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To synthesize high-fidelity samples, diffusion models typically requireauxiliary data to guide the generation process. However, it is impractical toprocure the painstaking patch-level annotation effort required in specializeddomains like histopathology and satellite imagery; it is often performed bydomain experts and involves hundreds of millions of patches. Modern-dayself-supervised learning (SSL) representations encode rich semantic and visualinformation. In this paper, we posit that such representations are expressiveenough to act as proxies to fine-grained human labels. We introduce a novelapproach that trains diffusion models conditioned on embeddings from SSL. Ourdiffusion models successfully project these features back to high-qualityhistopathology and remote sensing images. In addition, we construct largerimages by assembling spatially consistent patches inferred from SSL embeddings,preserving long-range dependencies. Augmenting real data by generatingvariations of real images improves downstream classifier accuracy forpatch-level and larger, image-scale classification tasks. Our models areeffective even on datasets not encountered during training, demonstrating theirrobustness and generalizability. Generating images from learned embeddings isagnostic to the source of the embeddings. The SSL embeddings used to generate alarge image can either be extracted from a reference image, or sampled from anauxiliary model conditioned on any related modality (e.g. class labels, text,genomic data). As proof of concept, we introduce the text-to-large imagesynthesis paradigm where we successfully synthesize large pathology andsatellite images out of text descriptions.

Learned representation-guided diffusion models for large-image generation