Unusual Capabilities of Nano Banana (Examples)

I've come to realize that I liked believing that there was something special about the human mental ability to use our mind's eye and visual imagination to picture something, such as how we would look with a different hairstyle. It's uncomfortable seeing that skill reproduced by machinery at the same level as my own imagination, or even better. It makes me feel like my ability to use my imagination is no more remarkable than my ability to hold a coat off the ground like a coat hook would.

Amazing model. The only limit is your imagination, and it's only $0.04/image.

Since the page doesn't mention it, this is the Google Gemini Image Generation model: https://ai.google.dev/gemini-api/docs/image-generation

Good collection of examples. Really weird to choose an inappropriate for work one as the second example.

Nano-Banana can produce some astonishing results. I maintain a comparison website for state-of-the-art image models with a very high focus on adherence across a wide variety of text-to-image prompts.

I recently finished putting together an Editing Comparison Showdown counterpart where the focus is still adherence but testing the ability to make localized edits of existing images using pure text prompts. It's currently comparing 6 multimodal models including Nano-Banana, Kontext Max, Qwen 20b, etc.

https://genai-showdown.specr.net/image-editing

Gemini Flash 2.5 leads with a score of 7 out of 12, but Kontext comes in at 5 out of 12 which is especially surprising considering you can run the Dev model of it locally.

This is amazing. Not that long ago, even getting a model to reliably output the same character multiple times was a real challenge. Now we’re seeing this level of composition and consistency. The pace of progress in generative models is wild.

Huge thanks to the author (and the many contributors) as well for gathering so many examples; it’s incredibly useful to see them to better understand the possibilities of the tool.

I recently released a Python package for easily generating images with Nano Banana: https://github.com/minimaxir/gemimg

Through that testing, there is one prompt engineering trend that was consistent but controversial: both a) LLM-style prompt engineering with with Markdown-formated lists and b) old-school AI image style quality syntatic sugar such as award-winning and DSLR camera are both extremely effective with Gemini 2.5 Flash Image, due to its text encoder and larger training dataset which can now more accurately discriminate which specific image traits are present in an award-winning image and what traits aren't. I've tried generations both with and without those tricks and the tricks definitely have an impact. Google's developer documentation encourages the latter.

However, taking advantage of the 32k context window (compared to 512 for most other models) can make things interesting. It’s possible to render HTML as an image (https://github.com/minimaxir/gemimg/blob/main/docs/notebooks...) and providing highly nuanced JSON can allow for consistent generations. (https://github.com/minimaxir/gemimg/blob/main/docs/notebooks...)

This is the first time I really don't understand how people are getting good results. On https://aistudio.google.com with Nano Banana selected (gemini-2.5-flash-image-preview) I get - garbage - results. I'll upload a character reference photo and a scene and ask Gemini to place the character in the scene. What it then does is to simply cut and paste the character into the scene, even if they are completely different in style, colours, etc.

I get far better results using ChatGPT for example. Of course, the character seldom looks anything like the reference, but it looks better than what I could do in paint in two minutes.

Am I using the wrong model, somehow??

Personally, I'm underwhelmed by this model. I feel like these examples are cherry-picked. Here are some fails I've had:

- Given a face shot in direct sunlight with severe shadows, it would not remove the shadows

- Given an old black and white photo, it would not render the image in vibrant color as if taken with a modern DSLR camera. It will colorize the photo, but only with washed out, tinted colors

- When trying to reproduce the 3 x 3 grid of hair styles, it repeatedly created a 2x3 grid. Finally, it made a 3x3 grid, but one of the nine models was black instead of caucasian.

- It is unable to integrate real images into fabricated imagery. For example, when given an image of a tutu and asked to create an image of a dolphin flying over clouds wearing the tutu, the result looks like a crude photoshop snip and copy/paste job.

I'm pretty sure these are cherry-picked out of many generation attempts, I tried a few basic things and it flat out refused to do many of them like turning a cartoon illustration into a real-world photographic portrait, it kept wanting to create a pixar style image, then when I used an ai generated portrait as an example, it refused with an error saying it wouldn't modify real world people...

I then tried to generate some multi-angle product shots from a single photo of an object, and it just refused to do the whole left, right, front, back thing, and kept doing things like a left, a front, another left, and weird half back/half side view combination.

Very frustrating.

I have two friends who are excellent professional graphic artists and I hesitate to send them this.

I added some of these examples into my Nanna Banana image generator: https://nannabanana.ai

While these are incredibly good, it's sad to think about the unfathomable amount of abuse, spam, disinformation, manipulation and who know what other negatives these advancement are gonna cause. It was one thing when you could spot an AI image, but now and moving forward it's be basically increasingly futile to even try.

Almost all "human" interaction online will be subject to doubt soon enough.

Hard to be cheerful when technology will be a net negative overall even if it benefits some.