How Gemini 2.5 Flash Image (Nano Banana) AI Model Work?

Image generation models are evolving rapidly and we can find new image generation tools and updates every day. Well, each models have its strengths, unparalleled in terms of features, and therefore different models have different use cases.

Some focus on creative style and speed while other focus on realism art generation. One such example is Gemini 2.5 Flash Image model which is also known by Nano Banana, extremely hyped for its speed and character consistency output.

In fact, Josh Woodward, VP@GeminiApp and @GoogleAIStudio shared an update on X that over 200 million images have been edited and over 10 million people using Nano Banana are new to the Gemini app.

Additionally, several tech journalists and professionals have praised Nano Banana for its features and capabilities. The Washington Post called it “masterful” at manipulating photos with simple prompts. TechRadar found it superior for character consistency, realism, and image-to-image fusion.

These lot of positive verdict genuinely pointing to one thing that Gemini 2.5 Flash Image model is well-versed trained with reinforcement learning techniques.

Understanding Workflow Of  Gemini 2.5 Flash Image (Nano Banana) Image Generation Model

1. User input the prompt (text/image/edit instructions)

The workflow starts to happen when a user provide the prompt. The prompt can be text-only, text + reference images, or even edit instructions for an existing image.

For example, Make the following edits to the image

• Remove all people from the background.
• Remove skin imperfections and marks.
• Make the hair color more vibrant and bright.

The above example incubates text + reference image along with multi-step prompt for editing.

2. Prompt understanding using Gemini (LLM layer with self attention mechanism)

Gemini which is an Large Language Model parses the input through tokenization (like any Transformer model), breaks text into tokens and mapped to embeddings.

It uses self-attention mechanism within the transformer architecture to understand and preserve the word to word meaning accurately.

For example, the given prompt breaks into token (grouping the words, number, and characters) and feeds into the self-attention mechanism to understand the context meaningfully.

3. Multimodal embedding comes into the picture where text encoder and vision encoder happen into vectors

It may sound technical and yes it is because after tokenization and context understanding, they turns into embeddings (a numerical representation often called vector that capture its semantic meaning) which stored in a high dimensional space.

Encoder LLMs are responsible for embedding process and therefore text encoder and vision encoder used to make shared embedding space.

Text encoder LLM converts your prompt into semantic embeddings. On the other hand, Vision encoder LLM encodes identity, colors, layout.

4. Decoder LLM interprets the embeddings and plan how to edit/generate the image

The decoder LLM which is the reasoning brain interprets embeddings. This step is not about understanding embeddings properly but about planning how to edit and generate the image.

For example., If you say “Put a cat in the Colosseum wearing a Roman helmet” → it builds a structured plan: [object=cat][scene=Colosseum][attribute=Roman helmet].

Now the diffusion pipeline kicks in. It starts from noise in latent space (guided step by step by the text-image embeddings). Nano Banana applies identity-preserving embeddings so faces, pets, or objects don’t drift between edits. This feature makes it stronger than MidJourney/Stable Diffusion for multi-turn editing.

5. SynthID Checking & Image Generation

At last, image generation happen and simultaneously SynthID watermark processing the image for safety filters such as violence, NSFW, political misuse, etc.

After these checking done, the final image is show to the user with affected requests given. Users can further enhance the image or make more tweaks and download locally.

Technologies Behind The Nano Banana Image Generation Model

1) Gemini LLM

Gemini 2.5 represents a sophisticated artificial intelligence developed by Google. It possesses the capability to comprehend and produce human language. Furthermore it can discern connections between various forms of information. This includes written words, visual pictures, and even moving images.

Essentially consider Gemini as the central intelligence for Nano Banana. It is the component that processes user requests. It then grasps the surrounding circumstances. This understanding is then conveyed to the visual generation system.

Consequently users can provide straightforward instructions. For instance one might request a photograph be transformed into a vintage painting style. This eliminates the need for complex specific commands. Gemini effortlessly connects everyday language with the creative abilities of artificial intelligence.

2) Encoder-Decoder Self-Attention Mechanism

An artificial intelligence system processes your words in a thoughtful manner. It carefully considers each part of your input. Think of it like someone listening intently to a story. They might mentally revisit sections to grasp every detail. This is similar to how the system operates.

The system first divides your message into understandable segments. It then identifies the most significant pieces of information. Following this the system reconstructs these pieces. This reconstruction allows it to produce a fitting reply.

Therefore the AI does not simply accept your words as they are. It also comprehends the underlying significance. Furthermore it recognizes the connections between different words.

3) Stable Diffusion Architecture

The system known as Stable Diffusion offers a way to generate pictures from written descriptions. Imagine an artist beginning a painting. They do not start with a finished product. Instead the process begins with a canvas filled with random patterns.

This is similar to a blurry starting point. The system then works through this randomness. It makes small adjustments over time. This gradual refinement leads to a clear picture. This approach enables the artificial intelligence to create images that are very lifelike and full of detail.

A key strength lies in its step by step creation. It does not simply produce an image all at once. Rather it carefully develops the image through distinct phases. This mirrors an artist’s journey from initial sketches to a polished artwork.

4) SynthID Checking

SynthID provides a discreet method for discerning images produced by artificial intelligence. Consider it a concealed mark within each AI created picture.

This mark remains unseen by people. However specific instruments can find it later. This capability holds significant value in our current environment. False or changed pictures can proliferate rapidly.

Utilizing SynthID allows organizations and individuals to track an image’s AI source. This facilitates distinguishing genuine photographs from those made by machines. It also assists in lessening the danger of incorrect information or improper application.

Frequently Asked Questions

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Bharat Kumar

Bharat is a content editor at The Next Tech for the past 3 years. He is studying Generative AI (GenAI) from Analytics Vidhya and share his learnings by writing on Generative Engines, Large Language Models, and Artificial Intelligence. In addition to his editorial work, Bharat is active on LinkedIn, where he shares bite-sized updates and achievements. Outside work, he’s known as a Silver‑rank Valorant player, reflecting his competitive edge and strategic mindset.

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