Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.

The recent large-scale text-to-image generative models have attained unprecedented performance, while people establishedadaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distributionconcepts. This failure is highly related to the low quality of training data.

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures.

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