified continuous text-image generation. The paper (submitted Aug 29) surfaced as the #2 paper on the Sep 1 HF Daily Papers listing.
Why it matters
41 upvotes, #2 paper on HF Daily Papers Sep 1; end-to-end latent training removes the frozen-VAE bottleneck shared by nearly all latent diffusion and autoregressive image models.
Importance: 2/5
#2 HF Daily Paper (41 upvotes), official sources