Diffusion without CFG: Correction Training markedly improves sample quality
Apprehensive_Sky892 · reddit · 2026-10-14
A Redditor shares a "Correction Training" method for diffusion models, exploring quality gains without relying on classifier-free guidance.
- Testbed: all experiments used the open-source NanoGen (diffusion-bench), a unified diffusion transformer for ImageNet and text-to-image; the base model was pretrained on BLIP-3o's Short-Caption, Long-Caption and JourneyDB subsets (36M image-text pairs).
- Results: compared to unguided samples, correction produces noticeably cleaner object structures.
- Outlook: the author hopes someone will validate the idea on larger models like Krea 2 or Qwen-Image 2.1.
Related event: UC Berkeley's Correction Training Frees Diffusion Models from CFG(3 posts)→
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