Data attribution methods trace model behavior back to its training dataset,
offering an effective approach to better understand ``black-box'' neural
networks. While prior research has established quantifiable links between model
output and training data in diverse settings, interpreting diffusion model
outputs in relation to training samples remains underexplored. In particular,
diffusion models operate over a sequence of timesteps instead of instantaneous
input-output relationships in previous contexts, posing a significant challenge
to extend existing frameworks to diffusion models directly. Notably, we present
Diffusion-TracIn that incorporates this temporal dynamics and observe that
samples' loss gradient norms are highly dependent on timestep. This trend leads
to a prominent bias in influence estimation, and is particularly noticeable for
samples trained on large-norm-inducing timesteps, causing them to be generally
influential. To mitigate this effect, we introduce Diffusion-ReTrac as a
re-normalized adaptation that enables the retrieval of training samples more
targeted to the test sample of interest, facilitating a localized measurement
of influence and considerably more intuitive visualization. We demonstrate the
efficacy of our approach through various evaluation metrics and auxiliary
tasks, reducing the amount of generally influential samples to $\frac{1}{3}$ of
its original quantity.

Diffusion-TracIn 和 Diffusion-ReTrac 方法针对扩散模型的时间动态性进行了研究，通过重新归一化适应性，以减少普遍具有影响力的样本数量，从而提供了更直观的可视化，并通过各种评估指标和辅助任务证明了方法的有效性。