解决工业缺陷生成中微小异常信息丢失问题，实现任意尺度高保真缺陷生成。
UniScale: Arbitrary-Scale Industrial Anomaly Generation
- 采用多尺度训练策略，抑制纹理重建中的插值误差，增强小异常特征学习能力。
- 在VisA和MVTec AD 2数据集上，异常生成质量提升45.86%和37.70%，检测性能显著提高。
- 适合需要高质量缺陷数据增强的工业质检场景，尤其关注微小异常的生成需求。
工业缺陷检测因真实异常样本稀缺而面临挑战。现有生成模型在处理小尺度异常时表现不佳，主要原因是扩散模型中极端下采样导致小异常信息在潜在空间中丢失。为此，我们提出UniScale，一种统一训练与推理的框架，实现任意尺度的高保真工业异常生成。训练阶段引入误差抑制多尺度训练（EMT），使模型学习到具有位置感知特性的丰富纹理，同时抑制纹理获取过程中的上采样插值误差，确保对小尺度异常的有效建模，且不影响常规尺度异常生成。推理阶段提出生成-融合去噪策略，将异常生成与背景融合解耦，防止小异常被淹没。大量实验表明，该方法在异常生成质量和下游检测性能上均优于当前最优方法：在VisA上相对IS(a)提升45.86%（1.81→2.64），在MVTec AD 2上提升37.70%（1.22→1.68）；同时在VisA上像素级IoU提升4.22%，在MVTec AD 2上AUROC提升6.55%。代码已开源。
原文摘要 · Abstract (English)
Industrial anomaly inspection faces a major challenge due to the lack of real-world anomaly samples. While generative models are used to create anomaly data, existing methods still struggle when handling small-scale anomalies. This failure occurs because extreme downsampling in diffusion models causes the information of small anomalies to be lost in the latent space. To address this, we introduce UniScale, a unified training and inference framework for high-fidelity industrial anomaly generation across arbitrary scales. During training, we introduce an Error-Suppressed Multi-Scale Training (EMT) strategy, which enables the model to learn the rich location-aware textures of anomalies, while suppressing upsampling-induced interpolation errors in texture acquisition, ensuring the model is capable of learning small-scale anomalies, while remaining effective for regular scale anomalies. For inference, we propose Generation-then-Fusion Denoising. It decouples anomaly generation from background integration, preventing small anomalies from being overwhelmed. Extensive experiments demonstrate that our method outperforms state-of-the-art competitors in both anomaly generation quality and downstream detection performance. It achieves a relative IS(a) improvement of 45.86% (from 1.81 to 2.64) on VisA and 37.70% (from 1.22 to 1.68) on MVTec AD 2, while also improving the downstream pixel-level IoU by 4.22% on VisA and AUROC by 6.55% on MVTec AD 2. Code is available at https://github.com/HUST-SLOW/UniScale.
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