Gemini 2.0 Flash Image Generation

gemini

Partner Model

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~5 sec

StatusActive

Version

2.0-flash-exp-image-generation

Updated1 day ago

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "gemini",
            "version": "2.0-flash-exp-image-generation",
            "input": {
  "image_url": "your_file.image/png",
  "prompt": "your prompt here"
}
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~5 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Gemini 2.0 Flash Image Generation is a model designed for generating high-quality images based on a text prompt and an optional reference image. It provides fast image synthesis with a focus on accuracy and coherence. Gemini 2.0 Flash Image Generation is capable of understanding detailed textual descriptions and can generate images that align with given inputs.

Technical Specifications

Uses advanced text-to-image generation capabilities to produce detailed images.
Supports multimodal input, allowing both text and image references.
Optimized for speed and efficiency, providing rapid response times.
Can generate diverse styles and compositions depending on input parameters.
Incorporates AI-driven enhancements to maintain visual consistency and realism.

Key Considerations

Gemini 2.0 Flash Image Generation may not always interpret highly abstract or ambiguous descriptions accurately.
Generated images might have slight inconsistencies in finer details.
Certain complex requests may require prompt refinement for optimal results.
When using image_url, ensure the reference image is relevant and clear to improve accuracy.

Tips & Tricks

prompt:
- Use structured prompts that include subject, action, environment, and style for more accurate results.
- Avoid overly generic phrases; be specific about the desired image elements.
- Example: Instead of "a cat," use "a fluffy orange cat sitting on a wooden bench in a park during sunset."
- If a particular artistic style is desired, explicitly mention it in the prompt.
image_url:
- Ensure the reference image is clear and relevant to guide Gemini 2.0 Flash Image Generation effectively.
- High-resolution images yield better results compared to low-quality references.
- The image_url should complement the prompt rather than contradict it.
- When using an image_url, try adjusting the prompt slightly to fine-tune the outcome.

Capabilities

Generates images based on textual descriptions with high fidelity.
Can adapt to various artistic styles depending on the prompt.
Supports conditional generation using both text and image inputs.
Maintains a balance between creativity and realism in outputs.
Handles a wide range of themes, from realistic to illustrative visuals.

What can I use for?

Creating concept art based on textual descriptions.
Generating variations of existing images using a reference.
Producing images for storytelling, content creation, and visual design.
Exploring different artistic interpretations of a single idea.
Enhancing creative workflows with AI-assisted image generation.

Things to be aware of

Experiment with different levels of detail in the prompt to see how it affects image composition.
Use descriptive adjectives and scene-setting words to refine results.
Test how modifying a single aspect of the prompt influences the generated image.
Provide an image_url with slight variations in the prompt to explore different creative outcomes.
Compare results using only a prompt versus using both prompt and image_url.

Limitations

May struggle with highly complex or abstract requests that lack clear direction.
Some generated images may have minor inconsistencies in fine details.
Requires careful prompt crafting to achieve specific artistic effects.
The effectiveness of image_url depends on the quality and relevance of the reference image.

Output Format: PNG

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