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This paper proposes a novel diffusion-based model, CompoDiff, for solvingComposed Image Retrieval (CIR) with latent diffusion and presents a newlycreated dataset, named SynthTriplets18M, of 18 million reference images,conditions, and corresponding target image triplets to train the model.CompoDiff and SynthTriplets18M tackle the shortages of the previous CIRapproaches, such as poor generalizability due to the small dataset scale andthe limited types of conditions. CompoDiff not only achieves a new zero-shotstate-of-the-art on four CIR benchmarks, including FashionIQ, CIRR, CIRCO, andGeneCIS, but also enables a more versatile and controllable CIR by acceptingvarious conditions, such as negative text and image mask conditions, and thecontrollability to the importance between multiple queries or the trade-offbetween inference speed and the performance which are unavailable with existingCIR methods. The code and dataset are available athttps://github.com/navervision/CompoDiff

CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion