A developer building Stitchboard, a marketing card tool, hit a wall trying to render crisp, editable text inside AI-generated images. Using SD3.5 Medium for backgrounds worked, but text output was illegible. Switching to a dedicated typography model solved the core problem, moving from scrambled letters to predictable results.

The author tested several Ideogram variants, finding V3 delivered the highest fidelity for text synthesis. A three-hour debugging session revealed a model-side error: 'typography_alignment_failed'. The fix wasn't prompt tweaking, but selecting a model family trained on typography-heavy datasets. This shifted the pipeline from a single, unreliable model to a specialized architecture.

The final shipped pipeline splits responsibilities: a fast model for background/style, Ideogram V3 for text compositing, and an SVG overlay to keep copy editable. The trade-off is increased complexity and cost from multiple model calls. For high-volume thumbnail generation, a single-model solution might still be preferable.