Image Generation with Gemini 2.0 Flash Experimental – Not Quite What I Expected!

Google is on a spree updating their GenAI stack with their all-new Gemini 2.0 Flash Experimental. The major updates have been made with their deep research and image generation features. With its text and image processing capabilities, the model has the potential to significantly improve our interactions with chatbots. It is set to bring a visual element to our conversations. In this blog, we will explore image generation with the Gemini 2.0 Flash (Experimental) model, understand its features, and test its capabilities. Let’s start.

What is Gemini 2.0 Flash?

Gemini 2.0 Flash (Experimental) is a multimodal model by Google that seamlessly integrates text and image generation under a single simplified framework. The 2.0 Flash (Experimental) LLM was launched in December for a small pool of testers, it is now available for developer experimentation via Google AI Studio and the Gemini API.

Why Use Gemini 2.0 Flash for Image Generation?

Gemini 2.0 Flash comes with a great set of capabilities. It caters to a diverse set of issues that we usually see with most of the image generation models like their inability to: 

1. Work with text
2. Maintain consistency across multiple images
3. Edit existing images
4. Merge images within conversations.

Along with important added functionalities, the Gemini 2.0 Flash model comes with the following features:

• Integrated Multimodal Capabilities: It generates text and also produces high-quality images that align with the provided narrative.
• High Responsiveness and Speed: The model can produce results faster than some other more computationally intensive models.
• Enhanced Reasoning and World Understanding: The model leverages advanced reasoning and broad world knowledge to generate images that are contextually accurate. 
• Conversational Image Editing: With its ability to engage in multi-turn dialogues, the model supports conversational image editing. 
• Superior Text Rendering: Unlike many image generation models that struggle with long text, Gemini 2.0 Flash excels at rendering extended sequences of text clearly and accurately. 

How to Access Image Generation in Gemini 2.0 Flash?

You can access the Gemini 2.0 Flash(experimental) either via Google AI Studio or through Gemini API.

Via Google AI Studio:

• Head to https://aistudio.google.com/welcome
• Sign in to your Google AI Studio account.
• Sign into your email account.

Once signed in, from the “Run Settings” panel on the right hand side, under the “Model” dropdown, select “Gemini 2.0 Flash Experimental”.

Via Gemini API:

• Make sure you have your Google API key with access to Gemini.
• Install the required client library (for example, the google.genai Python package).
• In your API request, use the model name “gemini-2.0-flash-exp” to call the experimental version.
• Configure your request to include both text and image output modalities. This enables Gemini to generate a multimodal response.

Code:

from google import genai

from google.genai import types

client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content(

    model="gemini-2.0-flash-exp",

    contents=(

        "Generate a story about a cute baby turtle in a 3d digital art style. "

        "For each scene, generate an image."

    ),

    config=types.GenerateContentConfig(

        response_modalities=["Text", "Image"]

    ),

)

Code Source

Also Read: I Tried All the Latest Gemini 2.0 Model APIs for Free

Generating Images with Gemini 2.0 Flash Experimental

I will now test Gemini 2.0 Flash Experimental on 4 different tasks:

1. Storytelling with Images
2. Interactive Image Editing
3. Real-World Image Generation
4. Accurate Text in Images

Now I’ll try each of these tasks with simple prompts. Let’s start with the first one:

Task 1: Storytelling with Images

Prompt: “Generate a 5-part story of a group of kids unboxing a treasure, inside which is a new red coloured chocolate bar, in 3D cartoon style. Generate an image for each scene.”

Output:

The output is a great amalgamation of text and images. The story is well written and the visuals are quite detailed. It feels like you are reading a comic book. With this feature, content creators and marketers can creatively bring their ideas to life. 

Task 2: Interactive Image Editing

Prompt: “add a bed in the middle of the room, opposite to the window, and add a painting on the center wall”

Output: 

The image editing with Gemini 2.0 Flash (experimental) is quite easy. The model follows the prompts exactly and gives the result. Although in some instances, it might not exactly follow the instructions, this usually happens when there are more tasks in a single prompt. Yet overall, the model can be a great tool for visualising ideas. 

Task 3: Real-World Image Generation

Prompt: “Give me the recipe to bake a strawberry cheesecake. Please give an image for each step.”

Output:

The output is a detailed guide to baking a cheesecake, complete with accurate text and corresponding images for each step. The model successfully generated both the instructions and visuals, bringing clarity throughout the process. This capability makes it particularly valuable for creating comprehensive manuals for machines and emerging technologies, where step-by-step guidance with visuals is essential.

Task 4: Accurate Text in the Image

Prompt: “create a billiboard, with a light background and words written in orange text “We are Back, ORDER NOW” with a small Pizza placed next to the text”

Output:

The response is truly impressive! The output not only delivered the text exactly as I specified, in the desired color, but also included a small image of a pizza as requested. Few models have successfully integrated text within images, but Gemini 2.0 Flash (Experimental) excels in seamlessly combining both elements. This level of precision and adherence to prompt details sets it apart from many existing models!

Also Read:

• Gemini 2.0 Flash vs GPT 4o: Which is Better?
• Google Gemini 2.0 Pro Experimental vs OpenAI o3-mini

Review of Image Generation with Gemini 2.0 Flash

Image generation with Gemini 2.0 Flash (Experimental) is impressively efficient, offering a seamless and conversational approach to creating and refining images. It feels as if you’re chatting your way through the creative process, making adjustments in real-time. However, the model does have a few limitations.

• It currently doesn’t support custom aspect ratios, and while it generates high-quality images, it may not always follow every detail specified in the prompt. 
• Though generally fast, response times can sometimes vary, leading to occasional delays. Additionally, while it can incorporate text within images, it doesn’t allow for precise text formatting. 

Despite these drawbacks, Gemini 2.0 Flash demonstrates immense potential, paving the way for advanced AI-driven image generation in the future.

Also Read: Is o3-mini Better Than o1 for Image Analysis?

Applications of Image Generation with Gemini 2.0 Flash

Gemini 2.0 Flash Experimental has diverse applications across industries, enabling seamless integration of text and image generation. 

• In storytelling with images, it can create illustrated children’s books, comics, and engaging marketing visuals while maintaining character and setting consistency. 
• Its interactive image editing capabilities make it ideal for graphic design, prototyping, advertising, and social media, allowing users to refine visuals through simple text prompts. 
• For real-world image generation, the model excels in producing accurate food illustrations for recipes, medical and scientific visualizations, and realistic product or architectural renderings. Additionally, its accurate text rendering ensures clear, well-formatted text for posters, invitations, social media ads, and educational presentations. 

These capabilities make Gemini 2.0 Flash Experimental a powerful tool for design, marketing, education, and business applications, streamlining creative workflows with AI-driven efficiency.

Also Read: Google’s Gemma 3: Features, Benchmarks, Performance and Implementation

Conclusion

Gemini 2.0 Flash (Experimental) brings a significant turn in AI-driven image generation, bringing a new level of interactivity and multimodal capabilities to large language models. Its ability to easily integrate text and visuals makes it a powerful tool for a wide range of applications – from storytelling and marketing to real-world simulations and instructional content. While the model has some limitations, such as the lack of aspect ratio control and occasional inconsistencies in following prompts, its strengths in conversational editing, world knowledge, and accurate text rendering set it apart.

As AI continues to evolve, Gemini 2.0 Flash paves the way for a future where chatbots are not just text-based assistants but also creative visual collaborators. 

I could show only a few examples of image generation using the new Gemini 2.0 Flash, but it can do much more. GenAI is so vast and impact our work in so many ways. In order to learn how to use it for improving you workflows – checkout our Free Course on Generative AI a Way to Life!

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Anu Madan

Anu Madan is an expert in instructional design, content writing, and B2B marketing, with a talent for transforming complex ideas into impactful narratives. With her focus on Generative AI, she crafts insightful, innovative content that educates, inspires, and drives meaningful engagement.