  The QUILT-1M dataset is the first openly available dataset containing images
harvested from various online sources. While it provides a huge data variety,
the image quality and composition is highly heterogeneous, impacting its
utility for text-conditional image synthesis. We propose an automatic pipeline
that provides predictions of the most common impurities within the images,
e.g., visibility of narrators, desktop environment and pathology software, or
text within the image. Additionally, we propose to use semantic alignment
filtering of the image-text pairs. Our findings demonstrate that by rigorously
filtering the dataset, there is a substantial enhancement of image fidelity in
text-to-image tasks.
