通过滑动窗口增强扩散模型的长程依赖，提升生成质量且无需训练。
Guiding a diffusion model using sliding windows
- 用自身限制感受野，引导主模型捕捉更远距离空间关系。
- 在ImageNet上超越现有无训练方法，Inception分数更高，无过饱和现象。
- 无需权重访问、额外训练或类别条件，适配性强，适合快速部署。
指导（guidance）是提升扩散模型生成质量的常用技术。本文首先通过二维示例表明，当辅助模型具有与主模型相似但更强的泛化误差时，指导效果最佳。基于此，提出一种无需训练的新型方法——掩码滑动窗口引导（M-SWG）。M-SWG通过有选择地限制主模型的感受野，增强其对长程空间依赖的建模能力。该方法不需访问前迭代模型权重、额外训练或类别条件。实验显示，M-SWG在无需训练的方案中取得更优的Inception分数，且避免样本过饱和。结合现有指导方法，M-SWG在使用EDM2-XXL和DiT-XL时，于ImageNet上达到当前最优的Frechet DINOv2距离。代码已公开于https://github.com/HHU-MMBS/swg_bmvc2025_official。
原文摘要 · Abstract (English)
Guidance is a widely used technique for diffusion models to enhance sample quality. Technically, guidance is realised by using an auxiliary model that generalises more broadly than the primary model. Using a 2D toy example, we first show that it is highly beneficial when the auxiliary model exhibits similar but stronger generalisation errors than the primary model. Based on this insight, we introduce \emph{masked sliding window guidance (M-SWG)}, a novel, training-free method. M-SWG upweights long-range spatial dependencies by guiding the primary model with itself by selectively restricting its receptive field. M-SWG requires neither access to model weights from previous iterations, additional training, nor class conditioning. M-SWG achieves a superior Inception score (IS) compared to previous state-of-the-art training-free approaches, without introducing sample oversaturation. In conjunction with existing guidance methods, M-SWG reaches state-of-the-art Frechet DINOv2 distance on ImageNet using EDM2-XXL and DiT-XL. The code is available at https://github.com/HHU-MMBS/swg_bmvc2025_official.
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