Create and edit images with Gemini 2.0 in preview

I've added/tested this multimodal Gemini 2.0 to my shoot-out of SOTA image gen models (OpenAI 4o, Midjourney 7, Flux, etc.) which contains a collection of increasingly difficult prompts.

https://genai-showdown.specr.net

I don't know how much of Google's original Imagen 3.0 is incorporated into this new model, but the overall aesthetic quality seems to be unfortunately significantly worse.

The big "wins" are:

- Multimodal aspect in trying to keep parity with OpenAI's offerings.

- An order of magnitude faster than OpenAI 4o image gen

This seems neat, I guess. But whenever I try tools like this, I often run into the limits of what I can describe in words. I might try something like "Add some clutter to the desk, including stacks of paper and notebooks" but when it doesn't quite look like what I want, I'm not sure what else to do except try slightly different wordings until the output happens to land on what I want.

I'm sure part of this is a lack of imagination on my part about how to describe the vague image in my own head. But I guess I have a lot of doubts about using a conversational interface for this kind of stuff

> what the lamp from the second image would look like on the desk from the first image

The lamp is put on a different desk in a totally different room, with AI mush in the foreground. Props for not cherry-picking a first example, I guess. The sofa colour one is somehow much better, with a less specific instruction.

The doodle demo is super fun

https://aistudio.google.com/apps/bundled/gemini-co-drawing?s...

Be a bit careful playing with this one. I tried this:

  curl -s -X POST \
    "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-preview-image-generation:generateContent?key=$GEMINI_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "contents": [{
        "parts": [
          {"text": "Provide a vegetarian recipe for butter chicken but with chickpeas not chicken and include many inline illustrations along the way"}
        ]
      }],
      "generationConfig":{"responseModalities":["TEXT","IMAGE"]}
    }' > /tmp/out.json

At 4c per image that's more than a dollar on that single prompt.

I built this quick tool https://tools.simonwillison.net/gemini-image-json for pasting that JSON into to see it rendered.

I generated 100 recipes with images using gemini-2.0-flash and gemini-2.0-flash-exp-image-generation as a demo of text+image generation in my open-source project: https://github.com/Yiling-J/tablepilot/tree/main/examples/10...

You can see the full table with images here: https://tabulator-ai.notion.site/1df2066c65b580e9ad76dbd12ae...

I think the results came out quiet well. Be aware I don't generate a text prompt based on row data for image generation. Instead, the raw row data(ingredients, instructions...) and table metadata(column names and descriptions) are sent directly to gemini-2.0-flash-exp-image-generation.

Of note is that the per-image pricing for Gemini 2.0 image generation is $0.039 per image, which is more expensive than Imagen 3 ($0.03 per image): https://ai.google.dev/gemini-api/docs/pricing

The main difference is that Gemini does allow for incorporating a conversation to generate the image as demoed here, while Imagen 3 is a strict text-in/image-out with optional mask-constrained edits but likely allows for higher-quality images overall if skilled with prompt engineering. This is a nuance that is annoying to differentiate.

Model outputs look good-ish. I think they are neat. I updated my recent hack project https://lifestyle.photo to the new model. It's middling-to-good.

There are a lot of failure modes still but what I want is a very large cookbook showing what known-good workflows are. Since this is just so directly downstream of (limited) training data it might be that I am just prompting in a ever so slightly bad way.

I'm getting mixed results with the co-drawing demo, in terms of understanding what stick figures are, which seems pretty important for the 99% of us who can't draw a realistic human. I was hoping to sketch a scene, and let the model "inflate" it, but I ended up with 3D rendered stick figures.

Seems to help if you explicitly describe the scene, but then the drawing-along aspect seem relatively pointless.

There are direct prompt tests and then there are tests with tooling.

If for example you use controlnets you can pretty much get very close to a style composition that you need with an open model like Flux that will be far better. Flux has a few successors coming up now

I use gemini to create covers for songs/albums i make, with beautiful typography. Something like this [1]. I was dying of curiosity, how ideogram managed to create such gorgeous images. I figured it out 2 days ago.

I take an image with some desired colors or typography from an already existing music album or from Ideogram's poster section. I pass it to gemini and give the command:

"describe the texture of the picture, all the element and their position in the picture, left side, center right side, up and down, the color using rgb, the artistic style and the calligraphy or font of the letters"

Then i take the result and pass it through an LLM, a different LLM because i don't like gemini that much, i find it is much less coherent than other models. I use qwen-qwq-32b usually and I take the description gemini outputs and give it to qwen:

" write a similar description, but this time i want a surreal painting with several imaginative colors. Follow the example of image description, add several new and beautiful shapes of all elements and give all details, every side which brushstrokes it uses, and rgb colors it uses, the color palette of the elements of the page, i want it to be a pastel painting like the example, and don't put bioluminesence. I want it to be old style retro style mystery sci fi. Also i want to have a title of "Song Title" and describe the artistic font it uses and it's position in the painting, it should be designed as a drum n bass album cover "*

Then i take the result and give it back to gemini with command: "Create an image with text "Song Title" for an album cover: here is the description of the rest of the album"

If the resulting image is good, then it is time to add font, i take the new image description and pass it through qwen again, supposing the image description has fields Title and Typography:

"rewrite the description and add full description of the letters and font of text, clean or distressed, jagged or fluid letters or any other property they might have, where they are overlayed, and make some new patterns about the letter appearance and how big they are and the material they are made of, rewrite the Title and Typography."

I replace the previous description's section Title and Typography with the new description and create images with beautiful fonts.

[1] https://imgur.com/a/8TCUJ75

I want to take a step back and reflect on what this actually shows us. Look at the examples Google provides: it refers to the generated objects as "products", clearly pointing toward shopping or e-commerce use cases.

It seems like the real goal here, for Google and other AI companies, is a world flooded with endless AI-generated variants of objects that don’t even exist yet, crafted to be sold and marketed (probably by AI too) to hyper-targeted audiences. This feels like an incoming wave of "AI slop", mass-produced synthetic content, crashing against the small island of genuine human craftsmanship and real, existing objects.