EvoSkillGen: Hierarchical Evolution of Compositional Skills for Agentic Image Generation

Image generators can synthesize high-fidelity images, but complex requests involving external knowledge, compositional and spatial constraints, or targeted edits with content preservation often require more than one-shot synthesis. Agentic image generation addresses such requests through retrieval, reasoning, and planning. Yet tasks demand different operations and information flows; selecting predefined workflows alone does not turn execution experience into improved reusable procedures. We propose **EvoSkillGen**, a self-evolving framework for text-to-image generation and image editing. It represents workflows as executable Skills composed of meta-operations with explicit instructions and artifact dependencies. Skill Adaptation (L1) selects task-specific workflows and constructs temporary Skills during evolution; Skill-Bank Evolution (L2) checks generated images against task requirements and uses execution traces to revise Skills and distill temporary workflows. The quality of generated images guides which candidate changes are retained in the Skill bank. We also introduce GenAgentEval, an evaluation suite built from six existing benchmarks to assess final-image quality, workflow-time overhead, and execution-trace diagnostics. Averaged over both judges, EvoSkillGen improves macro-averaged image-quality scores over the strongest baselines by 0.9–1.8 points for text-to-image and 2.3–2.8 for editing across agent configurations. Among agent methods, its workflow-time overhead is second-lowest for text-to-image and lowest for editing.