Structured Counterfactual Contrast for Compositional Text-to-Image Generation: A Pilot Study of Prompt Negation and Candidate Reranking

Text-to-image diffusion models often fail on compositional prompts involving attribute binding, spatial relations, and multiple objects. Such failures can be near-miss compositions: an image is broadly plausible but violates one local structural constraint, such as an attribute assignment or a relation direction. We study structured counterfactual contrast as an inference-time diagnostic and candidate-selection signal. We represent a prompt as a constraint graph and construct minimally perturbed counterfactuals through attribute swaps, relation inversions, and argument-role swaps. A frozen vision-language scorer then computes the margin between compatibility with the target prompt and compatibility with its nearest counterfactual alternative. We evaluate two lightweight instantiations without modifying the diffusion model. First, prompt-level counterfactual augmentation lowers the diagnostic margin in a matched pilot, indicating that textual negation is not a reliable way to express structured exclusions. Second, in a compute-matched three-candidate setting, counterfactual-aware reranking selects images with higher values of the margin than first-candidate and random-selection baselines. Because this margin is also the selection objective, we treat this result as a selection diagnostic rather than independent evidence of improved compositional fidelity. Our results establish a constrained empirical finding: explicit structured comparison is more promising than direct textual negation as an inference-time use of counterfactual information. We release a randomized method-blind evaluation protocol for assessing whether this signal translates into independently judged compositional fidelity, and identify latent-level counterfactual guidance as a direction for future work.