Correction Training lets diffusion models match CFG quality without guidance
RubenEVillegas · x · 2026-10-14
Researchers introduce Correction Training for Diffusion Models, arguing that poor unconditional image generation stems from undertrained models rather than a method limit. By learning a correction network that repeatedly refines the initial denoising prediction, the model produces high-quality unconditional outputs without classifier-free guidance at matching FLOPs. Project page and a thread with details are available.
Related event: UC Berkeley's Correction Training Frees Diffusion Models from CFG(3 posts)→
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