Correction Training lets diffusion models match CFG quality without guidance
kastnerkyle · x · 2026-10-14
Researchers from UC Berkeley and Reve (Donghoon Ahn, Alexei A. Efros, Taesung Park, Yash Belhe) introduce Correction Training for Diffusion Models. Key points:
- Without guidance, even large models like FLUX.2 Klein 9B produce poor samples; errors from early structural mistakes compound during denoising.
- Fix: train a corrector that refines the denoiser's prediction toward the same clean target, applied multiple times at each sampling step.
- Result: sample quality better than or competitive with classifier-free guidance on ImageNet and text-to-image, conditional and unconditional.
Project page with comparisons is live, code released, arXiv paper coming soon.
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