Researchers propose Correction step to improve diffusion model sampling quality
A new paper on arXiv introduces a correction step for diffusion models to address error accumulation during iterative sampling. The method trains a corrector on the base model’s predictions, refining intermediate denoising steps to prevent early artifacts from affecting final outputs. The approach improves sample quality while maintaining diversity, outperforming unguided generation and matching…
Key points
- New correction step refines diffusion model predictions iteratively to reduce sampling errors
- Method improves FID scores for unguided and unconditional generation across DiT, SiT, JiT
- Can be distilled into base model to cut inference-time overhead while retaining gains
The authors argue this explicit learning task bridges the training-inference gap, where denoisers are trained on real images with added noise but tested on imperfect predictions. The correction step refines predictions iteratively, allowing repeated application for better results without sacrificing diversity. The paper suggests this could reduce reliance on CFG while preserving quality.
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