AI

Correction Training for Diffusion Models

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Briefings

A new correction step for diffusion models reduces training-inference gaps by iteratively refining denoiser predictions at each sampling step. Evaluated across DiT, SiT, and JiT architectures, the method significantly improves FID scores for both guided and unconditional generation, matching or exceeding classifier-free guidance performance while maintaining diversity. The refined predictions can be distilled back into the base model, amortizing inference costs without quality loss.

Key points

• The correction step iteratively refines denoiser predictions at each sampling step to reduce early artifacts.
• The method achieves FID scores competitive with classifier-free guidance (CFG) across DiT, SiT, and JiT.

Context

Diffusion models struggle due to exposure bias where inference inputs differ from training data. This correction step addresses the training-inference gap by explicitly learning to correct predictions during sampling.

Limitations

Evaluation is limited to three architectures (DiT, SiT, JiT). Results are from a preprint (arXiv:2610.14136v2) and do not include real-world deployment data or long-term stability tests.

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