UC Berkeley's Correction Training Frees Diffusion Models from CFG
UC Berkeley researchers propose Correction Training, which lets diffusion models produce high-quality images without classifier-free guidance by addressing undertraining rather than a methodological ceiling.
2026-10-14 ~ 2026-10-14 · 3 related posts
- Correction Training lets diffusion models match CFG quality without guidance — kastnerkyle · 2026-10-14
- Correction Training lets diffusion models match CFG quality without guidance — RubenEVillegas · 2026-10-14
- Diffusion without CFG: Correction Training markedly improves sample quality — Apprehensive_Sky892 · 2026-10-14