给扩散模型加个“校对员”，无引导生成质量直追有引导
扩散模型生成图片时有个“自我污染”问题：训练时它只见过干净数据加噪声，但生成时每一步输入的都是自己上一步的“残次品”，没见过这种输入，于是错误越滚越大。这篇论文给模型配了个“校对员”：拿主模型的预测当输入，专门训练它去修正这些预测，让每一步的“草稿”更接近最终目标。结果，不需要任何额外引导（比如CFG），无引导生成的图片质量大幅提升，甚至接近有引导的水平；把校对员和CFG结合，还能减少CFG常见的过饱和。更妙的是，校对员的额外计算成本可以通过蒸馏消除——把修正后的预测“教”回主模型，推理时零额外开销。这不是你明天能直接用的工具，但它指向一个更省事的路线：与其在生成时加各种外部约束，不如让模型自己学会纠错。
📄 原文摘要(英文)
Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noise, but at inference its inputs are constructed from its own imperfect predictions from the earlier steps, something it has never seen at training time. This might be one reason diffusion models often struggle without additional help, such as CFG. We introduce a correction step for diffusion models which attempts to reduce this training-inference gap. The corrector takes the base model's prediction as input and is trained against the same ground-truth target. This makes correction an explicit learning task, with training inputs provided by the base model. During sampling, at each denoising step, we iteratively refine the denoiser's clean-sample prediction. This allows us to correct predictions at intermediate denoising steps, preventing early artifacts from persisting into the final sample. Although the corrector is trained only on predictions from the base denoiser, it can be applied repeatedly to its own outputs at inference for better results. This simple framework leads to substantial improvement in sample quality while largely preserving diversity. We evaluate our method across DiT, SiT, and JiT. Our method substantially improves upon previously reported FID scores for unguided generation and, notably, unconditional generation, where producing high-quality samples without guidance has been particularly challenging. Our method achieves performance competitive with classifier-free guidance (CFG), and combining correction with CFG further improves diversity and reduces oversaturation typical of CFG. Finally, we show that the additional inference cost of correction can be amortized by distilling the refined predictions back into the base diffusion model, retaining the quality gains without additional inference-time overhead.