Metaphorical text expresses meaning through cross-domain mappings rather than literal surface content, which makes it difficult for text-to-image systems to generate semantically faithful images. We propose CMIG, a structured prompting framework inspired by Conceptual Metaphor Theory (CMT). CMIG identifies source–target mappings, filters projectable source attributes, and selects a visual realization strategy in a reproducible reasoning workflow. Experiments on DALL⋅E 3, Imagen 2, and FLUX-1 show that CMIG consistently improves semantic alignment and yields a better overall balance of human-rated metaphor quality, visual coherence, and controllability on metaphorical prompts. To support systematic evaluation, we also construct a 3,500-instance visual metaphor benchmark.