Rare Concept Generation via Counterfactual Inference in Diffusion Models

TL;DR - CI-Diff is a diffusion approach that uses counterfactual/causal inference to generate images of "rare concepts" (objects with unusual attributes), overcoming the common-knowledge bias that makes standard diffusion models default to typical attribute renderings.

• Diagnoses the failure mode: diffusion models learned strong object–attribute associations during training, so they struggle to render atypical attributes or compose rare concepts correctly.
• Uses the Natural Direct Effect from causal inference to isolate the text prompt's independent influence, decoupling unusual attributes from the object and blocking common-knowledge bias interference.
• Reformulates classifier-free guidance to emphasize atypical attributes; claims to be the first to apply causal inference to rare concept generation.
• Reports superiority over state-of-the-art diffusion models on the RareBench benchmark; code released on GitHub.