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原文摘要

Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objects, counts, attributes, and relations. We introduce EPIC (Efficient Predicate-Guided Inference-Time Control), a training-free inference-time refinement framework for compositional T2I generation. EPIC casts refinement as predicate-guided search: it parses the original prompt once into a visual program of object variables and typed predicates, then verifies each candidate image against this program using visual evidence extracted from that image. A candidate is judged to satisfy the prompt when all predicates pass; otherwise, unmet predicates guide the next step, routing localized failures to targeted editing and layout-level or persistent failures to resampling. On GenEval2, EPIC improves the prompt-level Soft-TIFA GM score from 34.16% for single-pass generation to 71.46%. With the same generator, editor, and image-model execution budget, EPIC outperforms the strongest baseline by 19.23 points. It also uses 31% fewer image-model executions, 72% fewer multimodal large language model (MLLM) calls, and 81% fewer MLLM tokens per prompt.