ProGraf: Profile-Guided Planning for Step-by-Step Generation of Structured Non-Natural Images

Structured visual artifacts such as geometry diagrams, circuit schematics, and flowcharts are increasingly important targets for image generation, yet visual plausibility alone is insufficient---a single missing object, wrong relation, broken connection, or extra element can invalidate the output. Existing image generators remain brittle on such tasks, and generic step-by-step or agentic methods often fail because intermediate edits may corrupt previously correct structures and ignore generator-specific failure modes. To address this, we present ProGraf, a profile-guided framework for structured non-natural image generation. We first build a benchmark of 304 tasks across geometry, circuit, flowchart, and general diagrams, each annotated with fine-grained verification points. From repeated generator outputs, we derive model-specific capability profiles capturing category-level reliability, component-level weaknesses, and recurrent failure patterns. ProGraf uses these profiles to guide closed-loop decomposition, verifies each intermediate result, commits only accepted states, and replans after failures. On generator-specific hardest subsets, ProGraf recovers 43.7--77.8\% of previously failed tasks across generators and categories, outperforming fixed-step and adapted agent baselines; ablation studies further show that benchmark-derived profiles are a key source of the gain.