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Image-to-image reconstruction problems with free or inexpensive metadata inthe form of class labels appear often in biological and medical image domains.Existing text-guided or style-transfer image-to-image approaches do nottranslate to datasets where additional information is provided as discreteclasses. We introduce and implement a model which combines image-to-image andclass-guided denoising diffusion probabilistic models. We train our model on areal-world dataset of microscopy images used for drug discovery, with andwithout incorporating metadata labels. By exploring the properties ofimage-to-image diffusion with relevant labels, we show that class-guidedimage-to-image diffusion can improve the meaningful content of thereconstructed images and outperform the unguided model in useful downstreamtasks.

Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels