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Diffusion-based methods have demonstrated remarkable capabilities ingenerating a diverse array of high-quality images, sparking interests forstyled avatars, virtual try-on, and more. Previous methods use the samereference image as the target. An overlooked aspect is the leakage of thetarget's spatial information, style, etc. from the reference, harming thegenerated diversity and causing shortcuts. However, this approach continues aswidely available datasets usually consist of single images not grouped byidentities, and it is expensive to recollect large-scale same-identity data.Moreover, existing metrics adopt decoupled evaluation on text alignment andidentity preservation, which fail at distinguishing between balanced outputsand those that over-fit to one aspect. In this paper, we propose a multi-level,same-identity dataset RetriBooru, which groups anime characters by both faceand cloth identities. RetriBooru enables adopting reference images of the samecharacter and outfits as the target, while keeping flexible gestures andactions. We benchmark previous methods on our dataset, and demonstrate theeffectiveness of training with a reference image different from target (butsame identity). We introduce a new concept composition task, where theconditioning encoder learns to retrieve different concepts from severalreference images, and modify a baseline network RetriNet for the new task.Finally, we introduce a novel class of metrics named Similarity WeightedDiversity (SWD), to measure the overlooked diversity and better evaluate thealignment between similarity and diversity.

RetriBooru: Leakage-Free Retrieval of Conditions from Reference Images for Subject-Driven Generation