Nano Banana can be prompt engineered for extremely nuanced AI image generation
You may not have heard about new AI image generation models as much lately, but that doesn’t mean that innovation in the field has stagnated: it’s quite the opposite. FLUX.1-dev immediately overshadowed the famous Stable Diffusion line of image generation models, while leading AI labs have released models such as Seedream , Ideogram , and Qwen-Image . Google also joined the action with Imagen 4 . But all of those image models are vastly overshadowed by ChatGPT’s free image generation support in March 2025. After going organically viral on social media with the prompt, ChatGPT became the new benchmark for how most people perceive AI-generated images, for better or for worse. The model has its own image “style” for common use cases, which make it easy to identify that ChatGPT made it. Two sample generations from ChatGPT. ChatGPT image generations often have a yellow hue in their images. Additionally, cartoons and text often have the same linework and typography. Of note, , the technical name of the underlying image generation model, is an autoregressive model. While most image generation models are diffusion-based to reduce the amount of compute needed to train and generate from such models, works by generating tokens in the same way that ChatGPT generates the next token, then decoding them into an image. It’s extremely slow at about 30 seconds to generate each image at the highest quality (the default in ChatGPT), but it’s hard for most people to argue with free. In August 2025, a new mysterious text-to-image model appeared on LMArena : a model code-named “nano-banana”. This model was eventually publically released by Google as Gemini 2.5 Flash Image , an image generation model that works natively with their Gemini 2.5 Flash model. Unlike Imagen 4, it is indeed autoregressive, generating 1,290 tokens per image. After Nano Banana’s popularity pushed the Gemini app to the top of the mobile App Stores, Google eventually made Nano Banana the colloquial name for the model as it’s definitely more catchy than “Gemini 2.5 Flash Image”. The first screenshot on the iOS App Store for the Gemini app. Personally, I care little about what leaderboards say which image generation AI looks the best. What I do care about is how well the AI adheres to the prompt I provide: if the model can’t follow the requirements I desire for the image—my requirements are often specific —then the model is a nonstarter for my use cases. At the least, if the model does have strong prompt adherence, any “looking bad” aspect can be fixed with prompt engineering and/or traditional image editing pipelines. After running Nano Banana though its paces with my comically complex prompts, I can confirm that thanks to Nano Banana’s robust text encoder, it has such extremely strong prompt adherence that Google has understated how well it works. Like ChatGPT, Google offers methods to generate images for free from Nano Banana. The most popular method is through Gemini itself, either on the web or in an mobile app, by selecting the “Create Image 🍌” tool. Alternatively, Google also offers free generation in Google AI Studio when Nano Banana is selected on the right sidebar, which also allows for setting generation parameters such as image aspect ratio and is therefore my recommendation. In both cases, the generated images have a visible watermark on the bottom right corner of the image. For developers who want to build apps that programmatically generate images from Nano Banana, Google offers the endpoint on the Gemini API . Each image generated costs roughly $0.04/image for a 1 megapixel image (e.g. 1024x1024 if a 1:1 square): on par with most modern popular diffusion models despite being autoregressive, and much cheaper than ’s $0.17/image. Working with the Gemini API is a pain and requires annoying image encoding/decoding boilerplate, so I wrote and open-sourced a Python package: gemimg , a lightweight wrapper around Gemini API’s Nano Banana endpoint that lets you generate images with a simple prompt, in addition to handling cases such as image input along with text prompts. I chose to use the Gemini API directly despite protests from my wallet for three reasons: a) web UIs to LLMs often have system prompts that interfere with user inputs and can give inconsistent output b) using the API will not show a visible watermark in the generated image, and c) I have some prompts in mind that are…inconvenient to put into a typical image generation UI. Let’s test Nano Banana out, but since we want to test prompt adherence specifically, we’ll start with more unusual prompts. My go-to test case is: I like this prompt because not only is an absurd prompt that gives the image generation model room to be creative, but the AI model also has to handle the maple syrup and how it would logically drip down from the top of the skull pancake and adhere to the bony breakfast. The result: That is indeed in the shape of a skull and is indeed made out of pancake batter, blueberries are indeed present on top, and the maple syrup does indeed drop down from the top of the pancake while still adhereing to its unusual shape, albeit some trails of syrup disappear/reappear. It’s one of the best results I’ve seen for this particular test, and it’s one that doesn’t have obvious signs of “AI slop” aside from the ridiculous premise. Now, we can try another one of Nano Banana’s touted features: editing. Image editing, where the prompt targets specific areas of the image while leaving everything else as unchanged as possible, has been difficult with diffusion-based models until very recently with Flux Kontext . Autoregressive models in theory should have an easier time doing so as it has a better understanding of tweaking specific tokens that correspond to areas of the image. While most image editing approaches encourage using a single edit command, I want to challenge Nano Banana. Therefore, I gave Nano Banana the generated skull pancake, along with five edit commands simultaneously: All five of the edits are implemented correctly with only the necessary aspects changed, such as removing the blueberries on top to make room for the mint garnish, and the pooling of the maple syrup on the new cookie-plate is adjusted. I’m legit impressed. Now we can test more difficult instances of prompt engineering. One of the most compelling-but-underdiscussed use cases of modern image generation models is being able to put the subject of an input image into another scene. For open-weights image generation models, it’s possible to “train” the models to learn a specific subject or person even if they are not notable enough to be in the original training dataset using a technique such as finetuning the model with a LoRA using only a few sample images of your desired subject. Training a LoRA is not only very computationally intensive/expensive, but it also requires care and precision and is not guaranteed to work—speaking from experience. Meanwhile, if Nano Banana can achieve the same subject consistency without requiring a LoRA, that opens up many fun oppertunities. Way back in 2022, I tested a technique that predated LoRAs known as textual inversion on the original Stable Diffusion in order to add a very important concept to the model: Ugly Sonic , from the initial trailer for the Sonic the Hedgehog movie back in 2019. One of the things I really wanted Ugly Sonic to do is to shake hands with former U.S. President Barack Obama , but that didn’t quite work out as expected. 2022 was a now-unrecognizable time where absurd errors in AI were celebrated. Can the real Ugly Sonic finally shake Obama’s hand? Of note, I chose this test case to assess image generation prompt adherence because image models may assume I’m prompting the original Sonic the Hedgehog and ignore the aspects of Ugly Sonic that are distinct to only him. Specifically, I’m looking for: I also confirmed that Ugly Sonic is not surfaced by Nano Banana, and prompting as such just makes a Sonic that is ugly, purchasing a back alley chili dog. I gave Gemini the two images of Ugly Sonic above (a close-up of his face and a full-body shot to establish relative proportions) and this prompt: That’s definitely Obama shaking hands with Ugly Sonic! That said, there are still issues: the color grading/background blur is too “aesthetic” and less photorealistic, Ugly Sonic has gloves, and the Ugly Sonic is insufficiently lanky. Back in the days of Stable Diffusion, the use of prompt engineering buzzwords such as , , and to generate “better” images in light of weak prompt text encoders were very controversial because it was difficult both subjectively and intuitively to determine if they actually generated better pictures. Obama shaking Ugly Sonic’s hand would be a historic event. What would happen if it were covered by The New York Times ? I added to the previous prompt: So there’s a few notable things going on here: That said, I only wanted the image of Obama and Ugly Sonic and not the entire New York Times A1. Can I just append to the previous prompt and have that be enough to generate the image only while maintaining the compositional bonuses? I can! The gloves are gone and his chest is white, although Ugly Sonic looks out-of-place in the unintentional sense. As an experiment, instead of only feeding two images of Ugly Sonic, I fed Nano Banana all the images of Ugly Sonic I had ( seventeen in total), along with the previous prompt. This is an improvement over the previous generated image: no eyebrows, white hands, and a genuinely uncanny vibe. Again, there aren’t many obvious signs of AI generation here: Ugly Sonic clearly has five fingers! That’s enough Ugly Sonic for now, but let’s recall what we’ve observed so far. There are two noteworthy things in the prior two examples: the use of a Markdown dashed list to indicate rules when editing, and the fact that specifying as a buzzword did indeed improve the composition of the output image. Many don’t know how image generating models actually encode text. In the case of the original Stable Diffusion, it used CLIP , whose text encoder open-sourced by OpenAI in 2021 which unexpectedly paved the way for modern AI image generation. It is extremely primitive relative to modern standards for transformer-based text encoding, and only has a context limit of 77 tokens: a couple sentences, which is sufficient for the image captions it was trained on but not nuanced input. Some modern image generators use T5 , an even older experimental text encoder released by Google that supports 512 tokens. Although modern image models can compensate for the age of these text encoders through robust data annotation during training the underlying image models, the text encoders cannot compensate for highly nuanced text inputs that fall outside the domain of general image captions. A marquee feature of Gemini 2.5 Flash is its support for agentic coding pipelines; to accomplish this, the model must be trained on extensive amounts of Markdown (which define code repository s and agentic behaviors in ) and JSON (which is used for structured output/function calling/MCP routing). Additionally, Gemini 2.5 Flash was also explictly trained to understand objects within images, giving it the ability to create nuanced segmentation masks . Nano Banana’s multimodal encoder, as an extension of Gemini 2.5 Flash, should in theory be able to leverage these properties to handle prompts beyond the typical image-caption-esque prompts. That’s not to mention the vast annotated image training datasets Google owns as a byproduct of Google Images and likely trained Nano Banana upon, which should allow it to semantically differentiate between an image that is and one that isn’t, as with similar buzzwords. Let’s give Nano Banana a relatively large and complex prompt, drawing from the learnings above and see how well it adheres to the nuanced rules specified by the prompt: This prompt has everything : specific composition and descriptions of different entities, the use of hex colors instead of a natural language color, a heterochromia constraint which requires the model to deduce the colors of each corresponding kitten’s eye from earlier in the prompt, and a typo of “San Francisco” that is definitely intentional. Each and every rule specified is followed. For comparison, I gave the same command to ChatGPT—which in theory has similar text encoding advantages as Nano Banana—and the results are worse both compositionally and aesthetically, with more tells of AI generation. 1 The yellow hue certainly makes the quality differential more noticeable. Additionally, no negative space is utilized, and only the middle cat has heterochromia but with the incorrect colors. Another thing about the text encoder is how the model generated unique relevant text in the image without being given the text within the prompt itself: we should test this further. If the base text encoder is indeed trained for agentic purposes, it should at-minimum be able to generate an image of code. Let’s say we want to generate an image of a minimal recursive Fibonacci sequence in Python, which would look something like: I gave Nano Banana this prompt: It tried to generate the correct corresponding code but the syntax highlighting/indentation didn’t quite work, so I’ll give it a pass. Nano Banana is definitely generating code, and was able to maintain the other compositional requirements. For posterity, I gave the same prompt to ChatGPT: It did a similar attempt at the code which indicates that code generation is indeed a fun quirk of multimodal autoregressive models. I don’t think I need to comment on the quality difference between the two images. An alternate explanation for text-in-image generation in Nano Banana would be the presence of prompt augmentation or a prompt rewriter, both of which are used to orient a prompt to generate more aligned images. Tampering with the user prompt is common with image generation APIs and aren’t an issue unless used poorly (which caused a PR debacle for Gemini last year), but it can be very annoying for testing. One way to verify if it’s present is to use adversarial prompt injection to get the model to output the prompt itself, e.g. if the prompt is being rewritten, asking it to generate the text “before” the prompt should get it to output the original prompt. That’s, uh, not the original prompt. Did I just leak Nano Banana’s system prompt completely by accident? The image is hard to read, but if it is the system prompt—the use of section headers implies it’s formatted in Markdown—then I can surgically extract parts of it to see just how the model ticks: These seem to track, but I want to learn more about those buzzwords in point #3: Huh, there’s a guard specifically against buzzwords? That seems unnecessary: my guess is that this rule is a hack intended to avoid the perception of model collapse by avoiding the generation of 2022-era AI images which would be annotated with those buzzwords. As an aside, you may have noticed the ALL CAPS text in this section, along with a command. There is a reason I have been sporadically capitalizing in previous prompts: caps does indeed work to ensure better adherence to the prompt (both for text and image generation), 2 and threats do tend to improve adherence. Some have called it sociopathic, but this generation is proof that this brand of sociopathy is approved by Google’s top AI engineers. Tangent aside, since “previous” text didn’t reveal the prompt, we should check the “current” text: That worked with one peculiar problem: the text “image” is flat-out missing, which raises further questions. Is “image” parsed as a special token? Maybe prompting “generate an image” to a generative image AI is a mistake. I tried the last logical prompt in the sequence: …which always raises a error: not surprising if there is no text after the original prompt. This section turned out unexpectedly long, but it’s enough to conclude that Nano Banana definitely has indications of benefitting from being trained on more than just image captions. Some aspects of Nano Banana’s system prompt imply the presence of a prompt rewriter, but if there is indeed a rewriter, I am skeptical it is triggering in this scenario, which implies that Nano Banana’s text generation is indeed linked to its strong base text encoder. But just how large and complex can we make these prompts and have Nano Banana adhere to them? Nano Banana supports a context window of 32,768 tokens: orders of magnitude above T5’s 512 tokens and CLIP’s 77 tokens. The intent of this large context window for Nano Banana is for multiturn conversations in Gemini where you can chat back-and-forth with the LLM on image edits. Given Nano Banana’s prompt adherence on small complex prompts, how well does the model handle larger-but-still-complex prompts? Can Nano Banana render a webpage accurately? I used a LLM to generate a bespoke single-page HTML file representing a Counter app, available here . The web page uses only vanilla HTML, CSS, and JavaScript, meaning that Nano Banana would need to figure out how they all relate in order to render the web page correctly. For example, the web page uses CSS Flexbox to set the ratio of the sidebar to the body in a 1/3 and 2/3 ratio respectively. Feeding this prompt to Nano Banana: That’s honestly better than expected, and the prompt cost 916 tokens. It got the overall layout and colors correct: the issues are more in the text typography, leaked classes/styles/JavaScript variables, and the sidebar:body ratio. No, there’s no practical use for having a generative AI render a webpage, but it’s a fun demo. A similar approach that does have a practical use is providing structured, extremely granular descriptions of objects for Nano Banana to render. What if we provided Nano Banana a JSON description of a person with extremely specific details, such as hair volume, fingernail length, and calf size? As with prompt buzzwords, JSON prompting AI models is a very controversial topic since images are not typically captioned with JSON, but there’s only one way to find out. I wrote a prompt augmentation pipeline of my own that takes in a user-input description of a quirky human character, e.g. , and outputs a very long and detailed JSON object representing that character with a strong emphasis on unique character design. 3 But generating a Mage is boring, so I asked my script to generate a male character that is an equal combination of a Paladin, a Pirate, and a Starbucks Barista: the resulting JSON is here . The prompt I gave to Nano Banana to generate a photorealistic character was: Beforehand I admit I didn’t know what a Paladin/Pirate/Starbucks Barista would look like, but he is definitely a Paladin/Pirate/Starbucks Barista. Let’s compare against the input JSON, taking elements from all areas of the JSON object (about 2600 tokens total) to see how well Nano Banana parsed it: Checking the JSON field-by-field, the generation also fits most of the smaller details noted. However, he is not photorealistic, which is what I was going for. One curious behavior I found is that any approach of generating an image of a high fantasy character in this manner has a very high probability of resulting in a digital illustration, even after changing the target publication and adding “do not generate a digital illustration” to the prompt. The solution requires a more clever approach to prompt engineering: add phrases and compositional constraints that imply a heavy physicality to the image, such that a digital illustration would have more difficulty satisfying all of the specified conditions than a photorealistic generation: The image style is definitely closer to Vanity Fair (the photographer is reflected in his breastplate!), and most of the attributes in the previous illustration also apply—the hands/cutlass issue is also fixed. Several elements such as the shoulderplates are different, but not in a manner that contradicts the JSON field descriptions: perhaps that’s a sign that these JSON fields can be prompt engineered to be even more nuanced. Yes, prompting image generation models with HTML and JSON is silly, but “it’s not silly if it works” describes most of modern AI engineering. Nano Banana allows for very strong generation control, but there are several issues. Let’s go back to the original example that made ChatGPT’s image generation go viral: . I ran that exact prompt through Nano Banana on a mirror selfie of myself: …I’m not giving Nano Banana a pass this time. Surprisingly, Nano Banana is terrible at style transfer even with prompt engineering shenanigans, which is not the case with any other modern image editing model. I suspect that the autoregressive properties that allow Nano Banana’s excellent text editing make it too resistant to changing styles. That said, creating a new image does in fact work as expected, and creating a new image using the character provided in the input image with the specified style (as opposed to a style transfer ) has occasional success. Speaking of that, Nano Banana has essentially no restrictions on intellectual property as the examples throughout this blog post have made evident. Not only will it not refuse to generate images from popular IP like ChatGPT now does, you can have many different IPs in a single image. Normally, Optimus Prime is the designated driver. I am not a lawyer so I cannot litigate the legalities of training/generating IP in this manner or whether intentionally specifying an IP in a prompt but also stating “do not include any watermarks” is a legal issue: my only goal is to demonstrate what is currently possible with Nano Banana. I suspect that if precedent is set from existing IP lawsuits against OpenAI and Midjourney , Google will be in line to be sued. Another note is moderation of generated images, particularly around NSFW content, which always important to check if your application uses untrusted user input. As with most image generation APIs, moderation is done against both the text prompt and the raw generated image. That said, while running my standard test suite for new image generation models, I found that Nano Banana is surprisingly one of the more lenient AI APIs. With some deliberate prompts, I can confirm that it is possible to generate NSFW images through Nano Banana—obviously I cannot provide examples. I’ve spent a very large amount of time overall with Nano Banana and although it has a lot of promise, some may ask why I am writing about how to use it to create highly-specific high-quality images during a time where generative AI has threatened creative jobs. The reason is that information asymmetry between what generative image AI can and can’t do has only grown in recent months: many still think that ChatGPT is the only way to generate images and that all AI-generated images are wavy AI slop with a piss yellow filter. The only way to counter this perception is though evidence and reproducibility. That is why not only am I releasing Jupyter Notebooks detailing the image generation pipeline for each image in this blog post, but why I also included the prompts in this blog post proper; I apologize that it padded the length of the post to 26 minutes, but it’s important to show that these image generations are as advertised and not the result of AI boosterism. You can copy these prompts and paste them into AI Studio and get similar results, or even hack and iterate on them to find new things. Most of the prompting techniques in this blog post are already well-known by AI engineers far more skilled than myself, and turning a blind eye won’t stop people from using generative image AI in this manner. I didn’t go into this blog post expecting it to be a journey, but sometimes the unexpected journeys are the best journeys. There are many cool tricks with Nano Banana I cut from this blog post due to length, such as providing an image to specify character positions and also investigations of styles such as pixel art that most image generation models struggle with, but Nano Banana now nails. These prompt engineering shenanigans are only the tip of the iceberg. Jupyter Notebooks for the generations used in this post are split between the gemimg repository and a second testing repository . I would have preferred to compare the generations directly from the endpoint for an apples-to-apples comparison, but OpenAI requires organization verification to access it, and I am not giving OpenAI my legal ID. ↩︎ Note that ALL CAPS will not work with CLIP-based image generation models at a technical level, as CLIP’s text encoder is uncased. ↩︎ Although normally I open-source every script I write for my blog posts, I cannot open-source the character generation script due to extensive testing showing it may lean too heavily into stereotypes. Although adding guardrails successfully reduces the presence of said stereotypes and makes the output more interesting, there may be unexpected negative externalities if open-sourced. ↩︎ A lanky build, as opposed to the real Sonic’s chubby build. A white chest, as opposed to the real Sonic’s beige chest. Blue arms with white hands, as opposed to the real Sonic’s beige arms with white gloves. Small pasted-on-his-head eyes with no eyebrows, as opposed to the real Sonic’s large recessed eyes and eyebrows. That is the most cleanly-rendered New York Times logo I’ve ever seen. It’s safe to say that Nano Banana trained on the New York Times in some form. Nano Banana is still bad at rendering text perfectly/without typos as most image generation models. However, the expanded text is peculiar: it does follow from the prompt, although “Blue Blur” is a nickname for the normal Sonic the Hedgehog. How does an image generating model generate logical text unprompted anyways? Ugly Sonic is even more like normal Sonic in this iteration: I suspect the “Blue Blur” may have anchored the autoregressive generation to be more Sonic-like. The image itself does appear to be more professional, and notably has the distinct composition of a photo from a professional news photographer: adherence to the “rule of thirds”, good use of negative space, and better color balance. , mostly check. (the hands are transposed and the cutlass disappears) I would have preferred to compare the generations directly from the endpoint for an apples-to-apples comparison, but OpenAI requires organization verification to access it, and I am not giving OpenAI my legal ID. ↩︎ Note that ALL CAPS will not work with CLIP-based image generation models at a technical level, as CLIP’s text encoder is uncased. ↩︎ Although normally I open-source every script I write for my blog posts, I cannot open-source the character generation script due to extensive testing showing it may lean too heavily into stereotypes. Although adding guardrails successfully reduces the presence of said stereotypes and makes the output more interesting, there may be unexpected negative externalities if open-sourced. ↩︎
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