Reviewer Group: Senior Prompt Engineers and Generative AI Model Researchers.
Abstract:
This guide details optimal prompting strategies for the Nano Banana Pro (NBP) image generation model, emphasizing its versatility and advanced multimodal capabilities. NBP demonstrates high coherence across simple and complex prompts, but precision requires specific methodologies. Key prompting techniques include rapid iterative refinement, the effective use of negative prompts to manage default biases (e.g., rustic styles, unwanted elements like date stamps), and the application of structured JSON formats for granular control over technical and artistic parameters (e.g., lighting, aspect ratio, camera angle). The model excels in high-fidelity text rendering and maintaining consistent character identity across multiple references (up to five). Furthermore, NBP serves as a robust tool for visual reasoning challenges, object/style referencing, branding integration, real-time data grounding via Google Search, and high-quality image upscaling/restoration.
Nano Banana Pro Prompt Engineering Guide: Core Methodologies and Capabilities
Model Flexibility and Iteration: NBP is highly capable, requiring minimal input for coherent results. Initial interaction should focus on minimal prompts ("a photo") followed by iterative refinement and expansion to achieve complex scene definitions (e.g., detailed kitchen scenes, specific lighting conditions).
Being Detailed with Prompts: While NBP makes reasonable assumptions, specific outputs necessitate highly detailed prose, often requiring iterative adjustment to match exact user specifications.
Negative Prompting: This is a crucial technique for enforcing constraints and counteracting model defaults. Common negatives include no date stamp, no text, and not rustic, or specific content exclusions like No monkeys.
JSON Prompting (Structured Data): Utilizing JSON structure facilitates less verbose but more precise control over image characteristics, including promptDetails (style tags), scene (background/subject details), overlayObject (UI elements), and technicalStyle (aspect ratio, camera parameters, lighting type).
Text Handling: NBP possesses strong capabilities for rendering text in diverse styles (cursive, 3D word art) and orientations. For lengthy or critical text, the prompt must explicitly request a verbatim copy to ensure legibility and coherence, as long text generated organically by the model may become nonsensical.
Consistent Characters: Characters can be consistently rendered using reference images. High fidelity is achievable with a single image, but providing a range of references (close-ups, full body, varying poses/expressions) enhances accuracy and variation. The model supports high-fidelity consistency for up to five distinct characters simultaneously.
Reference Image Utilization (General): References extend beyond characters to influence object placement, style transfer, brand integration (logos), and color palettes. This capability is essential for depicting new products or items created after the model’s internal January 2025 data cutoff.
Advanced Visual Reasoning: NBP functions as a language model with strong multimodal understanding, capable of addressing visual challenges (e.g., solving crosswords) and interpreting image content (e.g., providing explanations or predicting subsequent events).
Tool Integration:
Google Search: When enabled, search grounding allows NBP to create images related to current events or real-time data, though the grounding is purely textual, not visual.
Upscaling and Restoration: NBP operates as a native high-fidelity upscaler, capable of enhancing small inputs (150x150) to 2K or 4K resolutions and faithfully restoring old or damaged photographs.