Failure-Aware Adaptive Recovery for Text-to-Image Generation: When to Resample and When to Rewrite

A failed text-to-image generation reveals what went wrong, but not how additional inference compute should be used for recovery. We show that post-failure recoverability is intervention-dependent: resampling the original prompt provides a strong baseline, while prompt rewriting recovers a complementary subset of failures missed by seed exploration. This motivates viewing recovery as a budget-allocation problem over stochastic and semantic interventions rather than as uniform prompt refinement. We introduce FARE, a failure-aware inference-time framework that uses evidence from the initial failed generation to guide both recovery allocation and early trajectory pruning. FARE estimates the recovery utility of feasible prompt–seed portfolios and conservatively retains resampling unless an alternative allocation provides sufficient predicted benefit. It then explores the selected candidates in parallel and uses the observed failed trajectory as a request-specific reference to prune candidates likely to reproduce the same failure. Across multiple T2I benchmarks, FARE improves recovery over fixed recovery policies under matched post-failure budgets, and its advantage persists as the available inference budget increases. Across SD2.1, SDXL, and FLUX.1-dev, failure-aware allocation improves recovery over the train-selected fixed allocation by 3.6–4.6 percentage points. We further find that matched failure references provide stronger early recovery signals than shuffled references, enabling conservative pruning that reduces redundant denoising while largely preserving recovery. These results recast post-failure prompt optimization as a failure-conditioned inference-time search problem, in which prompt rewriting is one intervention alongside stochastic exploration rather than the default response to generation failure.