Skip to main content

Gemini Nano Banana

Gemini Nano Banana: Create and Edit Images

Updated 23 September 2026. "Nano Banana" is Google’s name for image generation and editing models available through Gemini. Google’s current help describes Nano Banana 2 Lite, Nano Banana 2, and Nano Banana Pro. Names, limits, and availability can change, so check the linked Google help page before relying on a feature.

Which model should you choose?

OptionUseful forWhat to know
Nano Banana 2 LiteFast drafts and simple image ideasGoogle lists this with the Flash-Lite model. It is not designed for multiple image references or multiple edit turns.
Nano Banana 2Everyday image generation and editingGoogle lists this with Flash or Pro. It can use multiple reference images and is designed for improved text rendering.
Nano Banana ProMore detailed output and finer creative controlAccess depends on an eligible Google AI plan and model selection. Check current plan terms and daily limits in Gemini.

The table summarizes Google’s current documentation, not a guarantee that every option is available to every account or region.

Generate or edit an image

  1. Open Gemini and choose the image creation area or an available image model.
  2. Describe the subject, action, setting, composition, and visual style. Add lighting, colors, and aspect ratio when they matter.
  3. To edit an image, upload an image you have permission to use and describe the single change you want. State what must stay unchanged.
  4. Review the result closely. If it is nearly right, ask for one focused follow-up change rather than rewriting the whole prompt.

A prompt structure you can reuse

Make a [type of image] showing [subject] [action] in [setting].
Use [style], [composition], and [lighting]. Keep [important details] unchanged.
Use a [aspect ratio] layout. Do not add text unless I provide the exact wording.

Example: Create a clean editorial illustration of a small coastal market at dusk. Show three produce stalls beneath striped awnings, viewed from street level. Use a warm amber and deep blue palette, soft lantern light, and clear shapes. Leave signs blank and keep the scene uncluttered. Use a 4:5 portrait layout.

Editing example: Edit the uploaded product photo by changing only the background to a pale stone surface with soft side lighting. Keep the item, label, colors, and camera angle unchanged. Do not invent or alter any label text.

Check the result before using it

  • Proofread any words in the image; generated lettering can still be wrong.
  • Check small details, proportions, and factual diagrams. A polished image can still contain mistakes.
  • Use photos, faces, logos, and artwork only when you have the rights or permission to use them. Avoid creating misleading images of real people.
  • Check the current Gemini terms and plan limits before using an image commercially.

Official references

The images below are AI-generated examples retained from the earlier version of this page; they are illustrations, not photographs of real products.

AI-generated example image
AI-generated example image
AI-generated product mockup example

Comments

Post a Comment

Popular posts from this blog

AI Learning Roadmap for Beginners: From Basics to Projects

AI Learning Roadmap for Beginners: From Basics to Projects Updated 23 September 2026. Learning AI is easier when you build one layer at a time: understand the task, learn enough programming and data skills to experiment, study basic machine learning, and then choose a specialty. You do not need to master every branch before starting a small project. First, choose a goal AI is a broad field. Decide whether you want to understand how AI products work, build predictive models, work with generative AI, or evaluate AI systems. The first steps overlap, but the projects and depth of mathematics differ. This roadmap is a practical starting sequence, not a credential or a promise of a job. 1. Learn the basic vocabulary Start with the difference between a task and a technique. Artificial intelligence is the broad area of building systems that perform tasks associated with perception, reasoning, language, or action. Machine learning is one approach: a model uses examples to learn patterns f...
Post a Comment
Read more

AI in Video Games: What Works Today and What’s Still Experimental

Updated 24 September 2026. AI has been used in games for years, including for movement, opponent behavior, and procedural systems. Newer generative tools add possibilities such as conversational characters and assisted asset creation. These capabilities are uneven: a research demo or developer tool does not mean every released game has adaptive dialogue, a changing story, or an AI-controlled world. Game AI ranges from scripted rules to newer systems for generating or adapting content. AI already used in game development “Game AI” does not always mean a large language model. Developers can use rules, search, navigation, state machines, and other techniques to guide non-player characters (NPCs), opponents, or world systems. The behavior is designed for a particular game and its constraints. It is not necessarily learning from a player or inventing a response on the spot. Procedural generation is another established approach: software applies rules or algorithms to create or arrange cont...
Post a Comment
Read more

AI vs Machine Learning vs Deep Learning: What’s the Difference?

Updated 24 September 2026. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are related terms, but they describe different levels of a technology stack. This guide explains the relationship with one practical example and a checklist for choosing a starting point. AI is the broad field; machine learning is one approach within it, and deep learning is one family of machine-learning methods. How the terms fit together Artificial intelligence is the broad field of building computer systems that perform tasks associated with perception, prediction, language, planning, or decision-making. AI includes many approaches, including rule-based systems and machine learning. Machine learning is an approach in which a model uses examples to learn patterns for a defined task. People still choose the task, prepare data, select an objective, train the model, and check its results. The model learns parameters from data; it does not remove the need for instructions or e...
Post a Comment
Read more