Diffusion models have demonstrated a remarkable ability to generate photorealistic images. However, it is difficult to explain what causes the generated image. Tracing the output back to the training data and identifying the most influential samples is necessary to debug the model, find biases, or provide fair compensation to creators. While data attribution methods have been extensively studied in the supervised setting, data attribution for generative models such as diffusion models remains a challenge. The aim of this thesis is to provide an overview of existing methods for data attribution and evaluation methods. In the absence of a commonly used benchmark, a framework for evaluating data attribution methods was implemented as part of this thesis. Various experiments and evaluation methods allow a comparison between the different methods to better understand their use cases and limitations. Furthermore they lead to the proposal of new normalization method, called loss-normalization.
