让文生图模型的训练数据溯源快了上万倍，适合需要快速定位关键训练图像的场景。
Fast Data Attribution for Text-to-Image Models
- 将慢速溯源方法蒸馏到特征空间，实现高效检索。
- 在MSCOCO和Stable Diffusion上仅需几秒即完成溯源，速度提升2500至40万倍。
- 适用于真实世界大模型的可扩展数据溯源，尤其适合资源受限场景。
文生图模型的数据溯源旨在识别对生成结果影响最大的训练图像。现有方法每次查询需大量计算资源，难以应用于实际场景。本文提出一种新型可扩展、高效的溯源方法：将慢速的基于遗忘机制的溯源方法蒸馏到特征嵌入空间，便于快速检索最具影响力的训练图像。部署时结合高效的索引与搜索策略，无需运行昂贵的溯源算法即可定位关键训练图像。我们在中等规模的MSCOCO训练模型及大规模的Stable Diffusion模型上进行了广泛实验，结果表明，本方法可在数秒内完成溯源，性能优于或媲美现有方法，速度提升达2500倍至40万倍。该工作为在真实世界模型（如Stable Diffusion）上实现大规模数据溯源迈出了重要一步。
原文摘要 · Abstract (English)
Data attribution for text-to-image models aims to identify the training images that most significantly influenced a generated output. Existing attribution methods involve considerable computational resources for each query, making them impractical for real-world applications. We propose a novel approach for scalable and efficient data attribution. Our key idea is to distill a slow, unlearning-based attribution method to a feature embedding space for efficient retrieval of highly influential training images. During deployment, combined with efficient indexing and search methods, our method successfully finds highly influential images without running expensive attribution algorithms. We show extensive results on both medium-scale models trained on MSCOCO and large-scale Stable Diffusion models trained on LAION, demonstrating that our method can achieve better or competitive performance in a few seconds, faster than existing methods by 2,500x - 400,000x. Our work represents a meaningful step towards the large-scale application of data attribution methods on real-world models such as Stable Diffusion.
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