Generating Consistent Illustrations with Gemini Image Generation (www.tinystruggles.com)

Storylearner.app details a practical pipeline for generating hundreds of visually consistent book illustrations using Gemini multimodal models (chosen for speed and free experimental access). The team breaks the problem into three stages—idea generation (produce three rich scene concepts per excerpt), intelligent selection (an AI selector picks a diverse, non-repetitive set), and image generation (style guidelines + reference images + persistent chat sessions + retries). Key technical tactics include uploading a generated character image as a reference and instructing the model to “use the supplied image as a reference,” carefully managing seeds (same model+prompt+seed = identical results; small changes can produce big character drift), and avoiding face close-ups or violent content to reduce failure modes. A Colab companion demonstrates the approach and includes example code using GenerateContentConfig and response_modalities for text+image. The writeup emphasizes concrete engineering lessons: decouple ideation from rendering to improve quality and debuggability, craft granular style guidelines (the article gives a watercolor example with brushwork, palette, and texture rules), and keep sessions stateful but beware fragility—long chat sessions can degrade and accidentally copy elements across images. Overall, the piece is a hands-on blueprint showing how to move beyond isolated demos to production-ready illustration pipelines, with reproducibility, reference-driven consistency, and modular stages as the core levers for scaling narrative art generation.