TaxaAdapter: Scaling Fine-grained Species Image Generation To the Tree of Life

Accurately generating images across the Tree of Life is difficult: there are over 10M distinct species on Earth, many of which differ only by subtle visual traits. Despite the remarkable progress in text-to-image synthesis, existing models often fail to capture the fine-grained visual cues that define species identity, even when their outputs appear photo-realistic. To this end, we propose TaxaAdapter, a simple and lightweight adapter that incorporates the taxonomic embeddings of Vision Taxonomy Models (VTMs) such as BioCLIP to perform scalable fine-grained species generation over the entire Tree of Life. TaxaAdapter injects VTM embeddings using taxonomy-text dual conditioning into a frozen text-to-image diffusion model, improving species-level fidelity while preserving flexible text control over attributes such as pose, style, and background. Extensive experiments demonstrate that TaxaAdapter consistently improves morphology fidelity and species-identity accuracy over strong baselines. To better evaluate these improvements, we also introduce a multimodal Large Language Model-based metric that summarizes trait-level descriptions from generated and real images, providing a more interpretable measure of morphological consistency. Beyond showing improvements over standard benchmarking experiments, we observe that TaxaAdapter exhibits strong generalization capabilities in open-world settings, enabling species synthesis in challenging regimes such as few-shot species with only a handful of training images and even species unseen during training. Overall, our results highlight that VTMs are a key ingredient for scalable, fine-grained species generation.