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We present DiffInfinite, a hierarchical diffusion model that generatesarbitrarily large histological images while preserving long-range correlationstructural information. Our approach first generates synthetic segmentationmasks, subsequently used as conditions for the high-fidelity generativediffusion process. The proposed sampling method can be scaled up to any desiredimage size while only requiring small patches for fast training. Moreover, itcan be parallelized more efficiently than previous large-content generationmethods while avoiding tiling artifacts. The training leverages classifier-freeguidance to augment a small, sparsely annotated dataset with unlabelled data.Our method alleviates unique challenges in histopathological imaging practice:large-scale information, costly manual annotation, and protective datahandling. The biological plausibility of DiffInfinite data is evaluated in asurvey by ten experienced pathologists as well as a downstream classificationand segmentation task. Samples from the model score strongly on anti-copyingmetrics which is relevant for the protection of patient data.

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology