Diffusion models, which revolutionized image generation, are facing challenges related to intellectual property. These challenges arise when a generated image is influenced by one or more copyrighted images from the training data. Hence, pinpointing influential images from the training dataset, a task known as data attribution, becomes crucial for the clarity of content origins. We introduce MONTAGE, a pioneering data attribution method. Unlike existing approaches that overlook the internal workings of the training process, MONTAGE integrates a novel technique to monitor generations throughout the training via internal model representations. It is tailored for customized diffusion models, where training access is a practical assumption. This approach, coupled with a new loss function, enables enhanced accuracy as well as granularity of the attributions. The advantage of MONTAGE is evaluated in two granularity levels: Semantic concept (including mix-concept images) and individual image, showing promising results. This underlines MONTAGE's role towards solving copyright concerns in AI-generated digital art and media while enriching the understanding of the generative process.